rafmacalaba/data-use-mentions-extended huggingface dataset:
be resolved successfully. We allow for the expected costs of dispute initiation to be made up of two separate elements that include both the expected litigation costs and the expected political-economic costs to confronting the United States in a formal dispute. As we describe in more detail below, our hypothesis allows for economic interests to affect decisions, but we also include proxies for some of the institutional biases that WTO scholars have been concerned might also influence a country’s ability to stand up for its market access interests.

The primary _alternative_ hypothesis to the one we pursue here is that imposed trade remedies are not formally challenged under dispute settlement proceedings because of the _Agreement on Antidumping_ ’s Article 17.6 requirement that WTO Panels show deference to national authorities’ decision-making during antidumping

> 15 We will only be able to use cases involving manufacturing products due to our need to control for injury (requiring industry-level data, which is not comparably available for agricultural products.) This does not severely restrict the size of the sample, however.

9
|Public<br>Unemployed<br>OL|F|
||Private informal|**76**|10<br>5<br>4<br>5|53% (55%)|
||Private formal|43|**39**<br>12<br>3<br>3|14% (14%)|
|Egypt|Public formal|8<br>8|**82**<br>2|21% (23%)|
||Unemployed|56|17<br>11<br>**8**<br>8|4% (3%)|
||Out of labor force|56|14<br>11<br>5<br>**14**|9% (5%)|
||Private informal|Private formal<br>Public|formal<br>Un-employed<br>Out of lab|or force|

_Notes_ . The upper panel is simulated by Monte Carlo Method described in Section 4 Methodology. 100,000 simulations of transitions after six years were performed for each initial state, where the transition probability matrix per period is taken from the empirical annual transition matrix in Figure 8. The annual transition matrix relies on panel data from the ENOE in 2005 Q1 and 2006 Q1. We restrict our analysis to men aged 20 to 53 who were interviewed in both rounds. We use transition matrices to examine individuals’ transition of their work status/sector between the two dates. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2005 Q1, with the shares in 2006 Q1 reported in brackets. Panel weights are used. The lower panel relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our
approaches to estimate population counts. We also highlight the value of humanitarian data registries to inform ex-ante selection probabilities in contexts where those displaced are largely located in camps or areas covered by those data.

Although it was possible to solve many data gaps in this case, it is important to keep in mind that institutional solutions will make sampling frames more credible and compatible across surveys. Efforts to maintain detailed census cartography and consistent geocodes across surveys are central. As well as steps such as including refugees and non-nationals in national sample frames, which can provide a more sustainable infrastructure to design more accurate surveys and achieve more effective policies.

> 36 https://www.worldbank.org/en/country/bangladesh/brief/cox-s-bazar-panel-survey-briefs.

20
.498|0.445|0.418|
|Nigeria|0.294|0.307|0.225|0.531|0.565|0.440|
|Rwanda|0.259|0.252|0.256|0.492|0.494|0.499|
|Senegal|0.241|0.249|0.165|0.462|0.484|0.339|
|Sierra Leone|0.241|0.235|0.147|0.460|0.458|0.365|
|Swaziland|0.285|0.256|0.118|0.494|0.502|0.443|
|Tanzania|0.316|0.243|0.277|0.539|0.459|0.441|
|Togo|0.339|0.252|0.234|0.607|0.521|0.410|
|Uganda|0.377|0.211|0.264|0.634|0.419|0.534|
|Zambia|0.296|0.241|0.230|0.528|0.456|0.454|
|Zimbabwe|0.315|0.219|0.260|0.564|0.430|0.433|
|Mean|0.275|0.255|0.240|0.508|0.487|0.461|

Note: Data are drawn from DHSs. Means are population weighted. The table lists the proportion of underweight women, stunted children and wasted children who fall below the bottom 20th and 40th percentiles of the wealth index distribution. For example, 24.8 percent of underweight women fall below the bottom 20th percentile of wealth in Benin.

45
- **Treatment of electricity sector:** In our main results, the electricity sector is not treated as an EITE sector (it is not highly traded). However, in some real-world cases, the electricity sector is given special treatment under carbon pricing. Because of its large contribution to US emissions, and because it offers substantial cost-effective CO2 mitigation opportunities, treatment of the electricity sector is important in determining model results. In Section C.5, we show how model results are affected when the electricity sector is included in the set of EITE sectors. While inclusion of the electricity sector in the set of EITE sectors results in large numerical changes in results, the ranking of policy instruments and general conclusions from the analysis are preserved—notably, that the intensity-based policy variants achieve deeper CO2 reductions than the LSR and OBR policies in the EITE sectors, and that the intensity-based policies attenuate negative impacts of carbon pricing on output relative to the LSR policy.

- **Results for different countries:** Our main numerical analysis calibrates the CGE model to US data. To ensure that our conclusions are not driven by factors idiosyncratic to the US economy, we replicate our analysis after recalibrating our model to benchmark data from other countries in the GTAP data set. We show our analysis in Section C.3 for all G20 countries. While the numerical results differ from the results based on US data, the qualitative conclusions of the analysis remain unchanged: the intensity-based policy variants achieve deeper CO2 reductions in the targeted EITE sectors compared with the LSR and OBR policies and result in smaller curtailment in output in these sectors relative to the LSR benchmark.

- **Detailed subsector results:** In our main numerical results, we aggregate six energyintensive trade-exposed sectors (chemical manufacturing, iron and steel manufacturing, nonferrous metal manufacturing, nonmetal mineral manufacturing, oil refining, and pulp and paper manufacturing) into one aggregate sector for reporting purposes. In Section C.4, we report disaggregate results for each EITE subsector. Again
Table 3: Robustness II, Survival and Institutional Development.

The dependent variable is the hazard rate of trade relationships for product k of ...rm i operating in country c to destination country j: All regressions are estimated using the Cox Proportional Hazard Model. We control for destination country, year, ...rm and exporting country x HS2 ...xed e¤ects, and allow the baseline hazard to vary across HS8 product (strata). The variables of interest are de...ned in Table 1. Additional controls include the interaction between the sanitary risk and : country c overall economic development (GDPpcc x sanitary_riskc), country c Ease of Doing Business index (EDBcx sanitary_riskc ), country c Logistic Performance index (LPIcx sanitary_riskc), country c level of infrastructure (Infrustructurecx sanitary_riskc), and country c level of trade related infrustructure (TABcx sanitary_riskc). Robust standard errors clustered at (exporting country) � HS4 sector level are in parentheses. *, **, and *** denote statistical signi...cance at the 10%, 5%, and 1% levels, respectively.

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|FDcx sanitary_riskk|-0.590**|-0.257**|-0.291**|-0.169*|-0.345***|
||(0.243)|(0.099)|(0.146)|(0.101)|(0.130)|
|initial_exportcikj|-0.088***|-0.088***|-0.088***|-0.088***|-0.088***|
||(0.
new production facilities than it is to refurbish existing facilities. Both of these factors would suggest that the competitiveness effects of tightening up environmental policy would not be large in developing countries. On the other hand, if there are increasing returns or learning spillovers in the abatement process, or if substantial investments in infrastructure are required, then marginal abatement costs could be higher in a developing country.<sup>7</sup> It is likely that the net result of these two effects would vary across pollutants and sectors; but at this stage we have little or no evidence on the issue.

The applicability of the empirical work on plant location to an international context is also open to question. For example, early studies used US state-level data and found that environmental policy had little or no effect on which state that a firm chose to locate a new plant. However, studies such as Becker and Henderson (2000) found that environmental policy does have an effect at the county level. One interpretation is that environmental policy does not have much influence on which state a firm chooses as a site for its production; but that once a state is chosen, the firm looks for a location within that state that is attractive from a cost perspective, and that at this stage environmental policy could matter. If this interpretation is correct, then the implications of studies that find that county-level regulations within the U.S. affect plant location may not have much relevance for the effects of environmental policy on international location decisions.

There is as yet very little work that attempts to test the strong version of the pollution haven hypothesis, which is that trade liberalization causes pollution-intensive industry to systematically move to countries with weak environmental regulation. Antweiler et al. (2001) calculate the elasticity of the increase in ambient SO2 concentrations with respect to an increase in openness to trade for a large set of countries. The strong version of the

> 7 Another way to pose the question is to ask whether the response to pre-existing regulations has given developed
individual’s outcome. Rather, we measure the intention-to-treat effect, reflecting the availability of secondary education opportunities in a municipality.

The findings in this paper also contribute to a literature that looks at the impact that expanding education access has in developing communities. Several studies have previously looked at the quasi-random variation from the primary school expansion in Indonesia. This expansion is found to have a substantial impact on education and adult wages (Duflo 2001, 2004). In addition to labor market outcomes, women have fewer children as adults (Akresh, Halim, and Kleemans 2018). Providing evidence from an RCT, Burde and Linden (2013) find that introducing a primary school in Afghan villages significantly increases academic participation and performance for both boys and girls. However, the gains for girls are disproportionately large, significantly reducing the enrolment and the performance gap between boys and girls. An analysis of Brazil over the same period as our study finds a strong association between teenage fertility and income inequality within a municipality (Chiavegatto Filho and Kawachi 2015), and an inverse relationship between regional teenage pregnancy and regional measures of the human development index (Ferreira Vaz, et al. 2016). Our analysis provides some insight into the relative importance of access to secondary education in these relationships and adds to this observational evidence by providing evidence on a pathway through which school access may change gender-based inequalities in a municipality. If secondary access affects both fertility and social mobility, the school expansion we document may lead to fundamental changes in income inequality in the longer-run.

The between-state variation in teenage pregnancy rates (Figure 2) may be also reflect broader differences in economic and social issues. Kearney and Levine (2012) argue that the substantial geographic variation in US teen pregnancy rates reflects geographic differences in economic opportunity—both perceived and real. It is being on a low economic life trajectory that leads 22
the highestranking challenger candidate and the incumbent’s vote share. We use this difference as the running variable in our regression discontinuity (RD) design, described in Section 3.2.

**Administrative Data.** To measure bureaucratic performance, in addition to survey outcomes, we use administrative data from the 2014 and 2021 rounds of _Podes_ , a triennial village census, which we match to our survey sample. We use the 2014 data as the baseline and the 2021 data as the endline. When studying endline outcomes, we restrict the sample to villages that held their last election before 2021 and use the remaining villages for placebo checks. We also use predetermined geographic and socioeconomic characteristics from _Podes_ to conduct balance and other validity checks.

# **3.2 Regression Discontinuity Design**

Our goal is to identify how bureaucratic processes and service provision are influenced by turnover in the most recent village election. Before introducing our identification strategy, Table 1 presents correlations between some of these outcomes and the presence or the victory of an incumbent candidate in the last election. We first examine two measures of bureaucratic performance: the quality of public goods in the 2021 _Podes_ data, and the growth in service quality between the 2014 and 2021 _Podes_ waves. While there is no significant correlation between these outcomes and whether an incumbent runs (columns 1 and 3), incumbent wins are followed by a decline in public goods provision (columns 2 and 4). As one might expect, incumbent reelections are also associated with lower bureaucratic turnover (column 6).

> 2A small fraction (4%) of elections in our sample featured turnout greater than 100%. We later use this as a measure of data quality and show that this is uncorrelated with the occurrence of an electoral turnover.

> 3In Table 1, discussed in Section 3.2, we use the full sample of villages to document how villages where an incumbent competed in the last election differ from other villages. In this table and the RD analysis,
For completeness, we check for potential heterogeneous responses with respect to the start year of coal plants, as well as national pollution and income levels. The hypothesis is that countries with lower incomes and higher pollution levels may prioritize economic growth, and thus respond differently than high-income low-pollution countries. However, results reported in Table S3.2, S3.3 and S3.4 still show small and imprecise estimates on the interaction term with economic variables as left-hand sides, indicating negligible impacts of plant operations on income levels regardless of countries’ development stages. Section S2.2 summarizes findings from a set of sensitivity checks. Results remain strongly robust to alternative definitions of up- and downwind areas to construct the sample (i.e. alternative angles and distances).

There are at least three potential reasons for the null results in this section. First, adverse impacts of pollution on health and labor productivity may take many years to materialize. The time ranges considered in our sample may not be long enough to capture a meaningful reduction in income levels. Second, the GDP and HDI data are constructed using national economic values and subnational population as inputs (Kummu et al., 2018). Compared with our detailed pollution trajectory modeling, income data may not be granular enough to capture small-scaled local differences. Third, even if air pollution affects health outcomes and the quality of life, this may not translate into a significant reduction in income levels. On the contrary, high spending on healthcare could even push up measures of national production, such as GDP.

# **5 Conclusion**

Many countries continue to rely on coal power plants for electricity generation, despite their tremendous financial, societal, and environmental costs. This paper documents inequalities in people’s exposure to toxic air pollutant emissions from the world’s almost 4,000 coal-fired power plants. By modeling the wind-dispersion of air pollutants, we generate a high-resolution winddirected global air pollution map for coal plants. We find that, globally, 2.32 billion
we construct a binary indicator for “any job loss” that equals one if the worker suffered a job loss in any of the 3 waves compared to the prepandemic situation. For those who had a job prior to the pandemic, Figure 3 shows the percentage who lost their job in waves 1, 2, or 3 of the HFPS. Job losses were very large; the share of workers who lost their job in this very short time window was around 30 percent on average in the region. There are heterogeneities across countries, from 18 percent in Argentina to 47 percent in Colombia.

We are particularly interested in the heterogeneities of the impact of the crisis across socioeconomic characteristics, such as gender. Columns 1 and 2 of Table 3 show that job losses were larger for women than for men in all countries, and the gender gaps were as high as 22 percentage points in Paraguay. Also, job losses were larger for the youth (columns 3 and 4 of Table 3) and for the unskilled (columns 5, 6 and 7 of Table 3) in all the countries. Taking into account the pre-pandemic occupation, non-salaried workers experienced larger job losses in most countries (columns 8 and 9 of Table 3).

> 7Of course, the severity of the problem varies across countries. In Colombia, for example, there is very little difference between the share of elementary workers that arises from both sources.

8
# **1 Introduction**

Governments play a key role in economic activity. They are large employers, they set taxes and transfers, and they purchase goods and services from the private sector. These purchases are done by awarding _public procurement contracts_ to private firms. The size of public procurement varies over time and across countries, but it consistently represents a large fraction of GDP —12.8% in the OECD countries, 14% in EU countries, and 9.3% in the United States.<sup>1</sup> Because of its large size and high level of discretion, many governments use the public procurement process to allocate resources to specific firms. In the U.S., for example, the Small Business Act aims to “ensure that a fair proportion of federal contracts is awarded to small business”.<sup>2</sup> Similarly, in the EU, promoting the participation of small firms is at the core of the European Commission’s agenda for public procurement regulation.<sup>3</sup> A common justification for these policies is that small firms may face demand or financial constraints, preventing them from reaching their full potential and hence negatively affecting aggregate economic outcomes. Given the wide presence of these types of policies worldwide, it is surprising how little we know about their macroeconomic impact. This paper aims to fill this gap by asking if it is possible to achieve better aggregate outcomes when changing the composition of firms from which governments buy their goods and services.

Our research question is the following: how would the level of aggregate output, TFP, and provision of public goods be affected if governments change the composition of firms from which they buy while keeping total expenditure fixed? In order to address this question, we carry out an analysis that integrates a novel firm-level dataset with a macroeconomic model of firm dynamics. Our dataset merges administrative data on public procurement, credit allocation at the bank-firm level, and firm outcomes for the Spanish
# 1. Introduction

Institutional and governance challenges are key constraints that reduce Bulgaria’s economic potential and private sector productivity. Bulgaria continues to lag most European Union (EU) countries on governance indicators.<sup>1</sup> The gap with the rest of the EU is most pronounced along institutions critical for economic growth such as the rule of law, control of corruption, and government effectiveness.<sup>2</sup> One critical institutional area where governance weaknesses and state capture by private interests are evident is public procurement (European Commission-DG REGIO, 2022, chapter 7). Owing to the digitalization of public procurement data in the EU and the introduction of a comprehensive national e-procurement system, auditors and judicial institutions, government analysts, businesses, and civil society now have access to electronic public procurement data, including the details of individual contracts and tenders (World Bank, 2020). For the first time, these provide tools to work with big data by discovering the cost and beneficiaries of non-competitive public procurement tenders and linking corruption risks in public procurement to state capture and identifying their economic impacts. Among others, the <u>opentender.eu database on public procurement created by the EU-funded DIGIWHIST</u> project allows analysts to identify and validate ‘red flags’ for corruption that may signal competition flaws, misuse of public funds, or outright corruption.

Reducing corruption risk in public procurement has the potential to boost private sector productivity growth (World Bank, 2022). Public procurement amounts to more than 12 percent of gross domestic product (GDP) in Bulgaria. The access to contracts thus has a major impact on the allocation of resources in the economy. It can either achieve value for money out of scarce fiscal resources and support the growth of more productive firms or result in an inefficient use of public money through helping unproductive firms to accumulate scarce physical and human capital and stay in the market. Reducing corruption in public procurement also reflects on the enforcement of competitive market structures in the
SOFES-recipients more often work on the side than students without a SOFES loan, and the treatment effect of student loans is about 8%-point. One the one hand, this is a counter-intuitive finding, since we expected that student loans would reduce part-time work and free up time for studying. On the other hand, it could reflect a change in the composition of student population, in the sense that the loan option provides access to a new group of students that are more likely to work on the side. Indeed, the family income of students _without_ loans is on average 48% higher than the family income of students _with_ student loans. This is consistent with the hypothesis that students in the SOFES program are still credit constrained.

|**Table 6: Impact of the loan program on de**|**cision to take a job on the side, survey data.**<br>DWORK|
|---|---|
|_Treatment effect_||
|DCREDIT|0.078<sup>***</sup><br>|
||(0.027)|
|_Socio-economic controls_||
|TUITION (×1000 pesos)|-0.006<sup>***</sup>|
||(0.002)<br>|
|INCOME (×1000 pesos)|0.002<sup>**</sup>|
||(0.001)<br>|
|DFATHERTECH|0.127<sup>**</sup>|
||(0.058)|
|DFATHERUNIV|-0.054<sup>*</sup>|
||(0.028)|
|DMOTHERTECH|0.015|
||(0.038)|
|DMOTHERUNIV|<br>-0.019|
||(0.032)|
|DFEMALE|-0.038|
||(0