rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 5. Public Goods Expenditures and Election Timing**

||∆Total<br>Public<br>Investment<br>(log)|∆ Total<br>Number of<br>Projects|∆ Road or<br>Bridge|∆Draining<br>Facility|∆Irrigation<br>Facility|∆Land Infra-<br>structure|∆Protective<br>Forest|∆Environ-<br>mental<br>Protection|
|---|---|---|---|---|---|---|---|---|
|∆(Share of households in largest patrilineal clan ×|0.703|0.607|0.228|0.003|0.052|-0.015|0.004|0.029|
|One year before a regularly scheduled election)|(0.847)|(0.382)|(0.218)|(0.004)|(0.155)|(0.063)|(0.004)|(0.136)|
|∆(Share of households in largest patrilineal clan ×|0.498|0.002|-0.116|0.010|-0.050|-0.041|0.006*|-0.061|
|the year of a regularly scheduled election|(0.488)|(0.195)|(0.142)|(0.010)|(0.049)|(0.044)|(0.003)|(0.056)|
|∆(Number of deaths within the year× One year<br>|0.104|0.037|0.000|-0.001|-0.035|0.021|-0.001|0.030|
|before a regularly scheduled election|(0.226)|(0.114)|(0.056)|(0.001)|(0.034)|(0.019)|(0.002)|(0.030)|
|∆(Number of deaths within the year × the year of a|-0.085|-0.044|-0.013|-0.001|-0.004|0.022|-0.002|-0.006|
|regularly scheduled election|(0.163)|(0.088)|(0.043)|(0.002)|(0.024)|(0.020)|(0.002)|(0.013)|
|∆One year before a regularly scheduled election|-0.095|-0.021|0.024|-0.001|-0.023|-0.026|-0.003|0.004|
||(0.329)|(0.135)|(0.058)|(0.001)|(0.044)|(0.016)|(0.003)|(0.022)|
|∆The year of a regularly scheduled election|0.163|0.144|0.010|-0.000|0.020|0.002|0.000|-0.002|
||(0.237)|(0.098)|(0.045)|(0.001)|(0.040)|(0.020)|(0.001)|(0.016)|
|∆Number of deaths within the year|0.029|-0.028|-0.007|-0.000|-0.007|-0.022|0.001|0.003|
||(0.101)|(0.050)|(0.026)|(0.001)|(0.018)|(0.015)|(0.001)|(0.008)|
|Number of observations|495|495|495|495|495|495|495|495|

_Land Tenure Insecurity and Migration, Page 47_
# TABLE AI

ROBUSTNESS CHECK: IMPACTS OF PRICE INCREASE IN BILL 0 ON CONSUMPTION IN PERIOD 1

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|T|-15.901**|-17.415***|-15.266**|-16.637***|-15.577**|-17.013***|-15.597*|-16.670*|-16.296*|-18.000**|-14.780**|-16.371**|
|reatment|(6.491)|(6.342)|(5.929)|(5.656)|(6.153)|(5.934)|(9.194)|(8.996)|(8.105)|(7.922)|(6.882)|(6.583)|
|AAC|0.720**|0.822***|1.004**|1.169***|0.707**|0.806***|2.201|2.547*|0.759|0.876*|1.443**|1.603**|
||(0.290)|(0.272)|(0.412)|(0.368)|(0.272)|(0.251)|(1.441)|(1.282)|(0.451)|(0.450)|(0.612)|(0.604)|
|AAC *|||-0.619|-0.753|||-2.777|-3.335*|||-1.522*|-1.620**|
|Treatment|||(0.531)|(0.493)|||(1.968)|(1.823)|||(0.782)|(0.783)|
|AAC<sup>2</sup>|||||-0.011|-0.013|0.059|0.068|||||
||||||(0.012)|(0.011)|(0.069)|(0.061)|||||
|AAC<sup>2</sup><br> <sup>*</sup>|||||||-0.014|-0.011|||||
|Treatment|||||||(0.090)|(0.086)|||||
|C|433.271***|461.555***|436.134***|464.847***|434.620***|463.133***|439.962***|468.975***|433.000***|465.119***|438.133***|470.180***|
|onstant|(3.299)|(33.802)|(3.992)|(33.767)|(3.539)|(33.715)|(6.161)|(35.078)|(4.042)|(43.858)|(4.647)|(44.095)|
|Bandwidth|20|20|20|20|20|20|20|20|15|15|15|15|
|Areas<br>Effects|N|Y|N|Y|N|Y|N|Y|N|Y|N|Y|
|N|7190|7190|7190|7190|7190|7190|7190|7190|5417|5417|5417|5417|
|R-squared|0.001|0.056|0.001|0.056|0.001|0.056|0.001|0.057|0.001|0.057|0.001|0.058|

_Notes:_ The dependent variable is consumption in Period 1. The bandwidths determine the range of normalized annual accumulated consumption used to select observations for a particular regression. For example, in Columns (1) and (2) only individuals with normalized annual accumulated consumption between [-20, 20] are included. Average of the dependent variable is 425.49. The estimation method is OLS. Standard errors clustered by accumulated consumption in Period 0. Even columns present regressions with areas (neighborhoods) dummies. *** p<0.01, ** p<0.05, * p<0.1.

37
<!-- Start of picture text --> Table A.7: Robustness to Alternative and New Variables<br>Sample 1980-2007 Baseline Robustness to Alternative Measures: Robustness to Additional Variables:<br>REER (1) (2) (3) (4) (5) (6) (7) (8)<br>Terms of trade (psoy_cpiae) 0.642*** 0.670*** 0.860*** 1.012*** -0.020 -0.081** 0.627***<br>Productivity diff. (cpi_ppi) 1.456*** 1.460*** 1.768*** 2.456*** 1.533*** 1.659*** 1.448***<br>Reserves (res_cpi) 0.312*** 0.161* 0.357*** 0.294*** -0.130 0.231*** 0.172*** 0.332***<br>Trade openness (trade_cpi) -0.634*** -0.164* -0.697*** -1.047*** -0.337*** -0.141** -0.662***<br>Constant -2.720 -3.556 -0.392 -6.500 -12.964 -1.674 -0.776 -2.574<br>Alternative measures:<br>Terms of trade (psoy_cpiw) 0.278***<br>Productivity diff. (cpi_wpi) 1.336***<br>Productivity diff (ip_rel) -0.059<br>Trade openness (imp_cpi) -0.512***<br>Additional variables:<br>Money supply (m2_cpi) 0.964***<br>Net foreign assets (nfa_cpi) 5.5e-5<br>Government spending (ggcons_cpi) -0.250***<br>Real interest rate differential (usa_r) 0.005<br>Lags in VAR 1 1 1 1 1 1 1 1<br>Johansen coint. rank 1 2 1 1 3 2 3 2<br><!-- End of picture text --> Figure A.3: Robustness to Adj. CPI/PPI, Sample, and Methodology <!-- Start of picture text --> Adj-REER Overvaluation (%): Pre-2008 Adj-REER Overvaluation (%): Pre-2008<br>120 120<br>100 CPI/PPI (VEC) 100 CPI/PPI (FMOLS)<br>Adj. CPI/PPI (FMOLS)<br>80 Adj. CPI/PPI (VEC) 80 Large adj. CPI/PPI (FMOLS)<br>60 Large adj. CPI/PPI (VEC) 60 CPI/PPI (DOLS) Adj. CPI/PPI (DOLS)<br>40 40 Large adj. CPI/PPI (DOLS)<br>20 20<br>0 0<br>-20 -20<br>-40 -40<br>-60 -60<br>-80 -80<br>Adj-REER Overvaluation (%): Full Sample Adj-REER Overvaluation (%): Full Sample<br>120 120<br>CPI/PPI (VEC) 100 CPI/PPI (FMOLS)<br>Adj. CPI/PPI (FMOLS)<br>70 Adj. CPI/PPI (VEC) 80 Large adj. CPI/PPI (FMOLS)<br>CPI/PPI (DOLS)<br>Large adj. CPI/PPI (VEC) 60 Adj. CPI/PPI (DOLS)<br>Large adj. CPI/PPI (DOLS)<br>20 40<br>20<br>-30 0<br>-20<br>-80 -40<br>-60<br>-130 -80<br>1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br>1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> 37
|Constant|5.47***|6.54***|6.98***|6.59***|
|---|---|---|---|---|
||(0.06)|(0.03)|(0.02)|(0.02)|
|N|22567|40687|24913|71149|
|F-test|445.89|251.08|297.67|1444.5|
|AdjustedR2|0.36|0.15|0.26|0.37|

Note: The grand mean effects of the industries are calculated; additional controls include age squared, weekly hours worked, and industry dummies. Standard errors are in parentheses. Statistically significant coefficients at the 10,5, and 1 percent level are indicated by one, two, and three asterisks respectively. High-skilled workers are those with upper secondary or above. Source: Author's estimates using data from Bangladesh LFS.

**Table** **<u>6. Mincerian Wage Premium Estimates, with T&A and female dummy interaction</u>**

|||Logarithmo<br>|f realwages<br>||
|---|---|---|---|---|
||(1)<br>Only2005|(2)<br>Only2010|(3)<br>Only2013|(4)<br>Only2016|
|Female|-0.592***|-0.123***|-0.108***|-0.179***|
||(0.0148)|(0.0109)|(0.00625)|(0.00558)|
|Textile and apparel|0.0560***|-0.00425|0.0999***|0.0654***|
||(0.0151)|(0.00941)|(0.00631)|(0.00465)|
|Female*Textile and apparel|0.163***|0.134***|0.158***|0.103***|
||(0.0319)|(0.0218)|(0.0115)|(0.00991)|
|Age|0.0384***|0.0160***|0.0284***|0.0255***|
||(0.00200)|(0.00138)|(0.00103)|(0.000807)|
|High-skilled|0.699***|0.485***|0.249***|0.515***|
||(0.0130)|(0.00895)|(0.00543)|(0.00456)|
|Constant|5.405***|6.521***|6.966***|6.584***|
||(0.0388)|(0.0286)|(0.0192)|(0.0157)|
|N|22567|40687|24913|71149|
|F-test|431|244|296|1402|
|AdjR-square|0.37|0.15|0.26|0.37|

Note: The grand mean effects of the industries are calculated; additional controls include age squared, weekly hours worked, and industry dummies. Standard errors are in parentheses. Statistically significant coefficients at the 10,5, and 1 percent level are indicated by one, two, and three asterisks respectively. High-skilled workers are those with upper secondary or above. Source: Author's estimates using data from Bangladesh LFS.

29
**Table** 9: **Trade Openness and Industry Labor Demand for Blue Collar Workers in Colombia (1980-91) (standard errors in parenthesis)**

||Ln**L**t-1|Ln WBt|Ln WBt-**1**|Ln WWt|Ln WWt-**1**|Ln Industry<br>Value<br>Added|Constant|Wald<br>Test: p-<br>value(a)|Sargan<br>Test|Autoco.<br>Test<br>(2nd o.)|No. Obs<br>(Plants)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Trade Policy|0.475(*)|-0.656(*)|0.158|0.003|0.003|0.059(*)|-0.003|--|0.100|0.157|22992|
|Specification(b)|(0.112)|(0.170)|(0.124)|(0.075)|(0.089)|(0.018)|(0.010)||||(1916)|
|Variable* Tariff Rate|-0.0003|0.007|-0.0002|-0.003|-0.004||0.007|0.798||||
||(0.004)|(0.006)|(0.006)|(0.006)|(0.005)||(0.017)|||||
|Variable* Average Tariff|-0.003|-0.009|0.002|0.003|0.006|||0.427||||
||(0.004)|(0.007)|(0.007)|(0.006)|(0.005)|||||||
|Variable* Non-Tariff-|<br>0.0004|<br>-0.0004|<br>-0.0003|<br>0.001|-0.0003|||0.298||||
|Barriers<br>Variable* Real|(0.001)<br>-0.001(**)|(0.002)<br>0.003(**)|(0.002)<br>-0.002(***)|(0.001)<br>-0.0001|(0.002)<br>-0.002(***)|||0.029||||
|Exchange Rate|(0.001)|(0.001)|(0.001)|(0.0007)|(0.001)|||||||
|Wald Test: p-value(c)|0.032|0.015|0.218|0.103|0.447|||||||
|Trade Flows<br>Specification(d)|0.031<br>(0.202)|-0.672(*)<br>(0.224)|0.098<br>(0.157)|0.201<br>(0.158)|0.215<br>(0.150)|0.043(**)<br>(0.022)|-0.012<br>(0.013)|--|0.592|0.689|22992<br>(1916)|
|Variable* Import Ratio|-0.007(**)<br>(0.004)|-0.010<br>(0.008)|-0.006<br>(0.006)|0.008<br>(0.006)|0.005<br>(0.005)||0.031(***)<br>(0.017)|0.483||||
|Variable* Export Ratio|-0.003<br>(0.007)|-0.031(**)<br>(0.015)|0.013<br>(0.030)|0.019<br>(0.013)|0.0002<br>(0.026)||-0.004<br>(0.028)|0.473||||
|Variable* Real|0.003|0.005(*)|-0.0004|-0.003(**)|-0.002|||0.055||||
|Exchange Rate|(0.002)|(0.002)|(0.002)|(0.001)|(0.003)|||||||
|Wald Test: p-value(c)|0.229|0.012|0.699|0.128|0.088||0.186|||||
|Price-Cost-Margin|0.157(**)|-0.425(*)|-0.009|0.122(***)|0.064|0.054(*)|-0.007|--|0.914|0.503|22968|
|Specification(e)|(0.069)|(0.058)|(0.076)|(0.064)|(0.079)|(0.013)|(0.008)||||(1914)|
|Variable* Price Cost|0.002(***)|<br>0.0002|0.002|-0.002|-0.002||-0.002|0.505||||
|Margin|(0.001)|(0.002)|(0.002)|(0.002)|(0.003)||(0.008)|||||

Notes: GMM estimates with first-differenced data. Time dummies were included but are here omitted. All variables are assumed exogenous except for lagged employment. Instruments are based on the second and fuirther lags of employment and capital stocks. (*) Significant at the 1% level. (**) Significant at the 5% level. (***) Significant at the 10% level. (a) Wald Test of Joint Significance of the variables interacted with a given openness variable. (b) Additional Instrument: second and further lags of the tariff rate. (c) Wald Test of Joint Significance of openness variables interacted with a given variable. (d) Additional Instruments: second lags of import and export ratios. (e) Additional Instruments: second and further lags of price-cost-margins.
1 Fleisig (1996) highlights the problem with posting collateral in developing and transition countries with the example of financing available to Uruguayan farmers raising cattle. While cattle are viewed as one of the best forms of loan collateral by the US, a pledge on cattle is worthless in Uruguay. Uruguayan law requires for specific description of the pledged property, in this case, an identification of the cows pledged. The need to identify collateral so specifically undermines the secured transaction since the bank is not allowed to repossess a different group of cows in the event of nonpayment.

2 There is a parallel literature on financial development and growth at the country level. Specifically, crosscountry studies (King and Levine 1993; Beck, Levine, and Loayza 2000; Levine, Loayza, and Beck 2000) show that financial development fosters economic growth. Also see Levine (2005) for a review of the finance and growth literature.

3 See Dollar, Hallward-Driemeier, and Mengistae (2004), Gelb, Ramachandran, Shah, and Turner (2007), Carlin, Schaffer, and Seabright (2005) and Svejnar and Commander (2007).

4In the survey managers indicate that an obstacle is a problem by assigning a value greater than one, on a four point scale to that obstacle. The significance of the coefficient in the growth regression is usually sufficient to determine whether an obstacle is binding or not since the mean value of all obstacles exceeds one. However, in determining the relative impact, it is important to take into account the level of the obstacles.

5 DAG analysis is related to the use of different analytical methods to identify the most reliable predictors of economic growth such as the Extreme Bounds Analysis (EBA) used in Kormendi and Meguire (1985), Barro (1991), and Levine and Renelt (1992) and the technique in Sala-i-Martin (1997). DAG analysis has several advantages over these methods. While these methods start from an equation specified by the researcher, that embodies a causal ordering which is then tested, DAG can endogenously discover the causal ordering. Moreover, whereas EBA treats one relation at a time, the graphs produced by DAG show robust relations between all the variables being analyzed, taking into account the implications of robust relations elsewhere in the system on the ordering in a specific relation.

32
international agencies working on finance and/or development. Our data are drawn from the SSCI and SCOPUS.

In terms of journal article counts across all fields covered by the SSCI, the World Bank is hardly a star performer relative to these 14 universities. Over the same period, Harvard published almost 10 times as many journal articles as the Bank. Indeed, all of 14 universities used in our comparative analysis published more journal articles than the Bank. The Bank did, however, publish more than any other international financial or development institution; the second place went to the IMF, with more than 2,100 articles.

However, this comparison is rather uninformative. The Bank’s publications are highly concentrated in terms of disciplines and subject areas. If one calculates the Herfindahl index with respect to the SSCI subject areas, the Bank’s research is far more concentrated than any of these 14 universities.<sup>7</sup> Table 1 makes the comparison. The Bank and the other international agencies are much more specialized than the universities. The Bank’s Herfindahl index is 0.55, which is very close to the index one would obtain if all of the Bank’s research output was split equally between two headings (giving an index of 0.5). In fact, 91% of the Bank’s research output is in the SSCI headings of “economics” (68%) and “planning and development” (23%). By contrast, the top US universities have Herfindahl indices in the range 0.06-0.08. Even the London School of Economics (which specializes in economics and other social sciences) has an index of 0.13, much lower than the Bank’s. However, the World Bank’s Herfindahl index is similar to the other international agencies; the IMF’s index stands out at 0.93.

Naturally, the Bank’s research is far more concentrated on economics, and development economics in particular. If one focuses on the articles in the SSCI fields “Economics” or “Planning and Development,” one finds that the Bank has published more than any university except Harvard; the Bank published over 3,500 articles in these fields, as compared to an average of 2,500 across the 13 comparator Universities; Harvard published 4,400 (Table 1). The SCOPUS data tell a similar story. Focusing on publications in “Economics, Econometrics and Finance” (and excluding multidisciplinary articles), the Bank emerges second behind Berkeley in terms of publication counts. In both the SSCI and SCOPUS data, the Bank’s publication count is around 1.5 times the average publication count of these 14 top universities.

> 7   The Herfindahl index is a common measure of concentration given by (in this case) the sum of the squared shares of research output across the N SSCI headings. The index ranges from 1/N (when the shares are equal) to 1 (when they are very highly concentrated such that one heading has all the output).

8
members) in El Salvador (EHPM, 2022). Moreover, for every working-age adult within the household, there are approximately 0.27 dependents—children under the age of 14 or elderly aged 65 years and older. In addition, half of the households in our sample are located in rural areas, and approximately one-third of these households reported experiencing moderate to severe food insecurity. In terms of sources of income, about one-third of the household members work in non-farming related activities, and about one-third of the households receive remittances. Lastly, 54% of the household members are engaged in agricultural activities, including growing and harvesting crops, raising livestock and/or fish, and fishing.

Panel B shows that the average participant is 35 years of age; 55% of the participants are women; and 34% of the respondents have not been married. In terms of human capital, 28% of the respondents have completed high school or higher education, and 87% can read or write. Among the participants, 88% report having access to at least one mobile phone, but only 19% have access to the internet at home.

# **3.4 Validity of the Experiments**

To support the validity of our identification assumption in the experiment, we compare average household and individual characteristics across the three groups in our study. We present these results in Table 2. Columns (1) to (3) present the mean variables of the C, LOA, and ESR groups, respectively. Columns (4) through (6) provide _p_ -values for t-tests for equality of means between LOA and C (Column [4]), ESR and C (Column [5]), and LOA and ESR (Column [6]). Apart from three of the 15 variables tested, we do not find significant differences in these variables across the treatment and control groups at _p_ -values less than 0.1. We only find that households in LOA are less likely to work in agriculture (49% in LOA versus 56 and 57% in C and ESR, respectively; _p_ = 0.066 and _p_ = 0.035).

Moreover, ESR respondents are, on average, 1.4 years older than LOA and C respondents (relative to an average age of 35 years; _p_ = 0.014 and _p_ = 0.018) and are between 3-4 pp (out of 89 pp) less likely to know how to read and write ( _p_ = 0.066 and _p_ = 0.023). Lastly, LOA respondents are 3 pp less likely than C respondents to have access to a mobile phone (relative to 86 pp in LOA and 89 pp in C). Statistically, however, this is not surprising when testing across many variables and different groups. Overall, the differences in the respondents’ average age, reading and writing skills, and access to a mobile phone are relatively small. Nevertheless, to account for these differences, we control for these variables as a robustness check, as we explain in Section 4.

11
from 27 per year to 62 per year. This increase might, in part. be explained by the emphasis on the development of domestic statistical informption systems, which have incorporated studies as part of their design. The increase might also reflect the shift in emphasis within Bank education projects from infrastructure development (expansion of education systems) to software components (efficiency and quality improvements).

_Table 1: Planned, completed and available studies, 1972-1989_

||Studies initiated between||
|---|---|---|
|Studies<br>197|2-1982<br>198|2-1989|
|Total Planned<br>272|(100%)<br>436|(100%)|
|Total Completed 1972-1989<br>132|(49%)<br>184|(42%)|
|Completed 1972-1982<br>73|(27%)1-||
|Completed 1982-1989<br>59|(22%)1'<br>184|(42%)|
|Total Available 1972-1989<br>85|(31%)<br>84|(39%)|
|Available 1972-1982<br>55|(20%)||
|Available 1982-1989<br>30|(11%)<br>84|(19%)|

Percentages are **based** on **the total** number of planned studies for that time period.

Of the 59 completed studies 21 **were** the products of only two projects: Malawi **IV** and Thailand **V.** 14. Table 1 also shows that completion rate for studies improves over time. The completion rate for studies initiated during 1972-1982 was 27%, but if the time period for completion of the same studies is extended to 1989, the completion rate rises to 49%. This may in part be due to the completion of studies begun in the last two years of the sample time frame and completed thereafter. In the period 1982-1989, 42% of studies were completed, higher than for the same time span of the earlier studies. This suggests that feedback on research components has been slightly accelerated. However, completion rates remain low.

15. Geographical distribution. Table 2 provides data on completed studies by region. For the studies planned before 1982, Asia had consistently higher completion than the other regions. This trend is reversed for the period since 1982. The completion rates for Latin America and the Caribbean are skewed by one successful research project in Brazil. If this is excluded then completion rates for the region fall tc 43% for the period after 1982. The performance for Africa has remained constant.

5
2 as information on illnesses and whether and what kind of treatment was sought. A full SUSENAS survey is held once every three years. The present work is based on the 1978 and 1987 SUSENAS data sets.

It is also important to examine how utilization and the distribution of the benefits of public expenditures have changed over time. Indonesia has made great progress in alleviating income poverty during the 80s. This has been well documented in research (Ravallion and Huppi, 1991) and reported widely (World Bank, 1990 and 1991b). The evidence for the f.normous progress Indonesia has achieved in this area appears hard to dispute.

But, there has been some concern that improvements in certain social indicators (such as infant mortality rates and life expectancy) haven't been as good as that in the poverty measures. In particular, there is concern about whether increases in the incomes of the poor have been commensurately matched by higher access to and utilization of health and education services (World Bank, 1991a). This could explain the relatively poor performance of social indicators during this period.

The period from the late 1970s through the 1980s in Indonesiz is particularly interesting because it coincides first, with substantial declines in income poverty, and second, with a lot of emphaaie by the government on primary health care and basic education through both pricing and provision (World Bank, 1991a and b; Yahya and Roesin, 1990). Fees for primary schooling had been comy aly abolished by 1978, and there were large investments and substantial n.., initiatives in the primary health care system in the late 70s including the integrated family planning ana health post ("posyandu") system. Progress in these areas n.Ay have come under threat in the mid 80Os when Indonesia sustained various external shocks which resulted in substantial deterioration in the external terms of trade and a subsequent macroeconomic adjustment program involving, among other things, cuts in public expenditures.

These are the stylized facts and key issues concerning Indonesia's social sectors in the 1980s. The paper asks: 1) How does the utilization of social services and the incidence of subsidies in the social sectors in 1987