rafmacalaba/data-use-mentions-extended huggingface dataset:
We also characterize the population residing in close proximity to combustion power plants and show that the burden of excess PM2 _._ 5 falls disproportionately among groups with lower socioeconomic status.

The fourth set of results projects these costs into the future, accounting for possible changes in the climate, demography, and the electricity sector. Specifically, we extend the counterfactual calculations until 2059, which roughly corresponds to the expected operational lifespan of the combustion power plants in service. We begin by computing the evolution of the FHD using data from 22 climate and earth system models under three Shared Socioeconomic Pathways (SSPs). Concerningly, for the region, we find that the FHD is expected to increase between 22 and 24 percent over the next four decades. Consistent with the increase in the FHD, we also find that premature deaths and monetized losses are also likely to increase. When assessing the effect of the SSPs in conjunction with three highly stylized electricity sector scenarios, we find that merely flattening out the losses would require the most optimistic SSP scenario in conjunction with an electricity sector scenario where combustion power plants are phased out following regional pledges. These results are important because they indicate that the health burden of excess PM2 _._ 5 is largely permanent over the next four decades and is a first-order concern for policy makers in the region. Another important feature of these results is that there are considerable differences in sub-regional trends, for example, with the Andean Region (Colombia, Ecuador, and Peru) poised to experience a decrease in hydrological droughts and consequently in their FHD.

Our paper contributes to several stands of literature. Most narrowly, it extends recent work in the U.S. quantifying the relationship between hydrological droughts, energy generation, and air quality (Eyer and Wichman, 2018; Herrera-Estrada et al., 2018; Qiu et al., 2023). Specifically, we demonstrate that this relationship also exists in LAC, we recover the first region-wide estimates of excess PM2 _._ 5 for the region, and we
their first KUR loan, 11% of KUR borrowers had received at least one commercial loan from a bank. As shown in Table 1, eligibility criteria for KUR loans issued between 2015 and 2023 did not restrict access to firms without a lending history. In the five years before receiving KUR, 9 percent of borrowers received loans from a commercial bank, 10 percent from microfinance institutions, and 2 percent from regional development banks (Figure 5). After receiving KUR, the usage of these funding sources significantly decreased, indicating that KUR may be displacing other sources of formal lending for some borrowers. The finding of a possible crowding-out effect for some borrowers is supported by qualitative interviews in which some firms reported attempting to switch their non-KUR loans to KUR loans due to their lower interest rates.

Figure 5: After receiving KUR, fewer firms use alternative sources of formal and informal finance for their businesses **Formal Financing Used Before and After Receiving KUR** <!-- Start of picture text --> 12%<br>10%<br>10% 9%<br>8%<br>6%<br>4% 3%<br>2% 2%<br>2%<br>0% 0% 0%<br>0%<br>Non-KUR Loans Micro Finance Institutions Digital Lender Regional Development Banks<br>Prior to receiving KUR for the first time After receiving KUR for the first time<br><!-- End of picture text --> Source: Author calculations using survey of KUR borrowers While some studies suggest that microbusinesses may divert business loans to household consumption (Casano et al 2013), KUR loans are primarily used for business purposes. The primary use of KUR loans is working capital for the primary business (55.3% of loan value), followed by investments in physical capital for the primary business (27.2% of loan value, Table 3). Qualitative interviews shed light on the
/sup> = 0 _._ 011). I use spreads from the EMBI+ to parameterize the lenders’ stochastic discount factor. I assume that a period with high risk aversion is one in which the global EMBI+ is one standard deviation above the median over the sample period. With this procedure, I obtain 6 episodes of high risk premium every 20 28The time period starts in 1994 because the Mexican central bank independence reform was approved in 1993.

24
Law did not affect the ratio of federal transfers to a state’s GDP. However, our estimates in columns 11 and 12 suggest that tax rates in more ex-ante indebted states increased after the FD Law (by 10 percent on average), likely in an attempt to raise fiscal revenue. Overall, these results suggest that more indebted states reduce public spending and raise taxes to increase payments of their outstanding debt.

# _5.1.2. Economic Activity of States_

Table 2B presents our estimates from equation 1 using the state-quarterly data. Our first outcome of interest corresponds to the GDP of states. The next outcomes are states’ total employment, as well as employment in the primary, secondary, and tertiary sectors. All these variables are measured in quarterly growth rates. Overall, the estimates suggest that after the introduction of the FD Law, states with ex ante higher indebtedness experienced an increase in 23
|PPR|Working Paper Series|||
|---|---|---|---|
|Title|Author|Date|Contact|
|WPS11O Impact of Lower Oil Prices on<br>Renewable Energy Technologies|E. Terrado<br>M. Mendis<br>K. Fitzgerald|||
|WPS111 Recent World Bank ActivitiesioEnergy|Industry<br>and Energy<br>Department|||
|WPS112 A Visual Overview of the World Oil<br>Markets|Kay McKeough<br>Jose Escay<br>Sompheap Sem|||
|WPS113 Public Sector Pay and Employment<br>Reform|Barbara Nunberg|October 1988|J. Cheeseman<br>61703|
|WPS114 Africa Region Population Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodoifo A. Bulatao|October 1988|S. Ainsworth<br>31091|
|WPS115 Asia Region Population<br>Projections<br>1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswori<br>31091|
|WPS116 Latin America and the Caribbean<br>Region Population<br>Projections||||
|1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswori<br>31091|
|WPS117 Europe, Middle East, and North Atrica<br>(EMN) Region Population<br>Projections||||
|1988-89 edition|My T. Vu<br>Eduard Bos<br>Rodolfo A. Bulatao|October 1988|S. Ainswor;<br>31091|
|WPS118 Contract-Plans<br>and Public<br>Enterprise<br>Performance|John Nellis|October 1988|R. Malcolm<br>61707|
|WPS119 Recent Developments in Commodity<br>Modeling: A World Bank Focus|
average Jaccard similarity score of the topics covered by courses with the log of per-capita GNI (in PPP terms).<sup>6</sup> We see a positive and statistically significant correlation, with richer developing countries having more similar topic choices to the top-20 U.S. programs than poorer developing countries. We then turn to seeing whether this correlation persists after controlling for other potential explanations.

A second potential explanation for a difference in topic emphasis lies in the differing approach to the role of the state in development in different countries. Courses in countries in which the government plays a more active role in the economy may be more likely to emphasize topics of

> 6 Data from the World Development Indicators.

18
_R_<sup>2In addi-</sup> _ad j_<sup>, they could all be near the mean, with a sharp decrease in data variability.</sup> tion, logarithmic transformations are not immune to a re-transformation bias. There are various ways to solve this issue, but the most frequently used ones are based on scale correction of the predicted values. Both the relative Gini index and the relative Theil index are scale invariant,<sup>2</sup> which can render those corrections ineffective at reducing bias.

To summarize the discussion thus far, the regression to obtain a predictor is the right choice only if the _R_<sup>2</sup> _ad j_<sup>ishigh,alltheassumptionsarerespected,andthebiascaused</sup> by the log-transformation is negligible. In addition, it is important to bear in mind other potential sources of bias (Newhouse et al., 2014) arising from the differences between the survey design and the questionnaires.

# **4 Estimates of Biases Based on Moroccan Data**

In this section, using data from Morocco, we will show that the most common regression techniques can fail to accurately estimate three parameters: the Gini index, Theil index, and Head Count Ratio (HCR) with an endogenous poverty line equal to 60% of the median value of the regional expenditure per capita. We will compare estimates obtained with a direct estimator from the HBS at the rural/urban level in four different regressions. In all cases, the reported results will have a high negative skewed bias. The following regression techniques are used:

- Log-linear regression over two (split) data sets. We split the data set using a dummy (0= urban, 1= rural) variable, ran the regressions, and then computed the indices;

- Log-linear regression over the whole data set. The fitted values obtained are then split using the dummy (0= urban, 1= rural) variable and then the indices are estimated,
forcing the parameters to be the same across the entire distribution of individuals may be too restrictive and may hide important information.

A simple solution and yet a powerful tool is to make use of quantile regression techniques. The method of quantile regression amounts to minimizing the absolute sum of errors rather than, as in least squares, minimizing the sum of their squares.

Formally the method, first developed by Koenker and Basset (1978), can be formulated as<sup>9</sup> yi = xi ′ βθ + uθi = Quantθ(yi | xi) = xi ′ βθ (2)

where Quantθ(yi | xi) denotes the θ<sup>th</sup> conditional quantile of y given x, and _i_ denotes an index over all individuals, _i_ = 1,...,n.

In general, the θ<sup>th</sup> sample quantile (0 < θ < 1) of y solves Buchinsky (1995) examines various estimators for the asymptotic covariance matrix and concludes that the _design matrix bootstrap_ performs the best. In this paper, the standard errors are obtained by bootstrapping using 200 repetitions, in line with the literature.

# **4. Data description**

# _4.1. The Surveys_

The data used in this study are drawn from surveys of manufacturing firms in Côte d’Ivoire conducted in 1995 and 1996. The surveys are part of the RPED, a multi-year study of the manufacturing sector in several African countries (Cameroon, Côte d’Ivoire, Ghana, Kenya, Rwanda, Burundi, Tanzania, Zambia, and Zimbabwe). The RPED was organized by the Africa Region Technical Department of the World Bank.

The RPED is designed to provide an overview of the performance of manufacturing firms in the post-structural adjustment period, and focuses on a wide variety of aspects of firm behavior. The survey instruments include several modules covering creation of the enterprise; the enterprise in general; interior competition; labor markets; financial markets;
_Figure 8: Concentration and Incidence of Fiscal Interventions a. Concentration shares for all taxes and subsidies, across deciles_ <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>All direct All direct All indirect All indirect Net health Net education<br>transfers taxes (PIT) subsidies taxes transfers transfers<br>1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text -->

_b. Incidence of fiscal interventions (with respect to Market Income with Pensions), by decile_ <!-- Start of picture text --> 0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>-0.05<br>-0.10<br>-0.15<br>-0.20<br>1 2 3 4 5 6 7 8 9 10<br>Decile of Market Income Plus Pensions<br>All direct transfers excl contributory pensions All direct taxes<br>All indirect subsidies All indirect taxes<br>Net health transfers Net education transfers<br>Share of Market Income Plus Pensions<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17._ We also present the Kakwani coefficients and marginal impacts for each of the fiscal instruments within the broader categories. Kakwani coefficients provide a summary measure of how progressively taxes and benefits are distributed _relative to_ the distribution of income, in this case pre-fiscal income, or Market 27
Zain, and Orange reports, and World Bank ICT At-a-Glance.

Note: Converted to $ using annual average exchange rates (2010 figures using 2009 exchange rate). Penetration figures derived from population data of the National Statistical Institute. GSM = global system for mobile communications. * The first digital GSM network launched in 2001. Prior to that, there was an analog mobile network. — = Not available.

> 13 Société Nigérienne des Télécommunications. SONITEL’s privatization took place in December 2001, with 51 percent of shares being awarded to a Libyan-Chinese consortium; the government of Niger maintained 49 percent ownership.

> 14 ARM is also responsible for transport, energy, and water supply.

31
of Development, Industry and Foreign Trade. These data include information on firm-level export status in each year over 2009-2012, as well as the share of industry-level exports to each destination in 2008. To construct industry-specific real exchange rate instruments, we have supplemented these data with information on the consumer price index from the World Development Indicators of the World Bank and bilateral nominal exchange rates from the International Financial Statistics of the International Monetary Fund.

We further use detailed administrative records on training provision collected by the largest training provider in the manufacturing sector– _Servi ̧co Nacional de Aprendizagem Industrial_ (SENAI), the training arm of the national confederation of industry.<sup>3</sup> From these records, we extracted the set of workers trained in each year, worker-level information on demographics, occupation before starting the course, training category and course duration, as well as enrollment and completion dates. The data cover around 270,000 trainees per year.

SENAI provides five categories of job training: (1) technical upgrading; (2) apprenticeship; (3) initiation; (4) habilitation; and (5) qualification courses. The various course types have different objectives. In particular, technical upgrading courses differs considerably from the other training categories.

Technical upgrading courses are aimed at workers seeking to update, expand or complement the professional skills they have acquired in a specific occupation or field of work. Their duration varies between 40 and 120 hours. The curriculum is advanced and occupation-specific. They focus mainly on the skill needs resulting from technological innovations and new production and management processes. For instance, for industrial electricians, SENAI offers a course on electrical controls which lasts 80 hours.

> 3SENAI is the national service for training in the Brazilian industrial sector. It consists of a network of professional schools established and maintained by _Confedera ̧c ̃ao Nacional da Ind ́ustria_ (CNI), the national confederation of industry. SENAI is part of _Sistema S_ which is a set of vocational training institutions managed by CNI
Poverty rates are calculated for each of the 100 imputations and then averaged across imputations. The standard errors of the poverty estimates are computed following Rubin (2004). All estimates are carried out using version 2 of the Stata SAE package, developed by Nguyen et al. (2018).

In summary, we apply parameters from a model derived using CES 2011/12 to data from the SCS Health survey for 2017/18 to predict Indian poverty rates in 2017/18. We test the robustness of the model specification by varying the variable selection algorithm and the functional form of the rainfall shocks.<sup>14</sup> We test two different functional forms for the rainfall shock which is defined as the quarterly deviation of each district’s rainfall from the historical average (between 1981 and 2018). The first functional form of this variable uses the shock and its square. The alternative specification is a simple linear regression on a spline variable created at the 25<sup>th</sup> , 50<sup>th</sup> and 75<sup>th</sup> percentile points of the rainfall shock distribution (i.e. a dummy variable that indicates whether the household lives in a district where the rainfall shock falls in any of the four quartiles of the distribution of rainfall shocks). As previously mentioned, the framework may assume normality or allow for non-normality in the error terms. Our analysis allows for non-normality which is more flexible. All models are estimated for rural and urban areas separately. We run four model specifications, two using LASSO and two using Stepwise selection, where rainfall is specified either as a spline or a quadratic function.

The consumption models explain between 34 and 45 percent of the variance of the dependent variable, which is slightly lower than Newhouse and Vyas (2019). The explanatory variables vary across models because of the use of different variable selection algorithms (i.e. LASSO and stepwise), but sign and significance of the
3 calculated directly from the surveys, with little information available to adjudicate the choice.<sup>3</sup> In response, poverty economists have been developing a series of different poverty prediction methods, exploiting the comparability of subsets of data within and across surveys.<sup>4</sup> The methods differ in the predictors and prediction techniques used, but they generally share the critical and largely untested assumption that the estimated relation between the predictors and their welfare measure is stable over time. This cannot be taken for granted and has become a stumbling block in furthering the use of poverty prediction techniques to overcome data constraints in tracking poverty over time.

The need for comparing and validating poverty prediction methods is perhaps best illustrated by the ―Great Poverty Debate‖ in India (Deaton and Kozel, 2005). Following market liberalization in the early 1990s, the official poverty numbers for India showed a drop in poverty incidence from 36 percent in Round 50 of the National Sample Survey (NSS) (1993/1994) to 26 percent in Round 55 (1999/2000), or about a reduction in the number of poor people by 60 million.

However, these official numbers were received with skepticism. There was a widespread view that the underlying data were not comparable because reporting periods for various consumption items had changed between the two survey rounds. There were also lingering doubts about the accuracy of the price indices used to update the poverty lines (Deaton, 2008). Different poverty calculations were proposed, each of them

> 3 For instance, while households in Tanzania faced price increases of 93 percent between 2000/01 and 2007 according to the national Household Budget Surveys, the official CPI recorded only a price increase of 45 percent (Hoogeveen and Ruhinduka, 2009).  Adjustment of the CPI, which was largely based on urban consumption baskets, also proved to go a long way in remedying the 1994-2003 Growth-Poverty paradox in Burkina Faso (Grimm and Günther, 2006).

> 4 See for example,
# **Annex 5. Methodology: The Impact of Structural Reforms on GDP Growth**

For each policy variable, it is possible to compute the annual GDP growth that would be added if Serbia managed to close the gap with Germany in 20 years. In particular, assume that

- 0

- • 1 • zz = vvgglluuee ooff ssttssuussttuussggll vvggssvvggvvllee ffooss SSeessv gg zz = vvgglluuee ooff ssttssuussttuussggll vvggssvvggvvllee ffooss GGeessGGggllyy

- where Y is output per capita. yy = ln YY

The new level of GDP per capita for Serbia associated with the reform is computed as follows: 1 0 1 0)) where y0 is the level of GDP per capita in 2016 and yy = yy + ββ (zz is the weighted average of the estimated elasticity −zz 1 0 1 0)) for each structural variable along the relevant input channels, with weights as in equation (1) in the main text. ββ Therefore, g is the annual GDP growth rate that would be added by structural reforms if Serbia managed to close the gap with Germany in 20 years.

# **Annex 6. Details about Work-Time Regulation**

The CBR Labor Regulation Index Dataset (CBR-LRI) provides data on labor law for 117 countries from 1970 to 2013. A value between 0 and 1 is assigned to each indicator for each year covered, where 0 stands for absence of any protection for workers and 1 is assigned for the highest protection. Five areas of the law are covered: definition of the employment relationship and different forms of employment; work-time; dismissal; employee representation; and collective action.

The index is a measure of the extent to which a certain aspect is regulated (protected). Note that existence of a law regulating a certain aspect does not say anything about its actual application.

Table A6.1 reports on regulation of work-time for Serbia over time and in comparison to Germany.

**Table 13 Work-time Regulation, Serbia and Germany**

|**Regulation of work-time**||**Serbia**
Figure 5: Prediction of Mean Number of No Rain Days, by Remote Sensing Source and Agro-ecological Zone <!-- Start of picture text --> 66<br><!-- End of picture text --> <!-- Start of picture text --> Tropic-warm/semi-arid (n = 9,095) Tropic-cool/semi-arid (n = 2,840)<br>120 200<br>100<br>150<br>80<br>100<br>60<br>40 50<br>2008 2010 2012 2014 2016 2008 2010 2012 2014 2016<br>Tropic-warm/sub-humid (n = 9,009) Tropic-cool/sub-humid (n = 5,886)<br>120 200<br>100<br>150<br>80<br>60 100<br>40<br>50<br>20<br>2008 2010 2012 2014 2016 2008 2010 2012 2014 2016<br>Tropic-warm/humid (n = 3,280) Tropic-cool/humid (n = 2,960)<br>150 200<br>150<br>100<br>100<br>50<br>50<br>0 0<br>2008 2010 2012 2014 2016 2008 2010 2012 2014 2016<br>Year Year<br>CHIRPS CPC MERRA-2<br>ARC2 ERA5 TAMSAT<br>Days without Rain<br>Days without Rain<br>Days without Rain<br><!-- End of picture text --> _Note_ : The figure presents the mean number of no rain days, pooled across all countries and years, disaggregated by agro-ecological zones. Prediction made via Fractional-Polynomial.
nual for the period 1980-2019.<sup>38</sup> The sources are IFS (IMF) for GDP and government expenditure; World Bank for terms of trade; and Gruss and Kebhaj’s (2019) database for commodity export price indices.<sup>39</sup>

# 5.2 Effects of commodity prices on GDP

The first stage is to run a fixed-effects panel regression of output on commodity export prices. The results are summarized in Table 3. We use two control variables: terms of trade (as a way of controlling for trade effects) and the lagged dependent variable (to capture underlying growth unrelated to commodity prices).<sup>40</sup> We can see that the coeffi cient for EMDE (0.085) in Column (1) is about 3.5 times as much as the coeffi cient for AE (0.024) in Column (4).<sup>41</sup> In other words, an increase of 10 percent in commodity export prices increases GDP by 0.63-0.85 percent in EMDE and 0.18-0.26 percent in AE.<sup>42</sup> The estimates are quite robust across specifications.<sup>43</sup>

# 5.3 Effects of commodity prices on fiscal response

The second stage is to run a fixed-effect panel regression of government spending on commodity export prices. As in the case of Table 3, we control for terms of trade and the lagged dependent variable. The results are summarized in Table 4.<sup>44</sup> From

> 38For the computation of fiscal multipliers below, we use quarterly data from IFS (IMF).

> 39It is worth mentioning that the commodity price indices from Gruss and Kebhaj’s (2019) are countryspecific. Specifically, for each country, the change in the international price of up to 45 individual commodities is weighted using commodity-level data for the period 1962-2018.

> 40If we