rafmacalaba/data-use-mentions-extended huggingface dataset:
4</sup> Each of the emerging market crisis episodes has also been previously identified as being a Phoenix Miracle, where there is a recovery in output while credit still stagnates. Developing countries are most likely to the meet the conditions for the existence of Phoenix miracles following 3S episodes as firms in those countries are more likely to depend on short-

> 2 Publicly listed firms are typically the largest firms in an economy and thus presumably driving the recovery process. While we are limited by the unavailability of detailed balance sheet on private firms across countries as in other cross-country micro studies, we are able to examine the recovery process for a sample of private firms in Argentina (one of the 3S episodes in our sample) and find that the pattern mirrors the public firm sample.

> 3 Micro-evidence of recovery from 3S episodes are also of independent interest as  studies on economy-wide production and productivity forecasts (Miron and Zeldes, 1989; Bartelsman and Wolf, 2009; Young, 2009) have shown that micro-level evidence has more information and less measurement error than the macro data.

> 4 Calvo, Izquierdo, and Mejia (2004) argue that global capital market turmoil acts as a coordinating factor external to the emerging markets in many of the sudden stop episodes. They show that sudden stops are not a common feature in developed economies (83% of depreciation episodes in developed markets over the 1990s were not accompanied by sudden stops) since developed countries are likely to remain open during currency crises.

3
consumable income is still estimated to be positive, with extreme poverty declining from 8.9 to 5.9 percent relative to the situation in 2014 (Table 7).

<!-- Start of picture text --> Figure 8. Progressivity and Marginal Contribution – Family 500+<br>1.20 0.045<br>0.040<br>1.00<br>0.035<br>0.80 0.030<br>0.025<br>0.60<br>0.020<br>0.40 0.015<br>0.010<br>0.20<br>0.005<br>0.00 0.000<br>Kakwani Coefficient<br>Redistributive effect (Gini points, right)<br>Reduction of extreme poverty (percentage points, right)<br>Source: World Bank estimates based on HBS 2014.<br>Kakwani Index<br>--------------> Progressive Marginal contribution<br><!-- End of picture text --> The cost of the program is expected to amount to PLN 25.7 million per year (1.5 percent of GDP). However, when we take into account the likely increase in VAT and excise tax collections (assuming that households will consume a similar basket of goods after the program is put in

> 18 It is ignored only for the eligibility for all income concepts presented later the reported income is utilized. The value per hectare considered is PLN 2,506 per annum.

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br>24.8<br>23.4<br>20.8 20.018.9<br>20 17.0 16.3<br>11.7<br>10.29.1 8.9 10.0<br>10 3.43.1 2.32.0 3.95.8 4.7 4.34.8<br>0.20.2 0.00.0 0.00.0 0.30.4<br>0<br>Bangladesh Bhutan India Rural India Urban Sri Lanka Maldives Nepal Pakistan<br><!-- End of picture text -->

> 33 Data on spatial deflation was conveniently available for South Asia. Figures for Bangladesh are based on 2005 PPPs, and spatial deflators for Bangladesh are based on district level poverty lines.

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the following statement? Vaccines are effectiveness.” on a 1-5 scale, with response options of “Strongly agree,” “Somewhat agree,” “Neither agree nor disagree,” “Somewhat disagree,” “Strongly disagree,” As with the measure of vaccine safety, we reverse-coded the responses, so higher values indicate stronger agreement, and responses of “Don’t know” and “Refused” were dropped. We aggregate perceived vaccine effectiveness to the country level by calculating the mean response within each country. We then center country-level vaccine safety perceptions by subtracting the median country-level trust measure from each country-level mean.

# **2.5. Meta-Analytic Approach**

To summarize findings from our research program, we conduct a meta-analysis using a randomeffects, two-stage individual participant data (IPD) meta-analytic approach (Riley et al., 2021). The random effects model allows for variation in study effects, appropriate for our context where operationalizations of independent variables and outcome measurements differ slightly across studies. Moreover, IPD enables us to fully leverage the individual level data collected, allowing us to explore study-level moderators that could explain variation in messaging effects across studies as well as heterogeneity in treatment effects that could be explained by individual-level characteristics. IPD metaanalyses are commonly used in contexts such as clinical research, where individual participant data is more readily available, offering advantages over the traditional meta-analytic approach that rely solely on aggregate statistics (Burke et al., 2016; Debray et al., 2015; Tierney et al., 2015). As noted earlier, the operationalization of message framings and the primary outcome were tailored to each study’s context. All analyses were conducted with Stata (version 18).

To estimate the treatment effects for the framing experiment, we use logistic regression with treatment arm as the exposure and a binary measure of vaccine intention as the outcome variable (1 = intend to vaccinate). In our analysis, we control for a range of individual-level covariates, including gender, age, education,
earners in the tax data is inflated due to the underreporting of income.** According to tax data, 27.5% of employees are minimum wage earners. According to the raw EU-SILC data, this share is 13.5%. Based on the imputation of tax income into the EU-SILC data, our results show that the share of minimum wage earners is significantly higher, 22.3%.

**Underreporting of tax income is an important challenge for public policy in Romania.** The discrepancy between tax data and survey data should be further investigated. Tax evasion weakens the fiscal capacity of the country. Under-reporting of income in the bottom half of the income distribution may result in an incorrect allocation of means-tested benefits and limit the efficiency of the social policy. The estimates of the share of minimum wage earners based on the tax data should be treated cautiously. Our results suggest that approximately one-quarter of the minimum wage earners receive envelope wages and earn more than the minimum wage.

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# Coding Bootcamps for Female Digital Employment Evidence from an RCT in Argentina and Colombia<sup>_∗_</sup>

Julian Aramburu, Ana Goicoechea<sup>_†‡_</sup> , Ahmed Mushfiq Mobarak<sup>§</sup>

**_JEL Codes_ :** C21, I26, J16, J21, J24, J48, O12.

**_Keywords_ :** STEM, Computer Science, Coding, Technology, Labor Market Policy, Bootcamps, Gender, Skills, Employment, COVID-19, Argentina, Colombia.

> _∗_ We thank The World Bank team –Denis Medvedev, Martha Licetti, Andrew Beath, Sebastian Hurtado-Cano, Sarah Lenoble, and Victor Mulas– for their support and contributions throughout the design and implementation of the program. We also thank Digital House and Bogotá Institute of Technology for implementing the bootcamps in Buenos Aires, Argentina and in Bogotá, Colombia, respectively, and Bogotá Chamber of Commerce (CCB), Invest in Bogotá, BICTIA, and Facebook for their partnership to implement the program in both cities. The authors gratefully acknowledge the financial contributions of infoDev Trust Fund donors and the World Bank’s Gender Umbrella Trust Fund. Isabel Miranda, Agustina Suaya, and Inés Taylor provided excellent field coordination assistance. We thank Berk Ozler, Alaka Holla, Nate Barker, Paula Calvo, Zara Contractor, Lucas Finamor and participants in the Yale Labor/Public Economics Prospectus Workshop for helpful comments. Julian Aramburu thanks The MacMillan Center for International and Area Studies at Yale for funding travel to conduct field work. This randomized control trial was registered in the American Economic Association RCT Registry (ID AEARCTR-0003850) and approved by Yale University Institutional Review Board (IRB ID 2000025881). The views expressed herein are those of the authors and do not necessarily reflect the views of The World Bank nor Yale University.

> _†_ Yale University, julian.aramburu@yale.edu.

> _‡_ World Bank, agoicoechea@worldbank.org.

> §
way to control for institutional differences across countries and over time is to use enterprise data from several countries, i.e., enterprises under different institutional environments, and apply a random-effects model. This way one controls for differences in the legal and other aspects of the overall external environment, as well as enterprise-specific heterogeneity.

Third, the SV model indicates that the degree of stabilization can greatly affect enterprise performance. Improvements in enterprise performance may then wrongly be attributed to privatization when they are mainly due to hardening of the budget constraint. A cross-country comparison through a panel approach would correct for differences in overall monetary tightness. But, the hardening of the budget constraint may differ across enterprises, as the political costs and benefits of soft budgets may depend on the size of the enterprise, the labor intensity of production, type of products, etc. It is thus necessary to use firm-specific proxies for hardening of the budget constraint. One candidate is the (change in the) amount of new bank lending to the enterprise in question since, ex-post, much of it often ended up being subsidies in disguise. Direct subsidies from the state can also be included.

Fourth, and this a more general point, the SV model indicates that it is necessary to control for initial conditions at the enterprise level. Enterprises are differently endowed in terms of quality of machinery, labor, management, links with foreign markets, etc. This is especially the case in transition economies where the decisions on new investment, worker training, etc., were taken at the central level and were seldom driven by previous track record. It may have given some firms a competitive advantage or disadvantage once liberalization took place, leading to higher or lower profitability. Some of this can be captured by controlling for sectors, but not all. Provided there is no clear relationship of initial conditions with privatization, this still need not bias any results. There has been a presumption, however, that better firms are more likely to be privatized. As a result,
that refugees are employed 90 days after arrival is positively affected by the number of business owners in their network, but negatively affected by the number who are employees. We thus construct a “network” variable that indicates whether refugees had no family members or friends in the host country before they came. The interaction term between mental health scores and this network variable is negative and statistically significant (Panel F), which shows that, relative to those who have a network, increases in mental health scores of refugees without a network have a larger negative effect on the probability of employment and labor income.

Next, we examine the role of government and community supports in mediating the impacts of mental illness on labor outcomes. As discussed in Appendix C, the Australian government has implemented several programs to improve refugee employment integration. While we are unable to identify participants to these programs in the BNLA survey, we employ a variable indicating whether they receive government benefits as a simple proxy. We also use information on whether refugees receive assistance from ethnic or religious communities to construct an indicator of local community support. The summary statistics presented in Table B1 (Appendix B) show that about 53.4% of refugees in our sample receive benefits from the government, while that number is lower for those who receive support from the local community (49.8%).

The results in Panel G provide strong evidence for the beneficial role of government support. Specifically, we find that the impacts of mental health issues on labor outcomes are less pronounced for those receiving support from the government. This is consistent with findings from previous studies showing that government aid helps refugees better integrate into the labor market (e.g., LoPalo, 2019). Our findings are particularly important given that most respondents in our sample are newly arrived refugees who may have fewer resources, and thus are more vulnerable. However, we find no evidence of such impacts for local community support, as shown in Panel H.

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It is generally the case that verifiable information on program applicants’ level of consumption or income will not be available due to the difficulty and cost of collecting such information. Careful data collection and statistical modeling are required to ensure an accurate prediction of household welfare. Nevertheless, due to these inaccuracies, it is important for programs to include a grievance or appeals process available to excluded program applicants who may like to provide additional information in support of their application or have their case reviewed for potential errors (Sebastian et al. 2018).

Even with careful data collection and statistical modeling, the PMT score is still a prediction of a households’ actual welfare and subject to inaccuracies. The magnitude of inclusion and exclusion errors depends on the program eligibility threshold (the PMT score cutoff) and the true level of poverty rate in a country. Considering a fixed poverty rate in a country, at one extreme, a program that does not include any beneficiaries will have zero inclusion error, but an exclusion error equal to the poverty rate. Likewise, at the other extreme, a program that includes everyone will have zero exclusion error, but an inclusion error equal to percentage of the population not living in poverty. In practice, the PMT score cutoff can be selected to balance these errors based on policy and program objectives.

# 3. Data and Methods

The development of the Sudan PMT proceeds as follows: First, we identify a set of candidate variables from household survey data that we believe could serve as a good proxy for a household’s welfare—in the case of Sudan this is household consumption expenditure. Second, we perform statistical analyses to determine a subset of variables to be used in the final PMT. In this step, we also explore different model specifications. Third, we evaluate candidate models’ out-of-sample predictions across different PMT cutoff scores using several evaluation metrics. Finally, we explore the performance of hypothetical targeting methods and transfer schemes using the final PMT.

## _Data and Variables_

That data used to develop the
week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Usual earnings and usual hours of work taken from reports in baseline survey. All specifications include individual fixed effects. Column 1 is estimated by OLS and columns 2 and 3 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐interview dummies interacted with gender, week‐of interview dummies interacted with city, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
pricing scenarios ($15 to $25 per tonne of CO2) with developing country compensation** (Figure 2c). Of these, perhaps 5–10 percent might be remitted to implementing agencies, national or international, a precedent being that EU Member States are allowed to keep 10 percent of customs revenue to cover administration costs. Some might also fund clean technology development programs for the aviation and maritime industries. However, the amount that might be warranted on economic grounds to correct market failures, that is, the potential inability of innovators to appropriate spill over benefits of new technologies to other firms, would likely be a small fraction of the revenue raised. At most, therefore, international aviation might contribute around $4–7 billion for climate finance in these scenarios.

> 32 This ignores the (small) risk of offsetting emissions increases as the pattern of flights shifts from developed to developing countries.

> 33 For comparison, AGF (2010b) assumed no emissions reductions in response to similar charges to aviation.

> 34 This is based approximately on the projection in ICAO (2009a), scaled back by 20 percent on the assumption that ICAO‘s aspirational emissions mitigation goals (see above) will be met. It is assumed that charges are levied on flights that it is proposed to include in the EU-ETS. Issues arising from the coordination of such a charge with the EU-ETS are discussed in Section V.

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levels in tannery effluent. According to the United Nations Industrial Development Organization (UNIDO), effluent discharge into surface water typically is required to have BOD below 30-40 mg/l, while the typical BOD in _raw_ tannery effluent is approximately 2,000 (UNIDO 2011).

Use of a broad-based indicator of pollution raises the issue that it is likely affected by a variety of factors, not just the activities of tanneries. During the Supreme Court hearings, lawyers representing the tanneries argued that other firms, a negligent Kanpur municipality, and other negligent municipalities were contributing to the pollution around the tanneries (Mehta 2009: 78–82). This argument, however, was eventually dismissed in light of a report from the CPCB which demonstrated that the specific pollutant readings in the river were not even theoretically capable of being generated by any entity other than the cluster of tanneries (Mehta 2009: 83–84). Field reports of Jajmau report no other major industry in the area (Alley 2002).

In most of our analysis, we parameterize BOD as a dummy variable equaling one if average BOD in a district-month exceeds the national standard of 3 mg/l for bathing in surface water.<sup>2</sup> This 3-mg/l threshold is also a stated goal of the National River Conservation Plan, and is consistent with the crit-

> 2In addition to this BOD standard, suitability for outdoor bathing requires a total coliforms concentration of less than 5 per ml, pH between 6.5 and 8.5, and dissolved oxygen levels exceeding 5 mg/l.

8
_- 65 -_

**Argentina** Unemployment rate is drawn from CIA Factbook 1995 and refers to 1994. Paid employment is non agricultural activities is taken from Anuario Estadistico de la Republica Argentina, 1995 and relates to 1995. Javier Burgueno of Consultorio Burgueflo Pepe & Asociados was also a source of information relating to Argentina. Central Government Employment data are drawn from Public Sector Reform (Loan 3394-AR) Completion Report of June 29, 1995 and refer to 1993. They include decentralized agencies of the Central Government (76,000 in 1994). Primary, secondary and technical education are handled at the local level. Public Universities are at the central government level. University Teachers number 100,000 and include both full-time and part-time. In June, universities were given autonomous status within the public sector, and accordingly they became responsible for salaries of professors. It is not clear whether the number of teachers can include them or not. Local Government employment is taken from IMF Report No. SM/95/248 of September 20, 1995 and relates to 1994. It includes employees of the Provinces and the City of Buenos Aires (1,013), and employees of the Municipalities (348). Health sector employees are taken from WHO, 1993. State-owned enterprise employment is also taken from IMF Report SM/95/248 (see above) and relates to Central Government enterprise employees for 1994. Military employment data include conscripts (18500), but do not include personnel in paramilitary units, e.g., the Gendarmerie (18,000) under the authority of the Ministry of Defense and la Prefectura Naval (13,240). Data on GDP at market price is a 1993 estimate and is taken from World Tables 1995. Data on wages and salaries of Central Govemment as percent of GDP is taken from IMF Report SM/94/177 of July 8, 1994 and relates to 1993. GDP per capita, average Government wages, and wages and salaries as multiple of per capita GDP
intermediate demand. This allows for a fuller analysis than is possible when working strictly with IO tables.

We also make one adjustment to the data to ensure compatibility of the skilled-unskilled labor split over the years. All the versions of the GTAP data up to 2007 use only two types of labor – skilled and unskilled – while the latest version for 2011 (GTAP 9) classifies labor into five groups of occupational categories, such as officials and managers and service/shop workers (see Table A6 in Appendix 3). We checked that these categories cannot be reconciled with previous versions (see Figure A1 in Appendix 3), and instead exploit the fact that in previous versions of the data set the ratio of skilled-unskilled labor value added is constant over time for a specific country-sector pair.<sup>8</sup> Thus we apply the same skilled-unskilled labor value added ratio for each of these pairs to the 2011 data.

Since the GTAP database tracks interrelationships of an economy both for intermediate use and final demand (including exports), this complex structure of the database allows us to obtain the total value added content of final output and exports, including the compensation of employees’ component. This includes both the direct and indirect value added, based on the linkages of each sector with all other sectors in the economy. We can obtain both forward and backward linkages, where forward linkages count the value added of a given sector that goes into other sectors, while backward linkages count all the value added from other sectors that go into a given sector. Thus we can also account specifically for the direct value added attributable to labor, indirect value added (via backward or forward linkages), and total value added (the summation of direct and indirect value added via backward or forward linkages). We can further distinguish between the labor contributions from skilled and unskilled workers. Finally, we repeat the exercise for total final output as well as final output that is exported. This is explained in what follows more formally.

In