rafmacalaba/datause-encoder-data huggingface dataset:
# WPS3604

## **The Effect of School Type on Academic Achievement: Evidence from Indonesia**

## David Newhouse International Monetary Fund

Kathleen Beegle World Bank KEYWORDS: education, Indonesia, private JEL classifications: O12, O15, I21

## World Bank Policy Research Working Paper 3604, May 2005

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Direct Correspondence: David Newhouse, IMF, 700 19th Street NW, Washington DC 2043; Phone: 202623-5682; E-mail: dnewhouse@imf.org.  We thank Elizabeth King, Joe Newhouse, two anonymous referees, and participants of seminars at the World Bank Resident Mission in Jakarta, Minnesota Development Economics Conference, and Cornell University for useful comments.
<!-- Start of picture text --> SGP<br>KORTWN HKSYCPR GRCJPNESPIRL<br>0 2 4 6<br>1960 Log of % Income relative to the U.S<br>6<br>4<br>2<br>0<br>2009 Log of % Income Relative to US<br><!-- End of picture text --> <!-- Start of picture text --> 1960 Log of % Income relative to the U.S<br><!-- End of picture text --> <u>Figure 2: Relative income dynamics, 1960-2009</u> Figure 2 shows countries’ long run changes of their income relative to the U.S. The log of per capita income relative to the U.S. in 1960 is on the x axis, with the 2009 value on the y axis. Each axis is divided into three areas, representing the three income groups. Countries in the top-middle quadrant (in red) are those that “escaped” from middle income to high income over this period. The list of escapees includes Greece; Hong Kong SAR, China; Ireland; Japan; Puerto Rico; the Republic of Korea; Seychelles; Singapore; Spain; and Taiwan, China. Two countries that nearly make the list (the top of the middle quadrant) are Portugal and Cyprus, which are still classified as middle income in 2009. Table 1 below summarizes the number of countries in 1960 (and alternatively, in 1970, where we have more data).

The predominant fact that emerges from Figure 2 and Table 1 is that relative income levels are highly persistent. All high income countries in 1960 remained high income in 2009; a majority of middle income countries remained middle income;<sup>5</sup> and only a handful of low income countries joined the middle income group. A concern is that there might have been more fluid movements of countries between 1960 and 2009 - for example, some countries might have moved to high income and moved back, which Figure 1 would fail to capture. Figure A1 in the appendix shows that there are few such movements. Almost all of the countries that have ever moved into high income have stayed there. Two exceptions are the Czech Republic and Lebanon.<sup>6</sup> Another potential concern is that this persistence is an artifact of our selected

> 5 A few middle income countries declined to poor income groups (Bolivia, Ghana, Haiti, Honduras, Nicaragua, Paraguay, and Zambia).

> 6 Czech Republic was a high income country (52% of the US) in 1990, but then it dropped to middle income throughout the 1990s and early 2000s, and since 2005 it has again been high income. Lebanon became rich in the mid-1970s but went to middle income in the early 1980s as it had a long civil war in the 1980s.

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Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 16: Projected implicit pension debt* for civil servants (% of GDP) increases exponentially starting around 2030**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073<br>Year<br>variant 1 variant 2<br>Implicit pension debt (% GDP)<br><!-- End of picture text -->

**Notes: *** liabilities accrued by the system at a given point in time **Source** : WB calculations based on PROST

**Figure 17: Projected implicit pension* debt for teachers (% of GDP) only increases during the entire simulation period**

<!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073<br>Year<br>v ariant 1 v ariant 2<br>Implicit pension debt (% GDP)<br><!-- End of picture text -->

**Notes: *** liabilities accrued by the system at a given point in time **Source** : WB calculations based on PROST

- 46 -
**Table 6. Granger Causality Tests**

|**Results **|**for a VAR w**|**ith X= GDP**||
|---|---|---|---|
|||Chi-square|P-value|
|First segment|_HP_ ⇏_GDP_|0.81|0.37|
|(1960q1-1982q4)|_GDP_ ⇏_HP_|0.01|0.93|
|Second segment|_HP ⇒GDP_|10.86|0.00|
|(1983q1-2009q3)|_GDP_ ⇏_HP_|3.32|0.07|
|**Resul**|**ts for a VAR **|**with X=C**||
|||Chi-square|P-value|
|First segment|_HP_ ⇏_C_|1.16|0.28|
|(1960q1-1985q1)|_C_ ⇏_HP_|1.00|0.31|
|Second segment|_HP ⇒C_|20.74|0.00|
|(1985q2-2009q3)|_C_ ⇏_HP_|0.66|0.42|

_Note_ . The null hypothesis is the non Granger causality. The _⇒_ and ⇏ symbols denote the Granger causality and the non Granger causality, respectively, at 5% significance level.

Using macro-data from the last fifty years, this study finds that there is a robust structural break in the mid-1980s in the house prices effect on the macroeconomy, which is obtained when either GDP or private consumption is included in the multivariate specification. The paper also finds time series evidence that the overall impact of house prices on consumption and on economic activity has reinforced since the mid-1980s. Results in this study support the widespread hypothesis in the theoretical literature that the deep transformation in the mortgage market during the mid-1980s is the main cause of the intensification of the impact of housing prices, not only on household expenditures decisions, but also on the economic activity as a whole.

Future research could be focused on the consequences of the Dodd-Frank Act on credit conditions and to what extent it will affect the house prices macrolink. Indeed, a tightening in credit constraints could be expected following the Title XIV of the Dodd-Frank Act -“Mortgage Reform and Anti-Predatory Lending Act”- since its main objective is the imposition of restrictions on mortgage originators to only provide lending to borrowers who are likely to repay their loans.
|**Ln(Income)**|-3.1922***<br>(0.3255)|-3.676***<br>(0.3778)|-4.2984***<br>(0.4391)|-4.494***<br>(0.4596)|-5.0898***<br>(0.5197)|
|---|---|---|---|---|---|
|**Average loss in job accessibility (%)**|4.846|5.676|6.608|6.996|7.872|
|**Adjusted R**<sup>**2**</sup>|0.436|0.437|0.455|0.457|0.463|
|**Observations**|1126|1126|1126|1126|1126|

# Proximity of Non-Petroleum Based Modes and BRT with income controls

The variables of the previous two regressions were combined to see if the relationship was still true when considered altogether. The results are shown in Table 6, where these relationships do hold true, although the relationship between the losses in accessibility and proximity to each type of mode is not as robust.

_Table 6: Proximity to Non-Petroleum-Based Modes and BRT with Income Controls regression_

|||**Fu**|**el Price Increase=**|||
|---|---|---|---|---|---|
||**10%**|**20%**|**30%**|**40%**|**50%**|
|**Constant**|5.7551***<br>(0.7739)|8.5701***<br>(0.9972)|13.8378***<br>(1.497)|18.2618***<br>(1.9877)|23.4318***<br>(2.5188)|
|**Baseline Accessibilit**|-0.004|-0.004|-0.0101|-0.0143*|-0.0129|
|**y**|(0.0039)|(0.0046)|(0.0063)|(0.0081)|(0.0102)|
|**Tl i lbd d**|0.0002|0.0025*|0.0031*|0.005**|0.0073**|
|**rave tme to non-petro-ase moes**|(0.0014)|(0.0014)|(0.0018)|(0.0025)|(0.0029)|
|**l**|-0.0001|-0.0013|-0.0013|-0.0019|-0.0032|
|**Trave time to BRT**|(0.0011)|(0.0012)|(0.0019)|(0.0021)|(0.0027)|
|**L(I)**|-0.6996***|-1.0608***|-1.6946***|-2.2369***|-2.8708***|
|**nncome**|(0.1268)|(0.1657)|(0.2521)|(0.3356)|(0.4244)|
|**Average loss in job accessibility (%)**|1.14|1.592|2.68|3.553|4.634|
|**Adjusted R**<sup>**2**</sup>|0.154|0.275|0.381|0.428|0.431|
|**Observations**|909|909|909|909|909|
||Note: Robust sta|ndard errors inpa<br>**Fu**|rentheses. *p< 0.<br>**el Price Increase=**|1,**p< 0.05,**|*p< 0.01|
||**60%**|**70%**|**80%**|**90%**|**100%**|
|**C**|25.7193***|30.0218***|34.9514***|36.6066***|41.5761***|
|**onstant**|(2.739)|(3.2505)|(3.7304)|(3.9225)|(4.4563)|
|**Baseline Accessibilit**|-0.0182|-0.0175|-0.0241|-0.0254|-0.0238|
|**y**|(0.0111)|(0.0131)|(0.0149)|(0.0156)|(0.0177)|
|**l i lbd d**|0.0062*|0.0098**|0.0085**|0.0102**|0.012**|
|**Trave tme to non-petro-ase moes**|(0.0032)|(0.0038)|(0.0043)|(0.0045)|(0.0052)|
||-0.0031|-0.0027|-0.0048|-0.004|-0.0055|
|**Travel time to BRT**|(0.003)|(0.0034)|(0.0037)|(0.0039)|(0.0044)|
||-3.1203***|-3.6835***|-4.2302***|-4.4422***|-5.0461***|
|**Ln(Income)**|(0.4631)|(0.5497)|(0.6295)|(0.663)|(0.7523)|
|**Average loss in job accessibility (%)**|5.135|6.01|7.01|7.406|8.36|
|**Adjusted R**<sup>**2**</sup>|0.444|0.449|0.462|0.469|0.468|
|**Observations**|909|909|909|909|909|

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# **6 Results**

We now follow our empirical strategy to document, in three sets of empirical results, how pollution exposure shapes production activity in urban Uganda, and how high and low ability managers deal with this challenge.

## **6.1 Road Traffic Bundles Pollution Exposure with Market Access**

We first study the characteristics of available locations within the city to establish that road traffic bundles a good (market access) with a bad (pollution exposure). This result implies that managers face a trade-off in their location choice, in whether to avoid pollution or maximize firm revenues and profits.

We proceed in four steps. First, we establish that large roads are more polluted. Second, we find evidence that this pollution is generated by road traffic. Third, we show that firms located near larger roads have higher revenues and profits per worker, conditional on a rich set of controls. Finally, we explore the mechanism linking pollution to profitability using our rich survey data and argue that is primarily due to access to market demand.

**Larger roads are more polluted.** We follow the empirical specification in equation 3 to show that pollution is higher in grid cells with larger roads. The results are shown in Table 2: an increase in median road size in the cell of one unit is associated with an increase in residual pollution of about 7-8%, a result significant at the 1% level. Comparing column 1 with column 2 we note that the magnitude of the coefficient is barely affected by the introduction of sub-county fixed effects, thus showing that the relationship is driven by variation within as opposed to across sub-counties.

In columns 3 and 4, we check that these results are robust to conducting the analysis at the level of the single pollution measurement rather than the grid cell, by running the same equation 3 using variation across all the observed GPS coordinates _m_ , rather than aggregating at the grid-level.<sup>35</sup> Reassuringly, the results are remarkably similar.

One example to illustrate the empirical variation in our data is shown in Figures 3 and 4, which show pollution measurements and road size in one of our sample sub-counties. It is clear that pollution is higher on the larger roads.

> result of sustained exposure. Therefore, the empirical strategies adopted by related papers studying the impact of pollution on labor supply and productivity, which typically rely on high-frequency temporal variation in pollution exposure, would not be appropriate to answer the research questions we are after.

> 35In columns 3 and 4 of Table 2, 4,604 pollution observations (corresponding to 8% of the sample) are dropped because the size of their closest road was not available as they are more than 100m away from the closest road.

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_across Generations Around the World_ . Number 28428 in World Bank Publications. The World Bank.

- Noble, K. G., Houston, S. M., Bartsch, H., Kan, E., Kuperman, J. M., Akshoomoff, N., Amaral, D. G., Bloss, C. S., Libiger, O., Schork, N. J., Murray, S. S., Casey, B. J., Chang, L., Ernst, T. M., Frazier, J. A., Gruen, J. R., Kennedy, D. N., Van Zijl, P., Mostofsky, S., Kaufmann, W. E., Keating, B. G., Kenet, T., Dale, A. M., Jernigan, T. L., and Sowell, E. R. (2015). Family Income, Parental Education and Brain Development in Children and Adolescents. _Nature Nuroscience_ , 18(5):773–778.

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- Solon, G. (1999). Intergenerational mobility in the labor market. In Ashenfelter, O. and Card, D., editors, _Handbook of Labor Economics_ , volume 3 of _Handbook of Labor Economics_ , pages 1761–1800. Elsevier.

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There are several mechanisms through which income can affect labor supply. Most obviously, leisure may be a normal good. Second, liquidity constraints may prevent households from hiring permanent labor, changing household composition, or renting agricultural land.

Increases in income might relax this liquidity constraint and thereby affect labor supply. Third, higher household income may induce households to buy market substitutes for goods previously produced within the household (perhaps inefficiently because of liquidity constraints). As with changes in landholdings or hired labor, this may influence labor supply. In sum, if rice price increases are associated with higher household income, we would expect labor supply to decline.

Overall, the above discussion on income effects suggests that the association between changes in rice prices and an individual’s labor supply is not clear cut. Further, in conjunction with the conclusions in section 2.1, it is clear that the question regarding the impact of changes in rice prices on an individual’s labor supply can be best answered through empirical work.

Moreover, the interconnectedness of the mechanisms through which income influences supply with the value of time discussion above makes identification of the channels through which rice price changes influence an individual’s labor supply very difficult. Thus, the focus of the rest of the paper is on the reduced form relationship between rice price movements and labor supply.

# **2.3  Gender and Age Differences in Labor Response to Rice Price Changes**

The above discussion abstracts from the possibility that the labor supply response to increases in rice prices after market integration might vary across men and women. Previous work on female labor supply in developing world surveyed in Schultz (1999) suggests that female labor supply might respond differentially to shocks than male labor supply. Gender differences in the effect of rice price increases could arise for several reasons. First, the household may perceive differences in the disutility of work for men and women. This may 8
# **_Table 3: Forestry Credits for top 10 countries valued at 0.3 USD per ton_**

|**Country**|**Forest coverage 1990**<br>**(000’s of sq km)**|**Forest**<br>**coverage**<br>**2019 (000’s**<br>**of sq km)**|**Predicted forest**<br>**(000’s of sq km)**|**Forest**<br>**performance**<br>**(deviations from**<br>**regression**<br>**predictions –**<br>**000’s of sq km)**|**CO2**<br>**equivalents**<br>**(mil tons)**|**Credit (billion USD)**<br>**valuing excess stock at**<br>**$0.3per ton)**|
|---|---|---|---|---|---|---|
|China|1571.41|2180.99|1779.76|401.23|1203.68|0.36|
|India|639.38|718.94|595.92|123.01|504.36|0.15|
|Vietnam|93.76|145.67|92.53|53.14|382.64|0.11|
|Mexico|705.92|658.20|605.16|53.04|169.73|0.05|
|Congo, Rep.|223.15|219.61|172.90|46.71|714.72|0.21|
|Gabon|237.62|235.42|189.51|45.91|564.71|0.17|
|Spain|139.05|185.68|141.08|44.60|102.57|0.03|
|Guyana|186.02|184.25|147.88|36.36|389.10|0.12|
|Thailand|193.61|199.09|164.29|34.80|160.08|0.05|
|Colombia|649.58|593.41|563.36|30.06|336.63|0.10|

# **Implementation**

Once the broad contours of the framework are decided, some structures will have to be put in place.

- 1) The process of measuring actual carbon emissions by a country will have to be standardized. As with the calculation of any aggregates like GDP, there will have to be some assumptions as well as the use of rules of thumb. Since these decisions will imply actual money gained or lost by a country, it is best that these be set by a small independent technical group or secretariat. Further, advances in satellite image processing have made it possible to accurately measure carbon and methane in near real time. NASA’s OCO-2 and OCO-3 instruments, the European Space Agency’s METOP-A and TROPOMI (Sentinel-5P) platforms, China’s TANSAT, and the Japan Space Exploration Agency’s GOSAT and GOSAT-2 can be tapped for accurate and independent measurement (Dasgupta et al 2021).

- 2) There will be a debate about whether countries that are poorly governed should receive payments. In general, so long as there is a functioning government that is not kleptocratic, payments should not be withheld. If there is no government that meets these minimum conditions, payment could be held in trust by the World Bank for when such a government emerges. Furthermore, there should be no presumption that payments would be used for

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9 The causes in men are less well understood. HRP has recently started a multinational project to study the prevalence of past chiamydial and gonococcal diseases as a cause of male and temrnle n..ertility. It is also promoting the development of a simple diagnostic kit for chiamydial infection, testing new and established barrier methods **for** the prevention of reinfection with STDs, and testing drugs effective in preventing transmission. In addition, it is supporting preliminary studies on the development of a vaccine against chlamydial infection.

17. Behavioral and Social ePactors. HRP was one of the first, and remains one of the few, orgarizations to conduct research into the behavioral and social determinants of fertility regulation, exploring the attributes of methods that make them acceptable to users, and the processes by which users select, adopt and continue contraceptive use over time. Its studies have demonstrated the importance of providing a range of methods among which clients can choose, of counselling, **ai..** of continued contact between provider and client--all aspects of what have come to be called the "user-perspective". As the programme becomes increasingly involved with the introduction of new methods into family planning programs, such research becomes even more important.

18. Contraceptive Introduction. The introduction of the new contraceptive implant, NORPLANT, is a good example of HRP's involvement in the field. Once the contraceptive was developed by the Population Council, HRP coordinated with the Council and with Family Health International on its limited introduction in many countries and in post-marketing surveillance. A 20-center cohort study in 10 countries has been initiated to determine the mediumand long-term bei.efits or problems of NORPLANT use. No contraceptive compound has ever before been introduced with such care.

# Resources for Research

19. HRP helps national authorities and institutions in de 'eloping countries to establish research infrastructure in the area of reproductive health, giving priority to developing epidemiological and social science capability. The
|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>Dep. Var.: Private Credit/GDP<br>EFNX<br>2.587***<br>2.493***<br>2.307***<br>1.476**<br>1.315**<br>(0.629)<br>(0.608)<br>(0.690)<br>(0.592)<br>(0.567)<br>Log(Trade/GDP)<br>9.649<br>8.496<br>2.93<br>2.466<br>(6.621)<br>(6.470)<br>(5.914)<br>(5.407)<br>Log(Income)<br>15.847***<br>15.063***<br>(2.718)<br>(2.535)<br>French Legal Origin<br>-7.221<br>-8.839*<br>(7.231)<br>(5.039)<br>German Legal Origin<br>25.578<br>23.016<br>(18.665)<br>(17.703)<br>Scandinavian Legal Origin<br>8.704<br>-8.524<br>(11.882)<br>(11.876)<br>Socialist Legal Origin<br>-13.27<br>-13.756<br>(17.112)<br>(19.851)<br>Constant<br>-18.81<br>-55.130**<br>-43.738<br>-138.177***<br>-121.617***<br>(14.303)<br>(27.507)<br>(30.971)<br>(25.783)<br>(28.812)<br>Dep. Var.: EFNX<br>Predicted EFNX<br>1.188***<br>1.181***<br>1.104***<br>1.038***<br>1.047***<br>(0.184)<br>(0.189)<br>(0.216)<br>(0.175)<br>(0.212)<br>Observations<br>96<br>96<br>96<br>96<br>96<br>**_Panel A: Second Stage_**<br>**_Panel B: First Stage_**<br>Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%; Dependent<br>variable,_Private Credit/GDP_is credit to the private sector by banks and other financial institutions as a share of GDP;<br>_EFNX_is the external finance need of exports;_Log(Trade/GDP)_is log of exports and imports as a share of GDP;<br>_Log(Income)_is log of PPP-adjusted real per capita income from Penn World Tables;_French, German, Scandinavian, and_<br>_Socialist Legal Origin_dummies are as defined originally by La Porta et al. (1998)._Predicted EFNX_is the predicted external<br>finance need of exports constructed by first estimating sector-level gravity regressions, and then generating predicted export<br>shares for each country and sector. All of the variables are average values over 1970-99. Variable definitions and sources<br>described in detail in the text.|
|---|

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