rafmacalaba/data-use-mentions-extended huggingface dataset:
we examine the relationship between _contemporaneous_ exposure to pollution and hour worked. Note that an important omitted variable is local level of traffic. High traffic could raise air pollution and, by increasing transportation costs, also reduce labor supply. This alternative explanation would imply a contemporaneous relation between these two variables. However, the relationship is small and statistically insignificant (see column 2 Table 4).<sup>37</sup> Columns 3 through 5 in Table 4 check the robustness of our results to alternative specifications. Results are robust to using non-linear functions of weather, a narrower definition of susceptible population, and using a log-linear specification. In column 6, we also test alternative variance structures, and estimate standard errors using the procedure suggested by Conley (1999).<sup>38</sup> A key finding is that PM2 _._ 5 has different effects on hours worked in households with and without susceptible dependents. As discussed in Section 3.2, there are some observable differences between these two types of households. Households with susceptible dependents tend to have more income earners, relatively younger workers, and be slightly poorer. A relevant concern is that these differences, not the presence of susceptible individuals, are driving the heterogeneous results. This could happen, for instance, if poverty, worker’s age or household size influence how pollution affects labor supply.

We examine this alternative explanation by estimating a model using individuals in both types of households and adding interactions of PM2 _._ 5 with an indicator of having a susceptible individual and other observable characteristics (see Table B.3 in the Appendix). This specification allows us to examine other sources of heterogeneous effects of air pollution. Results, however, confirm the original findings: PM2 _._ 5 only seems to reduce hours worked among individuals in households with susceptible individuals.

Finally, we examine sample attrition as a source of selection bias. Note that, by using a panel sample and household fixed effects, we reduce concerns of
construct duration statistics and presents a few stylized facts. The results of our analysis are presented in section 4. Section 5 concludes.

# **1 Background: Definitions and Data**

Under the terms of the 1951 Convention Relating to the Status of Refugees –henceforth the Convention– later amended by the 1967 Protocol, a refugee is a person, who “owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion, is outside the country of his nationality, and is unable to, or owing to such fear, is unwilling to avail himself of the protection of that country.” Data on refugees and asylum seekers are collected by individual countries, international orga3
in Malawi, we are able to train and test models using different resolutions of satellite data, and we find that the models are surprisingly accurate in predicting street- and neighborhood-level variation in wealth within these cities.

Fourth, the censuses and extracts allow us to evaluate whether imagery-based models can make accurate predictions of changes in wealth over time. Previous efforts were again substantially constrained by ground data that did not repeatedly sample the same locations in different surveys [29]. As a result, it remains unclear whether an imagery-based model trained largely to predict spatial variation in wealth or consumption would be able to predict temporal variation, as the latter is typically both smaller and potentially driven by changes that are harder to detect in imagery. Repeated census data from the same locations 10 years apart in Malawi and Mozambique allows us to evaluate whether models can indeed extract information from imagery capable of predicting temporal variation in asset wealth.

# **Results**

## **Performance on prediction of country-level wealth**

For country-level wealth prediction, we train each model on each country individually and conduct country-wise five-fold cross validation for each model. The CNN model uses only Landsat satellite imagery as input. XGBoost utilizes geospatial features (geo-features) either alone or combined with satellite image statistical features. All models were trained to predict the asset wealth index (AWI) [29]. Estimates of AWI were generated and linked to imagery at fine administrative levels in Madagascar, Malawi, and Mozambique. In Burkina Faso, AWI estimates are only available for 334 communes (Table 1). This reduced the effective sample size of the training data in Burkina Faso, as seen in the clumped pattern of survey-measured asset wealth shown in the right panel of Figure 1d. This difference is reflected in the results. As shown in Figure 1a, a naive transformer model that does not condition on geo-features consistently outperforms other models across the Malawi, Mozambique, and Madagascar datasets. In those countries, predictions using the transformer model achieve values of 0.83, 0
|0.31|0.60|
|Age (15-29)|0.36|0.48|
|Age (30-49)|0.40|0.49|
|Age (50-64)|0.16|0.36|
|Age (>64)|0.08|0.28|
|N (unweighted sample size)<br>|4,770,735||

Source: Thai Labor Force Survey (1997 - 2005) Notes: N = unweighted sample size (individual respondents) for 32 quarters from 1997-2005. Mean and Std. Dev. are calculated using population weight given by the survey.
continued receiving monthly wage payments in cash. We measure treatment effects on learning and real outcomes using panel of surveys as well as administrative data. We also employ a machine learning approach (Chernozhukov et al. 2018) to understand which types of consumers are able to learn how to use the financial technology in a more effective manner through exposure to payroll accounts. We additionally examine the market-level implications of consumer learning with a complementary audit study that examines how the prevalence of illicit extra charges changes as consumer sophistication in the market increases and information asymmetries between consumers and intermediaries are diminished.

The setting of our experiment has several attractive features that help us trace how individuals engage with accounts and learn to overcome consumer protection challenges associated with a new financial technology. First, electronic payroll accounts are a financial technology with potentially high returns to adoption that is currently being introduced to millions of workers worldwide. At the same time, payroll accounts are susceptible to many common consumer financial protection problems, such as shrouded fees, commission-based incentives, and barriers to use among inexperienced consumers. Second, we measure behaviors and real outcomes, using comprehensive administrative data that captures all financial transactions made by study participants as well as self-reported survey data. Third, we are able to examine consumer learning in a controlled environment that allows us to induce random variation in access as well as incentives to use the technology among individuals in the same workplace environment. We hypothesize that being given an account alone lowers transaction costs and facilitates savings on the _extensive margin_ by relaxing constraints related to account opening. Adding direct deposit wage payments provides an incentive to actively engage with the account on the _intensive margin_ . This variation in the intensity of account use, generated by our experimental design, in turn allows us to identify whether frequent use causes consumers to learn how to use the financial technology in a more efficient and cost-saving manner.

We find compelling evidence of consumer learning as a result of active engagement with the financial technology. Our payroll account treatments increase active engagement with the account and cause
publishing LCOEs annually for various technologies since 2009. Lazard’s estimations of LCOEs reflect investors’ perspectives, and the analysis uses data for the United States. The International Energy Agency (IEA) has been doing the same in five-year intervals since 1982. IEA conducts analysis by collecting data from selected countries, but the sample includes mostly the OECD member countries, with only a few non-OECD countries included. The International Renewable Energy Agency (IRENA) publishes its LCOEs of renewable energy technologies every alternate year. IRENA’s analysis covers only renewable technologies; it does not include fossil fuels and nuclear technologies. The US National Renewable Energy Laboratory (NREL) and the financial institution Bloomberg also publish their LCOEs. These studies are, however, focus on the United States.

Based on LCOE, several studies claim that renewable energy technologies are cheaper than fossil fuel technologies. For example, Aghahosseini et al. (2020) find that solar photovoltaics (PV) and wind energy are the most cost-competitive technologies for electricity generation in the Middle East and North Africa (MENA) region. Collecting data from many countries around the world on the capital costs and other input data to calculate LCOE, IRENA (2020) finds that renewables are cheaper than fossil fuels to produce electricity on the basis of relative LCOEs. Lazard (2019) shows that LCOEs of utility-scale PV and wind recently have been smaller than that of the cheapest fossil fuel-based technology, gas-fired combined cycle (CC), in the United States despite historically low gas prices.

Over the last two years, utilities have signed power purchase agreements (PPAs) with private suppliers for renewables at very low prices. Deign (2019) reports on solar project auctions in eight countries at prices below $25/MWh. These are: (1) China’s Jinko Power offer at $24.89/MWh for 150MW in Jordan, (2) Norwegian company Scatec Solar’s bid for a 200MW plant at $24.4/MWh in Tunisia, (3) a
|5.55|10.70|4.83|3.84|0.59|10.80|5.60|
|Uruguay|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|USA|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

34
of manufacturing firms. As described in section 2, the training data include detailed information on several training categories provided by SENAI. Based on the theoretical mechanisms discussed in the introduction, technical upgrading is the training category that is more likely to be related to exporting. These are courses aimed at workers seeking to update, expand or complement the professional skills they have acquired in a specific occupation or field or work. The other categories consist of longer term investments seeking to reconvert unqualified workers or retooling for new occupations workers whose skills became obsolete. Hence, we estimate (1) separately for the share of workers who completed technical upgrading and other courses. Column 1 uses the pooled data controlling only for year effects. Column 2 adds log firm size and other firm controls, as well as state and sector dummies. Column 3 adds firm fixed effects, but excludes firm controls and state dummies. Column 4 reports results from our preferred difference-in-differences specification, which includes firm, sector and state effects, as well as firm controls. For all these specifications, the results indicate that export participation is positively associated with the share of workers receiving technical upgrading within firms. By contrast, the effects of export participation on the share of workers trained in categories other than technical upgrading are insignificant when including firm effects.

Table 5 presents estimates from specifications similar to those in Table 4, but considering a sample including only switchers. Once again, we find that export participation is positively associated with the share of workers who received technical upgrading. In all specifications, the coefficient of the export status is positive and statistically significant. The effects of exporting on the share of workers trained in other categories are once again insignificant.

As discussed above, a concern that remains after restricting the estimation sample to switchers is that there may be unobserved firm-specific shocks (e.g. changes in management practices) that induce some firms to both enter export markets and improve the skills of the workforce through worker training. This would lead to a positive bias in OLS
# **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Maria Shkaratan on power, Elvira Morella on water and sanitation, Michael Minges on information and communication technologies, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams, namely Mukami Kariuki, Negede Lewi, Adam Nelsson, David Rohrbach, Robert Schlotterer, Peter Silarsky, and Paolo Zacchia.
interest, rainfall, should mainly have an effect in agricultural areas. The locations of rural clusters from each round are displayed in Figure 2. The surveys are based on interviews of women of childbearing age, i.e. between 15 and 49 years old, who were either permanent residents of the household or visitors present in the household on the night before the survey took place. We pool the two waves together to create a sample of about 11,500 child-level records that includes birth dates, and detailed child health characteristics.

# Figure 2

We have decided to focus on the younger children (i.e. those between 0 to 35 months of age) for several reasons. Firstly, young children are believed to be especially vulnerable to shocks that lead to growth faltering. Secondly, body size at adulthood is strongly correlated with stature at age three, and that after this age children from even very poor localities will grow as quickly as children in developed economies. In turn, adult height is correlated with earnings and productivity, poorer cognitive outcomes, and premature mortality due to increased risk of cardiovascular and obstructive lung disease (Hoddinott and Kinsey, 2001). In addition, children experiencing slow height growth usually perform less well in school, score poorly on test of cognitive function, have lower activity levels, and fail to acquire skills at normal rates. Finally, the evidence suggests that the period since birth to age three is the most critical period for the development of the immune system.

We removed biologically implausible values (most likely as the result of errors in measurement or data entry) using the cut-offs recommended by the WHO. Thus, data were excluded if a child’s HAZ was below -6 or above +6 (1,569 observations), or WHZ was below -5 or above +5 standard deviations (1,321 observations). Our final sample has about 10,000 children age 0 to 35 months living in rural areas.

The summary statistics can be compared with the
.

At the country level, Allcott et al. (2016) similarly use hydroelectric power supply as an instrument and estimate that electricity deficits reduce Indian manufacturing firms’ revenues by 5% to 10% on average. They do not, however, find that shortages translate into losses in productivity which might be explained by the ability of plants to store inputs for later usage. Looking only at small firms, Zhang (2019) finds that they are disproportionately affected by outages and face production costs that are higher by 0.29% of revenue for every percentage point increase in electricity shortages.

Indeed, firm size is an important factor in determining the ability to cope with outages as confirmed for a set of Indonesian manufacturing firms (Poczter, 2017). In their sample, the negative effect of electricity unreliability on firm productivity is more than 50% larger for smaller than for bigger firms. Further confirming the disproportionate effect on smaller firms, a survey of Ghanaian small‐ and medium‐sized manufacturing firms revealed that major power outages between 2012 and 2015 decreased average monthly productivity by 10% (Abeberese, Ackah and Asuming, 2017). In Bangladesh, Zhang (2019) uses two different sets of firm‐level micro data to estimate the productivity impacts of electricity shortages. According to her estimates, a shortage of 10% causes total factor productivity reductions of 3.1% when using a survey of the manufacturing industry or of 4.1% when using Enterprise Survey data.

A problem related to, but distinct from power outages are so called brownouts, which refer to fluctuations in the voltage provided by the electrical grid. According to one study, one third of enterprises surveyed in Tanzania experienced damage to their appliances resulting from brownouts (Bensch et al., 2017). The relationship between power outages and foregone economic activity is not only visible at the firm‐ level, but also on a country‐wide scale. Andersen and Dalgaard (2013) estimate the impact of power outages on economic growth at the
gender discrimination in inheritance law has been questioned by a large social movement in Tunisia since 2018 but the reform proposal has never reached a parliamentary vote.

This study asks whether gifting is a second-best private alternative to the reform of gender discriminatory inheritance law and whether its use can be encouraged by an informational intervention. To answer these research questions, I conducted a nationwide phone survey, reaching a sample of more than 1,500 Tunisian adults. The survey gathered extensive descriptive and correlational evidence on Tunisians’ attitudes regarding women’s right to inheritance, as well as their use of gifting. With an informational experiment which was embedded in the survey, I moreover tested whether gifting one’s daughter can be encouraged via an informational treatment.

Tunisia, probably the country with the strongest feminist tradition in North Africa, represents an ideal case study for this research. The legal status quo in Tunisia remains unchanged: a son receives twice the share of a daughter, as foreseen by the Qur’an. My study shows that the persistence of Islamic inheritance law in Tunisia is consistent with the preferences of the majority of citizens. The descriptive data I collect shows that only about one in three Tunisian adults favors the introduction of a gender equal inheritance law regime. However, legal reform does not constitute the only way of increasing women’s share in the final wealth allocation. The Tunisian civil code allows parents to make gifts to their daughter during their lifetime. By doing so, they can attenuate the gender discrimination among their children in the final wealth allocation. I find that public opinion towards gifting daughters is much more favorable than towards legal reform: two in three Tunisians state that they would be willing to use gifting to achieve a more gender equal inheritance allocation. The descriptive evidence offered by this study thus confirms that gifting is a widespread and socially accepted practice in Tunisia.

Does gifting one’s daughter represent a private alternative to the nationwide introduction of secular inheritance law? To some extent, yes. I conclude that gifting represents
for example, that for every civil war battlefield death, roughly nine people are killed in interpersonal disputes. The relevance of interpersonal violence and crime for economic activity is beyond doubt. However, as we will explain in the following section, these effects are not showing up in economic changes at the macro level but in ways which are best studied at the micro level, i.e. by collecting data on the directly affected individuals and firms. We will review this literature but, in our analysis, focus on the relationship between political violence and poverty.

This report is structured as follows. We will first give a detailed overview over the existing literature on the economic costs of violence and explain the focus on armed conflict. We then discuss the crosscountry evidence followed by regional evidence and a detailed case study of Uganda.

# Complex Relationship between Conflict and Poverty

## Violence and Poverty: An Overview

There is a clear pattern in the cross-country data - the poorest countries are also the countries which suffer most from armed conflict. This broad cross-sectional correlation could be driven by a lot of different factors including geography and colonial origins which affect both the tendency for conflict and poverty.

A first strategy to shed light on the causal link between conflict and poverty is to simply control for other factors at the macro level or to look at changes across time. The macroeconomic impact of civil wars has been studied extensively in this way for more than two decades and several studies have shown that countries which suffer from an armed conflict also suffer economic collapse. In one of the first studies, Collier (1999) analyzes armed conflict and GDP growth using cross-county data on internal wars which occurred between 1960 and 1992. He finds that civil conflict is correlated with a contemporaneous reduction of GDP per capita growth of 2.2 percent. On a regional level, Abadie and 1
Randomization to different interview modes was adopted to guide an eventual transition to recall as a cheaper way of collecting IHSES data on food.<sup>9</sup> As non-food expenditures are already measured using different recall periods depending on the frequency of purchase, the experiment was limited to food measurements. Recall groups were defined and matched to their counterparts in the diary using the COICOP classification, yielding the quantities _Y_<sup>_d_</sup> and _Y_<sup>_r_</sup> defined above. Both quantities include household estimations of the market value of food items consumed that are not acquired from the market such as self-produced items and gifts. In practice, however, market purchases account for almost all entries in our case study. We used the square root of family size to equivalize survey measurements in our analysis. Acquisitions and valuations of recalled consumption, in Iraqi dinars, were transformed to US dollars throughout.

There are no incentives for households to report specific consumption levels, for instance, to meet thresholds for social programs. The most important social safety net, the Public Distribution System (PDS), is a universal in-kind food subsidy, and other much smaller public transfers were not meanstargeted at the time of the survey.<sup>10</sup> A separate IHSES module collected information about ration items received, consumed, bartered, sold or given away by the household during the last 30 days. The diary recorded market transactions to purchase ration items over and above the monthly allocation, but these transactions in our data are rare and small in magnitude. Because our focus is on comparing diary entries with recalled consumption, the consumption of rationed products is excluded from our analysis.

# **3.3 Covariate Balance and Raw Distributions**

Table 1 documents the demographic balance for households assigned to recall modules. This randomly selected group of roughly 8,000 households constitutes our “treatment” sample, while all remaining households form the “baseline” sample. This table shows treatment-control
which implies that total expenditure on pollution abatement is not strongly correlated with value of output.

In this paper firms' pollution control investment efforts under a combination of emission charges and abatement subsidies are modeled with an assumption that firms are minimizing pollution control costs subject to technical constraints. This requires an assumption that production process and end-of-pipe treatment costs are independent. The modeling is based on data collected on China's top polluters. The conclusion drawn in this paper applies only to those top polluters which are under close monitoring by the environmental agencies, and may not be valid for other industrial pollution sources.

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