rafmacalaba/data-use-mentions-extended huggingface dataset:
since the emissions implications will always remain very limited (at least if the energy- and carbonintensities are as good as historical standards). Therefore, our results show that the challenge to align the world’s development and climate objectives is not in reconciling extreme poverty alleviation with climate objectives _but in providing middle-income standards of living while decarbonizing the world economy_ . Our analysis faces important limitations. The modeling framework is deliberately simple to allow transparently comparing the emissions associated with different growth and income distribution scenarios consistent with poverty eradication. It is designed to explore scenarios, not to provide forecasts or predictions of future economic growth or carbon emissions.

Moreover, the model draws on historical data, which by construction constrains the range of modeled pathways to what has been observed in the past and makes the results overly pessimistic by ignoring the recent progress in green technologies and solutions. Indeed, new technologies and circumstances create new possibilities for future pathways that did not exist in the past.<sup>32</sup> For instance, new evidence shows that renewable energy sources, rather than fossil fuels, present the most cost-effective way to meet growing electricity demand in many low- and middle-income countries, suggesting that the carbon content of economic growth will be much lower in the future than historically.<sup>31</sup> Particularly relevant for extreme poverty alleviation is the potential from small-scale solar mini-grid in rural areas.<sup>33</sup> Also, there is growing evidence of the potential from energy efficiency measures to generate energy savings and economic benefits, especially linked to electrification of heat (e.g., with heat pumps) and transportation (from electric bikes to electric buses).<sup>34</sup> When low-energy and low-carbon options become more competitive than alternatives, trade-offs between climate and development objectives disappear, even though higher upfront costs and investment needs can represent a major financial challenge.

10
of this set of preconditions for the pace and sequencing of deregulation. This is because interest rate liberalization is not only about price reform; it also needs to involve removing all sources of frictions while building key institutions.

To contribute to a better understanding of IRCs around the world, this paper presents a new data set based on a survey of World Bank Group members. A questionnaire was submitted to financial sector authorities and bankers’ associations in more than 150 jurisdictions during 2019 to investigate the legal framework associated with the implementation of _de jure_ IRCs, document their application, and provide details on their functioning. We received responses from 108 countries at all levels of income and in all parts of the world. In an attempt to provide a potentially meaningful characterization of a country’s interest rate control regime, the data collected through the survey are then used to attempt to provide a preliminary estimate of the degree of bindingness of IRCs in a country and explore whether the degree of bindingness of IRCs is associated with other forms of financial repression.

This paper builds on and extends recent efforts to take stock of IRCs around the world. There have been various attempts in recent years to classify IRCs internationally. However, existing studies either take a narrow geographical perspective, focusing on EMDEs (Helms and Reille, 2004) or advanced economies (see Reifner et al., 2010, for the EU; and Dasgupta and Mason, 2019, for the US), or, when taking a global perspective, they focus on lending interest rate caps only (Maimbo and Gallegos, 2014; Ferrari et al., 2018). In a paper similar to ours, Jafarov et al. (2019) attempt to compile a database on all types of IRCs and construct a basic index (i.e. based on binary variables) of IRCs across economies. However, like in previous studies except for Reifner et al. (2010), it is based on data and information from secondary sources. To the

> 5 Recent literature finds
. The size of the gap between the two sources of data (i.e. how far below the 45 degree line most of the observations in the graphs are) illustrates how costly it would be not to try to strike a deal – and that assuming no deals are made does not, in fact, reflect what firms do.

# **III. Comparing Doing Business and Enterprise Surveys**

What is the connection between the DB indicators and the average value reported by the firms in the ES? For each of the three indicators (imports, operating license, and construction permit) we provide scatter plots (Figures 1a, 1b, 1c) and regression results using a variety of functional forms (Table 1). All three indicators have roughly similar patterns that: 8
be a census of all Brazilian firms in the formal sector, we cannot match 235 firms. Additionally, 323 of the matched firms did not report worker-level information in 2019, either because they had no active working contracts or halted operations during the reference year. Of the 1,067 matched companies, 129 are registered as fully public firms, and 238 are registered as mixed capital firms. We define as participated the 377 firms that are registered in RAIS as fully private firms but contain minority state participation as identified in Orbis. Table 2 presents the resulting number of firms by year in the sample, along with a decomposition between the number of privately-owned firms (POEs) and BOS, and their classification into each of the three BOS types after combining data from Orbis and legal nature information in RAIS.

Additionally, we use Orbis data combined with firm ownership structure information to obtain estimates for the share of government ownership in each BOS. Whenever firms are directly owned by the state, with only one public entity as the shareholder, we consider the percentage of government ownership provided by Orbis or by other sources used to validate

> 3 When computing age, we use data from 1996 to 2020.

> 4We only consider firms as participated when direct or indirect state participation is at least 10%.

> 5At this time, we only have access to firms with state participation in Orbis for the year 2019. We match these firms with information in RAIS for all years used in the analysis, from 2010 to 2020.

5
employment program evaluations in Europe and found that the programs tended to have larger impacts for women than men. However, Card (2009) found that programs tended to work equally well for men and women.

In general, program evaluations from developing countries show larger impacts than programs conducted in other regions. Based on 289 youth employment interventions in 84 countries, Betcherman et al. (2007) show higher impact in developing countries than in developed ones.<sup>6</sup> Most of the rigorous evidence on training programs in developing countries is from Latin America, where positive impacts are particularly pronounced (Gonzalez-Velosa et al., 2012; Attanasio et al., 2008).<sup>7</sup> Attanasio et al. (2008) evaluate the _Jovenes en Acción_ job training program in Colombia. _Jovenes en Acción_ provided three months of classroom training followed by a three month unpaid internship at a company. Attanasio et al. (2008) detect positive employment effects for women (4 to 7 percentage points), no employment effects for men and positive earnings effects for both men (8 percent) and women (18 percent). The study argues that the increase in earnings is due to increased employment in formal sector jobs upon training completion.<sup>8</sup> In Nepal, a wide variety of public and private technical education and vocational training (TEVT) programs are available to youth. Youth unemployment is frequently cited as a driver of the ten-year conflict from which Nepal emerged in 2006 (Macours 2011, ILO 2014), and since then international donors have invested heavily in the TEVT sector. Additionally, a combination of age, low educational attainment, and norms around marriage and childbirth confers multiple additional disadvantages to young women in the labor market. Their labor force participation rate is 43.0 percent compared to 51.7 percent for young men (ILO 2014). Concerns about the social exclusion of women, ethnic minorities, indigenous peoples, and other historically disadvantaged
how losing access to groundwater influences village-level outcomes. Wells deeper than 8m cannot use atmospheric pressure based pumps to extract water and therefore require more expensive mechanical pumps. This creates a clear threshold around water accessibility. Using this threshold, she shows that villages just below this threshold had poverty rates 9-10% higher than those just above and were 25% more likely to experience conflict over water. This builds on earlier work showing that a 1 meter increase in the depth to groundwater reduces food grain production by 9% and cash crop production by 5% (Sekhri et al., 2013).

# **3 Data**

## **3.1 Water depth data**

We collect data on groundwater levels from the Central Ground Water Board (CGWB) observation wells.<sup>6</sup> The CGWB monitors groundwater levels at nearly 23,000 monitoring stations across the country. Water depth data is collected in August (monsoon), November (kharif), January (rabi), and March/April/May (pre-monsoon), where the pre-monsoon collection times vary by state depending on the normal beginning of the monsoon (CGWB, 2021). On average, we have nearly 25 wells per seasons per district year. CGWB monitoring wells include a mix of open dug-wells and tube wells. Typically tube wells have a much deeper monitoring depth compared to dug wells.

## **3.2 Winter cropped area**

We collect data on area planted in winter crops from NASA’s Earth Observing System Data and Information System provided by the SEDAC center at Columbia University. This data provides a raster layer that indicates whether a pixel is planted in crops based on the peak Enhanced Vegetation Index value detected by the MODIS platform over the period October to March (Jain et al., 2017a,b). We aggregate the underlying 1-km pixel-level data to district level to determine the area of each district planted in winter crops in each year in the sample.

## *
earnings: based on estimates of the supply elasticity of women’s labor in Brazil (Vick, 2017), such a wage penalty would imply a 0.4 - 0.5 percent reduction in the labor supply of a woman working at minimum wage.

Finally, we account for the possibility that bystanders may _implicitly_ view women outside the reserved space as provoking harassment and assign responsibility for harassment to the victims. We interview men and women commuters on the platform and administer 948 social norm surveys and 291 Implicit Association Tests (IATs) to establish whether women face norms against riding in the public space when they have the choice of a reserved space, and whether this relates to their demand for the reserved space. Results suggest that men and women respondents both _implicitly_ and _explicitly_ associate women traveling in the public space with sexual provocation.

Overall, we find that a majority of men and women commuters consider using the womenreserved space as the “proper” choice for a woman commuter. Surveying women’s second order beliefs reveals that women commuters who perceive this attitude against women riding in the public space to be the prevailing norm are 79 percent more likely to report taking most of their rides in the reserved space. Our back-of-the-envelope estimates suggest that complying with this perceived norm may account for more than half of the demand for riding in the reserved space.

Taken together, our results paint a nuanced picture of the impacts of women-reserved spaces. On the one hand, the incidence of harassment is lower in the reserved space than in the public space, in line with the degree of adherence to the reservation rule. However, by playing into latent prejudice, the reservation policy appears to reinforce norms limiting women’s safe travel in the public, non-reserved space. We show that perception of this norm reflects back on women’s demand for the reserved space, imposing an additional cost on women traveling in the public space. Quantitative estimates of this cost suggest that it is at least as large at the protective value generated by the
70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Benue FCT Abuja Kogi Kwara Adamawa Bauchi Borno Jigawa Kaduna Kano Katsina<br>Nasarawa Niger Plateau Gombe Taraba Yobe Kebbi Sokoto Zamfara<br>d) e) f)<br>Protected Water Deprivation - Protected Water Deprivation - by Protected Water Deprivation - by<br>by State in South East - MICS State in South South MICS State in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>Children 0-17 Children 0-17 Children 0-17<br>Abia Anambra Ebonyi Enugu Imo Akwa IbomDelta BayelsaEdo Cross RiverRivers Jigawa Kaduna Kano Katsina<br>Kebbi Sokoto Zamfara<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 Note: Individuals with missing age were dropped from the analysis.

65
, we split the models by area of residence. Since districts are the lowest level of geographical aggregation in the EPH data, our results will capture the effects of an average weather shocks on income or poverty at the district level. In other words, there may be underlying heterogeneity this analysis will not be able to highlight, such as more nuanced differences in the intensity of weather shocks within districts, and may perhaps under-estimate the severity of the impact for highly-affected households. However, most districts in Paraguay are reasonably small, allowing for sufficient variation across units of analysis.

> 5 Ideally, we would cluster standard errors at the primary sampling unit (PSU) level. However, PSU codes vary across years in the EPH, so that it can currently not be used for clustering.

> 6We include those variables to control for differential weather conditions across areas of Paraguay, similar to previous studies like Azzarri and Signorelli (2020), Gao and Mills (2018) or Asfaw and Maggio (2018).

> 7We use the variable for ’representative departments’ in the EPH survey, which groups households into the following areas: Asunción, San Pedro, Caaguazú, Itapúa, Alto Paraná, Central, as well as other urban and other rural areas. ’Other urban and ’other rural’ contains all households who do not live in any of the aforementioned departments, divided by rural or urban area. Only the departments Boquerón and Alto Paraguay are not covered in the EPH due to the small population size living in those departments (1.4% according to the latest census). As a result, our study is representative of the Paraguayan population except for Boquerón and Alto Paraguay. As our weather shock variables are fixed at the district level, we cannot use district-fixed effects but use (representative) department-fixed effects instead.

14
Table 1: Balance: Impact Evaluation Sample: Treatment vs Control

|Variable|N|(1)<br>Control<br>Mean/SE|N|(2)<br>Treatment<br>Mean/SE|T-test<br>Diference<br>(1)-(2)|
|---|---|---|---|---|---|
|Number of Toilets|1197|14_._335<br>(0_._284)|1194|14_._091<br>(0_._262)|0_._244|
|Pupil Classroom Ratio|1188|108_._807<br>(1_._710)|1188|108_._229<br>(1_._683)|0_._578|
|Pupil Teacher Ratio|1196|80_._684<br>(1_._037)|1193|79_._708<br>(1_._122)|0_._976|
|Enrollment Grades 1-4|1197|608_._030<br>(12_._275)|1194|613_._517<br>(13_._909)|_−_5_._487|
|Enrollment Grades 5-8|1197|295_._145<br>(8_._354)|1194|307_._861<br>(9_._957)|_−_12_._716|
|Repetition Rate|1185|0_._259<br>(0_._003)|1181|0_._253<br>(0_._003)|0_._006|
|Dropout Rate|1185|0_._048<br>(0_._002)|1181|0_._044<br>(0_._002)|0_._004|

_Notes_ : Indicators extracted from EMIS 2017. The value displayed for t-tests are the differences in the means across the groups. Robust standard errors are reported in the second row (in parentheses
but instead report total labor income, including payments in kind. Given this and the fact that food payments in kind are the most common form of payment in kind, the income dimension includes not only wages, but also food payments in kind in the calculation of daily income if reported separately in a survey. To evaluate whether a job pays above the threshold, the income in local currency is adjusted to 2011 prices using the country-specific consumer price index values, and then the 2011 consumption-based PPP rate is applied (Ferreira et al. 2015).<sup>11</sup> _A job that pays above the threshold is counted as a success in the income dimension._ Converting labor income into a binary indicator leaves out valuable information regarding the quality of a job. However, the Alkire-Foster aggregation method followed in the analysis offers many desirable properties for the construction of an aggregate JQM, though it also requires binary indicators (Alkire and Foster 2011). However, collapsing labor incomes into binary indicators is widely used in the construction of JQMs (Brummund, Mann, and Rodríguez-Castelán 2018; González et al. 2021; Sehnbruch et al. 2020). The literature includes alternative composite indicators that are based on continuous indicators, such as the Human Development Index, which is constructed by the United Nations Development Programme as a geometric mean of three normalized continuous variables.<sup>12</sup> A potential area for future research would be the development of a JQM that exploits the continuous nature of labor income.

> 10 A household size greater than four may be more appropriate, especially in lower-income countries, where fertility rates tend to be higher. However, Anker (2011) mentions a few considerations. First, data on household size are often not available in labor force surveys, which usually exclude individuals outside the ages of 15–64. Second, using region- or country-specific estimates of completed family size can be complicated. For instance, observed average household size is affected by the distribution of household
# II. Background

Tobacco consumption Georgia faces high and increasing rates of tobacco consumption. The smoking prevalence in Georgia is among the highest in countries of the European region of the World Health Organization (WHO) (NCDCPH et al. 2016). Approximately one million Georgians are current tobacco users, and prevalence rates have increased over the last two decades (NCDCPH et al. 2016).

<mark>High prevalence of tobacco consumption is particularly concentrated among adult males (Table 1). In 2016, 57 percent of adult males were smokers and 51.5 percent were daily smokers (WHO 2017). Smoking among females is less common. Approximately 7 percent of women were smokers in 2016, and 6.2 percent were daily smokers (WHO 2017). More concerningly, the rate of current female smokers doubled in recent years (UNDP 2018). According to the Global Youth Tobacco Survey (GYTS 2017), 12 percent of teenagers (13 to 15 years old) consume tobacco, including 17 percent of boys, and 8 percent of girls.</mark> Smokeless tobacco is more common among the youth. Only 0.6 percent of adults (ages 18-64) consumed smokeless tobacco in 2010, but 3.4 percent of children (ages 12-15) consumed these products in 2014 (NCDCPH et al. 2018). Second-hand smoking affects a large share of the population. More than 40 percent of population—including children and pregnant women—are exposed to second-hand smoke (UNDP et al. 2016). Over 43 percent of students report suffering exposure to tobacco smoke at home (GYTS 2017).

Cigarette smoking is the most common form of tobacco consumption.<sup>2</sup> Filtered cigarettes are the most common type of tobacco product consumed, and only a small percentage of the population consumes unfiltered cigarettes or both (Little et al. 2019).<sup>3</sup> Evidence also suggests that smoking frequency (the number of cigarettes smoked daily)
# **7. Growth Effects from Panel Regressions**

We re-examine the growth effect of subsidy reduction, just visited from single country studies, but this time using the same dataset used in the Panel study of Section 5. We distinguish between long term and shortterm policies of subsidy removal since in theory the two may have opposite effects. For this reason, we adopt a panel Autoregressive Distributive Lag (ARDL) with Error Correction (EC). We adopt this approach due to its ability to distinguish between the short run and the long run effects. Results, reported in Tables 4, show that subsidies do _not_ matter for any of our three samples (world, MENA and MENA oil group) either in the long-run or in the short-run. The single country forward projections from the CPAT tool also suggested no long run effects, consistent with our finding here from historical data. Our results further suggest no short-run effect while the CPAT tool did suggest some short-run dip in the rate of growth.<sup>11</sup> Between the systematic regression results based on actual historical data, and the CPAT simulation tool, the policy implication of our finding is that removing fuel subsidies do _not_ negatively impact growth. (We cannot say the same about Carbon Taxes since those are only simulation-based results.) With both the climate and the budgetary benefits of subsidy removal from single country simulation on one hand, and the absence of harm to long-run growth on the other, the benefits of such a policy are clear and need to be advocated on this basis, not only on the basis of well known adverse effects on efficiency, fiscal space, and distribution.<sup>12</sup>

# **8.  Conclusion**

Numerous findings of this paper have been summarized as we have presented them. Here we emphasize some of the points that are worth repeating for policy purposes. Among the highlights are that for the MENA

> 11 A technical note: The error correction coefficient is negative and significant,
34 study whether the value of financial depth in 1960 predicts the rate of economic growth, capital accumulation, and productivity improvements over the next 30 years. Table 3 summarizes some of the results. In the three regressions reported in Table 3, the dependent variable is, respectively, real per capital GDP growth, real per capita capital stock growth, and productivity growth averaged over the period 1960-1989. The financial indicator in each of these regressions is the value of DEPTH in 1960. The regressions indicate that financial depth in 1960 is significantly correlated with each of the growth indicators averaged over the period 1960-1989.22 These results, plus those from more sophisticated time-series studies, suggest that the initial level of financial development is a good predictor of subsequent rates of economic growth, physical capital accumulation, and economic efficiency improvements over the next 30 years even after controlling for income, education, political stability, and measures of monetary, trade, and fiscal policy. 2 3 The relationship between the initial level of financial development and growth is economically large. For example, the estimated coefficients suggest that if in 1960 Bolivia had increased its financial depth from 10 percent of GDP to the mean value for developing countries 22 There are an insufficient number of observations on BANK, PRIVATE, and PRIVY in 1960 to extend the analysis in Table 3 to these variables. Thus, King and Levine (1993b) use pooled, cross-section, time-series data. For each country, data permitting, they use data averaged over the 1960s, 1970s, and 1980s; thus, there are potentially three observations per country. They then, relate the value of growth averaged over the 1960s with the value of, for example, BANK in 1960, and so on for the other two decades. They restrict the coefficients to be the same across decades. They find that the initial level of financial development is a good predictor of subsequent rates of economic growth, capital accumulation, and economic efficiency improvements over the next 10 years after controlling for many other factors associated with