Using an asset index to Using an asset index to assess trends in poverty in assess trends in poverty in seven Sub-Saharan African seven Sub-Saharan African

countriescountries

Frikkie Booysen, Servaas van der Berg, Ronelle Frikkie Booysen, Servaas van der Berg, Ronelle

Burger,Burger, Gideon du Rand & Michael von MaltitzGideon du Rand & Michael von Maltitz

Paper presented at IPC conference on Paper presented at IPC conference on The Many The Many Dimensions of PovertyDimensions of Poverty, 29-31 August 2005, , 29-31 August 2005,

Brasilia, BrazilBrasilia, Brazil

Outline

• Background

• Data

• Method

• Findings

• Conclusions

Background• Income-based cross-country poverty

comparisons difficult due to price conversions / fluctuations

• Comparisons within countries across time often not possible due to insufficient or incomparable surveys

• Data reliability an issue for many African countries’ official statistics

• Worse for income/expenditure data because complexity of surveying

Background• Sahn and Stifel (2000) propose used

of Demographic and Health Surveys (DHS) as solution to this problem

• Standardization of surveys ensures comparability across time and space

• Possession of assets, access to public services and characteristics of infrastructure easier to survey than income/expenditure

• Criteria for selection: three surveys available from late 1980s to early 2000s

• DHS conducted in different years for different countries, thus survey years are not matched

• To enable comparability over time:• First wave/baseline: 1987 - 1992• Second wave: 1992 - 1997• Third wave: 1998 - 2001

Data

• Seven African countries in our sample:

• Ghana• Kenya• Mali• Senegal• Tanzania• Zambia• Zimbabwe

Data

• Variables included in asset index• TV ownership• Fridge ownership• Radio ownership • Bicycle ownership

• Type of toilet facility• Type of floor material• Source of drinking water

• Apart from a few peculiarities in access to slow-moving assets, data appears reliable… BUT there is an inherent urban bias?

Data

• Multiple correspondence analysis used for constructing an asset index

• More appropriate than PCA/factor analysis often used in literature

• Aim is to find a number of smaller dimensions to capture most of information contained in original space

• Each of these dimensions are the weighted sum of the original variables

Method

Method

• MCA weights were allocated based on pooling of countries for the baseline (first) period, using mca command in Stata 8.2

• Explain 94% of inertia

• Logical distribution of weights across response categories, excl. “other categories”

Owns a radio 0.294

Does not own a radio -0.234

Owns a TV 1.568

Does not own a TV -0.103

Owns a fridge 1.630

Does not own a fridge -0.099

Owns a bicycle 0.022

Does not own a bicycle -0.006

Flush Toilet 1.147

Pit latrine -0.087

No toilet -0.308

Earth floor -0.270

Cement floor 0.359

Smart floor 1.830

Piped water 0.877

Public water -0.037

Surface water -0.223

Well water -0.229

Method

• MCAPi = Ri1W1 + Ri2W2 + … + RijWj + …

+ RiJWJ

, where MCAPi is the ith household’s composite

poverty indicator score, Rij is the response of

household i to category j, and Wj is the MCA

weight applied to category j

• Negative index values transformed into positive, non-zero values by adding 0.1785 to the index

MethodFigure 1: Assessing the robustness of poverty comparisons

Classification of household on welfare measure B

Non-poor Poor

Non-poor

A

B

Cla

ssif

icat

ion

of h

ouse

hold

on

wel

fare

mea

sure

A

Poor

C

D

Method

• Given the arbitrary transformation required to make all index values non-negative and the arbitrary poverty line, it was not deemed appropriate to calculate P1 and P2

• Poverty analysis confined to the poverty headcount ratio (P0) and the

investigation of stochastic poverty dominance, using cumulative density curves or functions

Method• Employed three poverty lines…

• 40th percentile of asset index• 60th percentile of asset index• Absolute poverty line: weighted sum of

categories that is deemed as representing an adequate standard of living:

• radio• bicycle• cement floor• public water • pit latrine• no refrigerator• no TV

Quintile 1 6

Quintile 2 18

Quintile 3 78

Quintile 4 128

Quintile 5 463

Total 693

Findings

Number of unique values per quintile

Findings

Household consumption always or continuously in deficit

13-item asset index (40th percentile poverty line)

Poor Non-poor

Poor 1,005 1,140

Non-poor 1,334 3,998

Asset index rankings compared to household consumption rankings (Uganda 1995)

Findings

Household head has no education or primary education only

13-item asset index (40th percentile poverty line)

Poor Non-poor

Poor 2,007 117

Non-poor 3,604 1,574

Asset index rankings compared to rankings based on education of household head (Uganda 1995)

Findings

Country

Mean asset index

Poverty headcou

nt

Asset

index

rankWDI $2

WDI rank

Ghana 0.267 71.7 5 75.2 4

Kenya 0.187 76.2 3 62.3 7

Mali 0.147 85.3 2 90.6 1

Senegal 0.319 60.9 6 63.1 6

Tanzania 0.108 89.3 1 72.5 5

Zambia 0.217 73.2 4 90.1 2

Zimbabwe 0.308 60.8 7 83.0 3

Poverty headcount across countries

Findings

CountryPeriod

1Period

2 Period 3

Asset index trend

WDI trend

Ghana 83.2 72.5 64.6 - -

Kenya 79.9 78.8 71.4 - +

Mali 95.6 88.8 80.9 - +

Senegal 75.8 59.5 57.3 - -

Tanzania 88.4 88.9 92.1 + -

Zambia 69.6 74.3 75.2 + +

Zimbabwe 63.5 63.7 57.0 - +

Poverty headcount over time by country

Findings

0.2

.4.6

.81

Cu

mu

lativ

e p

ropo

rtio

n of

ho

use

hold

s

0 .5 1 1.5Asset index (MCA)

Ghana1 Ghana2

Ghana3

Cumulative density curves for Ghana by period

Findings

0.2

.4.6

.81

Cu

mu

lativ

e p

ropo

rtio

n of

ho

use

hold

s

0 .5 1 1.5Asset index (MCA)

Tanzania1 Tanzania2

Tanzania3

Cumulative density curves for Tanzania by period

Approach

• “In places the density curves are almost indistinguishable. In most cases therefore it is not possible to reach strong conclusions on trends and disparities in poverty, giving rise to uncertainty as to whether there has been progress in terms of the alleviation of poverty.”

FindingsPoverty of what?

Findings

0.2

.4.6

.81

Cu

mu

lativ

e sh

are

of

popu

latio

n

0 .5 1 1.5Asset index (MCA)

Urban Rural

(MCA weights from period 1)Cumulative density curves Urban/Rural all countries & periods

FindingsOLS regression of country, time and place of residence on the asset index

Equation 1

Equation 2

Equation 3

Equation 4

Urban 0.344** 0.334** 0.334**

Ghana 0.159** 0.122** 0.113**

Kenya 0.079** 0.090** 0.081**

Mali 0.039** 0.033** 0.018**

Senegal 0.211** 0.152** 0.140**

Zambia 0.109** 0.061** 0.054**

Zimbabwe

0.200** 0.164** 0.154**

Period 2 0.014**

Period 3 0.044**

R-squared

0.36 0.07 0.40 0.41

Conclusions

• Evidence that overall poverty declined in Ghana, Kenya, Mali, Senegal and Zimbabwe, but increased in Zambia over this period

• Evidence that urban poverty declined in Ghana, Kenya, Mali, Tanzania and Zimbabwe, but increased in Senegal Zambia over this period

Conclusions, BUT caution required in interpreting results, given caveats of asset index approach…

• Not a complete measure of welfare• Sensitivity of results to choice of poverty line• Urban bias of the asset index means that analysis

of trends in rural poverty remains problematic• Aggregation conceals divergent shifts in

underlying variables and complicates policy recommendations, e.g. increased access to private assets versus decline in access to public assets

• Slow-moving nature of component variables: asset index not a good measure for assessing changes in welfare over short- to medium-term?