Targeting the vulnerable and the choice of vulnerability measures: review and application to Pakistan.
Kurosaki, Takashi
In this paper, the concept of vulnerability of the poor's
welfare and its practical measures are scrutinised in order to derive
implications for targeting poverty reduction policies toward vulnerable
households. As illustration, various measures of vulnerability proposed
in the literature are applied to a panel data-set collected from rural
Pakistan. The empirical results show that different vulnerability
rankings can be obtained depending on the choice of the measure. By
utilising these measures, we can identify who and which region is more
vulnerable to a particular type of risk. This kind of information is
useful in targeting poverty reduction policies. Since the nature of
vulnerability is diverse, it is advisable to use the whole vector of
various vulnerability measures.
JEL classification: I32, I38.
Keywords: Vulnerability, Poverty, Risk, Consumption Smoothing,
Pakistan.
1. INTRODUCTION
In this paper, the concept of vulnerability of the poor's
welfare and its practical measures are scrutinised in order to derive
implications for targeting poverty reduction policies toward vulnerable
households. How different is the concept of vulnerability from that of
poverty in a narrow sense and how significant is the expansion of the
poverty concept into vulnerability? How has the vulnerability concept
been operationalised into measures that can be estimated from
quantitative and qualitative data? And what is the weakness of these
measures we need to keep in mind when we would like to target our
policies toward vulnerable households based on these measures? These are
the issues addressed in this paper.
Recently, interest on the dynamic characteristics of poverty in
low-income countries has increased, partly due to the availability of
high quality panel data and partly due to the development of
microeconometric tools to analyse household dynamics under uncertainty
[Dercon (2005); Fafchamps (2003); Townsend (1994); Udry (1994)]. Much
attention is now paid to poverty dynamics and security issues in
designing poverty reduction policies as well [World Bank (2000)]. An
emerging consensus is that poor households are likely to suffer not only
from low income and consumption on average, but also from fluctuations
of their welfare. The concept of vulnerability is often employed in
these analyses of the poverty dynamics. In the non-technical literature,
Chambers (1989) described vulnerability as "defenselessness,
insecurity, and exposure to risk, shocks, and stress" (p. 1), while
the World Bank (2000) described it as "the likelihood that a shock
will result in a decline in well-being" (p. 139). This paper
accepts these non-technical definitions and attempts to translate them
into the terminology of economics. A natural way to define vulnerability
in economics terms is to define it as a loss in forwardlooking welfare
due to low expected consumption, high variability of consumption, or
both [Ligon and Schechter (2003)].
There exists an emerging literature in development economics that
attempts to operationalise the concept of vulnerability. (1) One strand
of the literature approaches this issue based on the expected utility
theory. Another strand proposes measures of vulnerability that are
readily estimable from household datasets, without specifying the
household utility function. These attempts are reviewed in the second
section of this paper.
As illustration, these measures of vulnerability are empirically
estimated in the third section, using a panel dataset collected by the
author in the North-West Frontier Province (NWFP), (2) Pakistan. The
empirical exercise investigates the robustness of ranking households
based on various vulnerability measures. (3) Pakistan is a part of South
Asia, where more than 500 million people or about 40 percent are
estimated to live below the poverty line [World Bank (2000)]. In recent
debates on poverty in Pakistan, the issue of vulnerability has been
mentioned frequently [e.g., Pakistan (2003); World Bank (2002)].
Furthermore, the poverty incidence in Khyber Pakhtunkhwa is higher and
agriculture is more risky than in other parts of Pakistan. These
additional hardships make the Kyber Pakhtunkhwa case study an
interesting one to investigate vulnerability. In the final section,
implications of vulnerability analyses to poverty reduction policies are
discussed.
2. ANALYTICAL FRAMEWORK
2.1. Basic Concept of Welfare under Uncertainty This paper assumes
that the welfare level of an individual belonging to household i in
period t is determined by the level of per-capita real consumption,
[y.sub.it]. The most important determinant of [y.sub.it] is household
income per capita, xit. Due to exogenous shocks occurring to the income
generating process, such as drought, flood, price changes in the world
commodity markets, sickness and injury to the labour force, and changes
in policies, [x.sub.it] fluctuates. However, [y.sub.it] need not to be
equal to [x.sub.it]. Households can smooth consumption over time and
across states of nature using various assets and insurance arrangements,
ex post [Townsend (1994); Udry (1994); Kurosaki and Fafchamps (2002)].
When households' ex post risk-coping measures are limited, possibly
due to the underdevelopment of credit and insurance markets in low
income countries, they may adopt income smoothing measures, such as
income diversification and asset portfolio choices [Morduch (1994);
Kurosaki and Fafchamps (2002)]. Since these attempts to avoid
unnecessary fluctuations in consumption are usually far from perfect,
fluctuations in consumption as well as income are commonly observed in a
household panel dataset, including the one used in this paper.
An implicit assumption underlying this discussion is that
households have risk-averse preferences. Since the focus of this paper
is on the well-being of people whose average consumption is low, a small
reduction of consumption might imply a serious survival crisis for such
people. Thus the assumption of risk aversion can be justified. Unwanted
fluctuations in future consumption indeed imply a loss in
forward-looking welfare. This loss is regarded as vulnerability in this
paper. The vulnerability concept thus captures an aspect that cannot be
captured by orthodox poverty measures that aggregate the deprivation of
current welfare below the poverty line. Herein lies the significance of
the vulnerability concept.
2.2. Vulnerability Analysis Based on the Expected Utility Theory
When the preference of household i is represented by avon
NeumannMorgenstern utility function, [U.sub.i]([y.sub.i]), with
[U'.sub.i](.)>0, [U".sub.i] (.)<0, and given the
distribution of [y.sub.i], we can calculate the value of the expected
utility, E[[U.sub.i]([y.sub.i])], which is a convenient measure of
welfare under uncertainty. Ligon and Schechter (2002, 2003) thus
proposed a convenient way of defining vulnerability, Vi, as the
deviation of the welfare from the level corresponding to the poverty
line without uncertainty:
[V.sub.i] = [U.sub.i](z) - E[[U.sub.i]([y.sub.i])] (1)
where z is the poverty line, exogenously fixed. Equation (1) can be
decomposed as
[V.sub.i] = {[U.sub.i](z) - [U.sub.i](E[[y.sub.i]])} +
[U.sub.i](E[[y.sub.i]]) - E[[U.sub.i](E[[y.sub.i]])|W])]} +
{E[[U.sub.i](E[y.sub.i]|W])] - E[[U.sub.i]([y.sub.i])]}, (2)
where E[[y.sub.i]|W] indicates the expected consumption level
conditional on a vector of aggregate variables W, such as weather
shocks. The first term on the right-hand-side of Equation (2) shows the
vulnerability due to income poverty, the second term shows the
vulnerability due to welfare fluctuations arising from aggregate shocks,
and the last term shows the vulnerability due to welfare fluctuations
arising from idiosyncratic shocks. By aggregating over individuals
belonging to a particular group, we can calculate the value of the
group's vulnerability with neat decomposition. This is what Ligon
and Schechter (2002, 2003) implemented for the case of Bulgaria.
One aspect that cannot be directly analysed in their approach is
endogenous income smoothing adopted by households. The size of income
shocks may not be a fixed household characteristic. Faced with
uninsurable income shocks, households may choose an income portfolio
that yields a low return and low risk. In such a case, the expected
consumption level, E[[y.sub.i]] in Equation (2), may decline, but the
real cause of the decline is not the income poverty but the uninsurable
aggregate or idiosyncratic risks. A straightforward but only recently
developed approach to incorporate this aspect into a vulnerability
analysis is to completely specify a stochastic dynamic programming model
for households and then to employ simulation analyses [Elbers and
Gunning (2003); Zimmerman and Carter (2003)]. Then, the total measure of
vulnerability can be further decomposed into several factors by
simulating the household economy under different counterfactual
scenarios.
However, this approach requires panel data with detailed household
information over a long period. Such high quality panel data are seldom
available from developing countries. In addition, the simulation results
of this approach are difficult to interpret due to its complicated
dynamic interference. Furthermore, to make the model computationally
tractable, the number of state variables needs to be limited to one or
two (or at most three). This limits the applicability of the simulation
approach. The methodology by Ligon and Schechter (2002, 2003) can be
understood as a shortcut to avoid this problem by employing drastic
assumptions to simplify the household's optimisation problem.
2.3. Measures of Vulnerability in the Existing Literature
In contrast to the utility-based approach described above, a more
traditional approach has been to use practical measures of vulnerability
that are readily estimable from household datasets without specifying a
microeconomic model of households. Panel data of households usually
include information on household income, consumption, demographic
characteristics, and assets. Since the household welfare is determined
by per-capita real consumption ([y.sub.it]), most of the vulnerability
measures are the transformation of the observed level and variability of
[y.sub.it] in one way or another. The transformation can be interpreted
as a crude approximation of [U.sub.i](z) - E[[U.sub.i]([y.sub.i])] in
Equation (1). In this review, such measures are broadly classified into
two: those based on the observed level of variability of [y.sub.it] in
the past and those capturing the expected poverty in the future. The two
are intrinsically interrelated. Since vulnerability is a forward-looking
concept, measures based on the dynamics of consumption in the past can
be interpreted as a proxy for the dynamics of consumption in the future.
2.3.1. Measures Characterising Consumption Changes in the Past
(i) Those who Fell into Poverty
If it is assumed that only the deprivation below the poverty line
(z) should matter when vulnerability is evaluated, a transition matrix
analysis can be employed. Given panel data with information on
[y.sub.it] and [y.sub.i,t+1], households are classified into four
categories: those who remained poor ([y.sub.it]<z and
[y.sub.i,t+1]<z); those who fell into poverty ([y.sub.it][greater
than or equal to]z and [y.sub.i,t+1]<z); those who escaped poverty
([y.sub.it]<z and [y.sub.i,t+1][greater than or equal to]z); those
who remained non-poor ([y.sub.it][greater than or equal to]z and
[y.sub.i,t+1][greater than or equal to]z). The second type of households
may be regarded as vulnerable. This analysis closely replicates the
nontechnical definition of vulnerability as "the likelihood that a
shock will result in a decline in well-being" [World Bank (2000),
p. 139]. See Sen (1981), Grootaert and Kanbur (1995) and Sen (2003) for
empirical application of this approach.
(ii) Size of Consumption Decline
It may not be necessary to employ poverty lines in vulnerability
analyses if the major concern is on the household's exposure to
downside risk regardless of the level of consumption. Then, given a
two-period panel dataset, the lower [DELTA][y.sub.i], (or
[DELTA]ln([y.sub.it])), the more vulnerable the household is. This is
the approach adopted by Ravallion (1995), Jalan and Ravallion (1999),
and Glewwe and Hall (1998).
(iii) Decomposition of Poverty Measures into Transient and Chronic
Components
When the household consumption level [y.sub.it] falls below the
poverty line z, the welfare level of the household may go down
substantially, accelerating as poverty deepens. Most of the popular
poverty measures, such as FGT measures [Foster, et al. (1984)], are the
average over individuals of an individual's poverty score function
p(z, [y.sub.it]), which takes the value of zero when [y.sub.it][greater
than or equal to]z and a positive value when [y.sub.it]<z. Then, the
increase of a household's poverty score attributable to the
variability of [y.sub.it] can be interpreted as a measure of
vulnerability. This is achieved by subtracting [p.sup.C.sub.i] (= p(z,
E[[y.sub.i]])), the chronic poverty score, from [p.sup.P.sub.i] , i.e.
the time average of p(z, [y.sub.it]), or the total poverty score
[Ravallion (1988)]. The residual component of observed poverty can be
attributable to risk, denoted by [p.sup.T.sub.i], which is a measure of
household-level transient poverty, thus a measure of vulnerability. (4)
Since this decomposition is both practically manageable and has a
theoretical foundation (the expected utility hypothesis), it has been
applied to a number of household datasets from developing countries to
analyse the dynamics of poverty [Ravallion (1988); Jalan and Ravallion
(1998, 2000); McCulloch and Baulch (2000)]. As an extension, Kurosaki
(2006b) investigated the sensitivity of this decomposition to the
poverty line or to the average consumption level and finds that poverty
measures associated with prudent risk preferences (such as
Clark-Watt's measures) perform better than FGT measures.
(iv) Excess Sensitivity of Consumption to Income
A variant to these approaches defines a household as vulnerable to
risk when [y.sub.it] shows excess sensitivity to shocks in [x.sub.it],
due to insufficient insurance. Typically, an empirical model
[DELTA][y.sub.it] = [a.sub.0] + [b.sub.vt][D.sup.v.sub.t] +
[[xi].sub.i] [DELTA][x.sub.it], + [DELTA][u.sub.it], (3)
is estimated, where [D.sup.v.sub.t] is a village-year dummy,
[a.sub.0], [b.sub.vt], and [[xi].sub.i] are coefficients to be
estimated, and [u.sub.it] is an error term. Then the size and
statistical significance of [[xi].sub.i] show how household i is
vulnerable to idiosyncratic income shocks. (5) Although Amin, et al.
(2003) is the first study that explicitly defines the estimate for
[[xi].sub.i] as a measure of vulnerability, followed by Skoufias and
Quisumbing (2005), earlier studies that estimate [[xi].sub.i] interpret
it as a measure of vulnerability implicitly, such as those by Jalan and
Ravallion (1999) and Dercon and Krishnan (2000). This measure of
vulnerability is a very partial one in the sense that it captures the
potential degree of suffering from adverse shocks in terms of how much
consumption is likely to fall when income is reduced by a fixed amount
due to exogenous shocks.
Kurosaki (2006a) extended the equation above by treating the
positive and negative shocks separately and defined vulnerability only
when a household hit by a negative shock reduces its welfare level. He
also allowed the vulnerability parameter to differ across households
systematically according to the household asset status. Therefore, in
the empirical model of Kurosaki (2006a), [[xi].sub.i] differs depending
on the sign of [DELTA][x.sub.it] and it is approximated as a linear
function of household attributes that are likely to affect the level of
consumption smoothing at the household level. In the next section,
[[xi].sub.i] is estimated based on the approach by Kurosaki (2006a).
2.3.2. Measures Capturing Expected Poverty in the Future
Another strand of studies propose a measure of "vulnerability
to poverty," defined as the expected value of a poverty score in
the near future, conditional on the information up to the last period of
the household (panel) data. A general model according to Chaudhuri
(2000) and Chaudhuri, et al. (2002) could be written as
[[pi].sub.i] = E[p(z, [y.sub.i,T+1]) | [I.sub.T]],
where [I.sub.T] is the information set included in the panel
dataset of length T. As a poverty score function, headcount index (HCI)
is the most popular one because [[pi].sub.i] in this case has an
intuitive meaning of the future probability of household i falling below
the poverty line given the current information. Although the HCI-based
measure of vulnerability is useful in assessing the poverty status of
households, it does not account for the depth of poverty below the
poverty line. Because of this shortcoming, it may not be a good
indicator of vulnerability to risk. For instance, when the variability
of welfare becomes larger (mean-spreading risk), the measure becomes
smaller for households whose average welfare status is below the poverty
line, although the welfare level of such households is likely to decline
because of the increase in risk. (6) Noticing this problem, Kamanou and
Morduch (2005) proposed that [[pi].sub.i] - p(z, [y.sub.i,T]) should be
a measure of vulnerability rather than [[pi].sub.i] itself and convex
functions such as those associated with the squared poverty gap should
be used for function p(.) rather than the one associated with the
headcount measure.
In estimating [[pi].sub.i], Chaudhuri (2000) and Chaudhuri, et al.
(2002) suggested that it can be estimated from cross-section information
only, if an identifying assumption is accepted that the expected level
of [y.sub.i,t+1] is a function of household attributes in t and the
time-series variance of [y.sub.i,+1] is the same as the cross-section
variance of [y.sub.it], which is also a function of the same variables.
(7) Since the identifying assumption is hard to accept, it is not
adopted in the next section of this paper. At the other extreme from
Chaudhuri's assumption, McCulloch and Calandrino (2003) estimated
[[pi].sub.i] using observed values of time-series means and variances of
[y.sub.it] for each i. This methodology is useful if T is sufficiently
large, but their dataset includes only five time periods. In between,
Pritchett, et al. (2000), Mansuri and Healy (2001), and Kamanou and
Morduch (2005) estimated [[pi].sub.i], using cross-section variation of
[DELTA][y.sub.it]. See Ligon and Schechter (2004) for Monte Carlo
experiments varying the number of periods T, in order to see how the
different measures perform.
For the case of Pakistan, Mansuri and Healy (2001) estimated
[[pi].sub.i] using five-year panel data collected by the International
Food Policy Research Institute (IFPRI), covering districts of Dir,
Attock, Faisalabad, and Badin, for the period 1986-87-1990-91. (8) It is
important that their estimates are based on the information on
cross-section variation of [DELTA][y.sub.it] (observed changes in
consumption), which is available only from panel data. Following their
approach, in the next section, the expected value of the headcount
measure is estimated for Khyber Pakhtunkhwa using a model where the mean
and variance of [DELTA][y.sub.it] are assumed to be functions of
household attributes in the initial period.
In non-technical literature, the vulnerable are sometimes defined
as those who are just above the poverty line z. For instance,
Pakistan's Poverty Reduction Strategy Paper calls those whose
income is between 100 percent and 125 percent of z "transitory
vulnerable" [Pakistan (2003), Figure 3.1, p. 13]. This concept can
be interpreted as an application of [[pi].sub.i] (the probability of
being below the poverty line in the near future). If we admit that
purely cross-section data do not contain meaningful information on the
individual-level income variability over time, the only alternative is
to assume that the variance of the individual-level income variability
over time is constant. With this simplifying assumption, the individuals
who were just above the poverty line z are those subject to the largest
risk of being poor in the near future among the non-poor. In other
words, the concept of the vulnerable as those who are just above z has a
theoretically-sound base. The underlying assumption is more acceptable
than Chaudhuri's (2000) assumption applied to a purely
cross-section data that the time-series variance of [y.sub.it] can be
inferred from its cross-section variance.
2.3.3. Measures Using Information other than Income and Consumption
Since economists tend to focus on monetary aspects of well-being,
vulnerability measures reviewed so far are defined on the consumption
space. However, we need to recall that consumption is only one of the
determinants of well-being. When other determinants such as education,
health, mortality, and so on, are controlled for, we can infer the level
and variability of welfare only from looking at the level and
variability of consumption.
Therefore, it is desirable to extend the vulnerability analysis
with a focus on welfare indicators other than consumption. In this
direction, Carter and May (2001) first searched for an asset that is
highly correlated with various determinants of welfare, and then applied
the vulnerability measures surveyed in this subsection to this asset.
Alternatively, Dercon and Krishnan (2000) regarded the change of body
mass index (BMI) as an index of individual's vulnerability and
applied the vulnerability measure of excess sensitivity to income shocks
([[xi].sub.i]) to the BMI change in Ethiopia. Similar analyses can be
applied to education investment as well, as done by Jacoby and Skoufias
(1997)
and Sawada and Lokshin (2009). These authors showed that less
landed households in South Asia are more vulnerable to education
interruption than more landed households.
3. EMPIRICAL APPLICATION TO PAKISTAN
3.1. Data
As illustration, this section applies the various measures of
vulnerability reviewed in Subsection 2.3 to a panel dataset compiled
from sample household surveys implemented in 1996 and 1999 in the
Peshawar District, Khyber Pakhtunkhwa. (9) The incidence of income
poverty in Khyber Pakhtunkhwa was estimated at around 40 to 50 percent
throughout the 1990s, the highest among the four provinces [World Bank
(2002)]. Not only income poverty but also the deprivation in other
aspects of human development is serious in Khyber Pakhtunkhwa.
Achievement in education and health development in Khyber Pakhtunkhwa is
lagging behind other provinces and gender disparity in education is
especially huge in rural Khyber Pakhtunkhwa.
Three villages surveyed are similar in their size, socio-historical
background, and tenancy structure, but are different in levels of
economic development (irrigation and market access). Table 1 summarises
characteristics of the sample villages and households. Village A is
rainfed and is located some distance from main roads. This village
serves as an example of the least developed villages with high risk in
farming. Village C is fully irrigated and is located close to a national
highway, so serves as an example of the most developed villages with low
risk in farming. Village B is in between.
Out of 355 households surveyed in 1996, 304 households were
resurveyed in 1999. From these sample households, a balanced panel of
299 households with two periods is compiled for analysis in this
section. Average household sizes are larger in village A than in
villages B and C, reflecting the stronger prevalence of an extended
family system in village A. Average landholding sizes are also larger in
village A than in villages B and C. Since the productivity of rainfed
land is substantially lower than that of irrigated land, effective
landholding sizes are similar among the three villages.
Real consumption per capita, [y.sub.it], was calculated by summing
annual expenditures on each consumption item including its imputed value
when domestically produced, divided by the household size and by the
consumer price index. (10) Average consumption per capita is lowest in
village A and highest in village C, although intra-village variation is
much larger than inter-village variation. During the three years since
the first survey, Pakistan's economy suffered from macroeconomic
stagnation, resulting in an increase in poverty [World Bank (2002)].
Reflecting these macroeconomic shocks, the general living standard
stagnated in the villages during the study period.
The official poverty line determined by the Government of Pakistan
is adopted in this section. It is set at 673.54 Rs in 1998-99 prices per
month per adult, which is estimated econometrically as the total
consumption expenditure amount corresponding to the food consumption of
2,350 kcal per day per adult. Based on this poverty line, 55.0 percent
of individuals are classified as "always poor" ([y.sub.it]
< z in both periods), 13.1 percent as "usually poor"
([average.sub.t][[y.sub.it]] <z and [max.sub.t][[y.sub.it]] [greater
than or equal to] z), 16.4 percent as "occasionally poor"
([average.sub.t][[y.sub.it],] [greater than or equal to] z and
[min.sub.t][[y.sub.it],] < z), and 15.5 percent as "always
non-poor" ([y.sub.it], [greater than or equal to] z in both
periods) in this dataset [Kurosaki (2006b)].
3.2. Empirical Results
The main question to be asked is: What is the best criterion for
targeting the most vulnerable? To answer this question, three candidates
for the targeting criterion were investigated: (i) geographical
targeting: villages A, B, or C, (ii) land-based targeting: households
belonging to the land-owning families versus others, (11) and (iii)
education-based targeting: households whose head was educated in formal
schools versus others.
Table 2 lists empirical measures estimated from the Pakistan data.
In addition to vulnerability measures based on per-capita real household
consumption, [y.sub.it], those based on education and subjective
assessment of vulnerability were also calculated. Regarding education,
the ratio of individuals belonging to households that experienced a
decline in children's enrollment (i.e., those households whose age
6-7 enrollment ratio in 1996 was larger than their age 9-10 enrollment
ratio in 1999) was calculated as a measure of education vulnerability.
The subjective assessment of vulnerability by the household head is
based on questions on whether the household experienced downside risk in
1996-99, and, if yes, how the household responded to the downside risk
in 1996-99. Unfortunately, the current dataset does not include useful
information on health. (12) In addition to the vulnerability measures,
measures of chronic poverty are also reported in the table for
comparison. All vulnerability measures in the table require panel data,
except for the subjective assessment of vulnerability that can be
elicited through retrospective questions. In contrast, most measures of
chronic poverty can be estimated from a single cross-section dataset.
The empirical results are shown in Table 3. (13) Among villages,
chronic poverty is most serious in village A and least serious in
village C. This reflects the survey design. Landed households suffer
less from chronic poverty than landless households and households with
educated heads suffer less from chronic poverty than households with
uneducated heads. The contrast is clearly shown regardless of the choice
of a particular measure of chronic poverty.
Among the seven vulnerability measures based on per-capita real
consumption, four measures show the contrast among villages, landholding
status, and education status very similar to the one found from chronic
poverty measures. The four measures include the average consumption
decline (Cons_decline), the ratio of individuals who experienced a
consumption decline (S_c_decline), the size of transient poverty a la
Ravallion (1988) (Trans_Pov), and the expected value of poverty
headcount index ([[pi].sub.0]).
On the contrary, the ratio of individuals belonging to the
"occasionally poor" (S_occ_poor) shows an exactly opposite
pattern: the ratio is higher in village C, among landed households, and
among educated households. This is because this measure of vulnerability
puts a heavy weight on consumption variability on the condition that the
chronic poverty level is not high. The reason for the ratio of
individuals who fell into poverty (S_fell_poor) to be higher in village
C is similar, although this ratio is higher among landless and among
uneducated households. The estimates for the excess sensitivity
parameter to income decline ([xi]_neg) show that landless households are
more vulnerable than landed households, reflecting the advantage of
landholding in consumption smoothing [Kurosaki (2006a)]. Against the
expectation that more educated households are more able to smooth
consumption, [xi]_neg is higher for educated households than for
uneducated households. Kurosaki (2006a) showed that the unexpected
result was due to a fact that households with educated heads were on
average richer than others so that they had room to reduce consumption
expenditure when hit by a negative shock without reducing the core
components of consumption. After controlling for the difference in
average consumption level, [xi]_neg was found to be smaller for educated
households than for uneducated households.
Table 3 also reports three vulnerability measures based on
education and subjective risk assessment. S_no_cope shows a contrast
similar to the one found from chronic poverty measures. This ratio shows
the household's subjective assessment that the household had no
other way to cope with income decline than to reduce their consumption.
Therefore, the inability to cope with downside risk through asset
markets or through reciprocity networks is closely related with the
depth of chronic poverty. Those who are chronically poor are also very
vulnerable in this sense. On the other hand, S_enrl_decline (the ratio
of individuals belonging to households who experienced a decline in
their children's school enrolment ratio) does not show such a
contrast. This is because this measure of education vulnerability
becomes positive only when households were able to send some or all of
their children to school in the initial period. In rural Pakistan, many
of the households who suffer from chronic poverty do not send their
children to school at all [Sawada and Lokshin (2009)]. In such cases,
this measure of education vulnerability is not very useful; measures of
chronic deprivation in education could be more useful.
Let us summarise the empirical answer to the main question. First,
among the three villages, households in village A seem more vulnerable
than those in villages B and C. Six out of the ten vulnerability
measures in Table 3 show this ranking. However, several vulnerability
measures that put a heavy weight on the decline of a determinant of
well-being do not agree with this conclusion (vulnerability is highest
in village C, not in village A), since these measures become positive
only when the initial welfare status is not at the bottom. Second,
households belonging to the land-owning families are less vulnerable
than others. Eight out of the ten vulnerability measures in Table 3
support this contrast. Here again, several vulnerability measures do not
agree with this pattern, especially when the measures are sensitive to
farming risk. Third, households whose head is educated are less
vulnerable than others. Six out of the ten vulnerability measures in
Table 3 show this contrast. Several measures, especially the measure of
education vulnerability, show the opposite pattern, mostly due to the
reason that they can take a positive value only when the initial
enrolment ratio was strictly positive. Fourth, these results show that
it is not possible to draw a definite conclusion regarding the best
criterion for targeting the most vulnerable: geographical, land-status,
or education-status. Depending on the choice of vulnerability measures,
the conclusion differs.
For those vulnerability measures that are the average of continuous
scores at the household level, correlation coefficients using micro
observations were calculated and reported in Table 4. (14) Most of the
coefficients among the four vulnerability measures were small in
absolute values. This indicates that these measures capture different
aspects of vulnerability. Since each of them has information not
included in others, these measures can be employed simultaneously as
complementary measures. When correlation coefficients between the
vulnerability measures and the chronic poverty measures were calculated
(Table 4), the expected value of headcount index ([[pi].sub.0]) was
found to be highly correlated with the chronic poverty measures based on
per-capita real consumption (Cons_low and Chron_Pov in the table). This
is as expected since the expected HCI decreases with the observed
consumption level by definition. Therefore, the information gain
additional to the one already included in chronic poverty measures may
not be large if the expected HCI is employed while it is likely to be
substantially large if other measures of vulnerability are employed.
Since these measures capture different aspects of the welfare cost of
consumption variability, all of them can serve as useful tools to extend
the poverty analysis in the dynamic context.
4. CONCLUSION
This paper surveyed the literature on the concept of vulnerability
of the poor's welfare and its practical measures and then applied
the measures to a panel dataset collected in rural Pakistan. By
specifying a household's utility and the expected flow of its
consumption, it is possible to decompose vulnerability into several
sources and to evaluate the impact of policy changes numerically.
However, this utility-based methodology requires drastic assumptions to
simplify the household's optimisation problem, or, simulations
based on a stochastic dynamic model using high quality panel data. In
contrast, there have been proposed a number of practical measures of
vulnerability that are readily estimable from household datasets, such
as the average consumption decline, the sensitivity of consumption
changes to income changes, the component of observed poverty
attributable to the fluctuation of consumption, and the probability of
falling below the poverty line in the future. The empirical exercise
showed that different conclusions can be drawn on the question who is
more vulnerable, depending on the choice of the measure.
These results suggest that the various measures of household
vulnerability to risk are useful tools to extend the poverty analysis in
the dynamic context. Each of the existing measures captures different
aspects of vulnerability. Most of them include information not included
in chronic poverty measures. This kind of information is especially
useful in targeting poverty reduction policies. Since the nature of
vulnerability is diverse, it is not advisable to search for a single
index of vulnerability. Instead, the whole vector of various
vulnerability measures could be employed as a useful source of
information. When the majority of the measures unanimously indicate a
particular group to be vulnerable, the group should be targeted with the
first priority for any type of poverty/vulnerability reduction policies.
When only a subset of the measures indicate another group to be
vulnerable, the group should be targeted with a policy that attempts to
reduce the particular type of risk.
The survey in this paper showed that most of the vulnerability
measures summarise micro-level information on consumption and income.
Since the welfare of an individual depends not only on consumption but
also on other non-monetary aspects such as education and health,
extending the vulnerability analysis to incorporate these aspects is
important. This is one of the areas that require more research.
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(1) See for example, Ligon and Schechter (2002), Hoddinott and
Quisumbing (2003), Calvo and Dercon (2005) and Dercon (2006) for a
survey of the literature on vulnerability analyses in developing
countries.
(2) In April 2010, the constitution of Pakistan was amended,
including the renaming of the former NWFP as "Khyber
Pakhtunkhwa." In this paper, since all data correspond to a period
before this constitutional amendment, the expression "NWFP" is
used to infer the current province of "Khyber Pakhtunkhwa."
(3) Among the existing studies, Ligon and Schechter (2004)
implemented a similar exercise of comparing the performance of various
vulnerability measures. They investigated the cases of Vietnam and
Bulgaria.
(4) Note that for this approach to be consistent with a risk-averse
behaviour of households, the poverty score function p(z, [y.sub.it],)
should be increasing and convex with the size of deprivation
z-[y.sub.it], For this reason, the squared poverty gap index is the most
popular choice as a functional form for p(z, [y.sub.it],).
(5) For a theoretical base of this interpretation, see
Townsend's (1994) model of Pareto- optimal risk sharing among
villagers. Since the model assumption of Pareto-optimality is unlikely
to be satisfied in the empirical reality, his theoretical model should
be regarded as a benchmark to evaluate the actual situation. See also
Ravallion and Chaudhuri (1997) for further notes required in
implementing empirical analyses based on his model.
(6) See also Ravallion's (1988) decomposition, where he
demonstrated that not all poverty measures respond positively to the
increase in consumption variance. The headcount index has the least
desirable property.
(7) Extending this approach based on the cross-section variation of
[y.sub.it], Christiaensen and Subbarao (2005) incorporated observed
time-series variation of semi-macro variables.
(8) Their methodology and results are summarised in World Bank
(2002), pp. 28-32, and pp. 135-138.
(9) See Kurosaki and Hussain (1999) and Kurosaki and Khan (2001)
for details of the 1996 household survey and the 1999 household survey,
including the definition of "household". Regarding the issues
discussed in this paper, Kurosaki (2006b) investigated the sensitivity
of Ravallion's poverty decomposition into transient and chronic
components, and Kurosaki (2006a) estimated the excess sensitivity
parameter of consumption to incomes, using the same dataset.
(10) The actual number of household members was used in this paper
as a measure of household size. Alternatively, the household size can be
estimated in terms of an equivalence scale that reflects differences in
sex/age structure and corrects for the scale economy [Lanjouw and
Ravallion (1995)]. Results under the alternative specifications were
qualitatively the same as those reported in this paper.
(11) To avoid endogeneity problems and to control for life-cycle
factors, we adopt the classification whether the household belongs to
the land-owning families, rather than the classification based on the
current landholding status. The two classifications are positively
correlated but the correlation coefficient is less than one.
(12) Health indicators based on the household head's judgment
were collected in the survey but they were subject to severe reporting
errors.
(12) Health indicators based on the household head's judgment
were collected in the survey but they were subject to severe reporting
errors.
(13) The values reported as [[pi].sub.0] and [xi]_neg are the group
averages of [[pi].sub.0,i] and [xi]_[neg.sub.i] that were estimated for
each household i. [[pi].sub.0,i] was estimated by a model reported in
Subsection 2.3.2 with the mean and variance of [DELTA][y.sub.it] as
functions of households' initial attributes such as the household
size, dependency ratios, the age and education levels of household
heads, sources of income, land assets, and other assets.
[xi]_[neg.sub.i] was estimated by a model reported in Subsection 2.3.1
(iv) with [[xi].sub.i] on the income decline approximated by a linear
function of similar variables [Kurosaki (2006a)].
(14) See Ligon and Schechter (2004) for similar exercises done for
the cases of Vietnam and Bulgaria.
Takashi Kurosaki <kurosaki@ier.hit-u.ac.jp> is Professor at
the Institute of Economic Research, Hitotsubashi University, 2-1 Naka,
Kunitacbi, Tokyo, Japan.
Author's Note: The author is grateful to anonymous referees of
this journal, to Laura Schechter seminar participants of the ASAE
Conference and the research meetings at Hitotsubashi University, the
Japan Bank for International Cooperation, and the United Nations
University for useful comments on earlier versions of this paper.
Table 1
Sample Villages and the Panel Data (Khyber Pakhtunkhwa, Pakistan)
Village A Village B Village C
1. Village Characteristics
Agriculture Rainfed Rain/Irrig. Irrigated
Distance to Main Roads (km) 10 4 1
Population (1998 Census) 2,858 3,831 7,575
Adult Literacy Rates (1998 25.8 19.9 37.5
Census)
2. Characteristics of Panel
Households
Number of Sample Households 83 111 105
Average Household Size
in 1996 10.75 8.41 8.95
in 1999 11.13 7.86 9.3
Average Farmland Owned
in 1996 (ha) 2.231 0.516 0.578
in 1999 (ha) 2.258 0.517 0.595
Average per Capita Income
in 1996 (Nominal US$) 194.4 231.2 336.6
in 1999 (Nominal US$) 147.8 164.7 211.6
Average per Capita Consumption
in 1996 (Nominal US$) 134.4 157.0 200.8
in 1999 (Nominal US$) 133.5 143.1 198.3
Source: The author's calculation (the same for the following tables).
Notes: (1) "Average per capita income" and "Average per capita
consumption" are averages based on individuals. They were calculated
as the household average with household size as weights.
(2) "Average farmland owned" is an average over all the sample
households.
Table 2
Definitions of Vulnerability/Poverty Measures Used in the Empirical
Analysis
Measure Definition
Vulnerability Measures (the
Larger Its Value, the more
Vulnerable)
l. Those Based on Per Capita
Real Consumption ([y.sub.it])
Cons_decline Average size of consumption decline
(group-average of
-[DELTA]ln([y.sub.it]))
S_c_decline Ratio of individuals who experienced
consumption decline ([y.sub.it] >
[y.sub.i,t + 1])
S_fell_poor Ratio of individuals who "fell into
poverty" ([y.sub.it] [greater than or
equal to] z and [y.sub.i,t + 1] < z)
S_occ_poor Ratio of individuals belonging to the
"occasionally poor"
Trans _pov Ravallion's decomposition: Squared
poverty gap attributable to
consumption fluctuations
[xi]_neg Parameter estimate for "excess
sensitivity" of consumption to income
decline according to the model of
Kurosaki (2006a)
[[pi].sub.0] Expected value of poverty headcount
index based on the information on
consumption changes
2. Those Based on Non-monetary
Measures
S_enrl_decline Ratio of individuals belonging to
households with the age 6-7 enrolment
ratio in 1996 larger than the age
9-10 enrolment ratio in 1999.
S_drisk Ratio of individuals belonging to
households with subjective risk
assessment that the household
experienced downside risk in 1996-99
S_no_cope Ratio of individuals belonging to
households with subjective risk
assessment that the household
responded to the downside risk in
1996-99 mainly by reducing
consumption
Measures of Chronic Poverty
(the Larger Its Value, the
Poorer)
1. Those Based on Per Capita
Real Consumption ([y.sub.it])
Cons_low Average deprivation below the poverty
line
[=(z-[average.sub.t]([y.sub.it]))/z]
S_chronic Ratio of individuals whose average
consumption was below the poverty
line
Chron_pov Ravallion's decomposition: Squared
poverty gap attributable to the low
level of average consumption
2. Those Based on Non-monetary
Measures
Edu_head Household head's schooling years as
the deprivation below the overall
average
Illiterate Adult (age 15 and above) illiteracy
ratio
S_enrl_low Ratio of individuals belonging to
households with the age 6-7 enrolment
ratio in 1996 smaller than unity
Table 3
Estimated Values of Vulnerability/Poverty Measures
(Klryber Paklitunkhwa, Pakistan, 1996-2000)
By Village
Total A B C
NOB 299 83 111 105
Vulnerability Measures (the Larger Its Value, the more Vulnerable)
1. Those Based on Per Capita Real Consumption ([y.sub.it])
Cons_decline -0.033 -0.008 -0.026 -0.063
S_c_decline 0.274 0.366 0.252 0.207
S_fell_poor 0.136 0.126 0.131 0.149
S_occ_poor 0.164 0.157 0.099 0.233
Trans_pov 0.017 0.021 0.016 0.014
[xi]_neg 0.084 0.053 0.092 0.105
[[pi].sub.0] 0.586 0.720 0.662 0.387
2. Those Based on Non-monetary Measures
S_enrl_decline 0.073 0.082 0.048 0.089
S_drisk 0.637 0.714 0.601 0.598
S_no_cope 0.323 0.416 0.359 0.202
Measures of Chronic Poverty (the Larger Its Value, the Poorer)
1. Those Based on Per Capita Real Consumption ([y.sub.it])
Cons_low 0.066 0.230 0.133 -0.152
S_chronic 0.681 0.816 0.755 0.484
Chron_pov 0.069 0.102 0.088 0.020
2. Those Based on Non-monetary Measures in 1996
Edu_head * 0.000 0.448 0.088 -0.507
Illiterate 0.753 0.809 0.804 0.651
S_enrl_low 0.361 0.538 0.361 0.192
By Land By Education
Landless Landed No Educ. Primary or More
NOB 159 140 217 82
Vulnerability Measures (the Larger Its Value, the more Vulnerable)
1. Those Based on Per Capita Real Consumption ([y.sub.it])
Cons_decline 0.008 -0.076 -0.023 -0.058
S_c_decline 0.334 0.212 0.294 0.221
S_fell_poor 0.156 0.115 0.143 0.116
S_occ_poor 0.140 0.190 0.156 0.187
Trans_pov 0.018 0.015 0.019 0.011
[xi]_neg 0.165 0.001 0.073 0.111
[[pi].sub.0] 0.679 0.490 0.610 0.522
2. Those Based on Non-monetary Measures
S_enrl_decline 0.076 0.070 0.067 0.090
S_drisk 0.634 0.641 0.631 0.652
S_no_cope 0.334 0.312 0.351 0.251
Measures of Chronic Poverty (the Larger Its Value, the Poorer)
1. Those Based on Per Capita Real Consumption ([y.sub.it])
Cons_low 0.171 -0.043 0.133 -0.110
S_chronic 0.810 0.548 0.732 0.545
Chron_pov 0.082 0.056 0.075 0.054
2. Those Based on Non-monetary Measures in 1996
Edu_head * 0.311 -0.322 1.000 -2.625
Illiterate 0.799 0.705 0.850 0.498
S_enrl_low 0.363 0.358 0.391 0.281
Notes: (1) All figures are weighted averages among households with the
number of household members as weights. Thus, these figures can be
interpreted as the individual-level averages. "NOB" gives the number
of sample households included in each category.
(2) * Indicates that the deviation is from the overall average and
then divided by the overall average. For example. the value of 0.448
for Ed"head in village A indicates that households in village A have
44.8 percent below the average in terms of the head's schooling years.
Table 4
Correlation Coefficients among Vulnerability/Poverty Measures
(Khyber Pakhtunkhwa, Pakistan, 1996-2000)
Vulnerability Measures
Cons_decline Trans_pov [xi]_neg [[pi].sub.0]
Vulnerability Measures (the Larger Its Value, the more Vulnerable)
Cons_decline 1.000 -0.049 0.170 0.536
Trans_pov 1.000 -0.006 0.003
[xi]_neg 1.000 0.224
[[pi].sub.0] 1.000
Measures of Chronic Poverty (the Larger Its Value, the Poorer)
Cons_low
Chron_pov
Chronic Poverty Measures
Cons_low Chron_pov
Vulnerability Measures (the Larger Its Value, the more Vulnerable)
Cons_decline 0.034 0.015
Trans_pov 0.084 -0.113
[xi]_neg 0.059 -0.067
[[pi].sub.0] 0.691 0.632
Measures of Chronic Poverty (the Larger Its Value, the Poorer)
Cons_low 1.000 0.627
Chron_pov 1.000
Note: Correlation coefficients are calculated among households with
the number of household members as weights.
