metadata: authors: 0: author: Pritchett, Lant
1: author: Suryahadi, Asep
2: author: Sumarto, Sudarno
count: Indonesia
docna: 0: docna: Quantifying vulnerability to poverty - a
            proposed measure, applied to Indonesia
docty: Policy Research Working Paper
subtopic: Educational Sciences,Social Development & Poverty,Gender and Development,Economics and Gender,Gender and Poverty,Gender and Economic Policy,Inequality,Gender and Economics
teratopic: Poverty Reduction
abstracts: cdata!: Vulnerability is an important aspect of
            households' experience of poverty. Many households,
            while not currently in poverty, recognize that they are
            vulnerable to events - a bad harvest, a lost job, an
            illness, and unexpected expense, an economic downturn - that
            could easily push them into poverty. Most operational
            measures define poverty as some function of the shortfall of
            current income, or consumption expenditures from a poverty
            line, and hence measure poverty only at a single point in
            time. The authors propose a simple expansion of those
            measures to quantify vulnerability to poverty. They define
            vulnerability as a probability, the risk that a household
            will experience at least one episode of poverty in the near
            future. A household is defined as vulnerable if it has 50-50
            odds, or worse of falling into poverty. Using those
            definitions, they calculate the "vulnerability of
            poverty line" (VPL) as the level of expenditures below
            which a household is vulnerable to poverty. The VPL allows
            the calculation of a "headcount vulnerability
            rate" (the proportion of households vulnerable to
            poverty), a direct analogue of the "headcount poverty
            rate". The authors implement this approach using two
            sets of panel data from Indonesia. First they show that if
            the poverty line is set so that the headcount poverty rate
            is twenty percent, the proportion of households vulnerable
            to poverty is roughly 30-50 percent. In addition to the
            twenty percent currently poor, an additional 10-30 percent
            of the population is at substantial risk of poverty. They
            illustrate the usefulness of this approach for targeting, by
            examining differences in vulnerability between households by
            gender, level of education, urban-rural residence,
            land-holding status, and sector of occupation of the head of household.
geo_reg_key: 517198,517191
repnme: repnme: Quantifying vulnerability to poverty - a
            proposed measure, applied to Indonesia
display_title: Quantifying vulnerability to poverty
            - a proposed measure, applied to Indonesia
listing_relative_url: /research/2000/09/693281/quantifying-vulnerability-poverty-proposed-measure-applied-indonesia
topicv3: Female-Headed Households m1326228 916,Poverty m1326687 695,Social Insurance m1326950 751,Education m1327048 470,Gender m1327621 615
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Environmt & Social Devl Sctr Dpt (EASES)
no_of_pages: 36
model_extractions: POLICY RESEARCH WORKING PAPER 2437

# **Summary findings**

Vulnerability is an important aspect of households' experience of poverty. Many households, while not currently in poverty, recognize that they are vulnerable to events-a bad harvest, a lost job, an illness, an unexpected expense, an economic downturn-that could easily push them into poverty.

Most operational measures define poverty as some function of the shortfall of current income or consumption expenditures from a poverty line, and hcnce mcasure poverty only at a single point in time.

Pritchett, Suryahadi, and Sumarto propose a simple expansion of those measures to quantify vulnerability to poverty. They define vulnerability as a probability, the risk that a household will experience at least one episode of poverty in the near future. A household is defined as vulnerable if it has 50-50 odds or worse of falling into poverty.

expenditures below which a household is vulnerable to poverty. The VPL allows the calculation of a "headcount vulnerability rate" (the proportion of households vulnerable to poverty), a direct analogue of the "headcount poverty rate." The authors implement this approach using two sets of panel data from Indonesia. First they show that if the poverty line is set so that the headcount poverty rate is 20 percent, the proportion of households vulnerable to poverty is roughly _30-50_ percent. In addition to the 20 percent currently poor, an additional 10-30 percent of the population is at substantial risk of poverty.

They illustrate the usefulness of this approach for targeting by examining differences in vulnerability between households by gender, level of education, urban-rural residence, land-holding status, and sector of occupation of the head of household.

Using those definitions, they calculate the "vulnerability to poverty line" (VPL) as the level of This paper-a product of the Environment and Social Development Sector Unit, East Asia and Pacific Region-is part of a larger effort in the region to develop a national poverty reduction strategy for Indonesia. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washi[ngton, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-473-3902, fax 202-522-1153, emnail address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Lant Pritchett may be contacted at lant_pritchett@harvard.edu. September 2000. (31 pages) _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countPtes they represent._ Produced by the Policy Research Dissemination Center
Quantifying Vulnerability to Poverty: A Proposed Measure, Applied to Indonesia Lant Pritchett (World Bank)

## Asep Suryahadi (SMERU)

Sudarno Sumarto (SMERU)

# Social Monitoring and Early Response Unit

# Jakarta

The findings and interpretations in this paper are those of the authors, and should not be attributed to the World Bank Group or to any agencies providing financial support to SMERU activities and report.

This paper is an extensive revision of the December 1999 version which adds estimates using a second panel data set (the Mini-Susenas). We thank Esther Duflo, Menno Pradhan, Martin Ravallion, and David Levine for their valuable comments and suggestions on the earlier version. We also thank Wenefrida Dwi Widyanti and Yusuf Suharso for their research assistance. We are grateful to BPS, UNDP, and UNICEF for providing access to the data.
_I) Introduction_ One aspect of poverty that emerges strongly from people's descriptions of their experience is the notion of vulnerability (Beard, 1998). Many households, while not currently in poverty, recognize that they are vulnerable and that events could easily - push them into poverty a bad harvest, a lost job, an unexpected expense, an illness, a lull in business.' This aspect of vulnerability is not captured when poverty is defined as a function of the shortfall of _current_ consumption expenditures from a "poverty line" (current consumption expenditures deficit or CCED concept).<sup>2</sup> We propose a simple expansion of these static poverty measures to include vulnerability. Vulnerability is defined as the risk a household will fall into poverty in the future, and we propose a simple empirical measure that allows the setting of a "Vulnerability to Poverty Line" and thereby estimates of the "Headcount Vulnerable to Poverty Rate," which is commensurate with traditional headcount poverty rate. While the proposed quantitative measure does not begin to capture all of the complex, multifaceted, dimensions of the concept of vulnerability, this measure at least begins to put vulnerability on a par with static poverty measures in analytic and policy interest.

In the developed countries context, Goodin _et al_ (1999) find that much income instability is strongly associated with ordinary lifecourse events. In addition to traditional lifecourse events driven by age, people now have increasingly to cope with new ones, in particular separation, divorce, and early retirement.

2 Examples of CCED measures of poverty are the FGT poverty indices from Foster, Greer, and Thorbecke (1984) as elaborated in Ravallion (1994) and many World Bank Poverty Assessments.

2
**_II)_** _Defining Vulnerability_

_Definitions._ While _vulnerability_ is a complex concept, to begin quantification we must both simplify and use what is measured. We define our empirical measure of vulnerability as the risk a household will fall into poverty at least once in the next few years. This means that a household's vulnerability is measured as a probability, hence households have greater or lesser degrees of vulnerability. Since the future is uncertain, the magnitude of vulnerability rises with the time horizon, so vulnerability over the next week will be quite low, over a year higher, and over several years the risk will be higher still. The _vulnerability_ of household h for n periods (denoted as R(-) for "risk") is the probability of observing at least one episode of poverty (in the usual CCED notion that real current consumption expenditures, e, are less than the poverty line) for n periods, which is one minus the probability of no episodes of poverty: _R(n, PL)_ = 1- [(I - _P(e7,^ < PL)) * .. * (1 - P(e7" < PL))J_ Four points on this concept. First, since expenditures at time t are known, it is also known whether a household is currently in poverty or not. In the future, however, many households currently in poverty will rise out of poverty in the next n periods, so the future vulnerability of the currently poor is less than one. Second, the poverty line (PL) is time invariant because the household's total real expenditure e is appropriately deflated, so that a constant poverty line on those expenditure units represents constant levels of welfare over time. Third, by defining the notion in terms 3
of observed expenditures, this measure of vulnerability already incorporates the existence and use of coping mechanisms. Some households may face large income variability and risk but have adequate mechanism to smooth over income changes and maintain expenditures relatively constant (e.g. savings, borrowing, informal or formal insurance). Hence observed expenditure vulnerability reflects both income risk and the utilization of smoothing.

Fourth, as a technical point, this is not the probability of at least one episode in n periods, but is a counterfactual: if one faced the one period ahead risk for n periods what is the probability of one of those periods having an episode of poverty. The more realistic problem of the evolution of poverty over time, where we take seriously the time scale of the observation of expenditures (which is usually one month) and calculate the evolution of expenditures following some dynamic process and calculate the probability of at least one month say, in 36 months being in poverty, is sufficiently more complicated we are not doing it that way.

While each household has some "vulnerability" (even millionaires could end up destitute), we want a more concrete measure of the number of households which are "vulnerable." We define a household as _vulnerable_ if the risk in n periods is greater than a threshold probability levelp:

# _V,h(p,n,PL)_ = _I4Rh(n,PL)_ > PJ

where I[-] is an indicator function. So, while vulnerability is a risk and comes in degrees (between zero and one), being _vulnerable_ is a state (either zero or one).

4
We take the threshold probability level that defines a vulnerable household to be 0.5. This has two attractive features. First, 50-50 odds is a nice "focal" point and it makes intuitive sense to say a household is 'vulnerable" if it faces even odds or worse. Second, if a household is just at the poverty line and faces a mean zero shock then this household has a one period ahead vulnerability of 0.5. This implies that, in the limit, as the time horizon _,n_ goes to zero, then being "in current poverty" and being "currently vulnerable" coincide.

_Implementation._ Take the first period in the expression for vulnerability. Define the change in expenditures in the natural way as Ae,+, = e+, - e,. Suppose that there is a time invariant trend (the expected increase in household h's income in each period, **[L)** and variability of the inter-temporal _change_ in expenditures for each household (a) - note this is not the usual variability _across_ households. Then the probability of a household with expenditures in the current period of _et_ falling into poverty in next period is just the probability that the negative shock to expenditures is greater than the current amount by which the household's expenditures exceed the poverty line _(et_ -PL) plus the expected change in income (p): _P(e,+ < PL) = P(Ae +, <-(e' - PL))_ or _P(e 1 <PL) = P((Ae +h _,uh)ICh < {- (e -PL) /h }/ h)_ The latter probability is: 5
_(PL-e; -#.h)1aI_ a _P Lff((Ae,+, ,uh) )ah )dAe_ - 0

# where f( ) is the density function of Ae.

As usual, to make more progress we need to make more, and stronger, assumptions.

First, assume that household expenditures is expected to be the same in each period so that _i_ = 0 and E(e+,,) = e, This assumption has two justifications. First, this is a plausible "base case" as a hypothetical question: _if_ incomes were to remain constant but the household faced the variability of income it currently faces, what is the probability it will fall into poverty? Hence, one should think of the calculations below as answering the question: if the level of income did not change but each household had variability in their expenditures repeated for n periods, what fraction of households would end up having at least one observed episode of poverty? Second, this assumption is easily modified later if one is willing to make clear and explicit predictions about the expected future growth (or fall) in earnings (either on average or for specific households).

We also make the even stronger assumption that Ae, is independently identically distributed (iid) in each period and that the distribution of the _changes_ in expenditures (not necessary the level) is normal. The assumptions of inter-temporal independence and normality are made for convenience in calculation and either could be relaxed with a calculation that is more complicated.

_6_
With these two assumptions we can compute the level of "vulnerability" of a household for any given level of current expenditures (e) as: <!-- Start of picture text --> (PL-e) / cr n<br>R(n,PL,e,c) = 1- f-|N(O01)<br><!-- End of picture text -->

We can also measure the number of households that are "vulnerable" by creating a "Vulnerability to Poverty Line" (VPL) as a function of the period length, probability of poverty, and the variability of the household's expenditures. The VPL is that level of expenditures such that, beginning from that level in period t, the probability of at least one episode of poverty in n periods is justp: _(PL--YPL)1/ VPL(p,n,PL,a) solves_ 1- fN(O,1) _= [1- p]f"f_ A quick illustration of what these numbers would be. Suppose that mean (log) per capita expenditures is 200 and the poverty line is half of that, 100, and p = **'/2** is the vulnerability threshold. Table 1 provides values of the VPL for a combination of years into the future and variability of the change in household expenditures. The variability is expressed as the standard deviation of the inter-temporal change in expenditures over the mean level of 200.

7
|Table 1: Calculat<br>expenditure variab|ion of hypothetical_Vulnerability to Poverty Lines_as a function of<br>ility and period (n). Values of VPL(O.5,n,100,a).<br>Ratio of standard deviation of household changes in expenditures<br>to mean expenditures|
|---|---|
|Years:|0.5<br>0.25<br>0.10|
|2|152.5<br>127.5<br>111.25|
|**3**|**182.5**<br>**142.5**<br>**116.25**|
|4|197.5<br>147.5<br>118.75|
|5|212.5<br>157.5<br>123.75|

The VPL behaves as expected: it is higher the larger the variability in expenditures. If the vulnerability horizon is three years ahead and if the variability of inter-temporal changes in expenditures is 1/2 of mean expenditures (100), then the VPL(0.5,3,100,100) is 82.5 percent higher than the poverty line, whereas if the variability of household expenditures is only 10 percent of mean income the VPL(0.5,3,100,20) is only 16.25 percent higher than the poverty line.

Increasing the horizon increases the VPL, and by a greater amount the higher the variability. So, if variability is high (50 percent of mean income), moving from a 3 year horizon to a 5 year horizon increases the VPL from 182.5 to 212.5 (higher than mean expenditures), an increase of 30 units. In contrast, if variability is low (10 percent of mean expenditures), then the same increase in horizon only increases the VPL by 7.5 units (from 116.25 to 123.75).

8
**_III)_** _Estimatesfrom Indonesian Data_

This section has three sub-sections. First, estimating the variability in income with some accounting for measurement error. Second, computing the average vulnerability and number of households that are either "in poverty" and "vulnerable to poverty." Third, calculating vulnerability and proportion vulnerable by various household characteristics.

# _A) Variability in Expenditures and Measurement Error_

Obviously in all of this, the key missing item is the variability of expenditures experienced by households over time. In a single cross section one can only estimate the variability of expenditures _across_ households. With a panel with only two observations per household one could have an estimate of the variability of expenditures household by household, but only with extremely large imprecision. However, one can use such a panel to estimate the variability of expenditures by groups of households<sup>3</sup> . For instance, landless households versus land owning households, or households with differing levels of education could face different variability of their incomes.

We are almost uniquely blessed in having not one, but two independent panel data sets to estimate this variability. We use the two different panel data sets, the "Mini-Susenas" and "100 Village Survey," to estimate standard deviations of the 3 An obvious extension is to estimate the variability as a function of a number of HH characteristics with a multivariate procedure and then use the HH's predicted variability in the vulnerability analysis.

9
change in expenditures for various types of households. The Mini-Susenas, with 10,000 HH sample, is a smaller version of the regular Consumption Module Susenas (the National Socio-Economic Survey), which has 65,000 HH sample. The MiniSusenas survey was first conducted in December 1998 and then repeated in August 1999 on the same sample frame, however only around 80 percent HH sample were surveyed in both rounds, providing a panel of 7,585 HH.<sup>4</sup> The 100 Village Survey was a panel survey that was first carried out in May 1997 for 12,000 HH in 100 villages (which include some urbanized areas, but no major cities) and repeated in August 1998, re-sampling only 2/3 of the households, providing a panel of 8,140 HH.<sup>5</sup> Both surveys were conducted by Statistics Indonesia (BPS), with the first round of Mini-Susenas was fmded by UNDP while the 100 Village Survey was funded by UNICEF.<sup>6</sup> The panels of households in both data sets make it possible to estimate the variability of changes in expenditures for households in category J: _O'A= e_ (Aeh -A<) _/(N 1_ ) However, there is an enormous problem with this procedure, namely measurement error. In most CCED poverty analysis, households are classified by the _measured_ expenditures. However, measured expenditures are only a very, very, rough measure 'For more details on Mini-Susenas, see BPS (2000). See Suryahadi and Sumarto (1999) for more description of the 100 Village Survey.

10
of actual expenditures. Observed household expenditures at time t can be decomposed into the "permanent" (P) component of expenditures (the part which is time-varying but is expected at time t to persist), a "transitory" (7) component of expenditures (time varying and expected not to persist), and a measurement error term (v): _el_ = _eh_ + _eh_ + Vh If the three variances (u<sup>2</sup> ) are uncorrelated, then ratio of measurement error (or "4noise') to total variance is: _a2 /(aI +a2 +al)_ How large is this noise to signal ratio in measured expenditures? A wide variety of evidence suggests that in cross sectional surveys measurement error is somewhere between 1/3 to **Y/2** of the total variance (see appendix for evidence with these data sets).<sup>7</sup> Measurement error is typically ignored in poverty analysis for three reasons.

First, pure mean zero measurement error does not affect the aggregate poverty rate.

**6** While the total sample size of Mini-Susenas is smaller than 100 Village Survey, the former is nationally representative while the latter is not.

' The method used to estimate measurement error in the appendix is similar to that used by Luttmer (2000).

11
Second, since measurement error will tend to attenuate differences, and hence "flatten" poverty profiles by lowering the gap between groups, the direction of the bias is known (unless of course there are differences in measurement error across groups). Third, it is not clear what can be done about it.

There are two good reasons why measurement error cannot be ignored in vulnerability analysis. First, in estimating vulnerability, if the standard deviation of "true" expenditures is less than the observed variability due to measurement error, this will imply that the level of vulnerability faced by households will be overstated. The analysis of vulnerability using the estimates uncorrected for measurement error can be thought of as measuring the likelihood a household will have an episode of _appearing_ to be in poverty, which could either be that they are actually in poverty or that there is measurement error in their expenditures.

Second, taking of first differences exacerbates measurement error by reducing the role of permanent expenditures in the total expenditure variability. Assuming that the variances of permanent, temporary, and measurement error are constant across time, then the ratio of noise to noise plus signal in the changes in expenditures is: (2av _2oVTTI)_ 1((2ap 2cPr **_PT,__** ) + (2 r - **_2.,__** ) + (2cV **_T,T-l))_** In the special case in which permanent income is time invariant and the innovations in temporary and measurement are uncorrelated with the previous period's innovation, then this implies that the measurement error problem in estimating changes is worse by eliminating the permanent component by first differencing: 12
_v_ ( /(a ' **_'T_** _+2 v' > v 2_ **_i '._** _+ + 2 *v ')_ So, while we can estimate the latter expression from a cross section, to move to the former requires some estimate of the permanent versus transitory innovations and the persistence of "permanent" innovations to expenditures.

As a provisional measure, we shall do two vulnerability calculations in the following sub-sections. First, using the estimate of the standard deviations of changes in expenditures, in aggregate and for groups of households. Second, using the estimate of the standard deviation of incomes scaled back by the estimated measurement error from appendix 1.

# _B) Estimating the Level of Vulnerability_

In this sub-section we estimate VPLs and headcount measures of vulnerability in both the Mini-Susenas and 100 Village Survey data. Since the level of poverty at any point in time is (more or less) arbitrary, we simply choose the poverty line so that the headcount poverty rate in both data sets is 20 percent.<sup>8</sup> Using this poverty line and the measured standard deviation of expenditure changes, we can calculate the vulnerability for the total sample. If we take a 3 year horizon as the _n_ and a vulnerability threshold of 0.5 for calculating the households who are vulnerable, the

> 8 This rate of headcount poverty is not completely unreasonable, for discussions on the poverty rates in Indonesia around the time of data collections, see Suryahadi _et al_ (2000).

13
VPL and the proportion of households who are vulnerable are as presented in table

# 2.9

Table 2: Estimates of Vulnerability and Proportions of Vulnerable HH's When Headcount Poverty is 20 Percent of the Population (May 1997 Prices)

||Mini S<br>(December 199|usenas<br>8 - August 1999)|100 Villa<br>(May 1997 -|ge Survey<br>August 1998)|
|---|---|---|---|---|
||Ignoring<br>measurement<br>error|Net of<br>measurement<br>error (300%)|Ignoring<br>measurement<br>error|Net of<br>measurement<br>error (50(10)|
|Mean of log percapita expenditures<br>in the initial period|<br>10.901|<br>10.901|<br>10.730|<br>10.730|
|Inverse of mean of log percapita<br>expenditures (Rp/month)|54,251|<br>54,251|<br>45,687|<br>45,687|
|Standard deviation of changes in log<br>expenditures durmig the period|<br>0.412|<br>0.288|<br>0.462|<br>0.231|
|Yearly standard deviation of<br>changes in log expenditures|0.617|<br>0.432|<br>0.370|<br>0.185|
|Yearly coefficient of variability|_0.057_|<br>0.040|<br>0.035|<br>0.017|
|Yearly probability of falling to<br>below poverty line|0.238|<br>0.154|<br>0.137|<br>0.015|
|Average vulnerability for three<br>annual shocks|0.577|<br>0.395|<br>0.358|<br>0.043|
|Average vulnerability three years<br>ahead|0.406|<br>0.367|<br>0.358|<br>0.233|
|Vulnerability poverty line<br>[VPL(0.5,3,PL)]|10.967|<br>10.815|<br>10.629|<br>10.477|
|Headcount vulnerable rate|58.91%|<br>47.49%|<br>42.10%|<br>30.18%/0|
|Ratio of vulnerable to poor|2.95|2.37|<br>2.11-|<br>1.51|

The results with the usual standard deviations, which would ignore measurement error, show very high levels of vulnerability, 59 percent in MiniSusenas and 42 percent in 1 00 Village Survey. In MiniPSusenas, the estimated yearly standard deviation of changes in log expenditures is 0.617, while the mean of 9 All empirical vulnerability calculations using both Mini-Susenas and 100 Village Survey data sets, with the results presented in tables 2 and 5, are based on May 1997 prices.

14
log expenditures is 10.90, resulting in a ratio of 0.0566.10 This implies that for a typical person in this sample, if the headcount poverty rate is 20 percent, the probability of at least one episode of apparent poverty in three years is 58 percent, while the probability that this person will be in poverty at least once in three years is 41 percent. To be "invulnerable" to poverty when facing such large variability in expenditures requires a level of log expenditures of at least 10.967 (which is called "Vulnerability to Poverty Line"), which is even higher than the mean (log) expenditures of 10.90. Because of this, 59 percent of the population is vulnerable to an apparent episode of poverty, almost three times of the currently poor." The level of vulnerability in 100 Village Survey sample is lower because the coefficient of variability is smaller. With a coefficient of variability 0.0345, the proportion of vulnerable population is 42 percent, about twice the poor.

This is interesting that the Mini-Susenas shows a _higher_ level of variability in expenditures, as this panel did _not_ span the worst of the crisis. One would have thought that the 100 villages data, by being conducted before and after the worst of the crisis would have much larger variability. There are a couple of possible explanations. First, we have done nothing about seasonality-because we can't. So perhaps the May to August comparison has less seasonal fluctuation than December '°This is a handy ratio, but it is not the "coefficient of variation" as this is the ratio of the standard deviation _offirst differences_ to the average of the _level._ We call this ratio the "coefficient of variability." " One way of thinking about this vulnerabili **t** y opoverty is a fornalization of the concept of "near poor," which is often used to illustrate the sensitivity of poverty rates to the poverty line. Vulnerability defnes "near poor" using a normalization based on the variability of changes in 15
to August. Second, the Mini-Susenas is nationally<sup>representative and perhaps the</sup> 100 villages population is, by virtue of being smaller,<sup>poorer, villages also less</sup> variable. Third, maybe the Mini-Susenas<sup>has worse inter-temporal measurement</sup> error - but we know of no way of checking that.

These estimates of vulnerability are probably<sup>too high as they do not account</sup> for measurement error. We try to address this<sup>by the admittedly crude expedient of</sup> reducing the estimated standard deviation<sup>of changes in log expenditure by the</sup> estimated measurement error from the appendix,<sup>i.e. 30 percent for Mini-Susenas and</sup> 50 percent for 100 Village Survey data. In<sup>this case the level of vulnerability of the</sup> household at mean income and with mean variability<sup>of Mini-Susenas is 0.39,</sup> roughly a 40 percent chance of an episode of<sup>poverty in three years. It is still the</sup> case that even though the poverty line is chosen<sup>so that only 20 percent are "poor,"</sup> nearly a half of the population (47 percent)<sup>are vulnerable to poverty, in the sense of</sup> facing even odds (or worse) of one episode<sup>of poverty in three years. The lower</sup> variability in 100 Village Survey, meanwhile,<sup>resulting in 30 percent headcount</sup> vulnerable rate.

These high levels of vulnerability confirm the<sup>voluminous literature based on</sup> qualitative assessments of the importance of vulnerability<sup>in the analysis of poverty.</sup> The poor at any point in time are only a fraction<sup>of those who must worry about, and</sup> struggle to avoid, falling into poverty.

expenditures. This eliminates the arbitrary nature<sup>of defining near poverty line as simply a chosen</sup> percent above the poverty line.

16
These findings are also consistent with analysis of other definitions of vulnerability. Using a six-year panel on households in rural China, Jalan and Ravallion (1998) estimate the inter-temporal "coefficient of variability" for consumption expenditures of 53.2 percent. They also found that the inter-temporal "coefficient of variability" for consumption tends to remain roughly constant as mean consumption expenditures increase.

These results are also consistent with calculations and analysis of other definitions of vulnerability. Jalan and Ravallion (1999), using a six year panel on households in rural China, examine "chronic" and "transient" poverty by examining which households were either persistently poor (expenditures in each period below the poverty line), chronically poor _(mean_ expenditures over all periods less than the poverty line but not poor in each period), transiently poor _(mean_ expenditures over all periods above the poverty line but experiencing at least one episode of poverty), and never poor. The results, reproduced here in table 3, are consistent with a large level of "vulnerability," as even though only 6.2 percent of households were "always poor" and a cross section in any given year would find less than 20 percent poverty, 54 percent of the sample experienced at least one episode of poverty.

17
Table 3: Characterization of transient and chronic poverty in households from China over six years, 1985-1990.

||Chro<br>(mean ex<br>pov|nically poor<br>penditures below<br>erty line)|Transiently poor<br>only (mean<br>expenditures above|Never poor|
|---|---|---|---|---|
||Always<br>poor|Not persistently<br>poor|poverty line)||
|Full Sample|6.21|14.38|33.38|46.03|
|Guangdong|0.40|1.04|18.31|80.25|
|Guangxi|7.12|16.07|37.38|39.43|
|Guizhou|11.90|21.20|40.17|26.73|
|Yunnan|4.88|18.04|35.55|41.53|

Notes: Adapted from Jalan and Ravallion (1999), table 2.

Other panel evidence shows similar transitions in and out of poverty. A summary of research using panel data that matches poverty data in households with at least two observations in Baulch and Hoddinott (1999), reproduced here in table 4, shows that the fraction of households which have experienced an episode of poverty is at times much larger than either those who never experienced such an episode or those who were persistently poor. This is consistent with our findings that levels of vulnerability to poverty are much higher than poverty rates themselves.

18
Table 4: Changes in poverty from panel surveys in selected countries

|Country|Period||Headcount (%)||
|---|---|---|---|---|
|||Always Poor|Sometimes<br>Poor|Never Poor|
|Zimbabwe|1992/93-1995/96|10.6|59.6|29.8|
|Pakistan|1986-1991|3.0|55.3|41.7|
|South Africa|1993-1998|22.7|31.5|45.8|
|Russia|1992-1993|12.6|30.2|57.2|
|Ethiopia|1994-1997|24.8|30.1|45.1|
|Cote D'Ivoire|1987-1988|25|22|53|

Notes: Adapted from Baulch and Hoddinott (1999).

Of course the reverse side of vulnerability of the non-poor is that the poor are also escaping poverty. A relatively small number of households remain in poverty consistently. This suggests that "the poor" from time to time are not a fixed but fluid group of households.

# _C) Differences in Vulnerability_

In addition to the _level_ of vulnerability, there is also interest in capturing the fact that different groups face different levels of risk. For example, even though two groups may have the same level of expenditures and hence one group has the same headcount poverty measure as the other, it is possible that one of the groups faces a higher level of risk so that they are more vulnerable. In this section we use only the estimates with the measurement error corrected estimated standard deviation to compare levels of vulnerability across various groups of households as presented in table 5. A handy way to compare results across groups is to compare the ratio of those "vulnerable" to those "poor" as this indicates how relatively important transient poverty is for these groups.

19
Table **5:** Estimates of poverty and vulnerability<sup>across groups</sup>

|<br>|Mean of log<br> <br>percapita<br> <br>expenditures r<br>in the initial<br>period|Headcount<br>poverty<br>c<br>ate (%)<br>|Yearly<br> <br>oefficient<br>of variability f<br>|Average<br> <br>vulnerability<br>or three<br> <br>annual shocks|Headcount<br>vulnerable<br>rate**(%)**<br>t|Ratio of<br><sup>vulnerable</sup><br>o poor|
|---|---|---|---|---|---|---|
|**Using Mini-Susenas:**|||||||
|By gender:||||<br>|<br>|<br>|
|a. Male<br>|10.9009<br>|<br>20.50<br> <br>|0.0392<br>|<br>0.3899<br> <br>|<br>47.11<br> <br>|<br>2.30<br> <br>|
|b. Female|10.9071|<br>21.23|0.0440|<br>0.4410|<br>50.97|<br>2.40|
|By education:||<br>||<br>|<br>|<br>|
|**a.**Less than primary|10.6840|<br>32.04|0.0404|<br>0.6611|<br>64.94|<br>2.03|
|b. Primary|10.8279|<br>21.15|<br>0.0381|<br>0.4624|<br>49.67|<br>2.35|
|c. Lower secondary|11.0430|<br>10.06|<br>0.0399|<br>0.2544|<br>34.20|<br>3.40|
|d. Upper secondary & higher|11.3333|<br>4.24|0.0399|<br>0.0783|<br>17.69|<br>4.17|
|By urban-rural:|_<br>|<br>|<br>|<br>|<br>|<br>|
|**a.**Urban|11.1640<br>|<br>7.93<br> <br>|<br>0.0405<br> <br>|<br>0.1697<br> <br>|<br>29.10<br> <br>|<br>3.67<br> <br>|
|b. Rural|10.7284|<br>28.88|<br>0.0389|<br>0.5963|<br>59.17|<br>2.05|
|By land owning (rural househo|lds only):||||_<br>|<br>|
|a. Landless|10.4631|<br>58.30|<br>0.0318|<br>0.8732|<br>75.74|<br>1.30|
|b. Landed|10.7325|<br>28.42|<br>0.0390|<br>0.5919|<br>58.87|<br>2.07|
|By sector:||<br>|<br>|<br>|<br>|<br>|
|a. Agriculture|10.6567<br>|<br>33.76<br> <br>|<br>0.0389<br> <br>|<br>0.6837<br> <br>|<br>65.79<br> <br>|<br>1.95<br> <br>|
|b. Industry|10.9881|<br>15.24<br>|<br>0.0381<br> <br>|<br>0.2812<br> <br>|<br>39.77<br> <br>|<br>2.61<br> <br>|
|c. Trade|11.0661|<br>10.55|<br>0.0416|<br>0.2575|<br>36.33|<br>3.44|
|d. Services|11.1270|<br>9.46|<br>0.0399|<br>0.1867|<br>30.50|<br>3.22|
|**Using 100 Village Survey:**|||||||
|By gender:|||<br>|<br>|<br>|<br>|
|a. Male|10.7197|<br>20.47|<br>0.0172|<br>0.0481|<br>30.80|<br>1.50<br>|
|b. Female|10.8450|<br>14.53|<br>0.0177|<br>0.0100|<br>22.66|<br>1.56|
|By education:||<br>|<br>|<br>|<br>|<br>|
|a. Less than primary|10.6357<br>|<br>24.78<br> <br>|<br>0.0168<br> <br>|<br>0.1184<br> <br>|<br>35.56<br> <br>|<br>1.44<br> <br>|
|b. Primary|10.737<br>|<br>18.29<br> <br>|<br>0.0178<br> <br>|<br>0.0463<br> <br>|<br>28.83<br> <br>|<br>1.58<br> <br>|
|c. Lower secondary|10.859|<br>13.71|<br>0.0171|<br>0.0061<br>|<br>21.13<br> <br>|<br>1.54<br> <br>|
|d. Upper secondary & higher|11.0797|<br>6.74|<br>0.0173|<br>0.0001|<br>12.6|9<br>1.88|
|By urban-rural:||_<br>|<br>|<br>|<br>|<br>|
|**a.**Urban|11.0174|<br>5.52|<br>0.0167|<br>0.0002|<br>10.2|9<br>1.86|
|b. Rural|10.667|3<br>23.13|<br>0.017|4<br>0.0942|<br>34.47|<br>1.49|
|By land owning (rural househo|lds only):||||||
|<br>a. Landless|<br>10.879|9<br>9.84|<br>0.018|4<br>0.0083|<br>18.9|2<br>1.92|
|b. Landed|10.621|4<br>26.00|<br>0.017|1<br>0.1477|<br>37.6|9<br>1.45|
|By sector:|||||||
|a. Agriculture|10.603|8<br>26.87<br>|<br>0.017<br> <br>|3<br>0.181<br> <br>|8<br>39.0<br> <br>|0<br>1.45<br> <br>|
|b. Industry|10.850|5<br>11.58|<br>0.016<br>|<br>0.005<br> <br>|0<br>20.0<br> <br>|7<br>1.73<br> <br>|
|c. Trade|10.918|5<br>7.71|<br>0.017|7<br>0.003|3<br>14.2|1<br>1.84|
|<br>d. Services|11.004|51<br>6.96|<br>0.016|8<br>0.000|4<br>12.7|0<br>1.82|

20
_Gender of household head._ One of the most persistent gaps between the quantitative and qualitative measures of poverty is that the quantitative measures very rarely find that female headed households are less well off (or have higher poverty rates), while in qualitative and participatory poverty assessments female headed households are often identified as the poorest of the poor. There are three possible reasons for this discrepancy. First, inadequate accounting for economies of scale in household consumption expenditures in the quantitative estimates versus people's experience and perceptions (Dreze and Srinivasan, 1997). Second, the category of female headed households is heterogeneous as it includes households "headed" by females at the time of the survey because the husband was absent, but was providing remittances and households in which women are truly supporting themselves (and others) alone such as widows and divorced and single mothers. Third, is that while female headed households have the same level of expenditures, they are more vulnerable to shocks **and** hence more at risk of poverty.

Our calculations on the Mini-S-usenas sample indicate that female-headed households have slightly higher mean per capita expenditures, but their poverty rate is slightly higher than male headed households."<sup>2</sup> Furthermore, the data suggest that female-headed households have greater expenditure changes variability and, hence, higher proportion of vulnerable households (2.4 for FHH versus 2.30). The 100 villages sample also indicate that female-headed households have a slightly greater

> **12** This indicates that female-headed households have higher expenditures variability _across households_ than male headed households.

21
variability in expenditure changes than male headed households. However, in this sample their mean expenditures is much higher and their poverty rate is much lower and, hence, their headcount vulnerable rate is much lower than male headed households. In data set as well, however, the ratio of vulnerable to poor households is slightly higher for female than male headed households (1.56 to 1.50).

This means that both data sets agree that female-headed households tend to have higher mean per capita expenditures and, at the same time, greater variability in expenditures changes than male headed households. However, the two data sets do not agree on the relative headcount poverty and vulnerable rates between the two groups of households, but agree on the ratio of both rates. Hence, these results give some light to, but stop for short of resolving, the quantitative vs. qualitative puzzle for female headed households.

_Educational level._ The analysis of vulnerability by education level using both data sets in general gives expected results. The higher the level of education of household head, the higher mean of per capita expenditures and the lower the poverty rate. There is no particular pattern on the variability of expenditure changes with respect to the education level, but they seem to be roughly the same across the level of education. Therefore, the average vulnerability and headcount vulnerable rate are lower the higher the education level of household head. However, in relative terms, the ratio of vulnerable to poverty rate is increasing with education level. So, in the Mini Susenas data even though the poverty rate of the most educated is only 4.2 percent, 17.7 percent are vulnerable, while the result is less dramatic in the 100 villages data, it is still 12.7 percent vulnerable even if the highest education group.

22
_Urban versus rural._ In comparing urban versus rural areas in the 100 Village Survey sample, one must keep in mind that the "urban" areas in this survey do not include any major cities but rather are smaller cities and urbanized areas. Also, the data represent a period of a very severe crisis, in which many urban areas and occupations were hard hit.'<sup>3</sup> Nevertheless, both data sets seem to agree on many aspects. Urban households have a much higher mean of per capita expenditures and much lower poverty rate than rural areas. The variability of expenditure changes in both areas seem to be roughly the same, but the two data sets disagree on the ordering. The resulting average vulnerability and headcount vulnerable rate are much lower in urban than rural areas. However, as with education the ratio of vulnerable to poor households is much higher in urban than rural areas, Partly because it begin from a lower base. ]3ut the urban results in the Mini-Susenas show what a difference vulnerability analysis can make - while poverty is only 8 percent and hence might be thought to be "not on issue" almost 30 percent of households are vulnerable.

_Landed versus landless rural households._ Interestingly, in this category the two data sets disagree on just about everything. According to the Mini-Susenas sample, rural households which own land have higher mean of per capita expenditures, lower poverty rate, higher variability in expenditure changes, lower average vulnerability, lower headcount vulnerable rate, and higher ratio of vulnerable to poor than the rural landless. On the other hand, the 100 Village Survey sample See Poppele _et al_ (1999) and Sumarto _et al_ (1998).

23
suggest that it is the rural households which do not own land which have such characteristics. At this juncture, it is still a puzzle as to why the two data sets convey completely conflicting stories. This disagreement is not a weakness of this study, but reveals a strong as we actually have and are using two independent XXX to corroborate results. Had we not used the second XXX we XXX would not have been able to report the difference 9as in the first version of the paper).

_Sector of occupation._ Both data sets indicate that households in agriculture sector have the lowest mean per capita expenditures and the highest poverty rates. This is followed by manufacturing, trade, and services as the sector with the highest mean per capita expenditures and the lowest poverty rates. Meanwhile, variability of expenditure changes is roughly the same across sectors. Hence, the ordering of sectors by average vulnerability and headcount vulnerable rate is the same as poverty rate ordering, However, the ordering of ratio of vulnerable to poor is reversed. "Trade" has the highest ratio of vulnerable to poor, followed by services and then industry. Especially in the Mini Susenas where poverty is 10.5 percent but vulnerability is 3 8 percent the importance of acknowledging variability is obvious.

24
_Io Conclusions and Implications_ Like the notion of poverty itself, the concept of "vulnerability" to poverty is complex and multifaceted and will never be adequately summarized in a single measure. However, we think the definitions and measures proposed here provide a step forward in the dialogue on vulnerability to poverty as it allows application of household survey data to explore the notion of vulnerability quantitatively. The strong assumptions we make allow just one additional parameter, the standard deviation of inter-temporal changes in expenditures, to open up an entire line of analysis of vulnerability. This also means that one can estimate "coefficients of variability" by group with one data set and then apply these to subsequent cross sections to do vulnerability analysis, even without a panel.

We find that vulnerability is important quantitatively. In a sample in which the headcount poverty rate is set to be 20 percent of the population, an additional 10 to 30 percent of households are "vuilnerable" to poverty (that is, at even odds of at least one episode of poverty in 3 years), and hence 30 to 50 percent of the population is "vulnerable" to poverty. We also find significant differences across groups that would have been missed by the static measures of current consumption expenditures deficit (CCED) poverty.

Expanding the analysis of poverty to vulnerability has several policy implications. First, the issue of targeting becomes more problematic. Are programs intending to reach only the "persistently poor"? If so, they will fail to capture a large swath of the population who, while they may not be "always poor," experience episodes of poverty. Without the ability to observe _current_ incomes or expenditures 25
on a frequent basis, targeting to the presently poor would be very difficult. That is, suppose a beneficiary group was chosen based on observed incomes at one point in time, how accurate would that be if the same beneficiary group were maintained for one year? Two years? Three years? Obviously the targeting accuracy deteriorates the larger the household variance and the longer the period.

Second, this raises the issue of risk and security. Many "social protection" or "social insurance" schemes (e.g. unemployment insurance, disability benefits, and health insurance) attempt to reduce the variability of income by providing transfers _not_ to the poor but to those that have experienced shocks. In this sense these programs act more as a mountain climber's "safety rope" (a rope that fixed at a progressively higher levels and protects the climber from a fall of more than a fixed distance) than as a trapeze artists "safety net" that catches only at the bottom. That is, while often both are referred to as "safety nets," there is an analytic distinction between social _insurance_ programs in which the benefits are contingent on the realization of some event - unemployment, flood, fire, health shock, old age, disability (safety ropes) - and _poverty_ programs in which the benefits or participation are intended to be contingent on expenditure (or income) level (safety nets). It may well be that insurance programs will be as important as poverty programs in reducing vulnerability.'<sup>4</sup> Third, this may provide insights into the political economy of targeting. While there is only a small proportion of the population who are chronically poor (and one 26
would conjecture these would tend to be relatively politically powerless), there are many many more who are vulnerable to poverty and would, for entirely selfinterested reasons, be interested in programs that reduce the risks they face. In models in which the budget for poverty programs is endogenously determined by majority voting, programs that are well targeted to the poor can be _worse_ for the poor than programs supported by the "middle" group interested in reducing their vulnerability (Gelbach and Pritchett, 1997 and 1999). In their model, this is because the budget for well targeted programs is so low compared to programs with more broad based support that the poor are worse off with a larger share of a smaller pie.

Finally, vulnerability may alter the target groups for poverty or social insurance programs. In the Indonesian contexlt, certain occupational groups (such as landless rural workers, urban informal sector workers (e.g. scavengers), or fishermen) or certain socioeconomic groups (e.g. widows) _may_ have quite highly variable incomes and hence merit attention even if their average level of expenditures is not _on average_ too much different from others. This is a possibility to be considered on a policy level, as it is not clear that this vulnerability can be properly identified or measured, or once identified there may be no programs that would be able to address this vulnerability.

4 For more discussions on this, see Sumarto _et al_ (2000).

27