rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5269

# **Abstract**

Using data from surveys of enterprises in Sri Lanka after the December 2004 tsunami, the authors undertake the first microeconomic study of the recovery of the private firms in a developing country following a major natural disaster. Disaster recovery in low-income countries is characterized by the prevalence of relief aid rather than of insurance payments; the data show this distinction has important consequences. The data indicate that aid provided directly to households correlates reasonably well with reported losses of household assets, but is uncorrelated with reported losses of business assets. Business recovery is found to be slower than commonly assumed, with disaster-affected enterprises lagging behind unaffected comparable firms more than three years after the disaster. Using data from random cash grants provided by the project, the paper shows that direct aid is more important in the recovery of enterprises operating in the retail sector than for those operating in the manufacturing and service sectors.

This paper—a product of the Finance and Private Sector Development Team, Development Research Group—is part of a larger effort in the group to study microenterprise dynamics. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **<mark>Competing Priorities: Women’s Microenterprises</mark>**

**<mark>and Household Relationships</mark>**

# **Sophia Friedson-Ridenour***

**Rachael S. Pierotti****

<mark>JEL classification: D13, Household Production and Intrahousehold Allocation Key words: microenterprise, gender, intrahousehold allocation</mark> _*_ Centre for Research on Gender and Women, University of Wisconsin-Madison **Corresponding author: The World Bank, email: rpierotti@worldbank.org
# **Public Primary School Expansion, Gender-Based Crowding Out, and Intergenerational Educational Mobility**<sup>**1**</sup>

**Md. Nazmul Ahsan,** Saint Louis University

**M. Shahe Emran,** IPD, Columbia University

**Forhad Shilpi,** DECRG, World Bank

**Key Words:** Public Schools, Intergenerational Mobility, Education, Heterogeneous Impacts, Most Disadvantaged Children, Social Norms, Indonesia, West Sumatra, Patrilineal, Matrilineal, Unintended Bottleneck, Gender-based Crowding Out, Full Count Census Data

**JEL Codes:** I24, J62, J16, O20

> 1 Emails for correspondence: fshilpi@worldbank.org (Forhad Shilpi). We are grateful to Margaret Triyana, Jan Stuhler, Daniel Suryadarma, and Hanchen Jiang for extensive comments on an earlier version.  We would also like to thank Jere Behrman, Marieke Kleemans, Tatjana Kleinberg, Arya Gaduh, Pablo Mitnik, Cynthia Bansak, M. Najeeb Shafiq, and participants at the GLO Global Conference 2022 for discussions and/or comments on an earlier draft. We are grateful to Esther Duflo for generously sharing data on school construction in Indonesia, and Sailesh Tiwari, Imam Setiawan, and Rinku Murgai for help with the census data. This paper supersedes and supplants an earlier substantially different version titled “Unintended bottleneck and essential nonlinearity: Understanding the effects of public primary school expansion on intergenerational educational mobility''.   Standard disclaimers apply.
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focus groups gave replies to qualitative questions that are almost wholly consistent with these quantitative estimates of improved attendance and savings of time. In one matter, however, they tell another story: the household data reveal no improvement in completing grades, but the respondents in most villages were adamant that their new road had made dropping out of secondary school a rarity, even among girls.

Turning to the effects of all-weather roads on health, the quantitative household data reveal none where both morbidity and mortality are concerned, though the time elapsed since the provision of the PMGSY roads was far too short for long-term effects to make themselves felt. The provision of such roads did, however, affect the choice of facility for treatment: the ensuing switch from PHCs to hospitals is both substantial and involves longer travel times, both induced by higher average speeds. Such an effect on behavior is a clear indication that there was an improvement in welfare in this domain. The survey respondents’ replies to the qualitative questions and the participants’ in the focus group interviews leave no doubt on this matter. In particular, the latter were of the firm opinion that their roads had brought about a definite fall in mortality: the average of the estimate was 2.5 deaths a year in the nine villages.

We close with two remarks. First, whereas the traditional method of estimating the benefits arising in the ‘commercial’ domain is well-established and relatively easy to implement in practice, the task of estimating those arising in the domains of education and health is altogether more difficult. Since the respondents ranked the combined benefits in the domains of education and health at least as highly as those in the commercial one, simply doubling the latter yields a conservative estimate of the total (money-metric) benefits, provided this restricted view of consumer sovereignty be accepted. Second, as expected, the before and after comparisons yield sharper results than their cross-section counterparts. Obtaining more precise estimates of the effects of providing villages with all-weather roads therefore demands a more systematic and larger panel survey design, and one of longer duration.

# **References**

- [1] Bell, C. (2011a), ‘The Benefits of India’s Rural Roads Program in the Spheres of Goods, Education and Health: Joint Estimation and Decomposition’, mimeo, University of Heidelberg, September. (To appear in the World Bank’s Working Paper Series.)

36
could not borrow on their own credit strength.<sup>23</sup> Although the latest data indicate that loan guarantees issued by states were lower at 2.8 percent of GDP by end-March 2009 compared to 3.3 percent of GDP in 2008, this does not incorporate the unfunded pension liabilities or the losses of the state public sector undertakings (PSUs). The Thirteenth FC estimated that by the end of 2007–08, about 1,160 state PSUs had accumulated losses of about Rs 659.24 billion (almost 1.3 percent of GDP), particularly the implicit liabilities associated with power utility companies, because their large accumulated losses represent a huge exposure for states.<sup>24,25</sup> The probability that these liabilities will devolve are not identical for each state, and thus cannot be treated uniformly in terms of their fiscal impact.<sup>26</sup> -As a rule of thumb, assuming that about onethird of such liabilities devolve to the states to service, figure 3 presents a broader concept of ―extended‖ debt, that is, debt inclusive of the likely devolvement of outstanding guarantees, to provide an assessment of the exposure and fiscal risk for the states. Extended debt is calculated as direct debt (explicit) plus one-third of the contingent liabilities<sup>27</sup> extended by the state governments (as reported by them). This adds to the stress scenario being faced by the states.

> 23 ―The Report of the Group to Assess the Fiscal Risks of State Government Guarantees,‖ (RBI, July 2002) reported that guarantees grew at an average rate of 16 percent per year during 1992 to 2001 (paragraph 5). To avoid an escalation of the fiscal risks, the RBI had issued regulatory guidelines to banks (which were the major investors) to only invest in state public sector undertakings (PSUs) if there was a clear revenue stream from the PSU/project, rather than that accruing from the state government budget. In addition, all the PSU issues were to be rated by at least two domestic credit rating agencies if banks were to invest in them. Also, such investments had credit risk weights and provisioning requirements.

> 24 Thirteenth FC, p. 103.

> 25 A recent note (2011) by Citi Investment Research and Analysis indicates that the total losses of state electricity boards in 2010–11 were Rs 635 billion, and those from five states (Bihar, Madhya Pradesh, Rajasthan, Tamil Nadu, and Uttar Pradesh) account for about 71 percent of the total losses.

> 26 ―The Report of the Group to Assess the Fiscal Risks of State Government Guarantees,‖ (RBI, July 2002), calculated the sectoral default ratios in 2001. The power sector was 15.09 and industry was 39.19. The total default ratio was 3.7.

27 These relate only to those that are reported by the states. Indirect and implicit liabilities, although a source of fiscal risk, are not included here. A comprehensive review is difficult because of inconsistencies and gaps in data coverage and definitions, and is not the remit of this paper.

15
measurement. South Africa is the most recent case that updated its CPI price database so that subnational PPPs can be calculated from it.

# **References**

Almas, I. 2012. “International Income Inequality: Measuring PPP Bias by Estimating Engel Curves for Food.” _American Economic Review_ 102 (1): 1093–1117.

- Almas, I., T. K. M. Beatty, and T. F. Crossley. 2018. “Lost in Translation: What Do Engel Curves Tell Us About the Cost of Living?” CESifo Working Papers.

- Almas, I., and A. Johnsen. 2018. “The Cost of a Growth Miracle: Reassessing Poverty and Inequality Trends in China.” _Review of Economic Dynamics_ 30: 239–264.

- Almas, I., and A. Kjelsrud. 2017. “Rags and Riches: Why Consumption Patterns Matter for Inequality.” _World Development_ 97: 102–121.

- Almas, I., A. Kjelsrud, and R. Somanathan. 2019. “A Behavior-based Approach to the Estimation of Poverty in India.” _Scandinavian Journal of Economics_ 121 (1): 182–224.

- Aten, B. H. 2017. “Regional Price Parities and Real Regional Income for the Unites States.” _Social Indicators Research_ 131 (1): 123–143.

- Atkin, D., B. Faber, T. Fally, and M. Gonzales-Navarro. 2018. “A New Engel on the Gains from Trade.” Unpublished manuscript.

- Balcazar, C. F., L. Ceriani, S. Olivieri, and M. Ranzani. 2017. “Rent Imputation for Welfare Measurement: A Review of Methodologies and Empirical Findings.” _Review of Income and Wealth_ 63 (4): 881–898.

- Balk, B. M. 2008. _Price and Quantity Index Numbers: Models for Measuring Aggregate Change and Difference._ New York: Cambridge University Press.

- Berry, F., B. Graf, M. Stanger, and M. Ylä-Jarkko. 2019. “Price Statistics Compilation in 196 Economies: The Relevance for Policy Analysis.” IMF Working Paper WP/19/163.

- Biggeri, L., and L. Tiziana. 2014. “Sub-national PPPs: Methodology and Application by Using CPI Data.” Paper prepared for the IARIW 33rd General Conference.

- Caves, D. W., L. R. Christensen, and W. E. Diewert. 1982. “The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity.” _Econometrica_ 50 (6): 1393–1414.

- Chakrabarty, M., A. Majumder, and R. Ray. 2015. “Preferences, Spatial Prices and Inequality.” _Journal of Development Studies_ 51 (11): 1488–1501.

- CPI Manual. 2004. “Consumer Price Index Manual: Theory and Practice.” Published for ILO, IMF, OECD, UN, Eurostat, and The World Bank by ILO, Geneva.

- Dabalen, A., I. Gaddis, N. T. V. Nguyen. 2019. “CPI Bias and Its Implications for Poverty Reduction in Africa.” _The Journal of Economic Inequality_ : 1–32.

- Deaton, A. 1988. “Quality, Quantity, and Spatial Variation of Price.” _The American Economic Review_ 78 (3): 418–30.

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30

**Figure 4:  Real wage and Poverty in Brazil, 1981-2004**

<!-- Start of picture text -->
-70 0.15<br>-75 0.14<br>0.13<br>-80<br>0.12<br>-85 0.11<br>-90 0.10<br>-95 0.09<br>0.08<br>-100<br>0.07<br>-105 0.06<br>-110 0.05<br>-115 0.04<br>0.03<br>-120<br>0.02<br>-125 0.01<br>-130 0.00<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>(Minus )Real Wage (Jan 2003=100) FGT(2)<br><!-- End of picture text -->

# **6) Conclusions and More Questions.**

After rising between 1960 and 1976, and declining between 1976 and 1981, Brazilian income inequality resumed its upward trend in 1981. Between 1981 and its peak in 1989, Brazil’s Gini coefficient rose by five points (or 9%) from 0.574 to 0.625. After oscillating between 1989 and 1993, the Gini then fell to 0.564 in 2004. The accumulated decline from 1989 to 2004 was of six Gini points or 10%. Similar “inverted-U” patterns are clearly discernible for other inequality measures as well, including the two Theil indices presented in Figure 1. These changes remind us that, while still very high by international standards, Brazil’s inequality is not immutable.

Over the same period, poverty also followed a non-linear evolution, rising during the 1982-1983 recession, falling sharply during the mid-80s recovery and the _Cruzado_ year, and then rising again at the end of the decade. Poverty was still higher in 1993, the year which preceded successful economic stabilization, than it had been in 1981. In the ten years following the Real Plan of 1994, poverty fell steadily, by various measures and lines. By the standards of the “administrative poverty line” of R$100 per capita per month, incidence fell by ten percentage points, or a third. While most of this decline was driven by Brazil’s (modest) economic growth, the decline in inequality observed over the same period also contributed. In fact, inequality reduction during 1993-2004 accounted for
outcomes, it also studies the ASP’s indirect effects on nonenrolled children and investigates emotional regulation as a plausible mechanism to explain the direct effects of the intervention. To address these questions, we rely on the first stage of Dinarte’s (2018) experimental setting.<sup>16</sup> The randomization process and sample distribution of this first stage are shown in Figure 1, panel A, which shows there are two groups in this study. The first sample of “enrolled” children comprises the 1,056 students who applied to participate in the program and then were randomly assigned to treatment (T) or control (C) groups. The second sample of “nonenrolled” students consists of 1,364 children who were not interested in the ASP. Using available administrative data for both groups, we are able to compare enrolled and nonenrolled children’s characteristics. We return to this later.

Students from the “enrolled” sample were the unit of randomization and were randomly assigned to either C (25%) or T (75%) within school-by-educational-level “blocks,” as shown in Figure 1, panel B. Each education level consists of three years of schooling: the first is from first to third grades, the second from fourth to sixth grades, and the third from seventh to ninth grades.

This uneven distribution between both T and C groups addresses the need to simultaneously reduce the number of children left out of the program during 2016 in light of the high out-of-school risk these children face, but it guarantees there is enough power to capture the effects (Dinarte, 2018).

Children in T group could participate in the two sections of the ASP. Students in C group were supposed to leave school facilities after classes ended. We were able to collect their information at follow-up because we gave them an “enrollment coupon” redeemable the next year that guaranteed their participation in the traditional clubs in 2017.

Since enrolled students were assigned at the school-by-education-level block, it is important to note that the share of treated children from each grade within each education level—after controlling by the share of enrolled children—was quasi-exogenous. This generates an important variation that allows us to estimate spillover effects on nontreated participants. We discuss this in more detail in an upcoming section.

> 16The second stage of the experimental approach is described in Dinarte (2018). The author incorporated additional features in the design to study how peer composition, according to a predicted violence level at baseline, changes the effectiveness of the intervention. Further details and results can be found in that study.

11
**Table 1 Mean, Median and Maximum PM2.5 Concentrations in Cooking/baking areas of Households who did and did not Receive Mirt Stoves in 2013**

||Mean|Median|Maximum|
|---|---|---|---|
|Mirt, Generally with<br>Traditional Too|1162.11 (1883.21)<br>[98]|145.12 (789.81)<br>[99]|45,465.21 (36,595.46)<br>[98]|
|Traditional Only|1297.03 (1990.24)<br>[103]|149.39 (710.42)<br>[105]|49,649.25 (45,976.89)<br>[105]|

Standard deviations in parentheses and sample size in brackets. Though the stoves may not be used, all treated households have Mirt Stoves in place.

Consistent with other studies in similar environments, maximum concentrations are extremely high in areas where biomass is burned. The average of the maximum concentrations is 47,629  g/m<sup>3</sup> .<sup>10</sup> Households that received a Mirt stove have lower average maximum concentrations (45,465  g/m<sup>3</sup> versus 49,649  g/m<sup>3</sup> ).

In the overall sample statistics reported in Table 1, we do not find statistically significant evidence that the Mirt ICS reduces PM2.5 concentrations (average mean, maximum and median). Using two-tailed t-tests that assume normality and allow for unequal variances and Kruskal-Wallis rank-sum <sup>2</sup> tests with ties that allow for non-normality, we do not find evidence of statistically significant differences.<sup>11</sup> We next consider differences in the impact of the Mirt stove based on the location of the cooking environment, focusing on those who cook in their primary living areas. Approximately 3⁄4 of households (n=151) in the sample have located their cooking/baking areas outside the main house where people generally live. All but one of these cooking/baking areas are indoors, though in a separate building. The remaining 53 households have cooking/baking areas located in the house where people live, so these areas serve many purposes besides cooking. There is no difference in treatment assignment depending on whether injera baking occurs in the main house or a separate cooking/baking area (i.e. Mirt stoves did not tend to be differentially installed in main houses or separate cooking/baking areas). We therefore do not conflate cooking location with our treatment effect. Results are available from the first author.

We test randomization across treatment and control subsamples, but now restrict our analysis to the subsample of households who cook in their main living areas, again using the 23

> 10 We omit one observation that may be an outlier, because the measured maximum concentration was over 400,000  g/m<sup>3</sup> .

> 11 Based on Shapiro-Wilk W tests for normality, we are able to reject normality at < 0.01 level.

11
8 institutions. We proxy the quality of infrastructure following Limao and Venables (1999) and construct an infrastructure indicator based on the density of road, paved road, rail lines, and telephones.<sup>5</sup> All of these variables are taken from the World Bank, WDI database. For institutions, we use the customs component of the World Bank Logistic Performance Indicators (LPI)<sup>6</sup> . We proxy trade policy-induced incentives using the overall tariff trade restrictiveness index of the World Bank applied by the exporter to the rest of the world.<sup>7</sup> Each variable is standardized to have a mean that equals zero and a standard deviation that equals one.

To estimate the gravity equation of trade, we conduct cross-section estimates for the period 2008-2010 by taking the past three years averages values of exports and of the time-variant explanatory variables. This also helps smoothing the effects of the 2008-2009 global crisis. We follow Freund and Rocha (2010) and proxy all the importer variables with importer fixed effects. Concerning the exporter-related variables, we use a remoteness variable to proxy for the Πi term. Exporter-related infrastructure, incentives and institutions are proxied using the same variables described above and we further add a landlocked exporter dummy.<sup>8</sup> We proxy the average size of exporter by using the logarithm of average value of the nominal GDP for the past three years. Finally, bilateral trade costs Dij include a bilateral distance variable between the exporter and the importer, and a common colonizer, common language, and a common border dummy. All these bilateral costs are time invariant. We further add bilateral tariff rates; since the inclusion of this variable reduces dramatically the sample size, the results are presented mainly as a robustness check.

All countries for which data are available in all the years under analysis are included. (See Annex for a list of countries included in the regression and for the list of data sources and the data descriptive statistics).

> 5 As in Limao and Venables (1999), the infrastructure indicator is constructed by transforming the variables such that their mean across the sample is equal to 1 and taking the average across the various components. 6 The LPI did not come into existence before 2006 and the follow up survey were undertaken in 2009. We use the 2006 value also for the years 2005 and 2007 and 2009 values also for the years 2008 and 2010.

> 7 The variable is available for the years 2007 and for 2009.   We use the 2009 value also for the years 2008 and 2010 and 2007 values also for the years 2006 and 2005.

> 8 Note that the importer dummy captures all time-invariant importer related variables.