rafmacalaba/data-use-mentions-extended huggingface dataset:
Aassve et al. 2020).

Yet countries differ greatly in terms of the pandemic’s spread and mortality. In terms of the number of accumulated confirmed COVID-19 cases as measured in the John Hopkins University (JHU) COVID-19 data on November 19, 2020, several countries, such as Brazil, India, and the United States have had more than 5 million of cases. In contrast, 11 countries or regions have had fewer than 100 confirmed cases. For the 187 sample countries or regions in the JHU data, COVID-19 cases per million residents range from about 3 to 78,634, with the highest observed in Andorra, Bahrain, Belgium, Montenegro, and Qatar, all above 47,500. Meanwhile, Cambodia, Lao PDR, Tanzania, Qatar, and Vietnam have the lowest cases, all fewer than 19 per million residents. Countries around the world have also reported very different COVID-19 mortality rates (i.e., the number of COVID-19 deaths divided by the number of cumulative cases), ranging from 0 to 29 percent. What explains the vast differences in the pandemic outcomes?

The unprecedented challenges of the pandemic compel every country, no matter the 2
The paper uses undernutrition as the measure of individual welfare to explore the questions posed above. We use data for 30 countries in Sub-Saharan Africa (SSA), where chronic undernutrition among children is a major policy concern. The latest data at the time of writing indicate tha <mark>t the count of stunted children in SSA has risen by 12.5 million since 1990. The incidence of child stunting in SSA today is probably the highest of any of the standard geographic groupings of countries.</mark><sup>8</sup> <mark>W</mark> e draw on anthropometric data for 390,000 women and children from the Demographic and Health Surveys (DHS).

> 7 We provide references on these points later.

> 8 These observations are from the World Bank’s <u>website</u> on nutrition and the latest available estimates compiled by UNICEF. Historically, South Asia has been the region with highest incidence but that region has been making greater progress than SSA in this respect. Also see the discussion in Smith and Haddad (2015). Differences in population growth also affect this shift to SSA of the global share of the undernourished.

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March 13). _Xyt_<sup>_′_isavectorofcontrols</sup> that includes weather variables (temperature and humidity) and indicator variables for hour of the day, the day of the week and week of the year (i.e., an indicator of the week within a calendar year, starting from week 1), and _εyt_ is the error term.<sup>16</sup> We conduct the analysis comparing 2020, when we expect reduced mobility and levels of PM2.5, to 2023, when we expect mobility to have normalized in the city.<sup>17</sup> There was consistent data (more than 80 days during the analysis period) available from two regulatory-grade monitors in 2020, Guediawaye and Republique. We include data from both monitors and control for monitor fixed effects. For 2023, there is only data from a third regulatory monitor that was recently installed in Pikine. We use the hourly MERRA-2 data for 2020 and 2023 for the city. There is only one Purple Air monitor for which data is available for several months before the policy in 2020 and also after the policy, but this monitor does not have data beyond 2020. Instead, for 2023 we include data from all monitors, which includes data on at least 80 different days within the period of 110 days of the analysis. We control for monitor fixed effects. In addition to using the unadjusted Purple Air data, we also conduct the analysis using two off-the-shelf calibrations of the data from Barkjohn and Jaffe, as well as our local and satellite calibrations.

We conduct several robustness checks. We conduct the analysis at the daily level, using both the

> 15These included banning public and private gatherings, a curfew from 20:00 to 6:00, and closing schools.

> 16Precipitation is not included as a weather control because the period of analysis includes only the dry period in Dakar and precipitation is consistently zero.

> 17There were still some COVID-19 policies in place in 2021 that could
worker. However, percentage of high school graduates, distances to a major highway and to navigable rivers, and kilometers of paved road per person have no effects, once market potential is controlled for.

We now describe the model and estimation strategy employed in our analysis. The data used for the analysis have been produced through a joint research program between IPEA, Brasilia and the World Bank. Detailed description of the variables and their sources are provided in Appendix C, and a descriptive overview of Brazilian city growth is in da Mata et. al (2005). There is no official statistical or administrative entity in Brazil that reflects the concept of a city or urban agglomeration that is appropriate for economic analysis. Socioeconomic data in Brazil tend to be available for municípios, the main administrative level for local policy implementation and management. Municípios, however, vary in size. In 2000, São Paulo município had a population of more than ten million, while many other municípios had only a few thousand residents. Furthermore, many functional agglomerations consist of a number of municípios, and the boundaries of these units change over time. Our analysis therefore adapts the concepts of agglomerations from a comprehensive urban study by IPEA, IBGE and UNICAMP (2002) resulting in a grouping of municípios to form 123 urban agglomerations (Figure 1). Throughout this paper we refer to these units of analysis as agglomerations, urban areas, or cities.

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Y|Y|

Note: Sample restricted to firms with more than 100 workers. Treatment refers to firms with 100-300 workers. All dependent outcome variables represented in log forms here have been assigned an underlying value of 1 when they take a value of zero. Share of contract workers is defined as the share of contract worker in total worker hours used by the firm for that year. Implicit cost is defined using the expenditure on all raw materials as a base. Standard errors in parentheses, clustered at the firm level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.) Finally, we present analysis suggesting that the strength of the impact of the IDA amendment is linked to the inherent technological viability of contract labor in firms. Following the method used in Rajan and Zingales (1998), we proxy for the inherent viability of (or dependence on) contract labor in firms by using national averages of contract labor intensity by industry. Specifically, we measure the national average firm level share of contract labor in total employment in each industry, and classify industries with above (resp., below) median contract labor share as high (resp., low) contract labor intensity industries. Our assumption is that industries with higher 27
_β_ 3 will be biased upward. Behrman and Birdsall (1983) actually find only a small bias in their empirical estimates, however.<sup>11</sup> Finally, and contrary to the fairly benign results presented so far, using data from the 1988/89 Ghana Living Standards Survey, Glewwe (1996) finds significant selection bias in the estimation of equation (1) because it typically is estimated only on wage-employed workers, ignoring the large share of the labor force that is self-employed in Ghana. He also uses a unique set of test scores collected in the survey to control for ability. He finds that controlling for ability and selection into wage employment reduces the coefficient on years of schooling from 8.5 percent to between 3.6 and 6.1 percent depending on the age of the worker and whether he or she works in the public or private sector.

Given the weight of the evidence and Card’s general observation that the bias is unsigned, and lacking any data on ability or schooling quality, we proceed with the straightforward equation (2).<sup>12</sup> In all our estimates, we are unable to distinguish between wage-employed workers who attended public versus private school, so we must assume that the returns to public and private schooling are similar. In appendix A, we show that between 25 and 38 percent of primary school students are enrolled in private schools, but this share falls to 10 to 15 percent for secondary school and university students. As we show below, a significant part of the return to primary schooling is the option to continue on to higher levels where labor earnings increase more sharply with additional education and where the vast majority of students attend public schools.

# _Calculating monetary benefits of an additional year of schooling_

One difference between our paper and much of the literature is that our aim is to estimate the benefits, in Ghanaian cedis, of an additional year of schooling rather than a rate of return because we want to compare those
requires identifying a vector of rental values for the observed population—object of the distributional analysis. For each household, the rental value to be used in the construction of a welfare aggregate is the actual rent paid by market tenants ( h ), and the imputed rental value for home-owners and tenants not paying the market price ( h ). Information on rent paid by market tenants is generally available in TT rr household budget surveys as the regular payment to landlords for living in the dwelling, although data from OO rr other sources might be also used (e.g. administrative registers, listings, mortgage transactions, experts’ survey). Imputed rent for home-owners and non-market tenants must instead be estimated.

Several different imputation approaches have been developed, and no one has emerged as superior to others so far (Balcázar et al. 2017). Notwithstanding the lack of consensus about the best method to adopt, two approaches, in addition to excluding the housing component _tout court_ from the welfare aggregate, are commonly 7
alleviates the concerns of bias at the school level, especially given the small sample of schools. Therefore, we use the within-school control as our reference.

Table A1, in the appendix, presents a comparison of the descriptive statistics of GABECE scores, socioeconomic characteristics, WASSCE scores and researcher designed exam scores for the treatment group, within and between-school control groups before matching. It also shows the descriptive statistics of all the students in the study together. Regardless of the group, the grade 12 students are generally about age 20 years with similar socioeconomic backgrounds. However, the PSI-PMI group has fewer female students than in the other two groups. Students in the pilot program performed better on the GABECE in mathematics and science, compared with the within-school control students, and the pilot program students were comprehensively superior (in all subjects) to the between-school control group. Tables A1a and A1b in the appendix present GABECE scores and socioeconomic characteristics after matching at the student level, showing overall that the different groups are comparable pre-intervention.

This matching method, however, relies on the assumption that unobserved characteristics are also orthogonal to the participation in the program, conditioned on the observables, a somewhat stringent assumption. We supplemented this method with a preanalysis that builds further confidence in the estimates. Prior to students taking the final regional exam WASCEE 2018, we administered a test and used that test along with student-level data to conduct a first analysis and predict the impact of the program. When the students took the WASCEE at the end of the year, we then use that data to replicate the findings, building confidence in the estimates and obtaining similar results from different data sources made available at different points in time.

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# **2.4 Data on private and municipal preschool centers**

We use administrative data from _Censo Escolar_ on municipal and private preschool centers and their inputs for the years 2000 to 2006. _Censo Escolar_ is a compulsory yearly census conducted by the Ministry of Education, in conjunction with the state-level education departments. This data set gathers information on the universe of public and private preschool centers in Brazil. In each year, it comprises data on enrollment, group size, and teacher qualifications. In addition, it collects information on the infrastructure and equipment of each center, such as whether it has adequate sanitation for preschool, a playground, and a fridge. To ensure that the information is reported accurately, inspections are conducted every year on a random sample of centers. The administrative information from _Censo Escolar_ is widely regarded as highly reliable to monitor developments in the Brazilian public and private child care sectors.

Table 3 reports descriptive statistics on municipal and private preschool centers in different population intervals, using pooled data for the period 2000-2006. We observe that, in the set of municipalities considered, municipal supply accounts for about 88% of enrollment and 84% of the number of centers. We also observe that the proportion of enrollment accounted for by private providers tends to be higher in larger municipalities, reaching about 20% of the total in the largest population intervals. We further observe that participation in preschool among children aged 4 to 6 is at about 49%, of which 43 percentage points is accounted for by municipal enrollment.

It is also important to note that the _Censo Escolar_ data reveal that enrollment in preschool increased considerably over the sample period. In the municipalities that comprise the estimation sample, preschool enrollment rose by about 25%. This overall increase reflects an expansion of enrollment in both the municipal and private sectors (25% and 22.7%, respectively).

Table 4 reports summary statistics on the aforementioned quality indicators of municipal and private providers. We observe that private centers tend to have systematically higher quality
Centre, while mineral production data came from the United States Geological Survey.

Our study offers three main conclusions. First, when the analysis is aggregated, mining activities significantly increase spatial inequality in Africa, a finding that is consistent with the standard resource curse literature. Second, when the analysis is disaggregated to include individual minerals, our findings suggest that mining activities impact on spatial inequality both positively and negatively, with the effects varying quite considerably across different minerals. This finding suggests that mineral wealth can be both a blessing and a curse. More importantly, this finding—a unique contribution of this study—reinforces the idea that disentangling the effects of individual minerals is more revealing of the effects of mining activities on development outcomes, and thus is important for eliciting the relevant policy responses.

Finally, our study shows that the effects of mining activities are leveraged by other important forces that underpin mining operations. For example, our analysis suggests that the coefficients of interest largely depend on whether mining activities are active or closed, small or large scale, of low or high value, and extractive or transformational. We argue that these are also relevant variables for informing appropriate policy responses aimed at addressing the nexus between mining activities and spatial inequality in Africa.

> 24 We have not reported these results here due to space constraints, but they are available from the authors upon request.

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a range of poverty estimates that reflect uncertainty in the estimated pass-through rate and the underlying national accounts growth rates, as well as allow for changes in inequality.

Under our preferred specification, using a pass-through rate of 0.67 applied to growth in Household Final Consumption Expenditure in national accounts between 2015 and 2017, we estimate a national extreme poverty rate (those living below the $1.90 poverty line) for 2017 of 10.4 percent.<sup>5</sup> Using a survey-to-survey estimation, the national poverty rate would be slightly smaller (9.9 percent), but its confidence interval, between [8.1, 11.3] percent, includes the estimates from the pass-through method.<sup>6</sup> Our estimates indicate a considerable decline in poverty since 2011/12, when poverty was estimated at 22.5 percent. Important caveats in the methodologies adopted, as well as some robustness checks to control for different assumptions, indicate that poverty rates could be higher than our preferred estimate. But we find no evidence that poverty has actually increased, or the mean declined, between 2011/12 and 2017/18, thus contradicting estimates that have been circulated in the press based on a leaked report on the 2017/18 survey (see Appendix for further details).

The paper discusses three sources of evidence about the evolution of poverty in India. Section 2 uses alternative survey data, from both public and private organizations, to provide descriptive statistics on household mean consumption. Section 3 describes the survey-to-survey imputation method, whiles section 4 describes the results from the pass-through method. Section 5 summarizes and concludes. The Appendix includes additional robustness checks and further details on the methods.

# **2. Available survey data for India**

The Consumption Expenditure Surveys (CES) by the National Statistics Office are the main source of poverty and inequality statistics in India. These surveys have also informed the World Bank’s poverty monitoring and are used to track
could thus be expected. Annual cross-section regressions give R<sup>2</sup> s comparable to those obtained in similar cross-section regressions.<sup>13</sup> Leverage has a positive and significant coefficient for Tobin's Q. Leverage has, however, a negative coefficient for profitability. This may be because under central planning Czech firms financed their long-term investment needs differently from their working capital. Their subsequent corporatization, including the determination of the book value of equity, may have led to a negative association between high-leverage and initial profitability. The corporatization may have rneant, for example, that firms which received more investment loans ended up more leveraged. Since during the transition these high capital intensive firms were generally less profitable, a negative association between leverage and profitability could have arisen. The crosssection regression using 1995 data alone suggests that this may be the explanation as the coefficient for leverage in the profitability regression turns positive.

The result for the concentration variable is that the lower the dispersion of ownership (the higher H), the higher Tobin's Q and profitability (Table 2, regression i). Ownership by the top five investors, A5, has similarly a significantly positive influence on Tobin's Q and profitability (not 11. We tested the joint significance of sectors and year dummies in all regressions. The sector dummies are always jointly significant at the 99% level. The year dummies are jointly significant at the 99% level for all profitability regressions; they are jointly significant for Tobin's Q in Regression (i) at the 95% level, but turn insignificant in Regressions (ii) and (iii).

12. As noted, the correlation between Tobins Q and profitability goes up over time. In the year-by-year regressions, the R<sup>2</sup> s then also increase for the profitability regression.

13. For example, the R<sup>2</sup> s for the 1995-regressions (i) for Tobin's
Overall, the descriptive analysis suggests the following three findings: (i) firms in Kenya are those with larger ICT use; (ii) the differences in ICT use between manufacturing and services are not significant; and (iii) larger firms show significantly larger levels of ICT use than small firms.

_<u>Table 13 ICT Index by Country, Sector and Size</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|88.77|82.78|66.83|72.78|106.67|136.45|100.00|
|**Main Sectors**||||||||
|Manufacturing|91.05|82.39|72.84|71.92|124.63|134.99|102.33|
|Service|86.39|82.91|64.59|73.06|101.68|137.10|97.70|
|**Size**||||||||
|Small|70.61|73.00|60.26|70.17|98.31|114.76|75.85|
|Medium|116.54|120.12|86.39|66.72|111.73|160.99|127.69|
|Large|159.93|185.25|106.92|162.53|201.37|183.41|173.10|

Source: Author’s own elaboration from enterprise surveys

# **6. ICT use, innovation and productivity**

## **6.1 Are ICT adopters more productive?**

A
In terms of labor productivity, Argentina’s performance is similar to Chile’s and better than Brazil’s and Mexico’s (figure 3), but falls within the low labor productivity growth characteristic of the last two decades in the region. Argentina’s labor productivity increased by 7 percent between 2007 and 2016 but lagged behind Brazil (9 percent) and Chile (12 percent). These figures suggest that innovation investments, especially in the private sector, are not sufficient and/or effective enough to have an impact on the much-needed productivity growth. In addition, the fact that the ratio of labor productivity to R&D expenditures fell by 40 percent in Argentina but only by 30 percent in Brazil between 2007 and 2016 suggests that there might be room for improving the profitability of expenditures on R&D.

**Figure 3. Labor Productivity, Argentina and Comparators, 2007–16**

<!-- Start of picture text --> 15%<br>50<br>40 10%<br>30<br>5%<br>20<br>0%<br>10<br>Canada Chile Argentina- Mexico Brazil<br>Levels 2016 Growth rate 2007-16<br>Productivity growth 2007-16<br>Labour productivity per hour worked (USD) 2016<br><!-- End of picture text --> _Source:_ The Conference Board Total Economy Database.

To fully benefit from increases in R&D expenditure and encourage private participation, it is important to identify possible inefficiencies in the role of knowledge in the production function, which might be deterring the contribution of R&D to labor productivity. In fact, when we compare R&D intensity and increases in labor productivity by sector, we do not always find the positive relation we would expect. Some sectors have high levels of R&D intensity but low productivity growth and vice versa (figure 4). In addition, an important share of
management for the relationship between trading and the female labor share and finds that both expand the female labor share premium of trading firms. Finally, additional analysis explored the link between a firm’s female labor share and average wage rates and in particular the mediating role of the firm’s trading status. The results suggest that while average wage rates are lower for firms with higher female labor shares, this negative correlation is smaller for trading firms.

While this paper explored the relationship between trading and female labor participation from a variety of angles, i.e. including different types of trading and female labor participation at the firm level, we identify at least four areas for future research: (i) What is the role of policy in shaping the relationship between trading and the female labor share? For example, how does country or sub‐national flexibility of labor regulations shape the findings? (ii) Do patterns hold when more explicit measures of technology or innovation (beyond TFP) are included? (iii) Do the findings hold when worker characteristics are taken into account (requiring the use of firm‐level surveys or merging firm‐level data with datasets on worker characteristics)? (iv) And finally, can a sub‐group of countries with panel data be used for a stronger test of causality?

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