rafmacalaba/datause-encoder-data huggingface dataset:
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# I **Introduction**

The relationships between growth, inequality, and poverty in developing countries have been discussed at some length. The standard view is that broad-based economic growth is poverty reducing<sup>2</sup> . Yet, growth may also be associated with rising inequality, which then tends to offset part of the gains from growth for the poor. This point was first made by Kuznets (1955) who suggested that rural to urban migration would result in an inverted-U relationship between growth and inequality since urban areas have not only higher standards of living, but also higher inequality. Kuznets' views or extensions thereof remain present in the literature today (Anand and Kanbur, 1993; Watkins, 1995; Ram, 1995), even though they have been challenged or at least qualified recently (Papanek and Kyn, 1986; Bourguignon and Morisson, 1990; World Bank, 1990; Fields, 1989; Chen and Ravallion, 1997; Bruno et al., 1996)3.

While the theoretical arguments for explaining the links between growth, inequality, and poverty have been refined over time, most of the empirical work is still based on international panel data with growth, poverty, and inequality measures for a large number of countries at a few points in time. Empirical studies focusing on a single country have relied on somewhat less satisfying methodologies. First, researchers using single surveys have estimated the point elasticity of poverty to growth and inequality using formulae provided by Kakwani (1993) and Kanbur (1987). Although useful for short term comparative statics, these elasticities say nothing about the longer term relationships between growth, poverty, and inequality. Second, researchers have decomposed changes in poverty measures 2 The World Bank's (1990) World Development Report on poverty recommends growth as a privileged path for poverty alleviation, provided it is accompanied by policies to promote access to education, health and social services, and also by the provision of safety nets, especially during adjustment periods.

3 For example, as noted by Bourguignon and Morisson (1990) and Papanek and Kyn (1986), many factors other than growth per se may affect inequality. These factors include the education of the labor force (a progressively better educated labor force tends to raise inequality), the structure of exports (which may or may not be associated with rents - mineral exports is a case in point), and the presence of trade distortion (which tend to increase inequality while free trade favors the abundant factor which is labor in developing countries). When these factors are omitted, the link between growth and inequality may be spurious.
indirect taxes is similar (15 and 13 percent of the final income respectively), while direct taxes are on average account for 4 percent of final income.

The tax-benefit system is quite pro-poor in Moldova in the sense that 60 percent of the population are net recipients and only 40 percent are net payers into the system. However, the degree of reliance on the system varies a lot with income levels. The poorest decile depends almost entirely on transfers and other benefits. Households in this income group get as much as 96 percent of their final income as net benefits. For the second decile this share is almost 80 percent of the total and even in the third decile one receives more than half of one’s final income as net benefits (Table 9, Figure 7). Meanwhile, the top two deciles contribute 20 and 34 percent of their final income as net taxes into the redistributive system in Moldova.<sup>8</sup> The relative importance of each intervention also varies significantly by income groups. Pensions and other direct transfers are highly progressive in the sense that their shares in final income decrease with market income level. Lower deciles defined by market income rely on these two interventions – their shares in the final income for the bottom decile are almost 89 and 68 percent respectively (Table 9, Figure 7). For the top decile, their role is much smaller – together they account for less than 6 percent of the final income. In-kind health benefits are also more important for the lower part of the original income distribution. The middle of the distribution benefits most of all from in-kind education transfers. They account for 12 percent of final income for the third and fourth deciles and only 2 percent of final income for the bottom and top deciles. Direct taxes and pension contributions are highly progressive in the sense that higher deciles pay a much higher share of their income for these types of taxes, while indirect taxes (mainly VAT) are regressive – lower deciles pay up to 20 percent of their final income, while top deciles only 10 percent.

**Table 9. Incidence of main components of tax benefit system by deciles, share of final income, percent.**

|decile|SSC|Direct Taxes|Pensions|Direct<br>Transfers|Indirect<br>Taxes|In-kind<br>Health|In-kind<br>Education|**Total**|
|---|---|---|---|---|---|---|---|---|
|poorest|0.0|-0.2|89.2|14.5|-19.4|10.2|2.0|**96.3**|
|2|-0.1|-0.4|67.5|16.4|-18.8|6.8|5.7|**77.1**|
|3|-1.8|-0.8|40.9|12.6|-16.7|5.6|11.8|**51.6**|
|4|-4.9|-1.5|25.2|9.6|-15.6|5.7|11.7|**30.2**|
|5|-8.7|-2.2|19.2|7.5|-14.8|5.4|10.2|**16.7**|
|6|-12.3|-3.0|14.0|4.7|-13.7|4.4|9.0|**3.1**|
|7|-15.0|-3.5|12.6|3.3|-13.0|5.0|7.2|**-3.4**|
|8|-18.0|-4.5|9.1|2.4|-12.1|4.4|6.7|**-12.0**|
|9|-21.7|-5.3|7.9|1.2|-11.1|3.9|4.5|**-20.5**|
|richest|-26.7|-7.8|5.4|0.4|-9.7|2.3|2.1|**-34.0**|
|total|-14.8|-4.0|21.4|5.2|-13.3|4.7|6.2|**5.4**|

Note: deciles are defined based on market income. Source: authors’ calculations using HBS-2017 data.

> 8 Because of the survey bias the top deciles do not fully cover the richest class in Moldova as in many other countries.

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**Table 15: Structure of employment for the youth in 2007 and 2009 (%)**

|||**2007**|||**2009**||
|---|---|---|---|---|---|---|
||**All**|**Male**|**Female**|**All**|**Male**|**Female**|
|Employer|0.8|0.8|0.7|0.9|0.8|1.0|
|Self-account worker|18.4|14.7|22.5|17.5|15.9|19.2|
|Unpaid family worker|40.6|42.2|38.9|36.3|35.0|37.6|
|Wage worker|39.4|41.2|37.3|44.4|47.0|41.6|
|Member of cooperative|0.04|0.1|0.02|0.1|0.1|0.1|
|Others|0.8|0.9|0.6|0.8|1.2|0.4|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

_Source: (LFS, 2007, 2009)_ Interestingly, the share of employers is relatively high compared to other countries (Table 11). Because they have grown beyond a one-person business, employers can be considered as successful entrepreneurs. It shows workers are willing to take risks and to launch their own venture; it also suggests an environment favorable to such business expansion. It would be interesting to see how Vietnamese employers fare compared with employers in other countries in terms of their business success and job creation capacity.

# _Unskilled employment is significant_

A large proportion of workers have unskilled occupations (Table 16). The differences between men and women are consistent with usual occupational gender patterns: women tend to be more present in sales and personal services, men tend to be in manual occupations. Although women are largely under-represented in high level management positions, it is a good sign that there does not seem to be much gender differential in professional occupations/officers. However, they are more likely than men to be unskilled workers.

**Table 16: Occupational structure of employment (%)**

|**Occupation**|**Men**|**Women**|**All**|
|---|---|---|---|
|Leaders in all fields|1.5|0.4|1.0|
|Top-level professionals|4.6|4.7|4.6|
|Mid-level professionals|3.2|4.4|3.8|
|Officers|1.7|1.6|1.6|
|Skilled workers in personal services, security protection and sales|10.8|20.5|15.6|
|Skilled workers in agriculture, sylviculture, and aquaculture|16.1|13.4|14.8|
|Skilled handicraftsmen and other relating skilled manual workers|17.1|7.7|12.5|
|Assemblers and machine operators|8.8|4.4|6.7|
|Unskilled workers|36.2|42.9|39.4|
|Total|100.0|100.0|100.0|

_Source: (LFS, 2009)_ 18
# Damming the Commons:  An Empirical Analysis of International Cooperation and Conflict in Dam Location

# Sheila M. Olmstead and Hilary Sigman<sup>1</sup>

Large water development projects are a hallmark of modern and industrializing economies. Nearly onehalf of the world’s rivers have at least one large dam (World Commission on Dams 2000) and dam construction proceeds at a rapid rate.<sup>2</sup> Large dams have complex welfare implications. Dams provide valuable services to their beneficiaries, including hydropower, irrigation, urban water supply, navigation, flood control. However, for at least 50 years, economists have worried that the benefit-cost analysis methods employed to assess the welfare impacts of dams have overstated benefits and understated costs (Eckstein 1965).<sup>3</sup> This concern has grown with rising attention to (and willingness to pay for) the environmental amenities of free-flowing rivers in industrialized countries, and the social disruption that follows forced resettlement of populations in a new dam’s catchment area in developing countries.

Previous research has emphasized the limited systematic empirical evidence for the effects of large 1 The authors are at the Lyndon B. Johnson School of Public Affairs, University of Texas at Austin <u>(sheila.olmstead@austin.utexas.edu), and the Department of Economics, Rutgers University</u> <u>(hsigman@rutgers.edu), respectively. The authors gratefully acknowledge research assistance by Georgia Bush</u> and Ju Young Park, and they thank seminar participants at the AERE Summer Conference, Fordham University, and Oregon State University for comments. This research was partially supported by a grant from the World Bank’s Knowledge for Change Program. The views expressed in the paper are the authors' alone and should not be attributed to the World Bank or its member countries.

2 A “large dam” is one that is 15 or more meters tall, or between 5 and 15 meters with a water storage capacity of at least 3 million m<sup>3</sup> (Scudder 2006). The United States has more than 6,000 large dams, many of the largest (Hoover, Grand Coulee, Glen Canyon) constructed between 1935 and 1965 (Collier et al. 2000). Before 1949, China had fewer than 100 large dams; in 2006, it had 22,000, about one-half of the world’s total (Scudder 2006). In the Amazon Basin alone, 140 dams are in the planning stages in Brazil, Bolivia, Colombia, Ecuador and Peru (International Rivers 2010).

3 For example, an analysis of the Central Arizona Project, which was completed in 1987 and provides water to the City of Phoenix, suggests that the project was built 86 years too early, with a deadweight loss of more than $2.6 billion, and that exploiting groundwater sources to delay its construction would have been more efficient (Holland and Moore 2003).

1
high mortaility rate of young Russian firms likely explains the size distribution since this eliminates smaller firms from the average size estimation (the left-hand side tail of the distribution). Young firms tend to be small. that younger and smaller manufacturing firms tend to have a high mortality rate (not unusual in any economy) irrespective of their level of efficiency (a relatively less common finding) which is a cause of concern. In addition, as discussed later in more detail, this relatively high mortality rate is associated with the deep and long downturns that characterize some cycles in the short history of the modern Russian economy.

<!-- Start of picture text --> Figure 2: The Russian economy is dominated by larger firms<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2: The Russian economy is dominated by larger firms<br>Size distribution of firms based on labor force (log) Size distribution of firms based on labor force (log)<br>Russia vs. Rest of ECA Russia vs. Rest of the World<br>Rest of the World<br>Rest of ECA Russia<br>Russia<br>0 2 4 6 8 10 0 5 10<br>Distribution of observations Distribution of observations<br>Russia Rest of ECA Russia Rest of the World<br>Source: Enterprise Surveys comprehensive dataset (May 2012) Source: Enterprise Surveys comprehensive dataset (May 2012)<br>Size distribution of firms based on sales revenue (log) Size distribution of firms based on sales revenue (log)<br>Russia vs. Rest of ECA Russia vs. Rest of the World<br>Russia Russia<br>Rest of ECA<br>Rest of the World<br>5 10 15 20 25 30 0 10 20 30 40<br>Distribution of observations Distribution of observations<br>Russia Rest of ECA Russia Rest of the World<br>Source: Enterprise Surveys comprehensive dataset (May 2012) Source: Enteprise Surveys comprehensive dateset (May 2012)<br>.25 .3<br>.2<br>.2<br>.15<br>.1<br>.1<br>kdensity labor force (log) .05 kdensity labor force (log)<br>0 0<br>.2 .2<br>.15 .15<br>.1 .1<br>.05 .05<br>kdensity sales revenue (log) kdensity of sales revenue (log)<br>0 0<br><!-- End of picture text --> Of equal concern is the indication that the right-hand side of the size distribution of manufacturing firms in Russia may also be shorter than that of other economies. In other words, the biggest firms do not grow to be as big in Russia as in other parts of the world. Examining Figure 2 (above) once again, the reader can see that the right-hand side tail of the distribution is also shorter for Russia than in other economies. This finding calls into question whether even efficient firms get the resources they require to grow in the Russian economy. In well-functioning economies, markets efficiently allocate resources to the most productive firms irrespective of their size and age (Hsieh

> 10 The data are taken from the World Bank’s Enterprise Surveys on May 2012. For each country, only the latest survey is used. This size comparison controls for differences in the composition of manufacturing sectors across these economies.

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# **Table 9. Mangrove Extent and Threats in Countries with the Largest Poor Rural LECZ Populations**

|**Country**|**2012**<br>**Mangrove**<br>**Forest Area**<br>**(km2)**|**Mangrove**<br>**Loss (%)**<br>**2000-2012**|**% of**<br>**Coastal**<br>**Wetlands**<br>**Area**<br>**Impacted**|**Growth**<br>**(%) of**<br>**Rural**<br>**LECZ**<br>**Population**<br>**2000-2010**|**Annual**<br>**GDP per**<br>**capita**<br>**Growth**<br>**(%)**<br>**2000-2012**|
|---|---|---|---|---|---|
|India|797.8|3.3%|32.3%|20.5%|7.9%|
|Bangladesh|1,773.0|0.1%|24.3%|15.7%|5.9%|
|Myanmar|2,557.4|8.4%|50.2%|0.2%|..|
|Cambodia|323.2|4.5%|1.5%|12.0%|8.7%|
|Nigeria|2,654.0|0.1%|38.8%|35.8%|7.2%|
|Pakistan|11.7|0.3%|61.4%|22.9%|2.5%|
|Iraq|..|..|..|31.1%|1.7%|
|Mozambique|1,223.7|0.2%|47.1%|33.0%|6.4%|
|Senegal|155.3|0.0%|22.0%|22.6%|1.1%|
|Brazil|7,674.9|0.6%|11.5%|10.1%|2.5%|
|China|33.8|1.9%|39.8%|1.7%|16.5%|
|Indonesia|23,324.3|3.1%|27.0%|13.4%|5.0%|
|Philippines|2,064.2|1.3%|45.0%|25.0%|3.5%|
|Vietnam|708.2|1.1%|29.4%|11.3%|7.1%|
|Thailand|1,886.3|2.4%|14.7%|12.4%|4.5%|
|**Total**|**45,187.9**|**2.0%**|**31.8%**|**17.5%**|**5.7%**|

Data on mangrove extent and loss is from Hamilton (2014) "Creation of a high spatiotemporal resolution database of continuous measures of global mangrove tree cover for the 21st Century (CGMFC‐21): A big‐data fusion approach." Available at <u>https://docs.google.com/spreadsheets/d/1wy6uBL8XVXb7wbQ6bzkbZdxWVy50EMr6vmc5 NB3z‐5A/edit?usp=sharing. Total global mangrove extent in 2012 was 81,848.8 km</u><sup>2</sup> ; the above countries account for 55.2% of this total.

Data for % of coastal GDP, agricultural area and wetlands exposed to a 1 meter (m) sea‐level rise accompanied by a 10% intensification of storm surges are from Dasgupta, S., Laplante, B., Murray, S. and Wheeler, D. 2009. "Sea‐ Level Rise and Storm Surges: A Comparative Analysis of Impacts in Developing Countries." Policy Research Working Paper 4901, the World Bank, Washington, D.C. April. Available at <u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22270368~pagePK:642148 25~piPK:64214943~theSitePK:469382,00.html</u> Data on GDP per capita is from the World Development Indicators, available at <u>http://databank.worldbank.org/data/views/variableselection/selectvariables.aspx?source=world‐development‐ indicators.</u> 24
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Policy conflicts can also arise with respect to exchange rate policy. In no country has exchange rate policy been turned over entirely to the central bank (Swinburne and Casteilo-Branco, 1991). Moreover, actions by both the government and the ICB will affect exchange rate determination even if the narrow role of exchange rate regulation and intervention is specifically assigned to one of them. Goodhart 1 994) comments that proponents of central bank independence invariably support a price stability mandate for the ICB without first questioning whether monetary policy should be geared towards preserving the stability of the external, rather than internal, value of the currency. But the reality is that if the ICB is to be only charged with maintaining price stability the government must be prepared to relinquish other objectives of exchange rate policy. In their study of six developed-country central banks, Bernanke and Mishkin (1992) observed that in all countries the central bank modified its policy at some point in time to arrest what they considered to be an undesirable exchange rate trend.

Suppose for instance that, in the face of large foreign capital inflows, the government chooses to let the exchange rate float. In the absence of foreign exchange intervention, the induced appreciation of the exchange rate makes room for a non-inflationary monetary expansion by the ICB. This action by the central bank undermines the government's policy of letting the exchange rate appreciate. Conflict can be expected when the government and the ICB can both affect the exchange rate analogously but have different objective functions.

The need for coordination is even more important under fixed or managed
# Male Headed Households

<!-- Start of picture text --> Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40% 45% 43% 41% 42% 41%<br>30% 30%<br>20% 22% 21% 20% 19% 19% 20%<br>10% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Head of Households: Blue Collar<br>Rest of the Country Near the Syrian Border<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50% 50% 49% 48% 48% 47%<br>40% 40%<br>30% 28% 28% 27% 26% 25% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>2009 2010 2011 2012 2013 2009 2010 2011 2012 2013<br>Poverty Rate (%) Poverty Rate (%)<br>Poverty Rate (%) Poverty Rate (%)<br><!-- End of picture text --> _Notes_ : OLS imputation method, regionally deflated. Results displayed at the 95% Confidence Interval. _Source_ : LFS 2009-2013. World Bank author’s calculations.

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# _3.1 Study region_

This analysis focuses on Latin American and Caribbean (LAC) cities with more than 50,000 people in 2010. According to (UN-Habitat, 2012), Latin America and the Caribbean is the most urbanized region on the planet with more than 80% of the population living in urban areas, and built-up area continues to expand despite deceleration in population growth. The LAC region is characterized by large diversity of climatic conditions, including from tropical to temperate climates; and the terrain also shows high variability in altitudes due to the presence of the Andean Mountain Range. That diversity in climate and topographic conditions translates into a high diversity in the shape that urban areas have taken. On the one hand, there are cities located in the mountains constrained by hilly terrain; on the other hand, there are also some cities located in the lower lands in flat areas, constrained only by the presence of large rivers or lakes, and yet other cities have grown located at the Caribbean and Pacific coasts, whose shapes reflect the coastline. Speaking of urbanized area, in the region it accounts for 0.4% of the land, a much lower value than Europe (2.3%) and the United States (1.2%), somehow closer to Asia (0.7%), but higher than the former Soviet Union region (0.2%) and Oceania (0.1%) (Y. Zhou et al., 2015). When compared to other regions of the world, LAC cities show denser populations than Europe and the United States, greater regularity and lower open space (Angel et al., 2010a; Huang et al., 2007). However, Inostroza et al. (2013) reported a general trend towards low density growing for some of the largest LAC cities.

# _3.2 Use of Night‐time Lights Data for urban extents delineation_

The nighttime lights data (NTL) are based on nighttime imagery recorded by the Defense Meteorological Satellite Programs - Operational Linescan System (DMSP-OLS), which reports the recorded intensity of Earth’s surface lights. Nighttime lights products have high correlation to human activities (Hsu et al, 2015), and have been previously used for regional and global analysis of urbanization (Cheng et al., 2016; Pandey et al., 2013; Sutton et al., 2006; Zhang and Seto, 2011; Zhou et al., 2015a; Zhou et al., 2015), population modeling (Anderson et al., 2010; Lo, 2001), and economic performance (Cao et al.,2016; Forbes, 2013).
examining the relationship between exposure of coal miners to dust from coal mines and loss of their lung function, Coggon and Tylor (1998) confirms that coal mining increases the risks of COPD in coal miners. Through monitoring of emission levels of surface coal mining in Jharia Coalfield in India, Ghosh and Majee (2007) reports that emission levels of TSP, PM10 and benzene soluble materials are much higher in the mining area as compared to those in ambient air outside the mining area; obviously these pollutants cause adverse impacts on coal minors’ health.

Oil refineries also poses a health risks to workers as inhalation of oil vapors and dermal contact of crude oil, feed stocks, benzene could have adverse health impacts including cancers. Based on a review and meta-analyses of cohort studies of more than 350,000 petroleum workers in the United States, the United Kingdom, Canada, Australia, Finland, Sweden, and Italy, Wong and Raabe (2000) concludes that exposure to various petroleum chemicals could cause a small increase of skin cancer mortality (10%). Follow up investigation of a cohort of 1,583 workers employed during the 1949–1982 period in a northern Italy oil refinery plant, Consonni et al. (1999) observes elevated mortality from lymphoma and leukemia. The study also finds excess risk among workers with 15 or more years of employment.

A few studies are available examining occupational health impacts on electric utility workers. van Wijngaarden et al. (2001) finds, through an investigation of mortality patterns of a cohort of 138,905 male electric utility workers in the United States, that electric utility workers had excess risk of total mortality, cardiovascular disease and some cancers. Further investigation of deaths of electric utilities workers, Savitz et al. (2000) finds that both cumulative and average magnetic field exposure is positively associated with brain cancer mortality in the United States.

Mining and the production of nuclear fuels, such as Uranium, poses several risks to human health. Long exposure to radon decay products causes greatest risks from nuclear fuel mining and processing. Radon’s alpha-emitting radioactive decay products are strongly and causally linked to lung cancer in humans (NRC, 2012). Similarly, uranium’s radioactive decay by alpha, beta, or gamma emissions also pose hazards to uranium miners and processors. However, despite the fears of the general public or a bad perception in general, the existing literature does not present significant health risks from mining of nuclear fuels and nuclear power plants. Using data from randomly selected 136 nuclear facilities and using several models, Baker and Hoel (2007) conduct a meta-analysis to examines the linkage between childhood leukemia and proximity of nuclear 14