rafmacalaba/data-use-mentions-extended huggingface dataset:
completed or more but no college|0.42|0.52|0.42|0.51|0.25|
|=1 if not completed high school|0.09|0.17|0.03|0.09|0.05|
|Proportion Single Mothers||||||
|=1 if single-mother household|0.19|0.25|0.14|0.25|0.12|

Note: Table reports definitions and descriptive statistics of the estimation sample’s main variables. Sample extracted from the second wave of ECLS-B for children born in 2001.

# **C.2.3 Transfers**

As described in Section 4.1.2, we assign non-labor income equal to zero to two-parent households and equal to $41 a day to single mothers. This amount is calibrated to match the poverty line in 2003; the transfer is sufficient to allow single mothers to consume and purchase center-based care.<sup>43</sup> Of course, in reality two-parent households might also receive transfers, and transfers might not be homogeneous among households. We have chosen this simplified transfer rule because transfer amounts are not reported in our data, and the simplification reduces household-level heterogeneity thereby lowering computational burdens.

> 43We have tried alternative transfer amounts, including values extracted from the literature (Fang and Keane, 2004; Hoynes and Schanzenbach, 2018), values proportional to the minimum wage, and values proportional to the poverty line. Calibrating the transfer to the 2003 poverty line provides the best fit.

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It is important to note that the PD survey does not cover all countries in any region. Therefore, regional performance as overviewed in section four is simply based on countries where data is available.

# **(2) Methodology**

The results of the PD survey cannot be directly used to compare across indicators. The indicators are measured using different methodology and their targets are arbitrarily decided. For example, indicator 5a on ̳use of country public financial management (PFM) systems is measured as the share of assistance using country PFM systems out of total assistance (%), while indicator 6 on ̳strengthen capacity by avoiding parallel project implementation unit (PIUs)‘ is measured by the number of parallel PIUs. Indicator targets are set up in different ways without clear rationales. In other words, measuring performance against targets is not a sensible way to conduct comparison across indicators.

Therefore, in order to enable us to conduct such a comparison across indicators, standardized scores of the PD survey results are calculated using the following formula.

Standardized scores follow a normal distribution with a mean of _zero_ and a standard deviation of _one_ . Standardized scores show how many standard deviations the value is away from the mean. For example, when a standardized score of an indicator is 1.0, this means that the indicator is away from the mean by 1.0 standard deviation and the probability of exceeding 1.0 is 15.9 percent.

The analysis using the standard deviation has two advantages. One is that it enables us to calculate a composite global aid effectiveness score for both partner countries and development partners. The other is that the change between 2005 and 2010 can be also standardized to compare the improvement in performance between these periods.

While the standardization of partner country data is simple as data is available for all indicators in all partner countries, that of development partners needs some adjustments as not all development partners are active in all partner countries. For example, the Asian Development Bank is not operating in Africa and
cents. The universe of blocks (“manzanas”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To ensure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process.<sup>8</sup> As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children’s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communities, which account for 80% of refugees worldwide (Climate Center 2022). We address these all available information on Venezuelan settlements across difficulties, leveraging the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit organization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically.<sup>9</sup> Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found. This led to the of a list process compilation containing approximately 7,000 Venezuelan families with children and adolescents in Medell ́ın. From this sampling frame, families were randomly chosen to participate in our study, ensuring representation across different age, gender, and socioeconomic strata.

_Sample size._ The study covered 2,556 households, gathering information from a total of 3,169 children and adolescents, with 44% being Venezuelan migrants. The sample comprised 834 Colombian children between the ages of 5 and 10, and 944 adolescents aged 11

> 8After identifying the sampling blocks, the field team followed a protocol that (i) randomly identified the first house which they should
# **3. Description of the Program**

Colombia Mayor was launched in 2003 as the Programa de Protección Social para el Adulto Mayor (PPSAM). The aim was to provide basic subsistence through noncontributory pension benefits to elderly people who had no pension income and were living in extreme poverty. In 2010, program administration was outsourced to the privately run Consorcio Colombia Mayor, which reports to the Ministry of Labor. The purpose of the change was to accelerate rollout among all elderly who were living in conditions of extreme poverty. The amount of the transfer is adjusted annually based on budgetary considerations, but it can never exceed half the value of the minimum wage. The program also provides a number of indirect nonmonetary benefits that are supplied through specially established centers catering to the needs of the elderly population and administered and cofinanced by the municipalities.

There are three main program eligibility criteria. The first is that the beneficiary must not be within three years of reaching the official retirement age. During the period under study, this meant a maximum age of eligibility for the benefit of 52 years among women and 57 years among men. (These ages were subsequently raised by two years.) The second criterion establishes that the beneficiary’s household must not score above a certain threshold in Colombia’s system for the identification of potential social program beneficiaries (in the Spanish acronym, Sisben). The Sisben score represents an estimate of the living conditions of households registered in the system. It is based on information on households collected through a system survey, including the quality of the dwelling, the number of dependents, disability status, income, ownership of durable goods, and so on. The Sisben score is used to determine the eligibility for all the country’s social programs at the national level. It has gone through two mayor modifications, such that the current, third, version is usually referred to as Sisben III. Each program is associated with a unique maximum score to identify beneficiaries. The maximum scores usually differ
use confidential administrative tax records from Chile’s tax authority (Servicio de Impuestos Internos). These data contain monthly, firm-level information, including sales, materials expenditure, value added, number of workers, wage bill, net worth, age, industry, and municipality. They allow us to construct measures of pre-pandemic firm attributes (such as productivity, measured as value added per worker) as well as firm performance during the pandemic, using monthly sales during 2020.

Third, we work with publicly available firm-level data on firms that use the employment program. The employment authority (Direcci ́on del Trabajo) publishes these data monthly, containing the dates that each firm uses the program and the number of workers in each firm

> 22In Appendix Figure 1, we plot the evolution of the number of firms using each program, distinguishing between SMEs and non-SMEs. In both programs, SMEs make up the vast majority of participants, implying that the discrepancy in the dollar value of both programs reflects a strategic policy decision to scale up the credit program, not differences in firm size across programs.

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decision was made to exclude the 2000 Panama survey to minimize location re-coding. From 2001 onwards, Panama’s administrative units remained stable up to 2017. El Salvador was also excluded from the analysis because of potential issues related to the switch in currencies in the early 2000s. Choosing 2005 as the initial period did not help because this resulted in a substantial decline in the number of surveyed locations available in 2005 and at the end of the 2010s, which biases the estimation of the location premia. Appendix Table A2 summarizes information on data sources, level of geographic detail, and time coverage.

The income gap analyses employ only household survey data, which, unlike the census data, extend all the way to the end of the 2010s. In most countries, we are able to match the first and last periods in the income gap analyses with the first and last periods in the estimation of location premia. By linking the two types of analyses, we are able to perform consistency check (7).

We deflate labor earnings to address price variations across time and space and to ensure that the results are comparable both within and across countries. First, we convert labor income into constant 2011 US$ PPP. We then adjust the converted income to reflect differences across subnational regions, and whenever possible different types of areas within subnational regions (i.e., rural versus urban). Appendix Table A3 provides information on the data used to calculate the deflators.<sup>13</sup> Descriptive

> 11 See https://www.cedlas.econo.unlp.edu.ar/wp/en/estadisticas/sedlac/

> 12 Appendix Table A5 provides information on the year and sample size by country. In the case of Argentina, the surveys cover only urban areas so the categories are reduced to all urban residents, the bottom 40% of urban residents, and skilled urban residents. In the case of Uruguay, the surveys cover primarily urban areas.

> 13 In the absence of
_Source_ : Own calculations using WBSCPS, administrative data, and households’ surveys. The administrative data sources are the following: Annual Reports of the Social Security Institution ( _Relaҁão Anual de Informaҁões Sociais,_ RAIS) for Brazil for 2017, Social Security and Taxes database for 2019-2020 for Ecuador, _Observatorio Laboral para la Educacion (_ OLE) for Colombia for 2016, and _Ponte en Carrera_ for Peru for 2018. The household surveys are the original (unharmonized) surveys from these sources: Brazil: PNADC ( _Pesquisa Nacional por Amosta de Domicílios Contínua_ ) for 2018; Colombia: GEIH ( _Gran Encuesta Integrada de Hogares_ ) for 2018; Ecuador: ENEMDU ( _Encuesta Nacional Empleo, Desempleo y Subempleo_ ) for 2018; Peru: ENAHO ( _Encuesta Nacional de Hogares sobre Condiciones de Vida y Pobreza_ ) for 2017.

_Notes_ : This table presents a validation exercise for the outcomes reported in the survey data. Graduation, dropout, time to degree, and employment are expressed as percentages. * indicates that wages are expressed in annual USD PPP (2017) for Brazil when using administrative data (column 3). Column 1 presents outcome averages as reported by program directors in the WBSCPS; means are weighted by the WBSCPS sampling weights (see definitions of outcomes from survey data in Appendix 1). In column (2), we impute a formal employment rate based on the following survey questions: "Regarding the graduates of the program in recent years, how many were employed by a firm in the formal sector?" and "Regarding the graduates of the program in recent years, how many were self-employed in the formal sector?" Directors had to choose among three possible answers: almost all, some; almost none or none. We assume the following formal employment rates for those answers: 80, 40, and 10% respectively. Column (2) shows the resulting formal employment rate imputation. In column (3) we show average outcomes calculated using individual-level administrative data; wages refer to individuals working in the formal sector
1 2 3<br>Log GDP per capita, PPP (constant 2017) Gini index<br>Latin America Europe and U.S. Asia and Australia Linear fit<br>Share of workers who are<br>self-employed without employees<br>Share of workers who are<br>in businesses with up to 10 workers<br>Share of workers who are<br>in businesses between 11-50 workers<br>Share of workers who are<br>in businesses with more than 50 workers<br><!-- End of picture text -->

**Note** : Both variables in each graph have been standardized. Each dot represents a country in the sample. Japan and Nepal are missing in the right-hand panels because their Gini indices are unavailable in the WDI. Argentina, Costa Rica, Japan, India, and Nepal are missing in the bottom two panels because of the lack of separate reports for the 50+ category. The coefficient below each panel corresponds to the linear regression coefficient of the y- axis variable on the x-axis variable. **Sources** : Data on Gini and GDP per capita are from World Development Indicators (World Bank, 2023). GDP per capita and Gini Indices are the last publicly available for each country between 2016 and 2019.

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19 Although all the numbers in the right column of Table 7 suggest a low rate of stability of the groups participating in ATP 1, this can be explained by the relatively early stage of the program (second year), and by the inappropriate procedure for selecting the farmers in the various groups. However, the high level of farmer and group substitution in ATP I is associated with the high transaction costs of establishing new groups and re-assigning of new farmers to existing groups. These transaction costs are to be borne by the extensionists who assemble the groups. These factors should be taken into account when analyzing ATP l's performance.

Table 7: Continuation rate of groups participating in ATP 1 between 1995 and 1996.

|**Region**|**Number of Groups**|**Groups Continuing**|**% of Groups Continu-**|
|---|---|---|---|
|Al|43|8|18.6|
|A2|101|22|21.8|
|B3|94|35|37.2|
|B5|32|3|9.3|
|C6|41|2|4.8|
|Total|311|69|22.2|

Source: Garcia (1996) Recent data for seven ATPI extension-providing companies allows a three-year perspective on the stability of producer-participation (INTA, 1998). Of the total 15,587 producers contracted for 1997/98, 26% are three year veterans, 39% are two years veterans, and 35% are new participants in the program. These figures indicate a better long-term stability of producer participation. Indeed, such analysis should be done, using long-term and aggregated data in order to provide sensible results.

In calculating the cost of extension provision in ATP 1, we use actual data available for technical assistance programs in INTA (Estrada-Rizo and Garcia, 1997). These estimates are presented in Table 8
variation from national repatriation inflows, and ii) municipal variation on birth municipality of individuals repatriated in the period 1995-2002. Based on our 2010-2018 sample, we document that deported individuals overwhelmingly go back to their birth municipalities. Individuals who were repatriated between 1995 and 2002 came back to El Salvador before the United States drastically strengthened and changed its migration enforcement in response to the terrorist attacks of 2001. The changes in the United States’ migration policies not only included a sudden increment in the number of repatriations, they also modified the strategies of deportations.<sup>2</sup> We document that repatriation inflows have a negative impact on average wages in the formal sector. An increase of 0.04 percentage point in the inflow of returnees reduces the average monthly salary paid by formal firms by approximately 9.4 percent. These effects are predominantly driven by men’s salaries. The wage impacts are larger among formal firms in sectors that face more intense competition from informal ones. In the case of employment, while we do not find any aggregate impacts from repatriation inflows, the effects are negative and statistically significant among formal firms in sectors that are predominantly informal. When looking at other outcomes related to labor demand, such as firm entry and number of firms, we do not find any significant impacts. These findings are consistent with deportees representing a labor supply shock in the informal sector which tends to reduce wages, even among formal firms in typically informal sectors. At the same time, the reduction in formal employment among the latter is consistent with repatriation inflows inducing negative effects on formal firms and weakening their demand for labor as they face increased unfair competition from their informal peers, as documented by Rozo and Winkler (2019).

Our paper contributes to the growing literature that examines the effects of forced migration. The impacts of large international forced migration inflows in hosting countries have been largely centered around studying the impacts of forced migrants on the local workers;<sup>3</sup> with a smaller

>
# **IV. PCR Objectives and Design Parameters: A Policy Discussion**

As reviewed, the original motivations for the development of PCRs around the world have been many and varied. However, in many instances PCRs are now used for purposes going beyond their original objectives. In particular we argue below that irrespective of the original motivation, it is likely that an established PCR can be useful to bank regulators to analyze, more scientifically, banking regulations pertaining to capital and provisioning. However, we provide a broader policy discussion here on the often-competing objectives of PCRs and the implications and tensions in their design thus created. We focus the discussion on 5 basic objectives: (1) to improve credit-\ access, (2) to strengthen bank supervision, (3) to promote competition, (4) economic research to inform macroeconomic policy making, and (5) to improve bank regulation. The final objective is dealt with in somewhat more depth. A theme throughout is also the relation between private credit bureaus and a PCR.

# **_1. Improving access to credit_**

Perhaps surprisingly, according to World Bank survey results, two-thirds of the respondents (39/57) consider supervised financial institutions the primary users of PCR data, while bank supervisors were identified by only one-third (18/57) as the primary users. Moreover, more than three-quarters of supervisors (44/57), indicated that the PCR was the primary source of credit data for financial institutions – Miller (2003). There are several ways PCRs may enhance credit access. First, and as illustrated in the empirical results above, the use of a PCR may, for a constant level of risk, allow a financial institution to expand its loan portfolio. The marginal increase in the loan portfolio will particularly benefit those companies or individuals that have a sound repayment history but lack other obvious indicators of creditworthiness such as wealth or that lack guarantees.

The use of PCR data may then enable lenders to predict credit risk more
CLEMENS & TIONGSON quasi-experimental estimates with those we would have obtained with more common observational estimates.

The results show that migration by one household member causes large increases in remaining household members’ spending on health and education, quality of life, and durables, but no increases in savings. Migration causes substantial reductions in borrowing from family members outside the household. We find no significant effect on starting or investing in entrepreneurial activity or on labor suppy by other family members. Migration by the applicant causes children to be much more likely to attend private school and receive awards at school. Migration causes large changes in decisionmaking power between household members, but nonexperimental evidence suggests that most of the migration effect arises through the remittance mechanism rather than through changing household decision-making. We do find some evidence that migration affects home production technology, particularly in agriculture, by altering household composition. We find that widely-used nonexperimental estimators of migration effects can, in the same data, spuriously attribute causation to self-selection.

We begin by discussing the separate literatures on microeconomic effects of credit constraints and on household composition and decision-making, as well as the literature on the bundle of these effects that arises from migration. The next section derives a simple model of the migration effect and its mechanisms. The following sections discuss the natural experiment that created the policy discontinuity, and describe our new survey data. We then present evidence on reduced-form impacts of migration, and compare our quasi-experimental results to those obtained with more standard observational estimators. We conclude with a nonexperimental exploration of impact mechanisms, and a discussion of external validity.

# 2. Financial decisions in families split by work

Labor migration by one household member bundles different effects on the household. Of these, two of the most important have each been the subject of a separate literature: 2
attendance conditional on enrolment is very high (98.3%) since once enrolled children tend to attend classes, our approach consists of estimating the impact on average school attendance rates of all school age children, unconditional on their enrollment. This is a broad measure of school participation with direct policy relevance that accounts for the enrollment and attendance effects and is not confounded by changes in the share of the sample enrolled. We rely on a measure taken from school ledgers collected at the school level<sup>17</sup> : for each child, we compute the

> 16 A recent cash transfer program aimed at improving secondary school enrollment in Macedonia randomly gave conditional cash transfers to mothers or fathers, and evaluation results also found that specifically targeting mothers did not significantly improve school enrollment (Armand 2014).

> 17 We also collected parental self-reports for attendance but the recall period was the last two weeks and since some villages were surveyed after the end of the school year, we consider this measure to be less reliable.

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households with more educated members and greater ownership of assets are associated with higher per capita consumption.

|**Dependent variable:** Log consumption per capita|(1)<br>Rural|(1)<br>Urban|
|---|---|---|
|1-member household|0.74***|0.99***|
||(38.10)|(56.03)|
|2-member household|0.53***|0.64***|
||(46.90)|(47.17)|

> 22Figure 7 of the Appendix 2.2 shows the share of principal industry codes of households are also consistent across NSS-2011 and CPHS. Share of households with agriculture as the principal industry code are excluded in the graph for ease of representation: 39.1 percent of households in NSS-2011 and 33.1 percent of households (averaged across years) in CPHS belong to this category. In NSS-2011, principal industry code refers to the industry from which the households obtained their maximum income. In CPHS, we construct this variable based on the industry code of the household head. Households with missing principal industry code (due to head of household being unemployed or no member of the household being active in the labor market) are set to zero.

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