The Spatial Dimension of Welfare and Poverty: Lessons from a Regional Targeting Programme in Indonesia

Transcription

1 REGIONAL [Asian Economic TARGETING Journal 1998, 2001, Vol. IN INDONESIA No. 3] 4] 345 The Spatial Dimension of Welfare and Poverty: Lessons from a Regional Targeting Programme in Indonesia Takeshi Daimon International University of Japan This paper examines the spatial dimension of welfare and poverty in Indonesia and explores the effective policy interventions to remedy the regional economic disparity which was most vividly shown during the recent economic crisis. The econometric estimations confirm the existence of a spatial poverty trap, where poverty persists for generations. The inclusion of the placement of Impres Desa Tertingal (IDT) implemented during suggests that the regional targeting programme could fail to achieve its policy goal when the empowerment of local communities is insufficient. It is suggested that full-scale decentralization could remedy the targeting policy failure. Keywords: Poverty, regional targeting, decentralization, spatial econometrics. JEL classification codes: O15, O18, R15. I. Introduction Since the early 1950s, the World Bank has assisted over 300 development projects for poor areas. A number of widely recognized poor areas still exist in developing countries, for example, the eastern outer islands in Indonesia, southern inland areas in China, northeastern India, parts of northern Pakistan, southern Bangladesh, rural Ghana, northern Nigeria, and northeast Brazil. In a strict sense, there are no poor areas but only poor people who are geographically concentrated in specific locations. The problem is the existence of a vicious cycle of poverty that is locked in a certain location over generations. In other words, the incidence of poverty is not randomly placed over space but follows some systematic patterns. This phenomenon, often called a spatial poverty trap in the literature, describes structural relations between a geographic space and the incidence of poverty It should be conceptually distinguished from the more short-run phenomenon, i.e., when there is an economic shock or crisis and people in certain areas are more affected than those living in other areas. This is related to short-run or transient poverty (as opposed to chronic poverty).

2 ASIAN ECONOMIC JOURNAL 346 While it is widely recognized that a spatial poverty trap exists, the literature contains surprisingly little discussion on its precise generation mechanism, let alone defines it. This paper defines the spatial poverty trap as a situation in which a persistence of poverty is due to location-specific factors and the cost of mobility is excessive. The questions to be explored are twofold: first, how has poverty become geographically concentrated, and second, why does poverty tend to persist in the same location over generations? The first question is arguably related to a set of initial conditions in an area, which may include land infertility and poor naturalresource endowments, adverse geological and climatic conditions, a weak industrial base, low public and private investments, and insufficient public goods and services. Poor areas, once created, can become non-poor when favourable endogenous and exogenous factors raise their standards of living up to a critical minimum level beyond which economic growth can be self-sustainable. This has happened in many previously poor areas in newly industrializing countries in East Asia. In contrast, however, there is a situation in which such a take-off is not feasible and a spatial poverty trap is created. Typically, in these areas, lack of transportation, telecommunication and a proper infrastructure in general makes the mobility of labour very costly. In a traditional society, cultural or religious barriers (e.g., the caste system in India) would add to the non-economic yet substantive costs. Many other types of institutional barriers in developing countries also make the cost of labour mobility unusually high. A number of poor-area development programmes, often referred to as geographic (or spatial, regional) targeting programmes, have been implemented over the years. In public finance literature, a number of studies have attempted to explain the conditions under which the targeting could most effectively alleviate the poverty. The theoretical foundation of poverty-targeting literature can be traced back to the seminal work by Akerlof (1978) who has shown that the transfer payment made when the rich and the poor are targeted is strictly greater than the amount that they would have received in the absence of targeting. Besley (1996) and Gelbach and Pritchett (1995) have shown that the validity of the Akerlof model is constrained by political economy factors. Kanbur s (1987) poverty-minimizing budgetary model was developed into an algorithm by Chao and Ravallion (1989) for their analysis of geographic targeting in Indonesia, and extended to a general equilibrium framework by Thorbecke and Berrian (1992). Kanbur et al. (1995) considered the labour-incentive effects in his earlier model. Grosh (1994) examined targeting programmes in Latin American countries, providing evidence for a premise that the cost of targeting policies is an increasing function of the fitness of targeting. There is a rich strand of literature on geographic targeting programmes, reflecting the popularity of geography-based development programmes as a policy tool for poverty alleviation in many developing countries. 2 Recently there has 2. Above all, Baker and Grosh (1994) on Latin America, Jalan and Ravallion (1998a, 1998b) on China, Datt and Ravalllion (1997) on India, Ravallion and Wodon (1997) on Bangladesh, and Ravallion (1993) on Indonesia should be mentioned.

3 REGIONAL TARGETING IN INDONESIA 347 been a growing research interest in searching for whether the decentralized poor community development programmes, emphasizing a participatory approach, could achieve a more desirable policy goal than the centrally administered targeting scheme. The results are case dependent. 3 This paper examines the causes and effects of the spatial poverty trap, as defined at the outset of the paper, and discusses policy alternatives to mitigate the adverse effects of such a trap, using data from Indonesia. Indonesia is a country where regional tensions could easily shatter the social fabric since it is composed of over 13,000 islands and 300 ethnic groups. The history of Indonesia is a history of regional tensions. Political leaders have resorted to stick and carrot approaches to unite this heterogeneous archipelago over the past decades. Following the recent economic crisis and political upheavals, these regional tensions have become a central policy focus. There is a growing political demand for more decentralization, which could encourage the rise of separatist movement. The next section outlines regional dimensions of the economic crisis in Indonesia. Section III presents an econometric estimation of the spatial poverty trap. The estimation attests to the existence of such a trap, which provides a rationale for the regional targeting programmes. In order to interpret this finding in a policy framework, Section IV examines the causes and effects of placement of an actual regional targeting programme in Indonesia, focusing on the Presidential Instruction Programme on Less-Developed Villages (known by the Indonesian acronym IDT or Instruksi Presiden Desa Tertinggal). Section V concludes. II. Social Impacts of the Indonesian Crisis It is no exaggeration to state that the economic crisis in East Asia that erupted in 1997 has hit Indonesia more severely and decisively than any other country in the region. Droughts, forest fires, political upheavals, and ethnic tensions have combined their destructive forces to threaten the social fabric of the nation. Economic factors alone, including hyperinflation, increasing unemployment, a huge drop in food self-sufficiency due to drought, a sudden withdrawal of foreign capital, and a sharp decline in industrial production, were sufficient to generate severe social impacts on the most vulnerable members of society. II.1 Monetary measure of welfare and poverty From 1997 to 1998, the mean per capita consumption of the urban population decreased by 33.9% significantly more than its rural counterpart (13.4%) (Table 1). The headcount ratio (P 0 ) was found to have worsened from 9.2% to 12% in urban areas, and from 12.4% to 15.2% in rural areas, using the same deflation ratio between urban and rural areas within a province. These estimates 3. See Alderman (1998) on Albania as a success and Ravallion (1998b) on Argentina as a failure case. See also Chambers (1997) for an introduction to the participatory development.

4 ASIAN ECONOMIC JOURNAL 348 Table 1 Monetary Measure of Welfare and Poverty Per Capita Expenditure Change (%) Poverty Rate (monthly 000 Rp.) (headcount ratio) Full Sample (18) (05) Geographical Location Urban (41) (10) Rural (08) (05) Jakarta West Java Central Java South Kalimantan South Sumatra North Sumatra NTB Expenditure Strata I (lowest) II III IV (highest) Note: Figures in parentheses are standard deviations. Source: Indonesia Family Life Survey (various years). are sensitive to the method of deflation, as reported in Thomas et al. (1999). For example, using different deflators would result in an urban poverty headcount ratio of 15.8% and a rural headcount ratio of 23%, implying that rural poverty had worsened relatively more than that of the urban population. Recent data suggest a much greater worsening of the incidence of poverty during the crisis in both urban and rural areas. 4 The evidence also points towards a variation in social impacts across regions, with West Java and Jakarta the hardest hit. Households in the lowest two quartiles of the expenditure distribution in 1997 appear to have surprisingly increased their per capita expenditure, while those in the highest two quartiles appear to have experienced a reduction in their per capita expenditure. These results would be inconsistent with the worsening of the poverty incidence unless, either there was a significant increase in the variance of the income distributions of the lowest two quartiles following the crisis, 4. Ikhsan and Wikarya (1999), using SUSENAS data, estimated the headcount ratio to have increased between 1996 and 1998 from 10.6% to 20.3% in urban areas and from 25.7% to 41.4% in rural areas.

5 REGIONAL TARGETING IN INDONESIA 349 or there was a severe measurement problem. In general, measuring welfare based on expenditure data may not be a good predictor since these data are subject to large fluctuations because of hyperinflation during the economic crisis. For example, lower income households may have to increase their expenditure for basic needs such as nutrition when prices have risen, even if they make some adjustments through consumption smoothing by, for example, selling assets. There is overwhelming evidence that many sub-districts (kecamatan) a significant portion of the population, especially in Java, reported that an increased incidence of consumption smoothing (Table 2). Once these assets are Table 2 Incidence of Consumption Smoothing (%) Total Urban Rural DI Aceh North Sumatra West Sumatra Riau Jambi South Sumatra Begkulu Lampung Average Jakarta n.a. West Java Central Java DI Yogyakarta East Java Average Bali NTB NTT East Timor West Kalimantan Central Kalimantan South Kalimantan East Kalimantan Average North Sulawesi Central Sulawesi South Sulawesi Southeast Sulawesi Maluku Average Note: Figures represent sub-districts (kecamatan) reporting an increased incidence of consumption smoothing (i.e., sales of assets to meet basic needs) during the economic crisis. Source: Kecamatan Rapid Poverty Assessment (1998).

6 ASIAN ECONOMIC JOURNAL 350 sold, they are gone and the poor have exhausted whatever safety valve they had. Further consumption smoothing becomes impossible. Perhaps, then, the impact of the crisis was attenuated in 1998 by these distress sales and was felt much more severely in 1999 and subsequently. II.2 Spatial-Economy implications of the crisis All data on monetary welfare and poverty presented above suggest that there are significantly different social effects of the economic crisis across geographical locations. In order to make sense out of these results, it is useful to make a distinction between the level and change of welfare; the change (relative decline) has apparently been greater in urban areas, but the absolute level is lower in rural areas. Moreover, poverty is likely to be chronic in pockets of rural areas, while the poverty increase in urban areas is transient in nature. It is also possible that the negative repercussions of the economic crisis in rural areas operate with a time lag. In other words, the second round of negative effects could occur in the future and they might be potentially more destructive in that they would perpetuate the poverty in rural areas. The main thrust of these observations is closely related to the old dilemma of economic development in Indonesia, that while the national economy grows rapidly, its spillover benefits to the poor areas are not evident. As a consequence, the economic disparity across and within regions widens. International comparisons show that, as of 1993, the inter-provincial disparity of income in Indonesia was much greater than in Brazil (Takeda and Nakata, 1998). In the past, a number of projects promoted rural development in order to reduce regional disparities. The problem has, however, persisted if not worsened over the years despite these policy interventions. Since the late 1960s, the government has initiated intergovernmental transfer programmes, called Presidental Instruction Programs (Instruksi Presiden or INPRES) to provinces, counties and villages for basic infrastructure and social needs, but such programmes have demonstrated no evidence of substantive effects on national welfare because of the overly-centralized programme administration. 5 In order to remedy the regional imbalance of welfare and poverty, in 1993, the so-called IDT program was introduced by a Presidential Instruction No. 5 and was specifically intended to target poor villages in order to help reduce the poor from 25 million (official head count in 1993) to 12 million by the end of Repelita VI (the sixth five-year plan). The IDT was first introduced in 1994 and was extended until See Wuryanto (1996) for a detailed discussion of the INPRES programme and the numerical simulation on the decentralized INPRES administration.

7 REGIONAL TARGETING IN INDONESIA 351 III. III.1 Estimating the Spatial Poverty Trap in Indonesia Conceptual framework The rationale for these public investments for the poor based on geographic location is a clear indication of spatial poverty trap as defined earlier. However, when poverty persistence in a given location is largely due to characteristics independent of geographic location, for example, household characteristics, public spending should not target geographical location itself, but instead should concentrate on improving those aspatial characteristics. This section examines the extent to which the persistence of household-level poverty is attributable to such a trap. To this end, a household consumption model has been constructed with the following assumptions: A) Individual welfare is reasonably reflected in the monetary measure of the per capita consumption of a bundle of basic necessities; B) Each household uses the initial endowments and capital stock available to it in a given location to maximize welfare. These stocks and endowments include monetary assets such as land and business equipment, as well as human capital such as education and health; C) Household characteristics, such as the age and gender composition of the household and the size of the household, are also likely to influence the variability of demand for consumption goods; D) Spatial factors also drive household consumption patterns through three channels, including; i) the use of public infrastructure such as roads and water supply, and social capital 6 such as community orientation and participation; ii) migration; and iii) physical and geological characteristics, such as proximity to commercial areas; E) These spatial and aspatial effects on household consumption decisions could vary between the rich and the poor, given the differential in demand elasticities. Assumption (D) merits further explanation. Spatial factor is defined as a set of observable and non-observable variables affecting the costs of mobility, which could include, for example, the costs of transportation, information gathering 6. Although there is no consensus yet on its precise definition, the paper defines social capital as the networks and relationships that both encourage trust and reciprocity and shape the quality and quantity of society s social interactions. (World Bank, 1999, p. 18) There are positive and negative social capitals. See also the World Bank s social capital Web page: scaptal/indes.htm.

8 ASIAN ECONOMIC JOURNAL 352 and communication between different economic environments, political systems, culture, languages and religions. Infrastructure and physical characteristics are regarded as facilitating or impeding economic activities of households across geographical locations. Social capital may provide information on the market or may impose community norms on consumption behaviour in general. Social capital would also facilitate or impede access to credit and insurance. People would migrate to earn higher income, and thus consume more. All of these factors drive household consumption patterns, entering the household utility function, which is unobservable. Given these assumptions, the estimation model is simply formulated as ln C ij =β X ij +γ Z ij +ε ij (1) where i denotes a household in a location j. The left-hand side is the natural logarithm of per capita household consumption. X is an n k vector of household specific variables (control variables) including personal assets, human capital, and household member composition, and an n l vector of ones. Excluding these variables (X s) would result in an omission variable bias. Z is an n l vector of spatial variables including public goods, 7 social capital, personal migration history, physical characteristics of a district, and locational dummies. This specification imposes an additional restriction that spatial and aspatial effects on household consumption be additively separable. Policymakers are assumed to minimize the Foster, Greer and Thorbecke (1984) class of poverty, P α, subject to budget constraints. This paper uses the severity of poverty measure, P 2, which is distribution-sensitive. At the household level, this measure corresponds to the poverty gap squared. Hence the resulting equation is: 1P i =β X ij +γ Z ij +ε ij if C ij δ j P i * = 2 (2) 3 0 otherwise where P i = (C ij δ j ) 2 and δ j is a poverty line in district j. III.2 Data The variables are constructed using Indonesian Family Life Survey (IFLS) 1993 data, covering about 6,000 sample households drawn from 13 provinces. The data were processed in the following way. First, per capita expenditure (the dependent variable), assumed to reflect household welfare, was calculated from the monthly total expenditure on household consumption of food and non-food items (including household members own production and external gifts) and divided by the total number of household members. Per capita expenditure was 7. Some of the variables included in the variable list of public goods such as water and electricity are indeed impure public goods because of some degree of excludability.

9 REGIONAL TARGETING IN INDONESIA 353 then deflated by the 1993 provincial urban and rural poverty lines. This assumes that the official provincial poverty lines reasonably reflect regional price levels. The independent variables were constructed on the following principle. The household-specific variables (X s) were constructed from the IFLS data on household size, declared household assets, years of schooling, health status (dummy variable), age-composition of household, and age and gender of household head (dummy). The spatial variables (Z s) include: (1) district means of household access to electricity, clean water, and paved or gravel roads as proxies for public infrastructure, and the availability of village hall and family welfare associations as proxies for social capital 8 ; (2) number of migrations of the household head since the age of twelve; and (3) physical distance from the district capital, and rural residence dummies. III.3 Estimation results The ordinary least square (OLS) estimation for per capita household expenditure (Equation (1)) suggested that human capital measured by years of schooling generates a high return (Table 3, first column). The inclusion of non-wage earning members of the family (children and the elderly) tends to lower the level of per capita expenditure, and indeed a family with more dependent members is likely to be poorer. Spatial variables, including public goods and social capital, need more attention. Families with more frequent migration histories tend to spend more. It should be remembered in this context that the Indonesian government has encouraged the migration from Java Island to outer islands by providing subsidies to the migrants (the so-called transmigration (transmigrasi) policy). The results would seem to suggest that the motivation for migrating was to obtain higher income and indeed that the transmigration policy has had some positive results. It may be, however, that higher income earners tend to migrate more, since the cost of mobility would be too high for the poorest portion of the population. There is a potential simultaneity problem. Nonetheless, as the transmigration contains important issues that are beyond the scope of this paper, the detailed investigation is left for further study. The quality of public goods in a district (kabupaten) of residence as proxied by the availability of electricity and paved roads has a positive influence on individual welfare through the stream of benefits the household receives from these public goods. Water supply is not significant, suggesting that the benefits from this infrastructure are not shared by a majority of members of any given district. Location also matters: households in remote areas and in rural areas are likely to be associated with lower levels of welfare, justifying public spending in these areas. The availability of social capital as proxied by the existence of a family welfare association has a positive impact on individual welfare. 8. There is no consensus in the literature as to which proxies should be used for the social capital.

10 ASIAN ECONOMIC JOURNAL 354 Table 3 Econometric Estimation of Household Welfare and Poverty Dependent Variables Explanatory Variables Per Capita Expenditure (OLS) Poverty (Tobit) Household Assets HH assets declared (log) *** *** Years of formal schooling a *** *** Health status a (dummy) Household Characteristics Age a HH size (log) *** *** HH member under 15 years old *** HH member over 65 years old *** *** Female a (dummy) Spatial Factors Number of migrations since 12 a *** *** Availability of clean water b Availability of electricity b *** *** Asphalt or gravel main road b ** ** Distance from prov. capital b (log) *** *** Rural residence (dummy) *** *** Social Capital Village hall b Family welfare association b *** ** Goodness of Fit F/& 2 (all variables) c F (spatial variables) c R 2 /Pseudo R 2 d Number of Observations 5,666 5,666 Notes: The left hand side of the column in each model reports parameter estimates and the right hand side indicates t-statistics, with levels of significance: *** 1%, ** 5%, and * 10%. The right hand side column of the Goodness of Fit reports p-value. a. Asked of the household head. b. District share. c. For the whole model: F-statistics are reported for OLS and & 2 -statistics are reported for Tobit. For the spatial variables: F-statistics are reported. d. R 2 is reported for OLS, and Pseudo R 2 is reported for Tobit. The Tobit estimation for household poverty (Equation (2)) shows, as before, that controlling for aspatial characteristics (household assets and household characteristics), spatial variables pertaining to family (migration) and to location of residence (public goods and social capital) are significantly correlated with the incidence of poverty. A rural locational dummy variable has a negative significance, implying that the non-rural (i.e., urban) residence is associated with a higher sensitivity to household poverty. Note, however, that these results are likely to be highly sensitive to where the poverty line is drawn.

11 REGIONAL TARGETING IN INDONESIA 355 Our analysis confirms that, when aspatial factors remain unchanged, spatial factors influence both household welfare and poverty, which suggests the existence of a spatial poverty trap. This confirms the results of Jalan and Ravallion (1998a, 1998b). Ascertaining where the real trap exists needs a careful examination, though, since, according to the econometric estimations, rural residency is associated with lower household expenditure, while urban residency is associated with a higher sensitivity to household poverty. These seemingly inconsistent results can be explained only if welfare distribution in urban areas is likely to have a greater variance than in rural areas. It is sometimes the case that when the data show a systematic clustering pattern across a sample space, there is a potential bias of estimation from heteroskedasticity. In order to check the robustness of the estimation results, a class of semi-parametric estimations has been conducted (see Technical Appendix for details). By definition, this estimation assumes no functional forms on the disturbance and is distribution-free. The results confirm the robustness of the parametric estimation (Table 4). At every quantile of the estimation model, the Table 4 Semiparametric Estimation of Household Welfare and Poverty (a) Per Capita Expenditure Variable Quantile: 0.25 Quantile: 0.5 Quantile: 0.75 Household Assets HH assets declared (log) *** *** *** Years of formal schooling *** *** *** Health status (dummy) Household Characteristics Age ** HH size (log) *** *** HH member under 15 years old ** *** HH member over 65 years old ** *** *** Female (dummy) Spatial Factors Number of migrations since *** Availability of clean water * Availability of electricity *** *** *** Asphalt or gravel main road ** * Distance from prov. capital (log) *** *** *** Rural residence (dummy) *** *** *** Off Jawa-Bali residence (dummy) Social Capital Village hall * Family welfare association ** ** *** Goodness of Fit F (all variables) F (spatial variables) Pseudo R Number of observations 5,666 5,666 5,666

12 ASIAN ECONOMIC JOURNAL 356 Table 4 (cont d) (b) Poverty Gap Squared Variable Quantile: 0.25 Quantile: 0.5 Quantile: 0.75 Household Assets HH assets declared (log) * ** Years of formal schooling Health status (dummy) ** ** Household Characteristics Age HH size (log) * *** *** HH member under 15 years old HH member over 65 years old * Female (dummy) * *** Spatial Factors Number of migrations since ** *** ** Availability of clean water Availability of electricity *** *** Asphalt or gravel main road * ** ** Distance from prov. capital (log) * Rural residence (dummy) * ** * Off Jawa-Bali residence (dummy) Social Capital Village hall *** ** Family welfare association Goodness of Fit F (all variables) F (spatial variables) Pseudo R Number of observations a 5,666 5,666 5,666 sign and magnitude of coefficients are consistent with those obtained from OLS and Probit models as in Table 3. These results suggest, in theory, the case for spatial targeting programmes in Indonesia. The following section presents an evaluation of a targeting programme in practice. IV. Lessons from the IDT Programme Prior to the IDT programme, the major intergovernmental transfer schemes were the INPRES 9 and the Current Transfer Programme (SDO), both of which accounted for as much as 70% of revenue sources for lower levels of governments 9. The INPRES consists of general-purpose ( block ) transfer to provinces, counties and villages, and specific-purpose ( categorical ) assistance for road improvements, primary schools, health, and reforestation.

13 REGIONAL TARGETING IN INDONESIA 357 in the 1970s and 1980s. How effectively these programmes have functioned in order to reduce the regional disparity of welfare is unknown. A study by Wuryanto (1996) shows by the computable general equilibrium (CGE) simulation that the programme could have achieved higher national economic growth if the INPRES programme had been implemented through a more decentralized fiscal structure. The IDT programme, on the other hand, was conceived on the basis of experience of the INPRES programme. The new poverty-targeting programme was intended to be a decentralized extension of the INPRES. Akita and Szeto (2000) have shown that the IDT programme has contributed to reducing the overall level of regional inequality, although its welfare consequences at community and individual levels are yet to be explored. IV.1 IDT villages The programme is a block transfer of 20 to 60 million Indonesian Rupiah (Rp.) 10 a year to some 22,000 nationally selected villages to promote income-generating activities through labour intensive public works and local empowerment through participation by poor individuals. In the outer islands IDT villages (desa) 11 are in the majority. Using two official surveys: Socioeconomic Survey (SUSENAS) and Village Potential Survey (PODES) 1996, Daimon (2000) showed that the design of the IDT programme is flawed, which could well make the policy ineffective in achieving its goals. Descriptive statistics show that the IDT villages lag substantially behind their non-idt counterparts at both aggregate and individual levels. The IDT programme, as in any other regional targeting programmes, takes a village as a geographical unit of targeting, assuming that aggregate spatial characteristics observed in that unit reasonably reflect the state of individual welfare and poverty. Regressing the status of IDT on the welfare indicators should provide the first approximation of programme effectiveness in terms of minimizing the inclusion and exclusion errors. IV.2 Spatial econometric estimation There is a methodological difficulty, however, in employing a standard econometric model, because of the problem called spatial autocorrelation. To understand spatial autocorrelation, one may to think of a situation where the state of welfare and poverty in a given location is correlated with that in adjacent districts. This correlation occurs from two major sources. The first is that the poor area the area in which the poor households are concentrated does not correspond to the administrative unit. The second is that there are spillovers from economic activities from adjacent districts transmitted through public infrastructure, environmental conditions, and market (forward and backward) 10. Equivalent to 10 30,000 US dollars using the exchange rate 1 US$ = 2,000 Rp. 11. Desa indicates the smallest administrative unit in both urban and rural areas.

14 ASIAN ECONOMIC JOURNAL 358 linkages. As shown in the previous section, these spatial variables enter into the household welfare functions. There are two types of spatial autocorrelation. The first one is called the spatial lag model and the other is called the spatial error model. When the welfare status of a community is approximated by per capita consumption, it strongly suggests the existence of spatial autocorrelation of the first type (Table 5). Suppose that Table 5 Spatial Econometric Estimation of Welfare and Programme Endogeneity Per Capita Expenditure (96) IDT Variables (OLS) (ML: Lag) (OLS) Household Assets Education * ** * House ** ** ** Telephone * TV ** Radio Car Household Characteristics HH size (log) Female Farmer Spatial Factors Population (log) Primary school * Secondary school * Financial institution ** Market Public phone ** ** Off Java-Bali (dummy) ** Policy Variables IDT *** *** Spatial Autocorrelation ρ ** Goodness of Fit F Moran s I LM/LR (error) LM/LR (lag) R Number of observations Note: All variables are district means. The left-hand side of each column reports parameter estimates and the right hand side indicates t-statistics of OLS and z-statistics for ML model, with levels of significance: ***1%, **5%, and *10%. The right-hand side of the Goodness of Fit reports p-values. OLS reports LM test and ML reports LR test.

15 REGIONAL TARGETING IN INDONESIA 359 the variable C refers to the observable indicator of welfare in a given district, and Y refers to the vector of all explanatory variables. In the spatial lag model, the dependent variable is correlated with its own spatial lag so that: C =ρwc + Yθ +ε=(i ρw ) 1 Yθ+(I ρw ) 1 ε (4) 12 where θ =[β:γ] slope parameters, WC is the spatially lagged dependent variable with a spatial weight matrix W (see Technical Appendix for details), and ρ is the spatial autocorrelation parameter which measures the extent of spatial spillovers. Clearly, if ρ is different from zero, the least square-based estimation of this model yields inconsistent and biased estimators. 13 The existence of spatial autocorrelation of the first type is suggested by Morans s I and Lagrange Multiplier (LM) Tests. LM test has failed to reject the null hypothesis of ρ =0. The IDT variable in the table refers to the share of IDT villages out of all villages in the district. Note that IDT is negatively associated with the dependent variable, suggesting that those districts containing more IDT-funded villages are still likely to be associated with a lower level of district welfare even after the implementation of the programme. This result does not, however, tell much about the programme effect, since the dependent variable is simply the level of district welfare in the post-programme period rather than the change of that indicator during the programme implementation, for which consistent data are unavailable. All that could be said from this analysis is that when the programme is completed the districts that have received more funding from the programme remain distinctively poor compared with non-funded districts. One could ask why this paper chooses to aggregate individual observations into 131 regions by throwing away individual information. This has been done on purpose in order to estimate the aggregate relationship between the placement of IDT programme and its welfare consequences at the community level when there is a strong reason to doubt the existence of spatial autocorrelation. Future study will assess the effect of IDT placement on the welfare at the individual level. Further, there is a potential problem of programme endogeneity, i.e., the IDT programme placement could be influenced by some observable variables which themselves influence the welfare consequences. This has been informally checked by regressing the placement of IDT on the welfare variables. 14 For example, the positive significance of the variable primary school (the number of primary schools in relation to population) could be interpreted as a sign that a 12. Notations for equations (4) and (5) are due to Anselin (1988). 13. The second type of spatial autocorrelation occurs when the error term is correlated with its own spatial lag. In this case, the error term is in the form ε =λwε+ξ where λ is the spatial autoregressive coefficient for the error lag Wε, and ξ is an independently and identically distributed (i.i.d.) error term. Hence, the resulting expression is: C = Yθ+(I λw ) 1 ξ. When λ is different from zero, the regression estimation based on this structure still yields consistent but inefficient estimators. 14. A more formal treatment of this issue, using the panel-data analysis, can be found in Pitt et al. (1995)

16 ASIAN ECONOMIC JOURNAL 360 higher level of literacy in a district is likely to be associated with a higher level of primary education in a district. Unfortunately, the available data do not allow checking the presence of programme endogeneity to be checked more rigorously. IV.3 The IDT programme and decentralization Given the paucity of information available on programme implementation in some 40,000 IDT villages across the nation, a hasty and general conclusion about the programme should be avoided. The success and failure, including how to define them, are case-dependent, making it difficult to quantify. According to a discussion paper written by a group of local officials: [The] IDT programme did not seem to provide a good result as expected, notably in relation to the funding management and targeting.... Nevertheless, although it was not wholly effective in meeting its aim, [the] IDT programme has to some extent played an important role in temporarily improving the economic wellbeing [sic] of poor households (Irawan et al., 1999, p. 7). Careful case studies in the field would normally supplement the evaluation of the implementation. To this end, an informal field survey of selected IDT villages was conducted in 1998, 15 which included interviewing village officials and residents, and evaluating various IDT-funded activities. The survey has suggested that a possible cause of programme failure is related to the participatory approach taken by the IDT programme. Such decentralized operations normally function only if the implementation capacity is sufficiently mature, which is hardly to be expected in poor villages in Indonesia. In fact, there are a growing number of decentralized targeting schemes in the developing world, but they tend to fail when there is a limited administrative capacity of the implementing bodies, as in Argentina (Ravallion, 1998b). When there is enough capacity, the programme tends to be successful, as in Albania (Alderman, 1998). Thus in itself decentralization is neither beneficial nor harmful for poverty targeting. To push the argument further, consider where the sources of administrative capacity lie and how such a capacity can be strengthened. The administrative capacity is multifold: it contains at the least technical capacity and fiscal capacity. The first aspect can be significantly improved by training local officials. The second aspect, however, requires efforts beyond the local level. It specifically requires the higher level of government to delegate fiscal (i.e., tax collection and public service administration) powers to the lower governments. This aspect has important incentive implications. That is to say, if the implementing body is financially dependent on grants from a higher level of government, there is only 15. Field trips to IDT villages in West Java and South Sulawesi were conducted from June to August 1998.

17 REGIONAL TARGETING IN INDONESIA 361 a weak fiscal incentive to use those funds efficiently. In other words, the incentives for subnational governments to act independently of the central government are strongly determined by their access to independent tax bases, since: the most important determinant of the success of local selfgovernment is the availability of adequate financing both from its own revenue sources and from the national budget. The delegation of new functions to local government without the accompanying resources or authority to raise revenue to pay for services makes decentralization meaningless at best and dangerous for democracy at worst (Diamond, 1999, p. 140). Despite Diamond s concerns, most countries in the world tend to decentralize expenditure more than revenue (Figure 1). By international standards, Indonesia Figure 1 Decentralization Index (1997) degree 50 China 40 Russia India Revenue (%) 30 Brazil Germany US France South Africa Thailand Malaysia UK Indonesia Expenditure (%) Note: Expenditure (%) = Expenditure by Subnational Governments / Total Public Expenditure; Revenue (%) = Revenue by Subnational Governments / Total Public Revenues. Source: World Bank (1999).

18 ASIAN ECONOMIC JOURNAL 362 is not decentralized in either expenditure or in revenue. Even among neighbouring countries, the expenditure is less decentralized than in Malaysia, and revenue is less decentralized than in Thailand, noting that neither country has a highly centralized fiscal structure by international standards. The decentralization ratios in these countries are below even the traditionally centralist states such as France and the United Kingdom, let alone federalist countries such as the United States, Germany and Brazil. Among developing countries, China and India have been most decentralized, according to this statistics. 16 The decentralization exercise in Indonesia through the IDT programme therefore was one-sided at best in the sense that it focused only on the expenditure aspect of decentralization or non-existent at worst. The international statistic suggests that further decentralization on both the expenditure and revenue sides would provide stronger incentives for local governments to use funds efficiently. One of the limitations of revenue decentralization, however, is that it could worsen the regional disparity of welfare. As of 1994, per capita revenue in Jakarta (Rp. 244,650) was more than ten times that in Lembeng (Rp. 18,956) (Takeda and Nakata, 1998). Therefore, the enforcement of an equitable and efficient provincial transfer scheme by the central government must be established before launching into full-fledged decentralization. On the other hand, revenue decentralization could significantly improve the fiscal position of local governments. One of the problems of taxation in Indonesia is that the tax base is too narrow. As reported in Asher (1997), there is a significant amount of income tax evasion not captured or prosecuted by law enforcement. This informational asymmetry problem would be lessened if the taxing power were delegated to local authorities, who are less constrained in obtaining local information. The key to successful decentralization in terms of poverty alleviation, therefore, rests upon the extent to which the stakeholders are given the right incentives to responsibly achieve the intended policy goals. Otherwise, transferring power from the central authority to the lower tiers of government could simply result in the creation of rent-seeking activities or nepotism at the local level. This could worsen the efficiency of the programme implementation. In order to avoid this risk, the delegation of fiscal power to the lower levels of governments should be adequately controlled. Monitoring by a third party may be a possible policy alternative. Another risk is that, because of the decentralization, the traditional tension between centralization vs. localization could be simply transferred to the tension between (say) provincialization vs. localization. In order to avoid this risk, it is important to seek consensus among the different tiers of government and set out explicit rules about which level of government should be accountable for which public service. In the Indonesian context, it does not make sense to expect 65,000 villages across Indonesia to assume all fiscal functions. Intergovernmental cooperation would be a necessary condition to achieve a successful decentralization in the country. 16. See World Bank (Chapter 5, 1999) for an interpretation of these results.

19 REGIONAL TARGETING IN INDONESIA 363 V. Conclusions This paper began by discussing causes and effects of the spatial poverty trap. A fine-tuned targeting programme could in theory effectively mitigate the adverse welfare effects, and eventually would lever the standards of living in these trapped areas beyond the critical minimal level to attain sustainable development. The targeting errors the existence of a significant portion of the nonpoor programme beneficiaries and poor programme left-outs could be minimized through careful programme design, which is often costly for the central government in a large country because of informational disadvantages in diverse local economic and social situations. In order to overcome this problem due to the informational asymmetries between the centre and localities, a carefully decentralized programme could be a policy alternative as long as it provides local stakeholders with the right incentives to achieve the intended policy goals responsibly. The limitation of this conclusion is that decentralization is often a consequence of political pressures rather than of a planned solution. It is certainly the case in Indonesia, where pressures for decentralization could be easily translated into separatist movements (e.g., in East Timor, Aceh, and Irian Jaya). The federalist fiscal system in most of the countries in the world has been established as a consequence of political and historical events, specific to each country, rather than carefully designed economic calculations. There is in this sense a timely political opportunity in Indonesia to initiate the decentralization. Attention should be paid to the balance between the political sensitivity of concerned parties and the economic gain from a carefully designed decentralization. Another limitation lies in the implementation side of the programme evaluation. Field visits to a dozen IDT villages, out of some 40,000 target villages, have been conducted to supplement the analysis, but the realities of the poor are often unobservable to outsiders. This is closely related to participationist critiques. For example, Chambers (1997) categorically dismisses economists use of the poverty measurement as technocratic, an approach from above that tends to ignore the realities from below. There is an increasing awareness in the economics literature of the advantages of using non-monetary and qualitative perceptions of local people to measure poverty consistently (e.g., Pradhan and Ravallion, 1999). Further investigations in this direction are left for future study. Technical Appendix A.1 Semi-Parametric Estimation (Section III) An alternative to the OLS and Tobit models when heteroskedasticity is present in the Quantile Regression (QREG) model. The QREG estimators of the model (1) in the text are obtained by solving the minimization problem:

20 ASIAN ECONOMIC JOURNAL 364 Min Q( β, γ; q) = ( 1 q) βγ, + q lnc β X + δ Z lnc > β X + δ Z ij ij ij ij ij ij (ln C ij β X ij γ Z ij ) (ln C ij β X ij γ Z ij ) = h j ln C ij β X ij γ Z ij i 12q if ln C ij β X ij γ Z ij >0 where h j = 2 (A1) 32(1 q) otherwise and q is a quantile to be estimated. On the other hand, the minimization function for model (2) is Powell s (1986) censored Least Absolute Deviation (LAD) estimator: Min Q( β, γ; q) = h j ln C ij Min( β X ij +γ Z ij ) (A2) βγ, i where notations are the same as before. Computationally, the parameters are estimated using the Iterative Linear Programming Algorithm (ILPA) as follows. The first QREG regression is run on all the observations and the predicted values u X ij + v Z ij are calculated. Next, a QREG regression is run on the observations for which u X ij + v Z ij < 0 and repeats this iteration until the sets of observations in two consecutive iterations are the same, which is a sign of convergence. The QREG does not assume any functional form in error distribution such as normality. But since the regression takes a linear functional form, this is called a semi-parametric estimation in the literature. However, one of the problems of the QREG is that it tends to underestimate standard errors when the heteroskedasticity problem is present (which is often the reason for using the QREG in the first place!) One solution is to use an alternative way to calculate standard errors, by bootstrap replication. Because of its peculiar method of calculation, the standard errors reported each time are typically not the same. Moreover, the estimation may take too much time unless the replications are set within controllable rounds. In this study, the bootstrap has been replicated 60 times. See Buchinsky (1998) and Koenker and Basset (1978) for more formal treatment. A.2 Spatial Econometric Method (Section IV) (1) Spatial Weight Matrix: Given the spatial models in the text: C =ρwc + Yθ+ε=(I ρw ) 1 Yθ+(I ρw ) 1 ε and C = Yθ +(I λw ) 1 ξ The spatial weight matrix W in these models is a positive symmetric matrix that assigns to district j the average value of variate C in districts surrounding district j. This reflects an assumption that the spatial autocorrelation of welfare and