rafmacalaba/data-use-mentions-extended huggingface dataset:
# Childcare, COVID-19 and Female Firm Exit Impact of COVID-19 School Closure Policies on Global Gender Gaps in Business Outcomes<sup>∗</sup>

Markus Goldstein, Paula Gonzalez, Sreelakshmi Papineni,<sup>†</sup> and Joshua Wimpey<sup>‡</sup> Gender,Entrepreneurship,Firms [JEL]J16,L25,L26,O12

> ∗Corresponding author: Sreelakshmi Papineni: spapineni@worldbank.org

This paper has greatly benefited from discussions with Marianne Bertrand and we thank her for her insightful and invaluable comments. We thank World Bank GIL and DEC Global Indicators (DECIG) seminar participants for their useful comments, in particular, Jorge Rodriguez Meza and Norman Loayza; and Elia Pietro Boe for excellent research assistance. We thank Facebook Data For Good, especially: JW Schneider, Kelsey Mulcahy, Laura McGorman, Caroline Abadeer, Arturo Gonzalez, and Curtiss Cobb. This paper is an output of the World Bank GIL. We are grateful to the World Bank’s Umbrella Facility for Gender Equality (UFGE) for financial support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †World Bank Africa Gender Innovation Lab (GIL)

> ‡World Bank Development Economics Enterprise Analysis Unit (DEC)
Figure 3 – Demeaned Wages and Demeaned Migrants (for work only) <!-- Start of picture text --> cairo<br>6-Oct<br>alexandria<br>ismailia<br>kafr el sheikh qualiobiagiza port said red sea suez<br>north sinaihelwan<br>matrouh<br>new val ey l uxor damiettasouth sinai<br>bani suef<br>aswan<br>fayoum dakahlia<br>menoufia assiut qenagarbeyya<br>beheira menia sharkia<br>sohag<br>-400 -200 0 200 400<br>dmwage<br>denetmig2 Fitted values<br>10<br>5<br>0<br>-5<br>-10<br><!-- End of picture text --> Source: Authors’ calculations using Labor Force Survey

# **Do migrants earn higher wages?**

Migrants receive, on average, slightly higher monthly wages (EGP1133.) than non-migrants (1033.EGP). Migrants’ wage premium compared to non-migrants increases with educational attainment (see Table 11).

Table 11- Mean Wages (in EGP) by Educational Level for Migrants and Non-migrants

|Education level|Non-migrants|Migrants|
|---|---|---|
|Illiterate|953|853|
|Read & Write|955|1222|
|Less than Intermediate|865|807|
|General Secondary|1005|877|
|Tech. Secondary|990|976|
|Above Intermediate|977|1030|
|University|1275|1422|
|Above university|2385|4909|

An OLS model (where the dependent variable is log hourly wage) controlling for levels of education and introducing a migration dummy, reveals that migrants receive a 4.7 percent higher wage premium compared to non-migrants (see Table 12). Introducing age or experience 10
9.6|11.3|12.7|14.3|12.8|12.7|
|Sargan|0.91|0.86|0.90|0.98|0.88|0.99|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

28
very important in light of the reasons outlined in a large literature arguing that size and composition of the middle class is critical for strengthening and stabilizing the democratic system and its institutions, for a better economy‘s performance, and for social cohesion. Moreover, while our definition follows a vulnerability-to-poverty approach and empirically establishes a new lower threshold for middle class identification, other existing definitions have tried to answer the question of what income level to choose for middle class measurement, as discussed above. We contrast these definitions with our proposed range and found that the middle class has increased in Chile, Mexico and Peru during the period under study, contrary to the results presented from the application of the Ravallion and B&D methods (shown previously: see figures 1-2).

The divergences are rather evident: the absolute lower threshold defined by Ravallion and B&D ($2 a day) is a very low standard for middle class comparisons across the upper-middle income countries analyzed in this paper. Actually, the official poverty lines in all three countries are more than twice the $2 threshold, suggesting thus the inclusion of poor households as part of the middle class (see Kernel distributions in annex; figure A2). The latter implies that the decrease in the size of the middle class, according to B&D and Ravallion definitions, was motivated by the fact that a proportion of households climb out of poverty during the analyzed period: in Chile from 32.9 to 15.1 percent over 1992-2009; in Mexico from 23.1 to 20.1 percent over 1992-2008; and in Peru from 21.6 to 19.6 percent over 1997-2009. The vulnerability-to-poverty measure of middle class proposed in this paper corrects these trends.

# **7. Concluding remarks**

This paper proposes an empirical methodology to analyze the middle class based on the notion of vulnerability to poverty. Following a regression-based approach this paper exploits panel data to estimate the amount of comparable income –$10 dollars a day, associated with a
|1x1km Grid|1,337,567|113,908|1,271,094|95,603|106,534|124,674|136,769|
|Kebeles|15,714|3,157|10,220|1,956|2,233|2,781|3,020|

## **S8.2 OLS Results**

Table S4 shows results from OLS models; models (1)–(6) show results using 1x1km pixels as the unit of analysis and models (7)–(12) show results using Kebeles as the unit of analysis. The results show road upgrades associated with gains in nighttime lights and urban land, particularly in areas with medium-tohigh initial baseline nighttime lights.

S12
for age (5-9 y.o.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement 0.41 (-0.17_ 0.38)T, Dropout from school* (5-8 yo.) (Martinez et al. 2017) Maz Preschool Construction & TT & Parental Engagement > 0.14 (0.05, 0.22) T, Schooling index (5-2 y.o0.) (Berlinski et al. 2008) URY Preschool Construction 0.04 (-0.03, 0.02), Years of schooling at age 7 (7 y.0.) (Berlinski et al. 2008) URY Preschool Construction -0.00 (-0.03, 0.027, Years of schooling at age & (8 yo.) (Berlinski et al. 2008) URY Preschool Construction 0.04 (0.02, 0.03)7, Years of schooling at age 9 (8 y.o.) NOTE: Weights are from random effects analysis -2.07 Lt) 2.07
.66|0.00|71.20|
|_Cars_|||||
|log number of cars (N=1,252)<br>_Agricultural Land_|3.42|1.02|0.88|8.30|
|% of Village agriculture that is paddy|44.46|37.65|0.00|100.00|
|% of Village agriculture that is plantation|55.14|37.58|0.00|100.00|
|_Textural and spectral characteristics_|||||
|Pantex (human settlements) mean, scale 8m|0.56|0.50|0.00|3.95|
|Pantex (human settlements) mean, scale 32m|0.66|0.59|0.00|4.68|
|Gabor flter (scale 64m, features 6), mean|0.67|0.32|0.01|2.03|
|Gabor flter (scale 64m, features 14), mean|0.68|0.31|0.02|1.95|
|Histogram of Oriented Gradients (scale 16m), mean|37.91|9.66|0.00|146.18|
|Observations (N)||1,360|||

Note: GPW=Gridded Population of the World; GHSL=Global Human Settlement Layer. HIES=Household Income and Expenditure Survey; GUF= Global Urban Footprint; NDVI = Normalized Difference Vegetation Index. The NDVI scale refers to the size of the window used to calculate average NDVI. Summary statistics of LASSO-selected indicators across all models are reported.

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Gridded Population of the World (GPWv4) dataset, developed by Columbia University based on population and housing censuses (CIESIN, 2016).

Data are available in five-year steps for the period from 2000 to 2020 with a spatial resolution of 30 arc-seconds (which is equivalent to about 1 kilometer at the equator). We use the 2015 version for further analysis.

Income data is obtained from Kummu et al. (2018) who provide gridded global datasets for GDP, GDP per capita, and Human Development Index (HDI) for every year from 1990 to 2015. GDP and HDI estimates are provided at 5 arc-minutes resolution, while GDP per capita (PPP) has a resolution of 30 arc-seconds. To generate these datasets, Kummu et al. (2018) use World Bank datasets, the Central Intelligence Agency’s (CIA) World Factbook, and subnational GDP per capita data developed by Gennaioli et al. (2012), and employ country-specific interpolation and extrapolation. These gridded datasets are consistent worldwide and comparable across countries and years. Figure 2 shows gridded GDP maps for six years. Areas that do not have any population are dropped from our analysis.

# **3.2 Model**

We simulate the wind-dispersion of air pollutants from coal power plants in four steps.

First, we use the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) to simulate the movement trajectories of particles originating from power plants, in line with Du et al. (2020). We generate 96 forward trajectories for 6,321 power generation units. Four such trajectories are visualized in Figure 3. Pollutant movement is calculated based on the Global Data Assimilation System at a 1 arc-degree resolution. We assume each pollutant particle has a four-day (i.e. 96 hours) lifespan and an initial altitude of 100 meters. Starting from the originating plant, the location of particles is simulated beginning from 11 am on January 1st, 2013, in hourly increments until 11 am on January 5th. Each location is a three-dimensional coordinate
# **Appendix A. Further Details of the Modeling Approach**

Our focus is on elucidating the tradeoff between the abatement and productivity benefits of improved energy efficiency and the opportunity costs of acquiring expensive relatively expensive technology-specific capital. To that end, we strive to keep the analysis as transparent as possible by choosing to represent the remainder of the economy in a simple and straightforward fashion, following the canonical model in Sue Wing and Balistreri (2014). In sectors where discrete technology options are not represented, the production function is specified according to the nested CES structure of the non-technology component identified by the dotted area in Figure 2.B. In each region the representative agent has CES preferences denominated over the 14 commodities, and a consumption elasticity of substitution of 0.5. Production of the government good has a similarly parameterized CES structure. Distortions are specified in terms of ad-valorem equivalents and are maintained at their benchmark levels given by the GTAP database. We do not impose government budgetary balance, or constrain revenue and expenditure in the counterfactual scenario to match their baseline trajectories. Similarly, we simulate the economy’s full-employment equilibrium, eschewing labor market closures such as labor-leisure choice that capture the potential impacts of productivity improvements on, and introduce the additional general equilibrium feedbacks of, elastic labor supply. To be sure, results such as the employment consequences of energy productivity gains will be of interest to policy makers, but they are secondary to our purposes here, and as such we leave their elaboration to future research.

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<!-- Start of picture text --> 1990 1995 2000 2005 2010 2015<br>Year<br>China Indonesia<br>Malaysia Thailand<br>Philippines Vietnam<br>.8<br>.7<br>.6<br>Human capital (US human capital=1)<br>.5<br><!-- End of picture text --> Source: Penn World Table 9.0 Note: Human Capital, H, in the Penn World Table 9.0 is assumed to take the form (based on Mincer equations): H =exp(.134*min(yr_sch,4)+.101*max(0,min(4,(yr_sch-4)))+.068*max(0,yr_sch-8)) where yr_sch is country’s average year of schooling. Years of schooling for Indonesia between 2011 and 2014 are extrapolated.

A breakdown of contributions by TFP, capital accumulation, growth in labor force and human capital reveals that capital accumulation plays a key role for convergence toward the U.S. level of output per capita (red bars in Figure 6). This is true across countries and periods.

TFP seemed to also play a key role between 2000-2007, but the role of TFP was clearly smaller for many countries (China, Thailand, Vietnam, Malaysia) between 2008 and 2014. Average TFP growth for these countries was sharply lower between 2008 and 2014. China’s average TFP growth was 1.3%, Vietnam’s was 1.1%, Malaysia’s was -0.36%; and Thailand’s was 0.24%.

# **Figure 6 Growth contribution of TFP, capital accumulation and growth in employment and human capital**

8
country. Indeed, Do and Levchenko (2007) show that the variation in these coefficients across sectors is substantial, generating variation in predicted trade patterns across countries.

There is another potentially important issue, namely the zero trade observations. In our gravity sample, only about two-thirds of the possible exporter-importer pairs record positive exports, in any sector. At the level of individual industry, on average only a third of possible country-pairs have strictly positive exports, in spite of the coarse level of aggregation.<sup>10</sup> We follow the Do and Levchenko (2007) procedure, and deal with zero observations in two ways. First, following the large majority of gravity studies, we take logs of trade values, and thus their baseline gravity estimation procedure ignores zeros. However, instead of predicting in-sample, we use the estimated gravity model to predict out-of-sample. Thus, for those observations that are zero or missing and are not used in the actual estimation, we still predict trade.<sup>11</sup> In the second approach, we instead estimate the gravity regression in levels using the Poisson pseudo-maximum likelihood estimator suggested by (Santos Silva and Tenreyro, 2006). The advantage of this procedure is that it actually includes zero observations in the estimation, and can predict both zero and non- zero trade values in-sample

> 10These two calculations make the common assumption that missing trade observations represent zeros (see Helpman et al., 2008).

> 11More precisely, for a given exporter-importer pair, we predict bilateral exports out-of-sample for all 61 sectors as long as there is any bilateral exports for that country pair in at least one of the 61 sectors.

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farm households defined by the head's primary occupation had a secondary non-farm activity (as either a secondary activity of the head or an activity of the spouse), and only 7% of households classified as being in the non-farm sector by our criteria had a secondary farm activity.

Banks such as Grameen have relied heavily on female membership.! It was not feasible with these data to estimate a gender-specific measure of the gains from switching; there were only 180 female heads or spouses with occupations as recorded in the HES.

The survey data identify household location by 20 districts, and the data are representative at that level. One district (Chittagong Hill Tracts) did not have any observations in the sample, and two were aggregated to contiguous districts for sample size considerations.

We thus have 17 areas in all. Data on bank branches are also available by district.

# **5 Estimated gains from sector switching**

Table 2 gives our estimates of the parameters of equations (4.1 )-(4.2), and their HuberWVhite standard errors. We include variables describing primary occupation, location, education, land ownership and demographics. The Appendix gives our test results for sector-selection bias, which indicate we can safely omit correction terms from the consumption equations.

Some of our specification choices were difficult. Including occupation dummy variables raises a concern about possible endogeneity, although there is likely to be a trade off here with

**8** This is not necessarily because of gender-specific gains from setting up new enterprises; more plausibly, it is because of the organization's desire to promote female empowerment and because women appear to be less likely to default. In any case, we would conjecture that the gains from women alone shifting to the non-farm sector will be highly correlated with the gains from the head shifting, as we have measured them.

13
Beyond a country’s fiscal space and public expenditure levels, we identify four different types of factors that are significantly correlated with intergenerational mobility. First, child health indicators are seen to be positively correlated with intergenerational mobility. We observe a negative coefficient for stunting, a positive coefficient for immunization rates, and negative coefficients for the prevalence of anemia and mortality rates. When children from disadvantaged backgrounds do not have access to adequate nutrition and health care, this may have long-term negative effects on their cognitive development. The resulting gaps in cognitive development means that disadvantaged and advantaged children could be learning at different rates, even when they would have access to schools of comparable quality.

Second, proxies for school quality are found to be positive factors: Intergenerational mobility tends to be higher in countries where there are more teachers per student (i.e. lower pupil-teacher ratios) and where the share of children dropping out of school early is low.

Third, proxies for segregation are negatively correlated with mobility: countries where the share of total inequality in years of schooling that is due to between-district variation in schooling (versus within-district variation) tend to report higher levels of intergenerational mobility in education. Ethno-linguistic fractionalization is similarly negatively correlated with mobility, albeit weakly.

Fourth, factors that may be indicative of the reach of public interventions are positively correlated with relative mobility: countries with higher mobility levels are more likely to be countries that are relatively small in terms of population and/or land area and tend to be more urbanized (possibly because it is easier to organize high-quality public interventions with a broad reach when countries are small with the population living in closer proximity to each other and to public services).

Labor market variables are found to exhibit little to no correlation with intergenerational mobility in education. These variables will conceivably report stronger correlations with measures of income mobility which, unlike education mobility, will be more sensitive to the functioning of the labor market.

# **6. Robustness**

In this section we re-evaluate the trends and
which found that certain socio-demographic characteristics such as education, gender, and income level had a role to play in determining the level of financial inclusion of Sri Lankans. Fintech can also play a role in improving financial inclusion for rural communities, as seen in many parts of the world. A study by Colombage (2010) explored the relevance of fintech in the Sri Lankan context.

However, none of the aforementioned studies specifically focused on the implications of financial inclusion in contributing to inclusive growth in Sri Lanka. Analysis of the status of financial inclusion with a view towards creating a more level playing field and increasing opportunities for productive engagement needs to consider socio-demographic factors as well. This paper provides a comparative analysis of the state of financial inclusion in Sri Lanka, taking into consideration socio-demographic factors. It analyzes the determinants of financial inclusion using data from the 2018 National Financial Inclusion Survey for Sri Lanka. It concludes by highlighting some implications for financial inclusion and inclusive growth in Sri Lanka.

# **1.1. A Brief History of Financial Inclusion Initiatives in Sri Lanka**

The arrival of the British in 1828, and the establishment of the commercial plantation sector, saw the start of formal financial services in Sri Lanka (Seelanantha and Wickremasinghe, 2009). A parallel informal financial sector also emerged, composed primarily of moneylending and pawnbroking. As pawnbroking became popular, laws were enacted to protect customers<sup>6</sup> and implement anti-usury policies (Karunagoda, 2007). The 20<sup>th</sup> century saw the rise of the cooperative movement as an organized mechanism to serve the farming community’s financing needs. Sri Lanka’s central banking system was formally established in 1950 following independence from the British, where the Central Bank of Sri Lanka’s (CBSL) primary objective initially was on monetary policy.<sup>7</sup> In later years, the CBSL formally took on the role of managing and deploying development finance lending programs on behalf of the
34 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity

# **6. Conclusions**

This paper proposes a more integral way to measure urban form and its relationship with economic performance, measured as the density of radiance within the urban extent, _dr_ntl_2010_ , computed as the sum of the NTL DN values in 2010 divided by area, in square kilometers, of the urban extent in 2010. Instead of using population density as a proxy for urban form, we use seven variables that cover the three dimensions of urban form: shape of the urban extent, structure of the urban texture and land use pattern. For the empirical evidence we use two strategies for dealing with the endogeneity between urban form and productivity: First, using lagged explanatory variables as in Fallah et al (2010); and second, using instrumental variables as in Harari (2015).

Using information from 919 urban extents in Latin America, the results show that the shape of the urban extent, the inner-city connectedness, the level of urbanization and the population level have a statistically significant influence over the productivity level of the city. We also found evidence of the presence of a congestion effect on productivity since the results for _population_<sup>_2_</sup> and _fullness_<sup>_2_</sup> are both significant and negatively associated with productivity. According to these results, a compact, dense and well-connected city meets important conditions to be highly productive.

An important consequence of decomposing the concept of urban form into three dimensions is that a non-compact city can reach high levels of productivity by guaranteeing a high level of inner-city connectedness; or, it may happen that a compact city that is poorly connected can show low levels of productivity. Another feasible situation using our way to characterize urban form is that compact cities that are poorly urbanized can have low productivity. In summary, our empirical evidence shows that each city can find its way towards a
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20