revisiting growth and poverty reduction in …scinet.dost.gov.ph/union/downloads/balisacan am et al....

INTRODUCTIONBy international standards, Indonesiahas done remarkably well in the areasof both economic growth and povertyreduction. For two decades before theAsian financial crisis in the late 1990s,economic growth averaged 7% p.a.This was the norm for East Asia, andwas substantially higher than the aver-age growth rate of 3.7% for all devel-oping countries. At the same time,Indonesia’s poverty incidence fell from28% in the mid 1980s to about 8% inthe mid 1990s, compared with adecline for all developing countries(excluding China) from 29% to 27%.1

Indonesia’s record also compares wellwith those of China and Thailand,whose economies grew at an evenfaster rate.

The Asian financial crisis, exacer-bated by domestic political turbulence,hit the Indonesian economy hard. GDPper capita contracted by 13% in 1998,effectively returning it to its 1994 level.Poverty rose sharply, as indicated byboth official and independent esti-mates.2 Official figures issued by Indo-nesia’s Central Statistics Agency (BPS,Badan Pusat Statistik) show the pro-portion of people deemed poor increas-

REVISITING GROWTH AND POVERTY REDUCTION IN INDONESIA:

WHAT DO SUBNATIONAL DATA SHOW?

Arsenio M. BalisacanUniversity of the Philippines-Diliman and SEAMEO

Regional Center for Graduate Study and Research in Agriculture

Ernesto M. PerniaAsian Development Bank, Manila

Abuzar Asra*Asian Development Bank, Manila

Indonesia has an impressive record of economic growth and poverty reductionover the past two decades. The growth–poverty nexus appears strong at the aggre-gate level. However, newly constructed panel data on the country’s 285 districtsreveal huge differences in poverty change, subnational economic growth and localattributes across the country. The results of econometric analysis show that growthis not the only factor to affect the rate of poverty change; other factors also directlyinfluence the welfare of the poor, as well as having an indirect effect through theirimpact on growth itself. Among the critical ones are infrastructure, human capital,agricultural price incentives and access to technology. While fostering economicgrowth is crucial, a more complete poverty reduction strategy should take theserelevant factors into account. In the context of decentralisation, subnational analy-sis can be an instructive approach to examining local governance in relation togrowth and poverty reduction.

Bulletin of Indonesian Economic Studies, Vol. 39, No. 3, 2003: 329–51

ISSN 0007-4918 print/ISSN 1472-7234 online/03/030329-23 © 2003 Indonesia Project ANUDOI: 10.1080/0007491032000142782

Carfax PublishingTaylor & Francis Group

BIESDec03Q9 27/10/03 6:30 PM Page 329

330 Arsenio M. Balisacan, Ernesto M. Pernia and Abuzar Asra

ing from 17.7% in 1996 to 24.2% in 1998.But just as the economic contractioncaused a sharp increase in the povertyrate, the rebound in 1999 and 2000,albeit modest, caused it to fall, nearly toits pre-crisis level. Based on independ-ent estimates (Suryahadi et al. 2000),poverty incidence in late 1999 hadfallen to 10%, comparable to the level inearly 1996, after shooting up to 16% inmid 1998. These estimates suggest thatpoverty in Indonesia responds rela-tively strongly and quickly to largeshocks.

While the Asian crisis adverselyaffected the welfare of the Indonesianpeople, the country’s achievements ineconomic and human developmentover the past quarter-century remainimpressive, especially when viewedagainst the performance of South Asiaand other low and middle-incomecountries (table 1). The economic andsocial gains from the high-growthperiod could not be wiped out so easily.

Indonesia’s overall growth andpoverty reduction experience appearsto approximate the findings of studiesbased on cross-country regressions.Dollar and Kraay (2001), for example,show that the incomes of the poormove one for one with overall averageincomes, suggesting that povertyreduction requires nothing much morethan to promote rapid economicgrowth.

There is more to the growth–povertynexus, however, than the nationalaverages would imply. Growth andpoverty reduction vary enormouslyacross the island groups, provincesand kota/kabupaten (urban and ruraldistricts) of Indonesia.3 In recent years,this variance appears to be wideningrather than converging; and given itsethnic dimensions, this is becoming apolitically sensitive issue (Hill 2002).Recent history is replete with examples

to show that social or political tensionsarising from economic disparities tendto dampen the return to high growth,thus making it more difficult to win thewar against poverty.

An appropriate approach to socio-economic disparities requires a clearunderstanding of the policy and insti-tutional factors that account for differ-ences in the evolution of growth andpoverty in the various districts ofIndonesia. To what extent can differ-ences in growth explain the observeddifferences in poverty reduction acrossprovinces and districts? How impor-tant are government policies and pro-grams, as well as geographic attributesand local institutions, in influencingpoverty? What lessons can be learnedfrom recent experience to promotepoverty reduction in the poorest areas?

As a case study for addressing theabove questions, Indonesia offersadvantages not found in many otherdeveloping countries. First, as alreadynoted, the country is diverse both in itsgeographic and institutional attributesand in the economic performance of itsprovinces and districts. It is this diver-sity that permits an assessment of theinfluence of economy-wide policiesand ‘initial’ conditions of poverty. Andsecond, comparable cross-sectionaland time-series data on subnationalunits of government (provinces anddistricts) are available for the 1990s, a period characterised by markedchanges in the policy environment andeconomic performance. This facilitatesa sufficiently disaggregated analysis toenhance our understanding of thedeterminants of growth and povertyreduction.

This paper examines the key deter-minants of poverty reduction inIndonesia during the 1990s. The fol-lowing section describes data andmeasurement issues. The third section

BIESDec03Q9 27/10/03 6:30 PM Page 330

Revisiting Growth and Poverty Reduction: What Do Subnational Data Show? 331

TABLE 1 Selected Social Indicators: Indonesia versus Other Developing Countries

Indicator 1970 2000

Average per capita GDP (in 1999 PPP $)a

Indonesia 940 2,882East Asia and Pacific 875 4,413South Asia 1,051 2,216

1980 1999

Infant mortality (deaths per 1,000 live births) Indonesia 90 42East Asia and Pacific 55 35South Asia 119 74Low and middle-income countries 86 59

Life expectancy at birth (years) Indonesia 55 66East Asia and Pacific 65 69South Asia 54 63Low and middle-income countries 60 64

Primary school gross enrolment ratio (%)b

Indonesia 107 113East Asia and Pacific 111 119South Asia 77 100Low and middle-income countries 96 107

Secondary school gross enrolment ratio (%)b

Indonesia 29 56East Asia and Pacific 44 69South Asia 27 49Low and middle-income countries 42 59

Adult illiteracy (%, among those aged 15 and over)Indonesia

Males 13 9Females 27 19

East Asia and PacificMales 13 8Females 29 22

South AsiaMales 41 34Females 66 58

Low and middle-income countriesMales 22 18Females 39 32

aPPP: purchasing power parity. Figures are three-year averages, centred on the year shown.bThe most recent data on primary and secondary school enrolments are for 1997.

Sources: World Bank (2001); IMF (2001).

BIESDec03Q9 27/10/03 6:30 PM 31


uses consistently assembled district-level data to analyse the basic growth–poverty relationship. The fourth sec-tion probes the contribution of localattributes and time-varying economicfactors to the variation in district-leveleconomic performance vis-à-vischanges in poverty. A main interesthere is in assessing the extent to whichcertain policy measures can enhance ordiminish the impact of growth on theliving standards of the poor. The paperconcludes by assessing implicationsfor the design of pro-poor, growth-oriented policies and institutions inIndonesia.

DATA AND MEASUREMENTISSUESThe National Socio-economic Survey(Susenas, Survei Sosial EkonomiNasional) conducted by the BPS is themain source of data for analyses ofpoverty and inequality. The surveycomes in two sets: the consumptionmodule and the core data, hereafterreferred to as the Susenas module andthe Susenas core. The Susenas moduleprovides detailed consumption data, isundertaken every three years, andallows disaggregation only at theprovincial level. For the 1990s, suchdata are available for 1993, 1996 and1999. The Susenas core, on the otherhand, covers both consumption andother socio-economic indicators on anannual basis, with the specific indica-tors varying from year to year. Theconsumption data in the Susenas coreare not as detailed as those in the Suse-nas module and, indeed, give a differ-ent picture of the level of consumption:the consumption figures for 1993–99from the Susenas core are about 11%lower, on average, than those from theSusenas module. The advantage of theSusenas core is that the data allow dis-

aggregation at the district level. Offi-cial government poverty figures calcu-lated by the BPS are based on the Suse-nas module.4

We use the Susenas core, as its cov-erage of 285 districts—versus 26provinces for the Susenas module—yields a far greater number of observa-tions for each survey year.5 However,to obtain the same aggregate povertyprofile as that given by the Susenasmodule, we have adjusted the con-sumption data from the Susenas coresuch that the consumption expendi-ture means by quintile correspond to those obtained from the Susenasmodule.

On both conceptual and practicalgrounds, consumption is preferable toincome as a measure of household wel-fare. Microeconomic theory suggeststhat since welfare level is determinedby ‘life-cycle’ or ‘permanent’ income,and since current consumption is agood approximation of this income,current consumption is an appropriatemeasure of both present and long-termwell-being. Indeed, measured con-sumption is typically less variable thanmeasured income (Deaton 2001). Forpractical purposes, it is less difficult toacquire accurate information on con-sumption than on income, especially indeveloping countries where the gover-nance infrastructure is weak and localmarkets are relatively undeveloped(Deaton 1997; Ravallion and Chen1997; Srinavasan 2001).

The National Income Accounts(NIA) are another distinct source ofdata on average welfare. AlthoughGDP per capita is widely used to meas-ure welfare, the level of personal con-sumption expenditure (PCE) per capitais closer to the concept of average wel-fare, as measured by households’ com-mand over resources. In general, PCEas measured in the NIA, and house-

BIESDec03Q9 27/10/03 6:30 PM Page 332


hold consumption expenditure (HCE)as measured in the Susenas, do notnecessarily correspond either as tolevel or growth rate, largely because ofdifferences in definitions, methods andcoverage.6 For example, PCE mayexceed HCE simply because it lumpstogether spending by the non-profitsector (non-government organisations,religious groups, political parties) withspending by the household sector. Atany rate, in the Indonesian case, aver-age per capita levels of PCE and HCEmove broadly in the same direction, atleast for the 1990s (figure 1).

The chosen indicator of householdwelfare, per capita consumption expen-diture, has to be adjusted for spatialcost-of-living, or SCOL, differencesbecause prices in any given year varysubstantially across provinces and dis-tricts. The SCOL index is simply theratio of the cost of attaining a level of

utility in province k to the cost ofattaining it in a reference province, r.To the extent that spatial poverty linesare comparable in utility terms (that is,imply the same standard of living),then the ratio of the poverty line forprovince k to that of the referenceprovince r is an appropriate SCOLindex. For our purposes, we use the1999 official poverty lines for urbanareas to approximate SCOL differencesamong the 26 provinces, as periodicsurveys to construct the consumerprice index (CPI) cover only urbanareas. Using urban poverty lines andJakarta as the reference province(Jakarta = 100), we find large inter-provincial differences in the cost of liv-ing, ranging from 74% in SoutheastSulawesi to 116% in Bengkulu (appen-dix table 1).

Comparison of household welfareover time requires that the chosen

FIGURE 1 Average per Capita Expenditure: National Income Accounts versus Susenas (Rp ’000 at current prices)

1985 1987 1989 1991 1993 1995 1997 19990

2,000

4,000

6,000

8,000

GDP per capita PCE per capita HCE per capita

PCE: personal consumption expenditure; HCE: household consumption expenditure.

Sources: National Income Accounts; Susenas.

BIESDec03Q9 27/10/03 6:30 PM Page 333


welfare indicator, consumption expen-diture, be adjusted for nominal pricemovements during the 1990s. Astraightforward way to achieve thiswould be to deflate the SCOL-adjustedconsumption expenditures usingprovince-specific CPIs.7 For practicalpurposes, this would be sufficient ifprice movements had been uniformacross consumer goods during theperiod of interest. However, in realityprice movements varied across con-sumption items, especially during theeconomic crisis of the late 1990s.

We have constructed a CPI for eachincome quintile of the population totake account of the differential priceregimes faced by each group. The con-struction involves combining the infor-mation on province-specific CPIs withthe district-level expenditure shares(weights), based on the 1996 Susenascore, of each population quintile in thefollowing commodity groups: food;prepared food and beverages; housing;clothing; health, education and recre-ation; and transport and communica-tion. Table 2 summarises the results for1993–99.

Because of the sharp rupiah depreci-

ation from July 1997, overall priceinflation was much higher in 1996–99(at 121%) than in 1993–96 (27%). Inaddition, while price changes did notvary much across quintiles between1993 and 1996 (the pre-crisis period),they did vary greatly between 1996and 1999 (the crisis period). During thelatter period, consumer price inflationwas about 128% for the bottom quin-tile, compared with only 109% for thetop quintile. The very high inflationrate for the poor during the crisisperiod was caused by markedincreases in the prices of food, particu-larly rice, which accounts for a domi-nant share of the poor’s consumptionbasket (Sigit and Surbakti 1999).8

The resulting national distributionof per capita consumption expendi-tures for the three Susenas years isshown in figure 2. Note that the expen-ditures are in real terms (at 1999 prices)and have been adjusted for provincialcost-of-living differences. Thus, withthe poverty line (in real terms) known,it is straightforward to obtain povertyincidence for the various years fromfigure 2. For example, if the nationalaverage (population-weighted) official

TABLE 2 Consumer Price Index by Expenditure Quintile

1993 1996 1999 % Change

1993–96 1996–99

National average 100.0 127.3 281.3 27.3 121.0

QuintileFirst (poorest) 100.0 128.2 292.2 28.2 128.0Second 100.0 127.9 288.4 27.9 125.6Third 100.0 127.6 284.6 27.6 123.1Fourth 100.0 127.2 279.1 27.2 119.5Fifth (richest) 100.0 126.1 264.0 26.1 109.4

Source: Susenas.

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poverty line of about Rp 904,400 perperson is used, the resulting povertyincidence would be 26% for 1993, 13%for 1996 and 16% for 1999.9

As shown by Foster and Shorrocks(1988), two non-intersecting cumula-tive distribution curves will suggestthat the direction of poverty change isunambiguous even for all other plausi-ble poverty indices that satisfy certainproperties of a desirable poverty meas-ure. As figure 2 shows, this is the casefor 1993 and 1996, as well as for 1996and 1999. Thus, poverty is unambigu-ously higher in 1999 than in 1996, butstill much lower than in 1993, for virtu-ally all poverty norms and standardpoverty measures that have been sug-gested in the literature.

To some extent, the pattern ofpoverty change shown above is quali-tatively consistent with the observa-tions reported in previous studies.Using their ‘consistent’ estimates, Sury-

ahadi et al. (2000) found that povertyincreased by 6.5 percentage pointsbetween 1996 and 1999; based on offi-cial poverty lines, the ADB (2000)found that it rose by roughly six percentage points. We estimate that the poverty rate increased by approxi-mately three percentage points between1996 and 1999. Note, however, that thistakes account of substantial inter-provincial differences in the cost of living.

A caveat on the welfare distributionestimates for 1996 and 1999 is in order.The difference between the two years isstrictly not an estimate of the extent ofchange during the crisis. The crisis didnot begin in February 1996 and end inFebruary 1999, the months in which theSusenas data used in this paper werecollected. Economic growth continuedto be positive and to surpass popula-tion growth (while inflation remainedmoderate) for nearly a year and a half

Source: Susenas.

FIGURE 2 Distribution of Per Capita Consumption

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50

60

70

80

90

100

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

% o

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ulat

ion

Real per capita expenditure (Rp ’000)

1993 1996 1999

BIESDec03Q9 27/10/03 6:30 PM Page 335


after the 1996 survey. This could havecaused a further decline in poverty, inline with the norm for the 1980s and thefirst half of the 1990s. Thus, the increasein poverty during the crisis was proba-bly higher than the three percentagepoints shown in figure 2.

SUBNATIONAL DIFFERENCES IN WELFARE The available data show large differ-ences in natural endowment, agrarian

structure, institutions, policies andaccess to support services across thecountry’s 285 districts. Figure 3 high-lights these differences for a few indi-cators, namely schooling, farm charac-teristics and access to infrastructure,technology and finance. These indica-tors (some of which are discussed inmore detail in the next section) are district-level averages for the 1990s. Ingeneral, the values for districts arescattered widely around the overall(national) mean for each indicator. The

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Schools: District average for the distance of villages from the nearest junior secondary schooland the nearest senior secondary school.Information: District average for the proportion of villages with: public phones; public televi-sion; and post offices.Electricity: Proportion of villages in district with state-run electricity.Roads: Proportion of villages in district with paved roads.

BIESDec03Q9 27/10/03 6:30 PM Page 336


dispersion is quite substantial even fordistricts with similar levels of real percapita expenditure.

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summarised by the fitted line, which isobtained by ordinary least squares(OLS) regression of the mean welfareof the poor on overall mean expendi-ture (income).11 Note that both meansare expressed in logarithms; hence theslope of the fitted line can be inter-preted as the elasticity of the averageincome of the poor with respect tooverall mean income, henceforth

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Finance: District average for the proportion of villages with a bank and the proportion with acooperative.Farm size: Average farm size (ha).Irrigation: Ratio of total irrigated area to the total area comprising wetlands, garden drylands,shifting cultivation lands and grasslands.Agricultural worker households: Ratio of agricultural labourer households to total agriculturalhouseholds.

Sources: BPS, Village Potential Statistics (Podes) for 1993, 1996 and 1999; BPS, Jakarta.

BIESDec03Q9 27/10/03 6:30 PM Page 337


referred to as the growth elasticity ofpoverty. This elasticity is about 0.8,indicating that a 10% increase in district-level income raises the livingstandards of the poor by 8%.12 Thisresult is strikingly similar to Bhalla’s(2001) estimate, based on cross-countryanalysis, of poverty reduction from thelate 1980s to the late 1990s.

However, simply regressing the percapita income of the poor on overallper capita income is likely to yield aninconsistent estimate of the growthelasticity of poverty. Measurementerrors in per capita income (which isalso used to construct our measure ofthe average income of the poor) biasthe estimate of this elasticity. More-over, it is possible that the incomes ofthe poor and overall incomes arejointly determined. Recent theory andevidence show a link between inequal-ity (hence, the incomes of the poor)and subsequent overall incomegrowth. One school of thought sug-gests that income (or asset) inequalityinhibits subsequent overall incomegrowth (Alesina 1998; Deininger andSquire 1998); another posits the reverse

(Forbes 2000; Li and Zou 1998). Incon-sistency in the parameter estimates ofthe growth–poverty relationship in fig-ure 4 may also arise from the omissionof variables that have a direct impacton the welfare of the poor and are cor-related with overall average income(figure 3). Provincial indicators ofhuman capital, infrastructure and localinstitutions (for example, social capi-tal) appear to be strongly correlatedwith provincial mean incomes (Booth2000; Kwon 2000: Garcia Garcia 1998).

We address these statistical prob-lems by checking the robustness of thegrowth elasticity estimates and explor-ing other determinants of district-levelpoverty reduction. Figure 5 sum-marises our empirical approach.

To deal with the measurement error,we could use average income to instru-ment for average expenditure. How-ever, the income variable is not avail-able at the district level. The alternativeinstrument is district-level expendituregrowth, which also takes care of theendogeneity issue.13 In the case of theomitted-variables bias problem, weexploit the longitudinal nature of the

FIGURE 4 Welfare of the Poor versus District Mean Expenditure

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BIESDec03Q9 27/10/03 6:30 PM Page 338


district-level data and employ panelestimation techniques to control fordifferences in time-invariant, unob-servable district-specific characteris-tics. Specifically, we use two standardpanel estimation models—the fixedeffects model and the random effectsmodel—suited to addressing un-observed fixed-effects problems, but insuch a way that the endogeneity ofoverall mean income is observed.14

Table 3 summarises the results of theestimation. For comparison, we alsoshow the OLS regression estimatesimplied by the fitted line in figure 4, aswell as the instrumental variable (IV)regression estimates.

The panel estimation results indi-cate that the unobserved district-specific effects are indeed significant,leading to a reduction in the earlierOLS estimate of the growth elasticityof poverty (of nearly 0.8) to about 0.7.Both panel estimation techniques giveroughly the same elasticity estimate,including the values at the 95% confi-dence interval. Hence, we employ the

panel estimation technique, in particu-lar the fixed effects model, which alsoallows for the endogeneity of the over-all income variable. The assumption ofthe random effects model that theunobserved district-level effects andthe explanatory variables are uncorre-lated is not supported by the data. Thiscorrelation problem also applies to theIV estimation technique.

To sum up, our estimate of thegrowth elasticity of poverty is notnearly the one-for-one correspondencebetween increase in the welfare of thepoor and growth in overall incomefound in studies employing cross-country regressions. However, the esti-mate for Indonesia is higher than thatfor the Philippines; a similar study forthe latter country found this elasticityto be about 0.5 (Balisacan and Pernia2002). The comparison is instructivesince the two countries are at roughlysimilar stages of economic develop-ment. Thus, while other factors appearto have direct (distributive) effects onthe welfare of the poor, in the Indone-

FIGURE 5 Empirical Frameworka

aReverse causation running from welfare of the poor (average per capita expenditure) togrowth (overall average per capita income) is not shown.

Welfare of the poor

Per capita expenditure

Overall average per capita

income

Policy regime Political attributesInfrastructure Geographic attributesTechnology Agricultural land Finance attributes

Growth Other factors

BIESDec03Q9 27/10/03 6:30 PM Page 339


sian case changes in the welfare of thepoor in response to overall economicgrowth seem fairly large. This could be explained by the relatively morelabour-intensive and agriculture-basednature of economic growth in Indone-sia. Over the past two decades, the rateof agricultural sector growth has beensignificantly higher in Indonesia thanin the Philippines: 3.7% in the 1980sand 2.2% in the 1990s for Indonesiacompared with 1.9% and 1.8% for thePhilippines.

OTHER FACTORS INFLUENCINGPOVERTY REDUCTIONCertain economic and social factorsinfluence poverty reduction at the locallevel. These include overall per capita

income, relative price incentives,human capital and access to infra-structure, technology and finance. Weuse these variables to explain the widedifferences in the per capita incomes ofthe poor across Indonesia’s 285 dis-tricts during the 1990s. Guiding ourspecifications are development theory,data availability, and estimation sim-plicity.

The proxy for the human capitalvariable is the district-level averageyears of schooling of household heads.This is expected to influence the wel-fare of the poor directly (throughincome distribution), apart from itseffect on district-level income growth.Numerous studies suggest that thehigher the level of educational attain-ment, the higher a person’s expected

TABLE 3 Basic Specifications: Elasticity of the Income of the Poor to Overall Incomea

OLSb IVc 2SLSd

Fixed Random Effects Effects

(1) (2) (3) (4)

Log of mean expenditure 0.774 0.764 0.712 0.714(39.74) (8.65) (16.66) (20.95)

Constant 2.583 2.729 3.474 3.442(9.30) (2.16) (5.68) (7.05)

95% confidence interval for growth elasticity 0.73–0.81 59–0.94 0.63–0.79 0.65–0.78

F-test that all district dummy coefficients are zero 5.58

aThe dependent variable is the logarithm of the mean expenditure of the bottom 20% ofthe population. Except for OLS, all estimations instrument for mean expenditure usinglagged mean expenditure growth. Figures in parentheses are t-ratios. Data refer to a panelof 285 districts surveyed in 1993, 1996 and 1999.bOLS: ordinary least squares.cIV: instrumental variable. d2SLS: two-stage least squares.

BIESDec03Q9 27/10/03 6:30 PM Page 340


earnings over a lifetime (see, for exam-ple, Krueger and Lindhal 2001). Forurban Java, the private rate of return toeducation is about 17%, higher than inmost other countries (Byron and Taka-hashi 1989, cited in Lanjouw et al. 2001).The social rate of return is also quitehigh, at roughly 14% for junior second-ary education and 11% for senior sec-ondary education (McMahon and Boe-diono 1992).

Two alternative proxies for humancapital are adult literacy and access tobasic schooling. The first is defined asthe proportion of the adult populationwho can read and write. The second isthe average distance of a district’s vil-lages from a secondary (junior andsenior high) school. As is well known,since the late 1960s Indonesia has wit-nessed an enormous expansion of edu-cational opportunities at all levels.Duflo (2001) found that each primaryschool constructed per 1,000 childrenled to an average increase of 0.12–0.19years of education, as well as a 1.5–2.7%increase in wages. Household datasuggest, however, that whereas univer-sal primary enrolment was reached asearly as around 1986, the level of sec-ondary enrolment still varied enor-mously across provinces in the 1990s(Lanjouw et al. 2001; see also figure 3).This large degree of variation was truenot only between islands, or betweenJava and the rest of the country, butalso between provinces on the majorislands. For example, while West Kali-mantan did poorly in terms of educa-tion and poverty outcomes, the situa-tion was far less worrisome in CentralKalimantan.

Roads represent the infrastructurerequired to access markets, off-farmemployment and social services. Thisvariable, defined as the proportion ofvillages with paved roads, may be seenas an indicator of spatial connectivity

or, conversely, of spatial isolationimplying geographic ‘poverty traps’.15

The presence of natural resources(oil, gas and minerals) is expected toinfluence growth and poverty reduc-tion. This is defined in terms of the rel-ative importance of oil and gas in thelocal economy. The net effect of thisvariable on the welfare of the poor inresource-rich areas is, however, not apriori obvious.

The price incentives variable isgiven by the local terms of trade,defined as the ratio of agriculturalproduct prices to non-agriculturalproduct prices. Since poverty is con-centrated in agriculture in developingcountries (Pernia and Quibria 1999),including Indonesia (Asra 2000), thisvariable is expected to have a positiveinfluence on the incomes of the poor.

Electricity is a proxy for access totechnology, or simply the ability to usemodern equipment. It is defined as theproportion of villages with access tostate-run electricity. The communica-tion–information variable also servesas an indicator of access to technology.It is given here by a composite indexrepresenting the proportion of villageswith access to public telephones, tele-vision, post offices and newsagents(appendix table 2). We further combinethe electricity and communication–information variables into a singlecomposite index referred to simply as‘technology’. This variable is expectedto positively influence the welfare ofthe poor, apart from its positive impacton overall growth.

Access to credit is critical to manag-ing household consumption, particu-larly as far as the poor are concerned,because it affords them the means tosmooth their incomes in the event ofunfavourable shocks. It is likewise crit-ical in securing working capital, main-taining assets and expanding busi-

BIESDec03Q9 27/10/03 6:30 PM Page 341


TAB

LE

4 D

eter

min

ants

of t

he W

elfa

re o

f the

Poo

ra

(1)

(2)

(3)

(1a)

Exp

lana

tory

Reg

ress

ion

FSFE

Reg

ress

ion

FSFE

Reg

ress

ion

FSFE

Reg

ress

ion

FSFE

Var

iabl

eE

stim

ate

Est

imat

eE

stim

ate

Est

imat

e(m

ean

year

s of

sch

oolin

gof

fir

st q

uint

ile)

Ove

rall

mea

n in

com

e (Y

)0.

7244

***

0.71

44**

*0.

7149

***

0.72

28**

*(1

3.12

)(1

3.42

)(1

3.42

)(1

3.76

)H

uman

cap

ital

Ye

ars

of s

choo

ling

–0.0

392

0.04

470.

0166

**–0

.003

4(–

0.40

)(0

.60)

(1.8

8)(–

0.51

)A

dul

t lit

erac

y0.

1290

0.31

07**

(0.7

4)(2

.32)

Dis

tanc

e to

sch

ools

–0.0

173

0.01

66(–

1.19

)(1

.50)

Term

s of

trad

e0.

0006

*0.

0014

***

0.00

050.

0013

**0.

0006

*0.

0014

***

0.00

06*

0.00

14**

*(1

.77)

(4.8

3)(1

.35)

(4.5

7)(1

.64)

(5.0

4)(1

.63)

(4.9

4)Te

chno

logy

0.21

53*

0.02

870.

2063

*0.

0436

0.20

46*

0.04

020.

2266

**0.

0319

(1.8

4)(0

.33)

(1.7

7)(0

.50)

(1.7

6)(0

.46)

(1.9

7)(0

.35)

Fina

nce

0.03

51–0

.005

80.

0428

–0.0

124

0.03

35–0

.004

4(0

.48)

(–0.

10)

(0.5

8)(–

0.22

)(0

.45)

(–0.

08)

Roa

ds

–0.0

143

0.04

99**

–0.1

650.

0320

–0.0

116

0.04

79**

–0.0

176

0.05

16**

*(–

0.52

)(2

.34)

(–0.

56)

(1.4

1)(–

0.42

)(2

.26)

(–0.

79)

(2.5

3)O

il an

d g

as–0

.192

70.

3843

**–0

.294

80.

3641

**–0

.228

40.

4253

***

–0.2

691

0.41

55**

*(–

0.90

)(2

.35)

(–1.

36)

(2.2

1)(–

1.09

)(2

.64)

(–1.

28)

(2.5

6)L

agge

d g

row

th o

f Y

0.45

66**

*0.

4678

***

0.46

11**

*0.

4583

***

(24.

18)

(24.

8)(2

4.76

)(2

4.95

)In

terc

ept

3.27

78*

13.9

773*

**3.

2659

***

13.8

259*

**3.

2958

*14

.100

1***

3.16

29**

*14

.070

0***

(4.2

3)(1

01.5

4)4.

14(1

31.3

2)4.

27(2

77.8

3)(4

.15)

(280

.79)

R-s

quar

ed

0.74

10.

785

0.73

90.

789

0.73

20.

787

0.73

30.

785

F-ra

tio

145.

1114

5.23

146.

3716

9.81

Wal

d X

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1,00

0)24

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5924

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00

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494.

694.

673.

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leve

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den

otes

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irst

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ixed

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ects

reg

ress

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in w

hich

the

dep

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vari

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per

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xpen

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ure.

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in p

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the

2SL

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xed

eff

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tios

for

the

FSFE

.

BIESDec03Q9 27/10/03 6:30 PM Page 342


nesses. This variable is defined as thedistrict average for the proportion ofvillages with a bank and the propor-tion with a cooperative.

Table 4 summarises the results of theeconometric estimation, including theresults of the first-stage fixed effectsregression (FSFE), which indicate theresponse of overall growth to theexogenous variables. Appendix table 2provides the descriptive statistics onthe variables.

After controlling for the influence ofother factors (including unobserveddistrict-specific fixed effects), thegrowth of overall income appears toexert a significant influence on theincomes of the poor. Indeed, the esti-mate of the growth elasticity is quiterobust, consistently around 0.7 in thevarious specifications. Surprisingly,this estimate is close to that obtained in the basic specifications in which district-specific effects are controlledfor (regressions 3 and 4 in table 3).

Evidence on the direct effect ofschooling is mixed. The variable formean years of schooling is insignificant(regression 1), as is the variable for dis-tance to school (regression 3). Note,however, that the variable for meanyears of schooling is significant if it isdefined for the poor only (regression1a). It is possible that years of schoolingmay not adequately reflect differencesin human capital across the incomespectrum. However, for the poor, thenumber of years at school may corre-spond closely to achieved human capi-tal since school quality may be less heterogeneous within the group.

Adult literacy also appears not tohave a direct impact on the welfare ofthe poor (regression 2). However, itexerts a significant influence on overallgrowth, suggesting that an improve-ment in human capital reduces povertyprincipally through the growth pro-

cess. In other words, investment inhuman capital promotes growth, there-by indirectly reducing poverty.

Price incentives matter to povertyreduction, as indicated by the positiveand significant coefficient of the termsof trade variable. This means thatchanges in the price of agriculture rela-tive to the price prevailing in other sec-tors of the local economy have animpact on the welfare of the poor, bothdirectly by affecting income redistribu-tion and indirectly through their posi-tive effect on overall growth.16 It isworth noting that the country’s priceand trade policy regimes in the 1980sand 1990s tended to penalise agri-culture relative to manufacturing.Although significant trade reformstook effect in the 1980s and 1990s,directly conferring some protection onthe primary sector, the protectionregime as a whole has continued to taxagriculture, though to a lesser extent(Garcia Garcia 2000). This would havelimited the income gains from tradereform in provinces dependent onagriculture. Evidently, since agricul-ture is more tradable than either indus-try or services, and since agriculture ismore labour-intensive than industry,reducing trade and price distortionspromotes both poverty reduction andgrowth objectives.17

The technology variable is positiveand significant, supporting the expec-tation that it matters to the incomes ofthe poor. Recall that this refers to theavailability of electricity and publiclyprovided information channels at thevillage level. Villagers in areas wherethese services are absent may simplynot have an important avenue for rais-ing the productivity of their assets (inagriculture, mainly land and labour).The coefficient estimates, which aver-age around 0.2, suggest that a 10%improvement in access to these serv-

BIESDec03Q9 27/10/03 6:30 PM Page 343


ices raises the incomes of the poor byroughly 2%, other things being equal.

Surprisingly, the finance variable isinsignificant. This runs counter to thecommon claim that access to formalfinancial intermediaries, particularlyin agriculture, is critical for poor peo-ple. It may be that this variable is apoor proxy for access to credit.18 Thespecific location and scale of financialintermediaries vis-à-vis the village pop-ulation might be a better indicator, butdata on such a variable are not avail-able. Moreover, the proxy finance vari-able correlates strongly with the tech-nology variable. Nevertheless, deletingthe finance variable in the estimatingmodel does not significantly changethe parameter estimates of the remain-ing variables.

The roads variable does not appearto be significant, but it has a strongimpact on overall growth. This is con-sistent with the observation by, forexample, Hill (1996) that the publicprovision of roads is designed not as a vehicle for achieving intradistrict (or intraprovince) redistribution, butrather as part of a development strat-egy to spur economic growth, espe-cially in the countryside.

The variable representing naturalresources is not significant, although itdoes significantly influence overallgrowth. This supports the observationby Tadjoeddin, Suharyo and Mishra(2001) that there is no strong correla-tion between natural resource endow-ment and community welfare, definedin terms of human development indi-cators (HDI).19 The revenues generatedby natural resources have, however,been an important means of financingdevelopment projects, especially thoseaimed at keeping interregional inequal-ity low. Indeed, the New Order gov-ernment’s equalisation policy—whichwas achieved mainly through fiscal

policy instruments such as central gov-ernment transfers, interregional trans-fers and other initiatives within theSpecial Presidential Program (Inpres)for poor villages—was quite effectivein spurring growth outside the Java–Bali enclave, especially in the OuterIsland provinces.

DIFFERENTIAL EFFECTS ACROSSQUINTILESDo the welfare effects of the economicand social factors discussed in the pre-ceding section vary across incomegroups? The growth elasticity esti-mates of less than unity shown intables 3 and 4 indicate that people inthe upper ranges of the income distri-bution tend to benefit more than pro-portionately from overall economicgrowth. What policies or institutionalarrangements might enhance the bene-fits of growth for the poor?

We estimate the model for each ofthe other four income quintiles. In par-ticular, we focus on the variant of themodel in which the finance variable isdropped and the schooling variablepertains to the mean years of schoolingfor the relevant quintile.20 Recall thatin the earlier variant we used the meanschooling years of the first quintile,rather than the overall mean schoolingyears of all quintiles, as a regressor.This education variable yielded a posi-tive and significant impact on the wel-fare of the poor. The estimation resultsfor each quintile are summarised intable 5. For ready comparison, theresults reported in table 4 for the firstquintile (column 1a) are reproduced asthe first column in table 5.

Results for the other quintiles gener-ally do not diverge greatly from thosefor the first quintile. Apart from districtmean income, average schooling ineach income group directly and posi-

BIESDec03Q9 27/10/03 6:30 PM Page 344


tively influences the welfare of thatgroup, as expected. Natural resourceendowment (oil and gas), infrastruc-ture (roads) and terms of trade exert aninfluence on welfare through their pos-itive impact on overall income growth.

The growth elasticity of welfaretends to increase monotonically withthe income quintile, suggesting that

people in the higher income groups doindeed enjoy more of the benefits ofgrowth. Similar results have beenfound for the Philippines, except thatthe growth elasticities for the first twoquintiles (the bottom 40% of the popu-lation) are significantly lower.

It is also worth noting that thereturns to schooling are fairly similar

TABLE 5 Determinants of Average Welfare by Quintilea

(Q1 = poorest; Q5 = richest)

Explanatory Variable Q1 Q2 Q3 Q4 Q5

Overall mean income (Y) 0.7228*** 0.7729*** 0.8324*** 0.9191*** 1.1900***

Years of schooling 0.0166** 0.0215*** 0.0211*** 0.0162*** 0.0164***(–0.0034) (0.0026) (0.0111) (0.0056) (–0.0043)

Terms of trade 0.0006* 0.0002 0.0000 0.0001 0.0001(0.0014)*** (0.0014)*** (0.0013)*** (0.0014)*** (0.0014)***

Technology 0.2266** 0.1146 0.0752 0.0655 0.1626**(0.0309) (0.0327) (0.0230) (0.0282) (0.0412)

Roads –0.0176 0.0215 0.0044 0.0150 –0.0199(0.0516)*** (0.0484)** (0.0450)** (0.0477)** (0.0496)**

Oil and gas –0.2691 –0.2628 –0.1763 0.0278 0.0280(0.4155)*** (0.3950)** (0.3286)** (0.3727)** (0.4086)***

Lagged growth of Y (0.4583)*** (0.4587)*** (0.4543)*** (0.4554)*** (0.4645)***

Intercept 3.1629*** 2.7740*** 2.1245*** 1.0940*** –2.2130***(14.0705)*** (14.0429)*** (14.0033)*** (14.0214)*** (14.0910)***

Wald X2 (× 1,000) 24,504 54,710 70,432 94,042 52,379Prob > X2 0 0 0 0 0

*** denotes significance at the 1% level; ** denotes significance at the 5% level; * denotessignificance at the 10% level.aEstimation is by two-stage least squares (2SLS) fixed effects regression. The dependentvariable is the logarithm of the quintile mean per capita expenditure adjusted for provin-cial cost-of-living differences. Figures in parentheses are the results of first-stage fixedeffects (FSFE) regressions in which the dependent variable is the logarithm of the districtmean per capita expenditure.

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across quintiles. An additional year ofschooling raises per capita income byroughly 2%, other things being equal.21

This result thus affirms the commonclaim in the development literaturethat education represents an importantavenue for raising household wel-fare—even more so for the poor, whoseaccess to land and other assets is verylimited. Finally, it appears that accessto technology directly influences thewelfare of the poorest and the richestquintiles, but not those in between.

CONCLUSIONNewly constructed panel data onIndonesia’s 285 districts reveal hugedifferences in poverty change, sub-national economic growth and localattributes across the country. Econo-metric analysis of these data showsthat the welfare of the poor respondsquite strongly to overall incomegrowth: the growth elasticity of povertyis about 0.7. This growth–povertynexus seems significantly strongerthan in the Philippines, where the elas-ticity is estimated to be only about 0.5.This may be explained by the highergrowth rate of agriculture in Indone-sia, which is likely to have been moreemployment generating. Still, thegrowth–poverty relationship is farfrom the one-to-one correspondencerevealed by studies based on cross-country regressions. Growth is goodfor the poor in Indonesia, as in thePhilippines, but it is not good enough.

Factors other than economic growthexert direct distributive effects on thewelfare of the poor, apart from theirimpact on growth itself. Among thecritical ones are the terms of traderegime, schooling, infrastructure andaccess to technology. Although oftenreferred to in the literature as beingimportant to the poor, the access to

credit variable as defined by availabledata was not significant. Future workmust go beyond physical indicators offinancial services to include ‘meso’indicators pertaining to the distributionof physical assets (particularly land)and social capital. Empirical researchon poverty in Indonesia—and else-where in the developing world—haslikewise to give careful attention to theprocesses by which various local insti-tutions affect the welfare of the poor.

On the whole, the present study andsimilar studies analysing subnationaldata show that there is more to povertyreduction than merely promoting eco-nomic growth. While fostering growthis evidently crucial, and appears to bea relatively straightforward objectiveto pursue, a more complete povertyreduction strategy must take accountof the various redistribution-mediatingand institutional factors that matter, ifthe aim is rapid and sustained povertyreduction. Paying attention to theseother factors will be good for bothgrowth and poverty reduction.

NOTES* The authors gratefully acknowledge

the valuable comments of two anony-mous referees, the assistance with datagiven by P.T. Insan Hitawasana Sejaht-era, in particular Swastika Andi DwiNugroho, and the advice provided byLisa Kulp. We thank Gemma Estradafor providing very able research assis-tance. The views expressed here arethose of the authors and do not neces-sarily reflect the views or policies ofthe institutions they represent.

1 According to the World Bank’s interna-tionally comparable estimates basedon a poverty line of approximately $1 aday (in 1993 purchasing power parity);see Chen and Ravallion (2001).

2 See, for example, ADB (2000); Skoufias(2000); and Suryahadi et al. (2000).

3 See Hill (1996, 2002); Tadjoeddin,

BIESDec03Q9 27/10/03 6:30 PM Page 346


Suharyo and Mishra (2001); ADB(2000); Booth (2000); and Asra (2000).Similar variations are evident withinother developing countries, both largeand small. See, for example, Fan, Zhangand Zhang (2000) for China; Ravallionand Datt (2002) for India; Balisacan andPernia (2002) for the Philippines; andDeolalikar (2002) for Thailand.

4 Although the Susenas extends back tothe 1960s, provincial-level data arestrictly comparable only for the sur-veys carried out since 1993. This wasthe year in which the BPS imple-mented a heavily revised core ques-tionnaire and expanded the core sam-ple size from about 65,000 to around200,000 households.

5 The classification of districts is thatprevailing in 1993. The data excludeEast Timor.

6 Ravallion (2003) finds that, for devel-oping and transition countries, theproblem of comparability between sur-vey and NIA data is more serious forincome than for expenditure measures.

7 Note that the CPIs for the provincesshare a common base year and a com-mon value (of 100). Hence, the use ofthe provincial CPIs alone to deflatenominal expenditure does not fullycapture provincial differences in thecost of living at any given time.

8 A notable feature of the economic crisiswas that food prices rose much moresharply than non-food prices. The foodCPI rose by about 160% between 1996and 1999, compared with an increaseof only 76% for the non-food CPI.

9 If no allowance were made for differ-ences in the provincial cost of living,that is, if the Susenas expenditure datafor the three survey years wereadjusted only for price changes overtime, the estimates of poverty inci-dence would be higher by 4.3 percent-age points for 1993, 3.3 percentagepoints for 1996 and 3.4 percentagepoints for 1999.

10 Alternatively, as in common practice,poverty can be defined in terms of anexplicit poverty line, below which a

person is deemed poor. However, forour purposes this practice is not partic-ularly appealing, since it makes theestimate of poverty response sensitiveto assumptions about the poverty line.

11 From here on, for expositional pur-poses, we use the term ‘mean per capitaincome’ or simply ‘per capita income’for ‘mean per capita expenditure’,unless otherwise specified. We also usethe expression ‘mean welfare of thepoor’, or simply ‘welfare of the poor’ or‘living standards of the poor’, for meanincome or expenditure of the poor.

12 The estimated elasticity for each year—0.773 for 1993, 0.768 for 1996 and 0.775for 1999—indicates that the overallestimate of 0.8 is quite robust.

13 The assumption is that the measure-ment error in overall mean expendi-ture is invariant to survey years.

14 The fixed effects model utilises differ-ences within each district across time.The technique is equivalent to regress-ing the average income of the poor ona set of intercept dummy variables rep-resenting the districts in the data, aswell as on overall mean incomes. Therandom effects model is more efficientsince it utilises information not onlyacross individual districts but alsoacross periods. Its main drawback isthat it is consistent only if the district-specific effects are uncorrelated withthe other explanatory variables.

15 In a related vein, Gallup and Sachs(1998) find that the geographic locationof a country influences the speed of itseconomic growth, noting in particularthat landlocked countries tend to growmore slowly than those with directaccess to sea transport.

16 As noted earlier, income poverty inIndonesia is largely a rural phenome-non. Most rural poor are dependent onagriculture for employment andincome. As such, an improvement inthe terms of trade in provinces whereagriculture is a dominant componentof the local economy tends to raise thewelfare levels of the poor.

17 Since labour in Indonesia is fairly

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mobile (Manning 1997), even farmersin resource-poor areas should benefitfrom trade and price reforms.

18 The reason that it may not be a goodindicator of access to finance is thattwo districts with the same proportionof villages with banks/cooperativescould still have different levels ofaccessibility to credit (for example, thenumber of banks or cooperatives maydiffer between districts).

19 Tadjoeddin, Suharyo and Mishra (2001)identify 19 ‘enclave districts’ charac-terised by very high levels of per capitaoutput, including seven districtslocated in the four natural resourcerich provinces of Aceh, Riau, East Kali-mantan and Papua. They find thatindicators such as consumption, healthand HDI for the 19 enclave districts arefairly close to the national averages,despite these districts’ high levels ofper capita output.

20 Using any of the other model variantsreported in table 4 will not substan-tially change the results in terms of thepattern of impact across quintiles.

21 Note that the average number of yearsof schooling varies by quintile.

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APPENDIX TABLE 1 Urban Poverty Line and Cost-of-Living Index by Province, 1999

Urban Poverty Line Cost-of-Living Index (Rp per capita per month) (Jakarta = 100)

1 Aceh 78,286 0.8692 North Sumatra 84,342 0.9363 West Sumatra 100,131 1.1114 Riau 90,609 1.0065 Jambi 91,032 1.0106 South Sumatra 88,533 0.9837 Bengkulu 104,237 1.1578 Lampung 96,635 1.0729 DKI Jakarta 90,108 1.00010 West Java 88,471 0.98211 Central Java 80,369 0.89212 DI Yogyakarta 92,037 1.02113 East Java 83,223 0.92414 Bali 94,190 1.04515 West Nusa Tenggara 84,449 0.93716 East Nusa Tenggara 79,473 0.88217 West Kalimantan 95,767 1.06318 Central Kalimantan 95,220 1.05719 South Kalimantan 87,134 0.96720 East Kalimantan 79,350 0.88121 North Sulawesi 85,886 0.95322 Central Sulawesi 83,579 0.92823 South Sulawesi 77,513 0.86024 Southeast Sulawesi 66,290 0.73625 Maluku 95,556 1.06026 Irian Jaya (Papua) 76,250 0.846

BIESDec03Q9 27/10/03 6:30 PM Page 350


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BIESDec03Q9 27/10/03 6:30 PM Page 351

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