rafmacalaba/datause-encoder-data huggingface dataset:
**Policy, Research,'and External Affairs**

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*  _S  1<br>tEducaion  and Employment<br>WVPS 525<br><!-- End of picture text -->

- This paper a product of the Education and Employment Division, Population and Human Resources - Department is part of a larger effort in PRE to understand differences in educational effectiveness. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cynthia Cristobal, room S6-033, extension 33640 (26 pages).

Lockheed and Bruns use a multilevel modeling procedure to explore (I) the percentage of variance in secondary school achievement in Brazil that could be attributed to the types of school attended, (2) differences between schools in students' achievement in mathematics and Portuguese, and (3) differences between schools in reducing achievement differences based on students' socioeconomic status.

Students in federal technical schools outperformed students in general secondary, SENAI,* and teacher training schools in both mathematics and Portuguese, after holding constant for gender, age, family size, and the number of hours the student spent working. This could reflect differences in students' entry-level performance as admission to federal technical schools in Brazil is highly selective.

other schools, Lockheed and Bruns explored variations in their organization, quality, and social composition.

Factors significantly related to average mathematics achievement were class size (achievement was higher in larger classes) and the number of hours math was taught (the more time, the higher average achievement), and the school's average student socioeconomic status (family sociai O.Aass background), suggesting that student selection into the schools accounted for much of the observed difference.

Factors significantly related to average achievement In Portuguese were the school's organizational complexity, the ,.verage sociocconomic status, and the average number of hours students spent working.

For mathematics only, students in private schools outperforned those in public schools.

To explain why students in federal technical and private schools outperformed students in Performance was not different for schools paying higher salaries, day schools, high-fee schools, or schools where teachers attended university.

*SENAI secondary schools are financed by the federal govcrnmerit but administered by the National Confederation of Industry (a private association of industrial employers).

The PRE Working Paper Series disseminates the findings of work under way in the Bank's Policy, Research, and Extemal AffairsComplex. Anobjectiveoftheseries is to getthcse findingsoutquickly, even if presentations are less than fullypolished. The findings, interpretations, and conclusions in these papers do not necessariiy represent official Bank policy.

Produced by **the** PRE Dissemination Center
|**Category: Other control**<br>**variables**|**Personal**<br>**information**|||
|---|---|---|---|
|Gender|Wp1219||1 Male<br>2 Female|
|Age<br>Marital children = Computed<br>from marital status and number of<br>children|Wp1220<br>Marital_children|Combination to Wp 1223 and Wp<br>1230|Until 99<br>1 Married with<br>children<br>2 Married without<br>children<br>3 Single with children<br>4 Single without<br>children<br>5 S/D/W with<br>children<br>6 S/D/W without<br>children|
|Marital status<br>(Index construction)|Wp1223|What is your current marital<br>status?|<br>1 Single/never been<br>married<br>2 Married<br>|
||||3 Separated/|
||||<br>divorced/ widowed|
|Number of children<br>(Index construction)|Wp1230|How many children under 15<br>years of age are now living in your<br>household?||
|Religion|religion||1 Muslim<br>2 Non-Muslim/other|
|Migration status|Wp4657|Were you born in this country, or<br>not?|religion<br>1 Born in this country<br>2 Born in another<br>country|
|Level of education|wp3117||<br>1 Completed<br>elementary education<br>or less<br>2 Secondary - 3 year<br>tertiary secondary<br>3 Completed four<br>years of education<br>beyond high school<br>and/or received a 4-<br>year college degree|
|Employment status|emp_2010||<br>1 Employed full time<br>for an employer/<br>Employed part time/<br>do not want full time<br>2 Employed full time<br>for self<br>3 Unemployed<br>4 Out of workforce 5<br>Underemployed<br>6 Other|
|Government employee|Wp1227|Are you a government worker or<br>not?|1 Other<br>2 Yes<br>3 Undetermined|
|Household composition Adults|Wp12|Including yourself, how many<br>people who are residents of age 15<br>or over currently live in this<br>household?|<br>1 One<br>2 Two<br>3 More than two|
|Household income (US$,<br>thousands)<br>Month andyear of Interview|inc_001<br>m_year||Expressed in<br>international dollars|

**Appendix B2: Descriptive Statistics**

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# **Thriving beyond Borders? Understanding Refugee Children’s Life Outcomes**<sup>*</sup>

Tatiana Hiller<sup>†</sup> Andr ́es Moya<sup>‡</sup> Sandra V. Rozo<sup>§</sup>

**JEL Classification:** F22, J13, O15 **Keywords:** Refugees, Forced Displacement, Children.

*We are grateful for comments received by seminar participants at Universidad de los Andes, the InterAmerican Development Bank Research Department Governance and Development Seminar, LAPOP Learning Summit on Migration, and the Central Colombian Bank 100 Year Anniversary Summit, and the Workshop on Economic and Social Integration of Immigrants and Refugees. We are grateful to Juanita Ruiz, Magdalena Cortina, Mar ́ıa Alejandra Franco, Alonso Hern ́andez, and Peter Grinde-Hollevik for their excellent support as research assistants. We also thank iQuartil for data collection and Mara Minski for her support training enumerators in Peabody scale proficiency. This project was approved by IRB protocol from Universidad de los Andes (Acta 1603). We thank the Hilton Foundation and the Knowledge for Change Program of the World Bank for providing financial support for this project. The authors have no conflicts of interest to report. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †University of California Davis, E-mail: hiller@ucdavis.edu

> ‡Universidad de los Andes. E-mail: a.moya@uniandes.edu.co

> §Dev. Research Group, World Bank. Corresponding author, E-mail: sandrarozo@worldbank.org
||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES||Weight-for-|age z-score||
||(0.073)|(0.073)|(0.073)|(0.073)|
|Improved water|-0.103**|-0.105**|-0.116***|-0.109**|
||(0.042)|(0.042)|(0.043)|(0.042)|
|Improved sanitation|0.046**|0.049**|0.044**|0.046**|
||(0.021)|(0.021)|(0.021)|(0.021)|
|Currently breastfeeding|<br>-0.221***|<br>-0.220***|<br>-0.220***|<br>-0.222***|
||(0.025)|(0.025)|(0.025)|(0.025)|
|Wealth index 2|<br>0.071**|<br>0.085***|<br>0.074***|<br>0.076***|
||(0.028)|(0.027)|(0.028)|(0.027)|
|Wealth index 3|0.117***|0.141***|0.124***|0.127***|
||(0.029)|(0.028)|(0.029)|(0.028)|
|Wealth index 4|0.172***|0.208***|0.178***|0.185***|
||(0.032)|(0.031)|(0.032)|(0.031)|
|Wealth index 5 (Wealthiest)|0.365***|0.417***|0.371***|0.379***|
||(0.041)|(0.039)|(0.040)|(0.039)|
|Rural|-0.054|-0.056|-0.046|-0.047|
||(0.039)|(0.039)|(0.039)|(0.039)|
|Rajshahi|0.019|0.026|0.021|0.025|
||(0.033)|(0.033)|(0.033)|(0.033)|
|Khulna|0.161***|0.159***|0.151***|0.157***|
||(0.041)|(0.041)|(0.041)|(0.041)|
|Barisal|0.008|0.005|-0.000|0.002|
||(0.042)|(0.042)|(0.041)|(0.041)|
|Dhaka|0.100***|0.110***|0.091***|0.099***|
||(0.030)|(0.030)|(0.030)|(0.030)|
|Sylhet|-0.137***|-0.135***|-0.139***|-0.135***|
||(0.037)|(0.037)|(0.037)|(0.037)|
|Chittagong|0.057*|0.063*|0.056*|0.053|
||(0.033)|(0.033)|(0.033)|(0.033)|
|Season 2|-0.079***|-0.075***|-0.087***|-0.090***|
||(0.022)|(0.022)|(0.022)|(0.022)|
|Season 3|-0.027|-0.029|-0.026|-0.024|
||(0.023)|(0.023)|(0.023)|(0.023)|
|Constant|<br>-0.242**|<br>-0.346***|<br>-0.228**|<br>-0.237**|
||(0.099)|(0.103)|(0.099)|(0.099)|
|Observations|10,144|10,144|10,144|10,144|
|R-squared|0.233|0.233|0.233|0.233|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 HFIAS Score is household food insecurity access scale, child DDC is the dietary diversity score constructed for each child, FCS is the food consumption score and MDDS is the mother’s dietary diversity score.

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normal linear model tends to overestimate the true value more than the empirical errors model.<sup>15</sup> We can also observe from Table 2.4 that the estimated R<sup>2</sup> of the model specifications tends to decline and also stabilize as the number of observations increases, which is consistent with the well-known statistical result that estimates for R<sup>2</sup> in smaller samples may be larger than their population counterparts (see, e.g., Pituch and Stevens, 2016). In essence, good estimates can also be obtained with very small samples but samples of medium size, around 1,000 observations in our case, seem to offer reasonably stable estimates while containing survey costs. This sample size is also consistent with the theoretical results offered in Park and Dudycha (1974).

These results have practical relevance. The HV data used in this study were collected with field visits that covered about 5,000 households per month, or 60,000 households per year. We have shown that covering about one-sixtieth of this number, or 1,000 households per year, may be sufficient to provide reliable poverty statistics.<sup>16</sup>

# **IV.2. Related Measures of Poverty**

How does our proposed poverty imputation method compare with alternative estimation methods such as asset (wealth) indexes and proxy-means tests? We examine in this section each of these two alternatives, together with the related exercise of targeting. This is a particularly important question for the UNHCR, which uses asset indexes to measure well-being in place of consumption in many places where consumption is not available. Other development organizations

> 15 Note that we are only considering a single summary statistics for the whole population (the poverty rate). If we were to estimate disaggregated statistics by geographical areas or population groups for example, sample sizes would have to be reconsidered.

> 16 This result should not be interpreted as suggesting that 1,000 observations are sufficient for a multi-purpose survey. In our case, we estimate this number to be sufficient to estimate one statistic (the poverty rate) whereas most surveys have typically multiple objectives and require the correct estimation of multiple statistics. The latter are the reasons behind common tasks associated with designing a survey such as power calculations, stratification, and clustering of the sample.

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# A New Distribution Sensitive Index for Measuring Welfare, Poverty, and Inequality

Aart Kraay<sup>1</sup> `r` Christoph Lakner<sup>1</sup> `r` Berk Özler<sup>1</sup> `r` Benoit Decerf<sup>1</sup> `r` Dean Jolliffe<sup>1</sup> `r` Olivier Sterck<sup>2</sup> `r` and Nishant Yonzan<sup>1</sup> _Keywords_ : Welfare Index, Poverty Index, Inequality Index, Shared Prosperity _JEL Classifications_ : D63, I32 1 World Bank; 2 University of Oxford and University of Antwerp. We thank Matthew Adler, Benu Bidani, Francisco Ferreira, Laurence Chandy, Gaurav Datt, Maya Eden, Garance Genicot, Peter Lanjouw, Max Lawson, Luis-Felipe Lopez-Calvo, Daniel Mahler, Roy Van der Weide, and seminar participants at the World Bank’s Poverty and Equity Global Practice and the Normative Economics and Economic Policy e-seminar for helpful discussions and feedback. The names of the authors are in random order, using the author randomization tool of the American Economic Association, indicated by “ `r` ” in the author list and archived here. We acknowledge financial support from the UK government through the Data and Evidence for Tackling Extreme Poverty (DEEP) Research Programme. The views expressed here are the authors’, and do not necessarily reflect the official views of the World Bank, its Executive Directors, or the countries they represent. A verified reproducibility package for this paper is available <u>here. Stata and R scripts for implementing the new welfare, poverty and inequality indexes introduced in</u> this paper are available at the end of this blog.
generally positive and not insubstantial. For inequality figures, the results are generally weaker. Because the signal-to-noise ratio is lower in these inequality estimates, it is particularly important to take into account error in the estimates when applying them to research or policy applications.

The ability to provide confidence intervals is a crucial advantage to the ELL method as compared with alternative approaches to welfare mapping. In the analysis presented here, it was found that alternative simulation methods do influence the size of the estimated standard errors on welfare estimates. The numerical gradient approach, originally proposed in ELL(2002) was found to produce satisfactory standard errors, and similarly for the non-parametric simulation procedure outlined in Appendix 1. However, the parametric bootstrapping procedure described in Section 3 was found to yield standard errors that are somewhat understated. It is not entirely clear why this latter procedure should suffer from this propensity, and further research is needed to resolve this concern. An important objective of this analysis has been to document how important it is, when applying small-area estimation methods, to think hard about possible unobserved, community-level, factors that may influence welfare outcomes. Experience with “poverty mapping” in a large number of countries indicates that inclusion of censusmeans as regressors in the underlying consumption model (and/or the inclusion of household variables that capture “network” effects, or of additional community-level variables from tertiary datasets such as administrative and GIS data) can go a long way towards helping to secure specifications in which unobserved location effects are kept small. The analysis here has shown that failure to capture such location effects in this way can lead to markedly higher standard errors and also an increase in bias.

It is important to recognize the limitations of the analysis in this paper. The data used here are less well-suited to poverty mapping than those usually employed. First, the expenditure aggregate used is less comprehensive than that found in a typical developing country survey, and the general quality of the data may be worse than, for example, data collected in a World Bank LSMS survey. This reduces the potential for variation in expenditure to be explained by observed variables. Second, the data all come from poor households in rural Mexico. Consequently, there is relatively little variation in 19
classmates, or where peers had negative returns prior to the course, have the highest returns in their first year of trading–they outperform new investors from other courses and experienced students from their own classroom. Overall, the results are consistent with the hypothesis that selective communication encourages more market entry among uninformed investors.

To directly test how communication in the classroom is related to market entry and performance, we asked former students if they had any discussions about stock trading with classmates during the course.<sup>8</sup> Using the sample of survey respondents, we confirm our main findings. Students that report investment conversations with classmates in courses where peers had positive returns are more likely to start trading, but they underperform in their first year after the course.

A potential explanation for low returns among new investors might correspond to mean reversion in stock prices.<sup>9</sup> For example, if individuals systematically buy the best-performing stocks of their peers and large price increases precede reversals, new investors would display poor performance. To examine this possibility, we use the sample of market entrants and study the relation between an individual’s stock selection and the stock selection decision made by experienced classmates. We document a strong positive relation between the stock purchases of new investors and purchases of experienced students after the course. As new investors try to look for investment opportunities, we show that instead of following past trades from classmates or stocks with high returns in the peers’ portfolio prior to the class, they seem to select stocks in which peers are making new purchases.

Peer effects in stock selection have been widely documented in multiple social environments and are not surprising in our setting, but our evidence sheds new light on the role of information transmission bias. The mechanism is summarized as follows. Recent positive returns from experienced classmates attract new investors. Such outcomes, however, are the result of portfolios with high idiosyncratic volatility. Due

> 8 Details of the electronic survey are presented in Section 3.

> 9 Mean reversion refers to the tendency of asset prices to return to a trend path. Although the existence of mean reversion in stock prices is subject to much controversy, there is some evidence of mean reversion in stock prices in US and international markets (Poterba & Summers, 1988; Chaudhuri & Wu, 2003; Fama & French, 1988). Campbell, et al. (1997) summarize the debate concisely.

6
population density across a state, the ecological inference issue is alleviated as we directly test the propensity of any population group to experience a conflict.

Through disaggregation, we may succeed in supporting explanations based on variables such as the distance from the capital and the overall size of the country's population if we know at which locations conflicts occur. If conflicts are located mainly at some distance from countries' capitals, we might infer that large countries have more conflicts because of the difficulties of projecting governmental power. If they are located in population concentrations irrespective of location relative to the capita, other explanations should be sought.

The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis. The dataset currently codes the location of all reported conflict events in 14 countries in Central Africa in the 1960–2004 period. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper suggests some adaptions to a statistical method to allow for analyzing data at this level of analysis.

Related to the size of populations is their distributions. The Democratic Republic of Congo, for instance, is not only characterized by being enormously large, but also shows tremendous variation in population densities. The capital is also located in the far west of the country, and there is a large population concentration in the far east. The disaggregated research design also allows exploring how such patterns of population dispersion – and the resultant political geography of states – affect the risk of conflict events.

# **2 Population, Geography and Conflict: Location-Specific Factors**

We will look into three groups of size-related explanations of civil war. The first simply posits that every citizen has a constant and uniform propensity for becoming involved in a rebellion. The second relates to heterogeneity and military constraints posed by distance. The third set of explanations highlights the importance of population concentrations.

3
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# **Retail Distribution of Government Securities— Aiding the Development of Public Debt Markets**

Programs that support the primary distribution of government securities are crucial for effective public debt management. Many emerging markets face serious challenges in developing such channels. In many of these countries the distribution of government securities is highly concentrated, partly because of policies to establish primary dealer systems and partly because economies are small and financial systems underdeveloped. Complicating matters, some distribution networks are dominated by a few local banks that simply buy and hold these securities.

In the face of such constraints, programs for the direct retail distribution of government securities can help emerging markets develop their public debt markets.<sup>6</sup> The Philippines introduced such a program, the Small Investors Program, in 1998. Though suspended in 2001, the program was recently revived as the Expanded Small Investors Program. Such programs, and the issues they raise and respond to, trigger several questions:

- What are the main goals of the Philippine program, and how do they fit into the country’s overall debt management strategy? Moreover, are all the goals achievable?

- How can technology enable the retail distribution of government debt securities? What risks are involved, and can they be mitigated? Does the design ensure sufficient electronic security?

- What are the prerequisites for success in designing and implementing this type of program and the related technology?

- Who are the main players in such a program? How are public-private partnerships arranged? What are the incentives for each player? What are the break-even points—especially for a treasury— given the investments required and the different sources of savings and revenues?

- What criteria should be used to gauge the success of such programs in economies with limited retail investors and few distribution channels (often just banks)?

This study is aimed primarily at government authorities in the Philippines (mainly the Ministry of Finance and its Bureau of Treasury, both as the issuer of sovereign debt and as a regulator and policymaker) and elsewhere, as well as members of the development finance community interested in learning how technology can improve management of government debt and increase public access to government securities.

As with any analysis of this type, some important provisions should be kept in mind. First, the Expanded Small Investors Program was just being launched when this study was conducted, so the analysis had to rely on data from the original program and tests of the new one. Second, more research is needed on the characteristics of the small investors participating in the program. Such research was beyond the scope of this study.

> 6 By contrast, in industrial countries such as Canada and the United States the main motivations for such systems are to lower the costs of distributing small-value securities and to make it more convenient for investors to buy them, thereby increasing savings.

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