rafmacalaba/data-use-mentions-extended huggingface dataset:
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 people are exposed to air pollution originating from coal power plants. Globally, 160 million people face SO2 pollution from coal plants that exceed the World Health Organization (WHO) guideline value of 40 _μ_ g/m<sup>3</sup> – and this does not account for pollution from other sources. Moreover, 247.5 million people are exposed to transboundary pollution – namely, toxic air pollution originating from coal plants outside their country.

We find that, overall, air pollution from coal plants increases with income levels. Richer areas have more coal plants, thus more coal plant-related pollution. However, air pollution from coal plants increases at a diminishing rate at high income levels. This is consistent with the notion that high-income countries transition to more efficient less polluting power sources. Our results 20
of shocks to the drug sector. Poverty-related mechanisms include the dynamism of local economy, food scarcity, budget constrains and water insecurity. Rapacity-related mechanisms include rent capture and the funding of insurgents. Other mechanisms include exodus, grievance, imperfect information, regional destruction (incl. agricultural soils) and state capacity. _Source: Authors’ compilation from MRA database._ Figure 3 presents the proportion of collected estimates in each meta-regression subgroup. Our three main subgroups (AS-, AS+, and HS+) account for 90.2% of the baseline sample of 1,391 regressions and 66.8% of the baseline estimates concern agricultural shocks (71.4% of the total sample of 2,464 regressions). Other shocks only account for 6.5% of the total sample. Interestingly, while selected studies on the agriculture sector investigate equally positive and negative shocks, studies focusing on the hydrocarbon/mineral sector only investigate positive shocks. Detailed information on the types of shocks in each meta-regression subgroup is provided in Figure A1, Table A1, and Tables A5 to A8 in the Appendix.

11
with weights provided by the Nepal DHS in each year. Standard errors, in parentheses, are clustered by regionyear. The notation<sup>***</sup> is p<0.01,<sup>**</sup> is p<0.05,<sup>*</sup> is p<0.10. In each regression the mortality-year interactions are presumed exogenous and we do not instrument for them. The Widow/Sep/Div/HH Head sub-sample excludes women whose husbands have migrated. Source: Authors‟ calculations based on MHP/NE/MI (2007), MH/NE/ORC (2002), and Pradhan _et al._ (1997).

43
data used by PovcalNet. Most CPIs are from the IMF’s International Finance Statistics, while most PPP exchange rates are from the International Comparison Program (PPPs for household final consumption expenditure). More details on the price data used are available in Lakner et al. (2018) and Atamanov et al. (2018). Second, we need population data to <mark>aggregate poverty estimates across regions and globally. We use annual population projections for each country from the World Bank.</mark><sup>9</sup> Finally, to arrive at regional and global poverty rates, we also need estimates for the 3% of the world for which we have no distributional data. In these cases, we follow the aggregation method used by Chen and Ravallion (2010) and deployed by PovcalNet, which assumes regional poverty rates for countries without a poverty estimate.

# 4 Results

This section presents the results from the simulations described above. First, we show distribution-neutral poverty projections towards 2030, both at the global and regional level, focusing on the poverty rate measured at $1.90/day in 2011 PPPs.<sup>10</sup> Second, we explore what would happen if growth or inequality changes in a positive or negative direction, and the trade-off between increased growth and reduced inequality.

## 4.1 Impacts on Poverty: Global and Regional Trajectories to 2030

Figure 5 presents our simulated trajectories for the global poverty rate to 2030 for the three different distribution-neutral growth scenarios: that countries follow their growth patterns of the past 10 years, of the past 20 years, or that they follow the growth projections from the WEO.

All scenarios put the global poverty rate in 2030 in the range of 6-7%. The scenario using historical growth rates from 1997-2017 is a bit more optimistic, due to the high growth rates at the turn of the century. While the different growth scenarios do not matter much at a global level, there are starker differences at the
important overview of education in colonial Africa. He points out that in colonial Africa, as in Europe centuries earlier, popular or mass education was the purview of Christian churches. He charts broad directions in education for British, French and Belgian colonies, noting the long-term influence missionaries had on education in British colonies, and Roman Catholic supervision of schools in Belgian Congo as an arm of the state. The French system, especially in French West Africa, was more independent of mission control, and was characterized in Hailey‘s opinion ―by a greater precision of objective and unity of method.‖ Differences between French and British educational systems reflected different ideologies of rule, and the French acknowledgement that the African colonies were an integral part of France (Hailey (1957: 1135)).

The British deferred responsibility in education to the missionaries, and absorbed the products of missionary schools in the lower echelons of the colonial bureaucracy. This left room for missionary assertion, and when the British set up an Advisory Committee on Education in the Colonies in 1924, it was even in response to missionary prompting that the colonial office should take more interest in African education (Hailey (1957: 1166)). This was the context in which a rather minor American organization, the Phelps Stokes Fund, came to wield enormous influence on educational policy in British Africa. The Phelps Stokes Fund commissioned two surveys of African education, the first in 1920 and the second in 1923, building off its growing reputation in American Negro Education. Berman points out how the Fund succeeded in ―perpetuating the belief that the educational methods which had been hammered out for the freed slaves in the post-bellum period were particularly relevant for black Africans in the 20<sup>th</sup> century‖ (Berman (1971: 132)). The Fund emphasized the utility of industrial and agricultural aspects of education in America versus ―academic‖ schools, and suggested a similar approach for Africa. The British Colonial Office needed a policy on education in Africa but
_“It started improving in July to August, and by September, October 2021 it had reached to normal up to date._ _“Since June 2021, last year. I started noticing that the pandemic was over because I earned a lot of money.”_ Similar results emerged from the survey data, which indicate that sex work income has recovered from the initial price shocks that occurred at the beginning of 2020. Among participants who reported sex work as their primary source of income, the total monthly income from sex work reported in surveys significantly increased from 84 USD to 127 USD per month from 2020 to 2021. Total income also recovered by 2021 and was similar to income levels prior to the pandemic.

# **Discussion**

This study found that by late 2021 FSW in Dar es Salaam, Tanzania had mostly recovered from the economic insecurity following the initial phase of the COVID-19 pandemic in 2020. The restrictions introduced in early 2020 resulted in dramatic decreases in sex work income, leading to extreme financial vulnerability, food insecurity, and challenges in meeting other basic needs such as paying rent. However, participants reported that the demand for sex work substantially increased in summer 2021. Notably, despite decreases in the average number of unique weekly clients, the price per client and the total monthly sex work income had returned to pre-pandemic levels. This may potentially be explained by an increased number of repeat clients, which started to represent a larger proportion of all clients during COVID-19, even after restrictions were lifted. Additionally, this study revealed that the pandemic changed the landscape of sex work in many ways, including the places to meet new clients and ways of meeting and engaging with clients. In 2021, participants were still not meeting clients as regularly in bars and brothels and were more likely to meet clients on the street.

Our results are consistent with findings from studies in rural and urban Kenya which demonstrated sharp decreases in transactional sex activity, income, and numbers of clients in the early phases of the pandemic
all of the components, in this paper we group the variables into smaller sets, calculate the Shapley-Shorrocks estimates of these, and assess whether path dependence is indeed empirically important.

The paper focuses on Peru, Bangladesh and Thailand. These three economies have experienced fast poverty reduction during the last decade, with falls in the moderate national poverty headcount rate in each country of over 12 percentage points. Growth was very high during the decade in all three countries, well above 4 percent per annum during the period 2002-2008. In the case of Peru and Thailand, there was a sharp deceleration due to the financial crisis in 2009, only to rebound very fast the following year. Bangladesh came through the crisis unscathed.<sup>5</sup> In all countries employment and public social transfers increased, as did remittances. However,

> 3 Panel data that can track the life and labor histories of households over time can be used to answer questions about economic mobility and poverty dynamics. However, panels are often not available with the frequency required. Moreover, panel data are often not representative of the population as a whole; and if they initially are, it is unlikely that over the course of a decade the panel would remain representative of the population. Alternative methods using repeated cross sections have been used. One approach is to construct pseudo panels, which can delve into some issues of economic mobility (Lanjouw et al., 2011). However, these models are often troubled by their lack of precision and the fact that they often do not measure the contributions of different factors to poverty reduction.

> 4 See Shapley (1953) and Shorrocks (1999).

> 5 See Bangladesh Poverty Assessment, World Bank (2012a).

3
decade. We investigate the hypothesis that skill-biased technological change did in fact arrive in India in the 1990s, examining Indian registered manufacturing using the Annual Survey of Industry (ASI). We look for symptoms familiar from the manufacturing sectors of other industrialized and developing countries.

We use the ASI for 1984-1998, aggregated by 3 digit industry for each of the twenty one Indian states and union territories. Our analysis of these data show that the mid- to late 1980s were indeed a period of falling demand for skilled labor in manufacturing, as reported in the cruder data used in Berman-Machin (2000). This is true to a varying degree in most Indian States. However, following major economic reforms in the late eighties and early nineties, the demand for skilled labor in manufacturing increased throughout the subsequent period, fairly consistently over time, though not consistently across states.

The pattern of changes in the demand for skill in Indian manufacturing is different from that observed for other countries in two important respects. First, though we do find some evidence of increased capital-skill complementarity, the best predictor of skill-upgrading is increased output. Second, previous studies find skill upgrading in manufacturing tends to be concentrated in the same industries across countries. Indian manufacturing did _not_ undergo disproportional skill upgrading in those typical industries, indicating an increased demand for skill in manufacturing qualitatively different from that experienced by other OECD and middleincome countries.

3
exclude all data uploaded by any iNaturalist user who records an observation inside the same protected area between 30 and 365 days from their last visit, as such users could plausibly be protected area staff. Reconstructing the panel data as per our primary specification results in the same 3,625 observations, but reduces the dependent variable’s mean value among control areas to 0.352. Nonetheless, our analysis yields a similar result to our primary specification, indicating that potential inclusion of protected area staff data does not cause bias. The pre-trend remains flat and the ATT is 0.175, with a standard error of 0.040 (Figure S9).

We also return to our primary iNaturalist data and repeat our regression with log(number of iNaturalist visits per year) as the dependent variable (Figure S11a). Because the dependent variable is a natural logarithm, we restrict the data to areayears with positive visits. The number of observations in the regression is therefore 1,635. We obtain an ATT of 0.060 with a standard error of 0.037.

# **A.4 eBird Tourist Visits (Figures S10 and S11b)**

Figure S10 employs a similar analytical approach to Figure S8, now using eBird visits as an indicator of tourism. The dependent variable again takes the value of 1 if any eBird observations are recorded within a protected area in a particular year, and 0 otherwise.

Unlike the previous bird abundance analysis (Figure S2), which leverages eBird data at the level of individual birding trips, this analysis utilizes data at the level of 9
# **1. Introduction**

How women and men spend their time is one of the most important and least understood characteristics of people around the world. Data on the activities they do, the time they allocate to each activity and on who they were with at the time are not collected on a regular basis in most countries. However, such information could be effectively used to better understand the factors leading to gender inequalities in access to economic opportunities, well-being as well as the implications for policy making.

Time use studies are not new. They were first used in the early twentieth century to understand people’s lifestyle based on their time use patterns. Back in the 1930s, feminist scholars inquired into time use and the need to measure the “invisible” unpaid work of men and women (Reid, 1934). Further, the integration of the gender division of labor and unpaid work into the analysis of economic development and policies has been one of the main contributions of feminist economics to the field (Boserup, 1970).

In terms of large-scale data collection, Canada and the United Kingdom conducted time surveys in the 1960s, and were followed by Norway, Japan, Finland and Austria in the 1970s and 1980s (Ironmonger, 1999). By the end of the 1990s, developing countries started carrying out time use surveys and the instruments acquired a new focus. Currently, these surveys are being used around the world to improve estimates of national accounts (Suh and Folbre, 2016) and complement information on work missed in labor market surveys. For example, a comparison of time use and labor force surveys for South Africa reveals that the former captures more of paid work that both men and women do than the latter (Floro and Komatsu, 2011). Moreover, while the data to assess poverty rates at the individual level are not yet available, time use surveys have provided information to study the concept of time poverty.<sup>1</sup> This allows adjusting the measurement of
, but our results suggest that it will not happen without significant changes in the economic, social, and policy landscape.

One response to our results could be that the gravity-type models that urban and trade economists have used for years have long recognized the importance of agglomeration and economic opportunity as dominant forces driving migration. We acknowledge those models, but point out that this same class of models is often used to show that migration will occur in ways that substantially reduce the risks of climate change, implying that individuals move away from at-risk areas.<sup>9,33</sup> In showing that those at-risk areas are often centers of economic opportunity – at least in the U.S. – and that this opportunity appears to dominate the adaptive motivation for migration in the data, our paper complicates these existing models. Specifically, our empirical results suggest that the relative weights given to economic opportunity and climate change in gravity model calibration may need to be revised. Consistent with this conclusion, recent work utilizing these models calibrated to U.S. county data finds that migration may have a negligible impact on aggregate losses from climate change.<sup>36</sup> Our results are also subject to several significant caveats. First, it is difficult to prove a null 13
specifications. The results from the simple difference estimators (A3, panels A and B, column 4) provide reassurance in particular for the validity of the interest rate results, since baseline rates are imbalanced at baseline in the opposite direction as the estimated treatment effect.

# **5. Discussion**

Chronic indebtedness and exploitative moneylenders are well-established tropes in the fictional and cinematic accounts of poverty in rural India. Several studies, including an important recent survey by the National Sample Survey (NSS, 2014) and technical papers by the Reserve Bank of India (RBI 2007, RBI 2011) have documented the widespread reliance of rural households on high-cost debt from the informal sector at rates of up to 150 percent per annum. Over the past two decades, micro-finance institutions have rapidly expanded into markets previously served almost exclusively by traditional moneylenders. One would expect this influx of competition to affect existing credit markets, but the nature of these impacts is not obvious _ex ante_ .

The limited empirical literature on this question consists of observational studies and finds zero or positive impacts on lending rates charged by informal sector lenders. Despite the existence of a large literature on the impact of micro-credit programs, no randomized impact evaluation to date has documented a significant effect on interest rates. This is the first study to do so. The randomized roll-out evaluated in this study, conducted in seven of the 38 districts of Bihar, increased self-help group (SHG) membership by 50 percentage points. Those who joined a Jeevika SHG shifted almost 30% of their household debt from high-interest informal sources to Jeevika, on average. The massive influx of relatively low-cost credit extended through Jeevika allows us to detect, for the first time, the impact of a micro-finance intervention on informal credit markets through a randomized evaluation.

At baseline, annual informal interest rates paid by landless households were an average of 6.1 percentage points per year higher than those paid by the landed. SHG entry caused a shift in the informal lending rate offered to these households that reduced this gap
# **References**

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- ——— (2008): “Son Biased Sex Ratios in the US 2000 Census,” _Proceedings of the National Academy of Sciences of the United States of America_ , 105, 5681–5682.

- Almond, D., H. Li, and L. Meng (2010): “Son Preference and Early Childhood Investments in China,” _Working Paper_ .

- Amaral, S. and S. Bhalotra (2017): “Population Sex Ratios and Crime Against Women: Long Run Effects of Sex Selection in India,” Mimeo, University of Essex.

- Ananat, E. O., J. Gruber, P. B. Levine, and D. Staiger (2009): “Abortion and Selection,” _Review of Economics and Statistics_ , 91, 124–136.

- Ananat, E. O. and D. M. Hungerman (2012): “The Power of the Pill for the Next Generation: Oral Contraception’s Effects on Fertility, Abortion, and Maternal and Child Characteristics,” _Review of Economics and Statistics_ , 94, 37–51.

- Anderson, R. G. and A. Kichkha (2017):
due to attrition, along with bias for non-response to be discussed later, by calculating Lee (2009) bounds and Kling-Liebman bounds (Kling & Liebman, 2004).

# 6. Results

The 85 villages participating in the RMG program are among Lao PDR’s poorest and offer few job opportunities. According to the baseline data, only one-fifth of working-age individuals in RMG-eligible households had paid work outside the household. Over 70% of RMG-eligible women worked on the household farm or business in unpaid roles. When compared against rural households from a nationally representative Laos Expenditure and Consumption Survey (LECS) V, used for calculation of poverty rates in Lao PDR, the RMG-eligible households were poorer than the average Laotian rural households across a range of wealth indicators, including housing composition, ownership of durables, and nutrition consumption; wait-listed and RMG women are also less educated on average and less likely to be in the labor force (Baseline Report, East Asia & Pacific Gender Innovation Lab 2020). A supplementary survey confirmed that the program women are among the poorest in these already poor villages (Dervisevic et al. 2020).

## 6.1. Impact on Women’s Economic Situation

The RMG program was meant to directly change labor market activities by giving participants a regularly paying job for 18 months. We first confirm that the intervention has worked as intended before testing how GBV was impacted. To do so, we estimate the impact of treatment on whether the women were listed among the household members that regularly contributed wage income, and on household average monthly wage income contributed over the 12 months preceding the endline survey. We find increases in regular work for RMG members. RMG members were 77.4% more likely than the waitlist group to be regular income earners (Table 2). Monthly income (winsorized at the 99<sup>th</sup> percentile to minimize the effects of outliers) increases by 173,558.24 kip ($23.56) for RMG households, over 40%