the impact of natural disasters on household income, expenditure, poverty and inequality: evidence...

This article was downloaded by: [Queensland University of Technology]On: 06 November 2014, At: 19:21Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Click for updates

Applied EconomicsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/raec20

The impact of natural disasters on household income,expenditure, poverty and inequality: evidence fromVietnamAnh Tuan Buia, Mardi Dungeyb, Cuong Viet Nguyenc & Thu Phuong Phamb

a Department of Economics, Macquarie University, Sydney, Australiab Tasmanian School of Business and Economics, University of Tasmania, Hobart, 7001Australiac National Economics University, Hanoi, VietnamPublished online: 18 Feb 2014.

To cite this article: Anh Tuan Bui, Mardi Dungey, Cuong Viet Nguyen & Thu Phuong Pham (2014) The impact of naturaldisasters on household income, expenditure, poverty and inequality: evidence from Vietnam, Applied Economics, 46:15,1751-1766, DOI: 10.1080/00036846.2014.884706

To link to this article: http://dx.doi.org/10.1080/00036846.2014.884706

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

http://crossmark.crossref.org/dialog/?doi=10.1080/00036846.2014.884706&domain=pdf&date_stamp=2014-02-18

http://www.tandfonline.com/loi/raec20

http://www.tandfonline.com/action/showCitFormats?doi=10.1080/00036846.2014.884706

http://dx.doi.org/10.1080/00036846.2014.884706

http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/page/terms-and-conditions

The impact of natural disasters on

household income, expenditure,

poverty and inequality: evidence

from Vietnam

Anh Tuan Buia, Mardi Dungeyb,*, Cuong Viet Nguyenc

and Thu Phuong Phamb

aDepartment of Economics, Macquarie University, Sydney, AustraliabTasmanian School of Business and Economics, University of Tasmania, Hobart,7001 AustraliacNational Economics University, Hanoi, Vietnam

Natural disasters are expected exacerbate poverty and inequality, but little evi-dence exists to support the impact at household level. This article examines theeffect of natural disasters on household income, expenditure, poverty andinequality using the Vietnam Household Living Standard Survey in 2008. Theeffects of a natural disaster on household income and expenditure, corrected forfixed effects and potential endogeneity bias, are estimated at 6.9% and 7.1%declines in Vietnamese household per capita income and expenditure, respec-tively. Natural disasters demonstrably worsen expenditure poverty and inequalityin Vietnam, and thus should be considered as a factor in designing povertyalleviation policies.

Keywords: hazards; disasters; natural shocks; poverty; inequality; Vietnam

JEL Classification: O12; Q54; D12

I. Introduction

The frequency and severity of natural disasters has beenincreasing across the globe (IPCC, 2007). Guha-Sapiret al. (2004) find that the economic costs of natural dis-asters have increased 14-fold since the 1950s, to about US$67 billion per year. Natural disasters often generateadverse effects on household expenditure and income(Dercon, 2004; Masozera et al., 2007; Thomas et al.,2010; Mottaleb et al., 2013). For example, Dercon(2004) finds long-term household food consumptioneffects of rainfall shocks in Ethiopia. The effects are alsopotentially detectable at the individual household level:

Mottaleb et al. (2013) show that Cyclone Aila, which hitthe coastal region of Bangladesh in May 2009, resulted ingreater falls in expenditure on schooling and children’sschool admission for affected than unaffected households.

Although there is a consensus on the adverse impact ofnatural disasters on household income and expenditure,its effects differ between nations, regions, communitiesand individuals due to the differences in their exposuresand vulnerabilities (Clark et al., 1998). Poor householdsare more vulnerable to natural shocks in both responseand recovery phases, and regions which are moreexposed to natural disasters may also experience higherpoverty and inequality than those less affected (Peacock

*Corresponding author. E-mail: [email protected]

Applied Economics, 2014Vol. 46, No. 15, 1751–1766, http://dx.doi.org/10.1080/00036846.2014.884706

© 2014 Taylor & Francis 1751

Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

et al., 1997; Fothergill and Peek, 2004; Wisner et al.,2004; Krueger and Perri, 2010).

This article focuses on the effect of natural disaster onincome, consumption, poverty and inequality of house-holds using a unique data set available for Vietnam.Vietnam ranks fourth, tenth and sixteenth in terms of theabsolute number of people exposed to floods, high windsfrom tropical cyclones, and drought, according to theUNISDR (2009), and suffers from a wide range of naturaldisasters.1 Importantly, the country is subject to a spec-trum of natural disasters which are not associated withvery large events but cause significant loss of life (Noy andVu, 2010). Thus, our findings represent the effects of‘average’ natural disasters, which are highly relevant forthe many countries in South and South-Eastern Asia,Central and South America that face similar conditions –both in terms of similar climate conditions and stage ofeconomic development (UNISDR, 2009).

Our study utilizes the Vietnam Household LivingStandard Survey (VHLSS) 2008, which uniquely collectedinformation on natural shocks reported by surveyed house-holds. We investigate the effect of the natural disasters onvarious aspects of welfare, including income and consump-tion, but also extending to poverty and inequality. Althoughthere is a literature on the impact of natural disasters oneconomic growth (Skidmore and Toya, 2002; Strobl, 2011),the effect on income inequality has been under-researched –this article aims to contribute to that gap.2

Vietnam is a populous country (89 million residents in2012) with a high poverty rate. Strong economic perfor-mance in countries like Vietnam will be important inreducing global poverty (Chandy and Gertz, 2011).However, the effectiveness of poverty reduction policiesdepends on whether they address all the underlying factorsgenerating poverty. For example, Ravallion (2013) pro-vides a list of causes of poverty, but does not considernatural disasters.

We find that the level of economic vulnerability tonatural disaster is positively associated with the povertyhead count index of the World Bank in 2008 (seeAppendix 2 – Table A1). The more severely disastersaffect an economy, the worse the outcomes for poverty.This indicates the importance of taking into account nat-ural disasters as an important factor in poverty reductionand inequality policies. Our article utilizes the AverageTreatment Effect on the Treated (ATT) of natural disasterand finds that natural disasters have significant impact on

poverty and inequality.3 Thus, our study suggests thatpolicymakers should consider the potential impact of nat-ural disasters in formulating anti-poverty policies.

We propose a simple method to estimate the effect ofnatural disasters on poverty and inequality using a coun-terfactual analysis. We first estimate the effect of naturaldisasters on household consumption and compare thiswith a prediction of household consumption, povertyand inequality in the absence of disasters. We accountfor the potential endogeneity of natural disasters with thepresence of human activity. Most existing studies treatnatural disasters as exogenous, whereas in fact the prob-ability of a natural disaster in a certain location may becorrelated with unobserved variables such as the cultureand practice of local people. To correct for potential endo-geneity in the consumption and income equations, we firstapply district fixed effects to control for the average long-run steady state natural shocks faced by households. Then,we use instrumental variables to yield consistent esti-mates. Following Mansuri (2006) and Acosta (2006), weuse the proportion of households, who are exposed tonatural shocks, to total households in a certain district asthe instrumental variable for natural shocks. This propor-tion reflects the risk level in the district and satisfies thetwo conditions for a valid instrumental variable.

The remainder of this article proceeds as follows. In thenext section, we briefly summarize the VHLSS data.Section III describes the natural disasters and distributionof household income and expenditure in Vietnam. Themethodological approach employed in this study is pre-sented in Section IV. Section V reports our empiricalresults, and Section VI concludes.

II. Data Set

This article uses the 2008 VHLSS. The VHLSS has beenconducted by the General Statistics Office of Vietnam(GSO) every two years since 2002 and follows theWorld Bank’s Living Standards Measurement Study. Theselected sample comprises 9189 households representa-tive of the Vietnamese population based on the 1999population census. Information is collected by face-to-face interview with household heads, household membersand key commune officials and includes information ondemography, employment, labour force participation,

1 Including flood, storm, tropical depression, storm surge, inundation, drought, whirlwinds, flash flood, river bank and coastline erosion,landslides, saline water intrusion, forest fire, hail rain and earthquakes (SRV, 2004).2Rodriguez-Oreggia et al. (2012) are the only study which investigate the effect of natural disasters on poverty inMexico. However, theyexamine the effect at municipal level, not at household level as in our research.3 The negative impact of natural disaster on households might be more severe if income redistribution is not appropriately conducted(Peacock et al., 1997; Fothergill and Peek, 2004;Wisner et al., 2004; Krueger and Perri, 2010). An existing working paper on Vietnam byThomas et al. (2010) used earlier cohorts of the VHLSS in 2002, 2004 and 2006 and relied on historical weather data to proxy for extremedisasters. They show that income and expenditure of households in areas of Vietnam with high exposure to natural disaster are muchlower than the average, finding that riverine floods and hurricanes cause immediate welfare losses of 23% and 52%, respectively.

1752 A. T. Bui et al.

Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

education, health, income, expenditure, housing, fixedassets and durable goods, and involvement in povertyalleviation programmes, general economic conditions,agricultural production, local infrastructure and transpor-tation and social problems. The 2008 survey uniquelycontains data on natural disasters reported directly byhouseholds. Households were asked if any natural disasterhad occurred in their area in the last five years; where thesenatural disasters are defined as floods, storms, droughts,landslides, climate (too hot or too cold) and hoar frost.

III. Natural Disasters and Household Welfarein Vietnam

For each of the regions in the sample, we estimate theproportion of households affected by at least one naturaldisaster in their areas in the last 60 months. The results are

presented in Fig. 1, which show that nearly 30% of house-holds faced at least one type of natural disaster in the fiveyears prior to the 2008 survey. Thirty per cent of ruralhouseholds were affected by disasters compared with 10%of those living in urban areas. Households living in thecentral regions of Vietnam reported the greatest incidenceof natural disasters.

Table 1 reports the number of events and the estimatedvalue of the damages caused by natural disasters inVietnam during the 1989 to 2008 period. Flood andstorms are the two most frequent and dangerous disas-ters. There are 147 separate events in which flood andstorm hit the country with the associated damage exceed-ing VND 85 trillion (US$ 5 billion). These are highlyseasonal events, occurring mostly from May toDecember, and hit all of the eight regions. These resultsare consistent with other empirical studies in Vietnamsuch as Benson (1997).

10.0-

Red River Delta

North East

South East

Mekong River Delta

Total

North West

North Central Coast

South Central Coast

Central Highlands

20.0 30.0 40.0 50.0 60.0

All

Urban

Rural

Fig. 1. Percentage of households reporting natural shocks by regions

Table 1. The estimated value of the damages by disaster types from 1989 to 2008

Disaster typesNo. ofevents

No. of eventswith recordedvalue damage

Total value ofthe damages(VND billion)

% of totalvalue of thedamages (%)

Average valueof the damages/events(VND billion)

Storm 70 57 41 505 46 728Flood 77 64 44 908 49 701Flash flood/landslide 23 21 2789 3 133Tornado 20 18 1625 2 90Cold wave 2 1 20 0 20Others 1 1 92 0 92

Total 193 162 90 941 10 561

Source: GFDRR (2010).

Impact of natural disasters 1753

Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

Our estimates of household per capita income andexpenditure in 2008 are given in Table 2. At the countrylevel, average per capita income and expenditure are VND11 684 and VND 8 150 000, respectively, but the incomeand expenditure of households in rural areas are about halfof those live in urban areas. Rural households living in themost frequently exposed to natural disaster of theNorthwest and North Central Coast are the poorest groupin the country. This confirms the finding of Minot et al.(2006), Imai and Gaiha (2007) and Thomas et al. (2010)that income and expenditure of households living inhighly exposed to natural disaster in Vietnam are muchlower than the average.

Following the classification of GSO and the WorldBank, we define poor households as those that have percapita expenditure below the expenditure poverty line of

VND 3 335 000 (US$ 200) per year in 2008. We estimatethe percentage of poor households living in rural, urbanareas in the eight regions. The distribution is reported inFig. 2, which shows that rural areas have higher povertyproportion than urban areas, and the greatest concentrationis found in the North West.

As poor households typically have relatively low stocksof liquid assets, they are more vulnerable to shocks, asshown by Krueger and Perri (2010) and Wainwright andNewman (2011). The percentage of households that areexposed to natural disaster reported in Table 3 shows thatpoor households are more exposed to natural disasters inevery region of Vietnam – about 64% of poor householdswere affected by at least one kind of natural disaster in thelast 5-year period, far higher than that of 26% of the totalhouseholds.

Table 2. Household per capita income and expenditure by regions

Per capita income Per capita expenditure

Region Rural Urban All Rural Urban All

Red River Delta 10 060 17 046 11 850 7008 13 711 8725North East 7913 13 803 9214 5601 9837 6536North West 5563 15 228 7288 4267 11315 5525North Central Coast 7042 12 693 7869 5549 10 100 6215South Central Coast 8248 13 203 9737 6264 11 102 7718Central Highlands 8218 12 799 9645 6110 9769 7250South East 13 600 21 951 18 173 8735 14 032 11 635Mekong River Delta 11 350 14 474 11 999 6817 10 382 7558All Vietnam 9611 17 013 11 684 6561 12 234 8150

Source: Author’s estimation from the 2008 VHLSS.

50.0

45.0

40.0

35.0

30.0

25.0

20.0

15.0

10.0

5.0

Rural

Urban

All

Red R

iver D

elta

Nort

h E

ast

Nort

h W

est

Nort

h C

entr

al Coast

South

Centr

al Coast

Centr

al H

ighla

nds

South

East

Mekong R

iver D

elta

Tota

l

-

Fig. 2. Distribution of poor households


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

IV. Empirical Methodology

Effect of natural shocks on household income andconsumption

We apply two different methods to examine the effect ofnatural disasters on household income and consumption.First, we employ standard consumption and incomeregressions but include a dummy variable for the occur-rence of natural disaster as follows:

ln Yið Þ ¼ αþ Xiβ þ Diγþ ui (1)

where ln Yið Þ denotes the natural logarithm of per capitaincome or expenditure of household i; X is a vector ofhousehold- and community-level control variables, whichincludes household characteristics and geographical loca-tion. The detailed descriptions of control variables arepresented in Appendix 2 – Table A2; D is a dummyvariable which takes the value of 1, if the household isexposed to at least one type of natural disaster and 0otherwise; and ui is the error term.

The existing empirical studies on natural shocks usuallyconsider natural disasters as an exogenous variable.However, Thomas et al. (2010) argue that the possibilityof a natural disaster in a certain location is correlated withthe likelihood of its occurring in the first place, which mayin turn affect the level of consumption. Households inregions where natural disasters hit frequently are likelyto adapt to these unfavourable conditions. This may leadto potential endogeneity problem when we include adummy variable for natural disaster. Thus, we first apply

district fixed effects to control for the average long-runsteady state natural shocks faced by households in a parti-cular district. We employ instrumental variables (IV)methods to yield consistent estimates when the indepen-dent variable (natural disaster events) is correlated withthe error term. The instrumental variable must satisfy twoconditions; first, it is correlated with the endogenous vari-able, and second, it is not correlated with the error terms.

Following Mansuri (2006) and Acosta (2006), we usethe proportion of households, who are exposed to naturalshocks, to total households in a certain district as theinstrumental variable for natural shocks. This proportionreflects the risk level in the district. The estimates fromthe first stage in the 2SLS regression (presented inAppendix 2 – Table A3) suggest that the selected instru-mental variable is highly correlated with the dummy vari-able for natural disaster. No empirical evidence found inthe literature implies that the proportion of the exposedhouseholds in a certain district is correlated with house-hold income or expenditure, thus there is no immediateevidence against the validity of our instrument. We alsoconducted a Cragg-Donald weak identification test withthe resulting test statistic exceeding 10, supporting thevalidity of the instrument4 (see Appendix 2 – Table A3).Secondly, we propose to examine the impact of natural

disasters by the ATT. This measure, developed byHeckman et al. (1999), has been using extensively toevaluate the effectiveness of public policies. FollowingHeckman et al. (1999), the ATT of natural disasters onhousehold income and expenditure can be written asfollows:

ATT ¼ E Y1i � Y0ijDi ¼ 1½ �¼ E Y1ijDi ¼ 1½ � � E Y0ijDi ¼ 1½ � (2)

where E Y1ijDi ¼ 1½ � denotes the expected value of theoutcome Y of the affected households if the householdsare exposed to natural disasters; and E Y0ijDi ¼ 1½ � is theexpected outcome Y of the affected households if they arenot exposed to natural disaster.

Combining Equations 1 and 2, we estimate the ATT ofnatural disaster on household income and expenditure asfollows5:

AT̂T ¼ 1

nD1� e�γ̂� �XnD

i¼1

Yi (3)

where nD is the number of affected households ðD1 ¼ 1Þ,and γ̂ is the estimate of γ in Equation 1.

Table 3. Proportion of poor households that are affected bynatural disasters

Percentage of poor HHs

Region

Notaffected bynaturaldisaster

Affected bynaturaldisaster

Percentageof poorhouseholds

Red River Delta 42.1 57.9 7.5North East 35.8 64.2 20.6North West 48.2 51.8 38.0North Central Coast 42.1 57.9 20.1South Central Coast 35.1 64.9 12.6Central Highlands 46.4 53.6 19.5South East 19.0 81.0 3.0Mekong River Delta 38.6 61.4 10.5All regions 35.3 64.7 12.3

Source: Author’s estimation from the VHLSS in 2008.

4As a rule of thumb, if the test statistic is under 10, the instrument might be weak (Staiger and Stock, 1997).5Derivation of Equation 3 is presented in Appendix 1b.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

The effect of natural shocks on poverty and inequality

We measure the degree of poverty using three indicesdeveloped by Foster et al. (1984). These indices can bewritten in their general form as follows:

Pα ¼ 1

n

Xqi¼1

z� Yiz

� �α(4)

where Yi denotes a welfare indicator for person i, z is thepoverty line, n is the number of people in the sample, q isthe total number of poor people and α is a measure ofinequality aversion. Different values of α provide differentindices.When α ¼ 0, the index measures the proportion ofpeople who live under the poverty line (headcount index);when α ¼ 1, the index represents the depth of poverty(poverty gap index); and when α ¼ 2, the index charac-terizes square poverty gap (poverty severity index).Following the literature, we employ per capita consump-tion expenditure as a proxy for welfare (Razavi, 1998; Vanden Berg and Cuong, 2011).

Income inequality is measured by the three most com-mon indices: Gini, Theil L and Theil T. The Gini coeffi-cient, which is based on the Lorenz curve, is the mostwidely used to measure inequality due to its straightfor-ward calculation, flexibility across different populationgroups and independence from sample size and scale ofthe economy. The Gini coefficient is estimated by thedifference between the distribution of income and theuniform distribution that represents equality.

G ¼ nþ 1

n� 1� 2

nðn� 1Þ�YXni¼1

ρiYi (5)

where ρi is the rank of individual i by their income. ρi isequal to 1 for the richest and increase for individuals withlower income. The Gini coefficient lies in the range of0 to 1. The more income inequality, the higher Ginicoefficient is.

We also measure household inequality by two otherproxies, the Theil’s L and Theil’s T indices, which can bewritten as follows:

Theil L ¼ 1

n

Xni¼1

ln�Y

Yi

� �(6)

Theil T ¼ 1

n

Xni¼1

Yi�Yln

Yi�Y

� �(7)

Values of Theil L and Theil T vary between 0 and 1,where 0 represents an equal distribution and the higher

value represents the higher level of inequality. The impactof natural disasters on poverty indices is estimated usingthe ATT measure for both the exposed households sampleand the full sample including all households.

We calculate the impact of natural shocks on the pov-erty indices for affected households as follows:

ΔIDi¼1 ¼ IDi¼11 � IDi¼1

0 (8)

where IDi¼11 is one of the three poverty indices for the

sample including the exposed households, which can becalculated directly from the sample. IDi¼1

0 is one of thethree indices which are estimated using the counterfactualconsumption for the exposed households. We cannotobserve the consumption of the affected households ifthey were not affected by natural disaster. Thus, thiscounterfactual consumption is estimated as follows6:

YiðD¼0Þ��ðDi ¼ 1Þ ¼ e�γ̂YiðD¼1Þ

��ðDi ¼ 1Þ (9)

where γ̂ is the estimate of γ in Equation 1.For the full sample of all households, we also measure

the impact of natural shocks on poverty and inequalityindices by

ΔI ¼ I � ID¼0 (10)

where I is the observed poverty or inequality index for allhouseholds in the sample, and ID¼0 is the index for allhouseholds in the sample where the affected householdshad not been exposed to the disaster. Finally, we employbootstrap techniques (with 500 replications) to computethe SE of the impact of natural shocks on poverty andequality indices in both of the samples.

V. Estimation Results

The effect of natural shocks on income andconsumption

To examine the effect of natural shocks on household percapita income and expenditure, we control for two sets ofvariables of household characteristics and locations,where the households reside, in model 1 and model 2,respectively. Model 1 includes the exogenous control vari-ables that are widely used as determinants of householdincome and expenditure in the literature. These controlvariables include the location of the household, householdsize, proportion of dependency, proportion of female,ethnic minority, gender of household head, and here werefer to these as ‘the small set of the control variables’.

6Derivation of this estimation is presented in Appendix 1.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

Since the literature shows education of householdheads and spouses, and wealth of household measures(e.g. living area, annual crop land, aquaculture water sur-face and quality of their houses) may influence householdincome and expenditure, we extend model 1 to includethese in the set of control variables in model 2. This set inmodel 2 is referred to as ‘the large set of the controlvariable’. For each model, we employ district fixed effectsand district fixed effects with IV methods. The results ofthese regressions are presented in Tables 4 and 5.

Table 4 reports the effect of the exposure to naturalshocks on household per capita income. Model 1 showsthat natural disasters lead to a fall of 7.0% in the averageincome of households after controlling for the averagelong-run steady state natural shocks faced by householdsin a particular district and other control variables (seecolumn 1). Estimates using district fixed effects with IVyield similar results (see column 3), which suggests thatthe endogeneity problem is not serious after controllingfor the district fixed effects.

Consistent with the findings in model 1, the results ofmodel 2 report a negative impact of disasters on household

income. However, the magnitude of the effect is lower(3.5% and 4.1%) when a larger set of control variables isemployed in either two specifications (see columns 2 and4). Our findings are in line with Krueger and Perri (2010)and Masozera et al. (2007), who also report a negativeeffect of natural disasters on household income.

The effect of natural disaster shocks on household percapita expenditure is illustrated in Table 5. A negativerelationship between natural shocks and household percapita expenditure is expected given our findings inTable 4. Model 1 indicates that exposure to natural shocksreduces household per capita expenditure by 8.3% in 2008after controlling for the district fixed effects (see column1). Consistently, the estimated coefficient of the naturalshock dummy is 7.1% after we further correct the potentialendogeneity problem (see column 3). The effect of naturaldisasters on expenditure follows the same patterns as onincome when we apply model 2 with a large set of controlvariables. Columns 2 and 4 in Table 5 indicate that thedisasters result in a lower reduction of per capita expen-diture of 5.1% and 4.6% compared with the respectiveresults for model 1. Overall, household expenditure

Table 5. Regressions of log of per capita expenditure on natural shocks

Explanatory variables

Model 1:district fixedeffects regression


Model 1:district fixed effectswith IV regression


Natural shocks (yes = 1) −0.083*** −0.051*** −0.071*** −0.046***[0.014] [0.012] [0.019] [0.016]

Control variables Small set Large set Small set Large setObservations 9189 9181 9189 9181

R2 0.19 0.41 0.168 0.396No. of districts 643 643 643 643

Source: Estimation from the 2008 VHLSS.Notes: Income and expenditure are measured in ‘1000 VND’.***Significant at 1%.Figures in brackets are SE. SEs are corrected for sampling weights and cluster correlation.

Table 4. Regressions of log of per capita income on natural shocks

Explanatory variables





Natural shocks (yes = 1) −0.070*** −0.035** −0.069*** −0.041*[0.019] [0.017] [0.025] [0.022]

Control variables Small set Large set Small set Large setObservations 9189 9181 9189 9181

R2 0.12 0.32 0.105 0.314No. of districts 643 643 643 643

Source: Estimation from the 2008 VHLSS.Notes: Income and expenditure are measured in ‘1000 VND’.*Significant at 10%; **significant at 5%; ***significant at 1%.Figures in brackets are SEs. SEs are corrected for sampling weights and cluster correlation.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

declines more than the reduction in income, which sug-gests that income becomes more uncertain for the affectedhouseholds.

We also implement models 1 and 2 using OLS and IVspecifications as robustness checks. The results of theseregressions are in line with our findings in Tables 4 and 5,confirming the adverse impact of natural disasters onhousehold income and expenditure. The full estimationsof the regressions in Tables 4 and 5 as well as the robust-ness checks are presented in Appendix 2 – Tables A4and A5.

Table 6 reports the effect of natural shocks on the abso-lute value of per capita income and expenditure using the

ATT measure. We use the district fixed effects with IVregression and the large set of the control variables toestimate γ in Equation 1.7 Per capita income and expendi-ture of the exposed households are VND 8238.6 and5 892 000 per year, respectively. If the exposed householdswere not affected by natural disasters, their income andexpenditure, which are estimated by Equation 9, wouldhave been VND 8581.4 and 6168.2000 per year, respec-tively. As a result, the exposure to natural shocks causes adecline in per capita income and expenditure by VND342.8000 and VND 257.9000, consistent with the resultsreported in Tables 4 and 5. The findings imply that naturalshocks lead to a significant reduction in household incomeand expenditure.

Estimates of the effect of natural shocks on poverty andinequality

Since natural disasters reduce household income andexpenditure, it is likely to worsen poverty. We examinethe impact of natural disasters on household povertyindices for the affected households and for the wholesample. Table 7 shows the head count index for theexposed households suggests that 21.6% of the exposedhouseholds live below the poverty line. However, if thesehouseholds were not exposed to natural disasters, thepoverty index would have been dropped to 18.92%. Wedocument the same pattern for the impact of the disasterfor the full household sample where the results are ofsmaller magnitude (see the last three columns inTable 7). Overall, these estimations imply that naturaldisaster shocks result in a deterioration in the povertysituation of both the affected households and all house-holds on average.

Table 8 reports the impact of natural disaster on house-hold inequality. Natural disasters cause 0.2%, 0.1% and

Table 6. The ATT of natural shocks on per capita incomeand expenditure

Outcome indicatorsWith naturalshocks (Y1)

Withoutnaturalshocks (Y0)

Impact(Y1–Y0)

Per capita income(1000 VND)

8238.6 8581.4 −342.8**

[252.5] [272.3] [165.3]Per capitaexpenditure(1000 VND)

5892.3 6168.2 −275.9***

[102.1] [137.8] [104.5]

Source: Estimation from the 2008 VHLSS.Notes: **Significant at 5%; ***significant at 1%.Y1 is the average per capita income and expenditure of theexposed households, which are observable from the data set. Y0is the average per capita income and expenditure of the exposedhouseholds if the households were not affected by the disaster. Y0is the unobservable from the data set and is estimated usingEquation 9. Figures in brackets are SEs. SEs are corrected forsampling weights and estimated using bootstrap (nonparametric)with 500 replications.

Table 7. Impact of natural shocks on poverty

Outcome indicators

People were affected by natural shocks All people

With naturalshocks (Y1)

Without naturalshocks (Y0)

Impact(Y1–Y0)



Impact(Y1–Y0)

Head count 0.2160 0.1892 0.0268*** 0.1447 0.1375 0.0073***[0.0119] [0.0123] [0.0076] [0.0056] [0.0058] [0.0021]

Poverty gap 0.0526 0.0456 0.0070*** 0.0347 0.0328 0.0019***[0.0037] [0.0040] [0.0025] [0.0018] [0.0019] [0.0007]

Poverty severity 0.0184 0.0154 0.0029*** 0.0124 0.0116 0.0008**[0.0017] [0.0017] [0.0011] [0.0009] [0.0009] [0.0003]

Source: Estimation from the 2008 VHLSS.Notes: ***Denotes significant at 1%.Figures in brackets are SEs. SEs are corrected for sampling weights and estimated using bootstrap (nonparametric) with 500 replications.

7 The use of alternative model specifications and size of control variables yield similar results.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

0.3% rises in Gini, Theil L, and Theil T indices, respec-tively, indicating that natural disasters worsen the inequal-ity issues among households. Our results support Kruegerand Perri (2010), Wainwright and Newman (2011)andPelham et al. (2011), who find that poor households aremore affected by shocks.

Overall, the empirical results presented in Tables 4–8show that after controlling for other potential affectingfactors, natural disasters themselves significantly reducehousehold income and consumption by 6.9% and 7.1%,respectively. Disasters also have worse impacts on poorhouseholds, widening the gap between the rich and thepoor. Our article suggests that the policymakers need toconsider the impact of natural disasters as a potentiallyimportant dimension in their policies to reduce povertyand inequality.

VI. Conclusions

Using the VHLSS in 2008, this article examines the effectof natural disasters on various aspects of welfare, includ-ing income, consumption, poverty and inequality. Thearticle uses a variety of model specifications, controls fora wide range of household and community characteristicsand takes into account the potential endogeneity of thenatural shock events. The study also proposes a simplemethod of using ATT to measure the impact of naturaldisaster on poverty and equality. Our findings are robust inall of different model specifications.

We find that natural disasters lead to significant drops inper capita income and expenditure of the affected house-holds in Vietnam. The average fall lies in range of 4% to8% or around VND 3 million depending on the modelspecifications and the number of control variables. TheGini, Theil L and Theil T indices are found to increase by0.23%, 0.27% and 0.30%, respectively. The three povertyindices are higher with the occurrence of the natural dis-asters for both the directly affected households sample and

the full sample, which includes both unaffected andaffected households.

In conclusion, this analysis shows that natural disasterssignificantly exacerbate poverty and inequality. Thus,when designing policies to alleviate poverty and inequal-ity, policymakers would be well recommended to includethe role of natural disasters as potentially contributingfactors in achieving development outcomes.

References

Acosta, P. (2006) Labor supply, school attendance, and remit-tances from international migration: the case of El Salvador,Policy Research Working Paper Series 3903, The WorldBank, Washington, DC.

Benson, C. (1997) The economic impact of natural disasters inVietnam, Overseas Development Institute Working PaperNo. 98, Portland House, London.

Chandy, L. and Gertz, G. (2011) Poverty in Numbers: TheChanging State of Global Poverty from 2005 to 2015,Brookings Institution report Policy Brief 2011–01,Washington, DC.

Clark, G. E., Moser, S. C., Ratick, S. J. et al. (1998) Assessingthe vulnerability of coastal communities to extreme storms:the case of revere, MA., USA, Mitigation and AdaptationStrategies for Global Change, 3, 59–82.

Dercon, S. (2004) Growth and shocks: evidence from ruralEthiopia, Journal of Development Economics, 74, 309–29.

Foster, J., Greer, J. and Thorbecke, E. (1984) A Class ofDecomposable PovertyMeasures, Econometrica, 52, 761–6.

Fothergill, A. and Peek, L. A. (2004) Poverty and disasters in theUnited States: a review of recent sociological findings,Natural Hazards, 32, 89–110.

GFDRR (2010)Weathering the Storm: Options for Disaster RiskFinancing in Vietnam, The World Bank, Washington, DC.

Guha-Sapir, D., Hargitt, D. and Hoyois, P. (2004) Thirty Years ofNatural Disasters 1974-2003: The Numbers, PUL, Lovain-la-Neuve.

Heckman, J. J., Lalonde, R. J. and Smith, J. A. (1999) Chapter 31The economics and econometrics of active labor market pro-grams, in Handbook of Labor Economics, Orley, C. A. andDavid, C., (Eds), Elsevier, Amsterdam, pp. 1865–2097.

Imai, K. and Gaiha, R. (2007) Poverty, inequality and ethnicminorities in Vietnam, Brooks World Poverty Institute,Working Paper No. 10, Brooks World Poverty Institute,Washington, DC.

IPCC (2007) Climate Change 2007: Synthesis Report,Contribution of Working Groups I, II and III to the FourthAssessment Report of the Intergovernmental Panel onClimate Change, IPCC, Geneva.

Krueger, D. and Perri, F. (2010) How do households respond toincome shocks?, Mimeo, University of Pennsylvania.

Mansuri, G. (2006) Migration, school attainment and child labor:evidence from rural Pakistan, Policy Research WorkingPaper Series 3945, The World Bank, Washington, DC.

Masozera, M., Bailey, M. and Kerchner, C. (2007) Distributionof impacts of natural disasters across income groups: acase study of New Orleans, Ecological Economics, 63,299–306.

Minot, N., Baulch, B. and Epprecht, M. (2006) Poverty andinequality in Vietnam: spatial patterns and geographicdeterminants, Research Report No. 148, InternationalFood Policy Research Institute, Washington, DC.

Table 8. Impact of natural shocks on inequality

Outcomeindicators



Impact(Y1–Y0)

Gini 0.3557 0.3534 0.0023***[0.0054] [0.0054] [0.0008]

Theil L 0.2084 0.2057 0.0027***[0.0064] [0.0064] [0.0010]

Theil T 0.2271 0.2241 0.0030***[0.0086] [0.0085] [0.0011]

Source: Estimation from the 2008 VHLSS.Notes: ***Denotes significant at 1%.Figures in brackets are SEs. SEs are corrected for samplingweights and estimated using bootstrap (nonparametric) with500 replications.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

Mottaleb, K. A., Mohanty, S., Hoang, H. T. K. et al. (2013) Theeffects of natural disasters on farm household income andexpenditures: a study on rice farmers in Bangladesh,Agricultural Systems, 121, 43–52.

Noy, I. and Vu, T. B. (2010) The economics of natural disasters ina developing country: the case of Vietnam, Journal of AsianEconomics, 21, 345–54.

Peacock, W. G., Gladwin, H. and Morrow, B. H. (1997)Hurricane Andrew: Ethnicity, Gender and the Sociologyof Disasters, Routledge, New York.

Pelham, L., Clay, E. and Braunholz, T. (2011), Natural Disasters:what is the role for social safety nets?, SP Discussion PaperNo. 1102, The World Bank, Washington, DC.

Ravallion, M. (2013) The Idea of antipoverty policy, NBERWorking Paper Series No. 19210, NBER, Cambridge, MA.

Razavi, S. (1998) Gendered poverty and social change: an issuespaper, United Nations Research Institute for SocialDevelopment Discussion Paper No. 94, United NationsResearch Institute, Geneva.

Rodriguez-Oreggia, E., Fuente, A. D. L., Torre, R. D. L. et al.(2012) Natural disasters, human development and povertyat the municipal level in Mexico, The Journal ofDevelopment Studies, 49, 442–55.

SRV (2004) National Report on Disaster Reduction in Vietnam,For the World Conference on Disaster Reduction, Kobe-Hyogo, 18–22 January 2005.

Skidmore, M. and Toya, H. (2002) Do natural disasters promotelong-run growth?, Economic Inquiry, 40, 664–87.

Staiger, D. and Stock, J. H. (1997) Instrumental variables regres-sion with weak instruments, Econometrica, 65, 557–86.

Strobl, E. (2011) The economic growth impact of Hurricanes:evidence fromU.S,Coastal Counties, Review of Economicsand Statistics, 93, 575–89.

Thomas, T., Christiaensen, L., Do, Q. T. et al. (2010) Naturaldisasters and household welfare: evidence from Vietnam,World Bank Policy Research Working Paper WPS5491,The World Bank, Washington, DC.

UNISDR. (2009) Global Assessment Report on Disaster RiskReduction, United Nations International Strategy forDisaster Reduction Secretariat, Geneva.

Van den Berg, M. and Cuong, N. V. (2011) Impact of publicand private cash transfers on poverty and inequality: evi-dence from Vietnam, Development Policy Review, 29,689–728.

Wainwright, F. and Newman, C. (2011) Income shocks andhousehold risk-coping strategies: evidence from ruralVietnam, Discussion Paper No. 358, Institute forInternational Integration Studies, Trinity College Dublin,Dublin.

Wisner, B., Blaikie, P., Cannon, T. et al. (2004) At Risk: NaturalHazards, People’s Vulnerability and Disasters, Routledge,New York.

Appendix 1a: Estimates of the ATT Measure

Estimator of the average treatment effect on the affectedhouseholds is expressed as follows:

ATT ¼ E Yi D¼1ð Þ Dijt ¼ 1�� E Yi D¼0ð Þ Dijt ¼ 1

�� ¼ E Yi D¼1ð Þ Dijt ¼ 1

�� E eln Yi D¼0ð Þð Þ��Di ¼ 1h i

¼ E Yi D¼1ð Þ Di ¼ 1j� �� E e αþXiβþuið Þ��Dijt ¼ 1h i

¼ E Yi D¼1ð Þ Di ¼ 1j� �� E e αþXiβþuið ÞþDiγ�Diγ��Di ¼ 1

h i

¼ E Yi D¼1ð Þ Di ¼ 1j� �� E eln Yi D¼1ð Þð Þ�Diγ��Di ¼ 1

h i

¼ E Yi D¼1ð Þ Di ¼ 1j� �� e�γE Yi D¼1ð Þ Di ¼ 1j� �¼ 1� e�γð ÞE Yi D¼1ð Þ Di ¼ 1j� �

Appendix 1b: Estimates of CounterfactualIncome and Consumption

The counterfactual income and consumption of affectedhouseholds if they had not been affected by the naturaldisasters, YðD¼0Þ, is estimated as follows:

Yi D¼0ð Þ Di ¼ 1j ¼ eln Yi D¼0ð Þð Þ��Di ¼ 1

¼ e αþXiβþuið ÞþDiγ�Diγ��Dijt ¼ 1

¼ eln Yi D¼1ð Þð Þ�Diγ��Dijt ¼ 1

¼ e�γYi D¼1ð Þ Di ¼ 1j

Appendix 2: Tables

Table A1. Relationship between disaster levels and poverty head count index in 2008

Disaster level Number of country Mean SD Min. Max.

2 6 4.294 5.855 0.000 14.9633 10 8.304 11.764 0.000 36.2644 12 21.566 20.690 0.070 75.9064* 9 14.016 8.055 0.070 23.430

Source: http://data.worldbank.org/topic/povertyNotes: Poverty head count index at $1.25/day.Disaster levels are measured by economic losses relative to GDP and/or to capital stock (see UNISDR (2009)for detail)Countries in the sample: low and middle income (2, 3, 4) and middle income (2, 3, 4*).


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

http://data.worldbank.org/topic/poverty

Table A2. Summary statistics of explanatory variables

Variables Type Mean SD

Urban Binary 0.256 0.436Red River Delta Binary 0.212 0.408North East Binary 0.143 0.350North West Binary 0.047 0.211North Central Coast Binary 0.110 0.313South Central Coast Binary 0.093 0.290Central Highlands Binary 0.063 0.244South East Binary 0.129 0.336Mekong River Delta Binary 0.203 0.402Household size Discrete 4.163 1.658Proportion of children below 15 Continuous 0.205 0.206Proportion of old members above 60 Continuous 0.132 0.259Proportion of female member Continuous 0.518 0.200Ethnic minorities (yes = 1) Binary 0.150 0.357Male head (yes = 1) Binary 0.755 0.430Log of head's age Discrete 49.96 13.51Head no education degree Binary 0.261 0.439Head with primary school degree Binary 0.251 0.434Head lower-secondary degree Binary 0.251 0.434Head upper-secondary degree Binary 0.077 0.266Head technical degree Binary 0.114 0.318Head post-secondary degree Binary 0.046 0.209Head without spouse Binary 0.203 0.402Spouse no education degree Binary 0.223 0.416Spouse primary school degree Binary 0.219 0.413Spouse lower-secondary degree Binary 0.206 0.405Spouse upper-secondary degree Binary 0.055 0.227Spouse technical degree Binary 0.060 0.237Spouse post-secondary degree Binary 0.035 0.184Annual crop land (10 000 m2) Continuous 0.334 0.817Perennial crop land (10 000 m2) Continuous 0.121 0.732Aquaculture water surface (10 000 m2) Continuous 0.039 0.473Living area (m2) Continuous 68.05 40.30Permanent house Binary 0.258 0.437Semi-permanent house Binary 0.599 0.490Temporary house Binary 0.143 0.351

Source: Estimation from the 2008 VHLSS.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

Table A3. First-stage regressions of natural shocks

Model 1 Model 2

Explanatory variables Coeff. SE Coeff. SE

% of the exposed households within district 0.860*** 0.016 0.857*** 0.016Urban −0.065*** 0.010 −0.044*** 0.010North East −0.029* 0.016 −0.027 0.017North West −0.060** 0.026 −0.061** 0.027North Central Coast 0.010 0.019 0.009 0.019South Central Coast −0.004 0.018 −0.008 0.018Central Highlands −0.021 0.025 −0.030 0.025South East −0.022* 0.013 −0.029** 0.014Mekong River Delta −0.035*** 0.013 −0.046*** 0.015Household size 0.008*** 0.003 0.005* 0.003Proportion of children below 15 −0.010 0.024 −0.008 0.024Proportion of old members above 60 −0.006 0.019 −0.012 0.020Proportion of female member −0.013 0.020 −0.015 0.020Ethnic minorities (yes = 1) 0.051*** 0.017 0.044** 0.018Male head (yes = 1) 0.011 0.009 0.000 0.011Log of head's age −0.020 0.020 −0.016 0.022Head with primary school degree 0.004 0.012Head lower-secondary degree −0.002 0.014Head upper-secondary degree −0.037** 0.017Head technical degree −0.044*** 0.016Head post-secondary degree −0.056*** 0.022Spouse no education degree 0.028* 0.015Spouse primary school degree 0.011 0.015Spouse lower-secondary degree 0.041*** 0.015Spouse upper-secondary degree 0.016 0.018Spouse technical degree −0.008 0.019Spouse post-secondary degree −0.013 0.022Annual crop land (10 000 m2) 0.007 0.005Perennial crop land (10 000 m2) 0.008 0.006Aquaculture water surface (10 000 m2) 0.001 0.008Log of living area (m2) 0.000 0.008Semi-permanent house −0.002 0.010Temporary house −0.001 0.016Constant 0.112 0.081 0.108 0.091

Observations 9,189 9,181R2 0.409 0.413

Weak identification test (Cragg–Donald Wald F-statistic) 12 804 13 010

Source: Estimation from the 2008 VHLSS.Notes: *Significant at 10%; **significant at 5%; ***significant at 1%.Figures in brackets are SEs. SEs are corrected for sampling weights and cluster correlation.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

Tab

leA4.

Regressionsof

logof

per

capitaincome

Exp

lanatory

variables

Mod

el1:

OLS

Mod

el2:

OLS

Mod

el1:districtfixed

effectsregression

Mod

el2:districtfixed

effectsregression

Mod

el1:

IVmod

elMod

el2:

IVmod

elMod

el1:

districtfixed

effectswith

IVregression

Mod

el2:

districtfixed

effectswith

IVregression

Naturalshocks

(yes

=1)

−0.08

8***

−0.06

1***

−0.07

0***

−0.035

**−0.14

4***

−0.13

2***

−0.06

9***

−0.04

1*[0.018

][0.016

][0.019

][0.017

][0.035

][0.031

][0.025

][0.022

]Urban

0.35

7***

0.16

3***

0.16

8***

0.04

3**

0.34

7***

0.15

3***

0.16

8***

0.04

2**

[0.022

][0.020

][0.022

][0.020

][0.023

][0.021

][0.022

][0.020

]North

East

−0.05

5*−0.04

6*−0.05

9*−0.05

0*[0.030

][0.027

][0.031

][0.027

]North

West

−0.09

5**

−0.114*

**−0.10

1**

−0.12

1***

[0.046

][0.039

][0.046

][0.039

]North

CentralCoast

−0.23

8***

−0.17

4***

−0.23

1***

−0.16

5***

[0.028

][0.024

][0.028

][0.025

]Sou

thCentralCoast

−0.09

7***

0.03

4−0.09

7***

0.03

4[0.028

][0.026

][0.028

][0.026

]CentralHighlands

−0.06

60.01

3−0.07

0*0.00

8[0.042

][0.037

][0.042

][0.038

]Sou

thEast

0.24

5***

0.37

1***

0.23

3***

0.35

6***

[0.036

][0.033

][0.036

][0.034

]Mekon

gRiver

Delta

0.011

0.20

1***

00.18

6***

[0.027

][0.027

][0.028

][0.028

]Hou

seho

ldsize

−0.05

3***

−0.07

7***

−0.05

6***

−0.084

***

−0.05

3***

−0.07

7***

−0.05

6***

−0.08

4***

[0.005

][0.005

][0.005

][0.004

][0.005

][0.005

][0.005

][0.004

]Propo

rtionof

child

renunder15

−0.67

5***

−0.47

3***

−0.71

2***

−0.497

***

−0.67

4***

−0.47

1***

−0.71

2***

−0.49

7***

[0.048

][0.041

][0.039

][0.035

][0.048

][0.041

][0.039

][0.035

]Propo

rtionof

elderly

abov

e60

−0.42

5***

−0.25

7***

−0.41

8***

−0.275

***

−0.42

6***

−0.25

9***

−0.41

8***

−0.27

5***

[0.039

][0.034

][0.033

][0.029

][0.039

][0.034

][0.033

][0.029

]Propo

rtionof

female

mem

bers

−0.22

3***

−0.19

5***

−0.17

0***

−0.145

***

−0.22

4***

−0.19

6***

−0.17

0***

−0.14

5***

[0.044

][0.039

][0.035

][0.031

][0.044

][0.039

][0.035

][0.031

]Ethnicminorities

(yes

=1)

−0.42

7***

−0.29

2***

−0.39

6***

−0.262

***

−0.42

1***

−0.28

4***

−0.39

6***

−0.26

2***

[0.029

][0.025

][0.031

][0.028

][0.029

][0.025

][0.031

][0.028

]Malehead

(yes

=1)

0.02

8−0.03

60.06

8***

−0.011

0.03

−0.03

50.06

8***

−0.011

[0.023

][0.026

][0.017

][0.019

][0.023

][0.026

][0.017

][0.019

]Log

ofhead'sage

−0.03

4−0.01

9−0.05

8*−0

.03

−0.03

4−0.01

8−0.05

8*−0.03

[0.045

][0.037

][0.034

][0.032

][0.045

][0.037

][0.034

][0.032

]Headwith

prim

ary

scho

oldegree

0.09

7***

0.09

3***

0.09

8***

0.09

3***

[0.021

][0.018

][0.021

][0.018

]Headlower-

second

arydegree

0.20

5***

0.18

9***

0.20

6***

0.18

9***

[0.024

][0.020

][0.024

][0.020

]Headup

per-

second

arydegree

0.31

3***

0.26

4***

0.31

2***

0.26

4***

[0.036

][0.027

][0.036

][0.027

]Headtechnical

degree

0.38

0***

0.36

6***

0.37

6***

0.36

6***

[0.028

][0.025

][0.028

][0.025

]

(con

tinued)


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

Tab

leA4.

Con

tinued

Exp

lanatory

variables

Mod

el1:

OLS

Mod

el2:

OLS

Mod

el1:districtfixed

effectsregression

Mod

el2:districtfixed

effectsregression

Mod

el1:

IVmod

elMod

el2:

IVmod

elMod

el1:

districtfixed

effectswith

IVregression

Mod

el2:

districtfixed

effectswith

IVregression

Headpo

st-secon

dary

degree

0.59

6***

0.55

3***

0.59

1***

0.55

3***

[0.038

][0.036

][0.038

][0.036

]Spo

useno

education

degree

−0.08

2***

−0.069

***

−0.07

9***

−0.06

8***

[0.030

][0.024

][0.030

][0.024

]Spo

useprim

ary

scho

oldegree

−0.03

5−0

.019

−0.03

3−0.01

9[0.027

][0.024

][0.027

][0.024

]Spo

uselower-

second

arydegree

−0.04

−0.005

−0.03

6−0.00

5[0.029

][0.024

][0.029

][0.024

]Spo

useup

per-

second

arydegree

0.07

2*0.09

0***

0.07

3*0.09

0***

[0.040

][0.032

][0.040

][0.032

]Spo

usetechnical

degree

0.14

3***

0.17

1***

0.14

4***

0.17

1***

[0.034

][0.031

][0.034

][0.031

]Spo

usepo

st-

second

arydegree

0.29

0***

0.28

7***

0.28

9***

0.28

7***

[0.044

][0.039

][0.044

][0.039

]Ann

ualcropland

(1000

0m

2)

0.118*

**0.13

2***

0.118*

**0.13

2***

[0.008

][0.008

][0.008

][0.008

]Perennialcrop

land

(1000

0m

2)

0.07

5***

0.06

8***

0.07

6***

0.06

9***

[0.020

][0.009

][0.020

][0.009

]Aqu

aculture

water

surface(10

000m

2)

0.06

2***

0.07

5***

0.06

4***

0.07

5***

[0.014

][0.013

][0.014

][0.013

]

Log

ofliv

ingarea

(m2)

0.27

4***

0.26

8***

0.27

4***

0.26

8***

[0.017

][0.014

][0.017

][0.014

]Sem

i-perm

anent

house

−0.16

1***

−0.117

***

−0.16

1***

−0.117*

**[0.020

][0.017

][0.020

][0.017

]Tem

porary

house

−0.30

8***

−0.255

***

−0.30

8***

−0.25

5***

[0.029

][0.025

][0.029

][0.025

]Con

stant

9.67

4***

8.43

7***

9.73

4***

8.52

3***

9.69

3***

8.45

6***

9.73

3***

8.52

5***

[0.183

][0.157

][0.136

][0.136

][0.183

][0.157

][0.136

][0.136

]

Observatio

ns91

8991

8191

8991

8191

8991

8191

8991

81

R2

0.25

0.43

0.12

0.32

0.22

20.39

50.10

50.31

4No.

ofdistricts

643

643

643

643

Source:E

stim

ationfrom

the20

08VHLSS.

Notes:*

Significant

at10

%;*

*significant

at5%

;***

sign

ificant

at1%

.Figures

inbracketsareSEs.SEsarecorrectedforsamplingweigh

tsandclustercorrelation.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

Tab

leA5.

Regressionsof

logof

per

capitaexpenditure

Exp

lanatory

variables

Mod

el1:

OLS

Mod

el2:

OLS

Mod

el1:districtfixed

effectsregression

Mod

el2:districtfixed

effectsregression

Mod

el1:

IVmod

elMod

el2:

IVmod

elMod

el1:

districtfixed

effectswith

IVregression

Mod

el2:

districtfixed

effectswith

IVregression

Naturalshocks

(yes

=1)

−0.08

8***

−0.06

3***

−0.08

3***

−0.051

***

−0.13

8***

−0.12

7***

−0.07

1***

−0.04

6***

[0.015

][0.013

][0.014

][0.012

][0.028

][0.024

][0.019

][0.016

]Urban

0.43

0***

0.23

4***

0.20

7***

0.08

1***

0.42

2***

0.22

5***

0.20

8***

0.08

2***

[0.020

][0.017

][0.016

][0.015

][0.020

][0.017

][0.017

][0.015

]North

East

−0.07

4***

−0.05

9***

−0.07

7***

−0.06

3***

[0.027

][0.023

][0.027

][0.023

]North

West

−0.05

3−0.01

7−0.05

9−0.02

4[0.040

][0.034

][0.041

][0.034

]North

CentralCoast

−0.17

6***

−0.116*

**−0.17

0***

−0.10

8***

[0.024

][0.021

][0.024

][0.021

]Sou

thCentralCoast

−0.05

2*0.08

3***

−0.05

2*0.08

3***

[0.027

][0.023

][0.027

][0.023

]CentralHighlands

−0.01

40.09

1***

−0.01

70.08

6***

[0.035

][0.029

][0.035

][0.030

]Sou

thEast

0.16

3***

0.29

4***

0.15

2***

0.28

1***

[0.031

][0.026

][0.032

][0.027

]Mekon

gRiver

Delta

−0.06

1***

0.16

1***

−0.07

1***

0.14

7***

[0.022

][0.022

][0.023

][0.023

]Hou

seho

ldsize

−0.06

3***

−0.07

7***

−0.06

7***

−0.086

***

−0.06

2***

−0.07

7***

−0.06

7***

−0.08

6***

[0.005

][0.004

][0.004

][0.003

][0.005

][0.004

][0.004

][0.003

]Propo

rtionof

child

renunder15

−0.58

2***

−0.41

0***

−0.62

0***

−0.443

***

−0.58

1***

−0.40

8***

−0.62

0***

−0.44

3***

[0.040

][0.034

][0.030

][0.026

][0.040

][0.034

][0.030

][0.026

]Propo

rtionof

elderly

abov

e60

−0.30

7***

−0.15

0***

−0.29

7***

−0.165

***

−0.30

8***

−0.15

1***

−0.29

7***

−0.16

5***

[0.032

][0.028

][0.025

][0.022

][0.032

][0.028

][0.025

][0.022

]Propo

rtionof

female

mem

bers

−0.112*

**−0.08

6***

−0.06

9***

−0.051

**−0.113*

**−0.08

8***

−0.06

9***

−0.05

1**

[0.034

][0.029

][0.026

][0.023

][0.034

][0.029

][0.026

][0.023

]Ethnicminorities

(yes

=1)

−0.42

2***

−0.26

7***

−0.40

6***

−0.262

***

−0.41

7***

−0.26

0***

−0.40

7***

−0.26

3***

[0.026

][0.022

][0.023

][0.020

][0.026

][0.022

][0.023

][0.020

]Malehead

(yes

=1)

0.01

5−0.04

0**

0.06

0***

−0.001

0.01

7−0.03

9**

0.06

0***

−0.00

1[0.017

][0.018

][0.013

][0.014

][0.017

][0.018

][0.013

][0.014

]Log

ofhead'sage

0.011

0.04

−0.02

40.01

0.011

0.04

1−0.02

40.01

[0.042

][0.034

][0.026

][0.023

][0.042

][0.034

][0.026

][0.023

]Headwith

prim

ary

scho

oldegree

0.12

8***

0.116*

**0.12

9***

0.116*

**[0.017

][0.013

][0.017

][0.013

]Headlower-

second

arydegree

0.22

2***

0.19

4***

0.22

3***

0.19

4***

[0.019

][0.015

][0.019

][0.015

]Headup

per-

second

arydegree

0.30

1***

0.24

8***

0.30

0***

0.24

9***

[0.026

][0.020

][0.026

][0.020

]Headtechnical

degree

0.36

4***

0.34

2***

0.36

0***

0.34

2***

[0.023

][0.018

][0.023

][0.018

]

(con

tinued)


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

Tab

leA5.

Con

tinued

Exp

lanatory

variables

Mod

el1:

OLS

Mod

el2:

OLS

Mod

el1:districtfixed

effectsregression

Mod

el2:districtfixed

effectsregression

Mod

el1:

IVmod

elMod

el2:

IVmod

elMod

el1:

districtfixed

effectswith

IVregression

Mod

el2:

districtfixed

effectswith

IVregression

Headpo

st-secon

dary

degree

0.55

9***

0.49

1***

0.55

5***

0.49

1***

[0.033

][0.026

][0.033

][0.026

]Spo

useno

education

degree

−0.08

3***

−0.082

***

−0.08

0***

−0.08

2***

[0.023

][0.017

][0.023

][0.017

]Spo

useprim

ary

scho

oldegree

−0.03

4−0

.036

**−0.03

2−0.03

6**

[0.023

][0.017

][0.023

][0.017

]Spo

uselower-

second

arydegree

−0.02

40.00

3−0.02

0.00

3[0.024

][0.018

][0.024

][0.018

]Spo

useup

per-

second

arydegree

0.07

9***

0.06

2***

0.08

0***

0.06

2***

[0.031

][0.023

][0.031

][0.023

]Spo

usetechnical

degree

0.09

9***

0.08

8***

0.10

0***

0.08

9***

[0.027

][0.023

][0.028

][0.023

]Spo

usepo

st-

second

arydegree

0.24

1***

0.20

5***

0.24

0***

0.20

6***

[0.040

][0.029

][0.040

][0.029

]Ann

ualcropland

(1000

0m

2)

0.02

8***

0.04

1***

0.02

9***

0.04

1***

[0.008

][0.006

][0.009

][0.006

]Perennialcrop

land

(1000

0m

2)

0.05

3***

0.04

7***

0.05

3***

0.04

7***

[0.010

][0.006

][0.010

][0.006

]Aqu

aculture

water

surface(10

000m

2)

0.02

3**

0.03

3***

0.02

4***

0.03

3***

[0.009

][0.009

][0.009

][0.009

]

Log

ofliv

ing

area

(m2)

0.23

1***

0.23

2***

0.23

1***

0.23

2***

[0.016

][0.010

][0.016

][0.010

]Sem

i-perm

anent

house

−0.15

8***

−0.106

***

−0.15

8***

−0.10

6***

[0.016

][0.012

][0.016

][0.012

]Tem

porary

house

−0.28

4***

−0.238

***

−0.28

4***

−0.23

8***

[0.023

][0.018

][0.023

][0.018

]Con

stant

9.18

7***

8.04

5***

9.30

6***

8.21

6***

9.20

4***

8.06

2***

9.30

2***

8.21

5***

[0.172

][0.157

][0.102

][0.100

][0.172

][0.157

][0.102

][0.100

]

Observatio

ns91

8991

8191

8991

8191

8991

8191

8991

81

R2

0.35

0.53

0.19

0.41

0.27

10.44

30.16

80.39

6No.

ofdistricts

643

643

643

643

Source:E

stim

ationfrom

the20

08VHLSS.

Notes:*

Significant

at10

%;*

*significant

at5%

;***

sign

ificant

at1%

.Figures

inbracketsareSEs.SEsarecorrectedforsamplingweigh

tsandclustercorrelation.


Dow

nloa

ded

by [

Que

ensl

and

Uni

vers

ity o

f T

echn

olog

y] a

t 19:

21 0

6 N

ovem

ber

2014

the impact of natural disasters on household income, expenditure, poverty and inequality: evidence...

Documents