rafmacalaba/data-use-mentions-extended huggingface dataset:
excellent evaluations of PROGRESA have thus far not focused on the differential impact on indigenous peoples. Therefore, the objective of this paper is to analyze the impact of PROGRESA/OPORTUNIDADES on indigenous children’s progress in school and work activities. The determinants of schooling and work will be modeled according to Grootaert and Patrinos (1999).

We use the ENCASEH97, ENCEL99N and ENCEL00N household surveys.<sup>16</sup> These surveys are part of a round of surveys to evaluate PROGRESA.<sup>17</sup> It consists of a base survey and six consecutive surveys of the same household in a three-year period. This panel data is representative of the rural disadvantaged populations with 20 to 2,500 individuals in 7 states. It includes approximately 138,000 individuals in 26,000 households in 506 localities, with 320 as the treatment group and 186 as the control group. It includes micro-data on household characteristics, especially those that refer to education and health. It includes demographic characteristics of the household, with individual information for all family members.

In both years, population is separated into three groups: indigenous, Spanish and bilingual. Indigenous is defined as people that only speak an indigenous language but not Spanish. Bilingual is defined as the people that speak both an indigenous language and Spanish. Finally, Spanish stands for people that only speak Spanish but not an indigenous language. Children are defined as individuals between 8 and 16 years.

For the description of changes in incidence before and after the program, the groups are also divided into control and treatment groups. As explained in Behrman and Todd (1999), the process by which the treatment and control samples were collected is as follows. First, a subset of localities in Mexico was chosen to participate in the experiment. Randomization is being implemented at the locality rather than at the household level because PROGRESA benefits such as improvements in local schools and

> 16 ENCEL00N: Encuesta de Evaluación de los Hogares, November 2000; ENCEL99N: Encuesta de
richest group, a large number of students are beyond the theoretical age group of 18-23 years. In Panama, less than 20 percent of the bottom quintile of the age group is

|**Table 3: G**<br>**Ter**<br>|**ross Enrollm**<br>**tiary Educati**<br>|**ent Ratio**<br>**on**<br>|
|---|---|---|
|**Country**|**Circa 2001 **|**Circa 2009**|
|**Costa Rica**|39%|49%|
|**Panama**|50%|47%|
|**Guatemala**|16%|15%|
|**ElSalvador **|21%|26%|
|**Honduras**|16%|17%|
|**Nicaragua**<br>Source: Authors’ ca|26%<br>lculations based on|29%<br>several household|

<mark>Source: Authors’ calculations based on several household surveys circa 2001 and circa 2009</mark> enrolled in tertiary education, while for the top quintile, the enrollment ratio is close to 100 percent. In short, in both countries, the richest sections are not only likely to enter tertiary education, they also tend to stay longer than the theoretical duration of the program. For the four other countries in Central America, there is only a minimal representation of the poorest sections of the population at the tertiary level, and this percentage has hardly changed between 2000 and 2009.

The enrollment rate for the richest quintile has increased in all countries, with the sharpest increase in Costa Rica. Hence, although by one measure Costa Rica is more equitable as it is provides more opportunities to the poor than other Central American countries in
we show results by individual banking centers; an exercise we are not allowed to conduct with the restricted dataset due to confidentiality requirements.<sup>20</sup> As shown in Columns (7)-(10), the overall increase in haven deposits around aid disbursements is driven by accounts in Switzerland and Luxembourg while the responses in Belgium and Jersey (combined with Guernsey and Isle of Man) exhibit statistically insignificant changes. This is consistent with the notion that the increase in haven deposits around aid disbursements reflect diversion to secret private accounts. Throughout the period 1990-2010, Switzerland was a leading haven with some of the strictest bank secrecy rules in the world and a share of the global market for private wealth management of around 40% (Zucman, 2013; Zucman, 2017). There is evidence that as much as 90-95% of the wealth managed in Switzerland is hidden from the authorities in the owners’ home country (Alstadsæter et al., 2019).

> 19For the rest of the world (Column 2), the coverage of the publicly available deposit information is lower with 35% that cannot be allocated to individual banking centers.

> 20A limitation of this analysis is that the total deposits owned by small and relatively poor countries in small and relatively unimportant banking centers are not rarely zero, which translates into missing observations with our log-transformation of the dependent variable.

18
| POLICY RESEARCH WORKING PAPER 2029

# Summary findings

Dasgupta, Lucas, and Wheeler use new data from Brazil and Mexico to analyze relationships linking economic development, the size distribution of manufacturing plants, and exposure to industrial pollution. For lack of data, prior work in this field has been limited largely to water pollution and medium-size plants. This study examines air pollution and encompasses small plants (with 1 to 20 employees) as well as medium-size and large plants.

Four main questions are addressed (with answers from plant-level data): _* Are small plants more pollution-intensive than large facilities?_ Clearly, yes.

_* Are there proportionately more small plants in lowincome regions?_ The answer is yes, in thousands of Brazilian municipalities. Small plants dominate poor regions and are a relatively low source of employment in high-income areas.

* _Is industry more pollution-intensive in low-income regions?_ In Brazil, yes. For each municipality, the authors estimate the share of the six most pollution-intensive ("dirty") sectors in total industrial activity. They find that the dirty-sector share declines continuously with increases in municipality income per capita.

* Do _poor areas suffer more than wealthy areas from industrial air pollution?_ Paradoxically, no. The risk of mortality from industrial air pollution is much higher in the top two income deciles among Brazil's municipalities and the great majority of projected deaths is attributable to emissions from large plants. The scale of large-plant emissions dominates all other factors. Lower-income areas suffer much less from industrial air pollution in Brazil, despite the greater emissions-intensity of smaller plants and the prevalence of smaller plants in lower income areas.

- This paper-a product of Infrastructure and Environment, Development Research Group is part of a larger effort in the group to understand the economics of industrial pollution control in developing countries. The study was funded by the Bank's Research Support Budget under research project "Small Plants, Pollution, and Poverty" (RPO 682-66). Copies of this paper are available free from the World Bank, 1818
not strictly a CAMEL variable, size has usually been added in early warning studies as a proxy for "too big to fail" situations.'<sup>4</sup> In the specific case of the East Asian countries, following Kane (1998), we hypothesize that larger institutions are more likely to be subject to political intervention and links, 12 In particular, by using 1996-only data we avoid our estimations describing the behavior of supervisors, rather than the possibilities of impending distress. If we would have used later data, the estimations might have captured, for example, the fact that supervisors consider those financial institutions distressed which have low capitalization. At the same time, using 1996-only data is a strict criteria as for several countries the financial crisis was worse in 1998 than in 1997, and distress for some financial institutions only occurred in 1998. For that reasons, we could have used in some case also 1997 data. Also, the decision to close distressed financial institutions occurred largely in 1998, meaning we could have used 1997 data to analyze the importance of the financial condition of individual financial institutions at end-1997 in 1998 closure decisions. Furthermore, to account for differences across countries and periods, we could have added macroeconomic variables, such as GDP-declines, to explain the relative occurrence of distress. More generally, we could have estimated a cumulative distress (hazard) model, as is often done by bank supervisors in developed countries. On all these accounts, the use of 1996-only data biases our analysis against finding strong results.

**13** Definitions of non-performing loans varied widely across countries, and banks were given considerably leeway before the crisis in classifying loans. While all countries had capital adequacy requirements, the definitions of allowable sources of capital differed across countries.

**14** We would have like to use as well data on financial institutions' exposures to risky sectors (e.g., real estate or lending for securities markets) and possible maturity mismatches (foreign exchange, interest
19 prepared for industrial countries. However, the3e studies have been chiefly based on comparisons of several cross-sections, with no correction for sample selectivity biases in estimating the coverage of the MW had beer. done. Novetheless, this can be an extremely important factor in developing countries, where the absence of affected population in the sample is likely relatively more important than in industrial economies.

In this paper we have applied a statistical technique which is basically different from the traditional one used to analyze the effect of MWs across population groups. The statistical analysis presented in this paper reveals a significant underestimation when the sample selectivity bias is not considered in estimating a wage equation. In particular, we have shown the serious underestimation of the rate of return to human capital, which in turn may lead to originate misjudgments about investment prospects. We have used a sample in which we included both the total labor force and the seeking inactives. Even if the latter are excluded from the sample, a substantial selectivity bias is still apparent.

Our results also show that MWs are more binding for young workers and for the less educated, groups for which the employment probabilities are substantially notably affected. We have estimated that an increase in the MW level will more significantly affect both these groups than other groups, thus implying the existence of unequitable effects in terms of unemployment. More specifically, we have indicated that the probability of being affected by the MVW, and thus of being affected by its employment consequences, is highly and negatively correlated with the human capital stock.

The analysis performed indicates that the MW is a powerful tool having the impact of creating structural unemployment. The results do not imply that the MW is necessarily 'bad'. However, while more applied research is necessary
# **APPENDICES**

# **Appendix A. Tracking and Survey Instruments**

The sample for this long-term tracking exercise consisted of all individuals between 5 and 15 years old who were part of the 1,807 households sampled for the first LEAPS round in 2003 (5,865 respondents). We had last attempted to survey these respondents in 2011 before we started a tagging and tracking exercise in 2016. The 2011 survey was conducted at the household level, and we collected information about individual respondents through household rosters. In 2016, we were interested in tracking individuals.

The first step in the tagging and tracking process was to go back to the latest addresses we had for these respondents’ households. We could not locate the households of 285 respondents (Table A1). 79% of those we could not find belonged to households that had already attrited by 2011.

For the households we could find, we implemented the following protocol. If the target individual had migrated, we would collect their address and contact information. We attempted to survey all the respondents in person—by visiting them at their new address (for those who had migrated within the country) or by waiting for them to visit their relatives (for those who had migrated abroad). For any individual we were able to meet in person, we would administer five different instruments:

- Questionnaire

- Cognitive skills assessment (on paper)

- Adaptive Cognitive skills assessment (tablet-based)

- Socioemotional skills assessment (on paper)

- Socioemotional skills assessment (tablet-based)

We administered a shorter survey over the phone to individuals we could not meet in person. The phone-based survey was similar to the questionnaire administered in person but shorter. We could not conduct any cognitive or socioemotional skills assessment over the phone.<sup>24</sup> We are therefore missing skills information for all the individuals surveyed over the phone (Table A1).

Finally, we could not survey some respondents in person or over the phone. These respondents mainly fell
the 2010 round of Enterprise Surveys. However, these survival rates are high when compared with OECD averages, which could be explained in part by a low level of competition.<sup>3</sup> Firms the EMES sample also show surprisingly low employment growth rates since inception. Average annual employment growth in micro firms is only 5 percent (equivalent to one employee every four years for a 5-employee firm). Firms that started with more than 10 employees have added few workers and many have actually downsized, while firms which began with 11-50 workers have contracted by 2.4 percent per year. An important caveat is that the survey does not observe firms of more than 50 employees (14.7 percent of firms in these cities, according to the Economic Census), and also that for many firms downsizing brings efficiency gains that allow them to stay afloat, so negative growth in employment does not necessarily imply productivity loss.<sup>4</sup> Still, it is worrisome that employment growth is so low as it reflects the difficulties of these firms to expand the scale of their operations.

**Figure 3: Firm survival, age and growth**

<!-- Start of picture text --> A: Firm age B: New employees added per year, by size at start<br>20<br>15<br>10<br>5<br>0<br>-5<br>0 20 40 60 80 0 10 20 30 40 50<br>Edad de la empresa en años Firm employment at start, including owner(s)<br>Source: EMES<br>20<br>15<br>10<br>Por ciento<br>5<br>New employees added per year<br>0<br><!-- End of picture text --> The revenue generation capacity of sampled firms is also limited, with an average of US$55,000
participation rates for each age group and gender, remain the same in both scenarios. Hence, aggregated labor supply differs between the two scenarios only because different age groups (with different inclinations to participate in the labor force) have different population shares.

The contribution of migration to potential growth is calculated as the difference between actual potential growth and a counterfactual derived from an “no-migration” scenario. The counterfactual scenario is one in which population, population shares, fertility, and life expectancy indicators are estimated under zero international migration for each country, as calculated in the UN Population Statistics.

The results suggest that, in 2013-17, population aging has lowered global potential growth by 0.2 percentage point from its 2003-07 average. Advanced economies accounted for all of this decline. In EMDEs, with the exception of ECA, growing and younger working-age populations lifted potential growth marginally over the same period. Trends in female labor force participation benefited some EMDE regions’ potential growth while weighing on potential growth in others. Over the longer-term (1998-2017), higher female labor force participation contributed 0.3 percentage point to potential growth in LAC, while it did

> 13 Such thought experiments are widely used to assess the impact of demographics on growth or growth projections (Bloom, Canning, and Fink 2010; European Commission 2015).

9
the model’s results – for example, a finding that gains for unskilled labor are larger than those for skilled labor – should be reliable.

Major advantages of this type of model are that it represents the whole economy in a consistent and theoretically sound framework and that the structural features of the country investigated strongly influence the final results. Table 3 shows these features for Nicaragua in terms of sectoral shares of gross production, imports, exports and private demand; the middle panel details, for each sector, the U.S. weight in total trade; the right panel shows Nicaraguan tariffs against the U.S. and other partners and the U.S. tariffs against Nicaraguan products. For convenience, the bottom panel of the table reports measurements for aggregate macro sectors, although the model’s actual 28 sectors are shown in the top panel. In commenting on the results of the policy simulations, we will refer to data in this table.

The initial import protection, both in its level and sectoral variability, is among the key elements determining the outcome of the simulated trade reforms. Three key features are highlighted by the tariff data: 1) the overall trade-weighted protection rate is rather low, 2) its dispersion is high with a clear bias against agricultural imports, and 3) tariffs against the U.S. are generally above the trade-weighted average of tariffs against the Rest of the World.

Table 3 also shows that domestic Nicaraguan agricultural producers may be facing strong competition from U.S. imports, which are 41% of total imports of agricultural commodities. It is likely that a liberalization of U.S. imports, which reduces an anti-agricultural import bias, would lead to an increase of competition in the agricultural sectors with a potential initial negative shock for households strongly dependent on farming incomes. Clearly, the level of sector aggregation used in the model may exacerbate this potentially negative outcome. It may be that at finer sectoral levels, one finds that imports and domestic products are complements rather than
% in the exports of exposed firms within four quarters after the ban, even after accounting for seasonality. These stylized facts are robust to potential confounders arising from the COVID-19 shock and the broader economic crisis that unfolded in Sri Lanka.

While these stylized facts demonstrate the ban’s adverse impacts, they do not quantify its full GE effects. GE effects are less relevant in contexts where fertilizer availability changes only for a small subset of farmers or regions, but become critical when access is disrupted nationwide—as occurred during Sri Lanka’s fertilizer import ban but also during the Russia–Ukraine war and other geopolitical shocks. The second step of our analysis is thus to develop a novel quantitative GE model of trade, space, and agriculture with the goal of estimating average welfare losses and their distribution across regions and occupations, elucidating the relative importance of partial equilibrium (PE) and GE channels in driving observed effects, and conducting policy counterfactuals. Our flexible model features three sectors (agriculture, manufacturing, and fertilizer), three types of economic agents (landowning farmers, mobile workers, and immobile estate workers), multiple Sri Lankan districts, cross-crop heterogeneity in fertilizer intensity as well as geographic hetero-

> 6Furthermore, we find minimal evidence of substitution towards organic fertilizer: the organic share of fertilizer never surpassed 4% (for either fertilizer imports or agricultural use).

> 7This reduction was accompanied by a significant increase in rice imports, which were negligible prior to the ban. 8To account for local weather fluctuations, we combine granular weather data with novel pixel-level yield estimates constructed using a satellite-based remote-sensing methodology described in Ozdo ̆gan et al.<sup> ̈</sup> (2025).
# **1. Introduction**

The COVID-19 pandemic is testing global food and social protection systems at an unprecedented scale. The spread of the pandemic is disrupting food systems and undermining the food and nutrition security of households (Amjath-Babu et al., 2020; Barrett, 2020; Béné, 2020; Devereux et al., 2020; GAIN, 2020; Reardon et al., 2020b; Swinnen, 2020; Torero, 2020). There is concern that developing countries with poor health care and more limited social protection systems may be especially badly affected. Recent projections show that globally the pandemic is likely to push 88 million to 115 million people into extreme poverty in 2020 (World Bank, 2020), a third of these being from Sub-Saharan Africa. Globally, the pandemic is projected to double the number of people facing acute food insecurity by the end of 2020 (about 135 million people before the crisis) (WFP, 2020).

There are at least three ways in which the pandemic may affect household food security. First, individuals in some households may contract the virus and this will have both direct economic impacts such as loss of earnings and indirect effects through the need to meet medical costs. Even in the absence of direct contraction, fear of contracting the virus could reduce incomegenerating activities. Second, government restrictions on movement and gatherings aimed at slowing the spread of COVID-19 have disrupted livelihood activities, thereby reducing household incomes (Abay et al., 2020; Amare et al., 2020; Arndt et al., 2020; World Bank, 2020). Third, access to food has been affected by disruptions to markets and food value chains (e.g., Aggarwal et al., 2020; Hirvonen et al., 2020; Mahajan and Tomar, 2020). However, empirical evidence on the magnitude of the impact of the pandemic on household food security remains scant, partly because the pandemic is still unfolding and detailed household survey data are not available yet.

Within this context
<!-- Start of picture text --> Ethiopia Somalia South Sudan Sudan<br>Years of SchoolingSchool attendanceFood SecuirtyPregnancy carePhysical safetyEarly marriageWaste DisposalDrinking waterElectricityCooking fuel HousingSanitationUnemployment Legal idBank account<br>.03<br>.02<br>.01<br>0<br>Absolute contribution<br>-.01<br>-.02<br><!-- End of picture text --> Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Notes: A positive number means that the gender gap in that indicator is in favor of male-headed household (female-headed households are worse off), and a negative number means that the gap is in favor of female-headed households. The sum of all indicator gaps gives the overall gender gap.

Similar patterns are observed in Sudan, except that lack of legal ID does not contribute to the household level gender gap. In contrast, in Somalia, the gender gap that favors female-headed households is driven by the differential in education and living standard measures. In general, in countries where female-headed households are worse off, the gap in lack of legal ID, lack of bank account, early marriage, and physical safety are larger than the gap in other indicators. In Somalia where the gap is in favor of female-headed households, the gap in education is a much larger contributor to the gender gap, followed by gap in lack of legal ID.

As noted above, female-headed households are generally poorer than their male-headed counterparts in Ethiopia, South Sudan and Sudan. Existing literature shows heterogeneity among households headed by married women living with a husband or a nonresident _(de facto)_ and those headed by divorced, separated, and single women _(de jure)_ (Klasen et al., 2015; Milazzo and Walle, 2015). Thus, to explore heterogeneity of multidimensional