rafmacalaba/data-use-mentions-extended huggingface dataset:
, mainly through premature deaths and adverse effects on human health. Bakhsh et al. (2017) performed an empirical study of the relationship between economic growth and pollution in Pakistan. They concentrated on the role of foreign direct investment. They found that, once a particular threshold level of pollution is reached, further increases in overall pollution cause a significant reduction in economic growth in the country.

The narrower question of the impact of air pollution on labor productivity has also been the subject of several studies in recent years. Findings reported in the literature draw a complicated picture, depending on the identification strategy employed and the study’s spatial and temporal focus. The channels through which local pollution can affect labor productivity can be far-reaching. As an example of the wide-ranging related effects, Victor et al. (2014) studied the relationship between cognitive performance and ambient PM2.5 pollution exposure in Israel. They used a sample of high-school examinations during 2000-2002 and found evidence of a strong negative relationship between performance outcomes and pollution concentrations. Their results suggest that reduced cognitive performance due to pollution could provide a channel to reduced labor productivity. Chang et al. (2016) studied the relationship between PM2.5 and worker productivity using daily panel data from an indoor pear-packing factory in Northern California. They found that a 10 _μg/m_ 3 increase in PM2.5 reduces productivity by $0.41/hour (approximately 6 percent of average hourly earnings), with PM2.5 adversely affecting productivity even at levels lower than current US air quality standards. They did not find evidence of a labor supply response to PM2.5 flows. Likewise, they found that outdoor pollutants such as ozone have negligible effects on labor productivity.

Hanna and Oliva (2015) and Hansen-Lewis (2018) argue that pollution-related effects on productivity might be economically significant even if the pollution effects are small in absolute terms. Chen and Zhang (2021) argue that the causal effect strengthens at higher pollution levels, and the effect might be different for different
Rijkers, 2013). Hill (2007) suggests that slow recovery was caused by imperfect implementation of the reforms and political instability.

In summary, the collapse of the regime decreased the value of connections to Suharto. The restructuring of politically connected companies, the elimination of a number of production and trade monopolies, and restrictions on investment are arguably all manifestations of reduced capture. If they are, one would expect increased competition, especially in industries where firms with connections to Suharto accounted for substantial market share. That the reform agenda and changes in business practices were not more sweeping, however, may limit the size of the effects.

# **3 Data**

This study draws on a number of data sets that are described in more detail in the online appendix A. Our primary data comes from the plant-level Annual Manufacturing Survey ( _Survei Tahunan Perusahaam Industri Pengolahan_ ), collected by Indonesia’s Central Bureau of Statistics ( _Badan Pusat_ 8
protection. Using data from the World Values Surveys, we find that more educated people state their support for prioritizing environmental protection over economic growth.

In the case of local pollutants such as PM2.5 or water pollutants, the reduced form crossnational carbon dioxide Environmental Kuznets Curve shifts down and inward over time (Dasgputa et al. 2002). One explanation for this empirical finding is that as nations grow richer, they internalize the social costs caused by local pollutants. This fact raises the possibility that as developing nations grow richer their own people may support a “greening” of coal fired power plants in order to enjoy local air pollution gains.

Our cross-national Engel curve estimates suggest that this optimism has not held in recent years for carbon dioxide emissions. The sheer scale of consumption increases in large lower-middle-income nations such as China and India means that these nations must make significant carbon intensity progress to offset the scale effects associated with increased consumption (Acemoglu et al. 2019). In richer nations, greenhouse gas emissions continue to rise but at a decreasing rate.

The rest of this paper is organized as follows. Section II sets up the trade-offs individual voters face in determining their support for low carbon policies. Section III describes the empirical work that estimates Engel curves, the adoption of electric vehicles in the United States, within-country variation in carbon emissions in Europe, and the correlates of support for environmental policies. Section IV concludes.

# **II. Understanding Voter Preferences over Introducing Carbon Taxes**

A voter’s support for carbon pricing will be based on comparing the costs of a short run increase in energy prices versus the ongoing benefits of facing less climate change risk and gains from improved local air pollution (Weitzman 2014, 2017). In the discussion below, we focus on cost heterogeneity. Those who are terrified by future fat tail risks associated with climate change will place a great benefit on enacting a carbon tax because of its insurance benefits (Wagner and Weitzman 2016).

5
des Entreprises (RGE), which was the last establishment census led by the Agence Nationale de Statistique et de la D ́emographie suggests that formal and informal firms with 5 or more employees cover almost half of the workers and about 80% of the total sales measured by the RGE data, despite the fact that they represent only 6% of the establishments. These firms are very important for the economy of Senegal, but still, the large majority, including those that are large, formal, and located in Dakar, present low 39
Here we present results from all three regional analyses for thresholds of 50km and 150km, alongside the main results. Tables 3‐4 present results for the Mekong River Basin, India, and global results, respectively. In each table, results from 9 regressions are shown. The specification across all regressions are the same, with the only variation coming from the distance to monitoring station threshold and the share of cropland threshold. By changing the distance threshold, the station that each gridcell is matched to does not change, as gridcells are always matched to their nearest upstream station. Rather, what is changed is the sample of gridcells that are included in the regression, with more gridcells included as the threshold increases. Note that in each table, columns 2, 5, and 8 are the regressions depicted in 4 for cropland thresholds of 30, 75, and 90 percent, respectively. In all regions, the coefficient on EC is not sensitive to changes in the monitoring station distance threshold. Similarly, weather control variables as not sensitive as well, implying that changes to the sample to not have a significant effect on the estimates.

We next test different thresholds of high EC tested for in the Mekong River Basin and India regions. In the main results, we use 100mS/m. The true sensitivity of agriculture to EC varies by crop. The original threshold was chosen as this is the recommendation made by the Mekong River Commission and other sources as to where one should start seeing impacts on irrigated crops (Kongmeng and Larsen 2014; Tanji and Kielen 2002). However, as shown in figure 5, impacts globally are found at lower values of EC. While the Mekong River Basin and India data sets do not offer enough variation to precisely estimate a semi‐ parametric model like that in figure 5, we do use alternative cut offs of 75 and 125 mS/m. Results are shown in Tables 6 and 7 for the Mekong River Basin and India, respectively. In both cases, we see that impacts
Policy Research Working Paper 6123

# **Abstract**

Unbundling power generation, transmission, and distribution is not an end itself, but rather a means to achieve better performance. The key objective of the analytical framework of this paper is to explore the links between alternative market structures and performance (in terms of access, price, quality, and technical and financial performance). The results are crucial for providing policy advice, by offering alternative options to policy makers based on the lessons learned from the taxonomy of different market structures, tailored to different national contexts. The analysis is based on unique data, including a panel of 22 countries for the period beginning in 1989 and extending through 2009. The results of the analysis carried out for this study confirm the following conclusions for policy guidance on power market restructuring for developing countries.

First, unbundling delivers results in terms of several performance indicators when used as an entry point to implement broader reforms, particularly introducing a sound regulatory framework, reducing the degree of concentration of the generation and distribution segments of the market by attracting public and private players and private sector participation. Second, there seems to be a credible empirical basis for selecting a threshold power system size and per capita income level below which unbundling of the power supply chain is not expected to be worthwhile. Finally, partial forms of vertical unbundling do not appear to drive improvements, probably because the owner was able to continue exercising control over the affairs of the sector and hinder the development of competitive pressure within the power market.

This paper is a product of the Energy Unit, Sustainable Energy Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mvagliasindi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
enrollment drives. Countries should also use this opportunity to build a more resilient and inclusive education system that can continue to deliver learning in future crises.<sup>83</sup>

- Remote learning, now and in the future, can be made more effective by ensuring a multifaceted model and developing short-term and long-term learning plans.<sup>84</sup>

- Learning losses can be mitigated by adjusting expectations from the curriculum, and creating a rapid catch-up period once schools reopen (rather than forcing students through a curriculum for which they are far from ready).<sup>85</sup>

- Drop-outs may not need to materialize if school safety concerns are properly addressed and communicated with families, cash transfers reach the poorest (at a later stage) are tied to conditions of school re-enrollment for children, and policies and practices that prevent the enrolment of pregnant students are lifted.<sup>86</sup>

- Countries and development partners need to work together to build an understanding of what actions and interventions have been promoted by governments in response to COVID-19,<sup>87</sup> how households have perceived and taken up those actions,<sup>88</sup> and how effective those interventions actually were.<sup>89</sup>

According to global estimates of Learning Poverty, 53% of all children in the developing world cannot read and understand a simple paragraph by age 10.<sup>90</sup> The losses simulated here will make an already daunting challenge worse and will set the world back in its goal of helping every child learn the basics. The Learning Poverty target – which was to at least halve learning poverty to 27% by 2030 – was predicated on the assumption that countries could accelerate their performance to that of the 80<sup>th</sup> percentile of countries in their respective regions (Figure 12). Even if we were to assume that countries could accelerate their performance to this
% between 1995 and 2005, with enrolment in private schools amounting two thirds of expenditures in this field.

By looking at expenditure in education as a percentage of total family expenditure, it is possible to observe that households from different income levels allocate a similar share of their resources. For the poorer, meals and transportation represent most of their educationrelated expenditures, whereas the fourth and fifth quintiles are assigning proportionally more to enrolment fees and monthly payments<sup>8</sup> . This clearly confirms that the middle class and the richer are more willing and more able to pay for private education in order to obtain a perceived better quality of services. Still, in some cases, they just got better infrastructures

> 7 DR Central Bank. National Accounts 2005, “Supply-use matrix”. Thousands RD$

> 8 Calculations based in ENCOVI survey, 2004.

[12]
investment cost subsidies).

# **Future Research Needs**

# **Empirical Research**

Rental housing sector research has not been particularly strong in mature market economies outside the limited circle of countries with high rental sector shares and proactive rental policies (mainly Scandinavia, the Benelux, France, Britain, and Germany). Moreover, during the 1980s the rental sector was pushed ideologically onto the defensive and thus attracted less scholarly attention. Not surprisingly, the first wave of housing sector analysis for transition countries focused on homeownership and mortgage markets.

A second shortcoming lies in detailed monitoring of the housing market by governments and independent analysts. Most census specifications do not provide information about tenure forms and are hard for researchers to access. Surveys are rare, and microcensus analysis that would, for instance, track household moving chains is absent.

Improving the data support, which can sometimes be done simply by adding or reformulating questions in surveys or censuses, should be a first priority for governments interested in enhancing their rental sector policies. Follow-up studies are recommended with existing census and LSMS/HBS material, to differentiate the structure of demand and supply further. This could 64
Figure 18. Gini index change between 2004-2014, resource rich and non-resource-rich SubSaharan African countries.

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>Resource-rich Non-resource-rich<br>-15<br>Gini index change (2004-2014)<br>Mozambique Cameroon Chad Ghana Cote d'Ivoire Zambia Tanzania Congo, Dem Rep Sao Tome and Principe Botswana Uganda Madagascar Central African Republic Sierra Leone Mali Niger Mauritania Angola Guinea Guinea-Bissau Malawi South Africa Benin Ethiopia Kenya Senegal Eswatini Namibia Rwanda Burundi Gambia, The Cabo Verde Burkina Faso Comoros<br><!-- End of picture text --> Notes: Gini index of 2014 or the closest year of reported value minus the Gini index in 2004 or closest year with reported value. Blue bars correspond to fossil fuel-rich countries and orange bars correspond to mineral-rich countries according to IMF (2012). A decrease in the Gini coefficient indicates an improvement in the income equality of a country’s population. Source: authors’ calculations using World Bank PovcalNet data.

# **Outcome 4: Failure to transform and diversify**

## **Resource-rich countries in Africa recorded lower adjusted net savings rates and increased export concentration compared to before the boom**

The transformation of resource endowments into sustained prosperity is a multi-stage economic and political problem (Venables 2016). It requires governments to navigate a series of decisions - counties must secure investment, typically from abroad to explore for and develop resources. They must secure a fair deal that maximizes value for the country without deterring investors, even in conditions of weaker governance. Then they must 28
## **<u>Table 2:  Summary of perceptions of main social programs</u>**

|**He**|**alth**|
|---|---|
|_Positive_|_Negative_|
|The professional character of COF<br>Improvements in the growth and<br>development of children<br>Improvements in the relationships between<br>mother-child<br>Development of new social interventions in<br>the family and between neighbors<br>Awareness ofpreventative actions|Limited number of spaces|
|**Educ**<br>|**ation**<br>|
|_Positive_|_Negative_|
|The continuity of services<br>The range of activities<br>Expanding educational and occupational<br>opportunities for youth (particularly<br>through the cooperatives)<br>The presence of resource people<br>(educators) in the community<br>Indirect impacts on the family and their<br>habits|Low coverage<br>Difficulty in access (long waiting list)|
|**Training and Emp**|**loyment Programs**|
|_Positive_|_Negative_|
|Opportunities for cooperatives in the labor<br>market<br>Some level of education is required for<br>entrance to the program<br>Existence of skilled workers in the area<br>Integrated approach|Coverage restricts the youngest<br>Perception that the cooperatives are closed<br>to new members|

# **III. COST EFFECTIVENESS**

The project area of Novos Alagados II included 2020 households, or 7800 people. A total of R$22.1 million was spent from 2001-2004 on physical infrastructure, equivalent to R$11,000 (US$3928) per household. Unit cost data for a range of 11 indicators presented in Figure 1 show substantial variation.

The highest expenditure under the Project was for the construction of
up until now have recommended against vaccination for people with a wide range pre-existing health conditions, including having had a recent surgery) about who is eligible to be vaccinated, and conducting awareness campaigns for both health care professionals and the public at large to stress the message that vaccination is recommended in nearly all cases, including among people with pre-existing health conditions (World Bank, UNIFEF and JICA 2021).

_Figure 11: Reason for “definitely not” (upper panel) or “probably not” (lower panel) taking the COVID-19 vaccine, Facebook data for month of HFPS)_ <!-- Start of picture text --> Respondent who will "definitely not" take the COVID-19 vaccine<br>Belize<br>Brazil<br>Colombia<br>Nigeria<br>Costa Rica<br>Lebanon<br>Panama<br>Honduras<br>Peru<br>El Salvador<br>Guyana<br>Respondent who will "probably not" take the COVID-19 vaccine<br>Panama<br>Costa Rica<br>Uruguay<br>Nicaragua<br>Nigeria<br>Iraq<br>Argentina<br>Guatemala<br>Honduras<br>Guyana<br>Indonesia<br>0 50 100 150 200 250 300<br>Safety Efficacy<br>Don't trust the government Dislike vaccines<br>Don't need a COVID-19 vaccine Other people need it more than me<br>Against my religious beliefs Concerned about the cost<br>Other<br><!-- End of picture text --> Notes: Y-axes indicate the share of survey respondents; multiple answers were allowed. Data source: Facebook (2021) for same month as reported in the HFPS. The month for each country is reported in Appendix Table 1.

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# **A**

- **A.A** **_Guerrilla territories_**

- **Territories under control by 1991:** Following Casta ̃neda (2016), this study uses the maps that document FMLN-held areas as submitted to the United Nations and approved by the different political parties in El Salvador during the cease-fire process. Since the map originally had an image format, we used ArcMap to digitize it by hand and convert it to a shapefile format. Thus, this is the only part of the spatial analysis that is not coded.

- **A.B** **_Geospatial variables_**

- **Night light luminosity:** Data on night light luminosity comes from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS). This data was obtained from the US National Oceanic and Atmospheric Administration (NOAA) at https: //ngdc.noaa.gov/eog/download.html. This data has a resolution of 30 arc seconds (1 _km_<sup>2</sup> ) and spans 1992 to 2013. The challenge with night light luminosity data is the significant fraction of observations that take the value of zero and the existence of extreme values in the right tail of the distribution (Michalopoulos and Papaioannou, 2013; Pinkovskiy and Sala-i Martin, 2016). To account for this potential concern, we adjust the outcome of interest using the logarithm and the inverse hyperbolic sine transformation.<sup>38</sup>

- **Elevation:** Elevation was obtained from the Google Earth Engine Data Catalog and is available at https://developers.google.com/earth-engine/datasets/catalog/USGS_ SRTMGL1_003. This data provides elevation information in meters at the 3 arc-seconds spatial resolution (90 _mts_<sup>2</sup> ). The digital elevation model (DEM) was created based on the images of the Shuttle Radar Topography Mission (SRTM) of NASA. In this study,
frequency of the data one is analyzing. It is obviously important for the analysis of quarterly data (most people spend more than they eam over Christmas and other holidays, and somewhat less than they earn in other times), and almost as surely is a bad theory for understanding savings rates over a quarter of a century. See Deaton (1992) for a survey of evidence on intertemporal models of savings. 2 Moreover, this assumption is consistent with the strong home equity preference in OECD economies that has been documented by French and Poterba (1991) and Tesar and Werner (1992). Lewis (1995) surveys alternative explanations for this phenomenon.

2
margin).

# **5.3 Hiring More Supervisors vs. More Teachers**

Column 5 of Table 10 shows the extent to which the effective STR (ESTR) can be reduced by hiring enough supervisors to increase the probability that a school was inspected in the past three months by 10 percentage points (the same magnitude used in the calculations in the previous section). To compare the relative cost effectiveness of hiring more supervisors versus more teachers, we calculate the salary cost of hiring more teachers to achieve the same reduction in ESTR and report these numbers in column 6. Comparing columns 3 and 6, we see that hiring more supervisors would be 12.8 times more cost effective at reducing ESTR than doing so by hiring more teachers. The difference between the estimates in columns 4 and 6 stems from accounting for the fact that hiring more teachers will increase the absence rates

> 28We use DISE data on the number of schools in each state to calculate the number of supervisors who will be required to increase the probability of inspections in a 3-month interval by 10 percentage points. The cost estimates are conservative and assume that the salary costs are double that of a teacher and that the travel costs are equal to 80 percent of a full months’ salary (which is higher than the typical travel and daily allowance provided to education department employees to travel to/from a village to district headquarters).

19