sri lanka poverty disaster interface study march 26 · list of abbreviations cb - central bank...

Sri Lanka National Report on Disaster Risk, Poverty and Human Development Relationship

(Draft Report for Review)

Disaster Management Centre United Nations Development Programme in Sri Lanka

United Nations Development Programme Regional Centre, Bangkok

MARCH 2009

Sri Lanka National Report on Disaster Risk, Poverty and Human Development Relationship Draft - Not to be quoted

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INFORMATION SHEET Project Title Disaster Risk and Poverty Interface

Client United Nations Development Programme

Project Team

Disaster Management Centre (DMC)

Ministry of Disaster Management and Human Rights

Major General Gamini Hettiarchchi: Director General, DMC

Mr. U.W.L. Chandradasa: Director (Mitigation and Technology), DMC

United Nations Development Programme, Sri Lanka

Dr. Ananda Mallawatantri: Assistant Resident Representative and Team Leader for Environment, Energy and Disaster Risk Management Programmes

Mr. Ramitha Wijethunga National Programme Officer

Mr. Dinesh Rajapaksha: Information Manger and Project Manager for DesInventar Project

United Nations Development Programme, Regional Centre, Bangkok

Mr. Rajesh Sharma: Regional Information System Specialist

Sanny Ramos Jegillos: Disaster Risk Reduction Advisor

EML Consultants Study Team

Dr. S M F Marikar: Team Leader/Economist

Mr. Thilak Hewawsam: Disaster Specialist

Dr. Manitha Weerasuriya: Technical Advisor

Ms. S.K Anila: Communication Specialist

Mr. Nishantha.Medagoda: Statistician

Ms. Nafla Niyas: Project Manager


3


4

INTRODUCTORY NOTE


5

ACKNOWLEDGEMENTS


6

LIST OF ABBREVIATIONS

CB - Central Bank

CBDRM - Community Based Disaster Risk Management

CBO - Community Based Organization

CDMA - Code Division Multiple Access (Wireless Telephone System)

DCB.- Decentralised Budget

DCGS - Department of Commissioner General of Samurdhi

DCS - Department of Census and Statistics

DM - Disaster Management

DMC - Disaster Management Centre

DS - Divisional Secretary

EWHCS - Estate Worker's Housing Cooperative Society

FGT - Foster-Greer-Thorbecke

FSP - Food Stamp Program

GAR - Global Assessment Report

GDP - Gross Domestic Product

GN - Grama Niladari

HIES - Household Income and Expenditure Survey

ICT - Information and Communication Technology

IDP - Internally Displaced population

ISDR - International Strategy for Disaster Reduction

JP - Janasaviya Program

LA - Local Authority


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M/DM&HR - Ministry of Disaster Management and Human Rights

MFI - Micro Finance Institution

MOH - Medical Officer of Health

MSD - Ministry of Samurdhi Development

NCDM - National Council for Disaster Management

NDMP - National Disaster Management Plan

NEOP - National Emergency Operations Plan

NGO - Non Governmental Organization

NIC - National Identity Card

PG - Poverty Gap

PHC - Poverty Head Count

RPC - Regional Plantation Company-

SANASA - Cooperative Credit Society (in Local Sinhala Language)

SASL - Samurdhi Authority of Sri Lanka

SBS - Samurdhi Banking Societies

SEEDS - Sarvodya Economic Enterprise Development Society

SP - Samurdhi Program

SPGI - Squared Poverty Gap Index


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TABLE OF CONTENTS INFORMATION SHEET .......................................................................................................... 2

INTRODUCTORY NOTE ........................................................................................................ 4

ACKNOWLEDGEMENTS ....................................................................................................... 5

LIST OF ABBREVIATIONS .................................................................................................... 6

TABLE OF CONTENTS ........................................................................................................... 8

TABLE OF FIGURES ............................................................................................................. 13

LIST OF TABLES ................................................................................................................... 15

LIST OF MAPS ....................................................................................................................... 17

EXECUTIVE SUMMARY ..................................................................................................... 20

1. BACKGROUND CONTEXT .............................................................................................. 23

1.1 Introduction .................................................................................................................... 23

1.2 Governance and Administration .................................................................................... 25

1.3 Sri Lanka – Demographic Characteristics ..................................................................... 27

1.4 Economic Development Profile ..................................................................................... 27

1.5 The National Poverty Profile ......................................................................................... 31

1.6 National Hazard Profile ................................................................................................. 33

1.7 Analytical Framework for Examining the Risk – Poverty Relationship ....................... 35

1.8 Conclusions .................................................................................................................... 37

2 POVERTY HUMAN DEVELOPMENT PROFILE ............................................................ 39

2.1 Introduction .................................................................................................................... 39

2.1.1 National and Global Poverty Definitions ................................................................ 39

2.1.2 Demographic Context and Rural – Urban Dynamics ............................................. 41

2.1.3 Inequality and Poverty ............................................................................................ 45

2.2 Income and Consumption Poverty ................................................................................. 51

2.3 Health and Nutritional Status ......................................................................................... 57

2.4 Education Status............................................................................................................. 60

2.5 Poverty and Vulnerability .............................................................................................. 62

2.6 Key Poverty and Development Challenges ................................................................... 63

2.7 Past and Current Policies and Programmes to Address Poverty.................................... 65

2.8 Policies to Address Poverty ........................................................................................... 67

2.8.1 Microfinance ........................................................................................................... 68

3 DISASTER AND SHOCK PROFILE .................................................................................. 70


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3.1 Introduction .................................................................................................................... 70

3.2. DesInventar Disaster typology ...................................................................................... 70

3.2.1 Annual Time Series Distribution ............................................................................ 71

3.2.2. Seasonal Distribution ............................................................................................. 72

3.2.3. Spatial Distribution ................................................................................................ 73

3.2.4. Conclusion ............................................................................................................. 74

3.3 People Affected By Disaster ..................................................................................... 74

3.3.1 DesInventar Disaster typology ................................................................................ 74

3.3.2 Annual time series distribution ............................................................................... 76

3.3.3 Seasonal Distribution .............................................................................................. 77

3.3.5 Conclusions ............................................................................................................. 79

3.3 Loss of Life due to Disasters ..................................................................................... 79


3.4.2 Annual time series distribution .............................................................................. 80

3.4.3 Seasonal distribution ............................................................................................... 81

3.4.4 Spatial Distribution ................................................................................................. 82

3.4.5 Conclusions ............................................................................................................. 83

3.5 Building Destruction Damage by Disasters ................................................................... 84




3.5.4 Spatial distribution .................................................................................................. 87

3.5.5 Conclusions ............................................................................................................. 88

3.6 Agricultural Crop Loss due to Disasters ........................................................................ 88

3.6.1 DesInventar Disaster typology ............................................................................... 88

3.6.2 Annual time series distribution .............................................................................. 89



3.6.5 Conclusions ............................................................................................................. 92

4 HAZARD RISK AND DISASTER IMPACT ...................................................................... 93

4.1 Introduction to Geological Hazards .......................................................................... 93

4.2 Earthquake ................................................................................................................. 93

4.3 Tsunami ..................................................................................................................... 93


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4.4 Landslides .................................................................................................................. 94




4.4.4 People affected (Annual time series and Spatial Distribution) ............................... 96

4.4.5 Loss of life (Annual time series and Spatial Distribution) ..................................... 99

4.4.6 Building Destruction and Damage (Annual time series and Spatial Distribution)........................................................................................................................................ 100

4.4.7 Agricultural loss (Annual time series and Spatial distribution) ............................ 102

4.4.8 Conclusions ........................................................................................................... 103

4.5 Climatological Hazards ................................................................................................ 104

4.6 Drought ........................................................................................................................ 104

4.6.1 Annual time series distribution ............................................................................. 104

4.6.2 Seasonal distribution ............................................................................................. 105

4.6.3 Spatial distribution ................................................................................................ 105

4.6.4 People affected by drought (Annual time series and Spatial Distribution) .......... 106

4.6.5 Agricultural Crop Loss (Annual time series and Spatial Distribution) ................. 108

4.6.4 Conclusions ........................................................................................................... 110

4.7 Flooding (including floods, flash floods & heavy rain) ............................................... 110




4.7.4 People affected by floods (Annual time series and Spatial Distribution) ............. 113

4.7.5 Loss of Life (Annual time series and Spatial Distribution) .................................. 115

4.7.6 Building Destruction & Damage (Annual time series and Spatial Distribution) .. 117


4.7.8 Conclusions ........................................................................................................... 120

4.8 Extreme Wind Events (Cyclone, Gale, Strong wind, surge) ....................................... 121




4.8.4 People affected due to extreme wind events (Annual time series and Spatial Distribution) ................................................................................................................... 124

4.8.5 Loss of Life (Annual time series and Spatial Distribution) .................................. 126


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4.8.6 Building Destruction & Damage (Annual time series and Spatial Distribution) .. 127


4.8.8 Conclusion ............................................................................................................ 131

4.9 Other Hazards .............................................................................................................. 132

4.10 Fire ............................................................................................................................. 132

4.10.1 Annual time series distribution ........................................................................... 132

4.10.2 Seasonal distribution ........................................................................................... 133

4.10.3 Spatial distribution .............................................................................................. 134

4.10.4 People affected (Annual time series and Spatial Distribution) ........................... 134

4.10.5 Deaths (Annual time series and Spatial Distribution)......................................... 137

4.10.6 Building Destruction & Damage (Annual time series and Spatial Distribution) 138

4.10.7 Agricultural loss (Annual time series and Spatial distribution) .......................... 141

4.10.8 Conclusion .......................................................................................................... 142

4.11 Animal Attack ............................................................................................................ 143

4.11.1 Annual Time series distribution .......................................................................... 143


4.11.3 Spatial Distribution ............................................................................................. 144

4.11.4 People affected (Annual time series and Seasonal distribution) ......................... 144

4.11.5 Loss of life (Annual time series and Spatial distribution) .................................. 146

4.11.6 Building damage (Annual time series and spatial distribution) .......................... 147

4.11.7 Agricultural loss (Annual time series and spatial distribution) .......................... 149

4.11.8 Conclusions ............................................................................................................. 150

4.12 Lightning .................................................................................................................... 151

4.12.1 Annual Time series distribution .......................................................................... 151


4.12.3 Spatial Distribution ............................................................................................. 152

4.12.4 People affected by lightning – Annual time series distribution .......................... 154

4.12.5 Loss of life : Annual time series and Spatial distribution ................................... 154

4.12.6 Building damage due to lightning ....................................................................... 156

4.12.7 Conclusion .......................................................................................................... 158

5 INTENSIVE & EXTENSIVE RISK PROFILE ................................................................. 159

5.1 Introduction .................................................................................................................. 159

5.2 Intensive Risk Profile ................................................................................................... 161


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5.3 Extensive Risk Profile.................................................................................................. 164

5.4. Spatial Distribution of Intensive and Extensive Risks ................................................ 167

5.5. Conclusion .................................................................................................................. 175

6 RISK POVERTY RELATIONSHIP IN SRI LANKA ....................................................... 176

6.1 Introduction and Objectives ......................................................................................... 176

6.2 Qualitative analysis of disaster risk and poverty ......................................................... 176

6.2.1 Floods .................................................................................................................... 179

6.2.2 Drought ................................................................................................................. 183

6.2.3 Landslides ............................................................................................................. 185

6.2.4 Extreme wind events ............................................................................................. 189

6.2.5 Animal attack ........................................................................................................ 193

6.2.6 Fire ........................................................................................................................ 197

6.3 Statistical / Econometric Framework for Analysis ...................................................... 201

6.3.1 Methodology ......................................................................................................... 201

6.3.2 Data and Level of Analysis ................................................................................... 202

6.3.3 Data available for analysis .................................................................................... 202

6.4 Static poverty / hazard analysis .................................................................................... 206

6.4.1 Dependent / independent variables ....................................................................... 207

6.4.2 Results of Analysis ............................................................................................... 208

6.5 Correlation Analysis .................................................................................................... 216

6.5.1 Results of correlation analysis .................................................................................. 218

6.6 Regression Analysis ..................................................................................................... 219

6.6.1 Results of regression analysis for Poverty Gap and Expenditure ......................... 219

6.6.2 Results of Regression Analysis with Poverty Headcount and Hazard Risk ......... 222

6.6.3 Regression for Poverty indicators vs. Risk measures ........................................... 225

6.7 Conclusions .................................................................................................................. 226

6.8 Hazard Risk Factors ..................................................................................................... 227

6.9 Correlation Analysis at District Level ......................................................................... 227

6.10 Correlation Analysis at Divisional Secretariat Level (using actual impact data) ...... 228

6.11 Regression Analysis ................................................................................................... 228

6.12 Disaster Risk Reduction Policy and Programmatic Response ................................... 229

6.13 National Disaster Management Policy ....................................................................... 230

7 CONCLUSIONS AND, RECOMMENDATIONS ............................................................ 232


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7.1 Poverty Status .............................................................................................................. 232

7.2 Disaster Hazards .......................................................................................................... 233

7.3 Data Analysis ............................................................................................................... 236

7.4 Policy Recommendations ............................................................................................. 238

7.5 Next Steps ............................................................................................................... 239

REFERENCES ...................................................................................................................... 241

ANNEX.................................................................................................................................. 242

TABLE OF FIGURES Figure 1 : Growth Rate of GDP at current prices - Sri Lanka 1990-2007 ............................... 28

Figure 2 : GDP by Sector 2002 – 2007 .................................................................................... 29

Figure 3 : Trend of GDP by Sector – 2002 - 2007 ................................................................... 30

Figure 4 : GDP Growth Rates by Sector .................................................................................. 31

Figure 5 : Extensive Disaster Hazards and Impacts 1974-2008 .............................................. 35

Figure 6 : Intensive Disaster Hazards and Impacts 1974-2008 ............................................... 35

Figure 7 : Population Growth Rate 1948-2005 ........................................................................ 41

Figure 8 : Sri Lanka - Poverty Head Count and Poverty Gap -2007 ....................................... 46

Figure 9 : Sri Lanka - Inequality Indices – 2007 ..................................................................... 46

Figure 10 : Poverty Head Count ratio by District .................................................................... 56

Figure 11 : Infant Mortality ..................................................................................................... 57

Figure 12 : Nutritional Status of Children ............................................................................... 58

Figure 13 Literacy Rates .......................................................................................................... 60

Figure 14 School Enrolment Ratios ......................................................................................... 61

Figure 15 Education Completion Rate ..................................................................................... 62

Figure 16 : Disaster Typology ................................................................................................. 71

Figure 17 : Trend of disaster events ......................................................................................... 72

Figure 18 : Seasonal Distribution of Disaster Events .............................................................. 73

Figure 19 : People Affected by Disaster - DesInventar Disaster Typology ............................ 75

Figure 20 : People Affected By Disaster - Annual Time Series Distribution .......................... 76

Figure 21 : People Affected by Disaster – Seasonal Distribution ........................................... 78

Figure 22 : Loss of Life due to Disasters - DesInventar Disaster Typology ........................... 80


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Figure 23 : Loss of Life due to Disasters - Annual Time Series Distribution ......................... 81

Figure 24 : Loss of life due to disasters – Seasonal distribution ............................................. 82

Figure 25 : Building Destruction Damage by Disasters - DesInventar Disaster Typology ..... 85

Figure 26 : Building Destruction Damage by Disasters - Annual time series distribution ...... 86

Figure 27 : Building Destruction Damage by Disasters - Seasonal distribution ..................... 87

Figure 28 : Agricultural Crop Loss due to Disasters - DesInventar Disaster typology ........... 89

Figure 29 : Agricultural Crop Loss due to Disasters: Annual time series distribution ............ 89

Figure 30 : Agricultural loss due to disasters – Seasonal distribution ..................................... 91

Figure 31 : Annual time series distribution of landslides ........................................................ 94

Figure 32 : Seasonal distributions of landslides ...................................................................... 95

Figure 33 : People affected by landslides – Annual time series Distribution .......................... 97

Figure 34 : Loss of life due to landslides - Annual time series distribution ............................ 99

Figure 35 : Building Destruction and Damage: Annual time series Distribution .................. 101

Figure 36 : Agricultural loss due to landslides - Annual time series Distribution ................. 102

Figure 37 : Annual time series distribution of drought .......................................................... 104

Figure 38 : Seasonal distribution of drought .......................................................................... 105

Figure 39 : People affected by drought – Annual time series Distribution ........................... 107

Figure 40 : Agricultural loss due to drought – Annual time series Distribution.................... 109

Figure 41 : Annual time series distribution of flooding ......................................................... 111

Figure 42 : Seasonal distribution of flooding ........................................................................ 111

Figure 43 : People affected by floods - Annual time series distribution ............................... 113

Figure 44 : Loss of life due to floods – Annual time series distribution ............................... 115

Figure 45 : Building Damage and Destruction due to Flooding – Annual time series distribution – .......................................................................................................................... 117

Figure 46 : Agricultural Loss due to Flooding – Annual time series distribution ................. 119

Figure 47 : Annual time series distribution of extreme wind events ..................................... 121

Figure 48 : Seasonal distribution of extreme wind events ..................................................... 122

Figure 49 : People affected due to extreme wind events – Annual time series distribution .. 124

Figure 50 : Loss of life due to extreme wind events – Annual time series Distribution ....... 126

Figure 51 : Building Destruction and Damage due to extreme wind events – Annual time series Distribution .................................................................................................................. 128

Figure 52 : Agricultural loss due to extreme wind events – Annual time series Distribution................................................................................................................................................ 130

Figure 53 : Annual time series distribution of fire ................................................................. 132


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Figure 54 : Seasonal distribution of fire ................................................................................ 133

Figure 55 : People affected by fire – Annual time series distribution ................................... 135

Figure 56 : Loss of life due to fire – Annual time series distribution .................................... 137

Figure 57 : Building destruction and damage – Annual time series distribution................... 139

Figure 58 : Agricultural loss due to fire – Annual time series distribution ........................... 141

Figure 59 : Annual time series distribution of animal attack ................................................. 143

Figure 60: Seasonal distribution of animal attack .................................................................. 143

Figure 61 : Spatial distribution of animal attack .................................................................... 144

Figure 62 : People affected by animal attacks – Annual time series distribution .................. 145

Figure 63 : Loss of life due to animal attack – Annual time series distribution .................... 146

Figure 64 : Building damage due to animal attack – Annual time series distribution ........... 148

Figure 65: Agricultural loss due to animal attack – Annual time series distribution ............ 149

Figure 66 : Annual time series distribution of lightning ........................................................ 151

Figure 67 : Seasonal distribution of lightning........................................................................ 152

Figure 68 : People affected by lightning – Annual time series distribution .......................... 154

Figure 69 : Loss of life due to lightning – Annual time series distribution ........................... 155

Figure 70 : Building damage due to lightning – Annual time series distribution .................. 157

Figure 71 : Disaster Typology of Extensive and Intensive Risk ........................................... 159

Figure 72 : Intensive Disaster Events -1974-2008 ................................................................. 161

Figure 73 : Loss of Life due to Intensive Disaster Events – 1974-2008................................ 162

Figure 74 : Building damage due to Intensive Disaster ......................................................... 162

Figure 75 : People Affected by Intensive Disasters ............................................................... 163

Figure 76 : Agricultural loss due to intensive disaster impact ............................................... 163

Figure 77 : Extensive Disaster Typology - Number of events ............................................... 164

Figure 78 : Loss of life due to Extensive Disasters ............................................................... 165

Figure 79 : Building Damage due to Extensive Disasters ..................................................... 165

Figure 80 : People Affected due to Extensive Disasters ....................................................... 166

Figure 81 : Agricultural loss due to extensive disasters ........................................................ 166

LIST OF TABLES Table 1 : GDP and GDP Growth Rate of Sri Lanka 1990-2007 .............................................. 28

Table 2 : Hazard Events in Sri Lanka - 1974-2007 ................................................................. 33

Table 3 : Analytical Framework .............................................................................................. 36


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Table 4 : Sri Lanka Population by Rural and Urban Classification ......................................... 42

Table 5 : Sri Lanka Population by Ethnicity and Religion – 2001 .......................................... 42

Table 6 : Dependency Ratio by District ................................................................................... 44

Table 7 : Indicators of Poverty by Sector 2007 ....................................................................... 45

Table 8 Inequality by District .................................................................................................. 47

Table 9 : Per Capita Monthly Food and Non-Food Expenditure and Poverty Head Count by Sector, Province and District, 2006-2007*. ............................................................................. 52

Table 10 : Prevalence of Underweight Children Under Five Years of Age ............................ 58

Table 11 : Estimates of Vulnerable Population ....................................................................... 62

Table 12 : Extensive and Intensive Disasters – Aggregate Impact -1974-2008 .................... 160

Table 13 : Data on Poverty and Disaster Hazards ................................................................. 202

Table 14 : Poverty Indices by District ................................................................................... 203

Table 15 : Poverty Indices by Sector ..................................................................................... 205

Table 16 : Poverty Indices by Province (2006/7) .................................................................. 205

Table 17 : The risk factor of people being affected by all Hazards ....................................... 208

Table 18 : Risk of people being affected by Hydrodynamic Hazards (Flood, Lightning and Extreme wind effect) .............................................................................................................. 209

Table 19 : Risk of people being affected by Geological Hazards (Landslides) ..................... 211

Table 20 : Risk for Environmental or Infrastructure Damage by All hazards ....................... 211

Table 21 : Risk of Environmental or Infrastructure damage by Hydrodynamic Hazards (Flood, Lightning and Extreme wind effect) ......................................................................... 212

Table 22 : Risk of Environmental or Infrastructure damage by Geological Hazards (Landslides)............................................................................................................................ 213

Table 23 : Risk for Economic Losses by: All hazards ........................................................... 214

Table 24 : Risk for Economic Losses by Hydrodynamic Hazards ........................................ 215

Table 25 : Risk for Economic Losses by: Animal Attacks .................................................... 216


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LIST OF MAPS Map 1 : Location Map of Sri Lanka ......................................................................................... 24

Map 2 : Topographic Features of Sri Lanka ............................................................................ 25

Map 3 : Provincial, District and Divisional Secretary Division Boundaries of Sri Lanka ..... 26

Map 4 : Sri Lanka – Gini Coefficient - 2002 by districts ........................................................ 49

Map 5 : Sri Lanka – Gini coefficient 2006/07 by districts ...................................................... 50

Map 6 : Sri Lanka – Squared Poverty Gap 2006/07 by districts ............................................. 51

Map 7 : Sri Lanka - Number of poor persons by District ........................................................ 54

Map 8 : Sri Lanka – Poverty Head Count 2006-7 ................................................................... 55

Map 9 : Spatial Distribution of data records by Districts and DS Divisions .......................... 74

Map 10 : People Affected By Disaster Spatial distribution .................................................... 79

Map 11 : Loss of life due to disasters – Spatial distribution .................................................... 83

Map 12 : Building Destruction/ Damage by Disasters - Spatial distribution ......................... 88

Map 13 : Agricultural Crop Loss due to Disasters: Spatial distribution .................................. 92

Map 14 : Spatial distribution of landslides by Districts and Divisions .................................... 96

Map 15 : People affected by landslides – Spatial distribution ................................................. 98

Map 16 : Loss of life due to landslides - Spatial distribution ................................................ 100

Map 17 : Building Destruction and Damage: Spatial Distribution ........................................ 101

Map 18 : Agricultural loss due to landslides - Spatial Distribution ....................................... 103

Map 19 : Spatial distribution of drought by Districts and DS Divisions ............................... 106

Map 20 : People affected due by drought - Spatial Distribution ........................................... 108

Map 21 : Agricultural loss due to drought – Spatial Distribution.......................................... 110

Map 22 : Spatial distribution of flooding by Districts and DS Divisions .............................. 112

Map 23 : People affected by floods – Spatial distribution ..................................................... 114

Map 24 : Loss of life due to floods – Spatial distribution ..................................................... 116

Map 25 : Building Damage and Destruction due to Flooding – Spatial distribution ............ 118

Map 26 : Agricultural Loss due to Flooding – Spatial distribution ....................................... 120

Map 27 : Spatial distribution of extreme wind events ........................................................... 123

Map 28 : People affected due to extreme wind events – Spatial distribution ........................ 125

Map 29 : Loss of life due to extreme wind events –Spatial Distribution .............................. 127

Map 30 : Building Destruction and Damage due to extreme wind events –Spatial Distribution................................................................................................................................................ 129

Map 31 : Agricultural loss due to extreme wind events –Spatial Distribution ...................... 131


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Map 32 : Spatial distribution of fire by Districts and DS Divisions ...................................... 134

Map 33 : People affected by fire –Spatial distribution .......................................................... 136

Map 34 : Loss of life due to fire –Spatial distribution ........................................................... 138

Map 35 : Building destruction and damage –Spatial distribution .......................................... 140

Map 36 : Agricultural loss – Spatial distribution ................................................................... 142

Map 37 : People affected by animal attacks – Spatial distribution ........................................ 145

Map 38 : Loss of life due to animal attack – Spatial distribution .......................................... 147

Map 39 : Building damage due to animal attack – Spatial distribution ................................. 148

Map 40 : Agricultural loss due to animal attack – Spatial distribution ................................. 150

Map 41 : Spatial distribution of lightning .............................................................................. 153

Map 42 : Loss of life due to lightning – Spatial distribution ................................................. 156

Map 43 : Building damage due to lightning - Spatial distribution ........................................ 158

Map 44 : Impact of Extensive and Intensive risk: Spatial Profile on Loss of Life ................ 168

Map 45 : Impact of Extensive and Intensive risk: Spatial Profile on Number of People Affected .................................................................................................................................. 170

Map 46 : Impact of Extensive and Intensive risk: Spatial Profile on Number of Damaged and Destroyed Houses 1974- 2008 ............................................................................................... 172

Map 47 : Impact of Extensive and Intensive risk: Spatial Profile on Agricultural loss ......... 174

Map 48 : Poverty Headcount Ratio of Sri Lanka - 2002 ....................................................... 178

Map 49 : Poverty headcount Ratio and People affected by floods ........................................ 179

Map 50 : Poverty headcount Ratio and Deaths due to floods ................................................ 180

Map 51 : Poverty headcount Ratio and Building Damage due to floods ............................... 181

Map 52 : Poverty headcount Ratio and Agricultural Loss due to floods ............................... 182

Map 53 : Poverty headcount Ratio and People Affected by droughts ................................... 183

Map 54 : Poverty headcount Ratio and Agricultural Loss due to droughts ........................... 184

Map 55 : Poverty headcount Ratio and People Affected by landslides ................................. 185

Map 56 : Poverty headcount Ratio and Deaths due to landslides .......................................... 186

Map 57 : Poverty headcount Ratio and Building Damage and Destruction due to landslides................................................................................................................................................ 187

Map 58 : Poverty headcount Ratio and Agricultural loss due to landslides .......................... 188

Map 59 : Poverty headcount Ratio and People Affected by extreme wind events ................ 189

Map 60 : Poverty headcount Ratio and deaths due to extreme wind events .......................... 190

Map 61 : Poverty headcount Ratio and Building Destruction and Damage due to extreme wind events ............................................................................................................................ 191


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Map 62 : Poverty headcount Ratio and Agricultural loss due to extreme wind events ......... 192

Map 63 : Poverty headcount Ratio and People Affected by animal attacks .......................... 193

Map 64 : Poverty headcount Ratio and Deaths due to animal attacks ................................... 194

Map 65 : Poverty headcount Ratio and Building Destruction and Damage due to extreme wind events ............................................................................................................................ 195

Map 66 : Poverty headcount Ratio and Agricultural loss due to animal attacks ................... 196

Map 67 : Poverty headcount Ratio and People Affected by fire ........................................... 197

Map 68 : Poverty headcount Ratio and Deaths due to fire .................................................... 198

Map 69 : Poverty headcount Ratio and Building Destruction and Damage due to fire ......... 199

Map 70 : Poverty headcount Ratio and Agricultural loss due to fire ..................................... 200


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EXECUTIVE SUMMARY The main objective of this study is to prepare an assessment report for Sri Lanka by analyzing the two-way relationship between disaster risk and poverty, using both quantitative and qualitative approaches. The qualitative analysis has focused on determining any relationships that can be discerned from visually observing trends or correlations between data sets obtained for both poverty and disaster. The quantitative methods utilized were correlation and regression analysis to determine any relationship between poverty and disaster variables.

Poverty statistics indicate that poverty head count has declined from 26.1% in 1990 to 15.2% in 2006/7. Poverty head count has declined in the urban and rural sectors, but it has increased from 20.5% in 1990 to 32% in 2007 in the estate sector. The largest number of poor resides in the rural sector (82%), followed by the estate sector (11%) and urban sector (7%). Poverty reduction has not been equitable due to growth being concentrated in urban areas, particularly in the Western Province. A large proportion of the internally displaced people (IDPs) temporarily residing in conflict as well as other areas may have fallen into poverty due to the loss of employment, death or injury to the breadwinner or loss of productive assets.

The major problems faced by the urban poor are the lack of safe water, sanitation, poor housing and unhygienic living environment conditions because the slums are located in areas with poor drainage or next to solid waste dumping sites. Due to their location close to ill-drained areas, the urban poor are at risk to floods. Furthermore, fire hazard is a major risk of the urban poor as they live in crowded housing within a small area. Most of the urban poor lack regular employment and therefore their families suffer more nutritional disorders; including stunting and wasting, poor birth weights due to malnutrition of mothers and a greater level of respiratory and diarrhoeal diseases. Most rural poor have large households, with low educational attainments (high dropout in order to work in farms or home enterprises) and high levels of under-employment with inadequate resources or assets to engage in self employment activities. The housing conditions in the rural area are better in terms of space, but sanitation and access to safe water, electricity and good healthcare are limited. The nutritional status of poor rural households is better when compared to the urban poor in terms of minimum calorific requirements. This is due to the availability of home-grown food as well as due to comparatively lower prices for basic food in rural areas. However, the quality of food in terms of balanced diet may not be high and thus children may have signs of nutrient deficiencies.

The estate poor have fewer opportunities to obtain a supplementary source of income due to their affiliation to the plantation company that provides housing and employment and remoteness. Traditionally, the plantation company has provided poor housing (called line rooms), with 1-2 rooms and common toilet facilities. The estates also provide schools and health facilities for their workers, but the quality of education and health care is poor due to lack of facilities and staff. Although the wages of workers can be reviewed from time to time after negotiations with employers, it is generally fixed at a lower level than prevailing market rates. Thus the estate poor is the worst affected by poverty, with statistics on poverty confirming this.

Although poverty has been reduced considerably over the last two decades or so, such reduction has been rather slow or stagnant in the rural and estate sectors. Inequality has increased between and within sectors or regions due to the concentration of economic growth in the western region. Therefore, development needs to be distributed among other lagging


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regions to reduce the vulnerability of marginal and poor households. Those already considered poor may fall deeper into poverty, while others on the margin are in danger of slipping into poverty if effective poverty reduction programmes and policies are not put in place.

Sri Lanka has experienced a variety of natural and human induced disasters that have had a disastrous impact on human well being as well as economic welfare of the country. The 2004 tsunami is an extreme example of a devastating disaster from which Sri Lanka has yet to recover. Apart from the tsunami which could be taken as a very rare and unique event, Sri Lanka is impacted frequently by many hazards that occur more or less on a yearly or more frequent basis. The impacts of disasters on the poor have not been systematically documented, but reports on disaster incidents suggest that the poor are more severely impacted than the non-poor population. The seven most frequently reported disaster events in Sri Lanka are; animal attacks, fires, floods, extreme wind events, landslides, lightning and droughts.

The largest number of deaths occurred due to the Tsunami, followed by extreme wind events, animal attacks and landslides. More than 29 million people have been affected by all hazards during the period 1974-2008 or an average of over 850,000 persons (4.6% of population) affected annually by these hazards. The total crop area affected by these hazards is estimated at over 800,000 hectares over this period. Damage and destruction to houses by all hazards has exceeded 550,000. Floods and extreme wind events have caused the most destruction, followed by the Tsunami of 2004. Geological hazards have caused the next highest damage but the damage has been unevenly distributed spatially and chronologically.

About 95% building destruction and damage was caused by wind events, Tsunami and floods, followed by landslides and animal attacks. Crop damage has been caused mainly by drought (56%), floods (39%), and wind events (5%). Landslides, which were rare in the past, have shown a sharp increase since 2002, with a large number of events recorded in 2006 and 2007 due to better record keeping in recent years. Sri Lanka being an island nation located close to the equator is prone to warm weather conditions, and droughts have been recorded almost annually, with a severe drought experienced in 1992. Incidences of flooding appear to have increased in recent years, with large scale flooding reported in 2006. Deaths caused by floods are relatively low but damage to property and crops is high. Wind events are evenly distributed spatially, but the poor are more susceptible due to the low quality of their housing. Although deaths due to wind events are rare, the 1978 cyclone caused over 850 deaths. Animal (elephant) attacks are increasing mainly due to the loss of traditional elephant grazing lands, which have been taken over for agricultural development over the last 5-6 decades. The very large increase recorded in recent years is probably due to increased reporting and recording of animal attacks. Animal attacks cause considerable damage to buildings and crops, in addition to human and animal deaths.

Intensive disasters are defined as those causing more than 50 deaths and damage to over 500 buildings and the rest are defined as extensive disasters. Both intensive and extensive disasters have caused considerable impacts on human life, shelter, livelihood and the economy. The number of intensive disasters is very low in comparison to extensive disasters but the impact is very high. Number of deaths and impact on shelter is very high in intensive disasters, while the number of people affected and damage to agriculture is much higher in extensive disaster. Intensive risks are more localized whereas extensive risks are widely


22

distributed across the country. When the impact of the tsunami is removed from the data set, the national patterns of intensive risk are closely related to flood and cyclone risk

Although comprehensive time series data are available for establishing a fairly accurate hazard profile, there are data gaps that need to be addressed. For example, more data on the value of economic and social losses need to be incorporated to determine costs and benefits of disaster management programs. In the case of poverty, a major problem appears to be the lack of consistency in data collected and the consequent lack of comparability for assessment purposes. These problems can be thrashed out and a common uniform platform of standardized data collection system implemented for the benefit of all.

The statistical analysis could only be conducted according to data availability. While hazard data was available for 34 continuous years, poverty data was available only for the years when comprehensive socio economic surveys were conducted. This has limited the application of regression and correlation analysis to certain time periods. The results of the analysis are not conclusive but more systematic and regular collection of poverty data would enable us to conduct a better analysis of the relationships between poverty and disaster.

The results from the limited analysis suggest that poverty is associated somewhat with risk from many disaster hazards. Poverty does not increase or decrease due to the impact of hazards, which means that poverty per se cannot be influenced by the risks of hazard. Analysis of hazard risks and location shows that hazard risks are high in some poor districts whereas it is also high for districts that do not have much poverty. The results are not consistent enough to arrive at any valid conclusions. Hazard risk is probably more related to the location rather than where the poor are. For example, hilly areas have higher risk of landslides, while coastal areas have a higher risk for hydro dynamic hazards as are some interior districts. The regression results also confirm to some extent that hazards may not increase or decrease poverty. On the other hand, there is some evidence that poor people are more susceptible to landslides than the non poor. There were no significant results for the other hazards, but with more poverty data one may be able to analyze this further.

Since the above results were not conclusive, a correlation analysis was undertaken with poverty indicators and disaster impacts, using data at the Divisional Secretariat (DS) level. Both poverty and disaster hazard impact data at DS level was utilized for the analysis. Using actual figures of damage or death rather than estimated risk factors, the results were more encouraging. There was evidence of correlation between populations below poverty line and people affected and houses damaged due to floods and landslides, suggesting that disasters do affect the poor. Further research is needed to substantiate these results. The visual analysis using spatial data at DS level also show occasional matches between poverty and disaster, thereby confirming the results of the correlation analysis. Therefore, one must conclude that there is evidence of some relationship between poverty and disaster at a more disaggregated levels compared to data at a more aggregated level. Understanding the extent and spatial distribution of key disasters; the socio-economic status of vulnerable groups; characteristics of the climate, land use and landscape and geomorphology and water regime will help to improve the joint planning for disaster mitigation and service delivery to vulnerable groups. Incorporation of disaster risk reduction in poverty reduction policies and sector development strategies can benefit sustainable development and pro-poor economic growth. The current study is a good beginning and similar studies would assist policy makers to fine tune policies provided that problems are widely prevalent in the many countries involved in such studies.


23

1. BACKGROUND CONTEXT 1.1 Introduction

Sri Lanka, being an island nation with a third of its land area under hilly terrain, is subject to a greater variety of natural hazards than land locked countries. Natural hazards occurring frequently include floods, strong winds, landslides and droughts. As Sri Lanka does not sit atop any major geological fault line, it has been fortunate in not suffering any major earthquakes in its recorded history. However, it was the second most affected country by the 2004 Indian Ocean tsunami. Some historical writings suggest that tsunamis have struck Sri Lanka previously, including the tsunami resulting from the eruption of the Krakatau volcano in Indonesia in 1883, causing some damage and death, although not recorded as causing as much devastation as the 2004 Tsunami.

Statistics on disaster impacts in Sri Lanka appear to suggest that risks due to natural hazards, including animal attacks are increasing. The apparent increase in impacts of natural hazards may be due to the fact that information on disaster impacts is being gathered more systematically now than before. Reports of impacts of disaster hazards appear to indicate that the poor may be more vulnerable to disaster hazards than the non poor. Population increases may have resulted in poorer people settling in hazard prone areas such as low lying lands or hilly slopes, while the clearing of elephant habitats for agriculture may have increased human-elephant conflicts. The overall impact of hazards, including the impacts on the poorer population has not been systematically researched in Sri Lanka.

This report is a part of Global Assessment of Risk (GAR) report being developed and prepared by UN-ISDR globally. The Sri Lanka Country report covers the profile of natural hazards-disasters, poverty profile and risk-poverty interface analysis. The report analyzes the pattern and impacts of natural hazards and its linkages with poverty, in order to provide a greater insight on the two way relationship between poverty and disaster or how disaster impacts on the poor and whether poverty increases the risk of disaster hazards.


24

Country Location and Geography

Map 1 : Location Map of Sri Lanka

Source: Perry Castaneda Map Collection, University of Texas, USA, 2008

Sri Lanka, also known as the ‘Pearl of Indian Ocean’ is an island situated at the south eastern coast shores of India, in the Indian Ocean. Sri Lanka is a densely populated, culturally diverse and natural resource-rich country, with an area of 65, 610 sq km. About one third of the land area in the central region of the island can be classified as wet hilly terrain, and the rest of the area comprises largely of drier undulating plains with a few hilly outcrops. The island is dotted with thousands of small man-made tanks dependent on rainwater and about 500 medium and large reservoir based irrigation systems, largely dependent on river systems as their sources of water, irrigating about two thirds of the islands cultivable area (600,000 ha of lands irrigated). Approximately 50% of the country’s land area of 6.3 million ha is cultivated, with 30% under permanent agriculture, 16% under shifting cultivation (Chena) and the balance under other types of cultivation. The main crops grown are paddy, largely under irrigation in the drier areas and tea, rubber and coconut, grown as rain fed crops in the wet areas as well other food and horticultural crops both as irrigated and rain fed crops and forestry and pastures. The large number of manmade reservoirs (Tanks) can be observed in Map 2(a) (shown in blue). Map 2 (b) shows the topographical features of the island, where the central highlands sloping to the coastal plains can be observed.

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26

the lowest level of government administration. Some key functions and responsibilities of the Central Government have been devolved to the Provinces and a system of Local Government administration comprising of Provincial Councils, Municipal and Urban Councils and Pradeshiya Sabhas (at the lowest level) has been established. These Councils and Sabhas are administered by locally elected council members for a period varying from four to six years. The area under the lowest level of local government or the Pradeshiya Sabha is roughly equal to the area under a DS division and sometimes the boundaries are the same for both. Currently, there are 9 Provincial Councils, and 17 Municipal Councils, 42 Urban Councils and 270 Pradeshiya Sabhas involved in local governance in Sri Lanka. Map 3 provides shows details of the boundaries of the various administrative units in Sri Lanka.

Map 3 : Provincial, District and Divisional Secretary Division Boundaries of Sri Lanka

Source: Department of Census and Statistics, Sri Lanka


27

1.3 Sri Lanka – Demographic Characteristics

According to the latest estimates (2007), Sri Lanka’s population is about 20 million and accordingly the population density works out to 308 persons per square kilometre. The country’s ethnic mix constitutes Sinhalese (74%), Tamil (18%), Muslim (7%), Burghers and others (1%). Over 80% of the population resides in the south-western region of Sri Lanka on 55% of the land area covering six provinces, namely, Western, Southern, Sabaragamuwa, Central, Uva and North Western, (population density 445 persons / sq. km.), compared to 20% of the population living on 45% of the land area (population density 137 persons / sq. km.) in the balance three provinces (Northern, Eastern and North Central) in the north eastern region of the country. These three provinces are less populated due to the drier climate and harsher environment with less plentiful water resources for agricultural production or cultivation.

According to the latest (2007) household survey, 85% of the population is rural and 15% urban. About 23.4% of the households are headed by females. Over 80% of the female heads of households are above 40 years of age and have studied only up to grade 10, while about 65% are widowed, divorced or separated. The population growth rate is about 1% per annum and has declined over the last 60 year period from a high of 3.5% in 1948. The country’s population pyramid, with increasing aging is closer to that of developed countries. Only 25% of the population below 15 years of age and the potential working population (15-59 years) is about 63 percent of the total. The population above the age of 60 years has risen to over 11% causing an increase in the dependency ratio over the recent period.

Although rural to urban migration is increasing, it is not increasing at a rapid pace, because cheap transport and the extensive road network facilitates the employed population and the business community to operate from their homes located even 80-100 km from their workplaces..

1.4 Economic Development Profile

Since its independence from the British in 1948, Sri Lanka has undergone much political and economic transformation. The economy of the country was adversely affected by insurrections – in 1971, and 1987-89, while on-going ethnic conflict has affected economic development, particularly in the conflict zones. In 1977, there was a major shift in Sri Lanka’s economic policies from its previous Socialistic outlook as the country adopted an open market policy, thus making free markets and private enterprise as the chief engines of economic development.

The Sri Lankan economy recorded an annual growth of 6.8 % with a GDP of Rs. 3,378 billion (US$ 32.2 million) in the year 2007. This rate is slightly lower compared to 7.7% in 2006 and 6.2% in 2005. However, this is the first time since independence that a growth rate of above 6 % has been recorded for three consecutive years in Sri Lanka, demonstrating Sri Lanka’s capacity for sustained growth. Sri Lanka has now moved on to a higher growth path above 6 % per annum from the historical average of around 4-5 % (Central Bank Annual Report 2007). Sri Lanka’s economy is projected to grow at around 7.5% to 8.5% per year from 2006 up to the year 2009 and the growth rate to further increase to over 10% per annum from year 2010 to the year 2016. The per capita GDP of Sri Lanka at current market prices is estimated at US$ 1617 in 2007. It has increased from US$ 449 in 1990 to US$ 881 in 2000 and US$ 1241 in 2005.(Central Bank of Sri Lanka – Annual Reports) The annual average rate


28

of unemployment reached its lowest ever recorded level of 6 % in 2007. The GDP growth rate during the period 1998-2005 is given in Figure 1.

Figure 1 : Growth Rate of GDP at current prices - Sri Lanka 1990-2007

Source: Central Bank of Sri Lanka Annual Reports

Table 1 : GDP and GDP Growth Rate of Sri Lanka 1990-2007

Year GDP @ Current Market Prices Rs Billion

GDP Growth Rate %


GDP Growth Rate %

1990 322 6.2 1999 1,106 4.3

1991 372 4.6 2000 1,258 6.0

1992 425 4.3 2001 1,407 -1.5

1993 500 6.9 2002 1,582 4.0

1994 579 5.6 2003 1,819 6.0

1995 668 5.5 2004 2,087 5.4

1996 768 3.8 2005 2,440 6.2

1997 890 6.3 2006 2,939 7.7

-2

0

2

4

6

8

10

Gro

wth

Rat

e

Year

GDP Growth Rate

Growth Rate


29


GDP Growth Rate %


GDP Growth Rate %

1998 1,017 4.7 2007 3,578 6.8


Sri Lanka’s growth rate as illustrated in Table 1 and Figure 1 shows a recovery from a sharp decline to negative growth in 2001. Since then the economy has seen a rise in GDP growth rates from around 4% to 6.2% with an insignificant drop in 2004. The growth rate thereafter has shown a steady increase with the highest rate of 7.7% achieved in 2006 followed by a small decline in growth rate in 2007. The sector break up of GDP shows a declining contribution from agriculture and an increasing contribution to the GDP from the industrial and service sectors. Details are provided in Figure 2 and 3.

Figure 2 : GDP by Sector 2002 – 2007


0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

2002 2003 2004 2005 2006 2007

Rs.

mill

ion

Year

Gross Domestic Product at Constant 2002 prices

Agriculture

Industry

Services


30

Figure 3 : Trend of GDP by Sector – 2002 - 2007


The service sector continues to increase its share of the GDP, while the agricultural sector shows a slight decline (Figure 4). Both irrigated and plantation agriculture sectors have shown a declining level of investments contributing to the stagnation of these sectors. The emerging new rural work force has shunned the agricultural sector and is seeking white collar, urban or industry jobs, thus affecting output in this sector. The tourism sector with high growth potential has been affected by the war, while the industrial sector mainly powered by the clothing and garment industries has been affected by severe competition from other South Asian countries. Despite these deficiencies, Sri Lanka’s economy has shown some resilience. Although actual achievements are below the projected values, the figures are not that far below actual values.

The industry and services sub sectors have shown a high growth rate of 7%-8% in recent years, while the agriculture sub sector has been lagging behind around 3% -6%

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

2002 2003 2004 2005 2006 2007

Rs.

Mill

ion

Year

Gross Domestic Product at 2002 constant prices (National and Sectoral)

Agriculture

Industry

Services

All


31

Figure 4 : GDP Growth Rates by Sector


Although Sri Lanka’s economy has been growing at an average rate of about 5%-6% per annum over the last decade, there has been a combination of constraining factors and deficiencies that have limited growth and poverty reduction. However, Sri Lankan policy makers are optimistic and have targeted a growth rate of over 8% over the next few years, despite these shortcomings in the economy. Economic growth and poverty reduction in Sri Lanka can be accelerated further, if some of the basic macro economic imbalances can be corrected. For example, economic growth has been overwhelmingly concentrated in the western province, infrastructure development has not kept pace with the needs of development, the civil conflict has adversely affected investments, public debt has spiralled due to the war effort, growth in various critical sectors has remained stagnant due to poor policies, lack of modern skills in the labour force and stifling labour union regulations. The consequent inefficiencies in the labour market have turned away foreign investors. Thus Sri Lanka could achieve high growth rates if the critical economic imbalances are corrected and appropriate policies are adopted to attract new investments and reduce inefficiencies, provided that there are no severe external shocks.

1.5 The National Poverty Profile

Sri Lanka’s mix of development oriented and welfare policies over the last three to four decades have resulted in increases in average incomes across all income groups and more people are now in the higher income categories (Gunatilaka et al. 2006). With growth averaging over 5% percent in the last decade, mean per capita income reached USD 1,617 in 2007. Due to a long history of significant public expenditure in health and education, Sri Lanka’s Life expectancy of 72, is close to that of developed nations. In 2007 it ranked 99th globally with a Human Development Index of 0.743, underpinned by universal primary

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

2002 2003 2004 2005 2006 2007

Perc

enta

ge

Year

Growth of GDP at constant 2002 prices - National and Sectoral

Growth Rate -Agriculture

Growth Rate -Industrial

Growth Rate -Service

Growth Rate - All


32

school enrolment, impressive literacy rates and high gender equality. However, there are also significant disparities in levels of economic development between the commercialized Western Province (50.1 percent GDP share in 2007) and the rest of the country. It is estimated that about a fifth of the population still remains poor - the population below poverty line was estimated at 15.2 % in 2006/7, (Department of Census & Statistics, Sri Lanka) excluding the population in the Northern Province. One district in the Eastern Province, with a large proportion of the population is believed to live below the poverty line due to the ongoing ethnic conflict. Other estimates of poverty show that 5.6 percent and 41.6 percent of the population earns less than USD 1 and USD 2, respectively.

Sri Lanka is seeking to eradicate poverty and malnutrition across all regions and strata of society and promote peace and sustainable human development while protecting its environment which is prone to natural hazards and disasters. Currently Sri Lanka is poised to meet or possibly exceed the Millennium Development Goals (MDGs) before 2015. However, significant challenges remain in reducing income poverty, improving the geographic distribution of economic growth and achieving equality (World Bank Sri Lanka Country Assistance Strategy 2009-12) of services. A pro-poor growth strategy incorporating a rights-based approach; macro-economic stability; legal and institutional reform for good governance; and social justice with equitable and efficient service provision, will need to be implemented to meet the above goals.

Poverty in Sri Lanka can be broadly classified under three sectors- urban, rural and estate. There is vast disparity between urban and rural poverty chiefly because of the Colombo-centric economic activities. Sri Lanka’s poor are from the rural areas. Agriculture still remains the backbone of the Sri Lanka economy and the contribution of agriculture to the economy (11.9%) is largely from the rural areas. This justifies the intrinsic link between rural poverty and rural agriculture. Of the employed population 31% or 2.2 million persons are employed in the agriculture sector. About 25% of the households and 20% of population (4 million people) are dependent on agriculture for their livelihoods (Based on data from HIES, Department of Census and Statistics 2006/7).

Poverty statistics indicate that poverty head count has declined from 26.1% in 1990 to 22.7% in 2002 and to 15.2% in 2006/7. Poverty head count has declined in the urban and rural sectors, but it has increased from 20.5% in 1990 to 32% in 2007 in the estate sector. The largest population of poor reside in the rural sector (82% of the poor), followed by the estate sector (11%) and urban sector (7%). Poverty reduction has been not equitable due to growth being concentrated in urban areas particularly in the Western Province. The government has hence identified 119 most economically disadvantaged Divisional Secretariat Divisions (DS) to focus its longstanding poverty reduction programmes.

The internal security situation remains a major challenge, undermining economic growth and development potential. Two decades of armed conflict combined with the December 2004 Indian Ocean tsunami have caused heavy destruction and widespread human suffering. Official statistics of poverty levels among the Internally Displaced Persons (IDPs) due to the tsunami and the ongoing conflict are not available. However, a large proportion of the IDPs temporarily residing in conflict as well as other areas may have fallen into poverty due to the loss of employment, death or injury to the breadwinner or loss of productive assets. Therefore, official figures may not reflect prevailing poverty levels in this population. An effective poverty and risk mitigation strategy for Sri Lanka would need to address these contextual challenges.


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More inclusive economic growth will require easing specific constraints affecting particular sectors, regions, and groups, but priorities critical for all include improving the quality of education, access to infrastructure like electricity, connectivity to markets and urban centres, access to finance for micro-enterprises and assistance in evaluating and operating viable livelihood enterprises (World Bank, Sri Lanka Poverty Assessment, 2007 and other reports).

1.6 National Hazard Profile

Sri Lanka has experienced a variety of natural and indirectly human induced disasters that has had a disastrous impact on human well being as well as the economic welfare of the country. The 2004 tsunami is an extreme example of a devastating disaster from which Sri Lanka is yet recovering. Apart from the Tsunami, which could be taken as a very rare and unique event, very unlikely to be repeated in the near or distant future, Sri Lanka has been impacted frequently by many hazards that occur more or less on a yearly or more frequent basis. The impacts of disasters on the poor have not been systematically documented, but reports on disaster incidents suggest that the poor are more severely impacted than the non-poor population. A profile of the most important hazard events is presented in the following tables and charts.

Table 2 presents the number of events that have been recorded for each hazard type from 1974-2007. The seven most frequently reported disaster events represented in the table below include: Animal attacks, fires, floods, extreme wind events, landslides, lightning and droughts. The hazard that has been reported most frequently in the country is animal attack with a total of 7202 occurrences reported between 1974-2007; followed by fire with 2703 occurrences; floods with 1397 events, extreme wind events with 1288 occurrences, landslides with 1174 occurrences; lightning with 300 occurrences and finally drought with 283 occurrences reported

Table 2 : Hazard Events in Sri Lanka - 1974-2007

Hazards No of Events

Percentage Deaths No. of People Affected

Houses Damaged / Destroyed

Agricultural loss (ha)

Animal attack

7,202 50.2 875 29,912 5,358 3,541

Fire* 2703 18.8 86 8,093 1,328 22,931

Floods 1,397 9.7 419 13,485,520 232,236 312,580

Wind Events*

1,288 9.0 926 1,802,236 201,793 33,546

Landslides 1,174 8.2 815 121,684 11,664 1,410

Lightning 300 2.1 288 748 119 1

Drought 283 2.0 0 12,413,545 0 419,902


34

Hazards No of Events

Percentage Deaths No. of People Affected

Houses Damaged / Destroyed

Agricultural loss (ha)

Tsunami 1 0.0 30,959 1,076,185 105,293 10,397

Total 14,348 100.0 34,368 28,937,923 557,791 804,308

Source: Disaster Management Centre

*- Includes all other wind effects, cyclones, gales, strong winds,

#- Includes urban and forest fires

The largest number of deaths occurred due to the Tsunami, followed by extreme wind events, animal attacks and landslides. Damage and destruction to houses by all hazards have exceeded 550,000. Floods and extreme wind events have caused the most destruction to housing, followed by the Tsunami of 2004. More than 29 million people have been affected by these hazards over the period of 34 years or an average of over 850,000 persons affected annually by these hazards (4.6% of population). The total crop area affected by these hazards is estimated at over 800,000 hectares over this period.

Hazard has also been classified in terms of whether they are intensive or extensive. Intensive events are those that have resulted in more than 50 deaths or 500 houses damaged or destroyed. All the others are classified as extensive. Most of Sri Lanka’s disasters can be classified as extensive, but some highly intensive events like the 2004 tsunami tend to skew the distribution, with over 90 percent share of the fatalities. The tsunami accounted for nearly 30 percent of all houses destroyed and damaged.


35

Figure 5 : Extensive Disaster Hazards and Impacts 1974-2008

Hydro meteorological events were most responsible for all impacts including deaths affected people, damaged houses or crops destroyed. However, the largest number of deaths was due to animal attacks.

Figure 6 : Intensive Disaster Hazards and Impacts 1974-2008

Intensive events are also largely hydro-meteorological in origin. However, the huge impact of the Tsunami of 2004 has tended to mask the impacts of the other events.

1.7 Analytical Framework for Examining the Risk – Poverty Relationship

Quantitative and pragmatic evidence all over the world proves that there is a direct relationship between disaster risk and poverty, which plays a key role in the accumulation of extensive risk over time and space. This role is intrinsically linked as poverty is a factor in processes such as urbanization and environmental degradation that generate extensive risk

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Percentage

No of Events No of Deaths No of DamagedHouses

No of PeopleAffected

Area of CropDamaged (ha)

Impact

Extensive Hazards

Animal AttacksFireGeologicalHydro Meteorological

0%

10%

20%

30%

40%

50%

60%

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90%

100%

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No ofDeaths

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Affected

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Damaged(ha)

Impact

Intensive Hazards

GeologicalHydro Meteorological


36

while the outcomes of extensive risk, particularly the livelihood impacts contribute to perpetuating or exacerbating poverty. Poverty increases disaster risk and vice-versa. Recurrent and major disaster impacts seem to perpetuate poverty outcomes. The extent of vulnerability of communities to disaster risk differs in the urban and rural areas. Though disasters impact the social and economic landscape of a country tremendously, there is little evidence or systematic research to demonstrate the long-term impacts of disaster on the livelihoods of people in the urban and rural areas.

Table 3 presents the framework of analysis broken down by components, sample units, sub-divisions and analysis

Table 3 : Analytical Framework

Components Sample Unit

Sub division Analysis

Static poverty analysis

Cross-section of households or individuals

Identification of poverty

categorizing and quantifying distinct groups, identifying poverty indicators and measuring them

Experience of Poverty

explore the incidence, depth and severity of static poverty measures and their temporal analogues for dynamic poverty measures;

Explanation of Poverty

generating statements by the figures, poverty profiles, multivariate analysis, multiple correlates (regression, models)

Aggregate poverty trends

Panel at district and sub-district level







Poverty micro-dynamics (Economic

Households or individuals

(Two





Explanation generating statements by poverty profiles,


37

Components Sample Unit

Sub division Analysis

mobility) periods) of Poverty multivariate analysis, multiple correlates (regression, models)

Sri Lanka faced its worst natural disaster in December 2004, when the Indian-ocean Tsunami struck the island leaving nearly 31000 people dead and over 1 million displaced. This study exploring the relationship between disaster risk and poverty takes on even a greater significance in the aftermath of the Tsunami that devastated lives, livelihoods and property. Furthermore, the global poverty mitigation agenda is facing numerous challenges due to the emerging climatic changes, environmental degradation and the increasing number of natural disasters across the world.

This report aims to examine the trends and patterns in extensive risk and of the interactive links between risk and poverty. The reports analyses the two-way relationship between disaster risk and poverty using both quantitative and qualitative approaches.

1.8 Conclusions

Although Sri Lanka’s economy has been growing at an average rate of about 5% to 6% per annum over the last decade, there has been a combination of constraining factors and deficiencies that have limited growth and poverty reduction. Sri Lanka represents a paradoxical situation, on the one hand, it has achieved high levels of human development exceeding that of some wealthy countries but on the other hand, it ranks as one of the poorest countries in overall economic growth and sustainable development.

Sri Lanka’s mix of development oriented and welfare policies over the last three to four decades have resulted in increases in average incomes across all income groups and more people are now in the higher income categories However, about a fifth of the population remains poor - the population below poverty line was estimated at 15.2 % in 2006/7, (Department of Census & Statistics, Sri Lanka). While the poverty head count has declined in the urban and rural sectors, it has increased from 20.5% in 1990 to 32% in 2007 in the estate sector.

It is the rural and estate population that have shown a high incidence of poverty and poverty gap. The Poverty Head Count and Poverty Gap indices are the highest in the estate sector, suggesting a large proportion of the population are poor and that the depth of poverty is high. The latter entails a greater transfer of funds to reduce poverty. Because of the high rural population in Sri Lanka, the poverty problem assumes a greater magnitude in terms of financial resources as well as implementation efforts needed to reduce poverty in this sector. The Internally Displaced Persons (IDPs), displaced due to the conflict and tsunami have also fallen into poverty and official figures may not reflect prevailing poverty levels in this population, which is largely resident in the northern and eastern provinces of Sri Lanka. The impacts of disasters on the poor have not been systematically documented, but reports on disaster incidents suggest that the poor are more severely impacted than the non-poor population.

This study which aims to examine the trends and patterns in extensive risk and of the interactive links between risk and poverty will provide useful insights into the relationship


38

between disaster risk and poverty, if any, that will help policy makers to improve the effectiveness of policies adopted in reducing poverty and mitigating disaster impacts on poverty.


39

2 POVERTY HUMAN DEVELOPMENT PROFILE

2.1 Introduction

Poverty and development pose many challenges to the Sri Lankan policy makers and government. Although Sri Lanka’s performance in terms of overall economic growth and sustainability of development has been mediocre, it’s achievements in terms of human development is commendable. Higher levels of human development have been achieved through the provision of universal access to health and education and continued investment in social sectors by the successive governments since independence (1948). Economic development, however, has lagged consistently behind social development. Overall poverty indices have shown a decline, but rural poverty levels have declined relatively slowly compared to urban poverty. Data for measuring poverty is now being collected more systematically by state agencies so that poverty measurement can become more comparable over time.

2.1.1 National and Global Poverty Definitions Global Poverty Definitions

A basic problem in any work on poverty is how to define the poor and how to measure poverty. There could be many definitions of poverty as the number of poor themselves, or at least as much as the number of people who have attempted to define poverty. Traditional approaches to measure poverty have centred on the concepts of incomes and consumption levels, with poverty generally perceived in two distinct ways; absolute poverty and relative poverty. Absolute poverty is defined in terms of minimum consumption needs without reference to income or consumption levels of the general population. A relative poverty situation, on the other hand, is generally defined in relation to mean income or consumption of a population as a whole. A person is considered poor, in absolute terms, if his or her income or consumption falls below some minimum level necessary to meet basic needs – this minimum level is called the poverty line.

However, it has been argued that income poverty is a narrow concept and is not an adequate measure of poverty or well-being. In recent years, it has been increasingly recognized that poverty is a multi-dimensional concept, extending from income and consumption poverty to lack of education, sanitation, safe water and poor health, and includes other social dimensions such as powerlessness, insecurity, vulnerability, isolation, social occlusion and gender disparities. Similarly the concepts of sustainable livelihoods, basic capabilities and entitlements have broadened the concepts of poverty.

There are two basic concepts of income poverty, static and dynamic. Static concepts relate to measurement of poverty at a point in time. Dynamic poverty relates to changes in poverty over time. The concept of dynamic poverty is further categorized as chronic poverty and transient poverty. Chronic poverty is defined as a state where a household’s income (consumption) is constantly below the poverty line. Transient poverty is a state where a household’s average income (consumption) is above the poverty line, but the household is confronted with the possibility of temporarily falling below the poverty line. There are distinct policy implications underlying the two dynamic concepts of poverty. For example, when chronic poverty is dominant, continuous long-term policy interventions are necessary. Such policies may include in the case of rural areas, agricultural research and extension, land reforms, income re-distribution and price support policies. When transient poverty is more


40

prevalent, some form of insurance provision policies are more appropriate. For example, policies such as micro-credit, crop insurance, employment guarantee, or price stabilization policies may be needed.

Some literature from the Asian region suggests that transient poverty is more prevalent, with 50%-70% of the population identified as living in transient poverty (Sawada, 2000). The measurement of income or expenditure measures of poverty involves the identification of an indicator such as income or expenditure and specification of a level of income / expenditure level or threshold below which a person or household is considered poor – the poverty line. The Foster-Greer-Thorbecke (FGT) class of measures is the most commonly used measure of poverty, which captures three aspects of poverty; incidence, depth/intensity and severity of poverty, which are the Poverty Head Count Index (PHC), Poverty Gap Index (PG) and Squared Poverty Gap Index (SPG). National Poverty Definitions and Measurement

Though poverty can be measured on the basis of multidimensional concepts including basic services such as lack of shelter, access to school, medical facilities, livelihood, social vulnerability, etc., in Sri Lanka, poverty is measured on the income and consumption patterns. The Department of Census and Statistics (DCS) and the Central Bank (CB) are the two main sources of data for poverty analysis. Data from periodic censuses, socio-economic and labour force surveys, annual food balance sheets, household income and expenditure surveys of the DCS and the annual reports, consumer finance and socio-economic surveys of the CB provide the basis for inter-temporal analysis of poverty. Poverty in general terms can be defined as the inability to maintain a minimal standard of living. In Sri Lanka, household income and expenditure as well as dietary intake of calories have been used to determine poverty lines. Consumption poverty has been defined as those consuming less than a recommended minimal dietary intake of calories. Defining poverty solely as being deprived of money is, however, not sufficient. Social indicators and indicators of risk and vulnerability must also be considered and understood to obtain a clearer picture of poverty.

In Sri Lanka, poverty indices were estimated by the DCS, from 1980 to 1990, using data collected from various surveys including Family Budget, Labour Force and Socio Economic Surveys. Since 1990, a Household Income and Expenditure Survey (HIES) was designed primarily to obtain data required for estimating poverty indices and food consumption patterns. The Household Income and Expenditure Survey (HIES) is a yearlong national sample survey conducted by the Department of Census and Statistics of Sri Lanka once in five years and is the main data source for the calculation of poverty indices for Sri Lanka. The last HIES was conducted from July 2006-June 2007 and it estimates the value of key poverty indicators by comparing the household food and non food consumption and expenditure data collected. The HIES has been conducted regularly once in five years for compiling the following indices.

Food and non - food consumption patterns

Population by income and expenditure deciles

Income receiver’s income

Sources of income

Average nutrition intake values


41

Poverty line

Percentage of population below poverty line

Average household size

The official poverty line in Sri Lanka was estimated using data from the HIES conducted in the year 2002 and has been defined as the welfare level of a person who meets a certain minimal nutrition intake (2030 kilocalories) as of 2002. More precisely, the official poverty line of 2002 was defined as the per-capita expenditure for a person who would be able to meet the nutritional level of 2030 kilocalories in the year 2002. A full description of the estimation of poverty line is available on the website of the DCS (www.statistics.gov.lk). The estimated poverty line for the year 2002 was Rs. 1423. That is, people living in households whose real per capita monthly total consumption expenditure is below Rs. 1423 in the year 2002 in Sri Lanka are considered poor. The poverty line for subsequent years has been calculated using the 2002 base data and adjusting for inflation on an annual basis. Monthly, district, and sub-district level poverty lines have been also estimated on the above basis. The poor can be found among many occupations, including semi-subsistence farmers, low income market oriented farmers, self-employed individuals, urban workers and others employed in trade, non trade and informal sectors.

2.1.2 Demographic Context and Rural – Urban Dynamics

The demography of the country can be characterized by a continuing decline in the growth rate of population. The population growth rate shown in Figure 4 has declined from around 3.5% per annum in the 1950s to slightly above 1% in 2007. The current population of the country is estimated at 20 million of which 14 million is rural, 1 million estate and 5 million urban.

Figure 7 : Population Growth Rate 1948-2005

Source: International Centre for Ethnic Studies (Website)

The rate of population growth in Sri Lanka has been declining over the years. Although fluctuations can be seen through the years, the growth rate has remained within the rate of 1%

00.20.40.60.8

11.21.41.61.8

2

Gro

wth

Rat

e

Year

Annual Growth Rate of Population (%)

Population Growth Rate (%)


42

to 4%. The years 1965 and 1982 have seen sudden falls in the growth rate. Despite this the growth rate has been fairly stable and remains at a little over 1% per annum in recent years.

Table 4 presents the estimated the rural and urban population by the censuses of population conducted between 1963 and 2001.

Table 4 : Sri Lanka Population by Rural and Urban Classification

Population Urban Rural Estate Total Growth Rate % Census Year Million % Million % Million % Million

1963 2.02 19.2 8.56 80.8 10.52 3.06

1971 2.85 22.5 9.84 67.5 12.69 2.58

1981 3.19 21.5 11.65 68.5 14.85 1.70

2001 2.74 14.6 15.04 80.0 1.02 5.4 18.8 1.33

2007* 2.70 14.7 14.70 79.9 1.00 5.4 18.4*

Source: HIES 2006/7 – Department of Census and Statistics *Excluding the Northern Province and the Trincomalee district in the Eastern province

In 2001, a complete census was not undertaken in the northern and eastern provinces, but the gaps were filled by data from the Registrar General’s Department. Thus the 2001 census may not represent an accurate picture of the urban – rural break-up of the population. Further, the definition of urban and rural as used by the Census Department also may be underestimating the urban population. Populations from Municipal and Urban Councils are considered urban, while the lowest level of local government or the Pradeshiya Sabhas are considered rural. There are Pradeshiya Sabhas located in urban areas, whose populations are largely urban and therefore the actual urban population may be underestimated by the statistics. The ethnic and religious break-up of the population is provided in Table 5

Table 5 : Sri Lanka Population by Ethnicity and Religion – 1981 and 2001

Population by Ethnicity 1981

%

2001*

%

Population by Religious Affiliation

1981

%

2001*

%

Sinhalese 73.9 82.0 Buddhism 69.3 76.7

Sri Lanka Tamil 12.7 4.3 Hinduism 15.5 7.8

Indian Tamil 5.5 5.1 Islam 7.5 8.5

Sri Lanka Moor 7.1 7.9 Christianity 7.6 7.0

Other 0.8 0.7 Other 0.1 0.0


43

Total % 100 100 Total % 100.0 100

Population Million 14.8 18.8 Population Million 14.8 18.8

* - Note that the Census of 2001 was conducted for only 18 districts excluding all districts in the North and East except Ampara District and thus excluding a large proportion of Tamil and Muslim population

Source: Census of Population, Department of Census and Statistics, Sri Lanka.

The majority of the population is Sinhalese and Buddhists, with Tamils forming the second largest ethnic group followed by the Moors. Hindus are the second largest religious group followed by Muslims and Christians in Sri Lanka.

The dependency ratio has been estimated for years 2001 and 2006/07 and is presented in Table 6


44

Table 6 : Dependency Ratio by District

District Dependency Ratio (2006/7)#

Total (18 Districts) 58.5

Colombo 53.0

Gampaha 55.3

Kalutara 58.8

Kandy 64.7

Matale 62.0

Nuwara Eliya 62.6

Galle 65.5

Matara 62.1

Hambantota 56.0

Ampara 61.6

Kurunegala 62.6

Puttalam 56.5

Anuradhapura 58.9

Polonnaruwa 54.2

Badulla 56.2

Moneragala 61.3

Rathnapura 59.7

Kegalle 56.2

Dependency Ratio = (Population 60 years and above and below 15 years/ Population between ages 14-59)

Source: Department of Census and Statistics Sri Lanka *- Census data; # - HIES data

The dependency ratios do not vary much among the districts. The highest dependency ratios were observed in districts with high population such as Galle, Matara, Kurunegala and Ampara and in districts such as Kandy, Matale and Nuwara Eliya where a high proportion of estate population live. The estate workers are among the poorest of the rural population and high dependency ratios in these districts may be a reflection of the level of poverty.


45

2.1.3 Inequality and Poverty

Different countries use various indices to estimate poverty depending on the availability of data. In Sri Lanka, official estimates of poverty are based on income and consumption levels.

Poverty in Sri Lanka can be broadly classified under three sectors- urban, rural and estate. There is vast disparity between urban and rural poverty chiefly because of the Colombo-centric economic activities. The Western Province of the country is the chief driver and contributes the lion’s share to the GDP. Sri Lanka’s poor are from the rural areas. Agriculture still remains the backbone of the Sri Lanka economy and the contribution of agriculture to the economy (11.9%) is largely from the rural areas. This justifies the intrinsic link between rural poverty and rural agriculture. The highest levels of poverty can be observed in the estate sector as presented in Table 7.

Table 7 : Indicators of Poverty by Sector 2007

Sector PHC Index (%)

Poverty Gap Index (%)

Squared Poverty Gap Index (%)

Gini Coefficient

No. of poor (000)

Contribution to poverty (%)

Urban

Rural

Estate

6.7

15.7

32

1.3

3.2

6.2

0.4

1.0

1.8

0.43

0.38

0.26

184

2,303

318

6.6

82.1

11.3

Sri Lanka 15.2 3.1 0.9 0.40 2805 100

Source: Department of Census and Statistics- HIES 2006/7

Figure 5 illustrates that the rural and estate population having a high incidence of poverty and poverty gap according to the Household Income and Expenditure Surveys conducted in 2006/7.


46

Figure 8 : Sri Lanka - Poverty Head Count and Poverty Gap -2007


The Poverty Head Count and Poverty Gap indices are the highest in the estate sector, suggesting a large proportion of the population are poor and that the depth of poverty is high. The latter entails a greater transfer of funds to reduce poverty. In the rural sector both PHC and Poverty Gap are lower than the estate sector, but higher than the urban sector. Because of the high rural population in Sri Lanka, the poverty problem assumes a greater magnitude in terms of financial resources as well as implementation efforts needed to reduce poverty in this sector.

Figure 9 : Sri Lanka - Inequality Indices – 2007


The Urban sector reported the lowest (0.4%) value of the Squared Poverty Gap Index (SPGI) and highest Gini Coefficient (0.43) of the three sectors (Fig. 5). Thus inequality among poor

0

5

10

15

20

25

30

35

Urban Rural Estate Sri Lanka

Perc

enta

ge

Sector

Poverty Indices

PHC Index (%)

Poverty Gap Index %

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Urban Rural Estate Sri Lanka

Perc

enta

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Sector

Inequality Indices

Squared Poverty Gap

Gini Coefficient


47

as reported by the SPGI (low index means low inequality) in the Urban sector was the lowest, while the total inequality among both poor and non-poor is the highest as reported by the Gini Coefficient (index ranges from 0-1 and high index means high inequality). Thus there is high inequality if the rich and the poor are grouped together as done in the estimation of Gini Coefficient and low inequality among the poor as shown by the SPGI in the urban sector. This means that incomes of the urban poor do not vary much but there is a large gap between the poor and rich in urban areas. Exactly the opposite of the above is shown by the Estate sector where non-poor also are not much far beyond the poverty line as Gini Coefficient is low (0.26) for both the poor and non-poor and the inequality among poor who stay below the poverty line is high (1.8 percent of SPGI) which is the highest among sectors. In other words, in the estate sector, the gap between the rich and poor is not great but incomes among the poor do vary. The rural sector is in-between these two categories. This means that in the rural areas, there is a moderate level of inequality within the total population as well as among the poor.

Table 8 Inequality by District

Administrative District Gini Coefficient ( 2002)

Gini Coefficient (2006/07)

Squared Poverty Gap (2006/07)

Colombo 0.46 0.53 0.3

Gampaha 0.44 0.44 0.4

Kalutara 0.43 0.48 0.8

Kandy 0.49 0.48 1.2

Matale 0.48 0.43 0.6

Nuwara-Eliya 0.40 0.48 2.0

Galle 0.43 0.44 0.9

Matara 0.43 0.43 0.6

Hambantota 0.40 0.45 0.7

Batticaloa 0.43 0.4

Ampara 0.45 0.7

Kurunegala 0.46 0.50 1.0

Puttalam 0.47 0.45 0.7

Anuradhapura 0.43 0.43 0.8

Polonnaruwa 0.40 0.46 1.0

Badulla 0.46 0.46 1.7


48

Administrative District Gini Coefficient ( 2002)

Gini Coefficient (2006/07)

Squared Poverty Gap (2006/07)

Moneragala 0.56 0.50 2.8

Rathnapura 0.41 0.51 1.6

Kegalle 0.43 0.42 1.3

Sri Lanka 0.47 0.49 0.9

Source: Department of Census and Statistics

The Gini Coefficient which measures equality from among both the rich and the poor can range from 0-1. The higher the coefficient the higher the inequality. Thus overall inequality has increased slightly in Sri Lanka, but in 5 out of the 17 districts, inequality has declined between 2002 and 2006/7.


49

Map 4 : Sri Lanka – Gini Coefficient - 2002 by districts

Gini coefficient

Not covered by survey

0.04

0.41 – 0.459

0.46 – 0.5

0.51 – 0.6


50

Map 5 : Sri Lanka – Gini coefficient 2006/07 by districts

Gini coefficient


0 - 0.45

0.451 – 0.5

0.51 – 0.6


51

Map 6 : Sri Lanka – Squared Poverty Gap Index 2006/07 by districts

In Moneragala, which had the highest PHC, PG, SPG (Table 9) inequality has declined since 2002, and thus poverty and inequality do not appear to be correlated. Inequality among the poor as measured by the SPG (>1) was high in 8 districts showing that even among the poor there is an unequal spread of income. Both Gini Coefficient and SPG show high inequality levels in the districts of Moneragala, Kurunegala and Rathnapura.

2.2 Income and Consumption Poverty

The poverty headcount indices by Sector, Province and District 2006-2007 are given in Table 9.

The Household Income and Expenditure Survey conducted by the Department of Census and Statistics estimated the official poverty line in the year 2007 at Rs 2233 per person per month. The poverty head count index (PHCI) is the proportion of the national population whose incomes are below the official threshold/s set by the national government.

Squared Poverty Gap Index


0 – 0.5

0.51 – 1.0

1.1 – 1.5

1.6 – 2.0

2.1 – 3.0


52

Table 9 : Per Capita Monthly Food and Non-Food Expenditure and Poverty Head Count by Sector, Province and District, 2006-2007*.

Sector/ Province/ District

Mean monthly total real expenditure per-capita (Rs)

Poverty

Head Count Index (%)


Squared Poverty Gap

Index (%)

Gini Co-efficient

No. of poor persons (000)

Contribution to total poverty (%)

Sri Lanka 5436 15.2 3.1 0.9 0.40 2805 100

Sector Urban Rural Estate

7556 5200 3078

6.7

15.7 32

1.3 3.2 6.2

0.4 1.0 1.8

0.43 0.38 0.26

184

2,303 318

6.6

82.1 11.3

Province Western Central Southern Eastern North-Western North-Central Uva Sabaragamuwa

6935 4560 5302 4843 5035 5698 3879 3982

8.2

22.3 13.8 10.8 14.6 14.2 27.0 24.2

1.5 4.6 2.6 2.1 2.9 2.8 6.2 4.9

0.4 1.4 0.8 0.6 0.9 0.8 2.1 1.5

0.41 0.38 0.37 0.33 0.36 0.40 0.35 0.34

471 573 338 100 342 168 346 467

16.8 20.4 12.1

3.6 12.2

6.0 12.3 16.6

District Colombo Gampaha Kalutara Kandy Matale Nuwara Eliya Galle Matara Hambantota Batticaloa

7885 6693 5499 5151 4960 3254 5468 5205 5131 4757

5.4 8.7

13.0 17.0 18.9 33.8 13.7 14.7 12.7 10.7

1.0 1.4 2.7 3.8 3.7 6.8 2.9 2.4 2.5 1.5

0.3 0.4 0.8 1.2 1.0 2.0 0.9 0.6 0.7 0.4

0.42 0.41 0.38 0.39 0.39 0.29 0.39 0.37 0.34 0.32

125 196 149 230 89

254 146 119 73 36

4.5 7.0 5.3 8.2 3.2 9.1 5.2 4.3 2.6 1.3


53

Sector/ Province/ District

Mean monthly total real expenditure per-capita (Rs)

Poverty

Head Count Index (%)


Squared Poverty Gap

Index (%)

Gini Co-efficient

No. of poor persons (000)

Contribution to total poverty (%)

Ampara Kurunegala Puttalam Anuradhapura Polonnaruwa Badulla Moneragala Rathnapura Kegalle

4892 4924 5251 5754 5586 4172 3340 4073 3861

10.9

15.4 13.1 14.9 12.7 23.7 33.2 26.6 21.1

2.4

3.1 2.3 2.8 2.8 5.3 7.8 5.3 4.3

0.7

1.0 0.7 0.8 1.0 1.7 2.8 1.6 1.3

0.34

0.36 0.37 0.40 0.39 0.36 0.31 0.36 0.31

64 238 104 118 50

197 150 292 175

2.3

8.5 3.7 4.2 1.8 7.0 5.3

10.4 6.2

* The survey did not cover some remote areas in Batticaloa and Ampara where high poverty level is suspected

Source: Department of Census and Statistics HIES 2006/7

Table 9 shows that urban PHC is roughly about half of that of rural PHC, while rural poverty is roughly half of that of estate PHC. Consumption poverty reduction in Sri Lanka has been modest—about 3 percentage points (from 26 to 23 percent) from 1990–91 to 2002)—and uneven across sectors. Urban poverty halved between 1990–91 and 2002, while rural poverty declined by less than 5 percentage points, and poverty in the estates increased by about 50 percent—making this sector the poorest in the country. Another crucial factor is that over 80% of the poor are in the rural sector, while 11% are in the estate sector. Thus poverty is largely prevalent in the rural sector. The largest poor population was from the Central province (20%) comprising of largely estate poor, followed by the Western Province (16%), largely the urban poor and Sabaragamuwa Province (16%), comprising a mix of rural and estate population. The poor population in these provinces made up about 55% of the total poor. The largest poor populations (over 200,000) were from Rathnapura, Nuwara Eliya, Kurunegala and Kandy districts, while Badulla and Gampaha districts had 197,000 poor persons. The poor population in these 6 districts made up 50% of the total poor population. Thus these districts may be considered the worst affected districts in terms of poverty.


54

Map 7 : Sri Lanka - Number of poor persons by District

The highest proportion of population below the poverty line was in the Uva, Sabaragamuwa and Central provinces and in the following districts, Nuwara Eliya, Moneragala, Rathnapura, Kegalle and Badulla. (Map 7)

Number of Poor (‘000)


<50

550 - 100

100-150

150- 200

>200


55

Map 8 : Sri Lanka – Poverty Head Count 2006-7

Map 8 shows PHC by districts for 2006-7. Two districts of Moneragala and Nuwara Eliya have the high poverty headcount, above 30%. Badulla, Kurunegala and Kegalle have moderate levels of poverty with PHC ranging from 21-30 %. The rest of the districts covered by the survey show low levels of poverty with PHC below 20.

PHCI (%)


1-20

21-30

31-40


56

Figure 10 : Poverty Head Count ratio by District

Source: Department of Census and Statistics Sri Lanka In the year 1990-91, the highest poverty headcount was recorded in the district of Kandy, followed by Moneragala. The PHC was moderately high (above 30%) in districts such as Kalutara, Hambantota, Rathnapura and Kegalle. It was the lowest in the district of Gampaha, with the second lowest recorded in Colombo district. By year 1995-96, the PHC increased significantly in districts such as Moneragala (highest PHC in 1995/96), Rathnapura, Matale, Badulla, Nuwara Eliya, Matara, Kegalle and Puttalam. There were moderate increases in PHC in Kandy district, which already had a high value, and in districts such as Galle, and Anuradhapura. In the rest of the districts PHC declined moderately. In general, PHC appeared to have increased in most districts and particularly in the plantation districts, probably due to greater poverty recorded in the estate population. By the year 2002, the PHC declined in most districts including the plantation districts. Significant declines were recorded in districts such as Moneragala, Rathnapura, Matale, Nuwara Eliya, Kandy, and Colombo. PHC increased moderately in only two districts Polonnaruwa and Hambantota and remained unchanged in Puttalam district. There were moderate declines in PHC in the year 2002 in all other districts. Thus, the poverty levels appeared to have declined considerably, including many of the plantation districts which had recorded high poverty levels in 1995/96. Substantial decline in PHC was recorded in almost all districts in the year 2006/7. Only the Nuwara Eliya district showed a substantial increase in poverty, with PHC increasing from 23% in 2002 to 34% in 2006/7. The PHC in Moneragala declined marginally from 37% in 2002 to 33.7% in 2006/7. Thus only two districts, Nuwara Eliya and Moneragala recorded high PHC levels in 2006/7, while all other districts recorded low levels of PHC. Only three districts had PHC values above 20%, with Colombo and Gampaha districts recording PHC values below 10%. Thus, the poverty levels appeared to have declined in most areas in the 2006/2007, compared to previous levels.

Several factors may have contributed to this sharp declining trend in poverty. These factors include, improvement in the economy, decline in unemployment due to a higher level of

0

10

20

30

40

50

60

Perc

enta

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Districts

Poverty Headcount Ratio

1990-91

1995-96

2002

2006-07


57

public employment (700,000 new employment opportunities were created between 2002-2007, of which 278,000 was in the State sector - Census and Statistics and Management Services Departments). Other factors such as rise in wages, increased public investments as well as expenditure on rural development, expanded health and education facilities, increased access to public utilities, and favourable producer prices for agricultural produce, may also have contributed to the decline in poverty. Enhanced investments in education and health including nutritional intervention programs during the period, contributed to increased access to education and health, nutritional status as well as skills of youth. Moneragala remains one of the poorest districts, while high poverty levels in Nuwara Eliya may be attributed to the high poverty in the estate sector.

2.3 Health and Nutritional Status

Figure 11 : Infant Mortality


Despite the internal problems existing in the country, Sri Lanka has continued to maintain and improve the standards of their health services and thus the health conditions of its people. This is especially true with respect to the health status of infants and young children. This is quite evident in the trend line for infant mortality. The infant mortality rate for Sri Lanka has been decreasing and over a period of 13 years it had declined by nearly 10%. This decline is attributable to health programs that were implemented by the government focusing on child care. These programs were also initiated to educate mothers on the requirements and ability to maintain health standards for themselves and their children.

02468

101214161820

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Rat

e

Year

Sri Lanka - Infant Mortalitity Rate


58

Figure 12 : Nutritional Status of Children


The nutritional status of young children has also improved over the years especially with respect to the condition of stunting. However the other two conditions have only seen slight improvements. The improvement of the nutritional status of children was more prevalent within the Colombo Metro area. However, the decline in cases of stunting and wasting in rural and estate areas as well, suggests that the health programs have had an island wide impact and most parents have access to medical facilities for their children.

Table 10 : Prevalence of Underweight Children Under Five Years of Age

District

%

Colombo 14.1

Gampaha 11.6

Kalutara 16.9

Kandy 25.3

Matale 23.2

Nuwara Eliya 25.3

Galle 23.2

Matara 23.3

Hambantota 23.8

Batticaloa 27.5

0

10

20

30

40

50

60

1993 2000 1993 2000 1993 2000

Stunted Wasted Underweight

Perc

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ge

Year/Characteristic

Health Status of young children

Total

Colombo

Other urban

Rural

Estate


59

District

%

Ampara 22.0

Trincomalee 27.8

Kurunegala 20.6

Puttalam 19.2

Anuradhapura 25/0

Polonnaruwa 25.6

Badulla 32.8

Moneragala 26.6

Rathnapura 23.9

Kegalle 23.3

Sector %

Urban 16.6

Rural 21.7

Estate 29.7

Total 21.6

Source: Demographic & Health Survey 2006/07

Department of Census & Statistics.

Definition : Numerator: Number of children (0-59) whose weight for age

There is high incidence of underweight children in the Estate sector and to some extent in the rural sectors. Badulla district had the highest proportion followed by the Eastern Province conflict affected districts of Batticaloa and Trincomalee.


60

2.4 Education Status

Figure 13 Literacy Rates

Source: Time Trend of Poverty Indicators on Population, Employment and Socio - Economic situation 1981 - 2004

Department of Census & Statistics

The literacy rate can be defined as the percentage of people ages 15 and above who can, with understanding, read and write a short, simple statement on their everyday life. (Source: Time Trend of Poverty Indicators on Population, Employment and Socio - Economic situation, 1981 – 2004). The literacy rate of Sri Lanka, just as the health status has been maintained and continues to improve despite the internal problems it faces. The total literacy rate and the literacy rate for each gender have taken similar trends with gradual rises and a slight drop in 2003. Thereafter, it has taken a sharp turn upwards and has continued its upward trend. Within a period of 25 years the literacy rate has increased by nearly 6%. However, the Literacy rate among males continues to be higher than among females but this gap appears to be narrowing.

7678808284868890929496

1981 1994 2001 2004

Perc

enta

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Year

Literacy Rates of Sri Lanka

Male

Female

Total


61

Figure 14 School Enrolment Ratios



The ratio of enrolment to schools also seems to be increasing. Although, there was a sudden drop in 2001 and 2003 conditions appear to have improved thereafter. By 2004, the primary enrolment ratio had reached 98% which is one of the key factors attributable to the rising literacy rate in Sri Lanka.

92

93

94

95

96

97

98

99

1996 1997 1998 1999 2000 2001 2002 2003 2004

Rat

io

Year

Primary and Secondary School Enrolment Ratio

Net Primary Enrolment Ratio

Net Secondary Enrolment Ratio


62

Figure 15 Education Completion Rate



The above diagram shows the completion rate of primary as well as secondary education. Both these conditions have been rising over the years suggesting that young children are now in school longer than before.

2.5 Poverty and Vulnerability

Within the total population, some groups tend to be more vulnerable to poverty than others. Those already considered poor may fall deeper into poverty while others on the margin are in danger of slipping into poverty if effective poverty reduction programmes and policies are not put in place. Poverty reduction has been slow during the past decade or so and has also been uneven across sectors. Poverty reduction appears to have taken place much faster in the urban sector, while it has been rather slow or stagnant in the rural and estate sectors. Poverty data shows that inequality has increased between as well as within sectors or regions.

The concentration of economic growth in the western region may result in marginal groups in other regions to fall into poverty. Therefore, development needs to be distributed among other lagging regions to reduce the vulnerability of marginal and poor households. A better understanding of the patterns and causes of poverty requires representative household data for the entire country, including the North and East. The most vulnerable groups need to be identified and effective remedial action taken to prevent further increases in poverty. Table 11 provides an estimate of the various categories of vulnerable population

Table 11 : Estimates of Vulnerable Population

Category Population % of Total Pop No of Households % of Total HH

Urban poor 184,000 1.0 31,550 0.7

Estate poor 318,000 1.7 60,888 1.3

0

20

40

60

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120R

ate

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Category Population % of Total Pop No of Households % of Total HH

Rural poor 2,303,000 12.5 479,067 10.6

Total poor 2,805,000 15.2 571,505 12.6

Disabled(19 districts) 116,265 0.6

IDPs 800,000 4.3 195,200 4.3

Female Headed HH 4,333,700 23.5 1,057,000 23.4

Samurdhi Recipients 7,382,870 40.1 1,800,000 39.8

Unemployed >15 years 446,200 2.4

Under-employed 337,000 1.8

Total 18,400,000 100.0 4,524,000 100.0

Source: HIES, 2006/7 (conducted in 19 districts, excluding 5 districts in Northern Province and Trincomalee district) and Labour Force Survey 2007, (conducted in 17 districts), Department. of Census and Statistics.

Table 11 shows that about 570,000 households are living below the poverty line. In addition, a proportion of the 200,000 internally displaced households may also have fallen below the poverty line due to the conflict or other reasons. However, it is difficult to estimate the actual number of persons or households that need intervention, as persons from one category may also be counted in other categories. It is estimated that 4.5% of the labour force is under-employed, the largest rate of unemployment of 6.5% was in the agriculture, followed by industry 5.0% and services sector 2.8%. Thus vulnerable groups may comprise of, the estate poor, temporarily or partly employed landless rural workers and small business owners, farmers or small land owners lacking water sources for cultivation, temporarily employed urban workers and small enterprise operators, internally displaced people either due to the conflict or tsunami, households without any physical, financial or educational assets, and female headed households and households with too old, too young, sick or handicapped members. There is a sizeable population hovering just above the poverty line, belonging to the above mentioned as well as other groups, who risk falling into poverty due to unforeseen circumstances or external shocks such as inflation, loss of employment or death of sole breadwinner, or natural or manmade disasters

2.6 Key Poverty and Development Challenges

Some of the key challenges of poverty alleviation faced by policy makers can be summarized as follows. (This section draws heavily from the World Bank Report No 36568 LK “ Sri Lanka Poverty Assessment “of January 23, 2007

Urban Sector

• In the urban sector, over-crowding, poor housing, sanitation and environmental degradation and lack of financial resources are contributing to the continuation of


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poverty. Better urban planning and quality of urban services can help reduce the costs of over concentration and mitigate its impact on the poor.

• Migration into Western Province has increased in recent years due to rising gaps in economic opportunities, with a high proportion of migrants originating from poorer and conflict-affected districts. These migrants could fall into poverty and add to the urban poverty problem.

Rural and Estate sectors

• Poverty in rural areas is higher among agricultural households, due to high levels of under-employment and low agricultural incomes. Higher prices for agricultural products appear to have benefited the middle-men more than the producer. Improved farmer access to technologies, trade, land and irrigation will help raise agricultural productivity.

• Given limited opportunities in agriculture, poverty reduction can be improved through growth of the rural non-farm sector employment by reducing current constraints faced by rural poor in start-up of new enterprises. Improving connectivity of remote areas, providing infrastructure and access to energy and transport, as well as appropriate micro-finance resources, market information and markets will facilitate establishment of new enterprises and provide additional incomes.

• Improving education and skills in remote areas can enhance employment choices, including the ability to migrate. Expansion of coverage of social programs, including Samurdhi transfers to estate poor can reduce estate poverty.

• Economic opportunities are adversely affected by marginalization from the mainstream of the estate poor due to the current organizational structure of estates. Improving connectivity to towns, coverage of National Identity Cards and quality of health and education services can help improve economic opportunities.

• Income from outside enterprises and remittances from overseas migrants reduces poverty. But the ability of rural/estate households to diversify their sources of income is low compared with the rest of the country.

Conflict-affected area:

• The conflict-affected North and East lag behind the rest of the country in economic infrastructure and key human development outcomes. Sustainable peace remains a necessary precondition for sustained economic growth and poverty reduction in this region.

• Remittances appear to have contributed to safeguarding income and consumption levels to a certain extent.

• The increasing number of IDPs from the current conflict areas may have temporary impacts leading to poverty and malnutrition. Removing constraints on the mobility of people and goods, such as on fishing will yield significant economic benefits in areas of past or recent conflict.

General Issues

• Basic welfare indicators (e.g., child and maternal mortality, primary and secondary enrolments) are generally high for both the poor and non poor. But rich-poor gaps exist along certain dimensions that can have lifelong effects on earnings, and perpetuate poverty across generations.


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• The quality of learning could be improved by emphasizing English and information technology skills and ensuring teacher deployment to rural areas.

• Reducing income poverty is likely to reduce malnutrition in the medium to long run. In the short term, nutritional outcomes will benefit from improving access to safe water and sanitation and nutritional awareness.

• Existing social welfare programs like Samurdhi are not performing to their potential, primarily due to targeting problems. A better targeting system will improve the impact of Samurdhi programs.

2.7 Past and Current Policies and Programmes to Address Poverty

Although poverty has been reduced considerably over the last two decades or so, rural and estate poverty remains high enough to warrant suitable interventions by policy makers. Sri Lanka has been committed to a well-established social welfare program, providing free health and educational services, since the early 1900s.Public expenditure in health and education grew to 6% of the GDP during 1948-52 and remained at this level up to the 1970s. As a result of improved health care and education, mortality rates declined rapidly, and population increased at rates close to 3%, resulting in large population increases in the 1950s. However, improved education and other social welfare programs had an opposite effect on population growth rate, which started to decline by the early 1980s and has continued to decline up to the present. Apart from free education and healthcare services, the government introduced a food subsidy program to reduce the impact of World War II, in the 1940s and continued up to 1977. This program provided a fixed amount of rice and wheat flour at a subsidized price to all households in Sri Lanka.

With the opening up of the economy in 1977, an attempt was made to target the food subsidy program to the actual poor and needy population. In 1978, the food subsidy program was restructured and redirected to the poorest of the population. Consequently, food subsidy was issued only to households with income of Rs 300 or less per month for five or more persons, thereby reducing the number of people receiving subsidies by half. Towards the end of 1979, food subsidies in the form of a rationed quantity of food was abolished and replaced with a food stamp program (FSP), for the same category of people. An evaluation of the FSP showed that only 38% of the total food stamp payment reached the intended poorest 20% of the population. The remainder of the subsidy went to higher income groups and was therefore the FSP was later restructured.

An economic reform program was instituted in 1989, to promote sustainable growth. Some of the reforms introduced such as removal of subsidies, restoration of macroeconomic imbalances, and exchange rate re-adjustment would adversely affect the poor, as it would remove subsidies on wheat flour, rice, bus fares, and sugar and also devalue the Rupee. To address this problem, the government decided to set aside 3.0% to 3.5% of the GDP every year for programs to improve the living standards of the poorest 20% of the population.

Thus the first attempt at poverty alleviation was the Janasaviya Program (JP) initiated in 1989. The program intended to transfer Rs 2500 per month to each poor household for a period of two years. In addition, the JP provided for credit based entrepreneurial development, free mid day meals, uniforms and books for school children. An evaluation of the JP identified many shortcomings. In addition to being costly and unsustainable, the selection criteria were not defined properly and the benefits not related incomes leading to


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inequities and inclusion of non-poor in the program. The benefits were high compared to prevailing income levels, leading to disincentives to work. Poverty being a long- term problem, could not be resolved within the two year limitation of the JP and there was no provision for the inclusion of families falling into poverty after the selection process was completed. A mid-day meal program for the children was started in 1989. A total of US$ 50 million was spent annually in providing one meal a day to all children in primary and secondary schools. This program also failed because it was too costly to sustain and did not reach the group, which was nutritionally most at risk i.e. the pre-school children. The JP program was scrapped after the formation of the new government in 1994.

A more ambitious poverty alleviation program “Samurdhi” was put into effect in 1996, for the improvement of the economic and social conditions of youth, women, and disadvantaged groups of the society. The Samurdhi Program (SP) was basically an income transfer program, providing direct cash grants to over 2 million poor families or 55% of the population as of 1995. In addition, several other subsidiary activities were implemented through this program. These included community and infrastructure development projects, savings programs, banking and credit programs, social insurance, training and entrepreneur development programs as well as self employment schemes. About 80% of the funds allocated were used for income transfers, intended as a consumption supplement. The amount of income transferred was related to the income of the household and ranged from Rs 100 to Rs 1000 per family depending on the household size. The other components of the program were intended to expand the productive asset base of the poor and to create employment through community infrastructure development.

Both the design and implementation of the Janasaviya and the Samurdhi programs were flawed and their effectiveness in creating opportunities or empowering the poor to overcome economic and social barriers was minimized as a result. According to a World Bank evaluation of the schemes undertaken in 2000, the major reasons for their ineffectiveness were:

1. Political bias of the administrators/mobilizers of poverty programs, with party affiliations and voting patterns influencing the allocation of income transfers, which made the poor vulnerable to changes in the political climate.

2. Both programs covered up to 50% of the population, or twice the actual percentage living in poverty. The transfers from the poverty programs reached only between 55%-65% of those in the lowest income groups. Poor targeting resulted in thin spread of income transfers, diverting funds away from the most needy.

3. Central control of poverty programs has hindered the development of communal social capital, and collaborative social relations, reducing the participation of the poor in development.

4. The costly poverty programs (up to 1% of the GDP) have not created sufficient opportunities for the poor. Large expenditures on poorly targeted transfers, lack of sustained rural works programs, long-term administrative costs of hiring poverty workers (over 30,000 workers in the Samurdhi Program) and weak exit mechanisms are some of the basic issues that needed to be addressed.

In 2003, the activities of the program were reviewed and new plans were prepared to implement the SP as a more productive, efficient, sustainable and non-political program. The target of the Samurdhi Program is to cover approximately 1.9 million low income families in the country whose household income is less than Rs. 4500 per month. A review of the SP


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shows that as of August 2005 it did not reach a substantial section of the poor and low income families. A microfinance program was initiated under the Samurdhi scheme, through the establishment a Samurdhi Banking System (SBS). Currently SBS’s have a total of about 2.2 million members of which 63% are females. The growth of membership has been declining during the last few years. Although there is a very high potential for SBSs to increase its outreach, this has not happened due to ineffective procedures and high operational costs. Loan recoveries have been satisfactory but only about 30% of the 1.9 million Samurdhi beneficiaries have used the services for obtaining credit for livelihood and other services. Thus overall, the effectiveness of the Samurdhi Program for alleviating poverty has not improved substantially in the recent period. Not all the actual poor families are receiving benefits from the income transfer program due to poor targeting. The amount received by beneficiaries is not adequate enough to have any impact on poverty by those who receive such transfers, due to inflation as the grants are not pegged to inflation. The Samurdhi Banking System is not effectively reaching all membership due to cumbersome procedures and lack of suitable loan products. The community infrastructure development program is suffering due to lack of funds, because these development programs are being funded by another government program “Gama Naguma” or village development, which is implemented by another government institution. Programs funded by this program are probably not related to the needs of the local community or providing employment to locals.

2.8 Policies to Address Poverty

Comprehensive policies have been drawn up to combat poverty in a holistic manner. These policies address most of the poverty issues highlighted for Sri Lanka and can accomplish the proposed objectives if adequate funding is made available and implementation is effective. However, the funding requirement for such a program is high and unless the government is fully committed towards its objectives of poverty alleviation, the required funds may not be forthcoming. The comprehensive government policies formulated to address poverty in Sri Lanka are described below.

The two main components of the policies are:

(1) Livelihood Development (2) Social protection

The overall vision for the plan is a country in which people are empowered to develop and sustain their livelihoods and improve their standards of living, with special care being taken by the society to ensure that vulnerable people and geographical areas are adequately protected from risks including risks to life and sustenance arising from various sources and are not left behind in the process of development, so that poverty, hunger and deprivation have ceased to have any significance.

Even though economic growth is essential to reduce poverty, the experience during the last few decades suggests that the fruits of growth do not trickle down to the poor automatically. Therefore, there is a need to redesign the development programmes to give a larger share of the benefits to the poor.

However, the government believes that the provision of cash grants does not help to moving out the people from the poverty trap. Therefore, new strategic initiatives will be introduced to create opportunities for the poor in the participation in the economic growth process in productive manner. The strategy of creating opportunities will be characterised by:


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• Focus on livelihood developments for poor families, through microfinance. • Provision of concessionary funding, technical know-how, improved infrastructure and

marketing facilities.. • Invest in special poverty reduction projects and programmes in rural areas • Improved coordination of poverty projects and programmes to avoid duplication. • Establish community organizations to plan and implement poverty alleviation

projects. • Continue the relief assistance aimed at economically and socially disadvantaged

people through payment of direct cash grants. • Establish a Village Upliftment (Gama Neguma) program to develop rural villages.

The key policy objective of the Government with regard to rural development is to develop all villages in the country so that they emerge as micro-centres of growth on modern lines. Gama Neguma (Village Upliftment) will be the main rural development programme, bringing together a number of more specific programmes focused on livelihood development and poverty reduction at village level.

The Samurdhi Program will be reformed for more effective implementation. While confining the cash grants for needy families who are in abject poverty, the other marginal groups are expected to he graduated through income supported projects. Other programmes focused on entrepreneurial development of the poor families and infrastructure development at village level are aimed at reducing the existing poverty trap. Other programs for poverty alleviation will include the following:

2.8.1 Microfinance

At present a microfinance policy is not found in the country. In consequence, there is no institutional mechanism to coordinate the microfinance interventions with other policies, formulated policies. The lack of institutions for specialized training and research in microfinance is a major issue. The financial results of the majority of the MFIs, have failed to conform to internationally accepted standards for sustainability. Services offered do not sufficiently take into account the needs of the target group. Long term credit is lacking. Supply of financial services also differs from region to region. The offers are deficient since they are mainly used by non poor or people close to the poverty line. The microfinance financial services system needs to have a broader outreach to the poor and the very poor members of the population.

It is proposed to establish 500,000 new micro enterprises and upgrade about 10 percent of the existing micro businesses to the category of small industries. It is expected that both unemployment and poverty will drop by 2 percent as a result of these interventions.

The following strategies will be adopted to improve the effectiveness of micro financial institutions:

• Formulate and implement a new policy and strategy for the microfinance sector. Establish a high level body to develop and implement microfinance concepts.

• A separate unit will be established in the Central Bank of Sri Lanka for effective supervision and regulation of microfinance agencies.

32


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• A scheme of reform will be undertaken to make the necessary changes in microfinance institutions run by the Government.

• Establish a system to link the micro enterprise sector with the small and medium industry sector.

• Establish network between microfinance stakeholders for exchange of information and good practices and policy dialogue with the government

• Strengthen the capacity of providers of microfinance through training, knowledge and information transfer.

• The apex microfinance agencies will be strengthened for channeling long term refinancing funds to microfinance agencies.

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3 DISASTER AND SHOCK PROFILE

3.1 Introduction

This chapter aims at presenting the natural disaster and shock profile of Sri Lanka. The Disaster Profile of Sri Lanka is based on the Sri Lanka Historical Disaster Information System, designed by the Disaster Management Centre, Ministry of Disaster Management and Human Rights, in line with DesInventar system developed by the LA RED in Latin America. DesInventar methodology proposes the use of historical data about the impact of disasters, collected in a systematic and homogeneous manner in the process of identifying hazards and vulnerabilities and thus risks on specific geographical units of Sri Lanka, i.e. Districts and Divisional Secretariat Divisions (DSDs). The Disaster and Shock Profile outlines different type of disastrous events with the magnitude of their effects and spatial distribution, showing types of events and to what degree they are relevant compared to other events. Relevancy is based on in terms of the number of reports1 and different effect variables, such as (a) Number of people affected, (b) Loss of life, (c) Number of destroyed houses/buildings, and (d) Loss of agricultural crops. Selected disaster typologies are taken into consideration for the analysis and the data of the selected attributes of disaster typologies is assessed from different perspectives to portray the disaster situation of Sri Lanka for the period of 1974 to 2008.

The Disaster- Hazard Profile of Sri Lanka first presents the disaster typology and how disaster types have been distributed chronologically, seasonally and spatially. Then it focuses on (a) People affected by Disaster; (b) Loss of life due to disaster; (c) Buildings destruction/ damage by disaster, and (d) Agricultural crops loss due to disaster by illustrating different facets of disaster such as; (i) Type of disasters as per DesInventar Disaster Typology, (ii) Annual Time Series Distribution, (iii) Seasonal Distribution, and (iv) Spatial Distribution.

3.2. DesInventar disaster typology

The figure below shows that that there are many types of events in the country but the most common are Animal Attacks (7202 events ), Fire ( 2703 events ), Flood ( 1397 events ), Extreme wind events ( 1288 events), Drought ( 283 events), Landslides ( 1174 events) and Lightning ( 300 events). This diagram illustrates that when considering the total of events within the period of January 1974 to December 2008, the most common one is the animal attack representing 50% of the total, followed by the fire, flood, and extreme wind events representing 19%, 10% and 9% respectively. These four disaster types cover 88% of total number of events during the period under review. Tsunami, the most devastating disaster hit Sri Lanka in 2004 has not been considered for this analysis of disaster typology as Tsunami is considered as a single event.

1 Note: The total Number of Reports does not necessarily reflect the number of disaster events. The DesInventar methodology suggests that the effects of a disaster should be disaggregated in each of the affected geographic units. Under this approach a medium or large scale event that affects multiple geographic units will generate also multiple Reports in the database. This is why this variable will be named as number of Reports (or DataCards) and not ‘events’


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Figure 16 : Disaster Typology

3.2.1 Annual Time Series Distribution

Figure 17 below illustrate the chronological distribution of disaster types over a period from January 1974 to December 2008. It illustrates the dominating impact of the events of animal attacks over a period of time. The sudden increase of animal attack events after 1998 is mainly due to the commencement of systematic recording of events, rather than a sudden outbreak of animal attacks. There are two main trends shown in the diagram. First, from 1974 to 1997, there is slow growth in the number of disaster events fluctuating from 20 to 200 events with several peaks. The second is, from 1997 to 2006 showing a clear trend of very rapid increase of number of disaster data records from the range of 200 records in 1997 to more than 1800 records in 2006, with only break of the trend in 2001. There is a sharp break

Animal attack50%

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of this trend started in 2006, but it is too early to predict whether this is a start of a decline trend or not.

Figure 17 : Trend of disaster events

3.2.2. Seasonal Distribution

The seasonal distribution of disaster types within the period January 1974 to December 2008 is demonstrated in the figure below. It shows the cyclical distribution of events with two peaks, one from April to June and other is from October to December demonstrating a close link with two monsoon seasons in Sri Lanka, i.e. south-west and north-east monsoons. May, which is the peak of south- west monsoon, and September/ October- the peak of north-east monsoon. These two months are the peak of disaster events occurrence with the highest affected months exceeding 1400 events. On the other hand, February and August, two months in the center of the inter-monsoon periods, are the months with the least amount of recorded events, around 800 - 1000 events. Seasonal distribution of disaster typology shows that all disaster types appear to occur throughout the year, with the seasonal changes of scale. Records of floods and landslides are higher in October, November & December, while drought is more prominent in March and August, the inter-monsoon period. Comparative importance of number of animal attack events is less in the period of November to January.

0200400600800

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Figure 18 : Seasonal Distribution of Disaster Events

3.2.3. Spatial Distribution

Uneven spatial distribution of disaster data records from 1055 to less than198 by districts and from above 90 to less than 10 by DSDs is apparent from the Map 8, which illustrates the geographical distribution of hazards across the Districts and Divisional Secretarial Divisions in the country The highest number of events has occurred within the district cluster of Anuradhpura, Polonnaruwa, Matale and Kurunegala. Mullaitivu, Vavuniya, Mannar and Kilinochchi are the least hazards prone district with minimum events occurring.

The DS divisions which have the occurrence of large number of events are clustered around the above mentioned districts. However, few DS divisions in the Southern parts of the island like Tissamaharama also appear to have large number of events occurring.

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Map 9 : Spatial Distribution of data records by Districts and DS Divisions

Districts DS divisions

3.2.4. Conclusion

The overall disaster typology in Sri Lanka does not seem to be distributed evenly. Sri Lanka seems to be most affected by the disaster of Animal Attacks (50%). Although the other disasters do not hold such a high proportion to Sri Lanka’s disaster typology, they hold similar proportions with respect to each other. In terms of time series distribution, animal attacks seem to have increased within the years of 1999-2007 which is attributable to the recent availability of data about wild elephant attacks from the wildlife conservation department. However, disasters such as floods seem to take place every year. A look at seasonal distributions shows that all disasters appear to occur evenly throughout the year with May being the most affected by disasters. Further, Anuradhapura, Polonnaruwa, Kurunegala and Matale are the hotspot districts for disaster risk whereas districts such as Mullaitivu, Vavuniya and Mannar and Kilinochchi take on a cooler stance.

3.3 People Affected By Disaster

3.3.1 DesInventar Disaster typology

Figure 19 below shows that people in the country are quite affected by disaster. As illustrated in the diagram, 93% of the people affected by disaster are either affected by floods (48%) or droughts (45%) without taking into consideration the Tsunami. Extreme wind events are also


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responsible for affecting 7% of the disaster affected people. The data records shows that during the last 34 years nearly 29 million people were affected by natural disasters. The share of climatologically disasters is 99%. This shows the dominating importance of climatological hazards as other types of disasters hold a very negligible proportion with respect to number of people affected. Sri Lanka is an agricultural country mainly based on natural resources and lives of the majority of people are directly linked with the environment, the most dynamic element of which is the climate. Therefore any small variation of climate affects people.

Figure 19 : People Affected by Disaster - DesInventar Disaster Typology

A. With Tsunami

B. Without Tsunami

Animal attack0%Extreme Wind

Event6%

Drought43%

Fire0%

Floods47%

Landslides0%

Lighting0%

Tsunami4%

Animal attack0%

Extreme Wind Event7%

Drought45%

Fire0%

Floods48%

Landslides0%

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3.3.2 Annual time series distribution

The time series distribution with respect to people affected by disaster is fluctuating from 0 to 3 million affected people as per the records. Figure below shows that the drought and flood are the major common causes affecting people almost all years. However, the proportionate impact of these two main types of disasters drastically changes year by year as drought dominates about half of the years of the period, while the remaining half of the years, people are affected more by floods than drought. Other dominating disaster type is the extreme wind event, but it is not as frequent as flood and drought. A sudden increase in people affected by extreme wind effects can be seen in 1978 and 2000 only. During the period from 1974 to 2008, number of affected people due to disaster shows high fluctuation with an increasing trend in general.

Figure 20 : People Affected By Disaster - Annual Time Series Distribution

A. With Tsunami

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B. Without Tsunami

3.3.3 Seasonal Distribution

Figure 21 below shows the seasonal distribution of people affected by disasters. It shows a cyclical distribution with three peaks in May, August and December/ January, These peaks may have a direct correlation with the monsoon. The two peaks due to floods have a clear correlation with the monsoon rain and the August peak caused by drought show a relationship with the inter-monsoon period. The other months appear to take on a lower value.

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Figure 21 : People Affected by Disaster – Seasonal Distribution

A. With Tsunami

B. Without Tsunami

3.3.4 Spatial Distribution

Map 9 below illustrates the geographical distribution of people affected by disaster across the Districts and Divisional Secretarial Divisions in the country respectively. It shows the uneven spatial distribution of human impact of disaster from 131,902 to more than 3,605,544 (upper limit) disaster affected persons by districts and from 0 to more than 86,210 (Upper limit) by DSDs. The highest number of persons affected by disaster is recorded in the Batticaloa district while the districts of Kegalle, Matale, Kandy, and Nuwara Eliya appear to be the least affected. DS divisions in the Southern and North -Western parts of the island have a higher

010000002000000300000040000005000000600000070000008000000

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number of people affected. However, the magnitude of people affected is not distributed evenly throughout the island.

Map 10 : People Affected By Disaster Spatial distribution

District DS divisions

3.3.5 Conclusions

The highest number of people were affected by floods during the period of 1974-2008 and next highest number of people affected has been due to drought. Although the incidence of animal attacks are high, people are least affected by it. It is also important to note that people located in Batticaloa are most affected by disasters whereas those within Kandy, Matale, Kegalle and Nuwara Eliya are least affected.

3.3 Loss of Life due to Disasters


The diagram below shows the distribution of loss of life with respect to each disaster type. As seen in diagram A, 90% deaths during the period from 1974 to 2008 is due to the Tsunami, which occurred for a few hours. The diagram B shows the situation without Tsunami and the deaths appear to occur evenly by Extreme wind events (27%), Landslide (25%), and Animal attack (25%). 78% of total deaths due to disaster have resulted from the occurrence of these three disaster types. The proportion of loss of life by other disasters is not very significant


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compared to the above mentioned three disasters. Flood (12%) Lightning (8%) and fire (2%) are other important disaster types in respect of loss of life.

Figure 22 : Loss of Life due to Disasters - DesInventar Disaster Typology

A. With Tsunami

B. Without Tsunami


With respect to an annual time series distribution, the occurrence of deaths due to disaster has been quite low in general. During the period 1974 – 2008 reporting of more than 200 deaths per annum has shown only in three year, while in the remaining 27 years annual death rate has recorded below 100. The exceptional break in the normal annual distribution is seen in

Animal attack3%

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1978 with a high death toll exceeding 900, mainly caused by extreme wind events. There are also two secondary level peaks in 1989 and 2003.

Figure 23 : Loss of Life due to Disasters - Annual Time Series Distribution

A With Tsunami

B. Without Tsunami

3.4.3 Seasonal distribution

The seasonal distributions of loss of life have been quite cyclical without Tsunami as shown in Figure 24 (B. Without Tsunami). November appears to have the most loss of life which is mostly attributable to the extreme wind events. Apart from this, May and June have also experienced a higher rate of loss of life than average monthly rate of loss of life. The least death rates can be seen from February – May and July – October. The total picture

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completely changes with Tsunami, as shown in ‘A’ showing December as the dominating month.

Figure 24 : Loss of life due to disasters – Seasonal distribution

A. With Tsunami

B. Without Tsunami


Map 10 below illustrates the geographical distribution of loss of life due to disasters across the Districts and Divisional Secretarial Divisions in the country respectively. It shows the uneven spatial distribution of loss of life by disaster between 72 to 139 loss of life in Vavuniya, Mannar, Puttalam and Moneragala districts to more than 4615 loss of life due to disasters in Ampara Districts, which has the highest number of recorded loss of life due to

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disasters. Very uneven distribution of loss of life is seen in DS divisions showing 0 to more than 14 recorded loss of life.

Map 11 : Loss of life due to disasters – Spatial distribution


3.4.5 Conclusions

The most deaths caused by hazards in Sri Lanka are due to the extreme wind events, landslides and animal attacks representing 78% of total loss of life. Uneven chronological, spatial and seasonal distribution of recorded loss of life due to hazards is a common phenomenon showing a close links with the weather patterns in Sri Lanka, especially with the monsoon. Further, the most deaths due to natural disasters take place in the districts of Ampara, Mullaitivu, Hambantota and Galle.


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3.5 Building Destruction Damage by Disasters


Figure 25 below shows the distribution of records of building damage due to various disasters. It can be seen that most damage occurring to buildings are caused by the disastrous wind events and flood with a total of 95% of total number of building destruction/damage by these two disasters. Other important disaster types causing building damages are landslides and animal attack. However, disasters such as fire, lightning and drought cause no building damage.


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Figure 25 : Building Destruction Damage by Disasters - DesInventar Disaster Typology

A. With Tsunami

B. Without Tsunami


Over time building destruction appears to be quite low with an exception in 1978 and 2000. In these two periods most destruction has been caused by extreme wind events. Apart from these two exceptional peaks most of the damage to buildings is generally caused by floods.

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Figure 26 : Building Destruction Damage by Disasters - Annual time series distribution

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B. Without Tsunami


The seasonal distribution appears to take on a cyclical pattern, as illustrated in Figure 27 below. Most destruction and damage are caused during the months of November, December and January and once again in May. In the first cycle most damage appears to be caused by extreme wind events whereas during May most damage is caused by floods.

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Figure 27 : Building Destruction Damage by Disasters - Seasonal distribution

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3.5.4 Spatial distribution

The map below shows the spatial distribution with respect to building damage by disaster. It can be seen that the district most affected is Polonnaruwa, whereas districts such as Vavuniya, Mannar, Moneragala and Kandy are the least affected. Further, DS divisions located in the Northern districts and Eastern districts appear to have incurred very high building damage. However some of the DS divisions in the Northern districts appear to be the least affected.

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Map 12 : Building Destruction/ Damage by Disasters - Spatial distribution

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3.5.5 Conclusions

About 95% building destruction and damage are caused by disastrous wind events, tsunami and floods. Other important disaster types causing building damages are landslides and animal attack. Except in 1978, 2000, and 2004, general annual rate of building destruction appear to be quite low and in these two peaks most destruction has been caused by extreme wind events, whereas in the remaining years the main cause for damage to buildings is floods. The seasonal distribution appears to take on a cyclical pattern and most destruction and damage have been occurred during the period of November, December and January and in May. The most affected district is Polonnaruwa whereas the districts such as, Mannar, Vavuniya Moneragala and Kandy are the least affected

3.6 Agricultural Crop Loss due to Disasters


Agricultural crop losses also appear to take on a similar typology as building damage. Only three disaster types cause agricultural crop damages and the drought (56%) and flood 41%) hold the largest percentage (95%) while Extreme wind event share the balance.


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Figure 28 : Agricultural Crop Loss due to Disasters - DesInventar Disaster typology

A. With Tsunami

B. Without Tsunami


The annual time series distribution for agricultural crop loss takes on a cyclical pattern with three peaks in 1987, 2001 and 2004. Further, most of the damage appears to be caused by droughts in these periods. However, in 1978 and 1984 most of the crop loss is caused by floods.

Figure 29 : Agricultural Crop Loss due to Disasters: Annual time series distribution

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A. With Tsunami

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The seasonal distribution of agricultural crop loss due to disasters also appears to be cyclical. The first peaks take place in the months of November, December, January and February. During this period, most damage is caused by floods which can be attributed to the monsoon time periods. The next peak can be seen in August and September. Here, however, most of the agricultural loss is caused due to the drought. Since these two months are the harvesting periods, a slight drought would cause a significant crop loss.

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Figure 30 : Agricultural loss due to disasters – Seasonal distribution

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The spatial distribution shows that the district of Kurunegala and Ampara appears to have the highest damage by crop loss. However, districts such as Colombo, Kandy and Kalutara experience very low crop loss. This is because the agricultural sector is not as significant in these districts than in other districts. Further, DS divisions located in the Southern and Western parts of the island appear to be most affected unlike certain DS divisions in the Northern and Eastern parts of the island.

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Map 13 : Agricultural Crop Loss due to Disasters: Spatial distribution

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3.6.5 Conclusions

Mainly drought (56%) and flood (39%) and Extreme wind event (5%) have caused agricultural crop damages. The annual time series distribution for agricultural crop loss takes on a cyclical pattern with three peaks in 1987, 2001and 2004 and damage appears to be mainly caused by drought and flood. The seasonal distribution of agricultural crop loss shows cyclical distribution with two peaks. The district of Kurunegala and Ampara appears to have suffered the highest damage by crop loss.


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4 HAZARD RISK AND DISASTER IMPACT

4.1 Introduction to Geological Hazards

A geological hazard is a natural geologic event that can endanger human lives and threaten human property. Earthquakes, landslides, tsunamis, and volcanoes are all types of geologic hazards. The landslide is the most common geological hazard in Sri Lanka, which has limited experience in earthquakes. A debris flow is a combination of water-saturated loose soil, rock, organic matter, and air, with material varying in size from grains of clay to large boulders. Such flows are formed when loose masses of unconsolidated wet debris become unstable. Most landslides happen on steep or moderate slopes, but lateral spreads usually occur on very gentle slopes or in flat terrain.

The tsunami, hit Sri Lanka in December 2004, after more than 2000 years and as per our long recorded history, is the most devastating natural hazard ever recorded. Tsunamis are large, destructive waves that are caused by the sudden movement of a large area of the sea floor. Most tsunamis are caused by earthquakes, some are caused by submarine landslides, a few are caused by submarine volcanic eruptions and on rare occasions they are caused by a large meteorite impact in the ocean. The December 2004, magnitude 9.0 powerful earthquake near Sumatra produced the largest trans-oceanic tsunami in over 40 years, and killed more people than any tsunami in recorded history. Sri Lanka was the second-most devastated country (after Indonesia), with: more than 30,000 deaths; severe damage to seventy percent of its coastline; and more than a million persons rendered homeless.

4.2 Earthquake

Earthquakes of low to moderate magnitude have been recorded over the past 400 years in Sri Lanka, but with very limited damage. Therefore, no accurate data is available and has not been considered for this analysis.

4.3 Tsunami

At 0059 GMT, a massive earthquake registering 9.0 on the Richter scale struck off the coast of Sumatra, Indonesia. Sri Lanka has been extremely hard-hit in terms of loss of life, infrastructure, and economic assets. The 2004 tsunami is widely acknowledged as the largest, most devastating natural catastrophe in the long history of the country. Two hours after the first earthquake occurred, the tsunami waves struck almost two-thirds of the coastline of Sri Lanka across thirteen districts, including Jaffna in the north, the eastern and southern coast, and parts of the west coast as far as Chilaw. The waves penetrated inland areas up to 500 meters in many places, leaving behind few intact structures and killing or injuring tens of thousands of people. Coastal infrastructure systems, including roads and railways, power, communications, water supply and sanitation facilities, and fishing ports have all been severely damaged. The tourism sector was also affected due to physical damage and cancellation of future bookings.

It has been estimated that more than 30,000 people in Sri Lanka were dead. Displaced person estimates stand at 443,000, while the affected population is estimated between one to two million, out of a total population of 19 million people approximately. The Government estimates the number of damaged houses at more than 130,000, of which more than 99,000 have been completely destroyed. About 217,000 people are still living in relief camps, while approximately 226,000 people have moved in with friends or relatives.


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Though the tsunami is the most disastrous hazard ever hit Sri Lanka, no accurate historical statistics are available. This is an unusually rare, extraordinary and unique incident, the frequency of which is not known. Taking these factors into consideration, the impact of the tsunami is not considered in this assessment. 4.4 Landslides


Landslides had been traditionally considered as minor type of disaster and not a common occurrence in Sri Lanka. Until the year 2002, the annual average number of landslide records did not exceed 50. However, the data shows a sudden increase in the occurrence of landslides during the years 2003 – 2008. This increase does not reflect actual increase in the number of landslide occurrences but an increase in the availability of data due to better record keeping. It is possible that more landslides may have occurred prior to this period but had not been recorded.

Figure 31 : Annual time series distribution of landslides

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Seasonal distribution of landslides demonstrates a clear link with the distribution of the rainfall as illustrated in the figure below. The records of landslides are high in the months of May and June and once again from November to January, showing a clear relationship with two monsoon seasons in Sri Lanka. The second peak is higher than the first and November is the month with the highest recorded landslides exceeding more than 275 data records.

Figure 32 : Seasonal distributions of landslides


With respect to spatial distribution, most landslides appear to occur only in the Southern, Uva and Central province within the districts of Badulla, Nuwara Eliya, Kegalle and Rathnapura which are the most landslides prone districts having the highest incidences. A similar pattern can be seen on the map for DS divisions where the higher incidence can be seen within the Uva Province.

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Map 14 : Spatial distribution of landslides by Districts and Divisions

4.4.4 People affected (Annual time series and Spatial Distribution)

As the incidences of landslides are relatively low and most of them are minor incidents, the number of people affected is also quite low. However, there are significant number of people affected in 1986, 1989 and 2007.

As most landslides appear to occur only within the Southern, Uva and Central province people affected are centralized within this area. The highest impact can be seen in the districts of Badulla, Nuwara Eliya and Rathnapura. A similar pattern can be seen on the map for DS divisions where the highest effect can be seen within the Uva Province



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Figure 33 : People affected by landslides – Annual time series Distribution

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Map 15 : People affected by landslides – Spatial distribution



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4.4.5 Loss of life (Annual time series and Spatial Distribution)

The occurrence of deaths is greater and more frequent than the number of people affected. It can be seen that almost everywhere that the country has experienced a landslide, loss of life has taken place. The highest death toll due to landslides was experienced on the year 1989.

Spatially, too, loss of life has taken a similar pattern as people affected. However, the highest death toll was experienced in the districts of Nuwara Eliya, Kegalle and Rathnapura. A similar pattern is also depicted for the deaths experienced in the DS divisions.

Figure 34 : Loss of life due to landslides - Annual time series distribution

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Map 16 : Loss of life due to landslides - Spatial distribution


4.4.6 Building Destruction and Damage (Annual time series and Spatial Distribution)

Throughout the time period, the occurrence of building destruction have been quite low with the exception of 4 years, namely, 1986, 2003, 2006 and 2007. With respect to spatial patterns it takes on a similar pattern as that experienced by people affected and loss of life.


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Figure 35 : Building Destruction and Damage: Annual time series Distribution

Map 17 : Building Destruction and Damage: Spatial Distribution


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4.4.7 Agricultural loss (Annual time series and Spatial distribution)

The occurrence of damage to paddy and other crops by landslides are not very significant and evenly distributed chronologically. From 1974 to 1984, there were no recorded damages but 1985 to 1990 represent a cluster of years showing damages. Then from 1991 to 2001there were no records of damages, except for a few in 1997. However, a very high crop loss was experienced in the year 2007. This maybe attributable either to the magnitude of the landslide or the area in which may occurred. Further, a landslide occurring during a harvesting period would incur severe crop loss. Spatially, most of the crop loss is centralized within the Southern, Western and Uva province with most loss being experienced in the districts of Kandy and Badulla. A similar pattern can be seen in the DS divisions as well.

Figure 36 : Agricultural loss due to landslides - Annual time series Distribution

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Map 18 : Agricultural loss due to landslides - Spatial Distribution

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4.4.8 Conclusions

Until the year 2002, the annual average number of landslide records did not exceed 50. However, the data shows a sudden increase in the occurrence of landslides during the years 2003 – 2008. This increase does not reflect actual increase in the number of landslide occurrences but an increase in the availability of data due to better record keeping. It is possible that more landslides may have occurred prior to this period but had not been recorded. Further, landslides are most prone to occur in the months of November, December and January. With respect to spatial distribution, most landslides appear to occur in the districts of Badulla, Moneragala, Nuwara Eliya and Kegalle. People affected, loss of life, building damage and crop loss also appear to take the same trend with only the above mentioned districts being most affected.


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4.5 Climatological Hazards

Climatic hazards include storms of various types, damage to coastlines by ice or wave action, droughts, floods, snow, hail, lightning, and natural fires. Tropical hurricanes are the worst and most widespread natural hazard, causing damage not only directly by wind action but also by flooding. The most serious incident in recent years resulted in 500,000 deaths in Bangladesh in 1970, while flooding of the Yangtze River in China following typhoons killed 40 to 50 million people in the mid-19th century. Tornadoes—rapidly rotating circular storms, particularly prevalent in the United States—also cause substantial damage.

Floods and droughts are often closely linked in both space and time, with many parts of the tropics having alternating wet and dry seasons. Human interference has increased the severity of flooding in some areas, due to changes in land use such as urbanization and deforestation. Increased incidence of drought has caused major civilizations to collapse in the past and currently much of Africa is becoming drier, particularly on the fringes of the Sahara, where the problem is exacerbated by poor farming practices and over-exploitation of fuel wood.

Taking into consideration the historical data available in Sri Lanka, only Drought and Floods (Flooding including floods, flash floods and heavy rains) have been included as the main types of climatic hazards affecting people. While most serious fires are caused by humans and natural fires are rare, it has been included as other hazards.

4.6 Drought


Sri Lanka as an island located close to the equator is prone to warm weather conditions. This can be seen by the yearly occurrence of droughts in Sri Lanka. It is also important to note that the drought seems to have occurred to a large extent in the year 1992 where it has reached its peak surpassing 18 events. There are two secondary peaks in 1983, 1989, 2001 and 2004. There is no clear trend in overall occurrence of drought seen, but there is a clear trend in intensifying the extreme situation.

Figure 37 : Annual time series distribution of drought

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Seasonal distribution of droughts demonstrates a clear link with the distribution of the rainfall as illustrated in the figure below. The records of drought are high in the months of January to March and once again in August and September showing a clear relationship with two inter-monsoon seasons in Sri Lanka. The second peak is higher than the first and August is the month recorded with highest occurrence of drought surpassing 80 events.

Figure 38 : Seasonal distribution of drought


With respect to spatial distribution, the area that appears to have drought occurring mostly is Hambantota where it has surpassed the upper limit. Further, districts such as Gampaha, Colombo, Galle, Mannar and Mullaitivu do not experience as many droughts. This may be due to the geographical location of these districts. Further, with respect to the DS divisions those located in the Southern parts of the island and also those located within the districts of Anuradhapura and Kurunegala appear to experience the largest incidence of droughts. However, most of the DS divisions located in the Northern and Western parts of the island experience no droughts at all

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Map 19 : Spatial distribution of drought by Districts and DS Divisions


4.6.4 People affected by drought (Annual time series and Spatial Distribution)

People are most affected usually by the most severe of droughts and the most effect has come during three periods, initially in 1988, then in 2001 and finally in 2004 with the largest number of people being affected in 2001 (300,000).

With respect to spatial distribution, people located in Anuradhapura, Kurunegala, Puttalam, Hambantota and Moneragala are the most affected. However, people located in the districts of Gampaha, Kegalle, Colombo, Kalutara, Galle and Nuwara Eliya appears to be the least affected. With respect to DS divisions, a few of them scattered about the island appear to highly tax people in the event of a drought. Despite this, many DS divisions do not have a very large amount of people affected especially the South Western, Central and Northern parts of the island.


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Figure 39 : People affected by drought – Annual time series Distribution

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Map 20 : People affected due by drought - Spatial Distribution


4.6.5 Agricultural Crop Loss (Annual time series and Spatial Distribution)

With respect to agricultural loss, the annual time series distribution has been fairly consistent and does not take on a cyclical pattern. However, there has been an unusual increase in crop loss in the period 1988, 2001 and 2004. This may be attributable to the severity of the droughts in that year. Spatially, the largest amount of crop loss can be seen in the districts of Kurunegala where it has surpassed the upper limit. However, districts such as Kegalle, Gampaha, Colombo, Kalutara, Mullaitivu and Galle appear to be the least affected in terms of crop loss. With respect to the DS divisions, a majority of them incur no or very little crop loss. Only a very few DS divisions scattered around the island experience a large crop loss.


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Figure 40 : Agricultural loss due to drought – Annual time series Distribution

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Map 21 : Agricultural loss due to drought – Spatial Distribution


4.6.4 Conclusions

Sri Lanka as an island located close to the equator is prone to warm weather conditions. This can be seen by the yearly occurrence of droughts in Sri Lanka. It is also important to note that the drought seems to have occurred to a large extent in the 1992. A look at Seasonal distribution shows that droughts occur largely in the month of August. With respect to spatial distribution, the areas most affected appear to be the districts of Kurunegala, Puttalam, Hambantota, Moneragala and Ampara. People are most affected usually by the most severe of droughts that have taken place in the years of 2001 and 2004. Further, people located in the above mentioned districts are mostly affected by drought. With respect to agricultural loss, the spatial effects are similar to people affected. However, along with 2001 and 2004, the year 1987 has also seen a large loss in agriculture.

4.7 Flooding (including floods, flash floods & heavy rain)


The diagram below shows the occurrence of flooding through the periods 1974 - 2008. Throughout the years it appears to take on a consistent pattern. However, after the period


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1998 it has taken on quite a fluctuate pattern. The incidences of flooding seem to be most prevalent in the latter years with most flooding occurring in the year 2006.

Figure 41 : Annual time series distribution of flooding


Flooding in Sri Lanka appears to take on a cyclical pattern with peaks at two different points of time. The first peak can be seen in the May and thereafter a second peak can be seen in November and December. This may be attributable to the monsoon patterns.

Figure 42 : Seasonal distribution of flooding

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With respect to spatial distribution floods appear to occur mostly in the districts of Kalutara and also in areas like Colombo, Gampaha, Matara and Jaffna. With respect to DS divisions, the highest incidence of flooding appears to occur in the Western parts of the island while most other DS divisions have a low incidence of flooding.

Map 22 : Spatial distribution of flooding by Districts and DS Divisions



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4.7.4 People affected by floods (Annual time series and Spatial Distribution)

The time trend of people affected appears to be quite fluctuating over the period shown. However, it can be seen that people have becoming increasingly more affected by floods since 2000, with the highest number in the year 2008. Further, people located in Batticaloa appear to be the most affected by the flood along with people in Colombo, Gampaha, Rathnapura and Ampara. Further, people located in districts in the Central part of the island appear to be the least affected. A similar pattern with respect to DS division of people affected can be seen.

Figure 43 : People affected by floods - Annual time series distribution

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Map 23 : People affected by floods – Spatial distribution



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4.7.5 Loss of Life (Annual time series and Spatial Distribution)

The occurrence of loss of life due to floods is relatively quite low and stable over the time period. However, a large increase in the deaths can be seen in 2003 which is out of the norm.

Further, people located in the districts of Kalutara, Rathnapura, Hambantota, Matara and Galle are most prone to loss of life due to floods. In terms of DS divisions, a majority of divisions across the island experience a low death toll due to floods.

Figure 44 : Loss of life due to floods – Annual time series distribution

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Map 24 : Loss of life due to floods – Spatial distribution



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4.7.6 Building Destruction & Damage (Annual time series and Spatial Distribution)

Building Destruction and Damage seems to take on a fluctuating pattern. However, the most damage has been caused by the floods that occurred in 2008. Spatially most of the damage has taken place in the districts of Ampara, Batticaloa, Rathnapura, Kilinochchi and Polonnaruwa. However, districts such as Nuwara Eliya, Matale, Kandy and Vavuniya appear to be the least affected. With respect to DS divisions, most of the damage appears to occur to DS divisions located in the Southern and South Western parts of the island.

Figure 45 : Building Damage and Destruction due to Flooding – Annual time series distribution –

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Map 25 : Building Damage and Destruction due to Flooding – Spatial distribution



Damage to paddy has taken place mostly in the earlier years with the most effect in 1984. Despite this, losses appear to have been consistent and low throughout the time period. Spatially, most damage has taken place in the districts of Kurunegala, Polonnaruwa, Batticaloa, Killinochchi and Ampara whilst least damage has taken place in Kandy, Matale, Nuwara Eliya and Kalutara. With respect to DS divisions, most of the DS divisions are not severely affected.


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Figure 46 : Agricultural Loss due to Flooding – Annual time series distribution

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Map 26 : Agricultural Loss due to Flooding – Spatial distribution


4.7.8 Conclusions

The incidences of flooding seem to be most frequent in the latter years with the most flooding occurring in the year 2006. Further, the floods in Sri Lanka are most likely to occur in the months of May in the first cycle and in December in the second cycle. With respect to spatial distribution floods are most frequent in the districts of Matara, Kalutara, Rathnapura, Gampaha and Ampara.

People have become increasingly more affected by floods with the highest number in the year 2008. Further, people located in the districts of Gampaha, Colombo, Rathnapura and Ampara are most prone to floods and a similar case can be seen loss of life. However, the occurrence of deaths due to floods is quite low except for the year 2003 where it has nearly reached 200. Building Destruction and Damage also seem to take the same stance with most damage occurring in the districts of Rathnapura, Galle, Matara, Hambantota and Kilinochchi. Damage to paddy has taken place mostly in the earlier years with the most effect in 1984 and most damage has taken place in the districts of Kurunegala, Polonnaruwa, Ampara and Batticaloa.


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4.8 Extreme Wind Events (Cyclone, Gale, Strong wind, surge)


The occurrence of wind events over the time period has been quite consistent and low. However, a sudden increase can be seen in the latter periods with highest number of events taking place in 2007.

Figure 47 : Annual time series distribution of extreme wind events


Seasonally, Sri Lanka takes on a cyclical pattern with respect to the incidence of wind events. The incidence of wind events is high in the months from April to June and once again from November to December.

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Figure 48 : Seasonal distribution of extreme wind events


With respect to spatial distribution, wind events are most prevalent in the districts of Rathnapura, Badulla Anuradhapura and Colombo. Further, areas such as Mannar, Kilinochchi, Vavuniya, Hambantota and Matara have experienced lower incidence of wind events. With respect to DS divisions, a majority of them have a low incidence of wind events with a very few numbers experiencing a high incidence of wind events.

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Map 27 : Spatial distribution of extreme wind events



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4.8.4 People affected due to extreme wind events (Annual time series and Spatial Distribution)

As per the diagram below it can be seen that people in Sri Lanka are not very much affected by wind events. However, an exception to that are the years 1978 and 2000 where people affected is very high. Further, people located in the districts of Anuradhapura, Polonnaruwa, Trincomalee and Batticaloa are most prone to wind events which is in a cluster. With respect to DS divisions, those in the Eastern parts of the island have the largest number of people affected whilst those in the Northern parts have a very low number.

Figure 49 : People affected due to extreme wind events – Annual time series distribution

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Map 28 : People affected due to extreme wind events – Spatial distribution



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4.8.5 Loss of Life (Annual time series and Spatial Distribution)

The occurrence of deaths due to wind events is quite rare except for the year 1978 where it has reached nearly 850. Further, deaths appear to occur mostly in the districts of Batticaloa. Deaths in DS divisions are also very low.

Figure 50 : Loss of life due to extreme wind events – Annual time series Distribution

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Map 29 : Loss of life due to extreme wind events –Spatial Distribution



Building Destruction and Damage also do not occur very much due to wind events. However, large damage can be seen in the year 1978 and 2000 which may be attributable to the severity of the winds. Further, buildings located in the districts of Anuradhapura, Polonnaruwa, Trincomalee and Batticaloa appear to be most prone to wind events. With respect to DS divisions, those in the Eastern parts of the island have the largest number of damage while those in the Northern parts have a very low number.


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Figure 51 : Building Destruction and Damage due to extreme wind events – Annual time series Distribution

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Map 30 : Building Destruction and Damage due to extreme wind events –Spatial Distribution



Agricultural crop loss also appears to be an uncommon occurrence, with an exception in 1989 and 2000. Further, most agricultural loss appears to occur in the districts of Moneragala, Ampara, Polonnaruwa and Trincomalee while districts such as Matara, Galle, Matale, Vavuniya and Mannar appear to have the lowest crop loss. With respect to DS divisions, almost all of them experience very low crop losses.


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Figure 52 : Agricultural loss due to extreme wind events – Annual time series Distribution

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Map 31 : Agricultural loss due to extreme wind events –Spatial Distribution


4.8.8 Conclusion

Wind Events too seem to be most prevalent in the latter years with the largest occurring in the year 2007. Further, the wind events in Sri Lanka are most likely to occur in the months of June and November. With respect to spatial distribution, wind events are most prevalent in the districts of Rathnapura, Moneragala, Kalutara, and Colombo.

People in Sri Lanka are not very much affected by wind events. However, an exception to that are the years 1978 and 2000.

Further, people located in the districts of Anuradhapura, Polonnaruwa, Trincomalee and Batticaloa are most affected by wind events,

The occurrence of deaths due to wind events is quite rare except for the year 1978 where it has reached nearly 850. Further, deaths appear to occur mostly in the districts of Batticaloa.

Building Destruction ad Damage also do not occur very much due to wind events, however, large damage can be seen in the year 1978 and 2000. Further, buildings located in the districts of Anuradhapura, Trincomalee, Polonnaruwa and Batticaloa appear to be most prone to wind events.


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Agricultural crop loss is also not a common occurrence, with most taking place in 1989 and 2000. Further, most agricultural loss appears to occur in the districts of Moneragala, Ampara, Polonnaruwa and Trincomalee.

4.9 Other Hazards

4.10 Fire


Initially until 2002 the occurrence of fires has been relatively low. However, from 2002 – 2008 these occurrences have increased. The reason why records are higher for the period 2002-2006 as compared to any other years is because data was available only from the Colombo Municipal Council, Fire Services Department for that period.

Figure 53 : Annual time series distribution of fire

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The diagram below shows that fires occur evenly throughout the year. This may be attributable to the fact that this disaster is not caused naturally but is manmade. However, the highest occurrence can be seen in the month of January.

Figure 54 : Seasonal distribution of fire

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The district which has the highest occurrence of fires is Colombo and Gampaha which has surpassed the upper limit. Anuradhapura, Polonnaruwa, Moneragala and Kalutara also have high occurrence of fires. However, the districts located in the Northern parts of the island have a lower incidence of fires occurring. A similar pattern can also be seen spatially.

Map 32 : Spatial distribution of fire by Districts and DS Divisions


4.10.4 People affected (Annual time series and Spatial Distribution)

The number of people affected is quite low in the initial years but have increased over time. This may be attributable, once again, to the availability of data records. Spatially, Colombo and Nuwara Eliya has the highest number of people affected but other districts such as Puttalam, Kegalle, Anuradhapura and Ampara also have a large number of people affected. With respect to DS divisions, the most number of people affected are in the Central province.


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Figure 55 : People affected by fire – Annual time series distribution

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Map 33 : People affected by fire –Spatial distribution



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4.10.5 Deaths (Annual time series and Spatial Distribution)

The occurrence of deaths has been quite similar over the time period. The largest death toll was seen in 2003 and 2007. However, no deaths have taken place within the period 1987 – 1992. Spatially, most deaths have taken place in Gampaha and Kalutara; however other districts too have relatively high death rates. With respect to DS divisions, most of the have quite low death rates.

Figure 56 : Loss of life due to fire – Annual time series distribution

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Map 34 : Loss of life due to fire –Spatial distribution



The number of buildings damaged is quite low in the initial years but have increased over time. This may be attributable, once again, to the availability of data records. Spatially, Colombo and Nuwara Eliya have the highest number of people affected but other districts such as Puttalam, Kegalle, Anuradhapura and Ampara also have a large number of building damage. With respect to DS divisions, the most number of building damage are in the Central Province.


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Figure 57 : Building destruction and damage – Annual time series distribution

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Map 35 : Building destruction and damage –Spatial distribution



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4.10.7 Agricultural loss (Annual time series and Spatial distribution)

Over the years, agricultural loss due to fire has been very low. Within the period of 34 years the agricultural loss has been extremely low except in three years, namely, 1978, 1988 and 2008. Particularly, in 1988 the loss has been very high surpassing 1600 hectares. Spatially, losses throughout the island have been quite low. Only two districts have crop losses above 368 hectares and these include Moneragala and Rathnapura. With respect to DS divisions, only few have agricultural loss due to fire of which a majority have very low levels and only two DS divisions have losses surpassing 40.47.

Figure 58 : Agricultural loss due to fire – Annual time series distribution

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Map 36 : Agricultural loss – Spatial distribution

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4.10.8 Conclusion

Fire also appears to be an uncommon occurrence with most occurring within the years of 2002- 2007, which is due to the recent availability of records about fire incidents from the fire services department. Further, areas such as Anuradhapura, Polonnaruwa, Gampaha and Colombo appear to be the most affected by fires. With respect to people affected and building damage it appears to take on a similar pattern. However, the occurrence of deaths appears to be fairly consistent and high over the years.


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4.11 Animal Attack

4.11.1 Annual Time series distribution

Animal attack appears to have suddenly increased towards the latter part of the timeline. This is attributable to the fact that records of these events are only available in the latter years. During this period (1998 – 2008) the number of events fluctuates very much.

Figure 59 : Annual time series distribution of animal attack


Seasonal distribution is generally high and fluctuating. There are two peaks, one which can be seen in April/May and the other in October.

Figure 60: Seasonal distribution of animal attack

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Spatially, it can be seen that animal attack has occurred throughout the island. However, three districts have the highest number of animal attacks surpassing the upper limit of 813.These include, Anuradhapura, Matale and Kurunegala. However, districts like Mullaitivu, Mannar, Batticaloa and Colombo have very low levels of animal attacks. A similar pattern can be seen in respect to DS divisions,.

Figure 61 : Spatial distribution of animal attack


4.11.4 People affected (Annual time series and Seasonal distribution)

The number of people affected by animal attacks has also increased in the latter years. However, like number of events, the number of people affected has gradually declined starting from 3500 people in 1999 to less than 500 people in 2008. Spatially, as before, most people affected are around Anuradhapura, Matale and Kurunegala while districts like Colombo, Kalutara, Batticaloa, Mannar, Kegalle, Matara and Matale have a lower level of people affected. Spatially, too, a similar pattern can be seen.


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Figure 62 : People affected by animal attacks – Annual time series distribution

Map 37 : People affected by animal attacks – Spatial distribution


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4.11.5 Loss of life (Annual time series and Spatial distribution)

Loss of life due to animal attacks has been generally quite high with a peak of more than 100 deaths. However, it takes on a fluctuating pattern within the period 1998 – 2008. Spatially, deaths take on a similar pattern as people affected.

Figure 63 : Loss of life due to animal attack – Annual time series distribution

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Map 38 : Loss of life due to animal attack – Spatial distribution


4.11.6 Building damage (Annual time series and spatial distribution)

Building damage due to animal attacks has also been relatively high. Taking a similar pattern as number of people affected, over time it has gradually declined from peak of 700 buildings damaged in 2000 to nearly 200 in 2008. Spatially, too, it takes similar pattern as people affected and deaths due to animal attacks.


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Figure 64 : Building damage due to animal attack – Annual time series distribution

Map 39 : Building damage due to animal attack – Spatial distribution


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4.11.7 Agricultural loss (Annual time series and spatial distribution)

Agricultural loss due to animal attacks has been generally low. However, a peak can be seen in three years, in 1984, 1988 and 1989 where in 1989 agricultural loss has surpassed 800 hectares. Spatially, too, it has taken a different pattern, with districts like Killinochchi, Anuradhapura, Moneragala and Badulla having the highest agricultural loss. However, many districts have experienced low levels of agricultural loss and these are clustered around the Northern and South Western parts of the island. Spatially, however, the level of agricultural loss is quite low.

Figure 65: Agricultural loss due to animal attack – Annual time series distribution

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Map 40 : Agricultural loss due to animal attack – Spatial distribution


4.11.8 Conclusions

The number of events of animal attack has increased towards the latter parts of the 34 year timeline under consideration. This is attributable to the increase recording and collection of animal attack records in the recent years. Spatially, animal attacks are clustered around the centre of the island. People affected by animal attack have increased in the latter years, but has gradually declined. However, unlike people affected, deaths appear to be fluctuating within the last ten years. Building damage also takes a similar pattern as people affected. Spatially, all three factors follow a similar trend with most losses seen towards the centre of the island and districts like Batticaloa, Colombo, Mannar are least affected. Agricultural loss takes a different time trend with most losses seen in the middle of the time line. Further, districts like Killinochchi, Anuradhapura, Moneragala and Badulla have the highest agricultural loss and districts clustered around the Northern and South Western parts of the island have experienced low levels of agricultural loss.


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4.12 Lightning

4.12.1 Annual Time series distribution

The occurrences of lightning throughout the year have taken on a cyclical pattern. However, it can be pointed out that lightning is generally low with one sudden peak in 1995 and another gradual peak in 2007. These are the two highest peaks with the first peak experiencing nearly 24 events and the second one experiencing nearly 35 events.

Figure 66 : Annual time series distribution of lightning

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The seasonal distribution of lightning also takes on a cyclical pattern. There are two peaks, one which can be seen from October and November and the other in April and May. The peaks in April and May are much higher and surpass 60 events.

Figure 67 : Seasonal distribution of lightning


In general it can be seen that all districts have experienced lightning and the occurrences are quite high. However, districts like Gampaha, Kalutara and Rathnapura are the most affected with number of events greater than 21. However, districts like Batticaloa, Trincomalee and Mullaitivu have less than three numbers of events occurring. DS divisions where lightning has occurred are scattered about the island and there is no particular cluster. Only few DS divisions have surpassed the upper limit of 5.

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Map 41 : Spatial distribution of lightning



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4.12.4 People affected by lightning – Annual time series distribution

The number of people affected by lightning appears to be quite low till 2000 with few people being affected in between. Thereafter, there has been a gradual rise in the number of people affected with the highest number of people being affected in 2007.

Figure 68 : People affected by lightning – Annual time series distribution

4.12.5 Loss of life : Annual time series and Spatial distribution

The loss of life due to lightning is quite cyclical. In most of the years deaths caused by lightning has been below 10. However, in years like 1995 and 2007 it has shot up to very high levels, exceeding 25 deaths. Spatially, most districts appear to have quite high levels of death due to lightning. However, the districts with the highest effect appear to be the districts of Polonnaruwa, Gampaha and Kalutara. However, in other districts like Mullaitivu, Vavuniya, Trincomalee and Batticaloa the occurrence of deaths are quite low. With respect to DS divisions, only few of them have experienced losses of life and most of the numbers are quite low.

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Figure 69 : Loss of life due to lightning – Annual time series distribution

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Map 42 : Loss of life due to lightning – Spatial distribution


4.12.6 Building damage due to lightning

Building damage due to lightning appears to be quite low during the 34 years. However, a gradual increase towards the end of the timeline can be seen with the highest building damage of nearly 35 buildings being destroyed in 2007. Spatially, most districts do not appear to have very high building damage. Only Badulla and Kalutara have experienced very high building damage. Most of the other districts have low levels building damage. A similar pattern can be seen with respect to the DS divisions as well.


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Figure 70 : Building damage due to lightning – Annual time series distribution

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Map 43 : Building damage due to lightning - Spatial distribution

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4.12.7 Conclusion

Lightning is one disaster that has occurred throughout the timeline considered with a cyclical pattern. Seasonally, too, it has taken on a cyclical pattern. Although the people affected, building damage and agricultural loss is not very high, there have relatively high levels of death due to lightning.


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5 INTENSIVE & EXTENSIVE RISK PROFILE

5.1 Introduction

This chapter presents the risk profile according to whether they are Intensive Disasters - large scale or extreme disaster events or Extensive Disasters- small and medium sized disaster events. The comparative impact of large scale disaster vs. small and medium sized disaster events is analysed in order to assess the differences between the two types of impacts. DesInventar Disaster database of Sri Lanka for the period of 1974 – 2008 was used for this analysis.

The Intensive and Extensive Disasters have been categorised on the basis of following criteria in undertaking this analysis.

• Intensive disaster = deaths > 50 or Number of Damaged and destroyed houses > 500 • Extensive disaster = deaths < 50 or Number of Damaged and destroyed houses < 500

The Table below shows the comparative picture of aggregate impact of extensive and intensive disasters in Sri Lanka. The total number of disaster events during the period from 1974 to 2008 was 14,296. This is equivalent to an average about 420 events per year, or 35 events per month or a little over one event per day. More than 99% of the disaster events were extensive events. Although only 1% of the disasters were categorized as intensive, 94% of total deaths and 88% of the damaged and destroyed houses were due to the intensive disasters. However, eextensive risk was more pervasive accounting for, 77% of the people affected and 89% of damaged to paddy and other crops.

Figure 71 : Disaster Typology of Extensive and Intensive Risk

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Disaster Typology of extensive and intensive risk


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Table 12 : Extensive and Intensive Disasters – Aggregate Impact -1974-2008

Event No of events

No of deaths

No of damaged and destroyed houses

No of people affected

Damaged to paddy and other crops (ha)

A. Extensive Disaster

Animal attack 7202 875 5358 29912 3541

Fire 2653 85 1305 7906 2397

Forest fire 50 1 23 187 20534

Gale 601 13 7295 79910 13832

Lighting 300 288 119 748 1

Rains 304 13 551 656751 32064

Strong wind 651 21 11042 84949 4699

Surge 28 3 47 3452 915

Drought 283 0 0 12413545 419902

Flood 975 195 33860 8820712 218901

Urban flood 49 2 69 23851 1

Landslide 1170 635 6329 84174 1410

Sub Total Extensive Disaster 14266 2131 65998 22206097 718197

B. Intensive Disasters

Cyclone 3 889 180003 1612575 14100

Gale 4 0 2881 18725 0

Landslide 4 180 5335 37510 0

Strong wind 1 0 525 2625 0

Flood 69 209 197756 3984206 61614

Tsunami 1 30959 105293 1076185 10397

Sub Total Intensive Disaster 82 32237 491793 6731826 86111

TOTAL 14348 34368 557791 28937923 804308


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5.2 Intensive Risk Profile

During the last 34 years there were 82 reported intensive disaster events in Sri Lanka as shown in the Figure 72 below. Six categories of intensive disaster events were reported during the period under review. They were, (a) Tsunami, (b) Cyclone, (c) Gale, (d) Landslide, (e) Strong wind and, (f) Flood. Most of the large disasters, in terms of number of deaths, damaged and destroyed houses, people affected and damage to paddy and other crops were mainly the Tsunami, Cyclone and Floods, while the impacts from the other categories were marginal.

Figure 72 : Intensive Disaster Events -1974-2008

Loss of life due to Intensive Disasters

Figure below shows that the Tsunami that hit the Sri Lankan coast on December 24th 2004 killed over 30,000 people or 90% of deaths from all disasters, during the last 34 year period. Other important intensive disaster events responsible for loss of life were cyclones, (889 deaths), followed by floods (209 deaths) and landslides (180 deaths).

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Figure 73 : Loss of Life due to Intensive Disaster Events – 1974-2008

Building damage due to Intensive Disaster

The impact of intensive disaster on human shelter was more damaging than extensive disasters. Out of total number of houses damaged or destroyed due to hazards (557,791) more than 88% (491,793) were caused by intensive risk events. During the last 34 years, the highest aggregate impact on shelter was due to three main events, flood (197,757), cyclone (180,003) and Tsunami (105,293). Other events such as landslides (5,335), gales (2,881) and strong winds (525) caused only a marginal impact on human shelter.

Figure 74 : Building damage due to Intensive Disaster

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People Affected by Intensive Disasters

Intensive disasters were responsible for only 23% of total number of people affected. Most hazardous events in terms of people affected by intensive disaster , as illustrated in the figure below, were Flood; Cyclone and Tsunami, affecting 3,9 million , 1,6 million and 1,1million people respectively. Other events such as landslides, gales and strong winds caused only marginal impacts on the number of people affected

Figure 75 : People Affected by Intensive Disasters

Agricultural loss due to intensive disaster impact

The impact on agriculture by disasters causes significant economic losses, causing many rural families to fall into poverty. Intensive disasters are not as damaging as extensive disasters to paddy and other crops. Only about 11% of paddy and other crops were damaged by intensive disasters out of a total of over 800,000 hectares damaged by all disasters. Floods caused the highest damage in terms of crop losses by intensive disasters or 72% of total impact of intensive disaster events, followed by Cyclone (16%) and Tsunami (12%), as illustrated in the figure below.

Figure 76 : Agricultural loss due to intensive disaster impact

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5.3 Extensive Risk Profile

The number of extensive disaster events is much larger than intensive disaster events. Over 99% of the disaster events are extensive disaster. There were 12 extensive disaster types recorded, i.e., Animal attack, Forest fire, Gale, Lightning, Rains, Strong Winds, Surge, Drought, Flood, Urban flood, Landslide totalling 14,266 events during the last 34 year period. Out of 12 extensive disaster types, Animal attack was the most frequent with 7202 events, followed by the Fire with 2703 events. These two types comprise nearly 70% of total extensive disaster events during the period under review.

The figure below shows the distribution of number of events under each disaster type during the last 34 years.

Figure 77 : Extensive Disaster Typology - Number of events

Loss of life due to Extensive Disasters

As already discussed, the direct impact of extensive disaster on human life was not significant compared to intensive disasters, as the number of deaths recorded during the last 34 years was only 6% of total deaths from all disasters. As shown in the figure below, deaths due to the four disaster typologies, were as follows; Animal attack (875 deaths), Landslides, (635 deaths), Lightning, (288 deaths) and Floods (195 deaths) accounting for 94% of the total deaths from extensive disasters. Deaths due to other extensive disasters were as follows: Strong Wind (21 deaths), Gale, (13 deaths), Rains (13 deaths), Surge, (3 deaths), and Forest fire (I death).

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Figure 78 : Loss of life due to Extensive Disasters

Building Damage due to Extensive Disasters

The impact of extensive disaster on the buildings was only 12% of total from all disasters. During the last 34 year period, the largest impact on buildings was caused by four types of disasters - Flood (33,860), Gale (7295), Landslides (6329) and strong winds causing 89% of total damage to buildings by extensive disaster. Floods caused damage amounting to 51% of total number of buildings.

Figure 79 : Building Damage due to Extensive Disasters

People Affected by Extensive Disasters

As against the other two impact criteria already discussed, i.e., number of deaths and buildings damaged, people affected by extensive disasters appears to be the major impact of extensive disaster events as the total number of people affected was 28.9 million during the last 34 years. Under extensive disaster types, the largest number of people affected is due to

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drought (43%). This is followed by flood with more than 8 million people affected. Other important extensive disaster types with considerable impact on people are Rains, Strong winds, Landslides, Gale, Animal Attack and Urban flood.

Figure 80 : People Affected due to Extensive Disasters

Agricultural loss due to extensive disasters

Extensive disasters events play a dominating role in terms of economic loss, adversely affecting agriculture as compared to intensive disaster events. Out of the total number of crops damaged which has surpassed 800,000 hectares during the last 34 years, 89% is due to extensive disaster events. Drought appears to have caused the most damage to agriculture sharing 58% of the total.

Figure 81 : Agricultural loss due to extensive disasters

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5.4. Spatial Distribution of Intensive and Extensive Risks

This section focus on spatial distribution of Intensive and Extensive Risks presents a spatial profile providing visual comparison of geographical distribution of Intensive and Extensive Risks in Sri Lanka. The spatial profile delineates the following four main impact areas of Intensive and Extensive Risks.

A) Loss of Life

B) No of People Affected

C) No of Damaged and Destroyed Houses

D) Damaged to Paddy and Other Crops (ha)

Four spatial profiles have been developed to illustrate the geographical distribution of key thematic impact areas of Intensive and Extensive Risks. Each spatial profile contains four set of maps as given below in comparison of spatial distribution of selected them between Intensive and Extensive Risks by districts and DS Divisions

1. Map set 1: Four Maps- Spatial Profile on Loss of Life due to Extensive and Intensive events by Districts and DS Division

2. Map set 2: Four Maps - Spatial Profile on No of People Affected due to Extensive and Intensive events by Districts and DS Division

3. Map set 3: Four Maps - Spatial Profile on Spatial Profile on No of Damaged and Destroyed Houses due to Extensive and Intensive events by Districts and DS Division

4. Map set 3: Four Maps - Spatial Profile on Damaged to Paddy and Other Crops (ha) due to Extensive and Intensive events by Districts and DS Division

The spatial distribution of all hazards, intensive and extensive by Districts and Divisional level indicates that intensive risks are more localized whereas extensive risks are widely distributed across the country. When the impact of the tsunami is removed from the data set, the national patterns of intensive risk concentration are closely related to flood and cyclone risk. It is, therefore, important to understand the underlying causes of each major hazard type, to help identify their possible linkages with vulnerability and poverty and possible mitigation measures. Addressing extensive risk in Sri Lanka would therefore require a much more decentralized strategy that strengthens capacity and interventions at local level.


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Map 44 : Impact of Extensive and Intensive risk: Spatial Profile on Loss of Life

Extensive Risk Profile by Districts Intensive Risk Profile by Districts

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Extensive Risk Profile by DS Divisions Intensive Risk Profile by DS Divisions

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Map 45 : Impact of Extensive and Intensive risk: Spatial Profile on Number of People Affected


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Map 46 : Impact of Extensive and Intensive risk: Spatial Profile on Number of Damaged and Destroyed Houses 1974- 2008


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Map 47 : Impact of Extensive and Intensive risk: Spatial Profile on Agricultural loss


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5.5. Conclusion

The previous sections have presented intensive and extensive Disaster risk profiles of Sri Lanka. First, after an introduction it outlines the intensive disaster risk profile. Then it presents the extensive risk profile. Finally it delineates the spatial profile of intensive and extensive disaster risk. It is widely accepted that both intensive and extensive disasters play a very important role in generating a wide set of adverse impact on human life, shelter, livelihood and the economy. However, the pattern of disaster occurrence and impact of disaster vary from intensive (large scale) disasters to extensive (Medium and Small Scale) disasters, depending on their scale, number of disaster events, deaths and building damage. The number of intensive disasters is very low in comparison to extensive disasters while the scale and impact is very high. Number of deaths and impact on shelter is very high in intensive disasters, while number of people affected and damaged to agriculture is much higher in extensive disaster. All most all intensive disasters types are linked with nature being either geological or Hydro-meteorological. The spatial distribution of all hazards at districts and divisional level across Sri Lanka indicate that intensive risks are more localized whereas extensive risks are widely distributed across the country. When the impact of the tsunami is removed from the data set, the national patterns of intensive risk concentration are closely related to flood and cyclone risk.


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6 RISK POVERTY RELATIONSHIP IN SRI LANKA

6.1 Introduction and Objectives

The main objective of the study is to prepare an assessment report for Sri Lanka analyzing the two-way relationship between disaster risk and poverty, using both quantitative and qualitative approaches.

The qualitative analysis will focus on determining any relationships that can be discerned from visually observing trends or correlations between data sets obtained for both poverty and disaster. Results of similar studies on poverty and disaster will also be analyzed to highlight any qualitative or quantitative relationships in such studies.

The quantitative analysis will attempt to estimate statistically valid correlations between variables relating to poverty and disaster. Based on the conceptual frame work developed and statistical methods adopted, the following hypothesis will be tested.

Hypotheses to be tested on disaster risk and poverty

Hypothesis 1 a) Poverty leads to a higher risk of exposure of households to natural hazards b) Poor households suffer greater losses from hazardous events.

Hypothesis 2 c) Natural hazards result in increases in poverty as estimated by poverty indicators d) Natural hazards result in the deterioration of the ability of poor households to either avoid

or recover from poverty

6.2 Qualitative analysis of disaster risk and poverty

This section analyses the possible relationships that can be discerned from visual examination of spatially analyzed mapped data, segregated by 319 Divisional Secretariat Divisions of Sri Lanka. A poverty map based on Poverty Head Count (PHC) by DS divisions is compared with a disaster impact map based on DS divisions to discern any relationship. The disasters covered include, extreme wind events, floods, landslides, fire, animal attacks, and drought. The impacts analyzed include, deaths, number of people affected, damage or destruction to housing, and agricultural losses. The PHC data is from the year 2002 (latest available) while the disaster data is the cumulative values for the period 1974-2008.

Poverty headcount ratio measures the proportion of the population below the poverty line. For the year 2002, the DS division of Siyambalanduwa in the district of Moneragala had the largest proportion of population below the poverty line of 51.8%. All DS divisions with PHC, between 36.4% to 51.8%, are highlighted in red. 17 other DS Divisions falling into this category of PHC 36.4%-51.8% include, Madulla (Moneragala), Mahiyaganaya (Badulla), Rideemaliyadda (Badulla), Meegaahakivula (Badulla), Kandaketiya (Badulla), Lunugala (Badulla), Madulla (Moneragala), Udadumbara (Kandy), Minipe (Kandy), Godakawela (Rathnapura), Weligepola (Rathnapura), Kolonna (Rathnapura), Elapatha (Rathnapura), Vanathavilluwa (Puttalam), Mundalam (Puttalam), and Kalpitiya (Puttalam). The DS


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divisions having the second highest category of Poverty Headcount ranging from 28.1% -36.4% is highlighted in pink. The third category of DS divisions with a PHC ranging from 21.2%-28.0%, comprising of 25 DS divisions is highlighted in beige. The next category of DS divisions with PHC ranging from 12.5%- 21.2% is highlighted in light green. The lowest level of PHC 2.10 was observed in the Dehiwela DS division in the district of Colombo and this category of DS divisions with PHC ranging from 2.1% - 12.5% is highlighted in a darker green. No colour signifies lack of data for the particular DS division. In the case of disaster impacts, the colouring starts from light beige to dark brown from the lowest to the highest levels of impact. No colour signifies lack of data or no impact.


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Map 48 : Poverty Headcount Ratio of Sri Lanka - 2002



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6.2.1 Floods

People affected

Map 49 : Poverty headcount Ratio and People affected by floods

PHC People affected by floods

In some DS divisions, the numbers of people affected have surpassed the upper limit of 53,735 and these are mostly clustered around the coastal areas of the country. Some of the DS divisions which surpass the upper limit are within the districts of Batticaloa and Ampara, but poverty head count ratio of these divisions are not available. The DS division of Mundalam in Puttalam is one of the DS divisions where the numbers of people affected have surpassed the upper limit. In this division, too, the poverty headcount ratio is very high and falls into the highest category. Divisions such as Katana have very large number of people affected (surpassing the upper limit) but the poverty headcount in these divisions are quite low. The next level of DS divisions which fall into the category with an upper limit of 53,735, are highlighted in dark brown. Some of the DS divisions in this category have relatively high levels of poverty. However, DS divisions such as Kolonnawa and Wattala which have a large number of people affected have one of the lowest headcount ratios in the island.

The lowest category of people affected, have an upper limit 70. Some of the DS divisions which have people affected ranging from 1-70 have some the highest levels of poverty


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headcount ratios in the island. These include, Madulla, Siyambalanduwa, Rideemaliyadda, Minipe and Kandaketiya.

Deaths

Map 50 : Poverty headcount Ratio and Deaths due to floods

PHC Deaths due to floods

Deaths caused by floods in the island is very low and most of the DS divisions have deaths ranging between 0 -1. However, few DS divisions have deaths above the upper limit of 3 which can be considered as high. These include Pandiyasnuwara, Kotapola and Kalawana divisions of which poverty data regarding Karachchi is not available. The poverty headcount ratios of these divisions are relatively high.


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Building destruction and damage

Map 51 : Poverty headcount Ratio and Building Damage due to floods

PHC Building Damage due to floods

Building Damage due to floods appears to be very high throughout the island. Many DS divisions have experienced high damage surpassing the upper limit of 760. However, all these divisions do not have very high levels of poverty headcount. For example, DS divisions such as, Wattala and Thimbirigasyaya have very low poverty headcount ratios. Building damage of DS divisions, which have high poverty headcount ratios, ranges between relatively high to relatively low.


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Agricultural loss

Map 52 : Poverty headcount Ratio and Agricultural Loss due to floods

PHC Agricultural loss due to floods

Agricultural losses show a similar pattern as deaths, where losses due to floods is not too high. However, few DS divisions have experienced relatively high agricultural loss. The poverty headcount ratios of these DS divisions are relatively low like, Katana and Panadura. Apart from a few DS divisions, most have low agricultural losses ranging from 0-1 hectares.


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6.2.2 Drought

People affected

Map 53 : Poverty headcount Ratio and People Affected by droughts

PHC People affected by droughts

According to the map, it can be seen that not all parts of the island are affected by droughts. Most damage and effects caused by drought is clustered around the Central and South Eastern parts of the island. Few DS divisions have number of people affected by drought surpassing the upper limit of 88243. However, with respect, to the poverty headcount ratio, most of these DS divisions do not have very high levels of poverty. The DS divisions which have the highest levels of the headcount ratio also have relatively large numbers of people being affected by droughts. These include, for example, the Mundalama DS division.


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Agricultural loss

Map 54 : Poverty headcount Ratio and Agricultural Loss due to droughts

PHC Agricultural loss due to droughts

Agricultural damage by droughts has not been very intensive throughout the island. Most of the DS divisions have agricultural loss from 0-12.9. However, few DS Divisions like Padaviya, Ipalogama, Thanamalvila, and Matale have agricultural loss surpassing the upper limit of 7782 hectares. However, Padaviya and Thanamalvila have a low PHC ratio while the other two divisions have a relatively high PHC ratio.


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6.2.3 Landslides

As landslides are caused by a particular geographic situation (land elevation), most of the landslides have occurred closer to the Central parts of the island.

People affected

Map 55 : Poverty headcount Ratio and People Affected by landslides

PHC People affected due to landslides

Although the number of districts affected is relatively low, the number of people affected within each district is quite high. Few divisions have surpassed the upper limit of 797 and these DS divisions also have relatively high levels of poverty such as Kandaketiya, Haldmulla, , Elpatha and Walapane. However, most of the other DS divisions have large numbers of people being affected whilst the levels of poverty are low.


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Number of deaths

Map 56 : Poverty headcount Ratio and Deaths due to landslides

PHC Deaths due to landslides

Deaths caused by landslides within most DS Division range from 0-2. Only few DS Divisions have exceeded the upper limit, such as Bulathkohupitiya and Elpatha with relatively high levels of poverty. Most of the other DS divisions have low levels of death.


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Map 57 : Poverty headcount Ratio and Building Damage and Destruction due to landslides

PHC Building damage due to landslides

Most of the DS divisions which have been affected by landslides have very high levels of building damage surpassing the upper limit of 86 or at the next highest level. Most of these DS Divisions are clustered around the districts of Nuwara Eliya and Kegalle where the levels of poverty are relatively high. DS divisions affected by landslides outside this cluster have relatively low levels of building damage.


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Agricultural loss

Map 58 : Poverty headcount Ratio and Agricultural loss due to landslides

PHC Agricultural loss due to landslides

Agricultural loss caused by landslides is very low and ranges mostly from 0-0.3. Only one DS division, Kandaketiya, has surpassed the upper limit and this is one DS division which has very high levels of poverty headcount. Almost all the other DS divisions have relatively the same levels of crop loss with poverty levels varying between the divisions with no clear pattern.


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6.2.4 Extreme wind events

People affected

Map 59 : Poverty headcount Ratio and People Affected by extreme wind events

PHC People affected due to extreme wind events

People located in most parts of the island appear to have been affected by wind events. In few DS Divisions, the number of people affected appears to have surpassed the upper limit of 1873. Most of the DS Divisions in this category have quite low levels of poverty headcount, e.g. Medirigiriya, Hingurakgoda and Colombo. In a few DS Divisions, like Welimada and Imbulpe, with high levels of people affected, the poverty headcount ratio is also high. Further, most of the DS Divisions where the number of people affected is high are located in the Eastern parts of the island for which poverty data is not available. Many other DS divisions with high poverty levels also have fairly high levels of people affected.


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Deaths

Map 60 : Poverty headcount Ratio and deaths due to extreme wind events

PHC Deaths due to extreme wind events

Deaths caused by wind events are quite low throughout the island. Most of the deaths range between the 0-2. However, few DS Divisions have deaths which exceed the upper limit of 2. These include Nuwara Eliya, Udapalatha, Lunugala, and Opanayaka where the poverty headcount ratio is relatively high. Of these DS Divisions, Lunugala has one of the highest headcount ratios in the island. This range also includes divisions like Dambulla where the poverty headcount ratio is quite low.


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Map 61 : Poverty headcount Ratio and Building Destruction and Damage due to extreme wind events

PHC Building damage due to extreme wind events

Building damage also appears to be quite high in most DS Divisions throughout the island. In few divisions the building damage has surpassed the upper limit of 251. Some of these divisions have a low headcount ratio like, Hingurakgoda, Kalutara, Dehiwela while some divisions like, Imbulpe, Haldamulla, have a high PHC ratio. The rest of the DS divisions (falling below the upper limit of 251) in the island have very high building damage which also have quite high levels of poverty headcount.


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Agricultural loss

Map 62 : Poverty headcount Ratio and Agricultural loss due to extreme wind events

PHC Agricultural loss due to extreme wind events

Agricultural loss due to wind events appear to be quite low throughout the island. Most of the crop loss ranges from 0 – 1. Few DS divisions have crop loss greater than 1 and they are not clustered around a particular area. These DS divisions include Madulla, Walapane, Haldmulla where the poverty headcount ratio is relatively high of which Madulla has one of the highest poverty headcount ratios in the island. However, in that category there are DS Divisions where the poverty headcount ratio is relatively low like, Hanwella and Ingiriya


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6.2.5 Animal attack

Animal attack according to the map appears to be clustered mainly around the Central and Southern parts of the island. In the Northern and Eastern parts of the island they appear to occur very rarely.

People affected

Map 63 : Poverty headcount Ratio and People Affected by animal attacks

PHC People affected due to animal attacks

Most of the people affected by animal attacks appear to be clustered around the Central and Southern parts of the island. In some DS divisions, the number of people affected has surpassed the upper limit of 390. These include Hambantota, Lunugamvehera, Mahiyaganaya of which Mahiyaganaya is one of the divisions which have the highest poverty headcount ratio in the island. However, in some DS divisions where the people affected are high the poverty headcount ratio is relatively low, like, Nuwaragam Palath, Kekirawa.


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Deaths

Map 64 : Poverty headcount Ratio and Deaths due to animal attacks

PHC Deaths due to animal attacks

Some of the highest deaths caused by animal attacks appear to be clustered around the Central parts of the island. The deaths in these DS division surpass the upper limit of 11. These include Galgamuwa, Buttala, Ambanpola, and Tissamaharama where the poverty headcount ratio is relatively high. However, this category also includes DS divisions like Elahera and Dambulla where the poverty headcount ratio is relatively low. Most of the DS divisions with very high levels of poverty headcount ratio also have experienced relatively high levels of death due to animal attacks.


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Map 65 : Poverty headcount Ratio and Building Destruction and Damage due to extreme wind events

PHC Building damage due to animal attacks

Building damage also appear to take a similar pattern as the above two, with largest building damage taking place in Central and Southern parts of the island, particularly, the Southern parts of the Island. In few DS divisions, the building damage has surpassed the upper limit of 117. Most of these DS divisions have high levels of poverty like, Wilgamuwa, Buttala and also includes Mahiyanganaya which has one of the highest poverty headcount in the island.

However, other DS divisions which have the highest levels of poverty headcount in the island like, Mundalama, Madulla have very low levels of building damage (1-0).


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Agricultural loss

Map 66 : Poverty headcount Ratio and Agricultural loss due to animal attacks

PHC Agricultural loss due to animal attacks

Agricultural loss due to animal attack throughout the island is not very high throughout the island. In few DS divisions, it has surpassed the upper limit of 12.02. Further, it appears that most of the divisions that fall into this category have relatively high levels of poverty, like Haldmmulla, Haputale, and Polpithigama.


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6.2.6 Fire

According to the map it can be seen that not all parts of the island are affected by fire uniformly. The affected DS divisions are scattered around the island and not clustered.

People affected

Map 67 : Poverty headcount Ratio and People Affected by fire

PHC People affected due to fire

In very few DS divisions, the number of people affected has surpassed the upper limit of 100. These include Colombo, Sri Jayewardenepura Kotte, Ratmalana, where the poverty headcount ratio is relatively low. However, DS divisions like Haldummulla, Wellawaya, Medawachchiya where the poverty headcount ratio is high also have relatively large numbers of people being affected by fire.


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Deaths

Map 68 : Poverty headcount Ratio and Deaths due to fire

PHC Deaths due to fire

Deaths caused by fire are very low throughout the island. In most divisions where a death has occurred it is generally 1. In only three divisions, it has surpassed the upper limit of 3. These include Katana, Negombo, Medadumbara and Thamankaduwa all of which except for Medadumbara have relatively low levels of poverty.


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Map 69 : Poverty headcount Ratio and Building Destruction and Damage due to fire

PHC Building damage due to fire

Building damage due to fire also appears to be quite low and only few DS divisions have surpassed the upper limit of 21. These include Colombo, Sri Jayawardenepura Kotte, Moratuwa where the poverty headcount ratio is relatively low and also DS divisions like Kegalle, Nuwara Eliya, where the poverty headcount ratio is relatively high.

Further buildings in DS divisions where the poverty headcount ratio is the highest in the island have not been severely damaged or destroyed.


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Agricultural loss

Map 70 : Poverty headcount Ratio and Agricultural loss due to fire

PHC Agricultural loss due to fire

Agricultural loss throughout the island appears to be very low. Most divisions have agricultural loss less than 1.62. Only around three divisions surpass this limit. These include Wellawaya, Imbulpe and Embilipitiya of which all three have relatively high levels of poverty.

Thus the visual analysis shows some correlation between poverty and disaster, but there are an almost equal number of cases where there is no correlation. Thus, one can conclude that there is some relationship between poverty and disaster, particularly in landslides, animal attacks, floods and drought. However, one cannot discern any clear cut relationship from this visual examination of spatially arranged data. There is a need for further analysis using topographic features, rainfall, and wind patterns and more detailed poverty data such as poverty gap, inequality levels, and other substitute poverty variables to arrive at a conclusion.


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6.3 Statistical / Econometric Framework for Analysis

The statistical/econometric framework for analysis is provided in Annex 5 – Framework for Statistical Analysis. The initial steps include the identification of poverty variables to be utilized in the study. In Sri Lanka, the Household Income and Expenditure Survey (HIES) conducted periodically is the source of data for estimating expenditure and poverty indicators. In this analysis, the indicators such as poverty head count, poverty gap and severity of poverty as well inequality indices such as the Gini Coefficient and other human development indices will be selected based on availability.

The second stage of the analysis will seek to explain poverty and its relationship to hazard risk. This will involve the following;

• Define poverty over individual households • Aggregate over district , regional or national levels • Tabulate poverty incidence and indirect variables against set of disaster

characteristics. • Spatial correlations between poverty incidence and natural hazards using cross-

section data • Multiple Regression

The third step will be to estimate the different risks of disaster or hazard events in terms of hazard losses. The losses will include human, infrastructure and economic losses. A susceptibility index based on hazards and actual losses will be estimated for the analysis, as follows;

k l k j k l j k l jj

H E Sπ = ⋅ ⋅∑ ------------------------------------ (1)

• l loss type - human, economic, environmental or infrastructure

• k - geographic unit

• j - types of hazards flood, earthquake, etc

• H - hazard j index – Probability over k

• E – total no. of elements exposed to a hazard total population, total number of households, GDP

• S - Susceptibility index – ratio or function that estimates the proportion of loss of a particular variable due to an event.

6.3.1 Methodology

The main objectives of the study are the identification and experience of poverty and explanation of poverty through qualitative and quantitative methods to determine any correlation between poverty and other explanatory variables including the relationship between the various factors of hazard with poverty and between poverty indicators and


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disaster hazards. It is proposed to conduct poverty analysis in terms of; i. Static poverty analysis ii. Poverty trend and iii. Poverty micro-dynamics, aimed at testing the two hypotheses.

6.3.2 Data and Level of Analysis

The data available will be classified by geographical distribution of both poverty and disaster. The geographically disaggregated data for both poverty and disaster is only available at divisional, district, provincial and national levels. Hence, the analysis will be restricted to these levels of aggregation.

6.3.3 Data available for analysis

The analysis will be limited by the data available for the study. While disaster data appears to be quite comprehensive, data on poverty is rather limited. Table 13 provides a list of available data in these two areas.

Table 13 : Data on Poverty and Disaster Hazards

Poverty data for years 1990, 1996, 2002 and 2006/7

Hazard by Type at District and Sub-District Level for the period 1974-2008

Poverty Head Count, Poverty Gap and Squared Poverty Gap for some years and Gini Coefficient for selected years. based on Household Income and Expenditure Surveys

Type of hazard ( landslides, floods, drought, lightning, extreme wind events, animal attacks and other)

Share of income/expenditure of lowest (poorest) 20% income/expenditure group (only for few years)

Number of deaths

Adult Literacy rate Number of persons injured / affected

Dependency ratio Number of houses, shops, building destroyed or damaged

Infant Mortality / Live births Number of hectares of crop destroyed

Sanitation (% households either without toilets or with pit toilets)

% Households with access to safe drinking water

Nutritional status of children under five - % stunting or % wasting (only for some years)

Disaster data by hazard type and losses are available on an area basis (district and sub-district level) from 1974- 2007. However, poverty data (only Poverty Head Count) is available for


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four time periods (1990-91, 1995-96, and 2002 and 2006-07) at district level and divisional level. Data on Poverty Gap, Squared Poverty Gap and Gini Coefficient are available for 2006/07 and one or two previous years. Therefore, it is possible to undertake the analysis of poverty vs. disaster for these four time periods. An official poverty line (national and district level) was constructed by the Census and Statistics Department for the first time in the year 2002 - based on the Household Income and Expenditure Survey (HIES) conducted in 2002. This poverty line has been updated annually after this date by adjusting for inflation on an annual and monthly basis. However, poverty data (PHC) is only available for the four years in which HIES were conducted. Although poverty gap data can be estimated using the survey results of previous years, such data has been officially estimated by the Census Department only for 2006/7 HIES. Detailed survey data of the Census Department cannot be accessed by anyone unless it is released in Department’s Web Site. Further, some districts in the north and east were not covered by the survey and thus even PHC data is only available for certain areas. Thus the poverty/disaster analysis for Sri Lanka will be constrained by the availability of poverty data. Indirect poverty data such as share of income/expenditure of poorest 20% population group, education, dependency ratio and literacy levels, health data such as access to safe drinking water, sanitation, infant and adult mortality, nutritional status such as stunting and wasting are also available only for certain number of years and for certain number of districts. Where possible, the analysis will be conducted using these variables as well. It is proposed to conduct the analysis to determine the relationship between poverty and disaster using PHC and appropriate indirect poverty variables by disaster type (we will limit disaster type to hydro-meteorological events such as floods, drought, wind and lightning and geological events such as landslides). Disaster types such as animal attacks, epidemics and fire may be grouped into an “other” category and will be only analyzed if a visual examination of data reveals that there is high probability of a cause-effect relationship between these disaster events and poverty. Time constraints may also prevent an expanded analysis. Table below lists the data on poverty indices by district and by urban, rural and estate population groups. Unfortunately, hazard data is not available by the latter grouping, so that an analysis between hazard and poverty indices is not possible by this grouping. Poverty indices available by year and geographic spread are presented in Table 14

Table 14 : Poverty Indices by District

Poverty Head Count Ratio by district % Poverty Gap

Squared Poverty Gap

Gini Coeff.

Year 1990-91 1995-96 2002 2006-07 2006-07 2006-07 2006-07

National 26.1 28.8 22.7 15.2 3.1 0.9 0.40

Colombo 16 12 6 5.4 1.0 0.3 0.42

Gampaha 15 14 11 8.7 1.4 0.4 0.41

Kalutara 32 29 20 13 2.7 0.8 0.38

Kandy 36 37 25 17 3.8 1.2 0.39


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Poverty Head Count Ratio by district % Poverty Gap

Squared Poverty Gap

Gini Coeff.

Year 1990-91 1995-96 2002 2006-07 2006-07 2006-07 2006-07

Matale 29 42 30 18.9 3.7 1.0 0.39

Nuwara Eliya 20 32 23 33.8 6.8 2.0 0.29

Galle 30 32 26 13.7 2.9 0.9 0.39

Matara 29 35 27 14.7 2.4 0.6 0.37

Hambantota 32 31 32 12.7 2.5 0.7 0.34

Kurunegala 27 26 25 15.4 3.1 1.0 0.36

Puttalam 22 31 31 13.1 2.3 0.7 0.37

Anuradhapura 24 27 20 14.9 2.8 0.8 0.4

Polonnaruwa 24 20 24 12.7 2.8 1.0 0.39

Badulla 31 41 37 23.7 5.3 1.7 0.36

Moneragala 34 56 37 33.2 7.8 2.8 0.31

Rathnapura 31 46 34 26.6 5.3 1.6 0.36

Kegalle 31 36 32 21.1 4.3 1.3 0.31

Ampara N/A N/A N/A 10.9 2.4 0.7 0.34

Batticaloa N/A N/A N/A 10.7 1.5 0.4 0.32

N/A – Not available

Source: Household Income and Expenditure Surveys (1990/91, 1995/96, 2002, 2006/7)


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Table 15 : Poverty Indices by Sector

Poverty Indices Urban Rural Estate

PHC 1990/91 % 16.3 29.4 20.5

PHC 1995/6 % 14.0 30.9 38.4

PHC 2002 % 7.9 24.7 30.0

PHC 2006/7 % 6.7 15.7 32.0

Poverty Gap Index 1990/91 % 3.7 6.3 3.3


Poverty Gap Index 2002 % 1.7 5.6 6.0


Squared Poverty Gap Index 2006/7 % 0.4 1.0 1.8

Gini Coefficient 2006/7 0.43 0.38 0.26

Source: Household Income and Expenditure Surveys (1990/91, 1995/96, 2002, 2006/7)

Table 16 : Poverty Indices by Province (2006/7)

Sector/ Province/

Mean monthly total income

PHC %

Poverty Gap Index %

Squared Poverty Gap %

Gini Co-efficient

No.of poor persons (000)

Contribution to Total Poverty %

Sri Lanka 5436 15.2 3.1 0.9 0.4 2805 100

Western 6935 8.2 1.5 0.4 0.41 471 16.8

Central 4560 22.3 4.6 1.4 0.38 573 20.4

Southern 5302 13.8 2.6 0.8 0.37 338 12.1

Eastern 4843 10.8 2.1 0.6 0.33 100 3.6


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Sector/ Province/

Mean monthly total income

PHC %

Poverty Gap Index %

Squared Poverty Gap %

Gini Co-efficient

No.of poor persons (000)

Contribution to Total Poverty %

North-Western 5035 14.6 2.9 0.9 0.36 342 12.2

North-Central 5698 14.2 2.8 0.8 0.4 168 6

Uva 3879 27 6.2 2.1 0.35 346 12.3

Sabaragamuwa 3982 24.2 4.9 1.5 0.34 467 16.6

The poverty line was estimated using data from the HIES of 2002. This is the expenditure requirement for meeting the calorific needs of 2030 k calories per capita, which is considered the minimum requirement to avoid hunger and malnutrition. For the districts of Colombo, Gampaha, Kalutara, Kandy, Galle, Nuwara-Eliya, Rathnapura and Kegalle, the 2007 poverty lines are higher than the national figure suggesting that expenditure needed to be above poverty line is higher than the national level in these districts or that the cost of living in these districts are higher than other districts.

6.4 Static poverty / hazard analysis

The statistical methods proposed for the study include correlations, multiple regression and multivariate analysis. Since spatially distributed data for this study is limited, it has been decided to undertake the analysis at district level and if data is available extend the analysis to Sub-district (Divisional Secretaries) levels. The relevant variables and indicators will be identified. The population also needs to be categorized as poor and non poor in this section.

Some factors contributing to poverty have been identified and available data on these factors have been gathered. In addition, data on spatial distribution of populations below poverty line has also been gathered. Data on incidence and depth of poverty are available at national, district and sub district level for certain years only and have been calculated using Foster-Greer-Thorbecke model.

The main tasks in this analysis are:

• Identification of the characteristics of poverty and determination of the variables (independent variables) that can be used in the analysis based on the availability of poverty data.

• Measuring the poverty as a dependant variable using selected explanatory variables for poverty.


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• Determine relationship between poverty and hazard variables both as dependent and independent variables

6.4.1 Dependent / independent variables

As a first step, the incidence of poverty (PHC) will be used in the analysis, as data on other poverty statistics (poverty gap and squared poverty gap) are only available for selected years. Although data is available for many hazard types and losses, only four or five hazard types and a similar number of loss variables will be used in the analysis. The hazard index, which is defined as the probability of occurrence of a hazard type in a defined area, can be calculated using available hazard data. The susceptibility index, which is defined as the probability of a particular type of loss occurring from a type of hazard within a specified area (district or sub district), can be estimated from available hazard data, for the analysis.

• Only the following three type of losses will be considered;

o Housing / Shops (number damaged/destroyed)

o Agricultural losses (hectare of crop lost)

o Human losses (death and injury)

• Hazard index will be calculated for risks, flood, drought, wind, lightning and landslide

• The hazard will be normalized by the exposure factor

• Normalizing by population and hazard index:

Due to the limitation in the data, three types of analysis were completed

a. Risk Factor Analysis (Hazard loss manifested) b. Correlation Analysis c. Regression Analysis

Regression model will be fitted using the explanatory variables which are highly correlated with the dependant variables. First, the null model will be fitted and then variables will be added one by one and the resulting sums of squares considered in selecting the variables that will be included in the final regression model with the interaction terms included based on the sums of squares.

Dependent variables

• Mean household expenditures per capita and headcount indices of poverty

Explanatory variables

• Explanatory variables will be selected from among those considered most appropriate and for which data is available for the regression time period, area or population


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Core impact variables: deaths, buildings destroyed and people affected. Agricultural loss data was found to be reasonably reliable across the sample. Therefore, it may not pose a serious challenge for drought and flood analysis. Ideally it would be best if an analysis is done for rural and urban areas separately across these variable levels. This may not be possible for Sri Lanka because of the lack of availability of either hazard or poverty data at this level. Thus, the analysis will largely be undertaken at the district level

6.4.2 Results of Analysis

The analysis has been undertaken in three steps as follows:

Risk Factor Analysis (Hazard loss manifested)

Correlation Analysis

Regression Analysis

1. Risk Factor Analysis (Hazard loss manifested):

In the first instance, the risk of loss of life was calculated for all types of hazards including landslides, flood, drought, lightning, extreme wind effects, fire and animal attacks for each district. Data on the number of deaths was unavailable for drought and in the case of lightning; the number of times that the event occurred was not available. Risks for these types of hazards were calculated by adjusting the relevant parameters to 0.

No of times of occurrence - Total no of elements exposed – Total no of population

Table 17 : The risk factor of people being affected by all Hazards

District Risk District Risk

Ampara 2021.5 Kilinochchi 1334.1

Anuradhapura 441.6 Kurunegala 282.5

Badulla 1456.9 Mannar 0

Batticaloa 6715.4 Matale 122.1

Colombo 2340.0 Matara 11001.8

Galle 2914.1 Moneragala 17.3

Gampaha 2027.7 Mullaitivu 3.4

Hambantota 717.2 Nuwara Eliya 1392.4

Jaffna 567.0 Polonnaruwa 916.6

Kalutara 8054.3 Puttalam 804.5

Kandy 36.5 Rathnapura 95386.1


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The highest risk for all hazards is in the Rathnapura District, which has also the third highest PHC and Poverty Gap, the fourth highest SPG and also a low Gini coefficient which suggests that there is a high risk of people being affected from all hazards in this district which has high poverty. The other districts with somewhat high risk of people being affected by all risks are the districts of Matara, Batticaloa and Kalutara, where poverty levels are low to moderate suggestive of lower risks of poor being affected. Nuwara Eliya and Moneragala with very high poverty have low risk of people being affected by all types of hazards.

Table 18 : Risk of people being affected by Hydrodynamic Hazards (Flood, Lightning and Extreme wind effect)

In the case of hydrodynamic risks such as floods, extreme winds and lightning, Rathnapura district has the highest risk of people being affected. Moderate risks for people being affected were observed in the districts of Matara, Kalutara and Batticaloa like in the case of all hazards. Very low risk was observed for the poorest districts of Nuwara Eliya and Kegalle

Kegalle 931.7 Trincomalee 9.0

Vavuniya 0.00015

District Hydro Dynamic Risk District Hydro Dynamic Risk

Ampara 2012.1 Kilinochchi 1334.1












Vavuniya 0


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(there is no probably no impact because these two districts are in the hill country, where floods are rare.


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Table 19 : Risk of people being affected by Geological Hazards (Landslides)

District Geological Risk (Landslides) District Geological Risk (Landslides)

Ampara 0 Killinochchi 0

Anuradhapura 0 Kurunegala 0.98


Batticaloa 0 Matale 1.5

Colombo 0 Matara 0.65


Gampaha .00003 Mullaitivu 0


Jaffna 0 Polannaruwa 0

Kalutara 1.02 Puttalam 0


Kegalle 923.7 Trincomalee 0

Vavuniya 0

In the case of landslides, high risk was observed in the three districts of Nuwara Eliya, Badulla and Kegalle, which are all hill country districts with high risks for landslides and also have high poverty indices (except Kegalle) indicating that the risk of the poor being affected is high.

2. Risk for environmental or infrastructure damage

Table 20 : Risk for Environmental or Infrastructure Damage by All hazards

District Risk for All Hazards District Risk for All Hazards

Ampara 202.00 Killinochchi 11.54


Badulla 33.19 Mannar 0.02



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District Risk for All Hazards District Risk for All Hazards









Vavuniya 0.00

Risk for environmental or infrastructure damage was the highest in Anuradhapura district, followed by Batticaloa, and Rathnapura districts. Except for Rathnapura, the other two districts have lower poverty indices, showing little or no correlation of damage to infrastructure and poverty.

Table 21 : Risk of Environmental or Infrastructure damage by Hydrodynamic Hazards (Flood, Lightning and Extreme wind effect)

District

Risk for Hydrodynamic

Hazards (no of houses) District Risk for Hydrodynamic

Hazards










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District

Risk for Hydrodynamic

Hazards (no of houses) District Risk for Hydrodynamic

Hazards





Vavuniya 0.00

In the case of hydrodynamic hazards too, the districts of Anuradhapura, Batticaloa, and Matara and to a lesser extent Rathnapura and Ampara districts had the highest risks of damage. Except for Rathnapura, poverty is not high in the other districts with high risk. Thus, there appears to be poor correlation between poverty and risk of infrastructure damage due to hydrodynamic hazards.

Table 22 : Risk of Environmental or Infrastructure damage by Geological Hazards (Landslides)

District Risk for Geological Hazards District Risk for Geological Hazards













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District Risk for Geological Hazards District Risk for Geological Hazards


Vavuniya 0.00

Risk of landslide damage to infrastructure was the highest in the Rathnapura district. This is also one of the poorest districts with a high risk of landslides. Badulla is another poor district with moderate risk of damage to infrastructure by landslides. Therefore, there is some correlation between poverty and landslide damage as estimated by the risk factors.

3. Risk for Economic Losses (GDP)

Table 23 : Risk for Economic Losses by: All hazards

Province All hazard Index

Northern Province 11,692

Central Province 11,672

Sabaragamuwa Province 5,870

Western Province 39,056

Uva Province 29,641

Southern Province 134,542.

North Western Province 97,619

Eastern Province 89,035

North Central Province 184,874

Data on GDP was available only on a provincial basis and estimates of risk of losses to the economy was the highest in the North Central Province followed by Southern, North western and Eastern provinces respectively. In all these provinces the poverty level as estimated by the poverty indices were moderate to low, suggesting that economic losses may not be strictly affecting the poor when considering damage from all hazards.


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Table 24 : Risk for Economic Losses by Hydrodynamic Hazards

The largest risk of losses was recorded in the Eastern province followed by North Western and Western provinces and moderate losses in the Southern Province. All these provinces had low or moderate levels of poverty as indicated by the poverty indicators.

Province Hydrodynamics (Flood and Extreme

Wind events)

Northern Province 6,538


Sabaragamuwa Province 2,925

Western Province 39,055

Uva Province 15,739

Southern Province 22,524





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Table 25 : Risk for Economic Losses by: Animal Attacks

Province Animal Attacks

Northern Province 233


Sabaragamuwa Province 108

Western Province 0

Uva Province 31,993

Southern Province 0




Biggest loss was recorded by North Western followed by Uva provinces. The Uva Province had one of the highest levels of poverty as shown by poverty indices.

6.5 Correlation Analysis

(a) Results of correlation analysis between poverty variables.

• Strong correlation between expenditure and poverty gap (1% level) • Weak negative correlation between poverty head count and % households either without

toilets or with pit toilets (5% level) • Weak negative correlation between poverty head count and safe drinking water (5%

level) • Weak positive correlation between poverty head count and total dependency ratio (5%

level) • There is no significant correlation between education and poverty head count • No correlation between poverty head count and infant mortality

• No significant correlation between poverty head count 91-92 , 95-96 and 2002 and any of the poverty factors

• There is weak correlation between Poverty Gap 2006 – 2007 and safe drinking water and education OL passed and AL passed (5% level)


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• Significant correlation observed between expenditure 2006-2007 and the factors of, safe drinking water 2006 -2007, total dependency ratio, education up to grade 5, passed OL and passed AL 2006-2007 (1% level)

Correlation between poverty variables showed that only expenditure was correlated with poverty. Toilets, drinking water and dependency ratios were only slightly correlated with PHC. There was also weak correlation between poverty gap (2006/7) and safe drinking water, education. Expenditure is also significantly correlated with education, dependency and drinking water. The results suggest that poverty is associated somewhat with variables such as safe drinking water, education, dependency ratio and sanitation and is strongly correlated with expenditure. Due to this, expenditure per se can be used as a proxy for poverty.

(b) Results of correlation analysis between poverty and risks of hazards

• Significant correlation between risk of people affected by hydrodynamic hazards

and poverty headcount 2002 • Significant correlation between risk of people affected by geological hazards and

expenditure 2006-2007 and safe drinking water 2001 and 2006-2007 • Correlation between poverty head count 2006 – 2007 and risk of people affected

for all hazard types is significant at 5% significance level.

• Risk of environmental or infrastructure damage by hydrodynamic hazard is weakly correlated with poverty head count 1995 – 1996 and 2002 and infant mortality 95-96, at 5% significance level.

• There is correlation between risk of infrastructure damage from Geological hazard and Poverty head count for 2006 and Safe Drinking Water 2001 and 2006 -2007

• No correlation between risk of people affected by all hazard types with expenditure.

PHC was significantly correlated with hydrodynamic hazards and expenditure was significantly correlated with geological hazards but only weakly correlated with risks of all hazards. There is evidence to suggest that there is correlation between poverty as estimated by PHC, safe drinking water, and infant mortality to the risks of infrastructure damage from geological and hydrodynamic hazards. There is, therefore, evidence that poverty is related to risks from many hazards.

(c) Results of correlation analysis between poverty and impact of hazards

Since the above results were not conclusive, a correlation analysis was undertaken with poverty indicators and disaster impacts, using data at the Divisional Secretariat level. Both poverty and disaster hazard impact data at DS was utilized for the analysis.


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Data on the number of events recorded of each disaster type was correlated with poverty data. The poverty variables considered were:

• Headcount Ratio • Household Population below Poverty Line.

The total number of disaster events and their impacts at the DS level were used in the correlation analysis. The variables used were:

• No of events • No of Deaths • No of people affected • Houses Destroyed • Houses Damaged • Houses damaged and destroyed • Damaged to paddy (Ha) • Damaged to other crop(Ha) • Agricultural loss

Using actual figures of damage or death rather than estimated risk factors, the results were more encouraging and are presented below.

6.5.1 Results of correlation analysis

1. No Significant correlation found between Animal Attack and Poverty. 2. No Significant correlation found between Lightning and Poverty 3. Strong correlation (0.847 at 1% Significance level) between population below poverty

line– 2002 and houses damaged due to floods. 4. A less strong correlation (0.404 at 1% Significance level) between population below

poverty – line 2002 and number of people affected due to floods. 5. A weaker correlation (0.393 at 1% Significance level) between population below

poverty line – 2002 and number of families affected due to floods. 6. A weak correlation (0.203 at 1% Significance level) between population below

poverty line – 2002 and area under Paddy in hectares damaged due to floods. 7. No significant correlation observed between poverty and loss due to drought 8. Slight correlation (0.173 at 10% significance Level) between poverty head count and

loss of other farm lands in hectares due to extreme wind effects. 9. Slight correlation (0.238 at 5% Significance level) between population below poverty

line – 2002 and houses destroyed due to landslides. 10. A less strong (0.465 at 1% Significance level) correlation between population below

poverty line – 2002 and houses damaged due to landslides.

Thus, there is evidence of correlation between populations below poverty line and people affected and houses damaged due to floods and houses damaged or destroyed due to


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landslides, suggesting that disasters do affect the poor. Further research is needed to substantiate these results. Correlation analysis using data at the disaggregated level of DS divisions did show some positive results, in terms of relationship between poverty and disaster. The visual analysis using spatial data at DS level did show occasional matches between poverty and disaster, thereby confirming the results of the correlation analysis. Therefore, one must conclude that there is evidence of some relationship between poverty and disaster at a more disaggregated levels compared to data at a more aggregated level.

6.6 Regression Analysis

I. Poverty Indicators and Poverty Factors

(a) Poverty indicators and Poverty factors.

o Poverty indicators considered :

Poverty head count for 91-92 , 95-96, 2002 and 2007

Poverty Gap 2006 -2007

Expenditure 2006 -2007

o Poverty factors considered;

Infant mortality 91-92 , 95-96 and 2002

Proportion of households either without toilets or with pit toilets-2001, 2007

Total dependency ratio

Safe drinking water 2001, 2006 -2007

Education 2001, 2006-2007

6.6.1 Results of regression analysis for Poverty Gap and Expenditure

A regression analysis was carried out for Poverty Gap 2006 – 2007 and expenditure 2006-2007 as dependent variables.

Poverty Gap 2006 – 2007

A Linear Regression model was estimated for Poverty Gap 2006 – 2007 as the dependent variable with safe drinking water, education OL passed and AL passed as explanatory variables. The summary results of the regression are as follows:


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Model Summaryb

.780a .609 .530 1.23147Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), Education Passed AL 2006/7,Safe Drinking Water 2006 - 2007, Education OLPassed 2006/7

a.

Dependent Variable: Poverty Gap 2006 -2007b.

ANOVAb

35.390 3 11.797 7.779 .002a

22.748 15 1.51758.138 18

RegressionResidualTotal

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), Education Passed AL 2006/7, Safe Drinking Water 2006 -2007, Education OL Passed 2006/7

a.


Coefficientsa

12.565 2.097 5.991 .000

-.085 .028 -.552 -3.064 .008

-.057 .161 -.126 -.351 .730

-.138 .200 -.244 -.690 .501

(Constant)Safe DrinkingWater 2006 - 2007Education OLPassed 2006/7Education PassedAL 2006/7

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Poverty Gap 2006 -2007a.

The large R indicates that there is strong relationship with dependent and explanatory variables. R squared revels that more than half of the variation is explained by the model.

The model predicted is as follows:

Poverty Gap 2006 – 2007 = 12.565 – 0.085(safe drinking water) –0.057(OL passed) - 0.138(AL passed)

The results indicate that Poverty Gap which is an estimate of the depth of poverty is negatively correlated to safe drinking water, and education, with the former variable having a greater impact of poverty. This suggests that greater efforts are needed to provide drinking water or empower the household to get safe water in order to reduce poverty level. Similarly, education up to General Certificate of Education Ordinary Level (OL) or General Certificate of Education Advanced Level (AL) significantly impact poverty, i.e. lower the level of education the higher the poverty.


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Model Summaryb

.897a .805 .724 680.00115Model1



Predictors: (Constant), Education Passed AL 2006/7,Safe Drinking Water 2006 - 2007, Total DependencyRatio, Education OL Passed 2006/7, Education up toGrade 5 2006/7

a.

Dependent Variable: Expenditure 2006-2007b.

Coefficientsa

1067.559 4280.531 .249 .807

34.262 17.072 .306 2.007 .068

-100.755 39.714 -.402 -2.537 .026

124.089 105.013 .474 1.182 .260

125.437 107.376 .393 1.168 .265

196.674 141.770 .495 1.387 .191

(Constant)Safe Drinking Water2006 - 2007Total Dependency RatioEducation up to Grade5 2006/7Education OL Passed2006/7Education Passed AL2006/7

Model1

B Std. Error


Beta


t Sig.

Dependent Variable: Expenditure 2006-2007a.

Expenditure 2006-2007

R and R square are significant and therefore the model is acceptable.

The predicted model is as follows:

Expenditure 2006-2007 = 1067.559 + 34.262(Safe Drinking Water 2006 -2007) – 100.755(Total Dependency Ratio) + 124.089(Education up to grade 5) + 125.437(Passed OL) + 196.674(Passed AL)

The regression results show high correlation between Expenditure variables that can be used as proxy variables for poverty such as safe drinking water, education and dependency ratio. This suggests that for the Sri Lanka model, education and safe drinking water and dependency ratios are indicators of poverty on the basis of Poverty Gap and Expenditure as dependent variables. Dependency is negatively correlated with expenditure, i.e. higher the dependency ratio, the lower is the expenditure. There is positive correlation of expenditure with all levels of education, suggesting that educational attainments improve expenditures or incomes and reduce poverty

II. Poverty Indicators and Hazard Risks


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Model Summaryb

.149a .022 -.035 21886.1431Model1



Predictors: (Constant), Poverty Head count 2002a.

Dependent Variable: Risk of People affected forHydrodynamic

b.

Model Summaryb

.714a .509 .404 377.590301Model1



Predictors: (Constant), Safe Drinking Water 2006 -2007, Expenditure 2006-2007, Safe DrinkingWater2001

a.

Dependent Variable: Risk of People affected forGeological

b.

(a) Poverty Indicators and Hazard Risks

o Hazard Risks

Risk of people affected by Hydrodynamic hazards

Risk of people affected by Geological hazards

Risk of infrastructure damage for Hydrodynamic hazards

Risk of infrastructure for Geological hazards

o Poverty Indicators

Poverty head count for 91-92 , 95-96, 2002 and 2007

Poverty Gap 2006 -2007

Expenditure 2006 -2007

6.6.2 Results of Regression Analysis with Poverty Headcount and Hazard Risk

Risk of People affected by Hydrodynamic hazards

A regression was estimated with Poverty head count 2002 as the independent variable and the risk of people being affected by hydrodynamic hazards as the dependent variable. The summary result of the regression is as follows:

The low R and R square values indicate poor fit of the regression line, suggesting that there is no relationship between these variables.

Risk of people affected by Geological hazards

A regression was estimated with risk of people affected by geological hazard as the dependent variable and several independent variables. The summary results of the regression are as follows;


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ANOVAb

2071664 3 690554.793 4.843 .016a

1996042 14 142574.4354067706 17


Model1


Predictors: (Constant), Safe Drinking Water 2006 - 2007, Expenditure 2006-2007,Safe Drinking Water2001

a.

Dependent Variable: Risk of People affected for Geologicalb.

Coefficientsa

3011.485 907.874 3.317 .005.026 .127 .068 .201 .844

-10.233 19.697 -.201 -.520 .612

-25.304 12.117 -.598 -2.088 .056

(Constant)Expenditure 2006-2007Safe Drinking Water2001Safe Drinking Water2006 - 2007

Model1

B Std. Error


Beta


t Sig.

Dependent Variable: Risk of People affected for Geologicala.

The results show significant R and R squared values indicating a good fit for this regression model.

The predicted model is as follows: People affected for Geological hazards = 3011.485 + 0.026 (expenditure 2006 -2007) – 10.233 (Safe Drinking Water 2001) – 25.304 (Safe Drinking Water 2006 – 2007) The model shows that correlation between expenditure and drinking water in 2001 are not very significant. Thus, if we consider the independent variables such as safe drinking water


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(2006/7) as a proxy for poverty, then we can conclude that poverty is related to geological risks i.e. the higher the risk of people being affected by geological hazards (landslides) the higher the poverty as associated with reduced availability of safe drinking water. In other words, poor people are more susceptible to damage from geological hazards.


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Model Summaryb

.551a .304 .105 9.79213 .304 1.528 4 14 .248Model1



R SquareChange F Change df1 df2 Sig. F Change

Change Statistics

Predictors: (Constant), Risk of Infostructre for Geological, Risk of Infrastructure for Hydrodynamic, Risk of People Geological, Risk of People affected for Hydrodynamic

a.

Dependent Variable: Poverty Head count 1991 - 1992b.

ANOVAb

586.019 4 146.505 1.528 .248a

1342.402 14 95.8861928.421 18


Model1


Predictors: (Constant), Risk of Infostructre for Geological, Risk of Infrastructure forHydrodynamic, Risk of People affected for Geological, Risk of People affected forHydrodynamic

a.

Dependent Variable: Poverty Head count 1991 - 1992b.

6.6.3 Regression for Poverty indicators vs. Risk measures

It was attempted to fit the regression model for poverty factor (PHC) and Poverty Gap with hazard risks as explanatory variables. The results of these regressions were insignificant and therefore do not satisfy any model fitting assumptions.

Model for Poverty Head count

R and R squared are too low, while the regression sum of squares is not significant at 5% significance level.

The above regression suggests that poverty does not increase or decrease due to the impact of hazards. This means that poverty per se cannot be influenced by the risks of hazards. There are other causes that may affect poverty, but hazards do not appear to affect poverty according to this model based on Sri Lankan data.


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ANOVAb

22.967 4 5.742 2.286 .112a

35.171 14 2.51258.138 18


Model1


Predictors: (Constant), Risk of Infostructre for Geological, Risk of Infrastructure forHydrodynamic, Risk of People affected for Geological, Risk of People affected forHydrodynamic

a.


Model Summaryb

.629a .395 .222 1.58499 .395 2.286 4 14 .112Model1



R SquareChange F Change df1 df2 Sig. F Change

Change Statistics

Predictors: (Constant), Risk of Infostructre for Geological, Risk of Infrastructure for Hydrodynamic, Risk of People affected forGeological, Risk of People affected for Hydrodynamic

a.


Model for Poverty Gap 2006 – 2007

The second model uses poverty gap as the dependent variable against hazard risks as independent variables.

The results of this model is also not significant with low R and R Squared and the conclusion is the same as the previous model that hazards do not appear to affect poverty according to this model as well.

6.7 Conclusions

The statistical analysis could only be conducted according to data availability. While hazard data was available for a continuous period of 34 years, poverty data was available only for a few years when comprehensive socio economic surveys were conducted. Thus, it was possible to complete regressions and correlation analysis for certain time periods. The results of the analysis are not conclusive but more systematic and regular collection of poverty data would enable us to conduct a better analysis of the relationships between poverty and disaster. The conclusions that can be drawn from this initial analysis are summarized below.


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6.8 Hazard Risk Factors

• The highest risk for all hazards is in the Rathnapura District, which has also the third highest PHC and Poverty Gap, the fourth highest SPG and also a low Gini coefficient which suggests that there is a high risk of people being affected from all hazards in this district which has high poverty.

• Nuwara Eliya and Moneragala with very high poverty have low risk of people being affected by all types of hazards.

• In the case of hydrodynamic risks such as floods, extreme winds and lightning too, Rathnapura district has the highest risk of people being affected.

• Very low hydrodynamic risk was observed for the poorest districts of Nuwara Eliya (there is no probably no impact because this districts are in the hill country, where floods are rare).

• In the case of landslides, high risk was observed in the two districts of Nuwara Eliya and Badulla which are all hill country districts with high risks for landslides and also have high poverty indices indicating that the risk of the poor being affected is high.

• Highest risk for environmental or infrastructure damage from all hazards was the highest in Anuradhapura district, followed by Batticaloa, and Rathnapura districts (only district with high poverty).

• In the case of hydrodynamic hazards, the districts of Anuradhapura, Batticaloa, and Matara and to a lesser extent Rathnapura (only district with high poverty) and Ampara districts had the highest risks of damage to environment and infrastructure.

• Risk of landslide and damage to infrastructure was the highest in Rathnapura district. This is also one of the poorest districts with high risk of landslides. Badulla is another poor district with moderate risk of damage to infrastructure by landslides.

• Economic losses may not be strictly affecting the poor when considering damage from all hazards.

• Economic losses from hydrodynamic hazards were high in Eastern, North Western and Western provinces (poverty levels moderate in all three provinces)

• Economic losses from animal attacks were high in North Western and Uva provinces (highest level of poverty in Uva)

6.9 Correlation Analysis at District Level

• The results suggest that poverty is associated somewhat with variables such as safe drinking water, education, dependency ratio and sanitation and is strongly correlated with expenditure. Due to this expenditure per se can be used as a proxy for.

• There is evidence to suggest that there is correlation between poverty as estimated by PHC, safe drinking water, and infant mortality to the risks of infrastructure damage from geological and hydrodynamic hazards. There is, therefore, evidence that poverty is related to risks from many hazards.


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• No correlation exists between poverty variables such as PHC and population below poverty line and disaster variables at the disaggregated or DS level of analysis.

6.10 Correlation Analysis at Divisional Secretariat Level (using actual impact data)

• No Significant correlation found between Animal Attack and Poverty. • No Significant correlation found between Lightning and Poverty • Strong correlation (0.847 at 1% Significance level) between population below poverty

line– 2002 and houses damaged due to floods. • A less strong correlation (0.404 at 1% Significance level) between population below

poverty – line 2002 and number of people affected due to floods. • A weaker correlation (0.393 at 1% Significance level) between population below

poverty line – 2002 and number of families affected due to floods. • A weak correlation (0.203 at 1% Significance level) between population below

poverty line – 2002 and area under Paddy in hectares damaged due to floods. • No significant correlation observed between poverty and loss due to drought • Slight correlation (0.173 at 10% significance Level) between poverty head count and

loss of other farm lands in hectares due to extreme wind effects. • Slight correlation (0.238 at 5% Significance level) between population below poverty

line – 2002 and houses destroyed due to landslides. • A less strong (0.465 at 1% Significance level) correlation between population below


Thus, there is evidence of correlation between populations below poverty line and people affected and houses damaged due to floods and houses damaged or destroyed due to landslides, suggesting that disasters do affect the poor. Further research is needed to substantiate these results.

Correlation analysis using data at the disaggregated level of DS divisions did show some positive results, in terms of relationship between poverty and disaster. The visual analysis using spatial data at DS level also show occasional matches between poverty and disaster, thereby confirming the results of the correlation analysis.. Therefore, one must conclude that there is evidence of some relationship between poverty and disaster at a more disaggregated levels compared to data at a more aggregated level.

6.11 Regression Analysis

• Poverty Gap which is an estimate of the depth of poverty is negatively correlated to safe drinking water, and education, with the former variable having a greater impact of poverty. This suggests that greater efforts are needed to provide drinking water or empower the household to get safe water in order to reduce poverty level

• Education significantly impacts poverty, i.e. lower the level of education the higher the poverty.


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• The higher the risk of people being affected by geological hazards (landslides) the higher the poverty as associated with reduced availability of safe drinking water. In other words poor people are more susceptible to damage from geological hazards.

• Poverty does not increase or decrease due to the impact of hazards. This means that poverty per se cannot be influenced by the risks of hazards

The results of the regression analysis using estimated risk factors and poverty variables were not conclusive when the analysis was done at the district level. The results suggest that poverty is associated somewhat with variables such as safe drinking water, education, dependency ratio and sanitation and is strongly correlated with expenditure. Expenditure per se can be used as a proxy for poverty, and consequently one can conclude that there is correlation between poverty and infrastructure damage from geological and hydrodynamic hazards. In other words, poor people are more susceptible to damage from geological hazards but poverty per se cannot be influenced by the risks of hazard

Based on an analysis of hazard risks and location, the results show that hazard risks are high in some poor districts whereas it is also high for districts that do not have much poverty. The results are not consistent enough to arrive at any valid conclusions. Hazard risk is probably more related to the location rather than where the poor are. For example, hilly areas have higher risk of landslides, while coastal areas have a higher risk for hydro dynamic hazards as are some interior districts. The regression results also confirm this to some extent that hazards may not increase or decrease poverty. On the other hand, there is some evidence that poor people are more susceptible to landslides than the non poor. There were no significant results for the other hazards, but with more poverty data one may be able to analyze this further.

6.12 Disaster Risk Reduction Policy and Programmatic Response

Sri Lanka is affected by many natural hazards and recent data shows that the frequency of natural disasters has increased, especially during last few decades and could be attributed to uncontrolled development, environmental degradation or human intervention. There is some evidence to suggest that human intervention can increase the frequency or severity of certain types of hazards such as landslides, floods, drought etc. or cause hazards that were not previously experienced. With the population of the country growing coupled, with the scarcity of safe land, there is a greater tendency for people to occupy hazard prone lands increasing their susceptibility to hazards. Although uncontrolled development may yield immediate benefits, it will hinder the development process in the long-run by triggering new hazards and that may cause irreparable damage to development. Optimizing development and maintaining its sustainability will only be possible by safeguarding the environment, which in turn will help arrest the occurrence of human induced hazards. This emphasizes the fact that development, population growth, poverty, environment and disaster management are very closely inter linked.

In this context, managing disasters in the 21st century requires a concerted as well as an integrated national effort which needs to be highly coordinated at all levels. The overall objective should be to reduce the risk, by focusing on preventing the occurrence of disasters, mitigating their impact and ensuring that there is adequate preparedness to guarantee an effective response.


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Disaster is multifaceted, cutting across various sectors and levels. Therefore, comprehensive assessment of disaster risks and contributing factors should be undertaken in different parts of the country and in different social strata. This will ensure that disaster risk reduction interventions and disaster preparedness plans respond adequately if disasters do occur, ensuring recovery at the earliest possible instance. This requires a well designed national policy framework and programmatic responses for disaster management to be in place, committing all public, local government, non governmental and private organizations and communities to ensure the highest level of safety for all citizens, property and infrastructure facilities.

6.13 National Disaster Management Policy

Sri Lankan government has progressed considerably towards the establishment of a national policy for disaster management. The main aims of the policy are to establish/strengthen and maintain sustainable mechanisms, structures, programs, resources, capabilities and guiding principles for disaster risk reduction and management. Other broad objectives of the policies include, preparing for and responding to disasters and threats of disasters in Sri Lanka in order to save lives and property and mitigate the impacts of the disaster, minimise risk and ensure physical and psychological health of the survivors. Further, policies have also been formulated to minimise disruption to economic activity and damage to environment in order to ensure the sustainability of development. Plans have also been drawn up for immediate recovery of essential services in case of occurrence of a disaster and medium and longer term reconstruction and rehabilitation to a higher standard than before, in collaboration with all relevant stakeholders.

The Government of Sri Lanka, recognizing the urgent necessity for risk reduction and management as an integral part of sustainable development, has passed the required legislation to implement policies for disaster management. In May 2005, the Sri Lanka Disaster Management Act No.13 of 2005 was enacted with legal provisions for instituting a disaster risk management system in the country. The Act provides for the establishment of the National Council for Disaster Management (NCDM), which is a high- level inter-ministerial body, that provides direction for disaster risk management work in the country, and also the establishment of Disaster Management Centre (DMC), that will be the lead agency on disaster risk management. In January 2006, a separate Ministry for Disaster Management and Human Rights (M/DM&HR) was established with NCDM and DMC listed as organizations coming under the this ministry.

This new development has provided the infrastructure needed for the development of the National Policy for Disaster Management (DM). The draft DM National Policy has been prepared in consultation with key stakeholders with the objective of establishing a widely accepted uniform framework for risk reduction which will be accepted by all citizens in Sri Lanka in countering any disaster threat to the country. It is expected that that the DM policy would (a) facilitate the development and implementation of the National Disaster Management Plan (NDMP) and National Emergency Operations Plan (NEOP); (b) clarify roles and responsibilities for all stakeholders concerned with disaster management so that disasters can be managed more effectively; (c) enhance public awareness and training to help vulnerable communities to protect themselves from disasters and promote Community Based Disaster Risk Management (CBDRM), especially for the people living in vulnerable areas; (d) develop mechanisms for accessing and effective use of resources for disaster


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preparedness, prevention, response, relief, recovery, reconstruction and rehabilitation; (e) help in the assessment of the risks and vulnerabilities associated with various disasters; (f) build capacity among stakeholder institutions, in risk management and the application of disaster management and mitigation practices; (g); assist in pre-disaster planning, preparedness and mitigation.

The Main Components of the Policy are;

(a) Pre-disaster risk reduction — through mitigation/prevention and preparedness;

(b) Response - meeting the immediate emergency needs of disaster victims during and just after the disaster; and

(c) Post-disaster recovery/ restoration — through relief, rehabilitation and reconstruction & sustainable development, at all levels of the national structure and organizations — from national government level through provincial and local government, and community levels.

A set of guiding principles have been included in the draft policy and proposed institutional arrangement including an Advisory Committee to the DMC; National Emergency Response Committee; Technical Committees; Provincial Steering Committees; District DM Committee; Divisional DM Committee; LA level DM Committees and GN/Village level Committees. Strategic interventions needed for disaster response are also presented. Government approval for the draft policy is still being awaited.

The proposed policy if approved by the Government will be a big step towards improving disaster management, in Sri Lanka. However, disaster-poverty interface has not yet been duly incorporated into this proposed policy. There has been no concerted effort as yet to integrate disaster risk management and poverty reduction. Although we have empirical evidence of a strong connection between poverty and vulnerability to disasters, and despite frequent statements in national and international development policies about the importance of the two issues, knowledge about this relationship has not yet been sufficiently grounded in research and development practice. Furthermore, effective co-operation and coordination between public agencies as well as other agencies involved in disaster management and poverty have not yet been established, mainly due to the following constraints:

(a) Organizational limitations; (b) Varying perspectives, of different stakeholders; and (c) Current limitations of existing knowledge;

As for organizational aspects, many governmental agencies are assigned responsibilities for one or the other of the functions relating to disaster management and work without much coordination. In addition, activities relating to natural hazards often have been stereotyped as being specific event-driven/focus rather than process-oriented, whereas actions related to poverty reduction tend to be wider in scope and process orientated.


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7 CONCLUSIONS AND, RECOMMENDATIONS Sri Lanka is seeking to eradicate poverty and malnutrition across all regions and strata of society and promote peace and sustainable human development while protecting its environment which is prone to natural hazards and disasters. Currently Sri Lanka is poised to meet or possibly exceed the Millennium Development Goals (MDGs) before 2015. However, significant challenges remain in reducing income poverty, improving the geographic distribution of economic growth and achieving equality. Recent estimates of poverty show that over 15% of the population live in poverty. A further proportion is hovering just above the poverty line and is vulnerable to shocks that may precipitate them into either temporary or permanent poverty. These shocks may arise from economic or social causes or be induced through natural disasters. The poor and those bordering poverty tend to reside in disaster prone areas and are therefore at greater risk of falling into poverty due to natural or manmade disasters. This suggests that overall policies for poverty alleviation need to incorporate disaster risk mitigation strategies and joint action is needed to thwart the consequences of poverty and disaster. Understanding of the causes, spatial distribution, intensity and impact of disasters is a necessary precondition to drawing up suitable policies and plans for disaster management. In addition, an understanding of the socio economic profile and cultural behaviour patterns of the poor and near-poor population and other vulnerable groups will be essential to obtain insights into the relationships between poverty and disaster and vice versa. The current study is an initial attempt to determine the nature and causes of poverty and disaster and examine if there are any quantifiable/statistically valid or even qualitative relationships that can be discerned for use in drawing up appropriate independent as well as integrated policy responses for disaster mitigation as well as for poverty alleviation.

7.1 Poverty Status

Despite impressive growth, about a fifth of the population still remains poor - the population below poverty line was estimated at 15.2 % in 2006/7. In the north eastern areas, a large proportion of the population is believed to be below the poverty line due to the ongoing ethnic conflict. Other estimates of poverty show that 5.6 percent and 41.6 percent of the population earns less than USD 1 and USD 2 per day, respectively. Poverty in Sri Lanka can be broadly classified under three sectors- urban, rural and estate. There is vast disparity between urban and rural poverty chiefly because of the Colombo-centric economic activities. The largest population of poor reside in the rural sector (82% of the poor), followed by the estate sector (11%) and urban sector (7%). Poverty reduction has not been equitable due to growth being concentrated in urban areas particularly in the Western Province.

Historically, poverty in the urban sector has been concentrated in underdeveloped or undeveloped settlements or the so called urban slums. Over-crowding, poor housing, sanitation and environmental degradation and lack of financial resources are contributing to the continuation of poverty in the urban sector. Better urban planning and quality of urban services can help reduce the costs of over concentration and mitigate its impact on the poor. Many migrants originating from poorer and conflict-affected districts could fall into poverty and add to the urban poverty problem.


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Poverty in rural areas is higher among agricultural households, due to high levels of under-employment and low agricultural incomes. Due to seasonal nature of agricultural incomes and high level of indebtedness of the farming household, many rural poor experience considerable level of transient poverty. Higher prices for agricultural products appear to have benefited the middle-men more than the producer. Improved farmer access to technologies, trade, land and irrigation will help raise agricultural productivity.

Given limited opportunities in agriculture, poverty reduction can be improved through growth of the rural non-farm sector employment by reducing current constraints faced by rural poor in start-up of new enterprises. Improving connectivity of remote areas, providing infrastructure and access to energy and transport, as well as appropriate micro-finance resources, market information and markets will facilitate establishment of new enterprises and provide additional incomes.

Improving education and skills in remote areas can enhance employment choices, including the ability to migrate. Expansion of coverage of social programs, including Samurdhi transfers to estate poor can reduce estate poverty. Improving connectivity to towns, coverage of National Identity Cards and quality of health and education services can help improve economic opportunities.

The conflict-affected North and East lag behind the rest of the country in economic infrastructure and key human development outcomes. Sustainable peace remains a necessary precondition for sustained economic growth and poverty reduction in this region. The increasing number of IDPs from the current conflict areas may have temporary impacts leading to poverty and malnutrition. Removing constraints on the mobility of people and goods, such as on fishing will yield significant economic benefits in areas of past or recent conflict.

Existing social welfare programs like Samurdhi are not performing to their potential, primarily due to targeting problems. A better targeting system will improve the impact of Samurdhi programs.

Comprehensive policies should be drawn up to combat poverty in a holistic manner and address most of the poverty issues highlighted for Sri Lanka. Poverty alleviation programs can succeed if appropriate policies have been adopted to address the identified issues. Currently, government policies formulated to address poverty in Sri Lanka include livelihood development and social protection. The overall vision for the plan is a country in which people are empowered to develop and sustain their livelihoods and improve their standards of living, with special care being taken by the society to ensure that vulnerable people and geographical areas are adequately protected from risks including risks to life and sustenance arising from various sources and are not left behind in the process of development, so that poverty, hunger and deprivation can cease to have any significance.

7.2 Disaster Hazards

The systematic collection and analysis of disaster data began only recently in Sri Lanka. The DesInventar methodology developed for Latin America has been adopted for use in Sri Lanka. The methodology uses spatially distributed historical disaster data on hazards and their impacts to identify and estimate the occurrence, intensity and impacts of disasters. Currently, such data is being collected at the Divisional Secretariat level and aggregated to the district and national levels. Data is being collected for major hazards such as floods, wind


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events, landslides, fires, animal attacks, droughts, lightening, etc and their impacts on people, housing and crops.

The overall disaster typology in Sri Lanka does not seem to be distributed evenly. According to data collected from 1974 to 2008, Sri Lanka seems to be most affected by Animal Attacks (50% of total number of events), followed by fire, floods, wind events and landslides. However, their impacts vary considerably, with floods causing the largest impact on people, followed by drought and the tsunami. In terms of number of deaths, apart from the tsunami which caused the largest number of deaths, wind events, animal attacks and landslides caused the next most number of deaths. In terms of time series distribution, animal attacks seem to have increased during the period 1999-2007. This may be due to data not being collected systematically prior to that period. However, disasters such as floods seem to occur almost every year. A look at seasonal distributions shows that all disasters appear to occur evenly throughout the year with May being the most affected by disasters.

The most deaths caused by hazards (excluding tsunami) in Sri Lanka were due to the extreme wind events, animal attacks, landslides and floods representing 78% of total loss of life. Uneven chronological, spatial and seasonal distribution of recorded loss of life due to hazards is a common phenomenon showing a close link with the weather patterns in Sri Lanka, especially with the monsoon. Further, the most deaths due to natural disasters occurred in the districts of Ampara, Mullaitivu, Hambantota and Galle.

About 95% building destruction and damage was caused by disastrous wind events, Tsunami and floods. Other important disasters causing building damage were landslides and animal attacks. Except in 1978 and 2000, the annual rate of building destruction appears to be quite low and in these two peak years most destruction had been caused by extreme wind events, whereas in the remaining years the main cause for damage to buildings was floods. The seasonal distribution appears to take on a cyclical pattern and most destruction and damage have occurred during the period of November, December and January and in May. The most affected district was Polonnaruwa whereas the districts of Mannar, Vavuniya, Moneragala and Kandy were the least affected.

Mainly drought (56%), flood (39%) and extreme wind events (5%) caused agricultural crop damages. The annual time series distribution for agricultural crop loss takes on a cyclical pattern with three peaks in 1987, 2001 and 2004 and damage appears to be mainly caused by drought and flood. The seasonal distribution of agricultural crop loss shows cyclical distribution with two peaks. The district of Kurunegala and Ampara appears to have the highest damage due to crop loss.

Landslides had been traditionally considered as minor type of disaster and not a common occurrence in Sri Lanka. Until the year 2002, the annual average number of landslide records did not exceed 50. However, the data shows a sudden increase in the occurrence of landslides during the years 2003 – 2008. This increase does not reflect actual increase in the number of landslide occurrences but an increase in the availability of data due to better record keeping. Further, landslides occur mostly in the months of November, December and January. With respect to spatial distribution, most landslides appear to occur in the districts of Badulla, Moneragala, Nuwara Eliya and Kegalle. People affected, loss of life, building damage and crop loss also appear to take the same trend with only the above mentioned districts being most affected.


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Sri Lanka being an island located close to the equator is prone to warm weather conditions. This can be seen by the yearly occurrence of droughts in Sri Lanka. It is also important to note that major droughts occurred in the year 1992. An examination of the seasonality of occurrences of drought shows that droughts occur largely in the month of August. With respect to spatial distribution, the areas most affected appear to be the districts of Kurunegala, Puttalam, Hambantota, Moneragala and Ampara. A large number of people were affected by severe droughts that occurred during the years of 2001 and 2004. Further, people located in the above mentioned districts were the most affected by drought. Spatial impacts of agricultural losses due to droughts were similar to that of people affected. Large agricultural losses were experienced in 1987 in addition to the losses incurred in years 2001 and 2004.

The incidences of flooding seem to be most frequent in the latter years with the most flooding occurring in the year 2006. Floods in Sri Lanka are most likely to occur in the months of May in the first cycle and in December in the second cycle. With respect to spatial distribution, floods are most frequent in the districts of Matara, Kalutara, Rathnapura, Gampaha and Ampara.

People affected by floods have increased over the years, with the highest number recorded in the year 2008. People residing in the districts of Gampaha, Colombo, Rathnapura and Ampara were the most prone to floods and a similar pattern was observed in the case of loss of life as well. However, the occurrence of deaths due to floods was quite low except in the year 2003, when the death toll reached 200. Damage or destruction to buildings also appears to follow this pattern with most damage occurring in the districts of Rathnapura, Galle, Matara, Hambantota and Kilinochchi. Damage to paddy was mostly recorded in the earlier years with the highest impact recorded in 1984 causing the most damage in the districts of Kurunegala, Polonnaruwa, Ampara and Batticaloa.

Extreme wind events too seem to be most relevant in the later years with the most extreme wind events occurring in the year 2007. With respect to spatial distribution, wind events occurred mostly in the districts of Rathnapura, Moneragala, Kalutara, and Colombo.

Number of people affected by wind events in Sri Lanka was relatively small, except in the years 1978 and 2000. People residing in the districts of Anuradhapura, Polonnaruwa, Trincomalee and Batticaloa were most prone to wind events.

The occurrence of deaths due to wind events was quite rare except in the year 1978, when the death toll reached nearly 850. Batticaloa district was the most affected by deaths from wind events.

Damage and destruction to buildings due to wind events was rare, however, heavy damage was recorded in the years 1978 and 2000. Buildings located in the districts of Anuradhapura, Trincomalee, Polonnaruwa and Batticaloa appear to be most prone to damage from wind events.

Agricultural crop loss was also a rare occurrence, with much loss reported in the years of 1989 and 2000. Biggest agricultural losses occurred in the districts of Moneragala, Ampara, Polonnaruwa and Trincomalee.

Fire also appears to be an uncommon occurrence with most occurring during the years of 2002- 2007. The districts of Anuradhapura, Polonnaruwa, Gampaha and Colombo appear to be the most affected by fires. A similar pattern was observed with respect to people affected


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and damage to buildings. However, the occurrence of deaths appeared to be consistent over the years except during the years 1987-1992, when no deaths were recorded.

Analysis of data on disaster hazards shows that risk of hydro meteorological hazards are higher than that of other hazards, with wide spread impacts over time as well as spatially. The risk of geological hazards is the next highest, but the risk is unevenly distributed spatially as well as over time.

7.3 Data Analysis

The statistical analysis could only be conducted according to data availability. While hazard data was available for a continuous period of 34 years, poverty data was available only for years when comprehensive socio economic surveys were conducted. Thus, it was possible to complete regressions and correlation analysis for certain time periods. The results of the analysis are not conclusive but more systematic and regular collection of poverty data would enable us to conduct a better analysis of the relationships between poverty and disaster. The conclusions that can be drawn from this initial analysis are summarized below.

Hazard Risk Factors

• The highest risk for all hazards is in the Rathnapura District, which has also the third highest PHC and Poverty Gap, the fourth highest SPG and also a low Gini coefficient which suggests that there is a high risk of people being affected from all hazards in this district which has high poverty.

• Nuwara Eliya and Moneragala with very high poverty have low risk of people being affected by all types of hazards.

• In the case of hydrodynamic risks such as floods, extreme winds and lightening too, Rathnapura district has the highest risk of people being affected.

• Very low hydrodynamic risk was observed for the poorest districts of Nuwara Eliya (there is no probably no impact because this districts are in the hill country, where floods are rare).

• In the case of landslides, high risk was observed in the two districts of Nuwara Eliya and Badulla which are all hill country districts with high risks for landslides and also have high poverty indices indicating that the risk of the poor being affected is high.

• Highest risk for environmental or infrastructure damage from all hazards was the in Anuradhapura district, followed by Batticaloa, and Rathnapura districts (only district with high poverty).

• In the case of hydrodynamic hazards, the districts of Anuradhapura, Batticaloa, and Matara and to a lesser extent Rathnapura (only district with high poverty) and Ampara districts had the highest risks of damage to environment and infrastructure.

• Risk of landslide damage to infrastructure was the highest in Rathnapura district. This is also one of the poorest districts with high risk of landslides. Badulla is another poor district with moderate risk of damage to infrastructure by landslides.

• Economic losses may not be strictly affecting the poor when considering damage from all hazards.


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• Economic losses from hydrodynamic hazards were high in Eastern, North Western and Western provinces (poverty levels moderate in all three provinces)

• Economic losses from animal attacks were high in North Western and Uva provinces (highest level of poverty in Uva)

Correlation Analysis at District Level

• The results suggest that poverty is associated somewhat with variables such as safe drinking water, education, dependency ratio and sanitation and is strongly correlated with expenditure. Due to this expenditure per se can be used as a proxy for poverty.

• There is evidence to suggest that there is correlation between poverty as estimated by PHC, safe drinking water, and infant mortality to the risks of infrastructure damage from geological and hydrodynamic hazards. There is therefore evidence that poverty is related to risks from many hazards.

• No correlation exists between poverty variables such as PHC and population below poverty line and disaster variables at the disaggregated or DS level of analysis.

Correlation Analysis at Divisional Secretariat Level (using actual impact data)

• No Significant correlation found between Animal Attack and Poverty. • No Significant correlation found between Lightning and Poverty • Strong correlation (0.847 at 1% Significance level) between population below poverty

line– 2002 and houses damaged due to floods. • A less strong correlation (0.404 at 1% Significance level) between population below

poverty – line 2002 and number of people affected due to floods. • A weaker correlation (0.393 at 1% Significance level) between population below

poverty line – 2002 and number of families affected due to floods. • A weak correlation (0.203 at 1% Significance level) between population below

poverty line – 2002 and area under Paddy in hectares damaged due to floods. • No significant correlation observed between poverty and loss due to drought • Slight correlation (0.173 at 10% significance Level) between poverty head count and

loss of other farm lands in hectares due to extreme wind effects. • Slight correlation (0.238 at 5% Significance level) between population below poverty

line – 2002 and houses destroyed due to landslides. • A less strong (0.465 at 1% Significance level) correlation between population below


Thus, there is evidence of correlation between populations below poverty line and people affected and houses damaged due to floods and houses damaged or destroyed due to landslides, suggesting that disasters do affect the poor. Further research is needed to substantiate these results.

Correlation analysis using data at the disaggregated level of DS divisions did show some positive results, in terms of relationship between poverty and disaster. The visual analysis using spatial data at DS level also show occasional matches between poverty and disaster,


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thereby confirming the results of the correlation analysis.. Therefore one must conclude that there is evidence of some relationship between poverty and disaster at a more disaggregated levels compared to data at a more aggregated level.

Regression Analysis

• Poverty Gap which is an estimate of the depth of poverty is negatively correlated to safe drinking water, and education, with the former variable having a greater impact of poverty. This suggests that greater efforts are needed to provide drinking water or empower the household to get safe water in order to reduce poverty level

• Education significantly impacts poverty, i.e. lower the level of education the higher the poverty.

• The higher the risk of people being affected by geological hazards (landslides) the higher the poverty as associated with reduced availability of safe drinking water. In other words poor people are more susceptible to damage from geological hazards.

• Poverty does not increase or decrease due to the impact of hazards. This means that poverty per se cannot be influenced by the risks of hazards

The results of the regression analysis using estimated risk factors and poverty variables were not conclusive when the analysis was done at the district level. The results suggest that poverty is associated somewhat with variables such as safe drinking water, education, dependency ratio and sanitation and is strongly correlated with expenditure. Expenditure per se can be used as a proxy for poverty, and consequently one can conclude that there is correlation between poverty and infrastructure damage from geological and hydrodynamic hazards. In other words poor people are more susceptible to damage from geological hazards but poverty per se cannot be influenced by the risks of hazard

Based on an analysis of hazard risks and location, the results show that hazard risks are high in some poor districts whereas it is also high for districts that do not have much poverty. The results are not consistent enough to arrive at any valid conclusions. Hazard risk is probably more related to the location rather than where the poor are. For example, hilly areas have higher risk of landslides, while coastal areas have a higher risk for hydro dynamic hazards as are some interior districts. The regression results also confirm this to some extent that hazards may not increase or decrease poverty. On the other hand, there is some evidence that poor people are more susceptible to landslides than the non poor. There were no significant results for the other hazards, but with more poverty data one may be able to analyze this further.

7.4 Policy Recommendations

Understanding the extent and spatial distribution of key disasters; the socio-economic status of vulnerable groups; characteristics of the climate, land use and landscape and geomorphology and water regime will help to improve the joint planning for disaster mitigation and service delivery to vulnerable groups.

Poverty has a direct and indirect influence on the vulnerability of populations living in hazard prone areas in Sri Lanka. Incorporation of disaster risk reduction in poverty reduction policies and sector development strategies can benefit sustainable development and pro-poor economic growth. In that context, a number of policy related recommendations can be made:


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• Areas identified as most vulnerable to flood, droughts, landslides and cyclones can be brought under special management zones to introduce best practices on land use, building regulation, construction and service delivery and also to target improved resources management, emergency response planning and awareness development

• Poverty reduction programmes should not only focus on the groups below poverty line but also on the groups that may fall into poverty as a result of disasters. There is a sizeable population hovering just above the poverty line, who risk falling into poverty due to unforeseen or external shocks such as inflation, loss of employment, death of primary earner that can be caused or catalysed by natural or man-made disasters

• Disaster Risk Analysis and mitigation planning should be integrated into the design and financing of infrastructure projects such as roads, dams and landscape modifications in the high risk areas with vulnerable populations. One way to do this is to extend the present Environment Impact Assessment (EIA) process to include a more detailed hazard risk and vulnerability assessment in project approval state and educate and promote public participation in decision making on projects.

• Invest in decision making tools such as hazard and vulnerability profiles for different hazards and training of potential users in their effective use

• Analysis of the poverty–disaster interface to help in understanding disaster impacts on poor populations. Present household socio-economic data level needs to be strengthened by developing panel data, including intermittent shock surveys. Data on landscape and land use data, geological and hydrological features and characteristics at high spatial resolution will need to be made accessible to enable appropriate analysis.

• Poverty data in Sri Lanka is not available in conflict affected areas as well as at the required coverage and frequency in other areas. The next national household survey and other periodic baseline data should be modified to address these concerns.

• National budgetary process should investigate the costs and benefits of investing in disaster risk reduction vs. response. Improving disaster prevention may help Sri Lanka attract investments as the risks and external shocks to business and those to potential poor and vulnerable group employees

7.5 Next Steps

Although comprehensive time series data are available for establishing a fairly accurate hazard profile, there are data gaps that need to be addressed. For example, more data on the value of economic and social losses need to be incorporated to determine costs and benefits of disaster management programs. Disaster insurance systems can be improved to allow disaster affected persons to restore their livelihoods quickly.

In the case of poverty data there are many gaps. The major problem appears to be lack of consistency in data collected and the consequent lack of comparability, for assessment purposes. Time series data on income, expenditure are not available on a regular basis. The HIES conducted periodically also suffers due to comparability problems and data gaps due to leaving out large areas due to conflict or problems prevailing in the area. Definitional problems and collection of same or similar data leads to confusion among researchers and


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lack of comparability of research results. These problems can be thrashed out and a common uniform platform of standardized data collection system implemented to the benefit of all.

Elaborate policies have been drawn up for both disaster management as well as poverty alleviation. These policies need to be validated with sound research and data back up if these policies are to succeed. There is a need to bring together the best brains in these specializations including support from the staff of foreign or international specialized organizations to study the problems and examine if they policies drawn up will in fact resolve the problems that have been encountered in these fields.

The current study is a good beginning and similar studies would assist policy makers to fine tune policies provided that problems are widely prevalent in the many countries involved in such studies. Thus the selection of the right problem to study and the need to have uniformity for comparability purposes but allowing for the differences observed among the selected countries will help to improve the effectiveness of results and recommendations arising out of the studies.


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REFERENCES

Central Bank of Sri Lanka – Annual Reports 2006-2007

Disaster Management Centre-Towards a Safer Sri Lanka : A Road map for Disaster Risk

Management, Ministry Of Disaster management, Sri Lanka 2005 Vol I & II

Department of Census & Statistics, Sri Lanka

Disaster Management Centre-Sri Lanka- Disaster Hazard Report 2007

Gunatilaka et al. 2006

Gunatilaka, Ramani, “Poverty trends,correlates and policies in Sri Lanka:1990-2002”

Kelegama, Saman “Poverty Situation and Policy in Sri Lanka” 2001

Ministry of Finance and Planning –Annual Reports 2006 and 2007

Mahinda Chinthana – “A Ten Year Horizon Development Framework 2006-2016 –

Discussion Paper” – Ministry of Finance 2005

Narayan, Ambar and Nobuo, Yoshida, “ Poverty in Sri Lanka: The Impact of Growth and

Rising Inequality” July 2005, World Bank

Sawada, Yasuyuki, (2000) “ Dynamic poverty problem and the role of infrastructure” JBIC

Review No 3 December 2000

United Nations – Global Network of NGOs for Disaster Reduction – “ Linking Disaster Risk

Reduction and Poverty Reduction, 2008

Vidyaratne, D.B.P.S. & Nigamuni, W.J. “Time Trend of poverty indicators on

population,employment and socio-economic situation 1981-2004”

World Bank “Sri Lanka Poverty Assessment – 2007”

World Bank, www.desinventar.lk


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ANNEX Annex 1 : Country and regional hazard profile

Country and regional hazard profile: An introduction

Disaster No of events (1974-2008)

Animal attack 7202

Cyclone 3

Gale 605

Strong wind 652

Surge 28

Tsunami 1

Fire 2703

Land subsidence 79

Landslide 1095

Lighting 300

Rains 304

Urban flood 49

Flood 1044

Drought 283

Extreme Wind events (Cyclone, Strong Wind, Gale, Surge )

Flood (including Flash flood, urban flood, rain and flood)


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Annual time series distribution: 1974 - 2008

Y

ear

No of events – Annual time series

Ani

mal

att

ack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1974 3 4 3 5 1 7

1975 4 5 4 2 1 1

1976 7 7 5 9 3 1 2 1

1977 3 4 6 12 1 1 1 2

1978 15 1 9 8 38 6 13 6 3 16 3

1979 5 8 7 8 7 1 6 1

1980 11 12 13 4 3 6 4

1981 11 6 17 12 9 1 3 6 2

1982 11 13 10 8 3 8 8

1983 5 14 7 7 3 1 5 1 1

1984 30 4 38 10 4 1 4 2

1985 20 4 5 17 6 9 2 1 1

1986 33 12 12 10 2 45 6 3

1987 5 13 2 3 5 7 1 1 2

1988 12 8 7 11 1 3 4 10 1

1989 31 14 5 19 10 33 3 3 3

1990 24 8 19 5 4 2 1 4 1

1991 20 4 3 18 12 8 3 1 18


244

Yea

r

No of events – Annual time series

Ani

mal

att

ack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1992 30 1 19 6 42 8 9 12 1 6 5 10

1993 22 8 4 46 4 1 21 14 2 8 1

1994 21 4 5 33 3 4 5 4 1

1995 51 8 19 40 3 11 24 2 1 1

1996 52 8 8 14 4 6 2 1 3

1997 110 9 7 35 5 28 14 1 1

1998 575 8 14 35 19 6 12 7 6

1999 803 6 2 39 9 11 7 5 3 2

2000 795 1 8 24 36 20 4 4 19 22 2

2001 693 13 21 15 15 2 13 2 8 1

2002 686 6 454 29 49 16 11 14 30 1 4

2003 582 8 477 44 50 8 80 9 25 60 6

2004 686 13 452 49 64 7 36 14 15 36 1 1 4

2005 880 4 438 59 73 1 43 20 30 47

2006 602 5

473 116

100

37 324 25 77 100 18

2007 335 7 109 59 84 21 246 35 62 201 3 2

2008 29 30 111 27 4 94 14 22 45


245

Seasonal Distribution 1974- 2008

M

onth

No of events – Seasonal

Ani

mal

att

ack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1 427 70 276 103 36 11 158 10 42 20 2

2 436 61 247 29 8 2 20 5 23 3 1

3 534 52 255 33 35 1 48 22 24 40

4 767 37 206 30 62 3 46 65 11 97

5 774 33 207 98 45 5 156 65 14 94 9 6

6 668 32 195 74 53 9 80 12 5 168 2 8

7 680 35 217 27 27 2 28 6 2 54 1

8 601 84 257 5 23 1 8 7 2 36 1 1

9 614 58 202 42 38 2 26 15 7 26 1 3

10 783 25 197 142 49 6 115 37 36 38 18

11 507 2 11 175 231 136 29 262 44 69 32 11 8

12 411 1 20 219 230 93 8 148 12 69 44 4 1 1


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People affected by disaster

People Affected by Disaster - DesInventar Disaster typology 1974-2008

Event People Affected

Animal attack 29912

Cyclone 1612575

Drought 12453010

Fire 7906

Flood 12804918

Gale 112567

Land subsidence 2551

Landslide 136636

Lighting 763

Rains 656751

Strong wind 87574

Surge 3452

Tsunami 1076185

Urban flood 23851


247

People Affected By Disaster - Annual Time Series Distribution 1974-2008

Yea

r

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1974 0 6737 0 8100 1500 15

1975 0 0 3984 0 0 0

1976 80 1780 5 51750 0 0 0 0

1977 0 16965 15 6766 0 0 0 0

1978 0 615176 80000 530 203806 15 1195 4 10 0 414

1979 85 0 255 5800 254 100 0 0

1980 121 0 0 2135 0 5 0

1981 60 88035 125 46900 800 0 0 2950 0

1982 45 508875 290 212897 0 175 18

1983 0 463680 5 21878 0 0 3000 289 0

1984 135 1266 196167 85 0 50000 0 0

1985 10 225000 0 80323 75 313 0 0 0


248

Yea

r

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1986 300 636514 5 396143 0 24145 0 160

1987 0 110000 5 15050 345 15 0 0 0

1988 5 1624655 350 225655 140 70 60 90 0

1989 241 181275 0 332243 32675 18223 75 0 300

1990 178 615214 277159 900 55 0 2500 245 1

1991 355 0 82 219604 890 115 5 1750 295

1992 70 91250 608585 0 337295 1050 278 2 0 0 922 55

1993 110 28775 0 962568 6395 0 3725 0 5 330 50

1994 20 8220 0 779384 758 7 0 500005 0

1995 65 1380 154 223394 1000 398 18 125 0 0

1996 175 148795 7 104380 9485 10 0 4 4625

1997 510 723080 12 171632 10 2354 2 0 50

1998 2700 7485 275 145907 10040 13 0 58025 60

1999 3632 42185 9 560458 1903 49 0 600 760 2185


249

Yea

r

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

2000 3410 906149 50332 474 868408 765 29 15 130 865 720

2001 3133 3128011 293 94019 589 10 14 150 835 526

2002 3009 159847 223 249898 8174 1309 15 1083 2495 250 4300

2003 2705 186329 127 1350089 7642 110 19670 18 320 34146 0

2004 3018 2721335 100 446979 7678 179 8197 16 447 2192 0 1076185 4

2005 2458 18972 1648 781104 8596 4 1297 54 1638 6765

2006 1671 41899 1356 739992 7439 814 13710 125 31567 11437 17307

2007 1463 17784 879 548444 2467 1262 38714 222 1255 11012 230 0

2008 148 682 2134607 1327 182 2030 80 4262 7887


250

People Affected By Disaster - Seasonal Distribution 1974-2008

Month A

nim

al a

ttac

k

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

January 1404 1982970 1304 1744281 9822 548 55149 6 50906 2113 0

February 1663 106345 495 414698 565 340 741 0 142 275 0

March 1886 535157 389 447535 2226 44 3053 32 4266 3184

April 3152 505078 253 312653 3150 109 1096 216 228 37339

May 3479 333703 253 2000739 2897 10 24374 116 57 8809 200 0

June 2731 577991 916 1142404 12648 105 18324 29 43 11896 80 2240

July 2845 1167312 787 142122 9762 74 888 10 20 4644 0

August 2327 4275362 810 580 1180 375 297 31 15 1487 250 0

September 2455 785482 581 96143 4075 25 673 65 32 3761 526 4

October 3591 502080 185 791985 3226 552 6126 105 31653 5802 21607

November 2719 706426 62625 200 2305678 21580 239 14403 143 518349 1698 2071 0

December 1660 906149 1618905 1733 3406100 41436 130 11512 10 51040 6566 325 1076185 0


251

Loss of Life due to Disasters

Loss of Life due to Disasters - DesInventar Disaster Typology 1974-2008

Disaster No of deaths

Animal attack 875

Cyclone 889

Drought 0

Fire 85

Flood 404

Gale 13

Land subsidence 14

Landslide 850

Lighting 292

Rains 13

Strong wind 21

Surge 3

Tsunami 30959

Urban flood 2


252

Loss of Life due to Disasters - Annual Time Series Distribution 1974-200 Y

ear

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1974 2 0 0 1 0 42

1975 4 0 0 4 5 0

1976 3 0 2 1 0 3 7 0

1977 2 0 2 0 0 1 0 0

1978 16 879 0 0 9 0 31 10 0 0 0

1979 3 0 2 0 0 0 11 0

1980 9 0 2 0 0 6 0

1981 8 0 4 7 0 0 5 0 0

1982 3 0 2 1 0 39 8

1983 1 0 7 17 2 0 0 0 0

1984 13 0 2 17 5 0 0 0

1985 2 0 0 3 0 9 3 0 0

1986 17 0 6 2 0 75 7 1

1987 0 0 0 0 1 4 0 0 0

1988 0 0 0 2 0 5 4 0 0

1989 4 0 0 4 0 274 2 0 0

1990 6 0 1 0 12 3 0 0 1

1991 9 0 0 11 0 2 3 0 2

1992 12 4 0 0 13 0 10 15 0 0 1 0

1993 9 0 7 6 0 0 23 14 0 0 0

1994 14 0 1 1 0 7 5 2 0

1995 13 0 4 5 1 4 27 0 0 0


253

Yea

r

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1996 8 0 0 0 0 1 2 1 1

1997 15 0 3 1 0 22 20 0 0

1998 83 0 0 2 1 0 9 3 0

1999 115 0 0 9 0 4 8 0 0 0

2000 87 6 0 7 7 0 0 5 0 1 1

2001 75 0 6 0 0 3 16 0 0 0

2002 53 0 1 1 1 6 9 0 3 0 0

2003 58 0 9 170 1 0 150 9 0 4 0

2004 53 0 4 4 3 0 13 8 1 0 0 3095

9 0

2005 76 0 5 22 2 0 4 10 0 0

2006 85 0 2 32 3 11 26 9 3 0 2

2007 17 0 9 21 0 3 45 27 1 9 0 0

2008 0 0 49 1 0 16 15 2 0

Loss of Life due to Disasters - Seasonal distribution 1974-2008

Mon

th

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng

win

d

Surg

e

Tsu

nam

i

Urb

an fl

ood

1 49 0 7 6 0 11 113 10 0 1 0

2 63 0 9 2 0 0 2 8 1 0 0

3 83 0 15 6 1 3 6 24 0 0

4 118 0 2 18 0 0 20 75 0 2

5 74 0 2 201 1 0 196 56 0 3 0 0


254

Mon

th

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng

win

d

Surg

e

Tsu

nam

i

Urb

an fl

ood

6 78 0 10 34 1 0 279 11 1 10 0 0

7 75 0 10 4 0 0 58 10 0 0 0

8 97 0 6 0 1 0 0 5 0 4 0 0

9 93 0 8 5 0 0 27 18 1 1 0 0

10 76 0 2 31 4 0 26 21 2 0 2

11 38 883 0 10 54 2 0 70 37 4 0 2 0

12 31 6 0 4 43 3 0 53 17 4 0 1 30959 0


255

Building Destruction Damage by Disasters

Building Destruction Damage by Disasters- DesInventar Disaster typology 1974-2008

Event Destroyed and damaged houses

Animal attack 5358

Cyclone 180003

Drought 0

Fire 1305

Flood 231616

Gale 11275

Land subsidence 329

Landslide 11397

Lighting 121

Rains 551

Strong wind 11567

Surge 47

Tsunami 105293

Urban flood 69


256

Building Destruction Damage by Disasters - Annual time series distribution 1974-2008

Y

ear

Ani

mal

att

ack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1974 0 0 0 250 300 0

1975 0 0 0 0 0 0 0 0

1976 8 0 1 6002 0 1 0 0

1977 1 0 3 14 0 0 0 0 0

1978 0

81269 0 87 9749 0 57 1 2 0 6

1979 17 0 0 1 0 50 0 0 0 0 0

1980 29 0 0 0 4 0 0 1 0 0 0

1981 12 0 0 25 199 160 0 0 0 570 0 0

1982 0 0 0 55 1814 0 20 0 0 0 0 0

1983 0 0 0 1 1668 0 0 0 0 0 11 0

1984 27 0 0 0 3298 0 17 0 0 0 0 0

1985 2 0 0 0 33 15 5 0 0 0 0 0

1986 28 0 0 1 5168 0

2187 0 0 23 0 0

198 0 0 0 1 5 0 24 4 0 0 0 0


257

Yea

r

Ani

mal

att

ack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

7

1988 1 0 0 53 34 26 14 5 0 18 0 0

1989 68 0 0 0 7034 135 89 15 0 60 0 0

1990 23 0 0 0

11325 185 0 0 0 40 0 0

1991 71 0 0 20 104 178 8 1 0 45 0 0

1992 13

17063 0 0 2714 206 55 0 0 0 0 11

1993 5 0 0 0

10631

1279 0 168 0 1 66 7 0

1994 4 0 0 0 7445 152 0 1 0 0 0 0 0

1995 12 0 0 31 834 150 0 39 4 0 25 0 0

1996 35 0 0 2 198

1033 0 2 0 1 925 0 0

1997 101 0 0 3 1619 2 0 25 0 0 0 10 0

1998 532 0 0 55 801 453 0 3 3 6 12 0 0

1999 717 0 0 2 6167 381 0 2 0 0 82 0 27

2000 664

81671 0 27 9261 173 0 6 2 24 96 7 0

2001 568 0 0 59 2006 106 0 2 0 0 128 0 0

200 563 0 0 42 2311 555 0 75 4 20 511 0 0


258

Yea

r

Ani

mal

att

ack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

2

2003 497 0 0 27

35102

1525 22

3744 2 31

1322 0 0

2004 548 0 0 22 6356 927 21 662 6 19 393 0

105293 1

2005 344 0 0 299

13608

1242 1 101 12 238

1180 0 0

2006 234 0 0 273

13057

1348 156

1724 24 103

2298 0 30

2007 224 0 0 153

11836 451 92

2250 34 87

2200 6 0

2008 10 0 0 62

60969 243 37 116 3 19

1573 0 0

Building Destruction Damage by Disasters - Seasonal distribution 1974-2008

Mon

th

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1 265 191 3326

2 901 16397

7 1 37 339 0

2 274 85 1317 125 68 25 0 22 55 0

3 334 78 1929 539 5 55 2 23 501 0

4 584 52 5492 554 18 89 36 16224

9 0

5 660 48 4482

6 585 2410

5 14 12130

7 0 0

6 514 161 1091

9160

6 21 178 2 10238

4 16 38


259

Mon

th

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

7 516 122 70 304 4 59 2 4 903 0 0

8 375 97 6 252 75 11 6 3 295 0 0

9 443 83 565 774 1 30 5 6 662 0 1

10 616 39 1276

7 648 53 270 21 62123

5 0 30

11 448 9833

2 40 8265

5344

5 40157

4 30 228 343 13 0

12 329 8167

1 309 3780

8154

2 26102

4 2 128129

4 18 10529

3 0


260

Agricultural Crop Loss due to Disasters

Agricultural Crop Loss due to Disasters: DesInventar Disaster typology 1974-2008

Event

Damaged to paddy and other crops (Ha)

Animal attack 3541

Cyclone 14100

Drought 420064

Fire 2397

Flood 280515

Gale 14896

Land subsidence 0

Landslide 1411

Lighting 1

Rains 32064

Strong wind 4699

Surge 915

Tsunami 10397

Urban flood 1


261

Agricultural Crop Loss due to Disasters: Annual time series distribution 1974-2008

Year A

nim

al

atta

ck

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1974 0 4320 0 0 61 0 0

1975 0 9332 0 324 0 0 0 162

1976 11 3594 0 5261 0 0 0 0

1977 0 972 0 0 0 0 0 405 0

1978 0 1300 7285 409

36638 607 0 0 0 0 29

1979 4 0 1134 0 0 22 0 0 0 0 0

1980 109 0 405 0 0 0 0 0 0 0 0

1981 0 0 1469 0 1992 0 0 0 0 0 0 0

1982 0 0 24707 0 8863 0 0 0 0 0 0 0

1983 202 0 2024 0 4406 0 0 0 0 0 29 0

1984 632 0 121 0

69256 0 0 0 0 0 0 0

1985 105 0 0 0 4047 202 162 0 0 0 0 0

1986 134 0 684 2

22732 0 98 0 0 0 0 0

1987 0 0

102935 0 0 0 243 0 40 0 0 0


262

Year

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1988 672 0 1022

1659 0 0 0 0 0

4451 0 0

1989 835 0 6042 0 4657

10111 38 0 0 0 0 0

1990 32 0 11960 0 5176 0 81 0 0 0 248 0

1991 2 0 158 0 1211 809 0 0 0 0 0 0

1992 0 2064 17139 0 2439 526 0 0 40 0 305 1

1993 44 0 1243 0 2151 0 0 0 0 0 0 0 0

1994 8 0 2838 0

19541 0 0 0 0 0 0 0 0

1995 5 0 982 0 1560 0 0 0 0 0 0 0 0

1996 25 0 5277 0 4287 0 0 0 0 0 0 0 0

1997 3 0 4371 0 3635 0 0 9 0 0 0 0 0

1998 47 0 1907 0 2032 0 0 2 0 284 0 0 0

1999 5 0 519 0 7993 0 0 0 0 5145 0 0 0

2000 30

10736 2616 0

16029 0 0 2 0 3845 4 305 0

2001 63 0

110949 0 5907 9 0 0 0 361 9 0 0

2002 43 0 9102 0 3982 22 0 43 0 662 44 0 0


263

Year

Ani

mal

at

tack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

2003 28 0 14359 0

13708 79 0 69 0 39 117 0 0

2004 57 0 64982 0 8966 1716 0 3 0 31 14 0

10397 0

2005 131 0 413 0 2217 319 0 4 0 5541 11 0 0

2006 170 0 4035 0 4513 380 0 17 0

15430 9 0 0

2007 94 0 1169 0 4585 31 0 631 0 72 38 0 0

2008 51 0 328

12407 1 0 11 0 7 2 0 0

Agricultural Crop Loss due to Disasters: Seasonal distribution 1974-2008

Mon

th

Ani

mal

att

ack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

1 684 81738 0 58376 61 0 805 0 570 9 0

2 69 36286 2 76405 0 0 17 0 23798 0 0

3 501 34149 0 75081014

3 0 9 0 6 8 0

4 150 7755 0 11230 48 0 6 0 223 118 0

5 489 1234 0 16564 1 0 322 0 28 4092 0 0

6 74 10974 0 5461 535 0 164 0 300 449 0 1

7 40 8178 0 0 13 0 0 1 0 12 0 0

8 516 115418 368 0 202 0 0 0 0 0 0 0


264

Mon

th

Ani

mal

att

ack

Cyc

lone

Dro

ught

Fire

Floo

d

Gal

e

Lan

d su

bsid

ence

Lan

dslid

e

Lig

htin

g

Rai

ns

Stro

ng w

ind

Surg

e

Tsu

nam

i

Urb

an fl

ood

9 146 102351 2027 957 0 0 0 0 0 0 0 0

10 43 5791 0 3342 41 0 26 0 129 5 0 0

11 283 336

4 3397 0 54320 3428 0 49 0 5565 0 638 0

12 546 10736 12792 0 46352 424 0 13 0 1445 4 276

10397 0


265

Annex 2 : Poverty Profile

Nominal Poverty lines by district Poverty headcount Ratio by district

1990-91 1995-96 2002 1990-91 1995-96 2002 2006-07

National 475 833 1423 26.1 28.8 22.7 15.2

Colombo 518 908 1537 16 12 6 5.4

Gampaha 489 875 1508 15 14 11 8.7

Kalutara 494 866 1523 32 29 20 13

Kandy 485 850 1451 36 37 25 17

Matale 466 816 1395 29 42 30 18.9

Nuwara Eliya 494 841 1437 20 32 23 33.8

Galle 489 833 1466 30 32 26 13.7

Matara 470 816 1395 29 35 27 14.7

Hambantota 470 791 1338 32 31 32 12.7

Kurunegala 456 791 1352 27 26 25 15.4

Puttalam 461 841 1423 22 31 31 13.1

Anuradhapura 456 816 1380 24 27 20 14.9

Polonnaruwa 475 783 1366 24 20 24 12.7

Badulla 485 850 1409 31 41 37 23.7

Moneragala 480 791 1366 34 56 37 33.2

Rathnapura 494 833 1451 31 46 34 26.6

Kegalle 466 858 1437 31 36 32 21.1


266

Lower Poverty Line Higher Poverty Line

1985/86 1990/91 1995/96 1985/86 1990/91 1995/96

Sri Lanka 7 5 4 11 9 8

Rural Sector 8 5 5 13 10 9

Urban Sector 3 4 2 7 7 5

Estate 4 2 2 8 5 5

Annex 3 Health Profile

Infant Mortality: By year and district

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Sri Lanka 17.7 17.9 16.3 16.9 16.5 17.3 17.2 14.3 13.8 13.4 12.6 11.2 11.2

Colombo 27 28 24.7 25.4 21.3 21.6 22 17.4 17.8 18.1 17.1 16.2 15.2

Gampaha 9.9 10.6 10.9 8.4 8.7 11.2 9.4 8 7 5.5 5.2 5.2 6

Kalutara 16.6 19.6 14.8 13.8 13.4 10.6 5.8 4.5 6.8 6.2 4.4 4 4

Kandy 26.8 27.6 25.9 26.7 24 26.7 22.9 20.8 22.5 18.7 17.9 15.8 15.3

Matale 11.5 9 8.3 9.3 10.9 10.6 15.3 10.6 7.6 10.2 7.6 7.6 10.4

Nuwara Eliya 28.9 26.9 27.5 25.4 23.5 23.9 21.4 16 18.8 17.2 20 16 15.6

Galle 13 15.5 17.7 18.5 18.3 21.8 16.5 9.4 14.4 12 13.6 10.9 10.9

Matara 23.2 22.9 17.8 20.7 21 24.9 19.3 18.3 11.8 13.9 7.8 5.9 8.3

Hambantota 6.5 4.9 5 3.9 4.9 3.3 5.8 4.2 2.9 3.9 6.1 4.7 7

Jaffna 10.2 19.8 15.3 13.2 5.6 17.5 10.8 8.3 5.4 4.3 5.2 6 4.4

Kilinochchi 3.9 2.2 8.6 5.4 7.8 10.8 24.6 13.4 14 14.2 6.6 3.9 1.2

Mannar 41.7 5.3 11 4.5 6.8 7.2 8.7 14.4 3.2 4.3 6.7 6.9 2.6

Vavuniya 4.5 4.9 9.7 4 5.2 8.4 10.9 11.6 8 3.9 9.7 11.8 6.8

Mullaitivu 4.3 2.6 5.7 4.8 2.3 7.8 19.6 7.3 7 13.3 13.9 8.9 1.7

Batticaloa 12.4 12.2 10.2 11.3 7.5 12.6 12.2 10.6 14.9 16.9 18.4 15.2 19.6


267

Undernourished children under five years of age - 2000 & 1993

2000 1993

Characteristic Stunted

%

Wasted

%

Underweight

%

Stunted

%

Wasted

%

Underweight

%Total 13.5 14.0 29.4 23.8 15.5 37.7

Sector

Colombo metro 7.4 10.1 18.2 19.7 12.2 31.2

Other urban 8.6 6.3 21.3 16.8 16.8 29.9

Rural 12.8 15.9 30.8 22.9 16.4 38.3

Estate 33.8 11.8 44.1 53.7 9.5 52.1

Sex 15.3 12.6 29.8 25.1 15.4 40.9

Boys 11.9 15.1 29.0 22.7 15.6 34.8

Child’s age in months 3.9 1.3 0.7 4.9 3.1 5.8

6 – 11 5.7 10.3 20.2 11.8 6.8 17.9

12 – 23 16.2 18.2 28.8 25.7 18.2 36.3

24 – 35 12.4 13.3 34.0 23.8 15.1 42.4

36 – 47 13.4 13.9 30.7 27.5 18.2 46.7

48 – 59 19.1 15.9 37.9 28.7 17.6 43.0

Ampara 8 6.9 5.1 6.9 4 5.4 3.5 3.8 8.1 7.4 6.6 7 6.3

Trincomalee 5.6 5.9 6.6 4.3 7 1.4 1.6 3.9 3.1 4 2.6 2.5 2.5

Kurunegala 19.2 15.1 15.7 15.1 16.6 14.1 13.6 14.6 14.7 17.4 13.4 10.8 14

Puttalam 19.4 16.9 11.6 16.2 19.1 10.9 11 11.3 12.3 9.7 7.5 6.3 5.9

Anuradhapura 21 22.4 14.3 25.7 28 27.5 21.5 18.1 22.3 16.5 22.5 17.6 19.4

Polonnaruwa 6.7 12.9 17.7 18.4 13.2 18.2 19.7 7.9 7.1 8.4 12.4 16.1 19.5

Badulla 14.6 14.3 15.6 13.7 18.6 19.2 44.7 37.1 22.2 22 21 15.9 9.9

Moneragala 5.3 5 5.8 4.3 5 4.8 5.8 5.6 4.7 4.4 3.1 2 2

Ratnapura 22.6 20.5 18.6 22.4 23.7 22.7 21.4 19.9 13.9 16.2 12.5 13.5 13.2

Kegalle 14.3 13.9 10.8 12.9 14.1 17.1 13.8 16.8 15 15.1 9.5 9.2 7


268

2000 1993

Characteristic Stunted

%

Wasted

%

Underweight

%

Stunted

%

Wasted

%

Underweight

%Previous birth interval

22.0 21.2 35.6 28.1 17.8 48.12 – 3 years 18.6 14.2 36.8 28.3 16.7 43.7

4 years or more 14.7 13.1 30.8 21.2 14.7 32.7

First birth 9.4 13.4 24.6 19.9 14.4 31.5

Twins 14.8 22.2 25.9 29.4 14.7 44.4

Birth weight 22.9 25.6 49.3 37.2 21.1 53.5

2.5kg. <= bwt. < 3.0kg. 15.4 15.9 34.5 22.2 15.8 38.9

bwt. >= 3.0kg. 7.0 7.7 15.6 15.2 11.0 25.0

Educational level of mother No Education

35.7 18.4 48.0 46.0 16.7 53.9

Primary 23.8 15.9 41.4 33.6 18.7 47.8

Secondary 12.7 15.0 31.7 22.6 16.8 39.1

GCE O/L 9.9 14.7 24.8 13.0 11.3 24.6

GCE A/L & Higher 5.4 7.6 13.3


269

Annex 4 Educational profile

School Enrolment and Completion Rate

Year Enrolment Ratio

(% of Relevant age groups)

Completion Rate

Literacy Rate

(%)

(5+ years of age )

Adult Literacy Rate (%) (10 years of age)

Net Primary

Net

Secondary

Primary Junior Secondary

Male Female Total

1981 .. .. .. .. .. 91.1 83.2 87.2

1982 .. .. 84.2 60.7 85.4 .. .. ..

1983 .. .. 86.5 63.5 .. .. .. ..

1984 .. .. 86.4 62.9 .. .. .. ..

1985 .. .. 89.4 65.6 .. .. .. ..

1986 .. .. 87.7 65.7 .. .. .. ..

1987 .. .. 88.7 67.0 88.6 .. .. ..

1988 .. .. 89.8 72.6 .. .. .. ..

1989 .. .. 91.3 72.2 .. .. .. ..

1990 .. .. 86.8 66.4 .. .. .. ..

1991 .. .. 91.6 72.1 .. .. .. ..

1992 .. .. 88.7 70.5 .. .. .. ..

1993 .. .. .. .. .. .. .. ..

1994 .. .. .. .. .. 92.5 87.9 90.1

1995 .. .. .. .. .. .. .. ..

1996 96.6 94.0 .. .. .. .. .. ..

1997 97.0 94.8 95.8 76.3 91.8 .. .. ..

1998 97.3 95.4 94.5 75.1 .. .. .. ..

1999 97.5 95.8 .. .. .. ..

2000 97.7 96.9 96.5 80 .. .. .. ..

2001 96.8 96.0 98.2 82.6 .. 92.6 89.7 91.1


270

Year Enrolment Ratio

(% of Relevant age groups)

Completion Rate

Literacy Rate

(%)

(5+ years of age )

Adult Literacy Rate (%) (10 years of age)

Net Primary

Net

Secondary

Primary Junior Secondary

Male Female Total

2002 97.2 96.3 96.2 82.8 .. .. .. ..

2003 96.8 95.1 96.5 82.1 .. .. .. ..

2004 98.0 96.2 97.7 83.8 92.5 .. .. 92.5


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Annex 5 : Theoretical Basis for Analysis of Risk Poverty Relationship in Sri Lanka

Introduction and Objectives

The main objective of the study is to prepare an assessment report for Sri Lanka analyzing the two-way relationship between disaster risk and poverty, using both quantitative and qualitative approaches.

The qualitative analysis will focus on determining any relationships that can be discerned from visually observing trends or correlations between data sets obtained for both poverty and disaster. Results of similar studies on poverty and disaster will also be analyzed to highlight any qualitative or quantitative relationships in such studies.

The quantitative analysis will attempt to estimate statistically valid correlations between variables relating to poverty and disaster. Based on the conceptual frame work developed and statistical methods adopted, the following hypothesis will be tested.

Hypotheses to be tested on disaster risk and poverty

Hypothesis 1

a) Poverty leads to a higher risk of exposure of households to natural hazards b) Poor households suffer greater losses from hazardous events.( how poverty affects the loss from

hazard or poverty vs. loss from hazard )

Hypothesis 2

a) Natural hazards result in increases in poverty as estimated by poverty indicators b) Natural hazards lead to losses in the physical and human assets ( Value and productivity)

Impact of hazards on members and assets Assets - physical income generating activities goods/services Human capital assets (i.e., nutrition and health, death, sickness and injury)

c) Natural hazards result in the deterioration of the ability of poor households to either avoid or recover from poverty

Statistical / Econometric Framework for Analysis

Framework for Analysis

Table below presents the framework of analysis broken down by components, sample units, sub-divisions and analysis

Analytical Framework

Components Sample Unit Sub division Analysis

Static poverty

Cross-section of



Experience of

explore the incidence, depth and severity of static poverty measures


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analysis households or individuals

Poverty and their temporal analogues for dynamic poverty measures;


generating statements by the figures, poverty profiles, multivariate analysis, multiple correlates (Regression, models)

Aggregate poverty trends

Panel at district and sub-district level






generating statements by the figures, Poverty profiles, multivariate analysis, multiple correlates (regression, models)

Poverty micro-dynamics (Economic

Households or individuals




explore the incidence, depth and severity of static poverty measures and their temporal analogues for


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mobility) (Two periods) dynamic poverty measures;



Identification and experience of poverty

The identification and experience of poverty will depend on the welfare indicators that will be selected. In this case household expenditure and either income and expenditure poverty lines will be the indicators of choice. The initial step will be the choice of indicators, which will depend on data available. In Sri Lanka, the Household Income and Expenditure Survey (HIES) conducted periodically is the source of data for estimating expenditure and poverty lines. In this analysis, the indicators such as poverty head count, poverty gap and severity of poverty as well inequality indices such as the Gini Coefficient and other human development indices will be selected based on availability.

• Choice of welfare indicators • Discriminating poor and non-poor by Poverty line • Summing the statistics across the group – experience of Poverty

αnh

h=1

1 N

(z - c )P(c, z, α) ,Tz

⎡ ⎤⎢ ⎥⎣ ⎦

= ∑ ------------------------------- (1)

When α = 0 - Poverty Head Count (Incidence of Poverty) α = 1 - Poverty Gap (Depth of Poverty) α = 2 - Squared Poverty Gap (Severity of Poverty)

Where Ζ = Poverty Line Ch = Income/expenditure of individual below poverty line N = Total population n = Population below poverty line Data required:

i. Poverty line ii. Wealth indicator (income/expenditure) of household h

iii. Total population iv. Sum of Wealth indicator for poor group v. Incidence of poverty, poverty gap, severity of poverty, inequality, and HDIs.



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The second stage of the analysis will seek to explain poverty and its relationship to hazard risk. This will involve the following;

• Define poverty over individual households i.e Calculate P(c,z,α) • Aggregate over district , regional or national levels • Tabulate poverty incidence and indirect variables against set of disaster characteristics. • Spatial correlations between poverty incidence and natural hazards using cross-section

data • Multiple Regression

Hazard events or hazard loss

The third step will be to estimate the different risks of disaster or hazard events in terms of hazard losses. The losses will include human, infrastructure and economic losses. A susceptibility index based on hazards and actual losses will be estimated for the analysis, as follows;

k l k j k l j k l jj

H E Sπ = ⋅ ⋅∑ ------------------------------------ (2)

• l loss type - human, economic, environmental or infrastructure

• k - geographic unit

• j - types of hazards flood, earthquake, etc

• H - hazard j index – Probability over k

• E – total no. of elements exposed to a hazard total population, total number of households, GDP

S - Susceptibility index – ratio or function that estimates the proportion of loss of a particular variable due to an event.

Static poverty / hazard analysis – Hypothesis # 1

The statistical methods proposed for the study include multiple regression and multivariate analysis. Since spatially distributed data for this study is limited, it has been decided to undertake the analysis at district level and if data is available extend the analysis to Sub-district (Divisional Secretaries) levels.


The relevant variables and indicators will be identified. The population also needs to be categorized as poor and non poor in this section.

Some factors contributing to poverty have been identified and available data on these factors have been gathered. In addition data on spatial distribution of populations below poverty line has also been gathered. Data on incidence and depth of poverty are available at national, district and sub district level for certain years only and have been calculated using Foster-Greer-Thorbecke model, for experience of poverty (incidence, depth and severity of poverty).

The main tasks in this analysis are:

• Identification of the characteristics of poverty and determination of the variables (independent variables) that can be used in the analysis based on the availability of poverty data.

• Measuring the poverty as a dependant variable using selected explanatory variables for poverty.


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• Determine relationship between poverty and hazard variables both as dependent and independent variables

Dependent / independent variables

As a first step the incidence of poverty (PHC) will be used in the analysis as data on other poverty statistics (poverty gap and squared poverty gap) are only available for selected years. Although data is available for many hazard types and losses, only four or five hazard types and a similar number of loss variables will be used in the analysis, as indicated in Table 1. The hazard index which is defined as the probability of occurrence of a hazard type in a defined area can be calculated using available hazard data. The susceptibility index, which is defined as the probability of a particular type of loss occurring from a type of hazard within a specified area (district or sub district) can be estimated from available hazard data, for the analysis.

According to the guide line

• Only the following three type of losses will be considered;

o Housing / Shops (number damaged/destroyed)

o Agricultural losses (hectare of crop lost)

o Human losses (death and injury)

• Hazard index will be calculated for risks, flood, drought, wind, lightning and landslide

• The hazard will be normalized by the exposure factor

• Normalizing by population and hazard index:

Analytical steps

2. Calculation of the Hazard loss by k l k j k l j k l jj

H E Sπ = ⋅ ⋅∑ for all loss type and geographical

region

3. Normalize the losses to get susceptibility index using exposure factor

4. Estimate the correlation between susceptibility index and poverty.

Poverty analysis – Hypothesis #2


Aim: Fitting the Regression model for poverty indices and some explanatory variables. (Multiple Regression)

Dependent variables

• Mean household expenditures per capita and headcount indices of poverty

Explanatory variables

District/Sub District Level

o Share of individuals, Households or Localities


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o Explanatory variables will be selected from among those considered most appropriate and for which data is available for the regression time period, area or population (Some variables listed in Table 1).

Regional Level

o Geographical isolation, a low resource base, and other inhospitable climatic conditions

Regression model will be fitted using the explanatory variables which are highly correlated with the dependant variables. First the null model will be fitted and then variables will be added one by one and the resulting sums of squares considered in selecting the variables that will be included in the final regression model with the interaction terms included based on the sums of squares.


Aim: Evaluation of Poverty over time at district level. Hypothesis testing, Regression

o Compute changes in poverty in district level

o Test for significance (equal) by two tail and one tail tests

o Get the poverty rates for time t-1 and t (yearly) for the district for which changes are significant

o Hazard prevalence, group of covariates that affect poverty

o Correlation analysis using multivariate regression between poverty measures for each district (sub-district) and the set of observable characteristics

Inspect the impact of specific past hazard events (t-1) on poverty at time t o Detect the presence of a large-scale disaster through the country hazard profiles

o Calculate the changes in poverty or poverty levels between t and t-1 (Pt – Pt-1)

o Link the changes with a set of hazard covariates at t-1 (Xt) , (Xt-1)

o This can be expressed by the following regression model

1 1 2 1 3 1 4 1 HAZARDP P + X (X X ) D + t t k t k t t t k t k k tB− − − − −− = + + − + Φ +α β β β ε

Where φK – time invariant dummy variable

D – Time dummy

Aim is to estimate α s and β s using this regression model.

Time-series Analysis o project forward time series of poverty rates available across each state before the hazard

happened

o Compare the predicted values with the actually observed poverty indicator


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Comparison

Aim: Test whether changes in welfare between years is due to changes in structural conditions or changes in the behaviour of households between the two years

o Compare the prediction of regression model (house hold income or consumption) for year t-1 and t for the same year t


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Poverty micro-dynamics – Hypothesis #2

Transition Matrix

Transition Matrix is a two dimensional array of a category for a base period and a terminal period. The category may have two or more or several levels. The cells of the matrix contain the category value corresponding to i in the base year and j in the terminal year.

We have poor/non poor data for the transition matrix. For the logit model, we can use indirect explanatory variables for poverty such as consumption, expenditure, nutrition, access to safe water, sanitation, infant mortality, literacy etc.

The categories may be poor/Non poor or Income levels, etc

Calculation of the matrix:

• Some probability measures

• Correlation measures, mobility index M

• Measuring the association by Pearson chi-squared

Explanation of poverty

Aim is to fit regression model for the transition matrix category and some explanatory variables.

• Identify the set of characteristics as explanatory variables. This must include geographical regions, urban/rural residence, household and community characteristics, and the occurrence of natural hazards.

• The model will depend on the number of levels in the category

o If dichotomous dependant - Logit or Probit

o More than two levels - Proportional odds model

Asset poverty transitions

Aim: To assess the effect of hazards on assets in a quantitative way.

o Establish the threshold for asset poverty

Estimate the effects of natural hazards on households across time

• Calculate changes in consumption

• Fit the model 1 2 1 -1 3 4 HAZARD HAZARD (P * ) D Xh t h h t h h t t h v v h t h hvc −Δ = + + + +∑ β β β β ε

Where

• HAZARD - hazards between periods t-1 and t

• X - household characteristics

• P - dummy variable taking the value of 1 for poor, 0 for households lying above. Lastly


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• ε h - error term

Core impact variables: deaths; houses destroyed and houses affected. Agricultural loss data was found to be reasonably reliable across the sample. Therefore it may not pose a serious challenge for drought and flood analysis. Ideally it would be best if an analysis is done for rural and urban areas separately across these variable levels. This may not be possible for Sri Lanka because of the lack of availability of either hazard or poverty data at this level, Thus the analysis will largely be undertaken at the district level.

sri lanka poverty disaster interface study march 26 · list of abbreviations cb - central bank...

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