rafmacalaba/data-use-mentions-extended huggingface dataset:
are either based on relatively small samples or a sampling approach that is not explicitly designed to be representative of a target population (Schneider, 2020). Therefore, caution must be exercised in generalizing the results from these case studies.

This paper provides novel insights into gender differences in the impacts of the COVID-19 crisis on firms, drawing on a unique data set of around 40,000 mainly formal businesses (only five countries included informal firms in the sample). Our analysis exploits firm surveys conducted between April and September 2020 under the World Bank’s Business Pulse Survey (BPS) and Enterprise Survey (WBES) programs. These data cover businesses across 49 mostly low- and middle-income countries. This database has been analyzed by ApedoAmah et al. (2020) to document global firm-level impacts of COVID-19, but without any disaggregation by gender. We extend their analysis to shine a spotlight on the early impacts of the COVID-19 crisis on womenand men-led businesses. We capture three broad areas of interest. First, impacts of COVID-19 on business performance measures (i.e. temporary business closures, disruptions in supply channels, changes in sales revenues, financial risks and the owners’ expectations about the future); second, responses to the crisis (i.e. adjustments in labor inputs, technology adoption and product innovation); and third, access to public support.

Our paper offers descriptive evidence of a differentiated effect of the pandemic on women-led businesses using robust and timely data that are comparable across countries and that cover critical dimensions of the operations of a firm. In a crisis of the magnitude of the COVID-19 pandemic, evidence tracing the impact of the shock in a timely fashion is desperately needed to help inform the design of policy interventions. Our real-time glimpse into women-led businesses fills this need for timely and policy-relevant evidence, even if the data are not well suited to examine the underlying channels potentially explaining the patterns we document (channels which likely vary across countries).

Our analysis shows statistically significant gender gaps in different measures
sample is restricted to respondents who are married at baseline and do not live alone, except for the outcome capturing expenditures on girls for which the sample in addition only includes those who have had children at baseline. The bounds are calculated for these OLS estimates and tightened based on whether respondents were married at baseline.

66See Tables C.22 to C.26 in the Online Appendix.

> 67Note that I estimate Equation 1 with the husbands’ data but use the wives’ set of potential control variables for the PDS Lasso algorithm as in Section 4. Also note that some husbands were unexpectedly unavailable at the time of

24
to 1995.

# Congo

Central Govemment, Education and Health employment are taken from Recent Economic Developments, IMF Report No. SMW941123, and relate to 1993. Public enterprise employment is also from the IMF Report for 1993. It probably understates the actual number of employees as it covers only the largest key state-owned enterprises. C6te d'lvoire Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1990. Data are from official sources of the Ivorian govemment.

Unemployment is taken from the Private Sector Assessment of December 29, 1994 (pg. 4, para. 1. 13). Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers to 1993. It reflects data gathered through Establishment surveys, that is, data on the number of workers on establishment payrolls. This in tum may result in an underestimation of employment. Central Govemment employment is taken from 'Atelier Regional de la Fonction Publique" of the Ministry of Public Administration (July 1995) and relates to 1993. Local govemment employment is taken from the Appendix of the Municipal Supogrt Probect of March 1995 and relates to 1993. This document, the govemments policy letter, states that permanent positions number 11,832 for local administration and as many as 10,000 occasional workers. Occasional workers are not included in non-central Govemment employment estimate. Military employment data include the Presidential guards (1,100) and the Gendarmerie (4,400), but do not include reserves.
Yagan (2019).<sup>30</sup> The estimates in Appendix Table C3 are qualitatively similar to those in Figure 1. Moreover, we estimate the impact of GFC firm shocks on worker job displacement by constructing two indicator variables for a worker being displaced from a job based on the motives for the end of a worker’s employment spell at a firm from the RAIS database.<sup>31</sup> The first variable is based on either of two possible motives: (1) justified dismissal by the employer or (2) unjustified dismissal by the employer. The second variable is based on the second motive only. The results in Appendix Table C3 show that regardless of the variable used, the firing of workers increases significantly in 2009 in firms harder-hit by shocks.

Finally, we conduct further robustness checks estimating Equation (2) using two alternative shock measures that modify the export portfolio weights in Equation (1) to be either average weights in the entire pre-GFC period (2004-2007) or in the previous two years (2006-

> 28We redefine the vectors of worker characteristics and firm controls pre-GFC to be based on 2005.

> 29Commodity-related sectors are animal, forestry, fishing, mining, oil and petroleum, and tobacco.

> 30See Appendix A for the definitions.

> 31See GC Britto et al. (2022) and Gerard & Naritomi (2021) for studies linking similar worker displacement data for Brazil to crime and consumption smoothing, respectively.

14
Figure 6 Productivity (Real Output Per Employee ZIM$) <!-- Start of picture text --> 3.9<br>2.4<br>0.9<br>{~ ~~~ N N N N N N° C N C C C C C - 0 0l Cl C a9 0000IS<br><!-- End of picture text --> Data source: CSO and African Regional Database (World Bank). Note: All series are log Figure 6, continued Productivity (Real Output Per Employee ZIM$) <!-- Start of picture text -->
1.4<br>0.9<br><!-- End of picture text -->

Data source: CSO and African Regional Database (World Bank). Note: All series are log 9
for a relatively short period if a new health center recently opened in the area, even though the neighboring area already offered family planning services. Similarly, it is possible that changes in facility type might not be reflected in the start date, i.e. a change from clinic to centre that results in access to a wider set of services. These issues are also likely to result in a downward bias of the estimated effect of access.

<!-- Start of picture text --> Health Facilities<br>Family Planning<br>CBRH Programs<br>1965 1970 1975 1980 1985 1990 1995 2000 2005<br>Source: The World Bank Wereda Health Facility and CBRH survey.<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Figure 1: Percent Communities with access to Health Facilities, Family Planning or CBRHA Figure 1 shows the development in access to health facilities, family planning services, and CBRH programs over the 30 years prior to our household survey.<sup>14</sup> We focus on the effects of having access to family planning services in 1990, when approximately 25 percent of all communities in the sample had access to a family planning program.<sup>15</sup> The prevalence of programs was essentially constant the decade

> 14The introduction of CBRH programs is an interesting development, but happened too recently and in too many areas simultaneously to allow for an analysis of long-term effects on fertility. Access to health facilities and family planning services track each other closely making it impossible to estimate whether there is an independent effect of access to health facilities.

> 15Unfortunately, there is not enough power to predict years of community access as opposed to dichotomous access

8
performers. Among the criteria used for scoring the heatmap, two are especially relevant for the type of debt we analyze: sectoral coverage of debt reporting and debt instrument coverage. Sectoral coverage of debt reporting is important as SOEs, and not central government, are often the borrowing party. Debt instrument coverage is also important as it reveals whether the borrower has received a government guarantee in support of its loan repayment obligations. Across the RBL borrower countries scored on the heatmap, only the DRC has full coverage across both sectors and instruments. Guinea does not report SOE debt. Ghana reports on government-guaranteed SOE debt, but Ghana’s reporting on non-guaranteed SOE debt is missing. None of the other RBL countries reports on either of these aspects according to the heatmap.

The transparency of SOEs is critical given that they were the borrowers in nine of the 30 loans. These borrowers are Ghana Integrated Aluminum Development Corporation (GIADEC), Gécamines (DRC), SNPC (Rep. Congo), SHT (Chad), Sonangol (Angola’s national oil company), and Zimbabwe Electricity Supply Authority (ZESA)). Of these SOEs, Sonangol publishes audited financial statements yearly (latest available 2018). SHT recently released audited financial statements for the first time.31 SNPC recently released its audited financial statements for 2012-2018 for the first time, although consolidated financial statements for the SNPC Group have yet to be disclosed.32 ZESA does not have a functioning website. Gécamines produces audited financial accounts, which it files to the public registry and shares with the EITI secretariat but they are not made public.33 GIADEC does not publish audited financial statements, which may be because it was only recently established.34 In many additional instances SOEs played an important intermediary role (for example through an additional offtake contract offering to supply resources which are then deployed as collateral for repayment of the sovereign’s loan).

Only 15 of the 30 loans analyzed could be identified on the World Bank’s Debtor Reporting System. Of these 15
time is endogenous. The economy grows due to accumulation of capital (determined by investment and depreciation), labor (determined by demography), and other factors (following exogenous growth trends), as well as because of improvements in total factor productivity (TFP). Apart from an exogenous component, TFP depends on the levels of government capital stocks.<sup>10</sup> The disaggregation of MAMS varies widely across different applications depending on data availability and the kinds of questions the model is called upon to analyze. For the Liberian application, the database is disaggregated into some 60 accounts (for details, see Appendix Table A2.1), indicative of the aspects of Liberia’s economy that the model considers. Most importantly, the database includes 22 production sectors as well as two commodities without 9 Many individual production activities do not respond to changes in relative prices for exports and domestic sales as their output only has one destination, either exported in full or sold domestically in full. By the same token, domestic demanders do not have a choice between imports and domestic output for commodities that only have one source. Such structural features reduce the flexibility of Liberia’s economy.

10 In Appendix 2 (on the MAMS database), we discuss our treatment of the links between productivity and government capital stocks.

4
- Vanden Eynde, Oliver and Liam Wren-Lewis (2021). _Complementarities in Infrastructure: Evidence from Rural India_ . CEPR Discussion Paper 16139.

- Verdin, Kristine L et al. (2007). _Development of a Global Slope Dataset for Estimation of Landslide Occurrence Resulting from Earthquakes_ . Technical Report.

- Wooldridge, Jeffrey M (2010). _Econometric Analysis of Cross Section and Panel Data_ . MIT press.

28
ranged in between. The two Asian countries (Indonesia and Thailand) have similar contributions of 23% and 25%. The predictable WEALTH set of decompositions is of less interest to us as in most cases there are small effects on endowments and large negative on With cross-section data in a non-experimental context, it is very difficult to make definitive conclusions, and only tentative ones can be made that hint at some answers. The findings on Dev7 countries indicate that it is possible that a number of advantages that Vietnam enjoys, depicted in ED WEALTH, can only function effectively in combination. One way to consider this is through a cultural lens - meaning that there is something specific to Vietnamese culture, that enables Vietnam to benefit from hard working students and teachers, with the guidance of committed and involved parents, even in cities and small towns.

> 11For all other countries we used the respective country as the basis; for Shanghai, Vietnam is the base country in terms of equation (5).

36
based on tax data, we compare the share of minimum wage earners in survey data, tax data, and imputed survey data. We define minimum wage earners the same as Robayo-Abril, Zamfir & Wroński (2024); we cover separate minimum wage for the construction sector, and we do not cover the separate minimum wage for employees with higher education.<sup>16</sup> Due to the significant gap in the share of part-time workers in

> 16 Thus, we define minimum wage workers as those earning between 95% of the minimum wage for employees without secondary education and 105% of the minimum wage for employees with higher education and those who earn between 95% and 105% of the minimum wage for the construction sector. Robayo-Abril, Zamfir, and Wronski (2024) describe the institutional setting of minimum wage legislation in Romania in detail.

29
# **1 Introduction**

This chapter reviews the outcomes of those forcibly displaced due to conflict, violence and persecution and the factors that shape these outcomes.<sup>1</sup> It looks at refugees who flee across borders, as well as at those internally displaced in their own country.

The literature on outcomes of refugees and internally displaced persons (IDP) is nascent but growing. Much of the economics literature has focused on high-income countries because data are more readily available (Becker and Ferrara, 2019; Brell et al., 2020). Yet only onesixth of today’s refugees and nearly no IDPs are hosted in those countries. A descriptive picture of refugee and IDP outcomes in low- and middle-income countries is emerging with the recent surge in representative cross-sectional data (Krishnan et al., 2020; Mejia-Mantilla et al., 2019; Pape et al., 2018). Efforts underway to increase panel data for those forcibly displaced in low- and middle-income countries will allow more rigorous, causal analysis in the future.<sup>2</sup> In analyzing the outcomes of refugees and IDPs in their host communities, the literature has proceeded in two steps. First, it has sought to find the appropriate counterfactual to put the outcomes of refugees and IDPs in perspective, and in doing so has compared the outcomes of refugees and IDPs with other migrants and finally with residents of the host communities. Alternatively, studies have made comparisons with individuals who stayed behind. Where possible, such comparisons have been done over time, but only studies in high-income countries have been able to access panel or longitudinal registry data. Second, the literature has attempted to explain the observed differences or absence thereof, after controlling for observables. Two directions have been pursued. On the one hand, researchers have acknowledged that the conditions leading to the decision to flee create a selection process that in turn greatly influences the extent to which refugees and IDPs have been found to adapt to a new environment among
of transition out of employment.

These results seem to suggest that shadow employment is either the way to self-employment (be it registered or not) or to unemployment. It does not seem to be the path to full time/open term contracts or to any other type of open term contracts. It is important to underline that these results are obtained with a model specification where one controls for the type of contract, i.e., they may be interpreted as ―pure‖ effects of shadow employment probability on the relative transition probabilities.

**Table 6 - RRR for transitions to given type of contract compared to full time/open term contract resulting from unit change in shadow employment probability**

|**Year**|No<br>employment<br>versus<br>full<br>time<br>open term(FTOP)|Full time fixed term<br>employment versus<br>(FTOP)|Part time open term<br>employment versus<br>(FTOP)|Part<br>time<br>fixed<br>term employment<br>versus(FTOP)|Self Employment<br>versus (FTOP)|
|---|---|---|---|---|---|
|**2004**|22.7|6.1|insignificant|5.6|20.9|
|**2005**|16.7|3.4|insignificant|insignificant|263.4|
|**2006**|32.5|7.6|insignificant|10.1|287.7|
|**2007**|insignificant|insignificant|insignificant|insignificant|51.3|
|**2008**|5.3|insignificant|insignificant|insignificant|insignificant|

Source: Author‘s calculations based on LFS data sets from years 2003-2008.
We also verified the impact of the inclusion of weights in the covariates by testing it on predictions of millet yields in the 2017 data set. We find that in average, the standard errors of the imputed mean from a model with survey weights was only 0.38% larger than in the case of a model without weights. The bias is also slightly larger as we find than in average, the root mean square error is 0.5% when including weights. As these results seem to be of little practical concern, we believe it will not affect the lessons gleaned from the imputation exercises conducted in this work and we leave changes to the covariates sets that could allow for the inclusion of survey weights<sup>18</sup> for future work.

# **5.1.3 Prediction accuracy by crops**

Figure 5 provides a graphical representation of the distribution of the _R_<sup>2</sup> of the regression of the CC yields on the ML prediction for yields levels predictions,<sup>19</sup> by crop. There appears to be significant heterogeneity in the performance of the ML algorithms across crops: the _R_<sup>2</sup> for cowpea ranges between 0.1 and 0.42 while that of rice ranges between 0.41 and 0.7. The comparison of these crops is a good case and point for what is the most likely candidate explanation for the disparity in ML prediction performance across crops: the rate of intercropping, which refers to the practice of growing multiple crops in the same field simultaneously. In fact plots with cowpea are the highest intercropping rate (0.26) while rice is not on intercropped plots. Moreover the median performance of the ML algorithm is in general increasing with the rate of intercropping if we focus on sorghum, maize and groundnut. The predictions for millet have a higher performance than those of sorghum or maize even though it has a higher intercropping rate, but we can
Das and Žumbytė 2017, Solotaroff et al. 2020). Finally, studies that causally attribute the effect of childcare on women’s employment outcomes remain scarce, with a notable exception of an impact evaluation of a public child support program in India (Jain 2016).<sup>3</sup> This paper makes three contributions to the literature. First, labor force surveys typically omit questions on availability of childcare and, as a result, many previous studies use household beneficiary status in child development programs, access to child subsidies, or the presence of grandparents in the household to deduce whether or not families have access to childcare (see, for example, Maurer-Fazio et al. 2011, Posadas and Vidal-Fernandez 2013, Bick 2016, García-Morán and Kuehn 2017).<sup>4</sup> This paper uses a direct measure of childcare support, using women’s own responses to a survey question “who would take care of their young children when women go to work?” This makes the findings less dependent on

> 3 Integrated Child Development Services (ICDS), India’s largest child development program, provides preschooling as well as other services, including supplementary feeding, immunization, and health checkups.

> 4 Measuring childcare availability through child development programs may lead to biases because some parents, such as those from lower income groups, may not have access to such programs. Similarly, using presence of elders in the family as a proxy may be problematic, since parents may have access to childcare regardless of the presence of grandparents, and the presence of grandparents by itself does not guarantee that they actively provide childcare, as they may need care support themselves.

4
Table 7: Impact of expected dowry on fertility and sex ratio, NFHS rural data

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Dependent variable:**||**Nu**|**mber of bi**|**rths**||
|_Firstborn girl_ * _Expected dowry_||0.008|0.002|0.003|0.004|
|||[0.015]|[0.012]|[0.013]|[0.013]|
|_Expected dowry_||-0.084|-0.004|-0.004||
|||[0.057]|[0.037]|[0.016]||
|_Firstborn girl_|0.273***|0.249***|0.287***|0.287***|0.284***|
||[0.032]|[0.050]|[0.039]|[0.038]|[0.039]|
|N|60,248|60,248|59,737|59,737|59,737|
|Dep var mean for Firstborn boy|3.095|3.095|3.095|3.095|3.095|
|**Dependent variable:**||**Fraction**|**sons (pa**|**rity** _≥_**2)**||
|_Firstborn girl_ * _Expected dowry_||0.008*|0.008*|0.007|0.007|
|||[0.004]
Israel, Korea, Malaysia, Nigeria, Pakistan, Singapore, Thailand, United Kingdom, and Zimbabwe stress the rights of creditors, such that CREDITOR= 1. CREDITOR is an indicator of legal codes, however, it does not incorporate information regarding enforcement.

_2. Enforcement_

> 21 We could have redefined **AUTOSTAY** and **MANAGES** such that values of one indicated stronger (instead of weaker) creditor rights. This would have produced values of CREDITOR between 0 and **3** and would not have altered the results. We did not do this for consistency: the variables in this paper are defined the same as the variables in LLSV **(1997,1998).**
# **Distortions in the International Migrant Labor Market: Evidence from Filipino Migration and Wage Responses to Destination Country Economic Shocks**<sup>*****</sup>

David McKenzie _Development Research Group, World Bank and BREAD, CEPR, CReAM and IZA_

# Caroline Theoharides

_Department of Economics and Ford School of Public Policy, University of Michigan_ Dean Yang _Department of Economics and Ford School of Public Policy, University of Michigan, NBER, and BREAD_ _Keywords:_ international migration, migrant demand, labor output elasticity, minimum wages. _JEL codes:_ O12, J23, F22.

> * McKenzie: dmckenzie@worldbank.org; Theoharides: cbtheo@umich.edu; Yang: deanyang@umich.edu. We thank the Philippine Overseas Employment Administration (POEA) for access to the data, and Dunhill Alcantara, Helen Barayuga, Nimfa de Guzman and Nerissa Jimena for their assistance with compiling this database. We thank Leora Klapper and Dorothe Singer for tabulating the Gallup Poll Data for us. Carmela Azurin, Jackson Gan and Vanch Rongcales provided important insights into the recruitment process for Filipino workers. We thank participants at seminars at Georgetown University, Maastricht School of Governance, Trinity College Dublin, the University of Michigan, the World Bank, and the 3<sup>rd</sup> International Conference on Migration and Development (Paris) for helpful comments. The views expressed here are those of the authors alone, and do not necessarily reflect the opinions of the World Bank or the Philippine Overseas Employment Administration. Funding support was provided by the World Bank’s Gender Action Plan and for Caroline Theoharides by the National Science Foundation Graduate Research Fellowship.

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