rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Trade in goods increased from about 43 percent of GDP in 1995 to almost 60 percent in 2017 (World Bank 2020). Increasing globalization has sparked interest along several dimensions. One such dimension, the focus of the present study, is the impact of exporting activity on job market prospects of women relative to men. Theory suggests several reasons for such a relationship. For instance, greater competition associated with international trade raises employers’ cost of discriminating against female workers and thereby increases female employment. Reallocation of resources from non-exporting firms to exporting firms can affect female employment depending on how intensively females are employed by the two types of firms. Access to foreign technology and greater mechanization linked to trading activity shifts job requirements from less “brawn” to more “brains”, favoring females more than males.

The empirical validity of the relationship between exporting activity and female employment remains to be properly established, especially for the case of developing countries. The present paper attempts to fill this gap in the literature. We do so by using firm-level survey data for several developing countries to estimate the relationship between exporting activity and female (relative to male) employment. We provide several endogeneity checks. We also pay close attention to the mechanisms at play and the mediating factors that may enhance or mitigate the impact of exporting on female employment. These checks not only help to uncover the relationship between exporting activity and female employment, but also help raise our confidence against possible endogeneity concerns (discussed below).

There is a growing literature on the impact of trade on female employment. However, most of the studies in the area are restricted to specific countries, and the cross-country studies that do exist tend to rely on macro-level data. Available empirical evidence is mixed. Some of the studies 2
following example of part-time work: a low-skilled woman employed as a house cleaner, who must commute to a neighborhood with high socioeconomic status. Before the free rides program, she has a time-consuming commute on foot. This leads to an

> 38We use the PoI section data to identify husbands and wives, and the InP section data to determine individuals’ average monthly wages between January and March 2021, the three months prior to the scheme’s implementation. After matching data from these two sections, 21% of the observations did not respond to the income survey, raising concerns about potential selection bias. However, over 70% of those who failed to report income did so because interviewers were unable to reach the households during the designated time span. Among the matched sample with income observations, about 99% of the households reported the husband’s wage as being higher than or equal to the wife’s. In these households, husbands’ wages are, on average, twice as high as those of their wives.

> 39Fletcher, Pande and Moore (2017) show that the types of jobs Indian women report wanting vary by age, but are primarily of a part-time nature, reflecting the demands of other household responsibilities, particularly in the context of marriage and childbearing. 73% of women willing to take a job prefer regular, part-time work while only 22% report wanting regular, full-time work; the remaining 5% want a mixture of only occasional full or part-time work.

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the randomly drawn price was higher than their actual WTP and not more than their stated WTP, they would be expected to buy the solar lantern even if they did not want to. Neither of these outcomes is in the best interest of the respondent. Individuals were also informed that if their stated willingness to pay was greater than or equal to the randomly drawn price, they would be given the opportunity to purchase the solar lantern at the randomly drawn price. Payment could be made immediately or after about a month. In cases where a household decided to purchase the solar lantern immediately, the household paid and received the solar lantern immediately.

> 3 The exchange rate at the time of the survey was 1 USD = 28 birr. This implies that the range of 20 to 250 birr is about 0.7 to 8.9 USD. The price interval of 10 birr (about 36 US cents) is quite small. The 10 birr interval was chosen because respondents’ WTP was expected to typically be in multiples of 10. This is also confirmed by the distribution of WTP data we collected from this study, in which all respondents provided their responses in multiples of 10, except 7 of the 808 respondents, whose WTP was in multiples of 5. We also note that, while the maximum price of 250 in the range is more or less the same as the market price of the solar lantern, the minimum price of 20 birr was chosen with the expectation that households may typically have a WTP of 20 or more; this is also confirmed by the results of the study, which show that only 5 of the 808 respondents had WTP of less than 20.

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and paternal education, rural residence, and household size. Descriptive statistics and bivariate associations of child development outcomes, risk and protective factors, and demographics with child gender were first summarized within each age group in the non-imputed data. Bivariate relationships were explored using Pearson chi squared tests for categorical and dichotomous variables and unadjusted Ordinary Least Squares (OLS) linear regression for continuous variables. We adjusted for multiple hypothesis testing by computing sharpened false discovery rate (FDR) q-values for each of the bivariate associations (Benjamini et al., 2006; Anderson, 2008).

To answer research question 2, we conducted an initial exploratory analysis running separate multivariate OLS linear regression models for each risk or protective factor (henceforth factor) to examine whether child gender is a moderator in the association between the factor and child development outcomes in the imputed datasets. Prior to imputation, z-scores were computed for variables for school readiness, psychosocial stimulation, and parental distress using surveyweighted means and standard deviations to support interpretation of coefficients. A separate regression analysis was conducted for each factor. Model 1 contained the risk or protective factor and child gender as discrete predictors and child development as the outcome, adjusted for sociodemographic covariates. Model 2 additionally included the interaction between the factor and child gender. We adjusted for multiple hypothesis testing across interaction terms within each age group by computing sharpened false discovery rate (FDR) q-values (Benjamini et al., 2006; Anderson, 2008).

Lastly, we used multivariate OLS regression to simultaneously test associations of child gender, risk or protective factors and their interactions with child development outcomes in a fully interacted model. For each age group, direct effects of all independent variables on ECD were first 8
This nascent literature also includes work by OECD/SWAC (2020), which leverages data on populations and built-up area extents for urban agglomerations from the _Africapolis_ database. In this database, urban agglomerations are identified as contiguous built-up areas from Google Earth and other satellite imagery, with a population of at least 10,000. Although they focus on a much narrower set of settlements than Henderson et al.,<sup>5</sup> the authors similarly provide evidence of a rapid growth in the number of urban agglomerations between 1950 and 2015 (see also Heinrigs 2021; OECD/UN ECA/AfDB 2022). They further document the emergence of larger networks of cities, some of which straddle national boundaries.

Whereas the above studies focus on describing urbanization dynamics over time, this paper instead focuses on providing a detailed empirical description of the anatomy of SSA’s urbanization for a single year – _circa_ 2015. A notable advantage of this is that it allows us to make use of much higher quality gridded population data. In doing so, we avoid relying on data for built-up areas only, which allows us to capture the intensive margin of urbanization relating to population density. In particular, as mentioned above, our analysis relies on high-resolution gridded population data that is derived, in part, from the digitization of the footprints of _all_ buildings in the region from very high-resolution satellite imagery. Specifically, we use constrained _WorldPop_ data from the University of Southampton’s WorldPop group for SSA countries as an input into our algorithm for delineating urban areas. Unlike the more standard unconstrained WorldPop data, which allocates population more generally across grid cells, whether built-up or not, based on many spatial covariates of population, the constrained WorldPop algorithm restricts the spatial allocation of population from larger administrative units to only built-up grid cells, where built-up area is identified based on building footprints.<sup>6</sup> The building footprints, in turn, come from the Digitize Africa project, which applied Artificial Intelligence techniques to 50
Finally, column 6 in Table 1 presents the difference-in-differences estimates of the impact of the treatment on the implicit regulatory labor tax on firms. As discussed in Section 3.4, we conceptualize the regulatory burden associated with permanent employees as an implicit variable labor cost. Within-firm changes in this hidden cost can be backed out using the firm’s cost minimization problem; they are proportional to changes in the ratio of expenditures on labor and materials. The estimated change in the log of the implicit labor cost is -0.13, significant at the 10 percent level. This implies that the implicit labor cost fell by approximately 14 percent.

Table 2 presents the estimated impacts on the log of output, fixed capital, value added and value adder per worker. The results indicate that the repeal of IDA Chapter V-B did not have a detectable affect on output, investment and labor productivity in the treated firms.<sup>33</sup> Table 1: Firm Level Panel DiD on Employment Outcomes

||(1)<br>Log<br>total workers|(2)<br>Log<br>regular workers|(3)<br>Log<br>contract workers|(4)<br>Share of<br>contract workers|(5)<br>Likelihood of<br>usinganycontract work|(6)<br>Implicit cost<br>of Labor|
|---|---|---|---|---|---|---|
|RJ * Treatment L * Post|0.031|-0.177*|0.807***|0.098***|0.140***|-0.136*|
||(0.065)|(0.101)|(0.241)|(0.028)|(0.050)|(0.075)
our whole period of analysis. All data sources are described in Appendix B.

# **4 Empirical Strategy**

## **4.1 Measuring annual municipal displacement from Venezuela**

Direct measures of population outflows are rarely available for autocratic regimes. We therefore proxy municipal-level outflows using data from Colombia, the primary destination of Venezuela’s displaced population. Our measure combines two sources of variation. Annual variation comes from total Venezuelan inflows registered by Colombian migration authorities. Municipal variation comes from VenRePS, as above. From these, we construct Origin Share _m,_ 2018 as the fraction of survey respondents who originated from Venezuelan municipality _m_ . Combining these two sources, our municipality-by-year measure of migration outflows is: where Outflows _t_ denotes total annual Venezuelan outflows to Colombia in year _t_ and _Pm,_ 1990 is the municipal population in 1990, the last census before the crisis. The numerator of Migration Outflows _mt_ allocates total outflows to Colombia across municipalities of origin, while the denominator scales this count by baseline population to express the measure as the share of each municipality’s 1990 population that was displaced.

Since approximately 2.8 million Venezuelan migrants resided in Colombia at the time of VenRePS (out of more than 7.7 million who had left Venezuela), the Colombian data do not capture the full universe of displaced individuals. Nonetheless, as the primary destination for Venezuelan forced migrants, Colombia is the most reliable available proxy for national outflows, and the survey was fielded at the peak of the migration crisis.

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consumer uses a threshold criterion instead of adjusting the stock regularly in small increments to close the gap between desired and actual level of durables. In his model the consumers buy or sell durables when the stock exceeds a certain lower or upper threshold, but do not act otherwise. He shows that households adjust stocks infrequently and expenditures react more to large income shocks than to small shocks. Furthermore, desired stocks are not very sensitive to transitory income, upward adjustments happen quicker than downward adjustments and thresholds levels vary across households. He attributes the different speed of adjustment to imperfections in resale markets while the threshold heterogeneity across households is a consequence of liquidity constraints. When households are faced with a lack of access to credit or excessively expensive finance, the upper threshold level increases as they are unable to adjust their durables stock to the desired higher level. The low threshold on the other hand will be small because if the desired stock is lower than the actual stock, households might be tempted to resell their durables in order to be able to purchase the nondurables they cannot access due to credit constraints.

Attanasio (2000) provides additional insights into the relationship of the adjustment bands and consumer income. He models the purchases of cars by US households as an (S,s) rule defining his thresholds in terms of the ratio of durables to non-durables. Using a large microeconomic dataset he directly estimates the (S,s) parameters and finds that desired ratio of durables is lower for households with lower income. In addition, he also find evidence that the (S,s) bands are large. This implies pervasive inertial behavior and potentially high relevance for the determination of aggregate expenditure. Unfortunately, as shown by the paper itself, explicitly deriving aggregate implications proves difficult.

Such aggregate implications are attempted in Eberly (1994). Similarly to Attanasio (2000), this paper 6
> Sources: United Nations World Population Prospects: The 2022 Revision. World Bank.

Note: AEs = advanced economies; EMDEs = emerging market and developing economies; EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A. The sample of each study differs. Aiyar and Mody (2011): Indian states,1961-2001; Bloom and Williamson (1998): 78 countries, 1965-90; Bloom and Canning (2004): over 70 countries, 1965-95; Bloom et al. (2000): 70 countries, 1965-90; Amer and Cruz (2016): 160 countries, 1960-2010.

B.C. Population weighted averages. The working-age population is defined as people aged 15-64 years. D. GDP-weighted arithmetic averages. Derived using production function-based potential growth. “Other factors” reflects declining population growth, convergence-related productivity growth, policy changes, cohort effects, and a slowdown in investment growth relative to output growth. “Factor” reflects the percentage-point changes between the averages of 2011-21 and 2022-30.
<mark>Group.http://documents.worldbank.org/curated/en/099815204132219104/IDU0cdb267100a24104a01</mark> <u><mark>0b1ab03dd511e0d135</mark></u> Lara Ibarra, Gabriel; Campante Cardoso Vale, Ricardo. Brazil 2021 Data Update : Methodological Adjustments to the World Bank’s Poverty and Inequality Estimates (English). Global Poverty Monitoring Technical Note ; no. 28 Washington, D.C. : World Bank Group.

http://documents.worldbank.org/curated/en/099334304122319773/IDU0a5bb83fe02e000416c0a18f09 fbdd3950173 <mark>Lee, R. D., & Miller, T. (1990). Population growth, externalities to childbearing, and fertility policy in developing countries.</mark> _<mark>The World Bank Economic Review</mark>_ <mark>,</mark> _<mark>4</mark>_ <mark>(suppl_1), 275-304.</mark> <mark>Londoño-Vélez, J., & Querubin, P. (2022). The impact of emergency cash assistance in a pandemic: experimental evidence from Colombia.</mark> _<mark>Review of Economics and Statistics</mark>_ <mark>,</mark> _<mark>104</mark>_ <mark>(1), 157-165.</mark> <mark>Lustig, N. (Ed.). (2018).</mark> _<mark>Commitment to Equity Handbook: Estimating the impact of fiscal policy on inequality and poverty</mark>_ <mark>. Brookings Institution Press.</mark> Lustig, N. & Higgins, S. (2018). The CEQ Assessment: Measuring the Impact of Fiscal Policy on Inequality and Poverty. Commitment to Equity Handbook: Estimating the Impact of Fiscal Policy on Inequality and Poverty, 219.

<mark>MacDonald, J. M
13 almost no modem inputs (see table 8 below), is labor intensive depending primarily on household labor, and uses a few rudimentary tools with an almost complete lack of mechanization. The data from the HRDS confirm that the health and nutrition status of the population is very poor, with an infant mortality rate of 92 in 1991/92 and 47 percent of children show signs of stunting. The government's past emphasis on primary schooling means that although many adults in rural households have primary schooling and the average years of schooling completed for adults is about 4.5, very few rural residents have secondary schooling.

II. Social CapitW **and Incomes** We show that associational life is in fact social capital first by showing it is _capital_ and then by showing that this capital is _social._ After establishing a strong association between the social capital in a village and incomes of households in that village we use instrumental variables estimation to argue this association is due to higher social capital leading to higher incomes and not because higher incomes lead to greater associational life. We then show the impact of village social capital on household incomes is truly _social,_ by showing that there is an independent effect at the _village_ level.

Both the SCPS and HRDS collected data on the economic and demographic characteristics of households. Total consumption expenditures per person in the household were estimated, including imputations for own produced consumption and for consumer durables (e.g. housing). We use expenditures as a proxy for incomes (and use the terms interchangeably) for two reasons. First, when there is saving and dissaving (and especially with
3B, we see that with increasing mobility, differences in work stoppages between the top 20 and bottom 20 became larger, with the bottom 20 being more likely to stop working. Similar results can be seen in the regressions in Appendix Table 1. The coefficient on quintile 5 (relative to quintile 1, the omitted category) is negative and significant in periods of higher mobility (omitted), while this effect disappears when mobility is lower.

Figure 3. Work stoppages across country-rounds are negatively correlated with mobility. Poorer workers were more likely to stop working during times of high mobility.

- A. Mobility and work stoppages

- B. Mobility and Top 20 to Bottom 20 ratio in work stoppages

Note: Each point represents a country-round. The mobility index in an average of the percentage change in mobility to retail and recreation locations, transit stations, parks, and workplaces. The average of the mobility index is calculated for each countryround. The regression lines exclude outliers (i.e., PNG in Panel A and Vietnam in Panel B). See footnotes 17 and 18 for further details.

Source: HFPS, Google mobility reports Below, countries are categorized into groups to shed light on different experiences across countries in the region.

Mongolia and Myanmar – Between April and May 2020, Mongolia and Myanmar experienced significant economic shocks and a drop in mobility due to precautionary containment measures that were put in place, in addition to the decline in global demand, particularly from China (World Bank 2021a). Round 1 of both the Mongolia and Myanmar high-frequency surveys took place after this shock during a period when mobility was relatively high (Figure 1b). At this time, poorer workers in both countries were less likely to resume work, leading to higher work stoppages (Figure 4). In Mongolia, poorer workers were significantly more likely to be out of work in May-June 2020, with workers in the bottom quintile being twice as likely to have stopped working as those at the top. A similar downward gradient in work
|279|207|100|108|

a. Child Development Workers are found in ECD program areas only (see text). n.a. not applicable to this provider type. * Mean is significantly different between program and non-program children at the .05 level. _Source_ : 2003/2004 Early Childhood Development Provider Survey.

Table 2 shows that by the third survey round, midwives and day care providers in the program areas are substantially more likely than providers in non-program regions to have received training in topics such as parent education, management of child illness, and growth monitoring; two thirds of child development workers received training on educating parents about child health. Although barangay health workers were less likely to receive training than other types of providers, they are more likely to receive health and ECD-related training if they work in a program barangay than in a non-program barangay.

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**Figure 8: Investment Financing by “High-Growth” SME’s**

<!-- Start of picture text --> Developing<br>Economies<br>(Excluding Africa)<br>Africa<br>0% 20% 40% 60% 80% 100%<br>Internal Banks Supplier Credit Equity Other<br><!-- End of picture text --> _Source_ : World Bank Enterprise Surveys, various years. Available at: www.Enterprise Surveys.org. _Note_ : ―High-Growth‖ is defined as employment growth greater than 20%. Sample size is 106 countries. Africa includes 38 countries all from Sub-Saharan Africa. All country point estimates are weighted. All regional averages are simple averages.

# **5. Key Messages and Conclusion**

Despite the recent financial sector growth in Africa over the past decades, many individuals and firms are still excluded from access to financial services in African countries. Analysis of the usage and access of financial services by adults and enterprises shows that African countries lag behind other developing economies in both aspects, and that cost, distance, and documentation requirements are important obstacles. These barriers tend to decline as per capita GDP rises. Barriers faced by both households and enterprises also tend to be lower in countries with more competitive, open, market oriented and well regulated financial systems with more developed contractual and informational infrastructures (World Bank, 2008).

Removing physical, bureaucratic, and financial barriers to expand financial inclusion is challenging since this also requires addressing the underlying structural causes. Nevertheless, measures to improve contestability of financial systems and underlying information and regulatory environment are also likely to speed up the introduction and adoption of new products, processes, and technology that may help further lessen these barriers, especially in Africa. The most evident example is the recent success of mobile money in Sub-Saharan Africa 15