rafmacalaba/data-use-mentions-extended huggingface dataset:
of Supply**

Based on World Bank Enterprise Surveys conducted between 2010 and 2020, 8.4 percent of the firms in the world chose electricity as the biggest obstacle affecting their businesses. The main reason for this is the unreliability of power supply, with outages taking place 29 times on average per month in South Asia and eight times per month in Sub-Saharan Africa.<sup>4</sup> Despite the negative impacts of electricity shortages on growth and productivity, empirical evidence remains limited. However, the emerging literature supports the positive contribution of a reliable electricity supply to firm productivity (Grainger and Zhang, 2019; Fisher-Vanden et al, 2015) and competitiveness (Mensah, 2018), as well as to household income (Samad and Zhang, 2017).

One of the first studies to address this issue was Fisher-Vanden et al (2015), which examined how firms responded to severe power shortages in China in the early 2000s. The research found that firms in regions with greater shortages decreased factor shares of electricity for production, while substituting other factors of production, but without any evidence of an increase in selfgeneration of electricity. An overall decrease in other non-electrical sources of energy indicates that these primary energy sources are complementary inputs in producing the intermediate products that have been outsourced in response to electricity shortages. The unit production

> 4 https://www.enterprisesurveys.org/en/data/

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model, it is difficult to understand and interpret the economic content of the estimated intergenerational persistence. The theoretical analysis identifies a set of economic mechanisms determining the intercept and the slope of the intergenerational educational persistence equation. In particular, the household-level returns to education in the parental generation determine relative mobility as measured by the slope of the regression function (called intergenerational regression coefficient ( IGRC, for short) in the literature). The intercept which shows the expected schooling attainment of the children from the most disadvantaged family background (fathers with no schooling) is determined by intergenerational persistence in occupation, among other factors.<sup>11</sup> A substantial literature shows that Hukou restrictions affected intergenerational persistence in nonfarm occupations in rural China (Wu and Treiman (2007)).

A credible empirical analysis of the role of occupational dualism in intergenerational educational persistence needs to address two major challenges highlighted in the recent literature: (i) truncation bias due to coresidency restrictions in surveys, and (ii) the role of genetic correlations in the observed intergenerational persistence. Most of the available household surveys, especially in developing countries, suffer from serious sample truncation as household membership is defined in terms of coresidency.<sup>12</sup> Recent evidence indicates that the standard measures of relative mobility, such as IGRC, suffer from substantial _downward_ bias in coresident samples (Emran, Greene, and Shilpi (2018)). Our empirical analysis focuses on the father-son linkage in education, and takes advantage of two exceptionally rich data sets: the rural sample from the China Family Panel Studies (2010) for China and the Rural Economic and Demographic Survey (1999) for India. Both of these surveys are unique in that they include all the children of the household head irrespective of their residency status at the time of the survey.<sup>13</sup> This is especially important in a comparative study such as ours, because cross-country comparisons based on coresident samples can lead to wrong conclusions (see Emran,
# _Comparison with other countries_

The next question is how this relationship between the adoption of management practices and firm performance differs from the pattern found in other countries. The Enterprise Survey has collected similar management practices adoption data in 15 other countries and territories.<sup>15</sup> Figure 9 repeats the regression of Column 1 of Table 6, showing the (partial) correlation between firm performance, measured by log sales per employee, and the management practices score (normalized within each country), from both a conditional regression with controls and a unconditional regression without controls. Most countries, with the exception of Lao PDR, report a positive coefficient. The coefficient from Mozambique is among the highest, at a similar level as Gambia and Suriname, but higher than most Latin American countries as well as Kenya.

**Figure 9. Unconditional and conditional regression coefficients of a regression of log sales per employee on management practices, by country.**

<!-- Start of picture text --> Suriname 2018 0.48***<br>Gambia 2018 0.48***<br>Mozambique 2018 0.47***<br>Bolivia 2017 0.34**<br>Ecuador 2017 0.27**<br>Greece 2018 0.23***<br>Paraguay 2017 0.19***<br>Colombia 2017 0.11<br>Kenya 2018 0.09<br>Argentina 2017 0.08<br>Peru 2017 0.03<br>Lao PDR 2018 -0.34<br>-0.60 -0.40 -0.20 0.00 0.20 0.40 0.60<br>Coefficent<br>Unconditional Conditional<br><!-- End of picture text --> _Note:_ The reported coefficient is from a regression of log sales on management score<sup>16</sup> , with the
(1978), US immigrants are positively self-selected with respect to unobservable abilities and motivation. Furthermore, there is evidence of certain degree of heterogeneity in the assimilation rates of immigrants coming from different origin countries<sup>6</sup> .

Borjas (1985, 1995) and others<sup>7</sup> , on the other hand, highlight the importance of “cohort effects” – the unobserved quality of successive immigrant cohorts measured by ability and motivation – which has declined steadily over time. Once these are controlled for, the convergence between immigrants’ and natives’ earnings becomes less likely. In support of this argument, Borjas (1987) presents a more nuanced description of the selection mechanisms, which relate to the characteristics of origin (versus destination) countries.

More recent studies rely on combinations of cross-section and longitudinal sources rather than the widely used repeated cross-section data to produce assimilation estimates that control for biases caused by individual heterogeneity and out-migration. More recently, Lubotsky (2007) finds previous studies to have overestimated the wage progress of immigrants who arrived between 1960 and 1980. Focusing on period 1994-2004, Kim (2009a) obtains little evidence of convergence between earnings of immigrants and natives, both at aggregate level, and by migrant’s country of origin.

Most of these papers discussed above use wages and monetary compensation as measures of immigrants' performance which provide a useful metric for easy comparison across professions and locations. Yet, the focus on wages creates other complications. First, monetary compensation might reflect local living costs and other factors. Second, wages might exhibit changes over time such as due to inflation and labor market conditions. When dealing with assimilation effects over several decades, more stable metrics of performance might be preferred.

Chiswick (1977) and Jasso and Rosenzweig (1986, 1988) are among the first papers to the study migrants’ occupational placement. In a widely cited paper, Jasso and Rosenzweig (1995) identify the differences in occupational placement of migrants entering the United States through
# **3. The Severity of the Pandemic**

While the virus has spread rapidly and extensively, the reported death toll remains surprisingly concentrated in just a few geographies. As of May 23, the cut-off date for all data in this paper, the death toll has risen to about 340,000 people. That is roughly 2.2 times the number of deaths that we would expect to occur on an average day prior to the pandemic. As Figure 1 shows, five countries—the United States, Italy, the United Kingdom, Spain and France—account the bulk of the global death toll. While they only represent 7.5 percent of the world’s population, these five countries claim 65 percent of the total number of fatalities. The widening distance in the figure between the world and the top five also implies that the death toll in the rest of the world is now growing more quickly.

Figure 1. COVID-19 Death Toll: World and Top Five Countries <!-- Start of picture text --> World<br>300<br>Top 5: US, UK, Italy, Spain, France<br>200 Top 4: US, UK, Italy, Spain<br>Top 3: US, UK, Italy<br>100 Top 2: US, UK<br>Top 1: US<br>0<br>Mar 1 Apr 1 May 1<br>Number of COVID−19 deaths (thousands)<br><!-- End of picture text --> Source: Johns Hopkins University.

## **A New Indicator of Relative Severity**

In what follows, we will examine the unequal distribution and progression of the pandemic through the lens of the ‘relative severity ratio’. This indicator is defined as the ratio of (1) the total deaths attributed to COVID-19 over a given period to (2) the expected total deaths from all causes under the counterfactual assumption that the pandemic
online appendix

# **A Data appendix**

## **A.1 EBM data**

We use the universe of EBM transactions made through EBM II as our original data from November 2019 through August 2020. EBM II is a software-based teller system introduced in 2018 as an alternative to traditional physical EBMs (EBM I); additional details on each are presented in Appendix A.1.1.<sup>A1</sup> We restrict to data in November 2019 and later, because the number of active EBM II devices in November increased by 50% relative to October, as part of a large EBM II registration drive.<sup>A2</sup>

**Coverage** EBM is mandated for use by VAT taxpayers, described in Appendix A.3. Receipts issued by registered taxpayers are transmitted via an internet connection to the RRA. In November 2019, EBM II recorded 81.8 billion RwF of value added, or 10.4% of GDP on 470,000 receipts.

**Receipt data** Each EBM is identified by a unique Sales Data Controller (SDC) ID, and issues receipts which are digitally signed with a unique Sales Data Controller Receipt Signature (SDCS). A fictitious EBM receipt is depicted in Appendix A.1.1. Each receipt enumerates the transacted items, and records the prices at (and quantities in) which they were sold.

EBM II receipts include both a United Nations Standard Products and Services A1 EBMs, or Electronic Billing Machines, are no longer exclusively physical machines. For this reason, Electronic Invoicing Systems (EIS) nomenclature is used as an umbrella term for both physical and digital/software-based receipt generating systems. To align with the colloquial usage in Rwanda, in this Appendix we refer to any official receipt generating system as EBM, and delineate versions when referring to any specific form of EBM (EBM I, EBM II). In the body of the paper, for parsimony we refer to EBM
# **1 Introduction**

The latest push for industrialisation in Ethiopia has attracted much academic and public interest. While the domestic manufacturing sector is still relatively small, Ethiopia has made large investments to promote industrial development and has witnessed some early success (Oqubay, 2015). The flagship project is the creation of several industrial parks across the country. The most famous of these parks, in the southern city of Hawassa, has attracted significant foreign direct investment from international garment manufacturers. This park alone currently employs more than 15,000 workers and exports 100 million USD worth of goods, doubling the country’s foreign exchange earnings from garment manufacturing (Butler, 2018).

These early results are surprising in the context of the recent literature on ‘premature deindustrialization’ (Rodrik, 2015). This literature argues that in the last decades developing countries have moved out of manufacturing much faster than today’s rich countries did in the past, when they had comparable levels of income. This early exit from a labour-intensive sector is feared to be an obstacle to generating employment and broad-based gains from growth. Also, the international experience with special economic zones and industrial parks has been mixed (Khandelwal and Teachout, 2016).

In this paper, I assess Ethiopia’s competitiveness and attractiveness as an investment destination by comparing the productivity and input costs faced by firms based in Ethiopia to a sample of manufacturing exporting countries. Given the current strategic importance of the garment sector, I select comparison countries that are garment exporters in Africa (Kenya), and Asia (Bangladesh, India, Vietnam). I use harmonized data from the World Bank Enterprise Survey, which enables me to construct aggregates figures for sales for worker (a measure of productivity), total labor costs, capital stock, firm size, and a somewhat noisy measure of value added. I compute these figures for the whole manufacturing sector and for the garment sector. Further, I use data from the World Management Survey to study the quality of management.
ineligible industries. For individuals who work in an eligible industry in any given quarter, but did not work in an eligible industry in the previous quarter, figure 7 plots how many individuals were in each of the following three categories (i) working in an ineligible durable goods manufacturing industry with access to IMSS, (ii) working in any other industry – nondurable goods manufacturing or outside manufacturing, or (iii) not employed – including the unemployed and individuals out of the labor force. An average of about 840 people start to work in an eligible industry each quarter and about 130 of them worked in an eligible industry and had access to IMSS during the previous quarter, a number that is quite stable over time. I thus do not see evidence that eligible industries are increasingly hiring employees away from ineligible industries after the wage subsidy program.

I also examine whether eligible industries are increasingly hiring individuals who were laid off in ineligible industries. In select quarters, the ENOE included an amplified questionnaire that asks individuals if they lost their job in the past year, extending the panel information about any given individual beyond five quarters. For individuals who report having lost a job with access to IMSS in an ineligible industry during the past year, figure 8 plots the percentage of individuals working in an eligible industry where they have access to IMSS, for the quarter where the amplified questionnaire was applied and for up to four quarters following this quarter. The initial quarter includes only 21 individuals who report having lost their job with access to IMSS in an ineligible industry during the past year, on average, and the number of individuals drops each consecutive quarter due to the rotating panel nature of the ENOE, containing only 4 individuals on average four quarters later. Although based on very few individuals, figure 8 suggests that less than 10 percent of individuals who lose their job with access to IMSS in an ineligible industry get hired in an eligible industry in subsequent quarters and that this small percentage does not increase in quarters after the wage subsidy program.
# **Data sources**

The primary data set providing the individual- and household-level information necessary to allocate fiscal policy elements is the HIECS 2015.<sup>23</sup> This survey includes modules covering health, education, economic and labor market activity, household consumption expenditure, agricultural production, and rent (or, for owner-occupied housing, imputed rent). HIECS also provides a roster per household that provides individual, demographic, and dwelling characteristics. HIECS 2015 uses an updated frame from the 2006 population census as its sampling frame and is representative at the national level, by urban and rural areas and by governorate. The survey was administered to approximately 24,000 households. For this analysis, the Central Agency for Public Mobilization and Statistics (CAPMAS) granted access to a representative 50 percent sample of the survey microdata covering 11,988 households and about 52,254 individuals.

The source for total revenues collected by the government from households––via the personal income tax and GST–– during FY15 is the Ministry of Finance. To impute “effective” or actual prevailing rates (which may differ from statutory rates), we first scale down the expected tax take from HIECS households. Scaling is done so that the ratio of tax revenues in final or audited budget reports to Private Final Household Consumption Expenditure in Egypt National Accounts data are equivalent to the ratio of VAT collections from HIECS households to the value of cumulative HIECS household consumption expenditure.

Program administrative data from the Ministry of Social Solidarity on the Takaful and Karama programs were also used. The data included the total number of beneficiaries and benefits distributed by governorate. The estimated electricity and fuel subsidies received by households were generated directly within the HIECS using reference price and subsidy rate information provided by the Ministry of Petroleum and data from World Bank (2017).

# **Assumptions and allocation overview**

When and where possible, the study allocates fiscal policy elements to individuals or households based on direct observation. For example, when an individual
services_ , which consists of the availability of the following services: firm floor, electricity, drainage and water access in the property; (5) _IT items_ , which refer to the number of computers and cellphones; and (6) _IT services_ , which sums the home phone, internet and pay-TV; (7) _ln(imp cons)_ , is the imputed measure of non-durable expenditure on food personal products, and clothing. Data employed comes from 1997 ENCASEH and 2017-18 ENCEL. Panel A shows the result from a cross-section estimate which only uses data from 2017. Treatment is a dummy variable that identifies if the observation belongs to one of the 320 original Progresa RCT treatment localities. Panel B does a DiD estimation with 1997 and 2017 as the two periods considered. Panel C estimates an ANCOVA, which controls for the pre-existing level of the dependent variable (denoted as Y(97)). Controls include (from ENCASEH 1997): HH head age, education, speaking a dialect, HH size, % members under 15, dummies for home, productive land and draft animals ownership, and locality marginalization index. Standard errors clustered at the locality level. Asterisks indicate significance at the ***1%, **5%, and *10% level.

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# **6 Concluding remarks**

This paper attempts to increase the reliability of assessments of yield-input relationships when yields based on self-reported survey data on crop production are subject to non-classical measurement errors. We show analytically and empirically that it is possible to construct imputed measures of objective crop cut yields, using a machine learning model and a sub-sample of plot observations with both crop cut and self-reported yield measures in a household survey. The imputed crop cut yields are shown to be less biased vis-`a-vis self-reported yields, and the errors in the imputed measures are uncorrelated with input use per hectare. As such, the regressions of imputed crop cut yields replicate the true relationships between yields and inputs that are estimated with observed crop cut yields. These conclusions are robust to whether we use the data from a small-scale methodology study conducted in southwestern Mali or a nationally-representative survey.

From a data collection point of view, these findings carry the implications that more objective methods of collecting crop production data need to be integrated in the protocol of household surveys. We rely on two household survey data sets from Mali with the rare characteristic of having both self-reported and crop cut data. The first data set from a methodological experiment enables us to empirically show that it is possible to combine self-reported yields data to judiciously predict crop cut yields and, thus build a measure of yields that appears to be fit for a correct assessment of the relationship between yields and inputs with a random sample representing one-third of the entire sample. The second data set, a nationally representative survey, enabled us to validate the approach but also served as a stark reminder that even when a data collection method is theoretically objective, execution is key to ensure that meaningful inference can be made based on it. Although these results are promising, further work considering other crops should be pursued to improve the machine learning predictions, evaluate the limit of the inferences that can be carried out using this approach, and better understand its potential bias reducing properties.

Finally, our analysis expands on the
So far, the results have focused on the persistence of water quality impacts in downstream areas by measuring the direct spillover externality imposed by upstream pollution. In Table 3, we provide the estimated impact of the within‐district externality by measuring the impact of pollution on health in the same district using the 2SLS procedure outlined in equations (2) and (3).

The first‐stage is strong across all columns and the upstream concentrations are significant. When local concentrations are instrumented with upstream pollution levels in the second‐ stage, all specifications yield statistically significant estimates of the pollution impact and the effect of nitrogen pollution in water on height is negative, and large in magnitude. Diagnostic statistics for instrument relevance such as the Kleibergen‐Paap F (Kleibergen and Paap, 2006) statistic show that the instrument is very strong. The F‐statistic exceeds the Stock‐Yogo (Stock and Yogo, 2005) weak identification critical value for 10% maximal instrumental variables size.<sup>7</sup> The point estimates from the 2SLS procedure are relatively much larger than the corresponding downstream spillover impact in Table 2, supporting the logic that as water pollution decays with river flow and time, the downstream impacts are likely to be smaller in magnitude than the within‐district health impact. The results show that exceeding the nitrate‐nitrogen safety standards over the entire period decreases height by 2.81 centimeters.

Because adult height is associated with income, this implies a productivity loss of around 7% using decrease in height estimates under full exposure derived from column 1 and using estimates of the economic returns to height assumed in the World Bank Human Capital Project (Kraay, 2018). When using estimates of a decrease in height under mean exposure derived from column 1, this translates into a 1.7% fall in productivity or earning potential.<sup>8</sup>

# 5. Robustness Checks

We carry out several robustness exercises to further corroborate our baseline results.

In order to examine the possibility