metadata: authors: 0: author: Pradhan, Menno
1: author: Suryahadi, Asep
2: author: Sumarto, Sudarno
count: Indonesia
docna: 0: docna: Measurements of poverty in Indonesia - 1996,
            1999, and beyond
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Poverty Impact Evaluation,Poverty Monitoring & Analysis,Food Security,Inequality,Poverty Assessment,Poverty Diagnostics,Poverty Lines,Small Area Estimation Poverty Mapping
teratopic: Poverty Reduction
abstracts: cdata!: Indonesia's economic crisis has
            caused a consumption expenditures deterioration in the
            welfare of Indonesians. Focusing on only one dimension of
            individual, and family welfare - consumption expenditures -
            the authors analyze two issues associated with the
            measurement of poverty. The first issue is how to produce
            regionally consistent poverty lines - that is, how to define
            a level of spending for each region that produces the same
            material standard of living. Without comparable data on
            prices, there is a problem of circularity. Choosing the
            reference population is important for defining the price
            level by which to deflate money expenditures to reach the
            same welfare level, but one needs to know the price level to
            define the reference population as a group with the same
            real expenditures. To address the problem of circularity,
            the authors use an iterative approach to defining poverty,
            one that produces consistent results across regions. They
            then use those poverty lines to examine the common
            "poverty profiles" (by location, sector, and so
            on). The second issue is more conceptual: how to expand the
            narrow measure of poverty, based on spending for
            consumption, with extensions that expand how welfare is
            measured, and allow more consistent comparisons of different
            individuals' welfare levels.
geo_reg_key: 517198,517191
repnme: repnme: Measurements of poverty in Indonesia -
            1996, 1999, and beyond
display_title: Measurements of poverty in Indonesia
            - 1996, 1999, and beyond
listing_relative_url: /research/2000/09/693280/measurements-poverty-indonesia-1996-1999-beyond
topicv3: Agriculture and Food Security m1326205 435,Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707,Education m1327048 470,Jobs m1503931 2670
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Environmt & Social Devl Sctr Dpt (EASES)
no_of_pages: 50
model_extractions: | POLICY RESEARCH WORKING PAPER 2438

# Summary findings

Indonesia's economic crisis has caused a consumption expenditures deterioration in the welfare of Indonesians. Focusing on only one dimension of individual and Family welfare-consumption expenditures-Pradhan, Suryahadi, Sumarto, and Pritchett analyze two issues associated with the measurement of poverty.

The first issue is how to produce regionally consistent poverty lines-that is, how to define a level of spending for each region that produces the same material standard of living.

Without comparable data on prices, there is a problem of circularity. Choosing the reference population is important for defining the price level by which to deflate money expenditures to reach the same welfare level, but one needs to know the price level to define the reference population as a group with the same real expenditures.

To address the problem of circularity, the authors use an iterative approach to defining poverty, one that produces consistent results across regions. They then use those poverty lines to examine the common "poverty profiles" (by location, sector, and so on).

The second issue is more conceptual: how to expand the narrow measure of poverty based on spending for consumption with extensions that expand how welfare is measured and allow more consistent comparisons of different individuals' welfare levels.

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, Washington, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-473-3902, fax 202-522-1153, email 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. (44 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 he 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
Measurements of Poverty in Indonesia:

# 1996, 1999, and Beyond*

Menno Pradhan, Free University Asep Suryahadi, SMERU Sudamo Sumarto, SMERU Lant Pritchett, World Bank# Social Monitoring & Early Response Unit

## Jakarta

The findings and interpretations in this report 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 reports.

' This report is based on the full SUSENAS sample of 65,000 households. We update the same methodology previously applied to the accelerated sample of 10,000 households. We thank Wenefrida Dwi Widyanti and Yusuf Suharso for their research assistance. We are grateful to BPS for providing access to the data.
**Introduction**

Counting the poor is both complex and straightforward at the same time. If one accepts a narrow definition of poverty line as consumption at a certain level, then poverty measurement is straightforward: those with consumption below the line are considered "poor" and the rest are "non-poor." However, setting the poverty line is a complex exercise as it requires answer to many questions: what mix of food commodities are to be included in the food basket? What level of calorie intake should that food produce? What is the level of non-food purchases that is essential. But the answers are subject to social conventions.

But poverty is even more complicated as it has many faces. Consumption is just one dimension: security, access to health facilities, educational attainment, physical well being, and social status are examples of other dimensions of welfare which can be incorporated into a definition of poverty.

This paper is divided into two parts. The first part discusses setting a regionally consistent poverty line in the standard current consumption expenditures deficit (CCED) definition of poverty. Using these poverty lines we report poverty incidence across regions of Indonesia. We also present the usual poverty profiles.

The second part is a prognosis of the future of measurements of poverty profile, taking into account other dimensions of poverty.

2
# **I. Poverty Measurements and Poverty Profile**

The _level_ of poverty is more or less arbitrary as the level of household welfare that is chosen to be the threshold for "poverty" is simply a social convention. Fortunately what is typically relevant for policy discussions, the targeting resources or design of programs, is the "poverty profile," i.e. the differences in poverty across households, _social or economic groups,_ or regions.

The following discussion is grouped into four sections. The first section discusses the methodology for the construction of a poverty line across regions with different but unobserved price levels. The second section emphasizes the importance of a "reference population" in poverty line calculations. The third section discusses the distribution and the changes of poverty incidence across regions (provinces by urban and rural). Finally, the fourth section discusses poverty profiles across gender, occupation or sectors, and educational attainment.

# _A)_ **_Poverty line: Basic description_**

The common starting point of many poverty calculations is a food energy intake requirement of 2,100 calories per person per day (Ravallion, 1994). A food poverty line (FPL) is the expenditures necessary to achieve this caloric intake. However, this same caloric intake could be achieved in an infinite variety of ways with a corresponding array of expenditures. If a person were to only eat the cheapest possible source of calories, dried cassava flour (see Table Al in the appendix), the FPL would be only around 20,790 rupiah per person per month. Meanwhile, a "rice only" diet to achieve 2,100 calories would cost 45,990 rupiah per month while a diet 3
of only chicken would cost 273,420 rupiah per month. Obviously diet of only rice and cassava flour is unrealistic and unpalatable and is not consumed, even by the very poor. People are quite willing to sacrifice calories for variety and taste in a diet. In addition, calories are just a proxy for an overall nutritional adequacy, which requires proteins and micronutrients as well as calories, and hence a varied diet is important for other reasons, while the total amount of calories is fixed "absolutely" the _basket_ and _quality_ of those foods used to reach that level is ultimately a _social convention._ That is, the basket must be fixed, but the fixing of the basket, while based on reasonable criteria, is ultimately a social choice.

The method we use to choose the basket is common: use a basket of foods actually consumed by a "reference population" to fix the _mix_ of foods and their prices, then the _total_ is fixed by scaling the mix of foods up to achieve the level of 2,100 calories. More formally, let **_i_**<sup>**_1_**</sup> **_k_** denote the average quantities consumed of commodity k by the reference population, which is chosen on the basis of its level or real expenditures. The food poverty line basket is defined as the set _qk_ = _Oq*k_

**_K_** _k= 1,_ ..., K, where 9 = 2,100 _/qkck_ and _Ck_ is the unit calorie value of **_k=1_**

commodity k.

Estimated food poverty lines can be rather sensitive to the choice of the commodity basket (Chesher, 1998). In order to make our estimates as directly comparable as possible to those constructed by BPS, we use 52 commodity items following the practice adopted by BPS (BPS and UJNDP, 1999). The list of these 52 commodities is given in table Al in the appendix. Once the food basket has been 4
chosen, the food poverty line in each region is then established using the _basket_ of quantities of the _national_ reference group, but _region specific_ commodity prices.' We use unit values for our food price estimates obtained from dividing expenditures by reported quantities. Bidani and Ravallion (1993) and Ravallion and Bidani (1994) use separately collected price data. The main advantage of using unit price estimates is that they can be derived from the survey. Especially in a period of high inflation, it is important that the price and expenditure data correspond to the same reference time. A disadvantage is that products may not be homogenous within a commodity category. Wealthier household can consume more luxurious varieties of a commodity and therefore pay higher unit prices.

We attempt to correct for the 1product heterogeneity problems that arise from using unit prices instead of separate price data by using predicted prices at the poverty line. If households indeed switch to more luxurious varieties as they get richer, this would result in a positive significant estimate of per capita consumption in the unit price regression. By taking the predicted price at the poverty line, we use the unit prices that are relevant for the poor. We use quantile (median) regression methods because since a regression is performed for each commodity in each region sample sizes are small and median regression is less sensitive to outliers.<sup>2</sup> lOne could set multiple nutritional intake targets for the consumption basket to achieve with the gain of realism about nutritional adequacy with the loss of symphony.

**2** A similar procedure in the construction of poverty line in Indonesia is used by Alatas (1997). A quantile regression using the results to median is the same as the LAD (Least Absolute Deviations) estimate.

_5_
Using a reference population with total expenditures<sup>_e,_the food poverty line</sup> <!-- Start of picture text --> (FPL) for regionj is defined as: 52 r 2100<br>1) FPL = Eq e)*p( ( 20<br>k=1 2: E>qk(e) Ck<br>k=1<br><!-- End of picture text --> Choosing the allowance made for the non-food<sup>expenditures is ever more</sup> difficult, as there is no equivalent of a nutritional<sup>standard to provide even a weak</sup> anchor to the amount. We adopt the rationale of Ravallion<sup>(1994) and others that one</sup> plausible way of setting a non-food amount that is "essential"<sup>to word poverty is to</sup> use those households who only have the _total_ expenditures<sup>equal to the food poverty</sup> line spend on non-food. This produces a _low_ estimate.<sup>Meanwhile, the non-food</sup> component of the poverty line is calculated by estimating<sup>an Engel curve for food</sup> consumption. The non-food component of the poverty<sup>line is set at the expected non-</sup> food consumption for those whose total consumption equals<sup>the food poverty line.</sup> The estimated Engel curve is estimated using all<sup>household (i) for each regionj</sup> is specified as: _co,j = j + /3* log(e,j / FPLj) + error termj_ The poverty line (PL) for regionj that follows equals<sup>the FPL plus the non-food</sup> allowance (NFA) of those households with total expenditures<sup>just equal to the FPL:</sup> 2) _PLj = FPL, +NF4. = FPLj +(l -j)*FPLj = FPLj *(2j)_ 6
# _B) The (une-xpected) importance of the reference group_

An arbitrary but, as it turns out, crucial decision in implementing any method of fixing the poverty line is the initial choice of the reference population. The consumption pattern of this group (letermines the weights of the commodities in the food basket that form the basis of the food poverty line. Generally, one wants the reference group to reflect the consumption patterns of the poor. Most researchers therefore start of with a prior belief about the level of poverty and use this population group as the reference group. This method could lead, to some extent, to _selffulfilling prophecies._ Two researchers working on the same country with exactly the same data using exactly the same method but simply having different prior beliefs on headcount poverty will produce different poverty estimates. The one who believes poverty is high will choose a wealthier reference population. This richer reference group will consume a more luxurious food basket. Hence the calories per rupiah will be lower so the cost of obtaining a fixed amount of calories will be higher. Both the food and non-food component through two effects as (2-eo)*FPL will be higher because (o is lower and FPL is higher of the poverty line, will turn out higher as a result. This researcher will most likely get a higher estimated headcount poverty compared to the researcher who started off with a low prior. The relation of the poverty line with respect to expenditures of the reference group is shown figure 1.

7
<!-- Start of picture text --> PL PL<br>PLD*D9 Aer (e 9<br><!-- End of picture text --> Figure 1: Poverty Line and Food Poverty Line This means that the "standard" poverty methodology is incomplete and not well specified. Without a procedure for fixing the reference group, the "standard" method applied to the same country with the same data can produce different outcomes. As the next section shows, the difference is not a minor theoretical _curiousem_ but are potentially enormous.

# **_C)_** _An_ **_iterative method_**

To overcome this circularity problem between determining reference population and the resulting headcount poverty, we use an iterative method. This method estimates the poverty line using an initial reference group. The poverty line that emerged from these initial steps is used as the center of the reference group for the next step. The iteration converges and the process steps when the reference 8
group yields a poverty line that is the same as the midpoint of the reference group. This point corresponds to the intersections of the two curves in figure 1.

We start with a prior of what the poverty lines are (such as point A). This determines the reference group. Next, we determine the food basket typical for households whose total consumption equals this poverty line. We price this basket using unit prices typically paid by households who are at this poverty line (obtained as predicted prices that follow from a quantile (median) regression of unit prices on real per capita consumption). The non-food component of the poverty line is obtained using the usual Engel curve approach. The resulting poverty lines then serve as the prior for the next iteration. This method appeared robust with respect to the choice of the initial value of the poverty line. The precise steps involved in calculating the poverty line are outlined in the appendix.

Since an increase in the FPL line increases the PL more than proportionally (since with a higher FPL the share of non-food at that line is even higher, so that the NFA is a higher proportion of a larger number), it is important to understand the increase in FPL as a higher level of expenditures in the reference group chosen. Since higher expenditures affect all three terms of the FPL: prices per unit, mix of units consumed amongst various food items, and total caloric value, the derivative of FPL with respect to expenditures is complex. The most intuitive way of expressing the derivative is: 3) _FPL_ _j __ - +~_ L.- _1k-7 ae ( TC )_ tE _k=1 ( k=k 1=1E ( t ) (k )]_ 9
Where, for each commodity the:

**C's** are the "elasticities of price with respect<sup>to total expenditures," this is the</sup>

# increase in within commodity quality as expenditures,

a's are the shares in expenditure of each<sup>commodity,</sup> 's are the usual (Marshallian) income<sup>elasticities, which determine the income</sup> expansion paths, K''s are the rupiah per calorie of each commodity.

This within commodities is the "quality<sup>upgrading" term, the expression for the</sup> derivative breaks the total into two parts.<sup>The first term is an increase in price for a</sup> fixed commodity basket as, for a given<sup>mix of goods, consumers move to higher</sup> qualities. This is simply the expenditure<sup>weighted sum of the "quality" elasticities.</sup> The second term is the "quality upgrading<sup>across commodities" and is also</sup> quite intuitive, particularly using the simple<sup>example of two goods, say rice and eggs.</sup> The rupiah per calorie of rice is 0.73 while<sup>the income elasticity is also low, only</sup> 0.063 (see table Al). In contrast, the rupiah<sup>per calorie from eggs is 6.07 and the</sup> income elasticity is a high 0.582. So as<sup>the expenditure of the reference group</sup> increases, consumers shift to a basket of proportionally<sup>more eggs, which are a</sup> higher cost source of calories, with the contribution<sup>to increasing the poverty line in</sup> this case of (0.73-6.07)*(0.063-0.582) =<sup>2.77. Since there is a general tendency for</sup> higher income elasticities to be associated<sup>with higher rupiah per calorie, as the</sup> income level of the reference group increases<sup>the FPL increases because the mix of</sup> commodities chosen increases.

10
Note that changes in total calories<sup>with respect to expenditure, which are</sup> shown in figure 2, plays no<sup>role at all in setting the FPL.3Since by forrmula in (1)</sup> calories are re-scaled up (or<sup>down) to remain constant all that matters is the rupiah</sup> per calorie. The estimated relationship<sup>between rupiah per calories and expenditures</sup> is shown in figure 3 in two<sup>ways -either as semi-log (rupiah per calorie on natural</sup> log expenditures) or using a flexible<sup>functional form (a quartic). In either case the</sup> relationship is quite steep. This<sup>implies the poverty rate will be quite sensitive to the</sup> choice of reference group.

<!-- Start of picture text --> 1,500<br>Polynominal model<br>C' *<br>Semi log model<br>500 1<br>0 20,000 40,000 60,000 80,000 00,000 120,000 140,000<br>Expenditure (Rp/month)<br><!-- End of picture text --> Figure 2: Relationship between<sup>Calories Consumed and Expenditures</sup> The calculations in this figure<sup>are based on the 52 commnodities in the poverty basket only. The</sup> average caloric intake from<sup>these 52 commodities is 1,513 calories per person per day, while the</sup> average total caloric intake is<sup>1,850 calories per person per day.</sup> 11
<!-- Start of picture text --> 3.50 - -_- --------<br>3.00<br>2504<br>2001<br>Di 1 P50 olyrto rial i<br>PoI ~ modetognodel<br>0 20,000 40,000 60,000 80,000 100,000 120,000 140,000<br>Expenditure (Rp/month)<br><!-- End of picture text --> Figure 3: Relationship between Price of Calories Consumed and Expenditures Suppose one researcher believed the poverty rate was 15 percent and hence began with a reference group of the 15' percentile, while another believed poverty was 30 percent. They then estimate the poverty rate without iterating. Table 1 shows that the resulting poverty rates from the two researchers using exactly the same _method_ on exactly the same _data_ and differing only in their prior (and not unreasonable) beliefs about the appropriate reference group would produce estimates of the poverty rate that differed by 6.7 percentage points (more than 30 percent!).

12
|Table 1: Illustrat<br>A|ion of the Sensitivity of th<br>ssumptions about the Ref|e Estimated Po<br>erence Group|verty Rate to|
|---|---|---|---|
|Assumption|Mid point of reference<br>(Rp/month)|Poverty line<br>(Rp/month)|Poverty Rate<br>(%)|
|Reference group<br>centered on 15%|69,645|77,265|21.78|
|Reference group<br>centered on 30%|86,159|84,550|28.48|

# _D) Poverty incidence across regions_

Our approach for the inter-regional comparison has been to keep the quantities in the food basket constant. Theoretically, we want the poverty line to represent the same in utility. This approach guarantees that the poverty line suffices to purchase this national basket in each region. A disadvantage is that the applied basket is not necessarily optimal for every region. In a region with a very different set of relative prices compared to the national average, the same welfare (in utility terms) can generally be reached with a lower total expenditure than the poverty line would imply. This is the argument in favor of using region specific food bundles.<sup>4</sup> Chesher (1998) finds that moving to a regional poverty basket increases the extremes in measured poverty, raising provinces that are already high and lowering provinces that are already low.

Using Susenas February 1999 data, the resulting regional poverty rates from our iterative method are presented in table 2, while the associated poverty lines are .4 There are a number of compelling arguments against, which are discussed in length in Ravallion and Bidani (1994) or Ravallion (1994).

13
presented in table A2 in the appendix and the number<sup>of poor people are in table A3.</sup>

# For comparison, table 2 also shows the regional<sup>poverty rates according to BPS's</sup>

# approach.

Table 2: Regional Poverty Incidence (%) and Rank in<sup>February 1999</sup>

|||Ite|rative<br>|Method<br>|<br>|||<br>|BPS Me<br>|thod<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Province|Urb|an|Rur|al|Tot|al|Urba|n|Rur|al|Tot|al|
||Value|Rank|Value|Rank|Value|Rank|<sup>Value</sup>|<sup>Rank V</sup>|<sup>alue</sup>|<sup>Rank</sup>|<sup>Value</sup>|<sup>Rank</sup>|
|Jakarta|2.82|1|||2.82|1|6.59|1|-||6.59|1|
|Bali|10.67|11|15.61|5|13.62|6|9.80|3|9.89|1|9.85|2|
|Riau|8.53|7|9.62|1|9.21|2|11.43|4|14.98|2|13.65|3|
|Aceh|5.43|3|15.41|4|12.89|5|13.76|6|17.38|_5_|16.47|4|
|West Sumatera|8.78|9|9.74|2|9.47|3|17.43|12|16.48|3|16.75|5|
|Central Kalimantan|5.00|2|13.43|3|11.15|4|7.16|2|20.41|6|16.83|6|
|North Sumatera|10.81|12|18.91|6|15.27|7|17.50|13|16.64|4|17.03|7|
|East Kalimantan|8.74|8|35.06|15|21.67|10|12.65|5|22.83|9|17.65|8|
|WestJava|20.82|22|31.87|13|26.60|15|19.10|14|22.32|8|20.79|9|
|North Sulawesi|11.70|13|26.83|11|22.47|12|14.23|7|24.60|13|21.61|10|
|South Sulawesi|17.42|18|24.94|8|22.63|13|20.50|17|22.20|7|21.68|1I|
|South Kalimantan|7.99|6|26.38|10|20.64|9|16.37|11|25.03|15|22.33|12|
|Yogyakarta|22.12|23|36.78|16|26.95|16|20.13|16|27.68|18|22.62|13<br>|
|Bengkulu|10.41|<br>10|24.55|7|20.44|8|20.02|15|24.55|12|23.23|14|
|West Nusa Tenggara|30.17|27|44.71|22|41.78|23|25.94|23|23.42|10|23.93|is1<br>|
|Jambi|15.41|<br>16|25.25|91|22.18|11|23.27|21|24.63|14<br>|24.24<br>|16<br> <br>|
|South Sumatera|14.47|<br>15|27.93|12|23.81|14|24.77|22|24.27|11|24.42|<br>17|
|Central Sulawesi|16.72|<br>17|32.69|14|28.52|17|21.69|20|25.87|16<br>|24.78<br>|<br>18<br> <br>|
|Southeast Sulawesi|13.74|<br>14|44.44|21|36.61|21|14.28|8|29.34|20|25.50|<br>19|
|East Java|19.51|<br>20|40.87|20|33.31|20|21.55|19<br>|28.80<br>|19<br>|26.24<br>|<br>20<br> <br>|
|Central Java|23.72|<br>25|37.76|17|32.78|<br>19|26.06|24|27.52|17|27.01|<br>21<br>|
|West Kalimantan|6.17|5|38.04|18|30.76|<br>18|14.43|<br>10|34.25|22|29.72|<br>22<br>|
|Lampung|19.90|<br>21|40.57|193|6.80|22|21.14|183|2.92|21<br>|30.77<br>|<br>23<br> <br>|
|Maluku|18.64|<br>195|9.90|24|48.40|<br>24|28.52|25|41.50|<br>23<br>|37.88<br>|<br>24<br> <br>|
|East Timor|23.37|<br>24|59.38|<br>23|55.49|<br>26|39.35|<br>27|44.07|<br>24|43.56|<br>25|
|East Nusa Tenggara|28.67|<br>26|66.11|<br>25|61.18|<br>27|30.43|<br>26|47.15|<br>25|44.95|<br>26|
|Papua|6.07|<br>4|72.19|<br>26|54.89|<br>25|14.31|<br>9|59.30|<br>26|47.53|<br>27|
|Indonesia|16.34||34.10|_|27.13||19.98||25.85||23.55||
|Note: Sorted by aver|age pro|vincial p|overty<sup>b</sup>|<sup>y BPS</sup>|<sup>method</sup>||||||||

The results show that in February 1999, the poverty<sup>rate in Indonesia was</sup> 27.13 percent, implying around 55.8 million<sup>poor people. This poverty rate is</sup> 14
modestly higher than the BPS poverty rate of 23.55 percent. The ranking of provinces from least to most poor by our iterative method and BPS's method are quite consistent with a Spearnan rank correlation of 0.92.

While at the national level the difference in poverty rates between the two methods is only around 4 percentage points, the two methods differ wildly in the range of differences in poverty rate s across urban and rural areas. The rank correlation is also lower at 0.83 for urban areas and 0.88 for rural areas. The BPS method implies a difference of less than 6 percentage points in the difference between urban and rural poverty rates. The iterative method, meanwhile, has a much, much, wider range of almost 18 percentage points (34 versus 16). Table 3 demonstrates the reason for this.

Table 3: Urban-Rural Differences in Iterative and BPS Methods, February 1999

||Reference p<br>(Rp/ni<br>Lower limit|opulation<br>onth)<br>Upper limit|Poverty line<br>(Rp/month)|Poverty<br>Incidence<br>(%)|
|---|---|---|---|---|
|Iterative Method:<br>|||||
|Urban|72,392|108,588|90,490|16.34|
|Rural<br>_<br>|<br>64,947<br>|97,421<br>|81,184|34.10|
|Ratio|1.11|1.11|1.11|0.48|
|BPS Method:<br>|||||
|Urban|80,000|100,000|93,869|19.98|
|Rural<br>|60,000|80,000|73,898|25.85|
|Ratio|1.33|1.25|1.27|0.77|

The iterative method, which chooses the reference groups to reflect equivalent _real_ incomes of urban and rural groups in the reference basket, produces much lower differences in the poverty lines in urban versus rural areas. The method fixes a 15
poverty line only 11 percent higher in urban than<sup>rural areas. As a result, the poverty</sup> incidence in urban areas, which is 16.3 percent,<sup>is less than a half of the poverty rate</sup> in rural areas, which is 34.1 percent.

The BPS method, meanwhile, uses references<sup>groups that are chosen reflecting</sup> an _assumption_ of higher costs of living in<sup>urban than rural areas. They choose a</sup> reference group range that is non-overlapping<sup>(the lower limit of urban is Rp. 80,000,</sup> which is the same as the upper limit of rural) and<sup>which is between 25 and 33 percent</sup> higher for urban areas. The result is a poverty line<sup>that is 27 percent higher in urban</sup> than in rural areas. Not surprisingly, the poverty rate in<sup>urban areas by this method,</sup> which is around 20 percent, is 77 percent of that in<sup>rural areas, which is 25.9 percent.</sup> So, in spite of much lower nominal expenditures, the cost of attaining<sup>the poverty</sup> basket is _assumed_ to be much lower in rural<sup>areas. This implies that the differences in</sup> poverty rate between urban and rural areas<sup>are possibly as much an artifact of method</sup> and assumptions as they are a finding of "fact"- the<sup>poverty line is higher because it</sup> is assumed to be higher. However, there is no double<sup>check within the BPS method</sup> on the initial assumptions about the appropriate reference<sup>groups</sup>

# _E) Changes in regionalpoverty during the crisis_

During the crisis, there has been a significant<sup>deterioration in household</sup> welfare (Skoufias _et al,_ 2000). If this is true, we<sup>can expect that this will be reflected</sup> in poverty incidence. We examine this by comparing Susenas<sup>February 1999 and</sup> Susenas February 1996 -one and a half years before<sup>the crisis started. Specifically,</sup> the question we want to answer is that given the level<sup>of welfare implied by the</sup> 16
iterative method results on Susenas February 1999, what was the poverty incidence in February 1996 and, hence, what is the change in headcount poverty during this period.

Crucial in these comparisons over time is the choice of deflator to convert the February 1999 regional poverty lines to those of February 1996. Suryahadi _et al_ (2000) recommend a price deflator where the share of food in the deflator uses the share of food in the poverty basket (0.8). They admit that this deflator overstates poverty changes because it does not allow substitution and does not use the actual consumption bundle of the poor. But they argue that this deflator is defensible as the price index shares represent the actual consumption pattern of some group in poverty, although the group is considerably below the poverty line.

The regional headcount poverty rates in February 1996 and the percentage changes between February 1996 and February 1999 are shown in table 4 using this deflator and food price inflation from Susenas unit prices and non-food price inflation from CPI. Meanwhile, the associated poverty lines and number of poor people in February 1996 are shown respectively in tables A4 and A5 in the appendix.

17
|Table 4: Pover<br>(Food sh|ty Rates in<br>are of pove<br>|February<br>rty basket,<br>|1996 and<br>Susenas<br>|Changes<sup>1</sup><br>unit<sup>prices</sup><br>|<sup>996-99</sup><br><sup>)</sup><br>i|h|
|---|---|---|---|---|---|---|
|Province|Poverty<br>Feb|Incidence<br>ruary 199|(%)<br>6|Percentag<br>Feb 199<br>|e pont c<br>6 - Feb<br>|ange<br>1999<br>|
||Urban|Rural|Total|Urban<br>|Rural|Total<br>|
|Jakarta|0.78|-|0.78|2.04|-|2.04|
|North Sulawesi|6.83|23.69|19.22|4.87|3.14|3.25|
|Riau|2.53|7.20|5.59|6.00|2.42|3.63<br>|
|Papua|6.94|64.97|50.04|-0.87|7.22|4.85<br>|
|Bali|4.98|10.63|8.67|5.69|4.98|4.95<br>|
|West Nusa Tenggara|20.60|39.78|36.16|9.57|4.93<br>|5.62<br>|
|Aceh<br>.|3.25|8.32|7.27|2.18|7.09|5.63<br>|
|West Sumatera|2.40|4.01|3.60|6.39|5.73<br>|5.87<br>|
|North Sumatera|3.68|11.51|8.27|7.13|7.40|7.00<br>|
|Central Kalimantan|2.27|4.39|3.91|2.74|9.03|7.24<br>|
|South Sulawesi|5.84|18.74|15.07|11.57|6.20|7.56<br>|
|West Kalimantan|5.72|25.65|21.32|0.45|12.39|9.44|
|East Nusa Tenggara|21.70|56.48|51.59|6.97|9.63|9.59|
|Bengkulu|4.62|12.22|10.25|5.79|12.33|10.19|
|Yogyakarta|13.10|20.11|16.64|9.02|16.67|10.31|
|Central Java|12.28|25.18|21.03|11.44|12.58|11.75|
|South Kalimantan|4.95|9.99|8.46|3.04|16.39|12.18|
|Southeast Sulawesi|11.61|27.29|23.73|2.13|17.15|12.87|
|Central Sulawesi|5.74|16.97|14.49|10.98|15.72|14.03|
|Jambi|7.32|8.27|8.01|8.09|16.98|14.18|
|East Java|9.40|23.57|18.99|10.10|17.30|14.32|
|Maluku|10.47|41.51|33.81|8.17|18.39|14.59|
|West Java|8.41|14.59|11.93|<br>12.41|17.28|14.67|
|South Sumatera|2.00|11.70|8.75|12.47|16.23|15.05|
|East Kalimantan|0.75|12.36|6.52|7.99|22.71|15.15|
|Lampung|7.50|19.01|17.18|<br>12.40|21.56|19.62|
|Indonesia|7.22|20.54|15.74|<br>9.12|13.55|11.39|

Table 4 shows that all provinces experienced an increase<sup>in poverty incidence</sup> between February 1996 and February 1999, but the variation<sup>of these increases is</sup> very large. The highest increase in poverty incidence<sup>occurred in Lampung with</sup> almost 20 percentage points increase. This is almost<sup>double of national increase,</sup> 18
which is around 11.4 percentage poinits. Table A5 indicates that there were 30.8 million poor people in February 19915. This means that there were additional 25 million people who fell to below poverty line during the period of February 1996 to February 1999.

The percentage point increase ifor rural areas, which is 13.6 percentage points, is higher than in urban areas, which experienced an increase of 9.1 percentage points. In relative terms, however, the increaLse in poverty incidence is much higher in urban areas than in rural areas. In urban areas, the poverty rate increased by 126 percent, while in rural areas it increased by 66 percent.

# **_1)_** _Poverty profile: Household characteristics_

In making use of the poverty line discussed above, we can also examine some of the characteristics of poor households. These characteristics will help in identifying the poor even though these characteristics are far too broad to be directly useful for targeting purposes.

_Poverty and sector of occupation._ Poverty profile across sectors is important to identify the poor. Table 5 shows the poverty incidence across sectors as well as the contribution of each sector to total poverty in both February 1996 and February 1999.5 These and hereafter are based on the regional poverty lines presented in tables A2 and A4.

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Table 5. Poverty Incidence and Contribution to Total Poor by Main Sector of Occupation, February 1996 and February 1999 (%)

||Febru|ary 1996|Febru|ary 1999|
|---|---|---|---|---|
|Sector|Poverty<br>incidence|Contribution<br>to total poor|Poverty<br>incidence|Contribution<br>to total poor|
|Agriculture|26.29|68.54|39.69|58.38|
|Trade, hotel, and restaurant|7.96|8.10|17.63|11.13|
|Manufacturing industry|10.69|5.71|22.92|7.71|
|Civil, social, and private services|5.73|5.72|13.13|7.36|
|Transport and communication|8.85|3.32|24.02|5.58|
|Construction|14.04|5.42|28.97|5.52|
|Receiving transfer|6.58|1.86|_15.57_|_2.65_|
|Mining and quarrying|15.34|1.01|29.81|1.00|
|Others|13.29|0.10|32.00|0.27|
|Finance, insurance, and leasing|1.24|0.06|5.23|0.23|
|Electricity, gas, and water|6.10|0.16|14.48|0.17|

Note: Sorted by contribution to total poor in February 1999 Table 5 indicates that all sectors uniformly experienced an increase in poverty incidence during the period. This implies that there is no single sector, which was spared from the negative impact of the crisis. In relative terms, the finance, insurance, and leasing sector had the highest increase in poverty incidence, which was more than quadrupled from 1.2 to 5.2 percent. This probably reflects the financial nature of the origin of the crisis, so it is not surprising this sector was the hardest hit. The table also indicates that other modem sectors such as trade, manufacturing, and services were also proportionately hard hit by the crisis.

Nevertheless, the agriculture sector consistently had the highest poverty incidence as well as the highest contribution to the total number of poor people during the period. This reflects two things. First, people in the agriculture sector have always been relatively poorer than those in other sectors. Therefore, even 20
though this sector was not hit by the crisis as hard as the modem sectors, in the end the poverty incidence in this sector still the highest of all sectors. Second, the agriculture sector remains the largest sector in terms of employment. In fact, during the crisis many workers who were laid off in modem sectors returned to agriculture, so that between 1997 and 1998 the employment share of agriculture increased from 40.8 percent to 45 percent (Feridhanusetyawan, 1999). The combination of these two factors explains the persistence of agriculture sector as the largest contributor to the number of poor people, even though its importance has declined markedly from 68.5 percent in February 1996 to 58.4 percent in February 1999.

_Poverty and educational attainment._ Education level is presumably highly correlated with welfare. Those who can achieve a higher level of education will have greater opportunities to get better jobs, and hence improve the welfare of their families. This is clearly indicated by table 6, which shows poverty profile across the education level of head of households. The higher the education level, the lower the poverty incidence. Even after the educational progress that has occurred 87 percent of the poor have a primary school education or less.

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|Table 6. Poverty Pr<br>Februa|ofile by Educ<br>ry 1996 and<br>Febru|ation Level of H<br>February 1999 (<br>ary 1996|ousehold H<br>%)<br>Febru|ead,<br>ary 1999|
|---|---|---|---|---|
|Education level of<br>household head|Poverty<br>incidence|Contribution<br>to total poor|Poverty<br>incidence|Contribution<br>to total poor|
|Not completed primary and<br>illiterate|31.23|27.67|47.51|19.84|
|Not completed primary but<br>literate|21.63|35.09|36.68|31.82|
|Completed primary|15.03|30.15|29.66|35.34|
|Completed junior secondary|7.04|4.80|16.85|7.61|
|Completed senior secondary|2.44|2.19|8.59|5.05|
|Completed tertiary|0.41|0.11|1.98|0.34|

The table shows that even just removing illiteracy has a large impact on reducing poverty incidence, i.e. by almost 10 percentage points. The table also shows that by the level of junior secondary education, the poverty incidence is already lower than the national average. This implies that poverty incidence at the national level is very much affected by high poverty incidence among those who have only primary education or less. At the tertiary level, the poverty incidence is indeed very small, less than 2 percent in February 1999. Before the crisis, it was almost non-existent at 0.4 percent.

All of this points to the fact that improving the education level of the people in general is one of the best long-run strategies in reducing poverty. However, this is not as straightforward as it seems. There is an endogeneity between welfare and education. So it is not only education level affects welfare, but also the initial level of welfare affects educational achievement. Hence, if education is left entirely as a 22
private decision of families, there will be a cycle between being in poverty and low levels of education. Therefore, there is obviously a role for the government to play in breaking this cycle.

It is also clear that more and better formal schooling is not likely to affect aggregate poverty in the very short run. Those household head that currently have "no schooling" or "incomplete primary" or "primary" schooling are not going to return for additional formal schooling. The sheer fact of demographic persistence means that even if starting today all students complete a full nine years of basic education, this will take time until these newly educated graduates enter the labor market full time, and have their earnings reflected in the poverty figures.

Table 6 also shows that poverty incidence has increased for all levels of education between February 1996 and February 1999. This implies that the crisis has hit everyone, those with low level oi education as well as the educated ones. In relative terms, however, there is an indication that the higher level of education the greater the increase in poverty incidence. While among the illiterate poverty incidence has increased by 52 percenlt (from 31.2 to 47.5 percent), among those with tertiary education the poverty rate has increased by almost four-fold (from 0.4 to 2 percent). This again probably reflects the urban and modem sector nature of the crisis.

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# **II. Future Directions for Poverty Measurements**

There are two large issues in the future directions for poverty measurement: expanding regional measures and broadening the concept of poverty measured. We discuss each in turn.

# _A)_ **_Regional comparisons_**

As we have seen even, coming to consensus on estimates by urban and rural areas of provinces was difficult. However the process of decentralization and of expenditure targeting already demand more, and more frequent, data. For expenditure allocation decision making, both for targeted safety net programs and for the fiscal decentralization of general revenues the "Daerah Tingkat II" (level two) (or kabupaten/kota) will be the relevant jurisdiction. There are certainly large variations in poverty within provinces. As we move toward district level of aggregation there will be two major problems:

- _Regionally comparable prices._ As seen above, the lack of directly comparable price indices leads to enormous difficulties. Even now the best that can be done for non-food prices is to assume they are the same in an entire province as in the CPI surveyed city. There are efforts underway to create meaningful rural price indices.

- _Sample sizes._ Even with 65,000 observations, the imprecision of estimating poverty levels for over 300 kabupaten/kota will raise concerns, particularly if these estimates actually become part of the expenditure allocation process, in

24
which case all issues of measurement are likely to become (even more) hotly

# political.

These issues will be important because there is a significant amount of variation within provinces. Table 7 shows the amount of total household variation in poverty associated with each administrative level. In spite of the enormous differences across provinces in average levels of poverty illustrated in table 2, this only explains 5 percent of the variance. Moving to the level of the _kabupaten/kotamadya_ explains another 9 percent.

Table 7: Variance of Poverty Rates across Different Unit of Analysis

|Unit of<br>analysis<br>___|Number<br>_________|Variance of<br>poverty rates<br>across different<br>units<br>__|Percent of total<br>variance<br>explained by<br>variance across|The additional<br>"explanation" of<br>moving from<br>higher to lower<br>level|
|---|---|---|---|---|
|Province|27|<br>0.0090|<br>5.0%|<br>_5.0%_|
|Kabupaten/<br>kotamadya|306|<br>0.0256|<br>14.1%|<br>9.1%|
|Kecamatan|2,396|<br>0.0587|<br>32.4%|<br>18.3%|
|Households<br>(Total)|62,212<br>I_I|<br>0.1813|<br>100.0%||

# _B) (Re)defining poverty_

Like many words, the meaning of "poverty" is a social convention. The standard definition that applied so far captures only "current consumption expenditures deficit" (CCED) poverty. This does not capture all of the phenomena covered by "poverty." An adequate definition of poverty would recognize the above definition is just one element of a complex phenomena with at least six, intertwined, 25
dimensions. These expanded definitions do not contradict standard welfare economics, but rather are integral components of a rigorous economic definition of poverty in terms of welfare levels. We would argue these are usually ignored, not because they are analytically unsound or because of evidence they are less important, but simply because they are "too hard" to measure with the usual data at hand. Of course, having just seen how hard it is to make consistent inter-temporal and interspatial comparisons of even standard CCED poverty, there is some justification to this approach. However, ultimately economic analysis should expand to reflect the reality rather than attempting to restrict social phenomena to what can be easily quantified.

The six dimensions of poverty are: _Current consumption expenditure deficit (CCED) poverty._ This is the usual definition.

I think a fruitful way to think about poverty is the expenditures function. The Expenditures function is the indirect function that is the result of the solution of the dual of consumer welfare maximization. The expenditure function gives, for any set of prices and a level of utility the amount of money necessary (that is, the lowest amount) to achieve that level of utility at the given prices. In this formulation the social convention is choosing a level of utility below which people are "poor" - call at u°vevY. Then the poverty line for the **ith** household is defined as: _4) PLi = ei (pi, upoverry)_ 26
This formnulation is useful in tlree ways.

First, it clarifies the role of nutritional standards in poverty line calculations.

Some might believe that nutritional standards eliminate the arbitrariness in settling on a social convention for. what (CE]D poverty ought to be and do so by introducing a technical, physiological given "necessary" level of consumption. This while a convenient function, is a fiction. Rather nutritional standards merely give us a way to discuss and settle on a level of utility below which a household is poor.

Second, as we have shown in on earlier pages (Suryahadi, Sumarto, and Pritchett, 2000) the expenditure function is convenient in thinking about the inter temporal comparisons of poverty as there is a well developed literature on price deflation using the expenditures function.

Third, as we show below, this is a useful way to approach extensions to the definition of poverty.

_Insecurity poor or vulnerability._ A first additional dimension of poverty is that people who may enjoy current expenditures above the poverty line but have a high likelihood of experiencing episodes of poverty. Both quantitative data and people's responses in focus groups of participatory, open-ended approaches indicate that the dynamic of poverty vulnerability is a crucial aspect of how many people experience poverty. The panel data sets indicate a very high variability of the poverty level at the individual level. A recent study on the 100 villages data indicates that of the poor 27
in 1998, over a quarter were more than 50 percent above the poverty line in 1997.6 While some of this must represent measurement error, nevertheless since in agriculture and informal occupations the variability of income is often very high, there is no question that over a period of 5 to 10 years many households which are not CCED poor will experience one or more _episodes_ of CCED poverty.

The question is how to measure this vulnerability, beyond merely pointing out that those near the poverty line are "vulnerable." There are two possible ways forward, both of which require measuring expenditure variability at the household level, or at least amongst types of households (e.g. urban-rural, formal-informal, education level, sector of occupation, etc.). One is to regard households expenditures as a dynamic process, with both the mean (,u) and variability (a): _J) ei (t j =_ **_ei (p i, ,i )_** Then the probability of at least one episode of CCED poverty in the next, say, five years is:' _6) P({]ttE{t,t+5}:e 1 (t) < PL(t)})_

- **6** See Skoufias _et al_ (2000).

- 7 This is discussed further and applied to Indonesian data in Pritchett _et al_ (2000).

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With additional assumptions one could define a household as "vulnerable" if the probability of an episode of poverty is higher then some threshold value, say 0.5. The second approach would be to define a poverty line that incorporates both the mean and variability of expenditures directly into the utility function. Then, the reference utility level that defines the expenditures necessary to be out of poverty can be fixed by this expenditure uncertainty inclusive utility level:

# _7) PL = e(p, VI p_<sup>_0_</sup> _',t(p,.))_

This is not merely a matter of moving the poverty line up or down, as this would also affect the poverty profile as almost certainly different groups have different income variability. For instance, in a given year a farmer may have an exceptionally good harvest and have expenditures equal to that of a person with a steady formal sector occupation. However, the farmer's income is almost certainly more variable. Hence, seen in an inter-temporal perspective (which is after all, how people live, not a series of snapshots)., even if the farmers current expenditures are the same the farmer "real uncertainty adjusted utility" may still be lower.

_Prospects poor._ A second additional dimension of poverty is that those who may or may not be above the current poverty line in expenditures, but who are not making adequate investments in their own and their children's future. A household which is above the consumption defined poverty line because their 12 year old has dropped out of school to contribute to household income _is poor._ This approach 29
- would bring investments in human capital basic schooling, adequate investments

in health and nutrition - directly into the definition of poverty.

In addition to human capital investments, there are also some families who are not in CCED poverty, but only at the expense of their future financial prospects. Households become trapped in exploitative cycles of credit, pawning assets, taking on very short term, high interest credit, etc. Even if these households escape temporarily CCED poverty by such means, they are actually still in poverty.

A formalization of "prospect" poverty would emphasize future expected utility of all household members, which depends on their current net investments (which could of course be negative). So if a poverty level is set for that forward looking utility: _VPI"e"IY = V(e,,I,)_ then the poverty line will depend not only on expenditures but also on net investments: 8) _PL = PL(e,, I,')_ This inter-temporal dimension to poverty requires information in human capital investments and also on the accumulation (or decumulation) of assets and, preferably, some information about credit.

_Access poor._ A third additional dimension of poverty is that there are certain goods that most people believe are "necessities" or "merit goods" that everyone should have access to - such as education, clean water, some basic types of health - care, and perhaps, depending on social conditions, additional infrastructure e.g.

30
electricity, transport. One approach is to simply _define_ that people are access poor if they do not have feasible access to these goods.

The more consistent approach iis to build access into the expenditure function. Since what is meant by "access" is usually that the "true" cost includes non-price dimensions. Take the case of electricity. A household that is far removed from the grid faces a much higher cost for electricity than a household in an urban area, which is, near the grid. The cost may be sufficiently high that there will be no consumption of electricity as the household uses substitutes (lanterns). In this case: _9) PL(no access) = e(p"_<sup>_0_</sup> _', u )pove"]/)> e(p with access Uple") = PL(Access)_ The obvious question is how much higher should the poverty line be? What is the money income that would just compensate in utility terns for access to the grid? Or having a clinic or school 1 km closer? Or having access to piped water? Fortunately, there has been a great deal of analytical and empirical work on these issues.<sup>8</sup> This work could inform the relationship of access and poverty lines.

_Physically disadvantaged poor._ The fourth dimension of poverty are the mentally or physically disadvantaged. These who may be living in households with adequate consumption levels but may themselves have low welfare levels. Their 8This essentially the question of "willingness to pay" from the literature on consumer surplus. There has been a resurgence of interest in these questions in connection with adjustments to the US CPI for introduction of new goods, leading to a heightened interest (in one of those delightful intellectual twists that make being an economist such a pleasure) in the market for cold cereal.

31
"material standard of living" may be lower than those with equivalent money income.

This dimensions leads to the enormously tricky problem of interpersonal comparisons of utility. However, there are again two relevant literatures. First, the dealing with compensation for various injuries which establishes conventional valuation of a cash wide variety of morbidity conditions. Second, there is the literature on injured valuation of various morbidity conditions inferred from avoidance behaviors. For instance, hedonic wage regression of contains implied valuations of various health risks.

This is obviously a challenging agenda of broadening the definition of poverty in a consistent way. There are of course short-cut approaches being used, such as the "HDI" (Human Development Index) popularizes by agencies such as the UNDP. While this approach is useful in drawing attention to non-consumption expenditure aspects of human welfare it has two serious weaknesses (openly acknowledged even by its proponents).

First, to add incommensurate items there must either be a broader class of which both are members (table plus chair or pieces of furniture) there must be a set of scaling factors that transform the separate items into common units (table times dollars per table plus chair times dollars per chair dollars of furniture). In a "human development" index items like "poverty" and "literacy" and "infant mortality" are added together. However, the weights used to add them up are just completely arbitrary. Equal weight has no more rationale then any other weights.

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Second, suppose the purpose is to compare two areas. Then, with an expanded definition of poverty that included two criteria A and B. Then the total "poverty" is not households that meet criteria A p;lus all those that meet criteria B as this double counts households that meet both _A and B._ If the overlap of criteria A and B is not exactly the same in the two areas to be compared (and there is absolutely no reason it should be) then a simple sum of A and B will not produce valid comparisons of an expanded definition of poverty.

_Socially disadvantagedpoor._ T'he fifth added dimension to poverty are people who, for various reasons, are disadvantaged due to their social condition. This includes those who within a household suffer (e.g. women who suffer from domestic violence) or those whose household status leaves them at social disadvantage (e.g. widows, ethnic minorities in certain areas). This now gets very complicated, as while it is easy to compare poverty lines across the conditions households face (e.g. access to goods, income shocks) the conceptual grounding is much more subtle in allowing welfare levels to vary across households themselves, and even more difficult to allow welfare levels to vary directly by _individual_ characteristics. Nevertheless, in qualitative assessments and focus group type activities, certain social groups are (correctly) identified which the standard CCED misses.

_Summary_ It is impossible to say _a priori_ how incorporation of these additional dimension would affect the level or profile of poverty. Many of the features of poverty would - overlap so many of those who are "prospect poor" are also already "CCED" poor 33
_and_ vulnerable poor, so an inclusive poverty rate would not be the simple sum of the individual poverty rates. In addition, the pattern of other dimensions of poverty will likely differ from the pattern of CCED poverty.

# _C) An example: Prospect poor_

To give an illustration of an expanded definition of poverty, we use easily measured indicator in a possibly expanded definition of poverty, i.e. the prospect poor. In this case, we define a household as "prospect poor" if it has at least one child older than 6 but less than 18 years, who is currently not enrolled in school, and has not completed lower secondary education level (SLTP). Table 8 shows a headcount of poverty of this is used in addition to the usual CCED definition.

Table 8: Headcount of Consumption and Prospect Poor

||Februa|ry 1996|Februa|ry 1999|
|---|---|---|---|---|
||Headcount<br>_(%)_|Relative to<br>CCED|Headcount<br>_(%)_|Relative to<br>CCED|
|Consumption poor|15.74|100|27.10|100|
|Prospect poor|15.70|100|12.49|46|
|Consumption or prospect poor|26.80|170|33.52|124|

The table shows that in February 1996, the prospect poor at 15.7 percent is quite sizable, they are as large as the consumption poor with only less than 30 percent of households overlapping and in both categories. Therefore if "broad 34
poverty" is defined as being either CCED or prospect poor (but not both) then this was of 26.8 percent of the population.

However, while the crisis has increased.the consumption poor significantly to 27.1 percent, it did not eliminate the progress in education achieved during the period due to its more permanent nature, resulting in the reduction of headcount prospect poor to 12.5 percent. The overall poverty using either concept, however, still increased significantly by around a quarter to reach 33.5 percent in February 1999.

35
**Conclusions**

There are three major methodological suggestions that emerge that deserve ifurther consideration.

First, the quality of the food basket consumed, as measured by rupiahs spent per calories, is strongly responsive to the level of expenditures then the essentially standard method for setting poverty lines is not robust to arbitrary choices about reference groups. Only by interacting will the reference group and poverty line be consistent.

Second, regional comparisons should be based on an iterative methodology for setting the reference groups. Given the high sensitivity of regional poverty comparisons to reference groups, and given these reference groups are often chosen without any methodological justification, this makes a substantial difference to the poverty profile. The current BPS method for urban/rural poverty differential is based on assumptions that narrow reported urban/rural difference in poverty.

Third, the concept of poverty can be expanded to incorporate additional dimensions beyond the present _current consumption expenditure deficit_ definition of poverty. But to do so in a consistent way with empirically grounded basis will be require much additional work.

36