Shocks as a source of vulnerability: an empirical investigation from Pakistan.
Haq, Rashida
The objective of this paper is to investigate the incidence of
different types of shocks in rural Pakistan and identity the household
characteristics that are associated with this phenomenon. It is observed
that one-third of households experience an adverse shock, be it
natural/agricultural, economic, social or relating to health. The
natural/agricultural shocks have major share in the total burden of
shocks while the households' coping mechanism is overwhelmingly
informal and largely asset-based. The poorest of the households adopt
behaviour-based strategies like reducing food consumption, employ child
labour, work more hours etc. Overall, households of with less educated
heads, high dependency ratio, large household size, low welfare ratio,
farm household, ownership of land and residing in south Punjab or Sindh
are more vulnerable to suffer shocks, particularly of income.
Vulnerability in terms of a decline in consumption is observed for
households who are hit by natural/agricultural or health shocks. For all
these reasons, a gradual shift from traditional emergency relief
measures towards ex-ante actions to reduce and mitigate hazard impacts
should be encouraged along with non- exploitative credit and more
effective safety nets.
JEL Classification: C21, C25, 132
Keywords: Shocks, Vulnerability, Poverty
1. INTRODUCTION
Households in developing countries are frequently hit by severe
idiosyncratic shocks and covariate shocks which result in welfare loss
not only directly but also as a consequence of the costly measures used
by households to protect consumption from such shocks including less
risky but also less profitable agricultural investment [Fafchamps
(2009)]. The emphasis on the impact of shocks to consumption leads to
the concept of vulnerability analysis. The inability to avoid welfare
declines when hit by exogenous shocks can be called vulnerability. The
extent of vulnerability depends on the level of underlying shock, the
ability to cope with shock management strategies, and long-term income
generating capacity [Chaudhuri (2003)]. Some of these shocks can have
long-lasting effects in terms of perpetuating and increasing poverty and
in adverse human development outcomes [Foster (1995) and Jacoby and
Skoufias (1997)]. In developing countries where financial and insurance
markets are incomplete or even absent, poor households are exposed to a
variety of risks resulting in high income volatility [Baulch and
Hoddinott (2000); Dercon (2002); Paxson (1992)]. In this context, such
households may attempt to smooth income exante in the expectation of
natural disasters. For instance, farmers can choose crop portfolios to
reduce exposure to risk [Kurosaki and Fafchamps (2002)] or allocate more
of their labour to non-agricultural activities when weather risk in
agricultural production is high [Ito and Takashii (2009)].
Shocks emanating from different sources may result in economic or
non-economic loss spread across space and time, and may differ in
frequency, duration, intensity and scope. The typology of shocks
typically classified and based on scope are idiosyncratic and covariate.
Households' idiosyncrasy shocks comprise household-specific shocks
such as illness, injury, death, job loss, crop failure and loss of
transfers which are compounded by lack of financial intermediation and
formal insurance, credit market imperfections and weak infrastructure,
while covariate shocks such as weather adversity and market fluctuation
tend to have an impact on a larger group of population in the same area
at the same time [Dercon (2006)]. All these shocks can potentially
contribute to high income volatility of the households. Proper
conceptualisation and characterisation of the underlying dynamic process
is thus imperative from both theoretical and policy perspectives.
To generate well-being in response to negative affect of shocks,
households have tangible (natural, human, physical and financial
capital) and intangible social capital in the form of proximity to
markets, health and education facilities and empowerment at their
disposal. More specifically, human capital refers to the household
members' education and their health status while physical capital
is related to productive assets such as land, tools, equipment and work
animal, and household assets like housing and household services,
livestock, food and jewellery. Finally, financial capital refers to
cash, savings, and access to credit. Intangible assets consist of social
capital, the proximity to market, health and education facilities and
empowerment. Both types of assets are important in the context of risk
management [Siegel and Alwang (1999)].
Shocks can also be divided into following categories:
natural/agriculture; economic; political/social/legal; crime; health;
and life-cycle shocks. Natural/agriculture shocks include earthquake,
flooding, erosion, pestilence affecting crops or livestock. Economic
shocks include business closures, mass layoffs, job loss, wage cuts,
loss of remittances. Political/social/legal shocks include court cases
and bribery, long duration general strikes, violence, crime and
political unrest while health shocks include death, injury and illness.
The presence of these risk and shocks can distort household's
intertemporal resource allocation behaviour which can be economically
costly and may propel households into chronic poverty.
Households can smooth consumption not only across space but also
over time by saving and borrowing or by accumulating and selling
non-financial assets. In developing countries poor households may have
difficulties in adopting these strategies because they have limited or
no access to formal credit markets and they may find it hard to save or
be cautious in running down assets to smooth consumption. Moreover,
households may choose inputs and production techniques that reduce
variability or may diversify income sources. These strategies may have
long term consequences when risk leads poor households to choose safe
but less profitable choices or to reduce investment in human capital,
thereby increasing the gap between the rich and the poor and pushing
poor households into the poverty trap [Alderman and Paxson (1994)].
The number of natural disasters reported appears to be increasing
globally--it was less than 100 per year in the mid-1970s while it was
approximately 400 per year during the 2000 (EM-DAT). (2) Pakistan is
classified as being extremely vulnerable to natural disasters due to its
geographical location, the frequency of their occurrence, and the number
of affected people. The top 10 natural disasters occurred during the
period 1900 to 2013 out of which, fifth, seventh, and eighth disasters
in the top 10 category occurred during the 1990s and 2010s,
respectively, of which floods and earthquakes were major disasters.
Recently, in Pakistan earthquake, flood and drought have caused
tremendous damage to livelihoods and infrastructure, with severe
implications for food security; earthquake 2005, 2010 flood and 2014
drought/famine resulted in the great losses to human life, agriculture
and livestock. In this background, the role of risks, shocks and
vulnerability in perpetuating poverty is important because poor
households are relatively more negatively affected by uninsured shocks,
as they are likely to lack the necessary human and physical capital to
recover from them. In Pakistan incidence of poverty in 2010 was 20.7
percent: 22.4 percent in rural areas and 16.6 percent in urban areas
[Arif and Shujaat (2014)]. They are not only suffering from average low
consumption but also are subject to high fluctuations in consumption due
to income risk and the lack of safety net measures. In rural areas,
permanent non-farm employment is associated with the exit from poverty
while education is key to such employment. Livestock is more pro-poor
than crop agriculture but its role in economic growth may be limited.
Social safety nets are weak; especially those provided by formal
institutions, while private networks based on personal relations are
more important safety nets [Kurosaki and Khan (2001)]. Since the
majority of households in Pakistan depend on agriculture for their
livelihoods, frequent droughts, floods, and other unexpected adverse
events such as illness, loss of job, and conflicts, can lead to loss of
their income and assets. While doing nothing is an option in the wake of
a shock, many also tend to use several coping strategies including
informal insurance, savings, loans, receiving aid and remittances,
reducing consumption, and liquidating assets to at least sustain their
welfare levels maintained prior to the shocks.
Improving the understanding of shocks at household level is an
issue of increasing importance for Pakistan. This is particularly true
for natural disaster related covariate shocks. There is limited
knowledge of their incidence and the coping mechanisms adopted by
households to deal with them [Heltberg and Niels (2009); Alderman
(1996)]. Given the significance of risk and uncertainty associated with,
policy-makers are required to incorporate shocks into their economic
development strategies for quick reduction of poverty in Pakistan. In
this scenario this study attempts to fill these gaps in the literature
by investigating the following questions: What types of shocks most
frequently affect households? Which households are more vulnerable to
natural disasters such as floods and droughts? Which region is more
affected by these types of shocks? What are the socio economic
characteristic of the households hit by (self-reported) shocks? Finally,
what are risk management strategies adopted by these households?
In this scenario the study has four main objectives related to
shocks, vulnerability and coping mechanism: (i) to highlight frequency
and severity of different types of shocks that affected the households
in 2006-2010; (ii) to examine the correlation structure of shocks at
village level; (iii) to assess the probability of occurrence a shock by
a multivariate analysis; and (iv) to analyse which type of households in
rural Pakistan are more vulnerable to shocks in terms of a decline in
their consumption during such disaster.
The rest of the paper is structured as follows: the next section
will provide review of the literature on shocks in developing countries.
Section 3 lays out details of the data and methodology used for the
paper and Section 4 discusses results in detail. Section 5 concludes the
study.
2. REVIEW OF LITERATURES
In developing countries increased focus on risk and vulnerability
motivated a series of studies aimed at theoretically conceptualising and
empirically measuring household vulnerability to shocks. This section
begins with a brief review of available literature on risk, shocks and
vulnerability in Pakistan.
As one of the dimensions of vulnerability, Kurosaki (2006)
investigates the inability of rural dwellers to cope with negative
income shocks in KP province of Pakistan. Estimated results show that
the ability to cope with negative income shocks is lower for households
that are aged, landless and do not receive remittances regularly. While
illustrating various measures of vulnerability proposed in the
literature Kurosaki (2009) applies it to a panel dataset collected in
rural Pakistan. The empirical results show that different vulnerability
rankings can be obtained depending on the choice of the measure. By
utilising these measures, it can be identified who and which region is
more vulnerable to a particular type of risk. This kind of information
is useful in targeting poverty reduction policies. Kurosaki (2010) also
investigates the measurement of transient poverty when each
person's welfare level fluctuates due to exogenous risk.
Theoretical results show that poverty measures associated with prudent
risk preferences perform better than other measures in assuring that the
value of transient poverty increases with the depth of chronic poverty.
Using a cross-section survey Heltberg and Niels (2009) mapped and
quantified shocks from all sources, ex-post responses and outcomes for a
sub sample of relatively poor Pakistani households. They found high
incidence and the cost of shocks, with health-related shocks being the
worst. Two-thirds of the sample experienced at least one major shock in
the three years prior to the survey while more than half of the reported
shocks were related to health and 75 percent of the most important
shocks were idiosyncratic. These findings add to the evidence that
health shocks often dominate and impose severe coping costs in terms of
medical expenses while relying mostly on informal and ad hoc responses:
informal borrowing, spending savings, and working more were the most
frequently used responses.
The relationship between health and death risk and income decisions
in rural Pakistan was explored by Jacobsen (2009). He showed how
insurance against hospitalisation and accidental death influenced the
purpose of micro credit loans. He found that individuals were more
likely to maintain the same loan purpose as their previous loan if they
were insured. Their results suggest that households that are insured
against hospitalisation and accidental death pursue less diversified
income portfolios. Hidayat and Takashi (2007) attempted to quantify the
ill-effects of covariate shocks such as natural disasters on the
sustainability of microfinance in Pakistan. Based on the
difference-in-difference approach, contrasting regions that were hit by
the 2005 earthquake, and regions that were not, it was found that the
delay in repayment in the affected areas was 52 percent higher than that
in the unaffected areas. The observed difference in the repayment delay
was decomposed into changes in borrowers' composition and
borrowers' behaviour. The decomposition result showed that the
changes in borrowers' behaviour accounted for a large portion of
the difference, suggesting a serious difficulty faced by borrowers and
microfinance institutions in the earthquake-hit regions.
The literature on natural hazards typically perceives disasters to
be acts of God while restricting the examination of their causes to
biophysical and geographical explanations. Yasir (2009) takes a
different approach; first, he argues that disasters are socially
constructed and, second, he situates the interactions of large-scale
natural forces with local political-economic conditions within the
context of vulnerability to contend that disasters are consequences of
unresolved development challenges. Using the Pressure and Release (PAR)
Model his paper suggests the usefulness of the concept of vulnerability
that shapes local geographies of risk and weak institutions which
transform and enhance the negative impact of 'natural' hazards
into 'man-made' disasters.
An empirical model of profit variability at the individual farm
level was proposed by Kurosaki (1995) and was applied to Pakistan's
agriculture. Results show that adding idiosyncratic yield shocks and
adjusting for input costs makes the variability of net profits much
larger than implied by the variability of average gross revenues. It is
also demonstrated that the correlation between green fodder profit and
milk profit at the farm level is substantially negative. This negative
correlation implies an advantage, in terms of risk diversification, of
combining fodder and milk production in one enterprise, which is
commonly observed in the mixed farming system in Punjab province.
Based on fieldwork, theoretical modeling and empirical testing of
agricultural households in Punjab, Kurosaki (1997) found
households' characteristics affecting their production choices and
the relationship between the individual decisions and the incompleteness
of the rural market structure. He also observed that with substantial
income uncertainties, the sample farmers were unable to share the risk
efficiently with the outside world and they therefore had to diversify
the risk through individual means such as crop choice and livestock
management. He also sheds new light on the positive role of livestock in
enhancing the welfare of households, especially of small land holders.
Using three-year household data on production and consumption from
the Punjab province, Kurosaki (1996), explored that the household's
livestock holding contributed to a reduction in income variability
through the negative correlation of livestock income with crop income
and through ex-post decumulation of livestock assets contingent on
realised income in the crop sector. His results suggested that the rises
in the livestock share in agricultural value-added in Pakistan during
the 1980s should have improved the welfare position of smaller farm
households with substantial livestock holding through reduced income
variability.
Substantial evidence of consumption smoothing as well as
differences in savings propensities between the rich and poor households
was explored by Alderman (1996), using a three year panel data from
Pakistan indicating that even poor households, use credit markets to
maintain consumption in the presence of negative income shocks.
Displacement gives rise to particular vulnerability for those
affected by shocks, necessitating special measures for assistance and
protection that correspond to those vulnerabilities. The factors that
have caused internal displacement in Pakistan in the recent past are a
complex bunch and cannot be addressed by a one-size-fits-all approach.
However, the official response has been largely reactive and
characterised by a failure to formulate a comprehensive approach that
focuses on preventing internal displacement, by avoiding conditions that
may lead to displacement [Din (2010)].
This review of literatures 011 risk, shocks and vulnerability
relating to Pakistan indicates direct implications for welfare loss due
to health shock, agricultural shock and natural disaster that
ultimately, translate into income shock.
3. MATERIALS AND METHODS
3.1. Data
Households in developing countries are frequently hit by severe
idiosyncratic and covariate shocks, resulting in high income volatility.
Pakistan being a low human development country, is frequently hit by
major natural disasters, including earthquake of 2005 and flood of 2010
which resulted in huge human and economic losses. To study this
scenario, panel household's survey is an important source of
information but it is rarely available in developing countries. In
Pakistan a three waves panel data set named 'Pakistan Rural
Household Survey' is available. The first round, of Pakistan Rural
Household Survey was done in 2001. The second round done in 2004 was
restricted to two provinces Punjab and Sindh and the third round,
renamed as Pakistan Panel Household Survey (PPHS)-2010, marked the
addition of urban sample of four provinces. These longitudinal surveys
were conducted by the Pakistan Institute of Development Economics with
the financial assistance of the World Bank. This study is based on
'PPHS-2010' which covers all four provinces (Punjab, Sindh,
Khyber Pakhtoonkhawa (KP) and Balochistan) with their urban and rural
counterparts. The survey covered 16 districts (3) from all four
provinces of Pakistan. The household survey questionnaire consists of
two parts; a male questionnaire and a female questionnaire. The male
questionnaire constitutes thirteen modules while female questionnaire
has twelve modules. The total sample size of PPHS-2010 was 4142
households; 2800 in rural and 1342 in urban while Punjab 1878, Sindh
1211, KP 601 and Balochistan 452.
The data used in this paper are based on a household-level
'Risk response module' included in PPHS-2010 and similar to
that developed in Hoddinott and Quisumbing (2003), but modified for the
Pakistan context. The module administered only in PPHS-2010 round, asks
households to report any unexpected events that were outside of their
control and caused a drastic reduction in income during the last five
years prior to the survey i.e. 2006-2010. The survey provides
information on data by year and type of disaster to provide a check for
the consistency between the self-reported shocks and on the actual
occurrence of such shocks. These reported shocks are divided into a four
broad categories: natural/agricultural; economic; social
(political/social/legal); and health/lifecycle shocks that inflict
welfare loss. Natural/agricultural shocks include flooding, drought,
fire, earthquake and crop failure. Economic shocks include business
closures, mass layoffs, job loss, wage cuts, loss of remittances and
other reasons. Social shocks in Pakistan include court cases and
bribery, long duration general strikes, violence, crime and political
unrest. Health/life-cycle shocks include death, injury and illness. The
survey distinguishes between death or illness of the primary income
earner and other household members. The respondents were also asked
whether the household was affected by idiosyncratic (household-specific)
shocks or covariate shocks that affected larger group of population in
the same area at the same time and to report the monetary value of the
cost of shock. The frequency and intensity of major disasters is also of
great relevance to the recovery of households. Finally, households were
also asked about the four important coping strategies to manage the
reduction in income such as sale of assets including land, livestock and
stored crop, decrease food consumption, increase labour supply
particularly of women and children, saving, borrowing and assistances
from friends and relatives, etc. The present analysis has used this
information on the shocks and coping strategies together with
socio-economic characteristics (i.e., individual characteristics such as
sex (if male=l), age in years and formal years of education) and
household characteristics, like household size in numbers (taken as
adult equivalent), dependency ratio, (4) per capita consumption
expenditure (to be precise, 'per capita' implies "per
adult equivalence unit), poverty status, (5) the ratio of female in the
household size (6) (working age 15-55 years), agricultural land
ownership in acres, livestock ownership in numbers, access to formal
credit (yes=1), household member abroad (yes=1), welfare ratio, (7)
sector of employment of household head (agriculture=1), changes in
agriculture landownership in acres and livestock ownership used as proxy
of assets and welfare ratio (between 2004 and 2010). In addition to
individual and household level characteristics, place of residence like
Punjab and Sindh (yes=1) provinces also included. Since there is a
socioeconomic gap and a difference in historical legacies between the
northern and southern parts of Punjab, the analysis divided Punjab into
two portions, north and south (yes=1) regions.
As reported earlier the self-reported shocks occurred between 2006
and 2010. To assess the relationship between socio-economic characterise
and exposure to specific type of shocks, the data on such
characteristics is used from a prior wave of the panel survey,
PRHS-2004. Since the PRHS-2004 was restricted to only rural areas of two
provinces, Punjab (48 villages) and Sindh (46 villages), this paper has
used a sub-sample of the PPHS-2010 consisting of two provinces, Punjab
and Sindh. However, the frequency of shocks and their spread are
reported for the whole sample of PPHS-2010 as well as the sub-sample of
rural Punjab and Sindh.
3.2. Method of Analysis
This section will discuss methodologies to analyse the occurrence
of shocks that lead to loss of household income, reduction in
consumption, loss of productive assets, and serious concern/anxiety
about household welfare:
(i) Bivariate analysis; (ii) Correlation structure of shocks; (iii)
Multivariate analysis; and (iv) Fixed effect model.
In bivariate analysis simple cross tabulation with row or column
percentage is presented to analyse the different types of shocks against
socio-economic characteristics.
To understand the correlation structure of different shocks, factor
analysis is applied which is a standard technique used to find the
latent shocks that account for patterns of variation among observed
shocks. Factor analysis is a method used to reduce the number of
variables to a smaller number of underlying dimensions, with highly
covariant variables loading on the same factor; a loading is the
correlation between the variable and the component
In order to determine the characteristics of households which are
likely to be affected by the occurrence of an adverse shock, a
dichotomous dependent variable was constructed in this study which would
be equal to one if occurring, five years preceding the survey would lead
to loss of household welfare and would be equal to zero otherwise.
Because the indicator is dichotomous, a logistic regression model was
estimated. This model makes it possible to estimate the probability of a
shock conditional on independent variables. In the same way a
probability of natural/agricultural shock is also estimated.
To construct the broad group of shocks, households were classified
into three groups- those that had not suffered any type of shock, those
who face an income shock (natural/agriculture and economic shocks) and
those who had an event of societal shock (health and social shocks).
Because the variable is trichotomous, the multinomial logistic
regression model is estimated. The independent variables are classified
into three groups: individual, household and community-level factors for
the estimation of this model.
Finally, for rural households, vulnerability in terms of a decline
in their consumption is investigated when their village is hit by shocks
such as floods and droughts, etc. Fixed effect (FE) model is used to
explore the relationship between predictor and outcome variables within
an entity (village). Each entity has its own individual characteristics
that may or may not influence the predictor variables. When using FE it
is assumed that something within the individual may impact or bias the
predictor or outcome variables and which need to control for this. This
is the rationale behind the assumption of the correlation between
entity's error term and predictor variables. FE remove the effect
of those time-invariant characteristics so we can assess the net effect
of the predictors on the outcome variable. The standard Fixed Effect
model is estimated as:
[y.sub.it] = [x'.sub.it][beta] + [z'.sub.i][alpha] +
[[epsilon].sub.it]
There are K regressors in [x.sub.it], not including constant term.
The heterogeneity, or individual effect is [z'.sub.i][alpha] where
[z.sub.i] contains a constant term and a set of individual or group
specific variables, which may or may not be observed. If [z.sub.i] is
observed for all individuals, then the entire model can be treated as an
ordinary linear model and fit by least squares. If [z.sub.l] is
unobserved, but correlated with [x.sub.it], then the least squares
estimators of [beta] is biased and inconsistent as a consequences of
omitted variables. However, in this instant, the model:
[y.sub.it] = [x'.sub.it][beta] + [[alpha].sub.i] +
[[epsilon].sub.it]
Where [[alpha].sub.i] = [z'.sub.i][alpha], takes all the
observable effects and specifies an estimable conditional mean. This
fixed effects approach takes [[alpha].sub.i] to be group specific
constant term in the regression model. The term 'fixed' effect
as used here, indicates that the term does not vary over time [Greene
(2003)].
The present analysis takes the specification of fixed effect model
as:
[dlnc.sub.2010-2004] - [[varies].sub.i] + [beta], [H.sub.iv,2004] +
[gamma] x [S.sub.iv,2010] + [[epsilon].sub.iv]
where
dlnc is the outcome variable (namely, change in log real per capita
consumption of household i in village v between 2004 and 2010), ocf the
group specific constant term for each village, [gamma] x [H.sub.iv], is
a vector of variables of household and socio-economic characteristics in
2004, [beta] x [S.sub.iv] shocks to households experienced between 2004
and 2010, and [[epsilon].sub.iv] is an error term.
4. EMPIRICAL RESULTS
4.1. Shocks and Coping Mechanisms: A Descriptive Analysis
In this section the data on the distribution of shocks in the
sample are illustrated. The objective is to present a description of
what kinds of shocks occurred, who was affected by them and what kind of
coping mechanisms were adopted.
The section defines the frequency, category, costliness and impact
of shocks as reported by the sample households occurred during five
years (2006-2010) preceding the survey. The sample households also
identified the main coping strategies and several other details of the
shocks including whether the event affected only the individual
household (idiosyncratic) or the entire community (covariate shocks).
As reported in Table 1, almost one-third (33.4 percent) of the
sample households experienced one most severe shock over the five-year
recall period. The most common types of shocks are natural/agriculture
related (55.9 percent of total) and health shocks (33.7 percent) which
have resulted in major fall in income. The natural/agriculture events
include loss of personal and business assets due to natural disaster,
crop failure, loss of livestock and drop in crop income while health
shocks comprise illness or disability and death of an income earner or
other family members. Far less frequent are economic (2.0 percent) and
social shocks (8.4 percent). The economic shocks consist of loss of
personal or business assets due to violence or conflicts, business
failure due to low sale/demand, unsuccessful investment and job loss
while social shocks comprise internally displaced person and other
social shocks including land or family dispute, etc.
While analysing the spread of shocks, it is observed that the risk
of shock may emanate from two broad sources: idiosyncratic shocks; or
covariate shocks. Covariate shocks i.e., community level shocks, are
typically natural disasters like floods, draughts and pest attack which
affect agriculture production severely and potentially contribute to
high income volatility of households. It is indicated that natural and
agriculture shocks contribute a major share in covariate shocks.
Household's idiosyncratic shocks that are household specific are
shocks such as death of principal income earner, chronic illness or
unemployment/underemployment etc. Health shock added 91.4 percent share
in this category. Health shocks may be having more importance because
they affect the household's ability to produce and generate income.
These types of shocks are fairly common in developing countries
including Pakistan, mainly due to the absence of easy access to medical
care, drinking water, unhygienic living conditions, and limited
opportunities for diversifying income sources. These difficulties are
compounded by lack of financial intermediation and formal insurance,
credit market imperfections and weak physical infrastructure.
The effects of shocks are multi-dimensional and affect a variety of
aspects of household welfare. Table 2 reports that all types of shocks
invariably affect both poor and non-poor households while rural
households are disproportionately exposed to natural and agricultural
shocks and are less exposed to economic shocks, specific to a formal
economy. As far as family headship is concerned, female headed
households are more vulnerable to overall shocks and its impact varies
from shock to shock indicating a high share of health shock that is 51.1
percent of total shock while male headed households get major welfare
loss due to natural/agriculture shocks that is 51.6 percent of the
overall impact of shock. The impact of different types of shocks
classified by assets ownership shows that households which had ownership
of land and livestock suffer a major welfare loss due to natural and
agriculture shocks; 70.6 percent and 65.4 percent respectively.
The PPHS-2010 also provides information on data by year and type of
disaster to make consistent with the self-reported shocks and with the
information on the occurrence of such shocks as presented in Table 3. It
is reported that 67.8 percent of all shocks are occurred in 2009-10 in
which a major natural disaster in the form of flood was witnessed. It
was the one of the largest floods in the history of Pakistan causing
unprecedented damage and killing more than 1,700 people, affected over
20 million people; in undated almost one-fifth of the country's
land. The estimated cost of the flood to the economy was $9.7 billion in
losses through damages to infrastructure, housing, agriculture and
livestock, and other family assets.
The severity of shocks is elaborated in Table 4. The mean total
cost of the most severe shock as reported by sample households, is Rs
10894.9 (or $1230). This is equivalent to 40 percent of average per
adult annual household expenditures in Pakistan. In respect of average
cost of shock, social shocks (Rs 233456.9 per event) are the most
expensive followed by natural/agricultural shocks (Rs 113093.9 per
event), economic shocks (Rs 99217.4 per event) and health shocks (74900
per event). Because of their high frequency and high costs,
natural/agricultural shocks caused by far the largest share in total
cost of shocks comprising 58 percent of the total burden while health
shocks took 23 percent of the total burden.
Table 4 also highlights shocks according to scope indicating that
the major share of idiosyncratic shocks originates from health shocks
(90.9 percent) while a larger part of covariant shocks originates from
natural/agricultural shocks (78.3 percent). Health insurance is also
rare in Pakistan where out of pocket expenditures accounted for 71
percent of total medical expenses, compared to 13.2 percent in the
United States. When a risk materialises and becomes a shock it causes a
significant major income loss to these households. These shocks can be
large and may trigger substantial consumption fluctuation which can have
important consequences for household welfare in the short and long run.
The coping responses practised by households to deal with shocks
are illustrated in Table 5. Survey respondents were asked how they
managed the reduction in income caused by the most severe shock and
about their use of saving, credit and assistance in general. It is
observed that coping mechanisms are overwhelmingly informal and largely
asset-based using savings, sale of livestock or borrowing. The ex-post
coping strategies can be divided into four main categories: (i)
asset-based strategies; (ii) assistance-based strategies; (iii)
borrowing-based strategies; and (iv) behaviour-based strategies. These
strategies can depend on formal or informal coping mechanisms.
Asset-based coping strategies are adopted by 54 percent households
experiencing shocks. This coping mechanism includes use of saving and
sale of assets such as agricultural land, livestock or stored crop.
Saving is likely to be held in cash that constitutes 37 percent of
assets-based strategy while sale of livestock and other assets (land or
stored crop) contributes 52 percent and 11 percent respectively of all
asset-based responses as reported in PPHS-2010. These assets are used
primarily to cope with natural/agricultural and health shocks.
Assistance-based strategies were reported to have been used for 10
percent of shocks; assistance is used largely to cope with health shocks
(60.6 percent) and rarely to cope with economic shocks (2.1 percent).
All types of assistance received by respondents come from relatives and
friends while formal coping instruments (government/NGOs) are lacking.
These findings are quite comparable with Heltberg and Niels (2009) who
had reported the results of a novel survey of shocks, coping, and safety
nets in Pakistan. They found high incidence and cost of shocks borne by
these households and in the absence of formal and effective coping
options they use mostly self-insurance and informal credit.
Borrowing-based strategies are used by 18.7 percent shock affected
households. Credit is almost entirely informal, offered by friends (28
percent of all loans and credit), family (40 percent) and moneylenders
(22 percent); formal credit sources such as banks or microfinance (10
percent) are of marginal importance for this analysis. Informal
instruments of coping mechanism dominate across all strategies.
Behaviour-based strategies such as consuming less, increasing labour
supply or taking children out of school for work, were used as the
primary coping response in 16.8 percent of the households when hit by
the worst shocks. These type of coping strategies were practised more
often for natural/agricultural shocks than for economic shocks. In
addition, many households reduced food consumption, non-food consumption
and increased labour supply of children or women in response to shocks
as a secondary coping strategy.
Dynamics of poverty and type of shocks in rural Pakistan are
presented in Table 6. It is observed that non-poor households are more
affected by natural/agriculture shocks as they have productive assets
like land and livestock which are at risk when any hazard occurred.
Serious adverse natural/agricultural shocks affect households in a
variety of ways, but typically the key consequences work through assets.
Assets themselves may be lost directly due to the adverse shocks--such
as crop failure, loss of livestock, soil erosion, while assets also play
a central role in attempts to buffer income fluctuations, and may
therefore be used or sold, affecting the ability to generate income in
the future. Likewise, chronic poor and transient non-poor households are
relatively more suffered from health shocks which affect the possibility
of income earning opportunities for households and a rise in health
expenditure.
Shocks for the rich and poor against expenditure quintiles are
presented in Table 7. Natural/agriculture shocks hit the upper two
quintiles more than the bottom quintiles as the rich have land or
livestock that are more vulnerable to natural disaster. Social shock
makes the poor more vulnerable due to conflict/disputes, or funeral
expenditure. Health shock affects the second quintile as compared to the
richest households due to uninsured risk.
Different types of coping mechanisms are also given against
household's economic status indicating that the poorest bottom
quintiles adopted behaviour-based strategies which include reducing food
consumption, employing child labour, working more hours, etc. It is also
observed that when a shock hits, the main strategy adopted by households
is to use their assets in some way rather than to ask for help from
friends and relatives, while private and public social safety nets exist
but offer little effective protection. The poor are less resilient than
the rich and the coping strategies used by the poor damage their
prospects to escape poverty. Recent study shows that there are
considerable poverty related movements depending on the type of shocks
and degree of risk and uncertainty that households are faced with. Even
if aggregate poverty levels remain constant over time, the share of the
population which is vulnerable to poverty might be much higher [Azam and
Katsushi (2012)].
4.2. Correlation Structure of Shocks
To measure the degree of covariance of the occurrence of a shock at
a particular location all primary sampling units (PSUs) in which no one
reported experiencing a shock in last five years were excluded from this
exercise. First, the information on the incidence of the shocks at the
level of the primary sampling unit was aggregated, and then the
proportion of households reporting the shock was estimated in each PSU.
The present survey records information on 15 specific shocks, plus two
catch-all categories; idiosyncratic or covariate.
The standard variance-covariance matrix can be used to find the
pairs of shocks with the strongest association, i.e., 'business
failure--drop in income' pair. The standard technique used to find
the latent shocks that account for patterns of variation among observed
shocks is factor analysis which is a method used to reduce the number of
variables to a smaller number of underlying dimensions, with highly
covariant variables loading on the same factor.
Table 8 presents the component loadings (i.e. a loading is the
correlation between the variable and the factors) on the first five
factors (whose eigenvalues are greater than one). The higher is the
loading, the higher is the association between a variable and a factor.
The present study employed factor analysis in which five components
considered as 'bunched-shocks' are extracted. Factor one
includes three health shocks illness/disability of household member,
death of income earner and household member and loss of personal and
business assets due to conflicts are positively correlated at village
level. Factor two includes natural/agricultural shocks which contain,
crop failure, loss of livestock due to disease or other causes and loss
of personal assets due to natural disaster are moving in same direction
while factor three consists of economic shocks including drop in crop
income, unsuccessful investment and business failure due to low
sale/demand. The three social shocks such as internally displaced
persons, illness/disability of income earner and other social shocks are
in fourth factor while in fifth factor two shocks related to loss of
business assets due to natural disaster and job loss are correlated.
The frequency distribution of these reported shocks are also given
in Figure 1. It is observed that highest reported shocks are crop
failure (28 percent) and personal loss due to natural disaster (12.1
percent) while third and fourth shocks are related to health shocks;
disability/illness of household member (10.8 percent) and earner (10.5
percent). A significant number of households also reported death of
earner (5.2 percent) and member (7.2 percent) of households. Economic
shocks including job loss, low sale, loss in investment and loss in
business have small share in total shocks.
4.3. Multivariate Analysis
The result of the shocks estimated through logistic regression
models to determine factors influencing the incidence and occurrence of
shocks are reported in Table 9. Models in this table represent an event
of shock versus no shock which resulted in welfare loss due to decrease
in income. The data on shocks have been taken from the risk response
module of PPHS-2010. The shocks include natural/agricultural, social,
economic and health which were faced by households during 2006 to 2010.
It is important to highlight that most of the determinants of the
occurrence of shock are however, themselves affected by shocks. For
instance, while acquisitions of such assets as ownership of land and
livestock have been taken as determinant of shock, they themselves could
be influenced by shocks. Another vicious circle may exist between the
poverty status of the household and different types of shocks. To
overcome this issue a restricted sample of panel households of rural
Punjab and Sindh provinces is used to observe the impact of
'pre-shock' socioeconomic characteristics in year 2004 on the
probability of experiencing an adverse shock between years 2006 to 2010.
Three types of explanatory variables have been used: individual
characteristics of the head of household i.e. sex, age and years of
education; household characteristics including household size (as adult
equivalents), female ratio in the household, dependency ratio, welfare
ratio, productive assets such as agriculture land and livestock, poverty
status (poor/non-poor), household member abroad, formal credit, sector
of employment (agriculture/non-agriculture) and community level variable
i.e., province (South Punjab/North Punjab and overall Sindh/North
Punjab). In addition to these characteristics, the analysis also adds
difference in assets (ownership of land and livestock) and welfare ratio
between the 2004 and 2010 period.
A glance at Model 1 reveals that a number of patterns emerge while
using the panel households of rural Punjab and Sindh provinces. With
respect to individual level characteristics, male headed households are
more likely to experience a shock as compared to female headed
households. The years of formal education achieved by household head is
included in explanatory variables to capture the household ability to
adopt risk management strategy. It is indicated that as the years of
schooling increases, the probability of occurrence of a shock decreases.
This could be because the welfare level of educated households is higher
than uneducated households in general, implying that educated households
have larger room for consumption curtailment when hit by an adverse
shock [Kurosaki (2009)]. Household size is positively correlated with
shocks reporting rates across the board, as larger households are
exposed to more shocks from multiple dimensions. With regards to the
variables poverty status and female ratio in household became
insignificant while welfare ratio had negative and significant relation
indicating that as the welfare level of households increases, the
probability of suffering a shock decreases. Access to credit plays an
important role in smoothing consumption. In this analysis those
household who had obtained formal credit have negative and significant
relation in explaining the probability of shock because formal credit is
usually taken for investment in agriculture purposes which generate
stable consumption paths, even when shocks occur. Households with
productive assets such as ownership of agricultural land and livestock
have greater probability of reporting a shock than those which do not
own these assets because assets themselves may be lost directly due to
the adverse shocks--such as crop failure and loss of livestock. It is
commonly believed that households whose heads are employed in
agriculture sector report more shocks on average as agrarian households
are often exposed to a larger sets of shocks than non-farm counterparts
particularly, crop failure, loss of livestock, and natural hazards like,
flood/drought. This analysis confirms this belief. Those households
which are employed in agriculture sector (52 percent in Punjab and 60
percent in Sindh) are more likely to report different type of shocks.
The analysis has also included those household who had member abroad and
receive remittances showing less likely to hit by any type of shock but
turns out to insignificant in explaining this phenomenon. Geographical
location also plays an important role in determining risk and shocks.
This analysis indicates that rural South Punjab and Sindh provinces are
more vulnerable in term of experiencing shocks as compare to north
Punjab because districts located in these regions like Muzafargarh,
Bahawalpur, Nawabshah, Mirpurkhas and Badin were the worst hit in 2010
flood.
In model 2, differences in the values of three correlates
(landholding, livestock and welfare ratio) between the 2004 and 2010 are
added in the model. There is no major change in results when compared to
model 1 except that the livestock which was significant in model 1
turned out to be insignificant in model 2. However, all the three
variables--difference in two periods have significant relation with
probability of occurrence a shock. The difference in livestock and
welfare ratio has a negative and significant relationship with
probability of a shock while landholding has positive relation to
experience a shock. This analysis indicates that not only the initial
socioeconomic conditions of households but also a change in these
conditions overtime has correlation with the probability of a shock.
Thus, it can be concluded that households with positive changes in
livestock and welfare ratio can lead to less likelihood of experiencing
a shock as livestock can be used as buffer stock when households exposed
to risk. However, difference in landholding which is included to proxy
households' productive capacity and permanent income generating
potential has positive and significant relation with an occurrence of
shock.
Natural disasters such as floods, droughts, earthquakes, and other
weather-related phenomena can affect household welfare through the
destruction of physical and human capital stock. These shocks are more
frequent in developing countries, and the poor are more likely to suffer
damages from natural hazards as usually they can only afford to live in
marginal areas and have a limited ability to manage these risks [UNDP
(2007-08)].
In Table 10, model 3 explores the factors that make households more
likely to experience from natural/agriculture shock that had also
resulted in loss of income and assets. It is worth mentioning that the
findings of these models are not different from the outcome of model 1
and 2, with a few exceptions. Ceteris paribus, if the household head is
older, the household faces a lower risk of shocks. Similarly; more
educated household heads are less likely to experience a shock than
those with less education level. Large households' size, high
dependency ratio and sector of employment are more at risk to suffer a
shock. The poverty status of the household head which was insignificant
earlier came out to be significant indicating more likelihood to suffer
from natural/agriculture shocks while female ratio and welfare ratio
turned out to be insignificant. Household productive assets, like land
and livestock have positive and significant relation with experiencing a
shock. In terms of economic well-being Punjab province is better off as
compare to Sindh province while within Punjab, Southern region is worse
off in terms of human and social development as compare to Northern
region [Haq and Azher (2013)]. Finally, as expected households residing
in south Punjab and Sindh regions are more exposed to natural disaster
as witnessed frequent floods and droughts in this regions.
In model 4, differences in the values of three predictors
(landholding, livestock and welfare ratio) between the 2004 and 2010 are
added in this analysis to explore the probability of occurrence an
agriculture shocks. There is no major change in results as compared to
model 3 except livestock ownership turn out to be insignificant while
difference in landholding between these periods is significant and
households turned out to be more vulnerable to ill effect of shocks.
The shocks are multi-dimensional and affect a variety of aspects of
household welfare. For this multivariate analysis, all shocks are
decomposed into income shock and societal shock. Income shock is
computed by aggregating natural/agricultural shocks and economic shocks
while societal shock includes health shocks and social shocks. The
results of multinomial logistic regression model presented in Table 11
show the effects of the independent variables on the probability of an
income shock vs. no shock and societal shock vs. no shock. Income shock
constitute the highest burden of shock with 58.8 percent while societal
shock takes 41.2 percent in total welfare loss as reported in
descriptive analysis. With respect to the individual level
characteristics, a male headed household is found to be more likely to
suffer income shock while it is insignificant for societal shock. Age of
household head is insignificant in both models while household size has
positive and significant relation with the probability of occurrence of
an income and societal shocks indicating that as household size
increases households are more vulnerable to shocks. Education level of
household head reduces probability of income shock but insignificant for
probability of societal shocks. The dependency ratio which is used to
measure the pressure on productive population is positive and
significant showing that as this ratio increases, a household is more
likely to suffer an income shock. Women make essential contributions to
the agricultural and rural economies in all developing countries
including Pakistan. They often manage complex households and pursue
multiple livelihood strategies but many of these activities are not
defined as "economically active employment" in national
accounts but they are essential to the well-being of rural households.
To analyse the impact of working age female population in household
size, female ratio is included in the model, but this variable turn out
to insignificant in explaining the probability of experiencing a shock
in both models. Poverty status indicates deprivation of a household, had
negative and significant relationship with reference to income shock
while it increases the probability of societal shock. Welfare ratio
which is a measure of overall well-being of household turns out to be
significant indicating that as economic status of the household
increases probability of suffering an income shock reduces. When the
effect of ownership of productive assets is examined, it was found that
a household with land and livestock ownership significantly increases
the probability of income shock while it reduces the likelihood of
occurrence of societal shocks.
Access to formal credit is used to capture the household's
capacity to mitigate the effect of shock. It was observed that a
household with access to credit is less likely to report an income shock
while it is insignificant for probability of societal shocks. Sector of
employment demonstrates positive and significant relation with
probability of economic shock while it is insignificant for societal
shock. Significant regional variations exist in determining the
likelihood of shocks. In model 5, southern Punjab and Sindh provinces
are more vulnerable to hit an income shock while it is negative for
societal shock. When a shock hits a household, it affects household
assets. To capture this effect, the study had taken change in
landholding, livestock and welfare ratio between the two periods as
reported in Table 12. There is no major change in correlates of this
model except a couple of exceptions, i.e., ownership of livestock turn
out to be insignificant in both type of shocks. The sensitivities of
shock responses to differences in landownership and welfare ratio lower
the probability of societal shock while it is positively related to
income shock in case of land ownership. The changes in livestock
ownerships is negatively associated with probability of income shocks
indicating that positives changes in this productive assets is used as
ex ante coping mechanism to avoid an income shock.
These shocks can affect assets in many ways, first, through the
impact on their amount, value and productivity. This could be the direct
result from the shock or a ramification of its impact through the
absence or inadequate application of coping mechanisms. Poor households
tend to pay a higher cost for mitigating and coping with risk due to
their reduced asset base. Next section discusses vulnerability measured
in terms of sensitivity of consumption changes due to shocks.
4.4. Vulnerability: Sensitivity of Consumption Changes Due to
Shocks
In developing economies poor households are likely to suffer not
only from low level of welfare on average but also from fluctuations in
their welfare to their limited coping abilities [Fafchamps (2009);
Dercon, et al. (2005)]. The inability to avoid welfare declines when hit
by exogenous shocks can also be called vulnerability [Ligon and
Schechter (2003); Kurosaki (2006)]. Idiosyncratic and village-level
negative shocks may have been responsible for the consumption decline of
certain households when the country experienced a consumption increase
on average. Aggregate shocks such as droughts and floods cannot be
perfectly insured by risk sharing.
Given this inability, Kurosaki (2013) explored households which are
more vulnerable in terms of a decline in consumption when a village is
hit by shocks like flood, drought and health and what kind of
microeconomic mechanism underlies the household heterogeneity in
vulnerability, using two-period panel data collected in rural Pakistan
in 2001 and 2004. This study also investigates households in rural
Pakistan who are vulnerable to shocks in terms of a decline in their
consumption expenditure when their village is hit by covariate or
idiosyncratic shocks which is based on risk response module of panel
data of 2010 with base year 2004. To measure vulnerability change in
real per capita log consumption expenditure {dine) for the years 2004
and 2010 is taken as welfare measure. The average real consumption
expenditure increased between the two periods as presented in Table 13.
The increase is larger in Punjab province than in Sindh province while
within Punjab it is higher for northern Punjab as compare to southern
Punjab, indicating spatial disparity across the two provinces which
accounts for approximately 80 percent of Pakistan's total
population. This increase in the average consumption is not shared
equally among all households. Among the full sample of panel households,
the average of dine was 0.21, indicating an increase of 11.5 percent in
real consumption over the two survey periods. However, 35.4 percent of
individuals suffered from a decline in their welfare levels (i.e., dine
was negative). Thus, the aggregate figure hides the fact that certain
households suffered from a severe decline in their welfare during the
two survey period. The welfare changes can also be analysed by taking
households with different groups of shocks which was reported in
PPHS-2010 indicated that those households who are experienced by shocks
had less positive changes in consumption as compared to no shocks. In
addition, households who suffered health shocks due to
injure/sickness/death had the least positive growth in consumption per
capita as compared to other groups.
As controls for household characteristics that determine
consumption growth, the paper follow the standard literature on the
determinants of welfare in developing countries [Glewwe (1991)] and
include variables such as agricultural production assets owned by the
household, farmland and household assets like milk animals, bullock,
sheep and goats, etc., with other households characteristics in 2004.
The household level covariate/idiosyncratic shocks that occurred after
the first round of survey may have affected the consumption level due to
income loss. For this reason, four groups of shocks reported in the last
five years in PPHS-2010 that are exogenous to initial consumption are
included in the model.
The estimated results of village level fixed effect model (8) is
presented in Table 14. Among household characteristics, seven variables
have statistically significant coefficients: household head's age
(positive), household head's years of schooling (positive),
household size (negative), sector of employment (positive), welfare
ratio (positive), the size of owned land (positive) and number of
livestock (positive). The analysis shows that aged household heads with
more year of schooling and high welfare ratio had experienced higher
growth in consumption between the two periods. The coefficient of
household size is negative and statistically significant indicating that
as household size increases, require larger amount of consumption thus
growth in consumption decreases between the two periods. The finding
that households with land and livestock ownership are ahead forward in
consumption growth suggests that growth from 2004 to 2010 was based on
agricultural sectors in rural Pakistan.
With regard to coefficients on shocks, all are negative but only
natural/agriculture shocks and health shocks are significantly related
to welfare. The absolute value of the coefficient on
natural/agricultural shock is especially large, indicating that
households had to reduce consumption by 15 percent (9) when their
households located in particular village is hit by floods/drought/
earthquake. This implies a substantial decline in welfare capturing a
major disaster of 2010 flood especially in Punjab and Sindh province.
Analysis from Arif and Shujaat (2014) using the same panel data suggest
that those household who are suffered from agriculture shocks are more
likely to fall into poverty. On the other hand, the coefficient on
economic shocks and social shocks are statistically insignificant. In
addition to these shocks, health shock is significantly negative
specifying a decline of 8 percent in consumption when a household member
or earner get sick/injured indicting income loss due work days lost. The
decline in consumption can also captured due to death of earner which
suspended income flow in the family.
5. CONCLUSIONS
In developing countries, shocks from many sources strike frequently
and hit hard, causing loss of life, assets, and livelihoods which has
also established the fact that the cost of risk exceeds the impact of
shocks. The objective of this study is to investigate sources of
vulnerability defined as households' exposure to shocks and their
limited ability to mitigate the impact of shocks. It has used household
survey data from PRHS2004 and PPHS-2010 focusing on the risk response
module to explore the probability of shocks and sensitivity of
consumption changes due to shocks.
The findings of this study elaborate that approximately one third
of the rural households experience an adverse shock during the last five
years 2006-2010, including natural/agricultural shocks 55.9 percent,
economic shocks 2.0 percent, social shocks 8.4 percent and health shocks
33.7 percent. The incidence of shock is greater from
natural/agricultural events and health related shock. Households with
agriculture land and livestock ownership are more vulnerable to face
shocks. As far as the scope of shock is concerned, 53.7 percent
households suffer from idiosyncratic shocks, particularly health related
while 46.3 percent had covariate shocks focusing on natural disasters.
The natural/ agricultural shocks contribute the major share of loss due
to shocks. It is observed that coping mechanisms are overwhelmingly
informal and largely asset-based using savings or sale of livestock
whereas the poorest bottom quintiles adopted behaviour-based strategies
which include reducing food and non-food consumption, employment of
child labour and increased working hours, etc. The analysis also sheds
new light on the positive role of livestock in mitigating adverse impact
of shocks as 29 percent households' sale livestock as coping
measures.
To determine factors influencing the incidence of shock, the
available panel households from rural Punjab and Sindh are taken to
determine the pre shock characteristic of households. A number of
patterns emerge while using all type of shocks and natural/agricultural
shock: male headed households, large household size, land and livestock
ownership, employment in agriculture sector and resident of south Punjab
and Sindh are more vulnerable to suffer from shocks whereas educated
household head, high welfare ratio and access to formal credit reduces
probability of a shock. In addition to it, high dependency ratio and
poverty status of the households are more likely to increase the
probability of natural/agricultural shocks. However, positive changes in
ownership of livestock and welfare ratio between two time period, lower
the probability for occurrence of shocks..
When the sample is categorised into income and societal shocks, it
is observed that male headed households, large household size,
dependency ratio, land ownership, livestock ownership, sector of
employment, south Punjab and Sindh increase the probability of income
shock while welfare ratio and access to formal credit lower it. However,
land and livestock ownership, member abroad, south Punjab and Sindh
lower the probability of societal shocks.
This paper also elucidated which households in rural Pakistan are
vulnerable in terms of a decline in their consumption when their village
was hit by a shock. It is found that those households who experienced a
shock had less positive change in their consumption levels as compared
to those households who have experienced no shocks. The empirical
analysis of consumption vulnerability also found that households with
agricultural and health shocks are more vulnerable as compare to other
households.
Shocks will continue to occur, however to mitigate their impact in
the future requires a reduction in the socio-economic vulnerability and
increased resilience that can be achieved through policies geared
towards improving social conditions and living standards. In this
regard, access to micro credit to build up productive assets such as
livestock, as it smooth consumption, enables to do saving and productive
assets. Lastly, health insurance is imperative especially for the poor
segment of the society because in case of health shock they had not only
to bear health expenditure but also loss market hours of work.
Finally, to strength the 'National Disaster Management
Authority' which will be the focal point for coordinating and
facilitating the implementation of strategies and programmes on disaster
risk reduction, response and recovery, particularly in case of flood
which is a common phenomenon in case of Pakistan.
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Author's Note: The author is grateful to Dr G. M. Arif, Joint
Director and Dr Najam us Saqib, Senior Research Economist at Pakistan
Institute of Development Economics for their valuable comments on an
earlier draft of this paper.
(2) http://www.emdat.be/natural-disasters-trends (accessed on April
17, 2015).
(3) Punjab: Faisalabad, Attock, Hafizabad, Vahari, Mazaffargarh,
Sindh: Badin, Nawab Shah, Mirpur Khas, Larkana, KP: Dir, Mardan, Lakki
Marwat, Balochistan: Loralai, Khuzdar and Gwadar.
(4) The dependency ratio takes the sum of the population under the
years of 15 and over 64 and divided by the population in the
intermediate range of 15-64.
(5) The poor are defined as a household with per adult equivalent
consumption expenditure below tin poverty line Rs 878.64 and Rs 1671.89
per month for the year 2004 and 2010, respectively [Arif and Shaujaat
(2014)].
(6) Women make essential contributions to the agriculture' and
rural economies in all developing world.
(7) Welfare ratio is defined as consumption expenditure per adult
equivalent divided by poverty line in the respected year.
(8) With village fixed effects, a Hausman test comparing the fixed
effects (within) regression and the random effects regression gives a
p-value of .0005. The result of the test provides evidence in favour of
the village fixed effect being uncorrected with the other regressors and
helps confirm this specification.
(9) (1 - exp (-0.1708) =(1- 0.84366) = 0.157.
Table 1
Extent of Shocks by Selected Shocks in Rural Pakistan (%)
How Widespread was
this Shock?
Only Affected
Affected this few
Household Households
Reported
Type of Shock Shocks Idiosyncratic
Natural/Agriculture 55.9 21.7 8.1
(18.8)
Economic 2.0 74.2 25.8
(0.8)
Social 8.4 79.3 16.2
(2.5)
Health 33.7 91.4 4.0
(11.3)
Overall 100 51.1 7.7
(33.4)
How Widespread was
this Shock?
Affected Affected
many Almost every
Households Households
Type of Shock Covariate All
Natural/Agriculture 19.4 50.7 100
Economic 0 0 100
Social 4.4 0 100
Health 1.6 3.1 100
Overall 11.7 29.4 100
Source: Computations are based on the micro data of PPHS-2010.
Table 2
Incidence of Shocks by Household Characteristics: Rural Pakistan
Type of Shock (%)
Household Natural/
Characteristics Agricultural Economic Social Health
Poor 47.4 3.9 10.9 38.1
Non Poor 58.2 1.6 7.7 32.6
Agri Household 65.5 15.9 53.9 45.9
Credit Access 56.2 2.0 7.7 34.2
Male Head HH 57.4 2.0 2.5 33.0
Female Head HH 32.8 0 8.0 59.1
Land Ownership 62.5 1.9 5.4 29.8
Livestock Ownership 84.2 34.8 73.8 74.6
Punjab 52.7 1.6 7.3 38.3
Sindh 61.8 2.4 8.0 27.9
Total 56.1 2.1 8.1 33.7
Household Incidence of
Characteristics Shock (%)
Poor 31.7
Non Poor 35.6
Agri Household 55.8
Credit Access 42.0
Male Head HH 32.3
Female Head HH 47.9
Land Ownership 60.1
Livestock Ownership 79.1
Punjab 33.6
Sindh 41.3
Total 33.4
Source: Computations are based on the micro data of PPHS-2010.
Table 3
Major Shocks Occurred during the Last Five Years (%)
Type of Shocks
Nat/
Year of Shock Agriculture Economic Social Health Overall
2009-10 64.6 33.3 50.2 61.4 61.9
20008-09 22.3 46.4 21.8 19.3 21.9
2007-08 7.4 20.3 10.1 8.4 8.2
2006-07 2.5 0 12.8 11.0 5.9
2005-06 2.5 0 5.1 0.6 2.0
Source: Computations are based on the micro data of PPHS-2010.
Table 4
Costs and Scope of Shock, by Type of Shocks
Cost of Shocks
Mean Rupees Standard % of Total
per Shock Deviation Burden *
Type of Shocks
Natural/Agri 113093.9 169925.6 58.6
Economic 99217.4 91088.9 1.8
Social 233456.9 380357.2 16.2
Health 74900.6 127709.1 23.4
Overall 10894.9 185783.5 100
Scope of Shocks
Covariate Idiosyncratic
% of Shocks in % of Shocks in
Type of Shocks Category Category
Natural/Agri 78.3 (88.9) 21.7 (24.4)
Economic 20.3 (0.8) 79.7 (3.1)
Social 26.6 (4.1) 73.4 (11.1)
Health 9.1 (6.2) 90.9 (61.4)
Overall 49.7 50.3
Source: Computations are based on the micro data of PPHS-2010.
* % burden of shock is computed by taking % share of reported
shocks out of total cost.
In parenthesis percentage distribution of type of shocks are given.
Table 5
Ex-Post Coping Strategies by Type of Shocks: Rural Pakistan
Type of Shocks
Natural /
Strategy Agricultural Economic Social
Asset-based Strategies 58.9 1.5 7.3
(57.3) (39.4) (49.6)
Assistance-based Strategies 44.2 5.3 8.1
(7.9) (25.8) (10.1)
Borrowing-based Strategies 41.2 1.5 9.7
(13.8) (13.6) (22.5)
Behaviour-based Strategies 69.8 2.6 8.6
(20.9) (21.2) (17.8)
Total 56.1 2.1 8.1
Type of Shocks
Strategy Health Total
Asset-based Strategies 32.3 100
(51.8) (54.5)
Assistance-based Strategies 42.4 100
(12.5) (10.0)
Borrowing-based Strategies 47.7 100
(26.3) (18.7)
Behaviour-based Strategies 19.0 100
(9.4) (16.8)
Total 33.7 100
Source: Computations are based on the micro data of PPHS-2010.
In parenthesis percentage distribution of types of strategies
are given.
Table 6
Dynamics of Poverty and Type of Shocks: Rural Pakistan
Type of Shocks
Natural/
Poverty Status Agricultural Economic Social Health Total
Chronic Poor 51.1 6.8 9.7 32.4 8.8
(7.9) (30.4) (11.2) (8.4)
Transient Poor 51.0 3.5 7.5 18.1 18.0
(16.3) (31.9) (18.0) (18.1)
Transient Non-poor 46.6 1.6 8.9 42.9 13.1
(11.6) (11.6) (16.5) (17.7)
Never Poor 61.3 0.9 7.0 30.9 60.1
(64.1) (26.1) (54.3) (53.7)
Total 56.1 2.1 8.1 33.7 100
Source: Computations are based on the micro data of PPHS-2010.
Figures in parenthesis are column percentages.
Table 7
Shocks for the Rich and Poor: Rural Pakistan
Expenditure
Quintiles 2004
Type of Shock Q 1 Poorest Q2 Q3
Natural and Agriculture 46.7 55.1 54.5
Economic 2.8 3.8 1.4
Social 10.0 8.5 6.8
Health 40.4 32.6 37.3
Main Coping Strategies
Asset-based Strategies 50.2 43.8 54.1
Assistance-based Strategies 8.1 13.1 10.1
Borrowing- based Strategies 27.3 18.1 18.5
Behaviour-based Strategies 14.5 25.1 17.2
Expenditure
Quintiles 2004
Type of Shock Q4 Q5 Richest
Natural and Agriculture 63.3 64.0
Economic 1.4 0.9
Social 9.3 3.3
Health 26.1 31.8
Main Coping Strategies
Asset-based Strategies 63.5 61.1
Assistance-based Strategies 4.8 13.0
Borrowing- based Strategies 19.9 12.8
Behaviour-based Strategies 11.8 14.8
Source: Computations are based on the micro data of PPHS-2010.
Table 8
Bunched Shocks: Understanding the Correlation Structure using
Factor Analysis
Shocks Factor 1 Factor 2
Illness/Disability of HH Member .759 .006
Death of other Household Member .659 -.275
Death of an Income Earner .600 .086
Loss of Personal Assets_Conflict .548 .436
Loss of Business Assets_Conflict .494 .049
Crop Failure .037 .766
Loss of Personal Assets_Natural Disaster -.050 .701
Loss of Livestock_Disease/Causes .251 .467
Drop in Crop Income -.036 .030
Unsuccessful Investment -.157 .055
Business Failure_Low Sale/Demand -.230 -.164
Illness/Disability of Income Earner .066 .114
Internally Displaced Persons -.014 .557
Other Social Shocks .218 .478
Job Loss .065 -.108
Loss of Business Assets_Natural Disaster -.182 .014
Shocks Factor 3 Factor 4
Illness/Disability of HH Member -.080 .178
Death of other Household Member .336 .182
Death of an Income Earner -.142 -.215
Loss of Personal Assets_Conflict .012 -.215
Loss of Business Assets_Conflict -.038 -.091
Crop Failure -.034 -.091
Loss of Personal Assets_Natural Disaster .233 .037
Loss of Livestock_Disease/ Causes -.276 .168
Drop in Crop Income .757 .128
Unsuccessful Investment .733 -.122
Business Failure_Low Sale/Demand .340 .141
Illness/Disability of Income Earner -.122 .643
Internally Displaced Persons .107 .562
Other Social Shocks .090 .553
Job Loss .061 .046
Loss of Business Assets_Natural Disaster .155 .034
Shocks Factor 5
Illness/Disability of HH Member -.117
Death of other Household Member -.231
Death of an Income Earner .319
Loss of Personal Assets_Conflict .024
Loss of Business Assets_Conflict -.045
Crop Failure -.043
Loss of Personal Assets_Natural Disaster -.134
Loss of Livestock_Disease/ Causes .386
Drop in Crop Income -.048
Unsuccessful Investment .215
Business Failure_Low Sale/Demand -118
Illness/Disability of Income Earner -.107
Internally Displaced Persons .025
Other Social Shocks -.056
Job Loss .833
Loss of Business Assets_Natural Disaster .394
Source: Computations are based on the micro data of PPHS-2010.
Note: Only principal components with eigenvalues > 1 are shown.
Reported statistic: Factor loadings after oblique rotation.
Table 9
Effects of 2004 Socioeconomic Characteristics
on the Probability of Experiencing a
Shock between 2006 and 2010
Model-1
Shock/No Shock
Correlates (2004) Coefficient S.E
Male Headed Households 0.450 ** 0.190
Age of HH Head -0.002 0.002
Head Education(Years) -.015 ** 0.006
Household Size 0.048 * .006
Dependency Ratio 0.007 0.030
Poverty Status -0.032 0.059
Female Ratio 0.158 0.196
Welfare Ratio -0.073 * 0.024
Land Ownership (Acres) 0.012 * 0.002
Livestock Ownership (no) 0.014 * 0.004
Credit Access -0.253 * 0.055
Member Abroad -0.110 0.175
Sector of Employment 0.317 * 0.048
South Punjab/North Punjab 0.718 * 0.066
Sindh/North Punjab 1.114 * .062
Constant -1.89 0.239
Difference in Landholding -- --
Difference in Livestock -- --
Difference in Welfare Ratio -- --
LR Chi-square 511.77
-2 Log likelihood 8946.7
Pseudo [R.sup.2] 0.097
Observations 1335
Model-2
Shock/No Shock
Correlates (2004) Coefficient S.E
Male Headed Households 0.430 ** 0.190
Age of HH Head -0.002 0.002
Head Education(Years) -0.013 ** 0.006
Household Size 0.043 * 0.006
Dependency Ratio 0.007 0.030
Poverty Status 0.032 0.059
Female Ratio 0.165 0.196
Welfare Ratio -0.073 * 0.024
Land Ownership (Acres) 0.012 * 0.002
Livestock Ownership (no) 0.002 0.004
Credit Access -0.251 * 0.051
Member Abroad -0.110 0.248
Sector of Employment 0.335 * 0.066
South Punjab/North Punjab 0.734 * 0.066
Sindh/North Punjab 1.175 * 0.062
Constant -1.746 0.241
Difference in Landholding 0.007 * 0.003
Difference in Livestock -0.025 * 0.004
Difference in Welfare Ratio -0.082 * 0.022
LR Chi-square 662.29
-2 Log likelihood 7881.7
Pseudo [R.sup.2] 0.113
Observations
Source: Computations are based on the micro data
of PRHS-2004-05 and PPHS-2010.
* Significant at 1 percent, and ** Significant at 5 percent.
Table 10
Effects of 2004 Socioeconomic Characteristics on the
Probability of Experiencing an Agriculture Shock between
2006 and 2010
Model-3
Agri Shock/No Shock
Correlates (2004) Coefficient S.E
Male Headed Households 0.563 * 0.251
Age of HH Head -0.004 ** 0.002
Head Education(Years) -0.036 * 0.007
Household Size 0.038 * 0.007
Dependency Ratio 0.089 ** 0.035
Poverty Status 0.319 * 0.075
Female Ratio 0.129 0.206
Welfare Ratio -0.002 0.018
Land Ownership (Acres) 0.017 * 0.002
Livestock Ownership (no) 0.004 ** 0.002
Credit Access -0.291 * 0.061
Member Abroad 0.083 0.219
Sector of Employment 0.693 * 0.059
South Punjab/North Punjab 1.034 * 0.81
Sindh/North Punjab 1.006 * 0.070
Constant -2.927 0.299
Difference in Landholding -- --
Difference in Livestock -- --
Difference in Welfare Ratio -- --
LR Chi-square 609.81
-2 Log Likelihood 8560.97
Pseudo [R.sup.2] 0.062
Observations 1335
Model-4
Agri Shock/No Shock
Correlates (2004) Coefficient S.E
Male Headed Households 0.589 ** 0.252
Age of HH Head -0.005 ** 0.002
Head Education(Years) -0.036 * 0.008
Household Size 0.034 * 0.007
Dependency Ratio 0.093 * 0.036
Poverty Status 0.310 * 0.075
Female Ratio 0.120 0.206
Welfare Ratio -0.021 0.027
Land Ownership (Acres) 0.022 * 0.002
Livestock Ownership (no) 0.004 0.005
Credit Access -0.297 * 0.061
Member Abroad 0.034 0.218
Sector of Employment 0.659 * 0.060
South Punjab/North Punjab 1.031 * 0.082
Sindh/North Punjab 1.031 * 0.072
Constant -2.795 0.300
Difference in Landholding 0.012 * 0.003
Difference in Livestock -0.003 0.004
Difference in Welfare Ratio -0.016 0.025
LR Chi-square 630.7
-2 Log Likelihood 8624.1
Pseudo [R.sup.2] 0.064
Observations
Source: Computations are based on the micro data
of PRHS-2004-05 and PPHS-2010.
* Significant at 1 percent,
and ** Significant at 5 percent.
Table 11
Multinomial Logistic Regression: The Probability of
Experiencing a Shock
Model 5
Income Shock/
No Shock
Correlates (2004) Coefficient S.E
Intercept -2.245 0.336
Male headed Households 0.719 ** 0.277
Age HH Head -0.003 0.002
Head Education (Years) -0.028 * 0.008
Household Size 0.053 * 0.007
Dependency Ratio 0.0985 ** 0.037
Poverty Status -0.197 ** 0.078
Female Ratio 0.200 0.221
Welfare Ratio -0.015 ** 0.020
Land Ownership (Acres) 0.016 * 0.002
Livestock Ownership (no) 0.007 *** 0.004
Credit Access -0.447 * 0.064
Member Abroad -0.288 0.253
Sector of Employment 0.631 * 0.063
South Punjab/North Punjab 1.098 * 0.086
Sindh/North Punjab 1.249 * 0.076
Chi-square 853.977
-2 Log Likelihood 15730.0
Pseudo [R.sup.2] 0.104
Observations 1335
Model 5
Societal Shock/
No Shock
Correlates (2004) Coefficient S.E
Intercept -1.567 0.293
Male headed Households 0.194 0.225
Age HH Head 0.001 0.002
Head Education (Years) 0.002 0.008
Household Size 0.043 * 0.009
Dependency Ratio -0.080 ** 0.040
Poverty Status 0.153 ** 0.074
Female Ratio 0.110 0.215
Welfare Ratio -0.069 0.033
Land Ownership (Acres) -0.018 * 0.003
Livestock Ownership (no) -0.022 * 0.004
Credit Access 0.047 0.067
Member Abroad 0.334 *** 0.203
Sector of Employment 0.053 0.063
South Punjab/North Punjab -0.397 * 0080
Sindh/North Punjab -0.967 * 0.078
Chi-square
-2 Log Likelihood
Pseudo [R.sup.2]
Observations
Source: Author's computation is from the micro data
of PRHS 2004-05 and PPHS-2010.
* Significant at 1 percent, and ** Significant at 5 percent.
a. The reference category is: No shock.
Table 12
Multinomial Logit Model: The Probability of Experiencing a Shock
Model 6
Income Shock / Societal Shock/
No Shock No Shock
Correlates (2004) Coefficient S.E Coefficient S.E
Intercept -2.114 0.341 -1.259 0.299
Male Headed Households 0.736 ** 0.277 0.167 0.226
Age HH Head -0.004 0.002 0.001 0.002
Head Education (Years) -0.028 * 0.008 0.003 0.008
Household Size 0.049 * 0.008 0.037 * 0.009
Dependency Ratio 0.09 ** 0.038 -0.081 ** 0.040
Poverty Status -0.185 ** 0.079 0.099 0.075
Female Ratio 0.195 0.221 0.115 0.216
Welfare Ratio -0.053 ** 0.029 -0.069 ** 0.033
Land Ownership (Acres) 0.021 * 0.002 -0.015 * 0.004
Livestock Ownership (no) 0.005 0.005 -0.004 0.006
Credit Access -0.451 * 0.064 0.062 0.067
Member Abroad -0.218 0.252 0.371 *** 0.205
Sector of Employment 0.602 * 0.063 0.0129 * 0.062
South Punjab/North
Punjab 1.098 * 0.088 -0.444 * 0.082
Sindh/North Punjab 1.27 * 0.078 -1.073 0.081
Difference in
Landholding 0.012 * 0.003 -0.013 ** 0.006
Difference in Livestock -0.009 * 0.004 0.048 * 0.006
Difference in Welfare -0.035 0.026 -0.107 * 0.031
Chi-square 985.622
-2 Log Likelihood 15587.0
Pseudo [R.sup.2] 0.119
Observations 1335
Source: Author's computation is from the micro data of PRHS 2004-05
and PPHS-2010.
* Significant at 1 percent, and ** Significant at 5 percent
a. The reference category is: No shock.
Table 13
Household level Welfare Changes in Rural Pakistan from 2004 to 2010
Distribution of [dlnc.sub.i]
(changes in log consumption per capita)
Mean Standard Deviation % dlnc>0
Shock 0.18 0.71 62.5
No Shock 0.22 0.69 65.7
Agricultural Shock 0.18 0.67 62.9
Economic Shock 0.17 0.41 68.3
Social 0.27 0.62 73.0
Health 0.17 0.78 59.1
Overall 0.21 0.70 64.6
Punjab 0.23 0.65 67.2
North Punjab 0.30 0.60 70.0
South Punjab 0.16 0.70 64.5
Sindh 0.18 0.74 62.0
Source: Author's computation is from the micro data
of PRHS 2004-05 and PPHS-2010.
Table 14
Vulnerability: Sensitivity of Consumption Changes
and Household Characteristics
Dependent Variable: dlnc
(Change in log Consumption)
Explanatory Variables Coefficients Standard Errors
Intercept 0.27 0.04
Male Headed Households -0.15 0.144
Age HH Head (Years) 0.0035 *** 0.0014
Head Education (Years) 0.011 * 0.005
Household Size -0.08 * 0.019
Dependency Ratio 0.003 0.24
Female Ratio 0.26 0.13
Welfare Ratio 0.16 * 0.01
Land Ownership (Acres) 0.003 *** 0.001
Livestock Ownership (No) 0.035 * 0.014
Credit Access 0.010 0.006
Sector of Employment 0.024 * 0.009
Natural and Agriculture Shocks -0.17 ** 0.023
Economic Shocks -0.035 0.05
Social Shocks -0.0047 0.01
Health Shocks -0.036 ** 0.121
R-sq: Within Village = 0.27
Between = 0.16
Overall = 0.24
F(15,852) = 13.15
Prob > F = 0.0000
Fig. 1. Sources of Shocks in Rural Pakistan (%)
Crop Failure 28.0
Personal Loss_N disaster 12.1
Illness/disability_HH member 10.8
Illness/disability_earner 10.5
Drop in crop income 7.6
Loss of livestock_disease 7.5
Death_HH member 7.2
Death_earner 5.2
Other social shocks 4.7
Internaly displaced 2.1
Loss of personal assets_conflicts 1.7
Drop in crop income .8
Business failure_conflict .7
Loss of business assets_N disaster .6
Unsuccessful investment .5
Note: Table made from bar graph.
