rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9887

# **Abstract**

This paper studies factors that could account for the asymmetric impact of the COVID-19 pandemic in Latin America, by exploiting microdata from the World Bank’s high-frequency phone household surveys conducted immediately after the onset of the pandemic. The paper codifies the occupation variables in these surveys, constructs measures of the individual’s potential for work from home, and estimates fixed-effects models of job loss and other labor outcomes. In line with previous studies, the findings show that the impact of the COVID-19 shock was (i) harder for women and (ii) strongly decreasing in the ability to work from home. Importantly, the analysis finds that the mitigating effect of working from home on the severity of the impact was especially relevant for women with children. These effects were larger in countries/periods in which the containment measures implemented by governments against the spread of the disease were more stringent. The paper also provides suggestive evidence on a plausible mechanism underlying the results: women with children were more likely to stay home due to school closures and the traditional intrahousehold distribution of childcare responsibilities, and thus the possibility of working from home was crucial for them to keep their jobs.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at lgasparini@cedlas.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the
# Annex 1 Global Income Distribution Dynamics Model

We use the Global Income Distribution Dynamics (GIDD) model, developed by Bussolo, De Hoyos and Medvedev (2010), to estimate distributional effects. GIDD is a “top-down” micro simulation framework that exploits heterogeneity observed in household surveys to distribute macroeconomic shocks. These shocks are aligned with a macroeconomic model such as the CGE model used in this paper. More specifically, we impose consistency between the GIDD and the CGE models in this paper in various ways. First, both use the same United Nations projections in aggregate population and age and education structures. The GIDD then uses estimates from the CGE model as inputs into the household model. In particular, as inputs into the calculation of changes in per capita household income, the GIDD uses CGE model estimates of differentiated wages for skilled, unskilled, agricultural and non-agricultural labor and changes in the prices of agricultural and non-agricultural goods.<sup>20</sup> Finally, all household incomes are adjusted proportionally so that the percentage change in the aggregate of household incomes in the GIDD is consistent with the CGE model’s estimate of the percentage change in real income.

GIDD was developed by the World Bank and was inspired by previous efforts involving simulation exercises (Francois Bourguignon, Bussolo, & Pereira da Silva, 2008; François Bourguignon, Ferreira, & Leite, 2008; Davies, 2009). Previous examples of application using CGE outputs and GIDD include the effect of agriculture distortions in the global economy (Bussolo et al., 2010; Dessus, Herrera, & de Hoyos, 2008), the effect of demographic change on Africa (Ahmed, Cruz, Go, Maliszewska, & Osorio Rodarte, 2014) and external and internal shocks in Africa (Devarajan et al., 2015).

The first step in the microsimulation exercise is to implement a set of changes in the household surveys’ demographic structure. The population growth adjustment is particularly important in countries with high fertility rates, such as those in Sub-Saharan African
Policy Research Working Paper 8592

# **Abstract**

This paper presents the most expansive and current cross-country dataset on education quality. The database includes 164 countries/territories covering over 98 percent of the global population from 2000–2017. Of these countries, 111 are low or middleincome economies. Harmonized learning outcomes are produced using a conversion factor to compare international and regional standardized achievement tests. These tests include PISA, TIMSS, PIRLS, SACMEQ, LLECE and PASEC. In addition, this paper includes the Early Grade Reading Assessment (EGRA) for the first time. This enables extension of the database substantially. This methodological update paves the way to include a series of assessments that are increasingly common in developing countries that are often excluded from large international assessments. The database includes mean scores and disaggregates the data by subject, level, and gender. This paper further presents a series of methodological improvements including measures of uncertainty, fixed conversion factors for greater comparability over time and year-by-year data.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2019: The Changing Nature of Work_ —is a product of the Education Global Practice. 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://www.worldbank.org/research. The authors may be contacted at hpatrinos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its
years. Nonetheless, given the general similarity in the coefficients across years, we focus on the pooled sample results. The results for individual characteristics mirror the bivariate results presented earlier. As noted, the gender gap in education has closed over time. This is reflected in the coefficients of the gender variable for the regressions for the three years for which we have available information. In 1994, boys had a 2.4 percent higher probability of attending school, controlling for other factors. In 2000 there was no gender difference in attendance rates. By 2005, the gender gap had reversed, and girls had a 1.3 percent higher chance of going to school than boys.

> 13 In this case, the lowest level of geographical identifier that we have in the DHS data is the cluster, so we take the average of the wealth index for each of these units. To avoid the endogeneity of this variable, following Lanjouw and Ravallion (1999), we calculate the left out mean, i.e. generating the average of the wealth index in the household’s cluster, but leaving out the value of the wealth index for that household.

10
# **1. Introduction**

There a large number of studies on the relationship between informality and firm productivity and, for the most part, the literature finds a negative relationship between the two. For example, Perry et al (2007), using data from the World Bank’s Enterprise Survey Database, find a negative correlation between output per worker and various indicators of formality in Argentina, Bolivia, Mexico, Panama, and Peru. Although their analysis controls for a series of firm and location characteristics, it does not take into account the potential endogeneity of a firm’s formal or informal status. In fact, in a study specific to Peru—a country for which Perry et al (2007) find the largest gap in productivity between formal and informal firms—World Bank (2008) finds no statistically significant effect of formality on firm profitability once selection controls are added to the equation. On the other hand, country-specific studies in Bolivia (McKenzie and Sakho, 2010; World Bank, 2009) and Mexico (Fajnzylber et al, 2009) do find a negative relationship between informality and firm profitability even after controlling for the potential endogeneity bias. World Bank (2010) reports similar findings for the relationship between informality and firm productivity in Turkey and Fajnzylber et al (2006) identify a negative impact of informality on firm revenues in Brazil.

This paper estimates a relationship between informality and firm profitability using a new survey of more than 1,200 small urban manufacturing and service firms conducted in Ecuador in May-June 2011. The richness of the survey allows for a variety of controls for firm, owner, and geographic characteristics, as well as for identifying determinants of informality which attempt to address the endogeneity problem. The paper finds that informality has a significant negative impact on profitability and productivity for otherwise comparable firms, and this finding is robust to different measures of informality and profitability. The paper also provides some evidence on the determinants of this relationship, such as the ability to avoid fines, issue receipts
Since house rental cost is likely to be the largest expense when children decide to leave parental home, we would expect coresidency rates to be higher in locations where rental rates are higher (Nicoletti and Francesconi (2006)). We take advantage of the recently available built up density data as a source of variation in house rent. Note that built-up density is a measure of supply of housing in a location, with a lower house rent where built-up density (supply) is higher. This implies that we expect a negative relation between built up density and coresideny rates on a priori grounds. The source of built-up data is the Global Human Settlement Layer (GHSL) (Pesaresia et al. (2015)). The built-up data are at 300 meters by 300 meters grids. We super-impose the digital maps from the censuses on the pixel-level data to estimate the total built-up area at the district level.

We use 1975 built up density in a district interacted with time trend in the estimating equation where the dependent variable is a dummy indicating whether a child is coresident with his/her father (with location and year xed eects along with the other control variables used in the DiD design), and generate the estimated probability weights.<sup>2</sup> Built up density interacted with the time trend has substantial explanatory power in the coresidency equation; the estimated coecient is signicant at the 5 percent level, and has a negative sign consistent with the a priori expectations. To check if the IPW correction is eective, we estimate the linear and quadratic mobility models in Table B.6 discussed above using IPW correction for census data. The IPW corrected census estimates are reported in columns 3 (linear CEF) and column 6 (quadratic CEF) of Table B.6 in this appendix. The evidence suggests that the IPW correction makes the census estimates closer to the corresponding IFLS estimates.

We then apply the IPW correction to our estimates of the eects of Inpres schools reported
performance of East Forest farm households is mirrored in the outcomes for food crop farmer households. Food crop farmers in the East Forest had a 19 percent decrease in farm income compared to the 52 percent drop in the West Forest. While the household expenditure data are more or less in line with the farm income data, they nonetheless beg the question of why West Forest farmers fared so much worse than East Forest farmers during this period.

Table 4 Farm Income of Farmers Across Rural Regions _(in 1985 CFAFperyear)_

||_Percentagze charge, 1985-1988_|
|---|---|
|_Region_|_Export crop farmer_<br>_Food crop farmer_|
|East Forest|-17.9<br>-3.5|
|West Forest|-33.6<br>-51.6|
|Savannah|-42.1<br>-18.6|

Source: Grootaert (1995). Redeflated with the INS deflators.

Cocoa production data from other sources are at some variance with the household survey results for West Forest.8 The cocoa production data, put together by Agricultural Price Stabilization Fund (CSSPA), are broken down by administrative department and reflect the estimation of cocoa production based on cocoa sales (table 5). The survey data show that sales increased by roughly 20 percent in the East Forest if we compare 1985 with 1987 or 1988 and decreased by roughly 40 percent in the West Forest. Trends in the survey data are roughly consistent with the CSSPA data for East Forest in that both show an increase in the 1986/87, 1987/88 and 1988/89 campaigns compared to 1984/85. The strong increase in the sales per household evident in the 1988 East Forest survey data is 1985 and 1988, and by 28 percent in the West Forest. Coffee sales declined by 7 percent in the East Forest and by 47 percent in the West Forest over the same period.

The CILSS surveys were fielded over the following periods: 1985
and _L_ = education level (2) LLjj LL LLjj LLjj UUhii = ZZhii + vvii jj= 0, ... , JJ

> 7 Note that although it is possible to estimate specific models for salaried and non-salaried workers based on the microdata from the household survey, it was not possible in this case to use these models because this information is not generally available from the macro side. Macro-economic projections are calculated mainly for aggregate economic sectors, such as agriculture, industry and services, instead of wages or self-employed, formal and informal sectors.

> 8 McFadden (1974).

> 9 Bourguignon and Ferreira (2005) say that this interpretation is not fully justified because occupational choices may actually be constrained by the demand side of the market, as in the case of selective rationing, rather than individual preferences.

5
,tt XX revenue generated from other sources. We include the interaction between debt to GDP and tax threshold to allow for potential non-linearities in the impact of debt on growth. The terms κ and κ represent country and year fixed effects, respectively. We employ this model to analyze the j- cc period ahead prosperity gap as an alternative dependent variable. tt GJW methodology recommends choosing the threshold to maximize the R-squared of the model via a grid search across potential threshold values, but this method may present certain drawbacks. For example, the incremental increase in R-squared might lack statistical significance suggesting an inability to meaningfully choose across threshold outcomes. Moreover, the maximized R-squared does not necessarily mean that the coefficient of interest which reflects the magnitude of the discontinuous jump in the growth rate, will be at its optimal value at the same threshold. To address these concerns, our approach focuses solely on the coefficient of the tax dummy threshold, as it is more relevant for capturing any discontinuous jumps in the effect on growth or the dependent variable of interest.

# **4. Data**

The main variables used in this paper are described in table 1 and their descriptive statistics in table 2. The revenue-based indicators are from the International Monetary Fund’s Government Statistics Finance (GFS) database. We select the level of government for each country (that is, general, central, or budgetary central government) to maximize the number of non-missing observations in a set of spending and revenue categories over the sample. There is likely a vital role that fiscal decentralization can play in the relationship between tax and growth, however, given data limitations, we use aggregated data focusing on general where data is available or central government in the remaining countries. We also focus on tax revenue including social security contributions to GDP and use this definition of tax revenue throughout the paper. Since coverage of government debt data 7
systems with stronger rights. It is worth to investigate whether the bank-based versus market-based distinction has an independent influence on corporate risk-taking, over and above that of the legal framework of the country. Thus our final hypothesis is that corporations in bank-based financial systems have higher debt and overall higher measures of corporate risk; however, the relationship could possibly be indirect with the legal system being a common causal factor.

We explore a multitude of measures of firm financial risk, in addition to the commonly-used leverage and maturity structure of debt measures. We do so since there exist different sources of risks and since not all risk-measures need to go in the same direction. Much of a firm's risk arises from the variability of its income. These risks are not captured by leverage and maturity structure of debt measures, but rather by the relative variation of income or sales over time. Financial measures such as leverage, in contrast, capture only the exposure of firms to financial shocks, such as changes in exchange rates or shocks to the supply of fumds, and do not control for the operational risks of the firm. Measures such as the ability of a firm to cover interest payment from its operational income try to cover both financial and operational risks, but provide again a partial picture since the focus is on flow rather than stock measures of risks.

# **4. Data**

We collect data from Worldscope, a database which has been used in a number of recent papers. Worldscope covers publicly-listed corporations in 54 countries. The sample we use includes all companies except financial firms (SIC codes 6000-6999) and regulated utilities (SIC codes 4900-4999). We use a balanced sample of firms over the period 1995-1996, with the exception of five ratios that are computed over the period 12
|
||||||(0.231)|(0.229)|(0.228)|
|How Much Of An<br>|||||0.012|0.032|0.034|
|Obstacle: Labor||||||||
|Regulations?||||||||
||||||(0.301)|(0.301)|(0.300)|
|Population (logs)||||||-38.37***|-37.069***|
|||||||(10.833)|(10.729)|
|Urbanization (%)||||||1.630***|1.633***|
|||||||(0.406)|(0.411)|
|WBL Index||||||15.209*|14.790*|
|||||||(8.451)|(8.393)|
|Women can work the||||||2.882|2.999|
|same night hours as<br>||||||||
|men Y:1 N:0||||||||
|||||||(2.879)|(2.861)|
|Women can work in||||||-1.485|-0.858|
|the same industries<br>||||||||
|as men Y:1 N:0||||||||
|||||||(2.114)|(2.094)|
|Ratio of Female to||||||2.474***
6|5.9|
|ers|60–69|17.9|17.4|17.2|
|rch|50–59|27|27.8|27.8|
|sea|40–49|23.9|21.9|19|
|Re|30–39|13.8|13|13.1|
||Under 29|13.5|15.4|17|
||70 and older|20.3|22.2|24.7|
||60–69|36.1|34|32.4|
|Ds|50–59|28.1|29.7|29.9|
|Ph|40–49|13.5|12.3|11.3|
||30–39|2|1.6|1.8|
||Under 29|0.2|0.1|0.1|
|f|70 and older|5.9|7.4|9.7|
|s o<br>e|60–69|26.4|25.1|23.8|
|ate<br>enc|50–59|28.5|28.9|29|
|did<br>ci|40–49|23.5|21.4|18.8|
|an<br>S|30–39|12.1|13|14.3|
|C|Under 29|3.5|4.1|4.5|

Source: Indicators of Science (2009) – MoES, ROSSTAT and the Higher School of Economics

> 6 55 years for women and 60 for men.

15
spending is procyclical and falls with the realization of negative macroeconomic shocks, perhaps due to an inability of governments to borrow during bad times, then OLS estimates of the multiplier would be biased upwards. A further possibility is that, in aid-dependent countries such as many of those studied here, any procyclical tendencies in domestically-financed government spending are offset by countercyclical tendencies in aid-financed government spending, so that total spending could be either procyclical or countercyclical.

I address this endogeneity problem by constructing an instrument based on the lags between commitment and eventual disbursements on loans by official creditors to developing country governments. Some institutional background is helpful in order to better understand this identification strategy. My dataset, described in more detail in Section 3, covers loans from multilateral and bilateral official creditors to developing country sovereign borrowers. Table 1 provides some summary statistics on the lending activities of official creditors included in my dataset. I first report total disbursements, disaggregated by major multilateral and bilateral creditors, for the decades of the 1970s, 1980s, 1990s, and 2000s. Disbursements on these loans are substantial, totaling nearly $1.8 trillion in constant 2005 prices over the past 40 years. Over time, the importance of multilateral creditors relative to bilateral creditors has increased substantially, with the share of the former increasing from about one-third in the 1970s to nearly three-quarters in the 2000s. This reflects a steady shift on the part of most bilateral creditors over the past 40 years to providing aid in the form of grants (which are not reflected in the DRS database), rather than loans (which are).

These loans are a traditional vehicle for aid donors to provide financial assistance to developing country governments. Consistent with this objective, the loans in this dataset are highly concessional on average, typically with long grace and repayment periods, as well as below-market interest rates. The bottom panel of Table 1 shows that, on a loan-weighted average basis, these loans have a maturity between 20 and 25 years, and an initial grace period (during which no payment
|---|
|Schools|100|Discovery Schools (DS)|
|Administrators|200|Principals and assistant principals|
|Teachers|3,000|Users of e-content in DS|
|Students|80,000|Users of e-content in DS|
|Ministry experts|120|Writers and reviewers|
|Lab technicians|1000|Jobs created as an outcome of JEI|
|Youth|150|New college graduates offered internship opportunities|
|Local companies|5|Worked on developinge-content for the first time|

Source: Authors’ calculations

# **Performance**

Consistent with its objectives, JEI has been effective in: (i) establishing a test bed for innovation in the realm of ICT and education in Jordan; (ii) helping develop the ICT and education industry in Jordan; and (iii) developing and testing a model of education reform based on partnerships that can be applied by other countries.

6
topic models identify topics in text data, assigning a probability of belonging in a topic to each word. We run two unseeded models: the `Location Mentions` model with a four-way variable identifying locations as ‘IDP only,’ ‘Returnee Only,’ ‘Both,’ and ‘Neither’ as covariates for comparability and to be able to explore heterogeneity, and the `Return Date` model with a dichotomous return variable for pre- and post- return. The results below display our analysis on models with 20 topics, chosen to maximize topic coherence both mathematically and through observation of models with different topic numbers (Mimno et al. (2011)). For all structural topic model analysis, expert annotators labelled each topic by looking at the 30 most salient keywords in that topic both in terms of frequency “F” and frequency and exclusivity “FREX”. Throughout our results, we present the English translations of the topic labels.

11