rafmacalaba/data-use-mentions-extended huggingface dataset:
birth from every individual observed during the 2001 Nepal Population Census and calculate yearly district birth rates. As reported in Figure 6, there is no dierence in birth rates between the districts experiencing above and below the median conict intensity (months of war). Table A.4 reports regression results for the same test and we see no dierence in periods long before and just before the start of the conict.

# 5.3 Impact on Fertility, Education, Employment and Wealth

Estimates in Table 7 show the impact of conict on reproductive health and fertility using the baseline specication. Early childhood exposure to conict has no signicant impact on probability of miscarriage or of stillbirth among women in age cohorts 9 to 15 and 4 to 8 in 1996. Women experiencing conict during their entire growth period, however, are signicantly more like to have had a stillbirth or miscarriage: each additional month of conict exposure increases the risk of stillbirth by 0.03 and of miscarriage by 0.06 percentage respectively. Valente (2015) nds similar results that pregnancies that were exposed to Nepal's conict were more likely to result in a miscarriage. There is no signicant impact on the probability of abortion. Women exposed to conict during their early growing periods have signicantly more live births, 0.024 and 0.013 more births per month of exposure among cohorts aged 0 to 3 and 4 to 8 in 1996 respectively. The impact of conict on the number of infant deaths is zero (column 5).

Impacts of early childhood exposure to conict on other fertility outcomes are presented in Table 8. The conict has statistically no impact on the likeliness of contraceptive use (column 1), number of women's sex partners, age at rst sexual intercourse and age at rst birth. There are also no signicant dierences between women in control and treatment groups on their age at rst cohabitation with their domestic partner and
# **4 Conclusion**

This paper evaluates the use of three tree-based machine learning techniques and linear empirical best predictor (EBP) models for the purposes of contemporaneous small area estimation of wealth and poverty using household census data and publicly available geospatial indicators from seven countries. In addition to evaluating accuracy, we propose and implement a bootstrap algorithm to estimate the uncertainty associated with machine learning predictions and evaluate its performance using real-world data. Two of the three treebased machine learning methods evaluated – Cubist Regression and Extreme Gradient Boosting (XGBoost) significantly outperformed the empirical best predictor model traditionally used for small area estimation (Molina and Rao 2010; Tzavidis et al. 2018). A third machine learning method, BRF, generated estimates comparable to EBP. Both point estimates and uncertainty estimates generated using Cubist regression models are generally a bit less accurate than those generated using XGBoost.

The results make a strong case for the use of XGBoost or Cubist regression in cases where the sample includes a sufficiently large number of subareas, those subareas comprise a small share of subareas in the population, and transparency and parsimony are not first-order concerns. In general, in these contexts, it does not appear that the benefit of EBP, in terms of conditioning on the sample data, outweighs the restrictions of its linear function form. In addition, the acccuracy of BRF estimates in this context likely suffers from using random subsets of predictors and different subsets of the data for training and prediction. Nonetheless, appropriate diagnostics, such as estimating linear models, interpreting the results of machine learning models, and evaluating results using cross-validation techniques, remain crucial in practice. Furthermore, accuracy is not always the most important factor when selecting methods. In cases where transparency and parsimony are important, or the number of sampled subareas is small, linear mixed EBP models or Cubist regression with a small number of rules are viable options. We do not claim that these results hold for all types of samples and settings. While recent research has made some progress towards combining tree-based machine
transportation infrastructure. The study included a baseline survey in 2006, followed by the rollout of the school feeding interventions, and a follow-up survey in 2008.

The present study contributes to the literature on school feeding through a quasiexperimental longitudinal comparison of different school feeding modalities (on-site feeding vs. take-home rations). Our household-based sample avoids the problem of looking only at children currently in school. The study also contributes to the impact evaluation literature by explicitly addressing evaluation challenges through the construction of two counterfactuals, using difference-in-difference estimation with propensity-score weighting.

To preview our results, we find very little conclusive evidence that school feeding affected enrollment or nutritional status in this population. The evaluation presents several methodological challenges which are discussed in detail below. In an attempt to confirm our null findings, we undertake several additional analyses related to sample attrition, spillover effects, and program take-up and implementation. The study raises some concerns about the potential for school feeding programs where school capacity is limited.

The paper proceeds as follows: we first discuss the theory and prior evidence of school feeding programs, then introduce our evaluation strategy. Details on the Lao PDR setting and the school feeding intervention are provided, along with a description of the sample and measures. Results and discussion follow.

# 2 Food‐for‐education and child development: Theory and evidence

Providing food to school children, either during the school day in the form of a snack or in the form of rations to take home, has several goals. First, the transfer is intended to decrease the net cost of schooling and thereby shift parental demand for children’s educational participation, 3
<!-- Start of picture text --> 3<br>2<br>2<br>Qo<br>Cc<br>oa<br>[a]<br>1<br>[2 2018<br>3 2019<br>0<br>0 2 4 6 8 10<br>Rainfall z-scores<br><!-- End of picture text --> <!-- Start of picture text --> 38<br>6<br>2<br>2<br>5 4<br>[a]<br>2<br>ME 2018<br>3 2019<br>0<br>-2 0 2 4<br>NDVI z-scores<br><!-- End of picture text --> <!-- Start of picture text --> 6<br>4<br>2<br>Qo<br>Cc<br>oa<br>[a]<br>2<br>[Mm 2018<br>3 2019<br>0<br>4 -2 0 2 4<br>Temperature z-scores<br><!-- End of picture text -->
are taken from the Bureau of Labor Statistics export price series data. The results are qualitatively similar to the results in the paper: while we see little or no wage effects within manufacturing, occupational exposure to import price declines has affected workers across sectors. In particular, we find that a 1 unit increase in the price index for imports raises a worker’s real wage by .031 percent. We find no significant effect of our export price series on wages. For the wage impact of offshoring, our results are similar in sign and magnitude to those presented in the paper. This suggests that our core results are robust to our use of quantities for imports and exports. Since our data on quantity of imports and exports is more comprehensive than the price data we have for this period, we proceed in our other results with our quantity measures.

C. Imports and Trends in Wage Premiums by Occupation In Table A5, the most affected workers were shoe machine operators, for whom occupation-specific import penetration increased from 37.2 percent in 1983 to 77.4 percent in 2002. For these workers, the coefficient on import penetration in column (5) of Table 3, which is -.27, implies that their real wages fell by nearly 11 percent as a result of competition from trade. The contrasting experiences of workers in textiles and apparel related sectors compared to many service sector employees such as teachers helps to explain why some parts of the U.S. economy have been deeply affected by globalization while others have not. On average, occupation-specific import and export shares only increased from an average of 2 to 4 percent during the 1983 through 2002 period, in large part because of the importance of services and the lack of global competition in service occupations. Consequently, the average effect of an increase from .02 to .04 for occupation-specific import competition is quite small, equal to .02 x .27, which is a fall in real wages of half a percent. However, what Table A5 makes
Ukrainian government in the Budget Declaration 2011 for ordering the optimization of complete schools (levels I-III) may be very conservative, especially for urban schools, almost none of which are below this threshold and for which the estimated optimal point is often over 1000. This suggests there is a lot of room for consolidation.

From a policy point of view, our findings suggest that consolidating the network of schools through a downsizing in the number of small schools and a transfer process of students in those schools to neighboring ones is unlikely to harm school performance on the EIT. If anything it may even increase educational outcomes. Of course, an important caveat here is that this “no harm conclusion” is only likely to hold if the organization of the transfer and of the commuting to the receiving schools is organized efficiently.

While our results are suggestive, they further need to be interpreted with caution as we have not been able to investigate in detail to what extent our findings reflect causal relationships rather than correlation. Nor can we claim that these empirical findings can be extended to schools other than complete schools (level I-III), since the analysis was undertaken with measures of school performance for 12<sup>th</sup> -graders. Both these areas remain avenues for further research as well as the analysis of the student-level EIT data as opposed to the school-level analysis presented here<sup>34</sup> .

The consolidation of the network of schools in Ukraine is an important area of reform for improving the efficiency of educational spending and for achieving fiscal sustainability of the sector. If managed well, a sound optimization strategy can bring tangible results on the access, equity and quality fronts. But rationalizing the network of schools is usually one of the most difficult education reforms since it usually faces with strong resistance in communities, and among parents and teachers. Therefore, a necessary pre-requisite to embark in such reforms is a strong political will and coordinated efforts of Government agencies and local authorities to materialize. A detailed roadmap with a
to refugee settlements. In sum, whether proximity to refugees has different effects on host communities’ attitudes and behaviors in low-income contexts is an open question, which our study seeks to begin addressing.

Using the case of Uganda, we hypothesize that in low-income countries, communities with greater refugee presence will not experience a backlash against migrants and refugee policies as long as these policies (especially those related to resource allocation) ensure that said communities do not carry a disproportionate burden of hosting refugees. We examine redistributive efforts that are designed to ameliorate possible _congestion effects_ .

Uganda is an important setting for exploring how refugee presence might affect host communities. Home to 1.4 million refugees (the most in Africa), it is the fourth-largest refugee-hosting country in the world and the seventh largest in the world on a per capita basis (UNHCR, 2020). Uganda has adopted relatively generous hosting policies such as maintaining an “open door” for displaced persons; allowing refugees to freely move and participate in economic activities; granting plots of land for permanent shelters and farming; and, with the help of humanitarian aid agencies, providing access to health care and education services (Ronald, 2020). Given the sheer scale of the refugee inflow and its welcoming asylum and refugee policy, our finding that Uganda has _not_ experienced a backlash against refugees among those most directly affected by the country’s asylum and refugee policies offers important lessons.

To explore the effects of refugees’ presence in Uganda, we combine publicly available geocoded Afrobarometer survey data with newly constructed fine-grained georeferenced panel data on service delivery in three domains: access to (primary and secondary) education, health care access and utilization, and road density. We focus on service delivery inputs rather than outcomes since service providers—governments, humanitarian organizations, and UN agencies—can directly affect access, but linking access to outcomes (such as infant mortality) is more complex, materializes with a long lag, and depends on several factors that are outside policy makers’ control. We
lower costs are at the top of their economic plans.

|**Table 1. Medians, Trade meas**|**ures in SSA**|**Sub Saharan Africa**|
|---|---|---|
||Region|Resource Rich<br>Non-Resource Rich|
|**Trade Openness**|||
|Percent of GDP|||
|1990 - 94|57.09|71.87<br>54.93|
|2010 - 14|76.39|87.23<br>64.81|
|**Herfindahl Index**|||
|Product Concentration|||
|1990 - 94|0.31|0.80<br>0.27|
|2010 - 14|0.21|0.54<br>0.16|
|Market Concentration|||
|1990 - 94|0.14|0.29<br>0.12|
|2010 - 14|0.13|0.21<br>0.12|
|**Natural Resources Trade**|||
|Percent of GDP|||
|1990 - 94|11.78|46.19<br>9.21|
|2010 - 14|7.88|23.58<br>5.98|
|Percent of total exports|||
|1990 - 94|91.43|98.63<br>83.57|
|2010 - 14|77.25|86.48<br>74.92|
|**Regional Trade**|||
|Inter-regional, Percent of GDP|||
|1990 - 94|31.91|40.36<br>26.74|
|2010 - 14|34.83|48.57<br>31.39|
|Intra-regional, Percent of GDP|
Schultz, T.P., (1999). “Labor Market Reforms: Issues, Evidence and Prospects,” Papers 802, Yale - Economic Growth Center.

Shlrigrt, A., and Vishny, R., (1993), “Corruption”, The Quarterly Journal of Economics, August, pp.559-617.

Svensson, Jacob, (2003) “ Who must pay bribes and how much ? evidence from a cross section of firms” , The Quarterly Journal of Economics, February.

Tanzi, V., (1998), “Corruption Around The World: Causes, Consequences, Scope, and Cures”, IMF Staff Papers, Vol(45), Number 4.

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Van Rijckeghem, C., and Weder, B., (1997), “Corruption and the Rate of Temptation: Do low Wages in the Civil Service Cause Corruption ? , IMF WP/97/73.

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Page | 21
# **3.3 The composition of intangible expenditures by industry group**

This section discusses our industry-wise estimates of intangible expenditures in Brazil. As explained in Section 2, we obtained these estimates by aggregating firm-level data to the level of industries, or if this was not possible, by using the sector input-output coefficients in the national accounts tables. Specifically, among major categories, industry-level spending estimates for computerized information, other product development and organizational improvement are based on input-output tables and are therefore less precise than other bottom-up estimates.

We give an overview of our industry level findings again by categories of intangibles, grouping our 55 industries (as classified in the Brazilian national accounts) into six broad categories: agriculture, extractive industries (including petroleum), low tech manufacturing, medium/high tech manufacturing, ICT services (which includes R&D services) and all other services (including construction). Our division of manufacturing into just two groups is admittedly crude but quite effective at capturing key interindustry differences in intangible investment. The category called ̳medium/high tech manufacturing‘ includes industries generally considered to be more technologically sophisticated _on average_ , such as chemical and pharmaceuticals, machinery and equipment, electronics, instruments, motor vehicles and other transport equipment, basic metals and metal products. The category called ̳low tech manufacturing‘ includes industries like food products, textiles, leather, wood, paper, which are considered technologically less sophisticated on average.

As indicated by the share of computer software and database spending in total industry sales, the extractive industries and (unsurprisingly) ICT are by far the most IT-intensive industries in Brazil (Figure 7). All other sectors spend less than 0.75 percent of sales on computerized information, which is quite low by US standards. As expected, firms in low tech manufacturing spend even less than those in medium/high tech manufacturing. However, manufacturing is also seeing considerable growth in computerized information expenditures, with its share in industry sales doubling in the case of low tech manufacturing
to ten kilometers.

Besides the population surveys, cross-sectional health facility assessment (HFA) data were collected in 2010 through a number of techniques, including observations of client-provider interactions (CPIs) during consultations, client exit interviews, interviews with SPs, record reviews, facility inventories, and service statistics. The HFA was conducted in 20 contracted (ten offering STI and ten offering safe motherhood services) and ten comparison facilities. The facilities were specifically selected based on the type of services offered and their size in terms of client volumes. This report uses data from CPIs and interviews with SPs. Additional crosssectional data were obtained from (1) voucher sales and claims up to mid-2011, provided by the VMA, (2) semistructured interviews conducted in August 2010 with SPs from contracted and noncontracted facilities to examine workload and the level of job satisfaction among the SPs, and (3) cost data for the voucher program, obtained from the VMA in July 2011.

13
76.2|80.9|80.9|92.0|92.0|95.1|95.1|<br>95.1|<br>95.1|95.1|

Notes: (1) the aggregate is the average of scores ranging between 1 (no liberalization) and 4.5 (liberalization at the levels of highly developed market economies) for prices, foreign trade and exchange rate regime and small privatization. It has been normalized with 1=0 and 4.5=100; (2) SEE-4 include Albania, Bosnia and Herzegovina, Macedonia and Serbia and Montenegro. Source: own calculations based on data from EBRD Annual Transition Reports.

The second transition shares more similarities with the initial path of the radical Central European reformers than Bulgaria’s first, failed transition. In some respects, it progressed faster, as aggregate output rebounded in the next year after the implementation of the stabilization program, and inflation was brought under control in the second year of the program. In some others, it progressed slower. While it would be tempting to take the value of the EBRD-based first-generation reform index that Poland reached in the first year of its stabilization-cum-transformation program in 1990 as a frame of reference, this would be 1 These three policy areas constitute the core of first generation reforms. Scores for each area ranging between 1 for no reforms and 4.5 for reaching the average conditions in highly developed economies have been normalized with 1 equal 0 and 4.5 equal 100. The average of these three indicators amounts to the achieved progress in first generation reforms.
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choices, attitudes toward violence, and violence experience). We run all regressions for women and men separately because the majority of indicators (e.g., concerning violence and contraceptive use) are not fully comparable, i.e., the questions for men and women in the DHS survey are different.

The coefficient _β_ is of primary interest, capturing the impact of distant violent events recorded in the ACLED data. The key explanatory variable **VEdist**<sup>_g_</sup> _jt_ captures the number of violent attacks over the last five years that occurred in location _j_ and were conducted by group _g_ —i.e., either Boko Haram or Fulani. We conduct regressions looking at violent attacks conducted by each of these groups separately. To ensure that these are distant attacks, we restrict the set of locations to those that lie outside of the 50 km radius of the centroid of a household cluster. This spatial threshold is the largest within which, according to the literature (e.g., Bendavid _et al._ , 2021), nondisplaced women and children are still considered to be living dangerously close to armed conflict. We use a timeinvariant spatial-weight matrix **W** _cj_ based on an inverse function of the distance between violent events and population clusters (see e.g., Agrawal _et al._ , 2017; Kelejian _et al._ , 2013). This choice is consistent with results from alternative specifications that count all events that occur within different radii from the household cluster—i.e., 0–50 km, 51–100 km, 101–50 km, and 151–200 km—without weighting these events. In particular, we observe a dose-response relationship between the size of the spillover effects and the distance of the attacks that appears to roughly follow a linear function with a negative slope parameter, i.e., the farther the event, the smaller the impacts, in absolute terms (see Table
# **Give Me a Pass: Flexible Credit for Entrepreneurs in Colombia**

Lasse Brune, Xavier Giné and Dean Karlan<sup>*</sup> Originally published in the Policy Research Working Paper Series on _November 2022._ This version is updated on _January 2024._ To obtain the originally published version, please email prwp@worldbank.org.

# **JEL codes** : C93, D22, D25, D90, G21, G22, O12, O16

**Keywords** : microenterprise; microfinance; microcredit; lending contracts; repayment flexibility; flexible lending; moral hazard; randomized evaluation

> * Brune: <u>Lasse.Brune@northwestern.edu, Northwestern University; Giné: xgine@worldbank.org World Bank; Karlan: Karlan@kellogg.northwestern.edu, Northwestern University. Thanks to Diego Eslava, Javier Madrazo, David</u> McDevitt, Nathan Haddon, Pablo Olmos, Anna Segura, Ana Serrano, Guillermo Tovar and Jesús Villero for excellent research management and assistance. Thanks to seminar participants at the Asian Development Bank, Atteneo de Manila University, Carnegie Mellon University, Columbia University, DIW Finance and Development Workshop, DIW Productivity and Development Workshop, Haverford College, University of Göttingen, University of Pittsburgh and UW-Milwaukee for helpful comments. We thank Kiva and Google.org for funding. We maintained intellectual freedom to report and interpret the results. We thank the management and staff of Fundación Mario Santo Domingo for their exceptional cooperation. Registered in the American Economic Association Registry for randomized controlled trials (AEARCTR-0001123). Institutional Review Board approval for human subjects research from Innovations for Poverty Action #14181. The views expressed are those of the authors and should not be attributed to the World Bank, its executive directors, or the countries they represent.