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Background Paper
What are the Sources of Risk and How do people cope? Insights from household surveys in 16 countries Rasmus Heltberg, Ana María Oviedo & Faiyaz Talukdar The World Bank
What are the sources of risk and how do people cope? Insights
from household surveys in 16 countries
Rasmus Heltberg, Ana María Oviedo, and Faiyaz Talukdar
November 24, 2013
Abstract: We report on a major multi-country comparison of household surveys on shocks and coping.
Natural disasters, health shocks, economic shocks, and asset loss are the most commonly reported types
of shocks. People often cope using costly responses that increase their vulnerability to future shocks. We
conclude that household survey modules on shocks and coping largely fulfill their objective of providing
information on risk exposure yet do little to inform policy beyond providing broad diagnostics.
1. Risks are important, and can be researched and documented in many ways
People in developing countries are surrounded by risks of many kinds and large numbers of people
remain vulnerable amidst rapid growth and unprecedented reduction in extreme poverty. The literature
on risk and vulnerability has established that shocks from many sources strike frequently and hit hard,
causing loss of life, assets, and livelihoods. The literature has also established that the cost of risk
exceeds the impact of shocks, including also ex-ante adjustments people make in the knowledge that
there is risk (Morduch 1995, Kochar 1995, Ligon and Schechter 2003, Christiaensen & Subbarao 2005).
Risk management tools such as microfinance, social protection, and preventative health can both
mitigate poverty and serve as a springboard to enable pursuit of productive opportunities. However, the
best design of risk management tools and the best balance between different tools and policies has been
subject to debate and research for years (Ashraf, Karlan, and Yin 2006, Duflo, Dupas, Kremer and Sinei
2006, Banerjee, Cole, Duflo, and Linden 2007).
Hopes are high that understanding the more frequent and costly sources of risk, and documenting the
detrimental coping people are often forced to take, can underline appropriate policy responses. After all,
better data have often led to advances in the understanding of the most useful and desirable risk
management tools and policies. This hope has guided much research and data collection effort. National
statistical agencies, often supported by the World Bank and other agencies, have included modules on
shocks and coping in household surveys in a large number of countries. These surveys ask respondents
about types of shocks experienced by their household within a set reporting period (usually either one or
five years), how they responded to those shocks (consumed less, worked more, borrowed, migrated,
sought assistance, and so on), and sometimes what impacts resulted (loss of income or productive assets,
for example). Surveys have been collected in normal times and after major disasters (for example Carter
and others, 2007 after Hurricane Mitch in Honduras). Such surveys of self-reported shocks are fairly
simple and cost-effective to collect and particularly favored in social protection where they have been
used to inform several reports and strategies (the World Bank’s Africa Social Protection Strategy 2012-
2022 on Managing Risk and Promoting Growth is a recent example).
Self-reported shock data is not the only source of data on risks faced by people. The academic literature
on risk and vulnerability has often relied on “natural experiments” to identify the short and long-term
impacts of shocks, for example identifying life-long health and income consequences for children born
during severe droughts and other major systemic shocks (Hoddinott and Kinsey 2001). Another strand
of the literature, starting with Townsend (1994 and 1995), has focused on informal insurance
mechanisms used by communities to smooth consumption, often relying on panel data. This literature
sheds light on the extent to which idiosyncratic risks are shared within communities, finding that
households are partially able to reduce income variability, either through informal credit from other
members of the community (Kochar 1995), or by establishing risk-sharing networks with other
community members who are not faced with the same set of shocks (Ligon 2002). Determining causal
chains and patterns of behavior are key strengths of the academic literature. Since much of this research
draws on data from rural areas, and sometimes focused on specific events such as war, epidemic, or
drought, it often does not permit the broad comparative diagnostic of risk from all sources and covering
both rural and urban areas that ideally would inform policy.
In this context, the appeal of self-reported shock data is that it allows for an empirical comparison of the
frequency with which various types of shocks occur, thereby infusing a sense of the priority of various
risks into policy discussions. There are many types of risk: idiosyncratic shocks such as health shocks,
family breakup, and some loss of employment affect households in isolated incidents and are not
simultaneously experienced by other members of the community. Systemic (or covariate) shocks such as
drought, flood, price shocks, and recession simultaneously affect all or many households in the
community. Shocks and coping modules can document the perceived occurrence of all shock types and
the many and varied ways in which households respond to these shocks.
The literature on risk and vulnerability has debated the relative importance of different sources of risk in
the lives of poor and vulnerable people in developing countries. Some authors argue that idiosyncratic
shocks are by far the most frequent and costly to people (Deaton 1997, Gertler and Gruber 2002, Udry
and Kazianga 2006). Other authors are somewhat dismissive of idiosyncratic risk, arguing that although
such shocks are fairly common, informal insurance mechanisms often allow people to manage them
relatively well, unlike systemic (or covariate) risk, for which informal mechanisms are often inadequate
(Kochar 1995, Dercon 2002, Gunther and Harttgen 2009, Hoogeveen, van der Klaauw and van Lomwel
2011). This debate has important implications for social protection and other policies aiming to assist
households avoid costly coping that damage their long term livelihoods and human capital. In the first
view, policy should focus on addressing idiosyncratic risk, for example via health insurance and long-
term social protection for the chronically poor. In the second view, there is a more acute need for policy
response to systemic risk, for example via rainfall insurance and social protection that is more
responsive to crises and natural disasters.
We report in this paper on an analysis of surveys from 16 countries in all developing regions. The
analysis was undertaken as part of preparing the World Development Report 2014 on Risk and
Opportunity: Managing Risk for Development. Although we cover 16 surveys, only 15 of them have a
shock module and only 15 of them contain questions on coping. We report major cross-country findings
emerging from this analysis on the extent of household exposure to different types of idiosyncratic and
systemic shocks, and the coping responses used by households.
We conclude that both idiosyncratic and systemic sources of risk are frequent and potentially
impoverishing. Health shocks are a universal problem, while systemic shocks—drought and other
disaster events in particular—tend to be more pronounced in rural than in urban areas. People affected
by shocks commonly respond with a mix of consuming fewer food and nonfood items, working more,
seeking credit and assistance from formal and informal sources, and relying on savings and sales of
assets.
Reflecting on the analysis, we also conclude that these survey modules largely fulfill their objective of
providing information on shocks and coping responses, although there is room to improve and
standardize survey instruments. Yet beyond providing broad diagnostics, the information culled from
these surveys largely disappoint the aspiration to inform policy. This is mostly because these surveys
shed so little light on the obstacles to risk management, that is, the reasons that people and societies
often fail to take commonsense precautions in the face of known threats. Without major innovation in
this area, shock and coping surveys will likely remain more useful for broad diagnostics than for specific
policy recommendations.
2. Data
We review and analyze household survey data from Afghanistan, Bangladesh, China, Iraq, Laos,
Malawi, Maldives, Mexico, Nigeria, Peru, Sudan, Tajikistan, Tanzania, Uganda, Uzbekistan and
Vietnam. Surveys comprise a mix of Living Standard Measurement Surveys (LSMS), budget surveys,
and special-purpose surveys designed for social protection analysis. The main selection criterion was the
availability of a module asking respondents to list shocks they have experienced.
To facilitate comparison across surveys, we constructed aggregate categories of shock types (health,
employment, price shocks and so on) and of coping responses (work more, consume less, and so on).1
The surveys were:
Afghanistan National Risk and Vulnerability Survey (2005): The sample size is 30,822
households, taken from the 2003 FAO Livestock Census using a systematic sampling with a
random start. The sample is nationally representative and the module on shocks and coping has a
recall period of 1 year.
Bangladesh Welfare Monitoring Survey (2009): The sample size is 14,000 households, drawn
from the 2001 Population Census using a two-stage cluster design. The sample is nationally
representative and the module on shocks and coping has a recall period of 1 year.
China Rural Social Protection Survey (2005): A sample of 6,165 households from the Fujian,
Gansu, Guangxi and Zheijiang provinces were surveyed using a multi-stage sampling design.
The sample is only representative of the 4 provinces it was drawn from, and the module on
shocks and coping has a recall period of 1 year.
Iraq Household Socio-Economic Survey (2006/07): A sample of 18,144 households was drawn
from the 1987 Iraq Census using a two-stage sampling design. The sample is nationally
representative and the module on shocks and coping has a recall period of 1 year.
Lao PDR Vulnerability and Shocks Survey (2008): A sample of 600 households from the
Attapeu, Phongsaly and Viantiane provinces were surveyed using a multi-stage sampling design.
The sample is only representative of the 3 provinces it was drawn from, and the module on
shocks and coping has a recall period of 1 year.
1 Annex A has a detailed description of how the categories were constructed and of survey design and questionnaire quality.
Malawi Third Integrated Household Survey (2010/11): A sample of 12,271 households was
drawn from the 2008 Malawi Population and Housing Census using a random systematic
stratified two-stage sample design. The sample is nationally representative, and the module on
shocks and coping has a recall period of 1 year.
Maldives Vulnerability and Poverty Survey (2004): A total of 2,840 households from 200
inhabited islands were surveyed using a systematic sampling with a random start. The sample is
nationally representative, and the module on shocks and coping has a recall period of 5 years.
Mexico Family Life Survey (2002): A sample of 8,440 households was surveyed using the
sampling framework from the 2002 Mexican National Employment survey. The sample is
nationally representative and the module on shocks and coping has a recall period of 5 years.
Nigeria General Household Survey 2010/11: A sample of 4,986 households from the 2006
Housing and Population Census was surveyed using a two-stage probabilistic sampling design.
The sample is nationally representative and the module on shocks and coping has a recall period
of 5 years.
Peru Encuesta Nacional de Hogares Sobre Condiciones de Vida y Pobreza (2011): A sample of
26,456 households was selected from the 2007 Population and Housing Census using a stratified
three-stage sampling design. The sample is nationally representative, and the module on shocks
and coping has a recall period of 1 year.
Sudan National Baseline Household Survey (2009): The sample size is 7,920 households, who
were selected from the 2008 Population and Housing Census using a two-staged stratified
sampling design. The sample is nationally representative and the module on shocks and coping
has a recall period of 5 years.
Tajikistan Living Standards Measurement Survey (2009): A sample of 1,500 households was
surveyed using the sampling framework from the 2005 Tajikistan Multiple Indicator Cluster
Survey. The sample is nationally representative. Contrary to the other surveys, it does not
contain a shock module. It does, however, contain a module on coping (with unspecified shocks),
with a recall period of 1 year.
Tanzania National Panel Survey (2010/11): Some 3,924 households from the 2002 Population
Census were surveyed using a multi-stage clustered sample design. The sample is nationally
representative. It does not contain a coping module; its (quite informative) module on shocks has
a recall period of 5 years.
Uganda National Household Survey (2009/10): A total of 6,800 households from the 2002
Population and Housing Census were surveyed using a two-stage stratified sampling design. The
sample is nationally representative, and the module on shocks and coping has a recall period of 1
year.
Uzbekistan Regional Panel Survey (2005): A sample of 2,948 households was drawn from a
countrywide population review conducted in 2002 using a three-stage stratified sampling design.
The sample is nationally representative, and the module on shocks and coping has a recall period
of 1 year.
Vietnam Household Living Standard Survey (2008): Drawn from the 1999 Population and
Housing Census, 45,945 households were surveyed using a multi-staged sampling design. The
sample is nationally representative, and the module on shocks and coping has a recall period of 1
year.
Table 1 presents a summary of the types of information on shocks and coping collected by each survey.
The design of the shock module varies significantly across countries.
Table 1: Typology of questions asked in shock and coping modules
Experienced
shock
Shock
timing
Multiple
shocks
Costs
(type of loss)
Costs
(currency)
Did others
experience
it
Severity Individual
who was
affected
Has the
household
recovered
Coping
type
Coping
ranking
Afghanistan x
x
x x
Bangladesh x
x
China x
x x
x x
Iraq x
x
Lao PDR x x
x x
x x x
Malawi x
x x
x
x x
Maldives x x x x x
x
Mexico x x x
x x
Nigeria x x x
x x x
Peru x
x x
x x
Sudan x
x
x
x
Tajikistan
x
Tanzania x x
x x x x
Uganda x x
x x
x
Uzbekistan x
x
x x x
Vietnam x x x x x x
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3. Frequency and magnitude of shocks
The share of households reporting any shock varies significantly across countries; in some cases defying
explanation. Table 2 reports the percentage of households that reported experiencing a shock in any of
the eight broad categories created for comparative purposes. Disasters, asset losses, and health shocks
appear to be the most commonly reported shocks, which is consistent with most of the literature on risk.
However, the percentage of households reporting shocks seems surprisingly low in fragile states like
Afghanistan and Iraq (particularly in the category of crime and safety). In as Bangladesh, the
questionnaire design omitted disasters. A similar observation can be made from looking at Table 3,
which groups all answers about different shocks into indicators of whether the household experienced a
single shock or multiple shocks over the recall period. There is large variation in the percentage of
households reporting any shock across countries. And although surveys with 5 year recall tend to show a
higher incidence of shocks than surveys with one year recall, that difference is lower than expected. It
seems clear that not only objective variation in risk levels, but also survey design, survey
implementation, and respondents’ subjective interpretation of shocks affect the observed cross-country
patterns in Table 2 and 3.
Table 2: Percentage of all households reporting the following shocks
COUNTRY Recall
Period
Prices
(inputs,
outputs,
food)
Disasters
(natural)
Employment
(jobs, wages)
Health
(death,
illness)
Asset and
crop loss
(house,
land,
livestock)
Household
breakup
Crime &
safety Other
Afghanistan 1 year 2.2 34.2 4.4 11.5 15.8 - 4.9 -
Bangladesh 1 year - - 2.8 4.9 4.1 2.5 0.7 4.3
China 1 year - - 0.6 11.1 25.2 2.6 1.9 3.1
Iraq 1 year - - 10.1 2.0 - - 8.1 2.7
Lao PDR 1 year 5.3 18.5 5.0 25.2 23.5 0.6 3.5 -
Malawi 1 year 32.8 38.8 3.1 13.9 2.3 7.2 8.5 3.1
Peru 1 year - 6.7 4.2 8.8 - 0.8 3.3 1.7
Uganda 1 year 1.5 32.4 1.4 11.4 0.6 - 13.9 2.6
Uzbekistan 1 year - - 38.6 20.1 3.5 9.8 4.6 7.8
Maldives 5 years - - 0.9 14.5 1.6 0.8 - -
Mexico 5 years - 1.4 8.0 13.4 4.7 - 1.4 -
Nigeria 5 years 6.5 5.5 3.9 13.7 4.1 0.7 2.8 1.0
Sudan 5 years - 33.7 - 25.7 32.7 - 5.6 2.4
Tanzania 5 years 43.5 34.2 4.0 46.4 7.7 18.4 15.9 3.4
Vietnam 5 years 39.5 29.3 2.2 18.3 9.2 1.7 2.5 2.4
Note: Tajikistan (2009) does not ask questions on shocks.
Note: Countries are grouped by the survey recall period, where the first group has a recall period of 1 year while the latter group has a
recall period of 5 years.
However, looking within countries, rural households report more shocks than do urban ones: they are
more likely to report having experienced at least one shock than urban households, especially in the
lowest quintile (true in 9 countries out of 15 total for which we have shock data). Rural households also
report a higher number of shocks on average (with the exception of Maldives and Uzbekistan). The
8
urban-rural difference is particularly strong in Afghanistan, Lao PDR and Malawi. It is safe to conclude
that rural areas and rural livelihoods tend to be more risky than urban ones.
Table 3: Percentage of surveyed households reporting the incidence of single/multiple distinct shocks over the
specified recall period
COUNTRY Recall
Period
Experienced a
single shock
Experienced
multiple shocks
Mean number of
shocks reported
during recall period
T-statistic of the
difference in
means across
region
Urban Rural Urban Rural Urban Rural
Afghanistan 1 year 8.3 9.8 8.1 39.2 0.3 1.4 (-)52.6*
Bangladesh 1 year 11.1 11.5 2.9 4.4 0.2 0.2 (-)3.4*
China 1 year - 25.9 - 11.7 - 0.5 -
Iraq 1 year 9.0 8.2 8.0 6.8 0.3 0.3 3.7*
Lao PDR 1 year 22.5 36.0 11.9 36.1 0.5 1.2 (-)6.6*
Malawi 1 year 27.3 26.4 12.7 40.4 0.6 1.5 (-)37.5*
Peru 1 year 19.3 32.6 1.4 1.9 0.2 0.4 (-)14.6*
Uganda 1 year 24.1 40.6 5.6 15.6 0.4 0.8 (-)13.0*
Uzbekistan 1 year 29.8 27.7 20.9 17.7 0.9 0.4 9.1*
Maldives 5 years 19.0 16.9 4.6 1.1 0.3 0.2 4.1*
Mexico 5 years 22.4 22.2 6.7 9.4 0.4 0.5 (-)4.2*
Nigeria 5 years 17.9 18.3 5.8 12.0 0.3 0.5 (-)6.1*
Sudan 5 years 32.2 29.7 16.5 40.3 0.8 1.4 (-)19.56*
Tanzania 5 years 31.1 27.8 52.3 54.5 2.1 2.2 (-)1.02
Vietnam 5 years 34.9 27.2 24.8 39.3 1.0 1.5 (-)13.7*
Note: Tajikistan (2009) does not ask questions about shocks.
Note: The t-statistic represents the difference in mean number of shocks experienced by urban/rural regions. * denotes significance at the
1% level.
The Tanzania survey asked people whether a given shock was also experienced by other members of the
community. This permits an assessment of how covariate different shock types are (Figure 1). The
results confirm our priors: food price hikes, water scarcity, droughts, floods, and crop disease are mostly
covariate; death, illness, crime, household breakup, and business failures are mostly idiosyncratic.
However, the results also indicate that, in practice, the distinction between covariate and idiosyncratic
risk is rather graduated: most types of agricultural risk, for example, tend to affect the entire village, but
sometimes they affect many farmers, and occasionally just a single farmer (Christiaensen and Sarris
2007). Overall, six of the seven most commonly reported shocks are mostly covariate, reflecting the
agricultural nature of life in Tanzania.
9
Figure 1: Sources of shocks in Tanzania
Source: Authors based on data from the Tanzania National Panel Survey 2010/2011.
We group survey responses into broad categories that permit us to compare across countries. Price
shocks comprise input, output, and food price shocks. Disasters include drought, water scarcity for
various reasons, flood, crop disease, storms, and more. Employment shocks comprise reduced earnings
and wages and loss of job. Asset shocks denote loss of land, house, livestock, and machinery for various
reasons (of which livestock disease is one of the most common). Health shocks comprise death, illness,
accidents, and disability, while crime and safety comprise common theft and violence of all kinds.
Household breakup includes separations but also incidents involving the police and other authorities.
Table 4 shows how the incidence of these major shock types vary across countries and between rural
and urban areas.
Natural disasters, health shocks, economic shocks, and asset loss are the most commonly reported types
of shocks across countries, and can often result in the loss of life, health, property and livelihoods.
Natural disasters such as drought, water scarcity, and flooding are among the most frequently reported
type of shock in all countries for which we have data. These shocks are often the single most common
risk in rural areas. In Lao PDR, Malawi and Vietnam, almost half of the poorest rural population suffers
from disasters. Drought is the most common type of natural disaster, with flooding and crop disease also
important. Although more prevalent in rural areas, they remain high risks in urban areas as well. Where
surveys (Malawi, Nigeria and Tanzania) ask respondents to rank shocks by severity, rural households in
0 10 20 30 40 50 60
Loss of salaried employment
Loss of own land
Household business failure
Household breakup
Illness/accident of a working member
Crime
Death of a household member
Livestock death or theft
Large rise in agricultural input prices
Large fall in crop sale prices
Crop disease or pests
Drought or floods
Insufficient water
Death of an extended family member
Large rise in food prices
% of households that have experienced each type of shock in the past 5 years
Shock was mostly systemic (affected all/most other households in the community) Shock was idiosyncratic in most cases
39
12
11
27
24
30
27
34
22
8
5
3
8
5
58
10
particular tend to rank disasters highly. In rural Malawi, for example, nearly three-fifths of all
households rank disasters as the most severe shock experienced.
Death, illness, and accidents are another major risk category that ranks high in all countries with shock
data. Health shocks is the most commonly reported shock type in Maldives, Mexico, and Nigeria;
second only to natural disasters in rural India and in Peru and Uganda; and second to asset loss in rural
China. Health shocks are ranked as severe in Malawi and Tanzania, where they are the most severe
shock for more than half of the households who report them.
Price shocks are also very common: they are the most commonly reported type of shock in Tanzania and
Vietnam, and the second-most common in Malawi. This is hardly surprising since several of these
surveys were conducted during the height of the food price crises (Vietnam during the 2008 crisis and
Malawi, Peru, Nigeria and Tanzania during the 2nd
crisis in 2010/11); these numbers confirm other
studies finding widespread impacts of the food price crisis on both rural and urban households,
particularly in Africa (Heltberg, Hossain and Reva 2012; World Bank 2011).
Compared to price shocks, far fewer households report loss of employment as a major shock, perhaps
because there is so little regular employment to begin with. Employment shocks are more commonly
reported in urban areas, probably because that is where paid jobs are to be found. Loss of assets is the
fourth major type of shock looking across the countries for which we have data. A wide variety of risks
can threaten a household’s livelihood through the loss of productive assets (farm equipment, livestock or
stored harvest) and of durable goods. Asset loss is almost always more common in rural than in urban
areas, and it is the most common type of shock in rural China, Lao PDR, Mexico, and Sudan.
Other types of shocks are important only in specific countries. Crime and violence, including common
theft, are fairly common in some places such as Iraq, rural Afghanistan, and the African countries, and
relatively minor in most other places. Of course, this frequency data do not show the impact of shocks,
and there is reason to believe that crime and, in particular, collective forms of violence may have deeper
repercussions on trust, social cohesion, and business climate than many other types of risk (Petesch
2013). Household issues is a residual category that includes divorce or separation, dowry and marriage
payments, legal issues, getting arrested, and having trouble with the police. In a few places such as
Tanzania and Uzbekistan, these numbers are relatively high.
Looking across our sample, we conclude that disasters, health, and price shocks and asset losses clearly
stand out as the major types of shocks. However, in order to understand the nature of exposure that
affects the household’s reporting rate, we turn to regression analysis. We use ordinary least squares
regressions to link shock reporting rates to key household characteristics, namely: the size of the
household, the gender, occupation and education level of the household head, share of working
household members, region (rural or urban), and economic conditions measured by quintiles of per
capita consumption. We are interested in how household’s internal conditions are related to the
household’s likelihood to report shocks. We run OLS instead of a binary outcome model (such as probit)
or a latent variable model (such as multinomial probit) in order to minimize the number of imposed
assumptions, particularly on the distribution of the error term. By merely reporting correlates, OLS
provides coefficients that are more robust to specification errors.
11
We use the following model:
where ,our dependent variable, is a dummy that captures the type of shock experienced by the
household, and depends on a set of household and household head characteristics , and district-level
fixed effects . Unobservables are grouped into the error term . We run the regressions on the entire
sample of survey respondents in each country.
Table 4: Most common shocks for rural and urban households
Type of household most likely to report experiencing shock
Country Price shocks Disasters Employment
shocks
Asset/crop
losses Illness/death Safety
Household
breakup
Afghanistan Rural Rural
Rural Rural Rural
Bangladesh
Rural
Urban
Iraq
Urban
Rural
Lao PDR
Rural Urban
Malawi Rural Rural Rural Rural Rural Urban Rural
Maldives
Urban Urban
Urban
Mexico
Rural Urban Rural Urban Rural
Nigeria
Rural
Rural
Peru
Rural Urban
Urban
Sudan
Rural
Rural Rural Urban
Tanzania Rural Rural Urban Rural Urban Urban Urban
Uganda
Rural
Uzbekistan
Rural
Urban Rural
Vietnam Rural Urban Rural
Regressing a host of covariates pertaining to household (and individual level) characteristics on shock
incidence yields some consistent results across countries; the main (significant) results are listed below
and the full set of results are attached in annex B.
Household size is positively correlated with shock reporting rates across the board, as larger
households are exposed to more shocks from multiple dimensions. This is largely the case across
the typology of shocks considered with the exception of crime and safety shocks (which were
negatively or not correlated with household size for most countries).
Female headed households are more likely to report a shock, particularly in the case of health
shocks and natural disasters (Mexico is an exception).
Households whose heads are employed in agriculture report more shocks on average as agrarian
households are often exposed to or affected by a larger set of shocks than their urban
counterparts, particularly when it comes to price of inputs, outputs or staple food items, and
natural hazards. Employment shocks, on the other hand, are more frequently reported by
12
households whose heads are employed in non-agricultural sectors in some survey countries such
as Afghanistan and Uzbekistan.
The education level of the household head is negatively correlated with shock reporting rates,
particularly when dealing with asset or crop loss, in some countries.
Rural households report more systemic shocks. (there is no clear pattern for idiosyncratic
shocks).
Dwelling characteristics, particularly relating to the quality of the house, are often associated
with idiosyncratic shocks.
4. Households use a wide variety of coping responses, often not very appealing ones
Households cope with shocks in many ways, some more effective than others. Our study of coping
responses is made complicated by the fact that response categories were not uniform. We use two
different analytical strategies to achieve a degree of comparability across countries. First, we group
coping responses into comparable functional categories based on what households did, shown in Figure
2 for the countries with the most directly comparable data. The figure makes it clear both that there is
huge variation in surveys’ response categories, and that people affected by shocks commonly respond
with a mix of consuming fewer food and nonfood items, working more, seeking credit and assistance
from both formal and informal sources, and relying on savings and sales of assets.
Figure 2 Responses to shocks
Source: Authors based on data from household surveys, various years 2004–11.
0% 20% 40% 60% 80% 100%
Nigeria
Sudan
Maldives
Iraq
Afghanistan
Uzbekistan
Tajikistan
Uganda
Malawi
% of all coping responses when faced with a shock
Informal credit and assistance Formal credit and assistance Consumption reduction
Savings and sale of assets Employment or migration
13
Second, we distinguish ‘good’ and ‘bad’ forms of coping. ‘Good’ coping comprises use of savings,
credit, asset sales, additional employment and migration, and assistance (for example, from friends,
family, community members, and social safety nets); it often requires a degree of ex-ante preparation.
‘Bad’ coping comprise responses that may increase vulnerability to future shocks and include
compromising health and education expenses, productive asset sales, and consumption reductions; as
already noted, the rationale for anti-vulnerability policies such as social protection, disaster risk
reduction, and micro-insurance often center on avoiding ‘bad’ coping and the associated perpetuation of
poverty. This classification is shown in Table 5.
Using savings is a common coping strategy. Savings, as argued in the 2014 World Development Report,
is a key component of risk preparation, acting as an instrument for absorbing some of the losses
associated with hard-hitting negative shocks and reducing a household’s reliance on costly coping. Rural
households generally report a higher reliance on sales of non-productive assets such as furniture, basic
appliances and durable items than urban households do; this is most noticeable in the case of Bangladesh
and Uganda where the rural reporting rate is four times as high as with urban households. Migration is
also a common form of coping in some countries, particularly amongst rural households; this is
consistent with migratory patterns reported in most of the migration literature (Fields 1975, Stark and
Bloom 1985, Lucas 1997. In rural China and Tajikistan, every other household reports having a family
member who has had to take on additional employment after a shock, primarily through migration. The
emergence of micro-finance has paved the way for greater access to both formal and informal credit,
particularly in parts of Asia and Africa; in Bangladesh, 3 out of every 4 households hit by a shock used
credit to cope; in rural Iraq, half did so. Informal assistance, both monetary and non-monetary, from
friends, relatives, and neighbors is also common, especially in some of the African and East European
countries.
Turning to ‘bad’ (or costly) coping, reductions in food and non-food consumption are common. Some
households also rely on sales of productive assets, which often reduce future income earnings for
agricultural households; we see this particularly in the case of rural Afghanistan, Bangladesh, Nigeria,
Sudan and Tajikistan. In contrast, reductions in household expenditure on health and education are
relatively uncommon in most countries, with the exception of Tajikistan.
14
Table 5: Coping responses
Percentage of households reporting responses conditional on having experienced a shock
COUNTRY Savings
Sale of
non-
productive
goods
Employment
or migration
related
Credit Assistance
Reduction in
health and
education
spending
Reduction in
food
consumption
Reduction in
non-food
consumption
Sale of
productive
assets
Other
Afghanistan 20.9 2.4 10.1 26.1 9.6 2.8 35.3 37.9 14.9 4.3
Bangladesh - 4.0 - 77.3 - - - - 13.0 2.4
China 20.4 3.0 51.0 - - 5.5 56.3 - 10.2 -
Iraq 48.4 13.9 3.1 42.3 20.6 3.0 60.2 74.5 2.9 2.6
Lao PDR 18.4 0.7 - 2.2 4.4 - 43.8 0.6 29.8
Malawi 24.6 1.6 4.8 1.8 25.4 - 4.4 0.5 1.2 8.9
Maldives 31.1 0.7 23.0 25.2 9.7 - - - 0.9 6.6
Mexico 38.7 7.0 16.8 25.9 2.6 - 3.3 - 1.2
Nigeria 3.9 8.8 16.1 36.5 33.3 2.9 21.3 12.9 19.9 8.6
Peru 17.2 3.7 9.3 16.4 0.9 - 11.9 - 10.6
Sudan 14.3 10.4 20.4 15.2 23.6 0.6 4.2 2.8 13.7 -
Tajikistan 9.2 3.8 47.9 25.5 34.1 16.9 28.9 59.7 23.6 23.3
Uganda 33.2 3.0 23.5 12.0 21.7 - 38.7 0.5 4.7 34.4
Uzbekistan 24.8 - 14.1 26.1 15.5 0.5 0.7 0.7 13.7 52.5
Vietnam 38.3 0.6 - 12.1 20.3 - 35.2 2.7 -
Note: Tanzania (2011) has no information on coping mechanisms.
Note: We cannot distinguish between reductions in food and nonfood consumption for Lao PDR, Mexico, Peru and Vietnam.
15
We use ordinary least squares regressions to link good and bad coping to the type of shock reported and
key household characteristics, namely: the size of the household, the gender, occupation and education
level of the household head, share of working household members, region (rural or urban), and
consumption quintiles. We are interested in how households’ internal conditions affect their ability to
cope with shocks. We run OLS instead of a binary outcome model (such as probit) or a latent variable
model (such as multinomial probit) in order to minimize the number of imposed assumptions,
particularly on the distribution of the error term. Again, by merely reporting correlates, OLS provides
coefficients that are more robust to specification errors.
We use the following model:
where ,our dependent variable, is a dummy that captures the type of coping strategy used by the
household, and depends on the occurrence of any shock , a set of household and household head
characteristics , and district-level fixed effects . Unobservables are grouped into the error term . We
run the regressions on the set of households who have experienced at least one shock.
Type of household most likely to report coping strategy
Country
Use
savings/credit/
assets
Work
more/
migrate
Assistance
(government/family/community/NGOs)
Sell
productive
assets
Reduce
consumption
quantity/quality
Afghanistan Richer Richer Richer Poorer Poorer
China
Richer
Richer Richer
Iraq Richer Richer
Poorer
Malawi Richer
Mexico
Poorer
(credit/asset
sales) Poorer
Nigeria Richer
Peru Richer
Sudan Richer Poorer Poorer Poorer Poorer
Tajikistan
Richer Poorer
Uganda
Richer
(savings/sell
assets), poorer
(credit)
Richer
Poorer
Uzbekistan Poorer (credit) Poorer
Poorer
Vietnam
Richer
(savings),
poorer (credit)
Poorer Richer
16
The regression results for each survey country are attached in annex B, while the main results of note
(addressing, where relevant, the relationship between the utilized form of coping and each covariate) are
discussed in the following.
Gender of the household head: There is no strong cross-country pattern of female-headed
households using more costly coping strategies. In Afghanistan, for example, female-headed
households are more likely to use savings and sell durable goods in response to shocks; whereas
in Malawi they are more likely to increase their labor supply, sell productive assets or reduce
food consumption. In Uganda, they are more likely to resort to credit; while in Uzbekistan they
are less likely to do so (they also report using their savings and labor supply relatively more).
Occupation of the household head: The sale of productive assets is reported as a significant
coping strategy for farming households in 5 out of 12 countries. In Sudan, Uganda and Vietnam,
these households also report using their savings; while in Uganda, Uzbekistan and Vietnam they
also report having access to assistance (they are less likely to report using assistance in
Afghanistan and Nigeria). Despite the strong correlation between the location of the household
(urban or rural) and its economic activity (farming vs. other), there are some differences in the
reported coping strategies in a few countries. For instance, increasing labor supply is a common
coping strategy for rural households (typically through migration to urban areas) in 5 countries,
but in Afghanistan, Peru, and Sudan farming households are less likely to increase their labor
supply.
Household size: Larger households are more likely to rely on ‘good’ forms of coping (mostly
using savings or selling assets, and in some cases obtaining credit). In Afghanistan, Iraq and
Vietnam, however, these households are more likely to sell productive assets; while in Mexico
and Uzbekistan they are less likely to receive assistance.
Wealth: Richer households generally are more likely to use ‘good’ coping practices, although
contrasting results are observed in the case of credit (Mexico, Uzbekistan and Vietnam). Richer
households in Afghanistan, China and Iraq; and poorer households in Sudan and Uzbekistan are
more likely to increase their labor supply either by migrating or by increasing the hours worked.
Poorer households (except in China and Vietnam) are more likely to reduce the quantity and
quality of consumption. In Afghanistan and Uganda richer households are more likely to rely on
assistance from friends, family, the community, and NGOs, but this is true for poorer households
in Sudan and Vietnam. The sale of productive assets is more likely for richer households in
China and Tajikistan, and poorer households Sudan and Uzbekistan.
We conduct additional analysis for a subset of survey countries (Malawi, Nigeria and Uganda), where
we measure the conditional correlates between shocks, coping responses, and access to safety nets,
preventive measures, and financial services. These include receipt of conditional cash or food transfers,
income generating schemes and public loan schemes, access to bednets and preventive healthcare
services, and access to informal and formal credit and savings arrangements. We believe that these
indicators are suitable proxies for an enabling environment to conduct efficient risk management.
However, an important caveat is that correlates between these indicators and either shocks or coping
mechanisms cannot reveal whether such an environment causes risk management to improve or not (and
with no counterfactual such assessment is not possible). Rather, these estimates should be interpreted as
purely descriptive. The results are discussed below:
Access to safety nets: In Malawi and in Nigeria households that benefit from access to safety
nets are less likely to report increasing their labor supply. In addition, in Nigeria these
17
households are less likely to report using their savings, and reducing consumption and human
capital investment.
Access to preventive measures (access to bednets and preventive healthcare): In Malawi,
households that report using bednets also report fewer health shocks, whereas the opposite is
true in Nigeria and Uganda.
Access to financial services: In Uganda, households with access to financial services are more
likely to report greater use of credit and assistance.
5. Conclusions and recommendations for improved survey instruments
The analysis above has elucidated the types of shocks that people experience and how they respond. We
found that natural disasters, health shocks, economic shocks, and asset loss are the most commonly
reported types of shocks and often result in ‘bad’ coping responses that perpetuate vulnerability. On the
whole, we conclude that the self-reported survey modules on risk fulfill their purpose of providing
relevant information on shocks and coping. Yet we also have to conclude that the surveys leave room for
improvement and are somewhat disappointing from a policy perspective: little if any detailed insight on
how to conduct anti-vulnerability policy can be derived from these results.
An obvious room for improvement would be to set up a broadly harmonized format for shock and
coping modules to use across countries. This would help generate more complete and more comparable
data. In countries lacking panel data, suitably improved shock modules can be a strong second best for
poverty and vulnerability diagnostics. A good practice would be to ask which household members were
subject to each shock. The Vietnam and Lao PDR surveys did this by explicitly asking about shock
consequence for individual household members. Another small improvement would be to better match
the survey recall periods across modules so that, for example, information on health care and social
protection can be linked to shocks and coping. Further, more detail on post-shock credit and assistance
would be helpful. Only a handful of surveys clarify whether the source of credit used to cope was formal
or informal, a crucial distinction from a policy perspective. Sources of credit and assistance used to cope
should be included in future surveys. Likewise, access to and timing and usefulness of government and
NGO support post-shock would be useful information. Although safety nets are captured in most
household surveys, greater emphasis could be placed on how they help in coping with shocks.
However, the main problem comes from what was not measured in the surveys, and sometimes may not
even be measurable. Key details not contained in the surveys include shock frequency, damage caused,
and coping costs incurred. This information is measurable in principle, but may be hard to assess given
weak precision in respondents’ recall. Further, the surveys contain nothing on preventive measures and
risk preparation: what families did to avoid risk, such as engaging in low risk-low return livelihood
strategies. The full cost of risk is the sum of the cost of risk avoidance and the cost of coping with
shocks. We did not see a survey that attempted to assess all of these costs, nor do we know how to do so.
Actions of risk avoidance are hard to ask about in these types of surveys, and may not always be
measurable.
To address risk, preparation has to improve (World Bank 2013). Policies to reduce vulnerability need to
be rooted in an understanding not only of key risks facing the poor and near-poor, but also of the
constraints and obstacles to better risk management. These constraints operate at the level of individuals,
households, communities, enterprises, and government (World Bank 2013). The surveys offer no real
18
insights on this—and in some cases could not. For example, a stable well-paid job is ultimately the best
source of financial protection, yet household surveys are neither designed, nor able to, shed light on the
factors impeding job creation. We would argue that information on shocks and coping have served at
best to provide broad policy recommendations, for example that health shocks and natural disasters are
impoverishing and need to be addressed. They offer little on the specifics of how countries might
achieve this.
Reflecting on the apparent paradox that reported data on shocks and coping largely fulfill the intended
objective of providing a broad information base on risks and its costs but largely disappoint the
aspiration to inform policy beyond broad generalities leads us to discuss what additional pieces of
information might help to move policy forward. We contend that the nature of the public response
depends not only on the type of risk, but also on how well individuals, families, and communities
manage risks on their own and the reasons they sometimes fail to manage them. As argued by the WDR
2014, it is frequently the case that risks are fairly well-understood and that simple cost-effective steps to
address them are available and known. Yet people, families, communities, and societies often fail to
enact risk management. Seen this way, the key bottleneck for better policy design may be less about risk
information than with understanding the constraints and obstacles to better risk management, that is, the
behavioral, cognitive, social, and political reasons for apathy in the face of risk.
Therefore, we recommend greater attention to assessing people’s knowledge about risk and risk
preparation, and obstacles to risk management. Such research may well go beyond routine household
surveys and require specialized instruments and a combination of quantitative and qualitative methods.
19
References
Ashraf, Nava, Dean Karlan, and Wesley Yin. 2006. “Tying Odysseus to the Mast: Evidence from a
Commitment Savings Product in the Philippines.” Quarterly Journal of Economics 121 (2): 635-
72.
Banerjee, Abhijit V., Shawn Cole, Esther Duflo, and Leigh Linden. 2007. “Remedying Education:
Evidence from Two Randomized Experiments in India.” Quarterly Journal of Economics 122
(3): 1235-64.
Bertrand, Marianne, and Sendhil Mullainathan. 2001. “Do People Mean What They Say? Implications
for Subjective Survey Data.” American Economic Review 91 (2): 67-72.
Carter, Michael R., Peter D. Little, Tewodaj Mogues, and Workneh Negatu. 2007. “Poverty Traps and
Natural Disasters in Ethiopia and Honduras.” World Development 35 (5): 835-56.
Christiaensen, Luc J., and Alexander Sarris. 2007. “Rural Household Vulnerability and Insurance
against Commodity Risks : Evidence from the United Republic of Tanzania.” FAO commodities
and trade technical paper 10, Food and Agriculture Organization of the United Nations, Trade
and Markets Division, Rome.
Christiaensen, Luc J., and Kalinidhi Subbarao. 2005. “Towards an Understanding of Household
Vulnerability in Rural Kenya.” Journal of African Economies 14 (4): 520-58.
Deaton, Angus. 1997. The Analysis of Household Surveys : A Microeconometric Approach to
Development Policy. Baltimore, MD: Johns Hopkins University Press.
Dercon, Stephan. 2002. “Income Risk, Coping Strategies, and Safety Nets.” World Bank Research
Observer 17 (2): 141-66.
Duflo, Esther, Pascaline Dupas, Michael Kremer, and Samuel Sinei. 2006. “Education and Hiv/Aids
Prevention : Evidence from a Randomized Evaluation in Western Kenya.” World Bank Policy
Research Working Paper 4024, Washington, DC.
Fields, Gary. 1975. “Rural-Urban Migration, Urban Unemployment and Underemployment, and Job-
Search Activity in Ldcs.” Journal of Development Economics 2 (2): 165-87.
Gertler, Paul, and Jonathan Gruber. 2002. “Insuring Consumption against Illness.” American Economic
Review 92 (1): 51-70.
Gunther, Isabel, and Kenneth Harttgen. 2009. “Estimating Households Vulnerability to Idiosyncratic
and Covariate Shocks: A Novel Method Applied in Madagascar.” World Development 37 (7):
1222-34.
Heltberg, Rasmus, Naomi Hossain, and Anna Reva. 2012. Living through Crises : How the Food, Fuel,
and Financial Shocks Affect the Poor. New Frontiers of Social Policy. Washington, DC: World
Bank.
Hoddinott, John, and Bill Kinsey. 2001. “Child Growth in the Time of Drought.” Oxford Bulletin of
Economics and Statistics 63 (4): 409-36.
20
Hoogeveen, Johannes , Bas Van der Klaauw, and Gijsbert Van Lomwel. 2011. “On the Timing of
Marriage, Cattle, and Shocks.” Economic Development and Cultural Change 60 (1): 121-54.
Kazianga, Harounan, and Christopher Udry. 2006. “Consumption Smoothing? Livestock, Insurance and
Drought in Rural Burkina Faso.” Journal of Development Economics 79 (2): 413-46.
Kochar, Anjini. 1995. “Explaining Household Vulnerability to Idiosyncratic Income Shocks.” American
Economic Review 85 (2): 159-64.
Ligon, Ethan. 2002. “Targeting and Informal Insurance.” United Nations University Wider Discussion
Paper DP2002/08, World Institute for Development Economics Research.
Ligon, Ethan, and Laura Schechter. 2003. “Measuring Vulnerability.” Economic Journal 113 (486):
C95-C102.
Lucas, Robert E.B. 1997. “Internal Migration in Developing Countries.” In Handbook of Population and
Family Economics, edited by Mark Richard Rosenzweig and Oded Stark. Amsterdam ; New
York: Elsevier.
Morduch, Jonathan. 1995. “Income Smoothing and Consumption Smoothing.” Journal of Economic
Perspectives 9 (3): 103-14.
Petesch, Patti. 2013. “How Communities Manage Risks of Crime and Violence.” Background paper for
the World Development Report 2014.
Stark, Oded, and David E. Bloom. 1985. “The New Economics of Labor Migration.” American
Economic Review 75 (2): 173-78.
Townsend, Robert M. 1994. “Risk and Insurance in Village India.” Econometrica 62 (3): 539-91.
———. 1995. “Consumption Insurance - an Evaluation of Risk-Bearing Systems in Low-Income
Economies.” Journal of Economic Perspectives 9 (3): 83-102.
World Bank and International Monetary Fund (2011). Improving the Odds of Achieving the MDGs.
Global Monitoring Report. Washington, DC, World Bank; IMF.
World Bank 2013. World Development Report 2014 Risk and Opportunity: Managing Risk for
Development.
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