rafmacalaba/data-use-mentions-extended huggingface dataset:
When comparing the results of Colombia with other countries in the region, it turns out that Colombia is the second country, after Bolivia with the most regressive indirect taxes in the region and that additionally, indirect taxes in Colombia are the ones that most contribute to an increase in inequality throughout the region (Figure V-5- Panel B).

<!-- Start of picture text --> FIGURE V-6: PROGRESSIVITY AND REDISTRIBUTIVE EFFECT OF INDIRECT TAXES<br>(FROM MARKET INCOME + PENSIONS TO CONSUMABLE INCOME)<br>0.3000 0.0140<br>0.0120<br>0.2000<br>0.0100<br>0.1000 0.0080<br>0.0060<br>0.0000 0.0040<br>-0.1000 0.0020<br>0.0000<br>-0.2000 -0.0020<br>-0.0040<br>-0.3000<br>-0.0060<br>-0.4000 -0.0080<br>Kakwani Coefficient<br>Marginal Contribution<br>Source: World Bank Group staff estimates based on ENPH 2017<br>Kakwani Coefficient Redistributive effect<br>Regressive <---------> Progressive VAT Unequalizing <---------> Equalizing<br>All direct taxes and C.S.S. Consumption tax Tobacco Tax Tax on alcoholic beverages All taxes and C.S.S.<br><!-- End of picture text -->

# **_b) Direct transfers and subsidies_**

Total direct transfers, like the subsidies that households receive, are progressive and help reduce poverty. Direct transfers are progressive in relative and absolute terms since these not only represent a higher percentage of the income of the poorest, but a higher percentage of these is given to people from the lowest deciles. Thus, while
# **1. Introduction**

Haiti’s geographical location and preexisting fragilities make it particularly vulnerable to natural disasters. During the last decade alone, Haiti has suffered at least 44 natural hazards,<sup>6</sup> such as hurricanes and earthquakes, with the 2010 earthquake that killed an estimated 220,000 people standing out as the most devastating of them (Kolbe et al. 2010). At the same time, preexisting vulnerabilities, including a volatile and unstable political climate with high levels of violence and crime, limit disaster preparedness, response, and recovery. Prevailing socio-economic disparities may place certain population segments in disproportionate exposure to disasters. Limited resources at the household and community levels can limit the ability of a household to prepare for disasters, making poorer households more vulnerable. At the same time, natural disasters diminish the economic capacity of households, increasing vulnerability to future catastrophic events and thereby perpetuating a cycle of poverty. Kianersi et al. (2021) found that households severely affected by Hurricane Matthew in 2016 were more likely to be food insecure in the long term. Besides income, other socio-economic factors such as education, employment, and demographic characteristics, including age and gender, may exacerbate vulnerability conditions for marginalized populations (Llorente-Marrón et al. 2020a).

This paper uses data from a mobile phone survey carried out in 2021, 2022 and 2023 to examine socioeconomic factors that may explain differences in household vulnerability to and preparedness for disasters. Understanding the risks households face, and their preparedness capacity is hampered by acute data shortages. Haiti’s last national household survey, the “Enquête sur les Conditions de Vie des Ménages après Séisme” (ECVMAS), was conducted in 2012. To overcome this data gap, this note leverages mobile phone surveys carried out in July 2021, December 2021 and 2022, and March 2023.<sup>7</sup> While these surveys are useful to capture behaviors, and cost and time efficient, they also have limitations related to low
19-related labor market shocks by pushing them into occupations that are more amenable to working from home.

The literature on the labor market impact of the COVID-19 pandemic has grown exponentially over a very short period. As these papers all show, the impact of COVID-19 on labor markets has been very profound, with asymmetric effects across occupations and population groups (see for example, Adam-Prassl et al. 2020; Beland, Brodeur and Wright, 2020; or Coibilion, Gorodnichenko, and Weber, 2020). Several studies indicate that the employment risks associated with COVID-19 disproportionately affect workers who were already exposed to wage and job uncertainties prior to emergence of the pandemic (Casarico and Lattanzio 2020; Rio Chanona et al., 2020; Mongey, Pilossoph, and Weinberg, 2020; Papanikolaou and Schmidt, 2020; Yasenov, 2020). Similarly, Garrote-Sanchez et al. (2020) show that COVID-19 is likely to exacerbate inequality between workers – especially in richer countries, where better paid and better educated workers with secure jobs are more insulated from the shock. Using monthly data in the US for 2020, Montenovo et al. (2020), Liu and Mai (2020) and Cajner et al. (2020) show occupations that require greater physical proximity and less suitability for teleworking exhibited larger employment losses.

By focusing on another category of vulnerable workers – immigrants – our paper contributes to the recent literature on the occupational exposure to COVID-19 related risks. The handful of papers specifically comparing the COVID-19 exposure of immigrant and native workers finds that immigrant workers are typically more exposed to both economic and health shocks associated with the pandemic (Basso et al., 2020; Fasani and Mazza, 2020a and 2020b; Yasenov, 2020). Among the reasons proposed by Borjas and Cassidy (2020) is the higher concentration of migrants in jobs with lower potential to be performed remotely. Yasenov (2020) finds that only 30 percent of the migrants in the United States have jobs suitable for telework, compared to 45 percent of natives. While these
# **I. Introduction**

Women’s participation in the labor force has increased significantly throughout the world over the past 25 years (Duflo _,_ 2012). Despite this, research shows that systematic gender gaps in wages and productivity persist across both developed and developing countries (Blau and Kahn, 2000; Jarrell and Stanley, 2003; Terrell, 1992). Ensuring parity in male-female labor outcomes is not only a matter of equity but also one of smart economics and efficiency (Doepke, Tertilt, and Voena, 2014; Duflo, 2012). The 2012 World Development Report, _Gender Equality and Development,_ notes that eliminating gender discrimination against female workers could increase productivity by 25 to 40 percent (World Bank, 2012). Moreover, removing barriers faced by women in the labor market has implications for the next generation as well as for the inclusivity and representativeness of institutions and development policies (Hallward-Dreimeier and Gajigo, 2013).

Gender wage discrimination is a well-researched area and one that has been examined using a variety of estimation techniques. Previous studies on this topic have used various worker characteristics such as age, experience, education, occupation, and industry of employment in predicting male-female wages.<sup>4</sup> However, while recent research in labor economics has documented the critical role of skills, broadly defined, in predicting key life outcomes, including the associations between skills and earnings (Hanushek et al., 2013; Heckman and Kautz, 2011), the mechanisms through which cognitive and noncognitive skills are related to wages have received limited attention,<sup>5</sup> and most of the research has been conducted in countries belonging to the Organisation for Economic Co-operation and Development (OECD). Further, few studies have applied these skills measures to examine earnings differences between men and women.

This study aims to conduct a comparative analysis across seven low- and middle-income countries, examining the gender wage gap in these countries using data from the STEP Skills Measurement surveys
Policy Research Working Paper 7550

# **Abstract**

Evidence from developed country data suggests that cognitive and non-cognitive skills contribute to improved labor market outcomes. This paper tests this hypothesis in a developing country by using an individual-level data set from Peru that incorporates modules to measure cognitive and non-cognitive skills. The paper estimates a structural latent model with unobserved heterogeneity to capture full ability rather than just measured skill. It also applies standard ordinary least squares techniques for comparison. The analysis confirms that cognitive and non-cognitive skills are positively correlated with a range of labor market outcomes in Peru. In particular, cognitive skills positively correlate with wages and the probability of being a wage worker, white-collar, and formal worker, with verbal fluency and numeric ability playing particularly strong roles. The results are robust to methodology. The patterns are less uniform for non-cognitive skills. For instance, perseverance of effort (grit) emerges strongly for most outcomes regardless of methodology. However, plasticity—an aggregation of openness to experience and emotional stability—is only correlated with employment, and only when using the structural latent model. The ordinary least squares method also finds that the disaggregated non-cognitive skills of kindness, cooperation, emotional stability, and openness to experience emerge significantly, mostly for the wage estimates. The different results derived from the ordinary least squares and the structural model with latent skills suggest strong measurement bias in most non-cognitive skills measurement. These findings, although only correlational because of the use of a single cross-section, suggest that recent efforts by the Peruvian government to incorporate non-cognitive skill development into the school curriculum are justified.

This paper is a product of the Social Protection and Labor Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at wcunningham@worldbank.org.

_The Policy Research Working Paper Series
male enum*poor respo|||||||||||||-0.004|-0.030|-0.031|0.019|
||||||||||||||(0.074)|(0.080)|(0.057)|(0.063)|
|urban enum* poor resp|||||||||||||0.085|0.012|0.022|-0.032|
||||||||||||||(0.064)|(0.079)|(0.061)|(0.074)|
|experience* poor resp|||||||||||||-0.116|-0.103|0.012|-0.090|
||||||||||||||(0.081)|(0.083)|(0.046)|(0.077)|
|Observations|6,245|6,333|6,553|6,320|6,245|6,333|6,553|6,320|6,245|6,333|6,553|6,320|6,245|6,333|6,553|6,320|
|R-squared|0.490|0.469|0.240|0.484|0.490|0.468|0.239|0.484|0.490|0.468|0.239|0.484|0.490|0.468|0.239|0.484|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.
as well as the plant size distribution data are available for Türkiye. Using representative, plant-level data from the World Bank’s Enterprise Surveys (WBES), I show that while bigger enterprises pay higher bribes, they spend a lower fraction of their value added (VA) on bribery. More specifically, 6.8% of big plants and 8.7% of small plants<sup>3</sup> in Türkiye experienced a bribe request in 2013. However, big enterprises spent 5.2% of their VA on bribery, whereas small enterprises spent 18.3% of their VA on bribery. Moreover, according to the Annual Industry and Services Statistics of Türkiye, 99.6% of all plants are small. Although big plants constitute only 0.45% of the total plants, they account for 43.6% of all formal employment.

> 1See Restuccia & Rogerson (2008), Guner _et al._ (2008) and Hsieh & Klenow (2009).

> 2See Restuccia & Rogerson (2017) for a survey of the literature.

> 3Big plants refer to the plants that employ more than 100 workers and small plants refer to the plants with fewer than 100 employees.

1
**Annex D: Comparison between PODER students and the rest of the students in 1**<sup>**st**</sup> **grade within each school**

||**PODER students**|**Mean**|**SD**|
|---|---|---|---|
|COMIPEMS scores|-4.398***|51.795|20.577|
|Secondary average|(0.0313)<br>-0.776***<br>(0.023)|7.719|1.423|
|Scholarship Econ Need|0.026***<br>(0.009)|1.886|0.318|
|Num. of books at home|-0.024*<br>(0.048)|3.806|1.621|
|Internet at home|-0.000<br>(0.014)|1.291|0.454|
|Higher Edu Father|-0.037***<br>(0.013)|0.271|0.445|
|Higher Edu Mother|-0.014*<br>(0.013)|0.254|0.435|
|Observations|22.132|||
|Mean Dep. Control<br>Group<br>SD Dep. Control Group|0.518<br>0.500|||
|R-square|0.185|||

28
distribution as opposed to a smooth curve for the GPS measure. Finally, the comparison of the two distributions appears to support the case for treating the GPS measure as the more accurate of the two.

The systematic patterns in the difference between land measurements we have observed above have the potential to introduce a bias in the estimation of agricultural/land productivity. Reasoning through the data it is apparent, as we already mentioned, that the kind of pattern we observe in our data should, if anything, generate a bias opposite in sign to that needed to corroborate the IR hypothesis. If small farmers report to be cultivating more land than they actually are, their „true‟ yields are actually even larger than what one would compute using self-reported land quantities. To explore that point descriptively, Figure 2 draws the relationship between deciles of cultivated land and farm yields (computed as the ratio of the value of agricultural production over farm size). The slope of both lines is negative, pointing to an inverse yield-farm size relationship in both cases, but the line is even steeper when the GPS land measure is used.

Table 3 summarizes farm yields computed using GPS and self-reported land areas. Farms are categorized as small, medium or large. Small farms, those cultivating landholdings smaller than1.45

> 4 1 hectare is equal to 2.47 acres.

7
# **S4 Trends in Woreda-Level Outcome Variables Over Time**

This section shows trends in outcomes variables over time. Trends are shown across different groupings of Woredas, where Woredas are grouped by baseline levels of nighttime lights into dark Woredas (Woredas with no positive nighttime lights), and those with low, medium, and high baseline levels of nighttime lights (we use 3-quantiles of the maximum value of nighttime lights across Woredas with some positive nighttime lights to form the low, medium, and high groups).

Figure S4 shows average trends over time. Woredas across all groupings saw growth in nighttime lights and urban land; in addition, all followed a general pattern of increasing then decreasing cropland area. The figure shows a sharp increase in nighttime lights among Woredas with a maximum nighttime value of 0 at baseline. This increase is due to a difference in the underlying nighttime lights data; from 1992-2013, DMSP-OLS data is used; and from 2014 onwards, simulated DMSP-OLS data is used, which captures more low-level light.

Figure S5 shows the distribution in growth rates in outcome variables from 1992 to 2016. The figure shows that most Woredas saw growth in nighttime lights; while many Woredas saw growth in Urban area, a notable proportion saw no change in urban area. In addition, most Woredas saw no change in Cropland area. Among Woredas that did see change in cropland, Woredas were roughly equally split in seeing growth or a reduction in cropland, except for Woredas with high initial nighttime lights, where Woredas tended to see a reduction in cropland.

S5
earners, these facts may indicate that fiscal policy in Armenia seems to be associated with women (1) staying out of the labor force, (2) taking lower-paying employment, and/or (3) working in economic activities that tend to be in the informal sector.<sup>29</sup> The fiscal system significantly reduces gender gaps in incomes for all household types, but the gaps for households with dependents remain negative and the largest, indicating that dependents create demands on caregivers more so than they contribute to the supply of caregiving. On the other hand, the presence of stay-at-home elders reduces the gender gap relative to a household type with dependents but no assistance from elders. The reduction is most significant from pre-fiscal to disposable incomes, highlighting the association with household labor market decisions and the associated PIT liabilities.<sup>30</sup> For reference, the relatively small gender gap at pre-fiscal income in households with neither older-age nor younger-age dependents is actually reversed and becomes a gender “bonus” at the disposable and consumable income concepts. These patterns are consistent with older-age dependents contributing resources to care, social reproduction, and household production simultaneous with creating expenses associated with their care needs; they are also consistent with those expenses being assigned more frequently to women.

# c. Incorporating Time-Use Data

Worldwide, women spend over three times as much of their time on unpaid work, and approximately one-third as much of their time on paid work, as men (Charmes 2019). The volume of unpaid work may mean women have less time than men to engage in paid work; or women may feel restricted to jobs allowing for a balance between paid and unpaid work. Either way, a one-hour decrease in time spent on unpaid care work is correlated with a fivepercent increase in women’s labor force participation (Ferrant and others 2014).

Women in Armenia are much more likely than men to miss economic opportunities due to childcare, older dependent care and support, and other social reproduction activities.
roughly the same time in Indonesia. These contrasts were already foreseen in Manuelyan-Atinc and Walton (1998).

Finally, even if the impact of crisis on market price and incomes was the same the world over, the ability and willingness of the government to maintain or improve the safety net is most unlikely to be.

For all of these reasons, generalization from the Indonesian experience should be done with care. Nevertheless, each continent has seen important recent crises for which studies suggest that they have also been associated with narrowing inequalities.

# _Mexico, 1994_

Baldacci et al. (2002) present some detail on shifts in Mexican income distribution for the three Mexican National Household Income and Costs Surveys ( _ENIGH_ ) 1992, 1994 and 1996. Poverty incidence increased dramatically in the immediate aftermath of the crisis – moderate poverty (as nationally defined) jumped from 36 to 48 percent of the population and extreme poverty from 11 to 17 percent in the two years to 1996. Nevertheless, the lower deciles were not the worst affected in relative terms. The income share of the top decile fell from 44 percent to 42 percent, with gains in all of the other deciles (except the fourth). Accordingly, not only the Gini, but the Theil and Atkinson’s indices (for parameters 0.5, 1 and 2) all show improvements. This holds for both the data on income and expenditure. Baldacci et al. also show that the change between 1992 and 1996 in the probability of being poor in Mexico is systematically related to geographical and age characteristics: households in Yucatan province, and in urban areas, and those with very young or very old heads, all displayed a heightened propensity to be poor. Transfers kept 6.1 percent of the population out of poverty in 1996, compared with 4.5 percent in 1994 – suggesting little improvement in the scale or targeting of income transfers.

Actually, the Mexican case alerts us to just how severe are data problems on inequality from standard sources. Even as straightforward
it as our main empirical strategy for the gravity analysis. We also include country-specific fixed effects to address potential trade multilateral resistance –the barriers to trade each country faces with all its trading partners– and country-pair fixed effects to control for the endogeneity of trade agreements (Bair and Bergstrand 2007).

# • **Construction of the Data Set**

We employ the Centre d'Études Prospectives et d’Informations Internationales (CEPII) gravity data set to estimate the change in Egypt's imports following Egypt's trade agreements. The CEPII database contains total trade (summed across all HS codes) for each country pair over a long time span, but we limit the data to 1996-2019. The CEPII dataset includes an indicator for regional trade agreements (RTA) for each country pair-year covered by an RTA registered with the World Trade Organization. The CEPII database consists of 248 importers and 248 exporters, and we limit the sample to country pairs with positive trade flows. We draw upon the World Bank’s World Development Indicators data for the annual GDP.

As explained, the empirical strategy compares the change in trade between country pairs that have a regional trade agreement. We generate a dummy variable equal to 1 indicating when Egypt is an importer and another equal to one when Egypt is an exporter and interact each of

> 6 Chung and Wall (2005) find that unless heteroskedasticity is accounted for correctly, gravity models will typically overestimate the effects of integration with regards to trade.

7
. The means are equivalent to circles with radii of 27 and 21km respectively. This makes the district mean 50% larger than the mean area of the “ _zones d’emploi_ ” used in Combes (2000) and 100% larger than the mean of the Colombian municipalities used by Duranton (2016), although the mean is 50% smaller than the mean of the Indian districts used in Lall et al (2004).

Both firm censuses subsequently collected much more detailed production data from a nationally representative sample of the firms identified in each census (this sample was called phase two). This production data could be used to explore possible productivity effects of agglomeration but unfortunately is not representative at the district level, as the phase 2 sample was stratified across the 10 regions, sectors and several firm‐size categories only, but not districts. Since we thus do not have a reliable district‐level measure of productivity, we instead study possible effects of agglomeration on employment growth. Because the age of the firm was also asked in phase 1 of the censuses we can also examine the extent to which the location decisions of new firms are influenced by agglomeration forces.

Adjustments and improvements to the firm census data were undertaken for this paper. The NIC and IBES were collected using different industry classification codes, and some districts were split between the censuses. Thus, harmonization of the industrial classifications and districts was required. To harmonize the districts, we matched the IBES districts to the list of districts previously used in the 2003 NIC. This required combining (aggregating) those 2014 districts that had been created by the splitting up of any 2003 districts into a number of smaller (disaggregated) parts. In order to harmonize industry codes, we ensured that both NIC and IBES were matched to ISIC (revision 4) 4‐digit activity codes.

In separate research we have undertaken on the comparability of the Ghana manufacturing firm census data, we have found that there are a number of comparability issues between the 2003 and

> 3