rafmacalaba/datause-encoder-data huggingface dataset:
effect of the program on learning and explore whether the program had differential effects on girls and low-ability students. Lastly, we estimate effects of the program on school management outcomes.

# _Effect of the Program on Enrollment_

To measure the effects of the program on enrollment, we test the difference in average grade-level enrollment by estimating the following: (1) Ygsj � β0 � β1Tj � γ<sup>′</sup> Xgsj � εgsj where Ygsj identifies the number of students enrolled in grade _g_ in school _s_ in village _j_ , in either 2012 or 2013. Tjindicates whether village _j_ was assigned to the treatment group. The vector Xgsj is included in some specifications for statistical power and includes the number enrolled by gender prior to program implementation and cohort fixed effects. We estimate the equation with a linear probability model and cluster standard errors by village, the unit of randomization. We estimate the equation for all students and separately by gender.

# _Selection into Test-Taking_

Before estimating the effect of the program on learning outcomes, we examine whether the program affected the type of students we observe for learning outcomes. Since test scores are only observed conditional on enrollment and attendance on the day of the exam, if the program was successful at targeting low-performing students, the estimates of the program’s impact on test scores would be biased downward. Conversely, estimates may be biased upward if higherperforming students were more likely to be retained or attend on the test day in treatment schools. To address potential threats due to differential test-taking, we examine the effects of the 14
varying preferences for education. However, our analysis (not shown) does not find heterogeneous effects for different levels of household expenditure.

Since the factors shaping education decisions for children in primary, secondary, and tertiary school age can vary widely, additional analysis is conducted separately for individuals in these age groups. The first three columns of Table 9 present these results of impacts on enrollment. Even though the direction of the impact of migration on school enrollment appears positive for all age groups (column 1), such a conclusion is not evident since the empirical estimates cannot be distinguished from zero. Subject to this caveat about imprecise estimation, the size of the impact tends to be larger among older children aged 11 to 18 and smaller among children aged 6 to 10, whose initial enrollment rate is already high. The positive sign of the estimated impacts reflect what happens among households with male migrants rather than among those with female migrants, as shown in columns 2 and 3 of Table 9. The type of families sending male migrants and the type sending female migrants may be different for unobserved reasons, or alternatively, male and female migrants themselves have different impacts. Since women tend to be more involved than men in child care and monitoring children’s activities, when a mother migrates for work overseas, this action can have worse consequences for children’s schooling behavior than the father’s migration. In fact, columns 7-9 of Table 9 suggest that migration may increase children’s idle time among families with a migrating female, particularly for young children aged 6-10.

These results are very similar to findings about female migration and child outcomes in rural El Salvador. Acosta (2011) finds that male migration has null to slightly positive effect on children’s school enrollment while female migration appears to have the opposite effect. At the same time, female migration tends to reduce child labor, in contrast to male migration from El Salvador. The results are also consistent with the findings of Guzman et al. (2007) and Pfeiffer and Taylor (2007) that female migration, as opposed to male migration, is associated with lower household expenditure on education

# **7. Conclusion**

Using the large IFLS data, this paper is the first attempt to quantitatively assess the impacts of migration, differentiated by gender, from Indonesia on labor supply and child 22
Table 4: Inspection-level outcomes at midline

||Cl|assroom q|uality||T|eaching qu|ality||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Overall|(2)<br>Spatial<br>org.|(3)<br>Educ.<br>documents|(4)<br>Overall|(5)<br>Written<br>prep|(6)<br>Teaching<br>material|(7)<br>Class<br>practice|(8)<br>Refection|
|In-Kind|0.143<sup>_∗∗_</sup>|0.149<sup>_∗_</sup>|0.136<sup>_∗_</sup>|0.111<sup>_∗∗_</sup>|0.095|-0.022|0.115|0.256<sup>_∗∗∗_</sup>|
||(0.056)|(0.078)|(0.071)|(0.046)|(0.071)|(0.073)|(0.075)|(0.067)|
|Recognition|0.090|-0.020|0.200<sup>_∗∗∗_</sup>|0.079|0.210<sup>_∗∗∗_</sup>|-0.240<sup>_∗∗∗_</sup>|0.250<sup>_∗∗∗_</sup>|0.096|
||(0.060)|(0.087)|(0.073)|(0.051)|(0.075)|(0.078)|(0.079)|(0.078)|
|Control mean|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1056|1056|1056|1056|1056|1056|1056|1056|
|R-squared|0.050|0.034|0.117|0.080|0.098|0.124|0.075|0.053|
|Test:In-Kind=Recognition|0.354|0.050|0.355|0.508|0.104|0.004|0.072|0.024|

_Notes._ Each column represents a separate regression using equation 1. Data is at a teacher level. The dependent variables in columns (2)-(3) and (5)-(8) are the scores assigned by inspectors for different domains of classroom and teaching quality, standardized to have a control mean of zero and standard deviation of one. The dependent variable in column (1) is the mean of the dependent variables in columns (2) and (3). The dependent variable in column (4) is a mean of the dependent variables in columns (5) to (8). Standard errors are in parentheses and clustered at the school level. *p _<_ 0 _._ 1, **p _<_ 0 _._ 05, ***p _<_ 0 _._ 01
# **1 Secured Transactions and Movable Collateral: Current State in MENA**

Access to credit is crucial for economic growth and is the engine for private sector development. While access to credit varies from one jurisdiction to another, **constrained access to finance remains among the top three limitations on private sector growth in the developing world** . More than half of private firms in emerging markets have no access to credit. The number of firms that use loans to finance investments in the developing world is half the number of those of firms operating in OECD countries.<sup>1</sup> Creditor protection through modern secured lending legal regimes is associated with higher ratios of higher private sector credit to GDP (2006, Djankov et al). Increasing the protection of creditor’s rights and enforcement mechanisms can lead to a considerable increase in private sector credit to GDP.<sup>2</sup> As illustrated in the figure below, the MENA region clearly lags behind the rest of the world in firms’ access to private credit.

**Figure 1: Firms Access to Credit Around the World3**

<!-- Start of picture text --> Access to a Line of Credit or Loans Use of Bank Loans to Finance<br>from Financial Institutions (% of Investments (% of Firms)<br>60<br>Firms)<br>49.85<br>60 56.92 54.97 50<br>5040 45.02 37.54 4030 34.18 28.77 27.20 26.21<br>30 25.07 20 16.45<br>20 10<br>10 0<br>0 Eastern OECD Latin South Asia Sub-Saharan Middle East<br>Eastern Europe Latin America South Asia Sub-Saharan Middle East & Europe & America & Africa & North<br>& Central Asia & Caribbean Africa North Africa Central Asia Caribbean Africa<br><!-- End of picture text --> In the World Bank’s Enterprise Surveys, the MENA region has the lowest percentage of firms with credit lines or loans from financial institutions, at 25.07%, compared to 56.92% for Eastern Europe and Central Asia (ECA), 54.97% for Latin American & the Caribbean (LAC), and 45.02% for South Asia. The percentage of firms in MENA using banks to finance investments was also the lowest. Sixteen (16.45) percent of firms in MENA use bank financing for investment, compared to 34.18% in the OECD, and 49.85% in ECA. Based on the numbers in the same survey, MENA firms also identify access to finance as a major constraint, more than in

> 1 See World Bank Group Enterprise Surveys.

> 2 See ―Private Credit in 129 Countries‖, Simeon Djankov, Caralee McLiesh and Andrei Schleifer, 2006.

> 3 Based on the latest datasets available on www.enterprisesurveys.org on the date of access (February 10, 2010)

3
Table A.9: Heterogeneous treatment effects on clinic visits for family planning services

||Co|variate at basel|ine:|
|---|---|---|---|
|**Outcome:**|Wanted another<br>child|Had at least<br>one son|Had ever visited<br>a clinic for FP|
|**Visited any clinic**|(1)|(2)|(3)|
|Own Voucher|0.311***|0.026|0.122**|
||[0.069]|[0.094]|[0.059]|
|BAF Voucher|0.193***|0.112|0.138**|
||[0.065]|[0.085]|[0.058]|
|Own Voucher _×_ Covariate|-0.218**|0.195*|0.136|
||[0.096]|[0.110]|[0.111]|
|BAF Voucher _×_ Covariate|-0.117|0.063|0.014|
||[0.095]|[0.102]|[0.094]|
|Observations|604|604|604|
|Endline control mean<br>p-values:|0.193|0.193|0.193|
|Own = BAF|0.145|0.395|0.818|
|Own _×_ Covariate = BAF _×_ Covariate|0.371|0.285|0.331|
|Own + Own _×_ Covariate = 0|0.165|0.000|0.007|
|BAF + BAF _×_ Covariate = 0|0.277|0.002|0.042|

_Notes:_ All columns are fully-interacted regressions where all controls are interacted with the covariate used to examine heterogeneous effects; the main effect of the covariate is also included as a regressor. All columns also include balancing controls (i.e., the baseline variables used to balance randomization–whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module), other baseline covariates (i.e., baseline values of woman’s age, indicator variable for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index) and village fixed effects. FP denotes family planning, and BAF denotes bring-a-friend. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

57
# **Figure 5: Relationship between bank interest rates and spreads**

_High nominal and real lending rates are driven by intermediation inefficiencies, not high deposit rates. Intermediation spreads are closely aligned with net interest margins, but the link is weaker in countries with higher spreads, perhaps due to developmental challenges._ A. Median interest rates and spread in EMDEs (%)

- B. Interest spread vs deposit rate (2003-17 averages)

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>Deposit rate (median)<br>Lending rate (median)<br>Lending deposit spread (median)<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0 5 10 15 20<br>Deposit rate<br>Lending - deposit spread<br><!-- End of picture text -->

- C. Interest spread vs lending rate (2003-17 averages)

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0 10 20 30 40 50<br>Lending rate<br>Lending - deposit spread<br><!-- End of picture text -->

- D. Median interest rates by quintile of the intermediation spread (2003-17 average)

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Lowest spread Highest spread<br>Deposit rate Nom. lending rate Real lending rate<br><!-- End of picture text -->

- E. Median intermediation spread and net interest margin by quintile of the intermediation spread (2003-17 average)

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Lowest spread Highest spread<br>Lending - deposit spread Net interest margin<br><!-- End of picture text --> Source: World Bank Finstats 2019.

39
distortions. Reducing unexplained wage differentials may well reduce “occupational” and “informal” distortions; improving valuation parameters will partially compensate “social” distortions; eliminating _parafiscales_ will, instead, reduce “social” costs for formal salaried workers, while equalizing social security contributions among formal workers will reduce “social” costs in favor of formal self-employed workers. Hence, the attention given to the reform of _parafiscales_ (see Nuñez 2008; Santa María, García, and Mujica, 2009; Sánchez, Duque, and Ruiz, 2009) simply concentrates one part of the problem, but not on the most substantial in light of the wage gaps, contribution differentials, and sub-valuation of transfers.

Ultimately, the fundamental issue is whether or not the solidarity model chosen by Colombia is the most efficient to perform its purported social protection objectives. The answer, in light of the distortions, is not very rosy. The strategy of expanding spending in social security without institutional changes and augmenting the generosity of social assistance will clearly reinforce the distortional relationship between the labor market and social protection in the future. A debate on labor market reform cannot be understood without a discussion of the social protection reform –and although not discussed here, without a discussion on the growth, productivity and fiscal implications of perpetuating informality (see IADB 2010 for a recent analysis of these interactions). In that debate, also, policy makers need to internalize the mismatch between individuals’ appreciation of social programs and their willingness to pay for them. Future reforms, at the end of the day, will not be viable if social protection reforms create expectations of higher coverage and quality without having to contribute towards it. In Colombia, as currently in other countries, these need to be the critical considerations to discuss in order to ensure a sustainable minimum-standards comprehensive (or universal) social protection system that expands coverage and reduces distortions.

# **References**

Acosta, O., and J. Ramirez. 2004. “Las redes de protección social: modelo incompleto”, Serie financiamiento del desarrollo No. 141, Unidad de Estudios Especiales, CEPAL, Santiago de Chile.

Albrecht, J., L. Navarro, and S. Vroman. 2009. “The Effects of Labour Market Policies in an Economy with an Informal Sector.” _Economic Journal_ . Vol. 119, pp. 1105-1129.

Bernal, R., A. Camacho, C. Flórez, A. Gaviria, C. Jaramillo, O. Nupia, X. Peña, C. Rodríguez, F. Sánchez, M. Urrutia. 2009. “Desarrollo económico: retos y políticas públicas.” Serie Documento CEDE 01-2009. Universidad de los Andes, Bogota, Colombia.

BID (Banco Inter-Americano de Desarrollo). 2008. Calidad de vida: más allá de los hechos. Washington DC: BID.

Superintendencia de Subsidio Familiar (2010) Boletín Mensual Enero 2010- Definida Cuota Monetaria Departamental.

DNP (Departamento Nacional de Planeación). 2006. “Red de Protección Social contra la extrema pobreza.” Documento CONPES Social 102, Bogotá, Colombia.

19
# The Structure of Production as a Determinant of the Demand for Human Capital in Peru

# by Indermit Gill

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|The Theoretical<br>Framework|6|
|3.|Some Theoretical<br>Extensions|12|
||Experience as a Factor of Production|13|
||Forming of Expectations|14|
|4.|Household Level Empirical Evidence|17|
||Definition<br>of Dependent Variable:<br>Schooling Shortfalls|17|
||Definitions<br>of Independent Variables|19|
||Ree-its of the Schooling Regressions|23|
|5.|Province Level Empirical Evidence|35|
||Definitions<br>of Dependent Variable:<br>Illiteracy Rates|35|
||Definitions<br>of Independent Variables|36|
||Description<br>of the Data|38|
||Results of the Schooling Regressions|41|
|6.|Conclusions<br>and Policy Implications|51|
|Ref|erences|53|
|App|endix|55|
Research to date has been confined to relatively limited sets of impacts<sup>7</sup> and locations.<sup>8</sup> In this paper, we broaden the assessment to 84 coastal developing countries in five regions.<sup>9</sup> We consider the potential impact of a large (1-in-100-year) storm surge by contemporary standards, and then compare it with intensification which is expected to occur in this century. In modeling the future climate, we take account of changes in sealevel rise (SLR), geological uplift and subsidence along the world’s coastlines. Our analysis includes impact indicators for the following: affected territory, population, economic activity (GDP), agricultural land, wetlands, major cities and other urban areas. As far as we know, this is the first such exercise for developing countries.

Our analysis is based on the best available data for estimating the relative vulnerability of various coastal segments to increased storm surge. However, several gaps in the data limit our analysis. First and foremost, the absence of a global database on shoreline protection has prevented us from incorporating the effect of existing man-made protection measures (e.g., sea dikes) and natural underwater coastal protective features (e.g., mangroves) on exposure estimates. Second, lack of sub-national data on impact indicators has prevented us from including small islands in our analysis. Third, in the absence of reliable spatially disaggregated projections of population and socioeconomic conditions for 84 developing countries included in this analysis, we have assessed the impacts of storm surges using existing populations, socioeconomic conditions and patterns of land use. Human activity is generally increasing more rapidly in coastal areas, so our estimates are undoubtedly conservative on this score. On the other hand, we also have not attempted to estimate the countervailing effects of planned adaptation measures related to infrastructure (e.g., coastal embankments) and coastal-zone management (e.g., land-use planning, regulations, relocation). Fourth, among the 84 developing countries included in this analysis, we restrict our analysis to coastal segments where historical storm surges have been documented. Fifth, we did not assess the relative likelihoods of alternative storm surge scenarios. Following Nicholls et al. 2007, we assume a

> 7 For example, Nicholls et al. (2007) assess the impacts of climate extremes on port cities of the world. 8 For example, the impacts of storm surges have been assessed for Copenhagen (Hallegatte et al., 2008); Southern Australia (McInnes et al. 2008); and the Irish Sea (Wang et al. 2008).

> 9 We have employed the five World Bank regions: East Asia & Pacific, Middle East & North Africa, Latin America & Caribbean, South Asia, Sub-Saharan Africa.

4
Google Trends data and their usage require some caveats. First, the Google Trends platform calculates comparable search indices for a maximum of five search terms and five geographic areas only. This technical restriction forces users to download search volume indices for multiple countries – 182 countries in our case – and multiple terms separately, rendering them incomparable across geographic areas and terms. Therefore, users need to renormalize the indices with a reference country to make them comparable across geographic areas. While there are different approaches to do so, we use the approach suggested by Narita and Yin (2018) to make the search index for our terms comparable across countries.<sup>5</sup> See Appendix A for a detailed description of the renormalization.

Second, potential differences in search language across countries call for a more careful approach to extracting the Google search data. In a given geographic area, some people could submit their search queries in local (non-English) language and others in English. In order to best capture search popularity for our selected terms in countries where English is not the main language, we request search terms in English and in a local language that is widely spoken in the country. We mass translate our search terms from English to the respective local languages using Google Translate API for all countries and terms.<sup>6</sup> We extract sector-specific search queries both in English and the most dominant local language. For the United States, for example, search intensity is measured in English, whereas in Japan, it is measured both in English and Japanese.

The COVID-19 data on confirmed cases and deaths are extracted from the Johns Hopkins University Center for Systems Science and Engineering for the period from January 22, 2020 to April 30, 2020. We aggregate the number of daily new cases and deaths to create weekly confirmed cases and deaths for each country. This allows us to link the weekly Google Trend data with COVID-19 cases and deaths. The number of COVID-19 cases and deaths are expected to inform consumer behavior and government policy responses to reduce the spread of the virus and the economic cost of the pandemic. These may impact demand for services through different, but related channels.

The aggregate impact of COVID-19 on consumer demand for services is the combined effects of (i) a reduction in the supply of services due to social distancing and lockdowns measures,

> 5 We have also applied the renormalization method by Stephens-Davidowitz (2014), but it makes no difference to our results. Results from this approach are available upon request.

> 6 We mass translate our search terms from English to the respective local languages using Google Translate API for all countries and terms. See also at https://github.com/MarkEdmondson1234/googleAnalyticsR

7
early childhood health among the poorest of the poor in a post conflict setting such as Nepal is an important policy goal and this paper provides crucial evidence towards this.

Our paper is most closely related to the recent work by Macours, Schady, and Vakis (2012), who study the impacts of cash transfers to households in Nicaragua under the _Atencion a Crisis_ program on child development. They find that cash transfers improve overall child development and that the positive effects of the program last long after the transfers stop. This suggests that transfers can lead to long lasting changes in behavior. Our paper can directly test the value added of cash over and above information; we add an explicit information based intervention to a basic cash component, while the _Atencion a Crisis_ program seems to have included a large number of programs as part of its treatment (including informational sessions). Importantly, we explicitly measure maternal knowledge about best practices regarding child care; hence, a key contribution here is whether maternal knowledge improves as a result of the intervention and whether knowledge is better put into practice when cash is additionally given.

# 2 Experimental Design

## 2.1 Randomization

The intervention was implemented in four food insecure districts in Nepal (4 of 75 districts nationwide), through a Community Challenge Fund (CCF) administered by the Ministry of Federal Affairs and Local Development.<sup>1</sup> The CCF specifically targeted high risk communities within Community Organizations (COs) supported by the Nepal Poverty Alleviation Fund (PAF). PAF is a program created by the Government of Nepal that seeks to improve outcomes in poor, marginalized communities by community driven development. COs supported by PAF are designed to hold monthly meetings, facilitated by a local Social Mobilizer (SM), that bring together people from the community. PAF supports community infrastructure and income generating activities for poor and socially vulnerable households. These four districts supported by the CCF cover Nepal’s diverse geography, from flat lying agricultural based areas to more mountainous regions.<sup>2</sup>

> 1 Under a Social Safety Nets Project financed by the World Bank.

> 2The four districts are Sarlahi, Rautahat, Sindhuli, and Ramechaap. Sarlahi and Rautahat are in the terai, a low lying region that consists of marshy grasslands at the foot of the Himalayas. Sindhuli and Ramechaap are in the hills.

4