rafmacalaba/datause-encoder-data huggingface dataset:
# **4.1 Population exposed to power plants’ emissions**

Using the modeled air pollution map, we estimate global exposure headcounts to coal power plant pollution around the world. We estimate that globally 2.32 billion people are exposed to SO2 emissions from coal-fired power plants, 1.29 billion of which are in China and India alone. Figure 5 shows the top 10 countries with the highest population and highest proportion of population affected by coal plants’ pollution. China, India, and the U.S. have 895.7, 392.4, and 157.0 million people exposed respectively, due to their high plant concentrations and population densities. The left panel of Figure 7 visualizes the proportion of population that are exposed to any level of coal plants’ pollution relative to national population in each country. Most countries show similar patterns as Figure 1 given within-country pollution dispersion. Czechia and Germany have 97 percent and 83 percent of their population exposed, including to high transboundary pollution flow from foreign plants. The proportion of affected population ranges from 0 to 97 percent, with an average of 12 percent; 13 countries have over half of their population exposed to air pollution from coal plants.

Focusing on cross-border pollutant flows, Figure 8 shows the share of national populations that are exposed to foreign plants’ pollution (left) and that face higher foreign pollution than domestic pollution (right). Overall, 247.5 million people world-wide are exposed to transboundary air pollution from foreign coal plants. The top affected countries are shown in Figure 6. Transboundary pollution affects 24.1 percent of the population in Czechia, where Poland and Germany are the primary sources of transboundary flows.

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. These WHO threshold values are evidence-informed quantitative recommendations based on a systematic review of the evidence of adverse health effects and dose-response functions. SO2 of 40 _μ_ g/m<sup>3</sup> is the upper limit of the 24-hour mean. The proportion of population exposed to SO2 over 40 _μ_ g/m<sup>3</sup> in each country is visualized in the right panel of Figure 7.

According to the WHO, ‘achieving the recommended guideline levels will deliver substantial health benefits globally’.<sup>4</sup> This implies that even if other natural and anthropogenic pollution sources could be mitigated, coal-fired power plants alone are leading to substantial health risks for large population groups.

> 4Detailed explanations can be found on the WHO website: https://www.who.int/news-room/questions-andanswers/item/who-global-air-quality-guidelines

11
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# **2 Lao PDR Context**

Lao PDR is a small, landlocked, least developed country in SE Asia, which has experienced a gradual opening to the international economy: merchandise trade as a share of GDP has risen from ~10% in the 1980s to ~50% at present but is still low by international standards It is a member of ASEAN and plans to join the WTO. The country faces a rest-of-world average trade weighted tariff of just 1.4%. However the average rate applied to agricultural goods is rather higher at 6.3%. Foreign investment inflows have also increased rapidly, mostly from the region, in both resource (~80%) and non-resource sectors: hydropower, mining, agriculture, processing, and tourism (Record and Nghardsaysone 2009, World Bank 2009). Agricultural production (~50% of GDP) and most manufacturing production (18% of GDP) are now privately owned, while State Owned Enterprises cover only ~1% of employment. Overall, ~97% of manufacturing units are small (less than 10 employees), with 35% of the medium and large firms being privately owned by Lao citizens and 55% joint ventures with foreigners.

Historically, Lao trade has relied on its three big neighbors (Thailand, Vietnam and China). The sheer weight of Thailand in Lao exports is also reflected at the micro level: almost 70% of exporters are linked to Thailand, and close to 80% of Lao product types (at the HS-6 level) make their way there. As can be expected for a resource-rich developing country, its exports are dominated by large yet diversified firms: a large share of exporting is carried out by fairly sophisticated firms, serving six or more destination countries (73%) and selling 30 or more products (67%) (Stirbat, Record and Nghardsaysone 2011).

The garment sector has been a key driver of economic growth since the 1990s. Lately, though, trade in garments has decreased from almost 40% of exports in 2004 to 14% in 2010 due to the relatively slow growth in this sector and the very rapid growth in hydro and mining exports. Raw materials (and in particular copper) dominate by far, raising concerns about excessive concentration of exports which could act as a drag on economic growth and development. The Lao export basket is comparatively unsophisticated and dominated by products with low value addition and few linkages to products with higher value addition, and the country does not appear to be catching up with regional neighbors in terms of export sophistication (Record and Nghardsaysone 2009).

To showcase the skewed nature of Lao exports, they are grouped by destination regions in Table 1: Thailand is destination to 69% of the value over the period in the analysis sample, whereas Asia-Pacific (Vietnam, China, Australia etc.) only 11%. A more “natural” grouping of ASEAN destinations together changes little in the distribution, since Laos’ exports to ASEAN members other than Thailand (including Vietnam) are 6
# **3. Empirical framework**

We use as our basic testing framework the structural dynamic investment equation of Bond and Meghir (1994), which extends the Euler equation of a neoclassical model of investment under symmetric (quadratic) adjustment costs to the possibility of liquidity constraints arising from differences in cost between internal finance and external finance in general and debt finance in particular. Such differences could arise from, for example, bankruptcy costs or the differential taxation of income from equity and interest income. It could also arise from credit rationing by relatively uninformed lenders. Bond and Meghir (1994) show that the Euler equation for firm investing under no financial constraints leads to an estimable investment equation of the form where _K it_ is capital stock of business _i_ at the end of fiscal year _t_ ;<sup>_I_</sup> _it_<sup>is</sup><sup>_i_’s fixed</sup> investment in year _t_ ; Π is annual gross profits, _Y_ is annual sales,; _D_ is long term liabilities;<sup>_d_</sup> _t_<sup>and</sup> η _i_ are unobserved time effects and firm effects, which, between them, capture the user cost of capital; υ _it_ a forecast error uncorrelated with any of the other right-hand side variables; and β _j_ ’s are parameters assumed to be constant across firms and over time. The Euler equation underlying this investment function fully incorporates the firm’s expectations of future profitability, these being captured by one-step-ahead investment forecast. There is therefore no reason why more profitable firms should invest more as long as the marginal cost of internally financing the investment is the same as the marginal cost of external finance. Indeed, in the absence of financial constraints to investment, the model implies that β 3<sup>></sup> 0 so that the coefficient of the profitability term, (Π / _K_ ) _it_ − 1 , is negative. The sign configuration of the rest of the parameter set in the absence of financial constraints is β 1, β 2 , β 4 , β 5 ≥ 0 . The term ( _Y_ / _K_ ) _it_ − 1 arises from the assumption of product market imperfection so that the price elasticity of product demand is finite. The coefficient of this term, β 4 , decreases in the price elasticity of demand and is consequently smaller in more competitive industries, as do the absolute values of the coefficients of (Π / _K_ ) _it_ − 1 and ( _D_ / _K_ ) _it_ 2 − 1 . The absolute value of each of the three 2 financial variables (Π / _K_ ) _it_ − 1 , ( _D_ / _K_ ) _it_ − 1 , ( _Y_ / _K_ ) _it_ − 1 also decease in decease in adjustment costs (that is in the output foregone in the process of installing the additional productive capacity) but increases in cost of long term borrowing. The absolute value of 2 the coefficient of each of the rate of investment terms ( _I_ / _K_ ) _it_ − 1 and ( _I_ / _K_ ) _tt_ − 1 also increases in the cost of long term borrowing (while decreasing in the rate of depreciation).

Equation (1) subsumes a key variable of the neoclassical model of investment in the error component<sup>_d_</sup> _t_ +η _i_ . This is the user cost of capital, which in turn is an increasing function 10
First, the efficiency wage model with labor tumover captures well a number of features of LDC labor markets, and in particular the Mexican case we consider. The literature suggests that the self-employed informal sector comprises both workers rationed out of formal salaried jobs as well as a relatively prosperous "upper tier" that may prefer self-employment.<sup>4</sup> In other words, the literature is consistent with one of the central ideas of the efficiency wage model with labor turnover, namely that at each point in time workers are voluntarily leaving their formal-sector job for self-employment and simultaneously self-employed workers are unsuccessfully trying to reenter the formal sector. Moreover, neither minimum wages nor unions are credible explanations for the observed segmentation.<sup>5</sup> Finally, Constitutional proscriptions against firing suggest quittiing as the dominant mode ofjob separation.

Second, our data set has an important time dimension which allows us to estimate the quitting response of individual workers to changes in macroeconomic conditions. Given our final goal of quantitative macroeconomic analysis, this feature ofthe data set seems essential. In addition, the data on self-employed workers offer a measure of the benefits (payoffs) to not being employed in the formal sector that displays substantial variations over time. These variations in selfemployment benefits are important since they provide us with an additional test of the "quitting theory" which predicts that labor turnover is positively correlated with expected benefits to selfemployment and that the quit-rate function is symmetric: the benefits-elasticity of quitting, is equal to the negative ofthe wage-elasticity of quitting. Moreover, if the symmetry property ofthe quit-rate function is supported by the data on self-employed workers, we may use it as a working hypothesis (until refuting evidence is forthcoming) and apply it to unemployment. This opens the door for an assessment of the quantitative macroeconomic effects of changes in unemployment benefits without 4 Harris and Todaro (1970) offer the canonical statement of the dualistic (rationing) viewv and Fields (1990) discusses the "two-tier" view of the informal sector.

5Bell(1996) finds no evidence that minimum wages are binding. Maloney and Ribeiro (1998) find evidence of union influence on employment, but none on wage setting.

2
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1151 Is Growth Bad for the Environment<br>Pollution,<br>Abatement,<br>and<br>Endogenous<br>Growth|? Charles van Mairewijk<br>Federick<br>van der Ploeg<br>Jos Verbeek|July 1993|J. Verbeek<br>33935|
|WPSI 152 Population,<br>Health, and Nutrition:<br>Annual Operational<br>Review for Fisca<br>1992|Denise Vaillancourt<br>l Stacye Brown<br>and Others|July 1993|0. Nadora<br>31091|
|WPS**1**53 North American Free Trade<br>Agreement:<br>Issues on Trade in<br>Financial Services for Mexico|Alberto Musalem<br>Dimitri Vittas<br>Asli DemirgOg-Kunt|July 1993|P. Infante<br>37664|
|WPS**1**54 Options for Pension Reform in Tunisi|a Dimitri Vittas|July 1993|P. Infante<br>37664|
|WPS1155 The Regulation<br>and Structure of<br>Nonlife Insurance<br>in the United<br>States|Martin F. Grace<br>Michael M. Barth|July 1993|P. Infante<br>37664|
|WPSI 156 Tropical Timber Trade Policies:<br>What<br>Impact Will Eco-Labeling<br>Have?|Panayotis N. Varangis<br>Carlos A. Primo Braga<br>Kenji Takeuchi|July 1993|D. Gustafson<br>33714|
|WPS1 157 Intertempor**a**l nd Interspatial<br>Comparisons<br>ofIncome: The Meanin<br>of Relative Prices|Sultan Ahmad<br>g|July 1993|E. O-Reilly-<br>Campbell<br>33707|
|WPS1158 Population<br>Growth, Externalities,<br>and Poverty|Nancy Birdsall<br>Charles Griffin|July 1993|E. Homsby<br>35742|
|WPS**1**59 Stock Market Development<br>and<br>Financial Intermediary<br>Growth:<br>A Research<br>Agenda|Asli Demirg0v.-Kunt<br>Ross Levine|July 1993|P. Sintim-<br>Aboagye<br>38526|
|WPS**1**60 Equity and Bond Flows to Asia and<br>and Latin America: The Role of Globa<br>and Country Factors|Punam Chuhan<br>l Stijn Claessens<br>Nlandu Mamingi|July 1993|R. Vo<br>31047|
|WPS1 161 Increasing<br>Womens Participatio**n**<br>i<br>the Primary School Teaching Force an<br>Teacher Training in Nepal|Molly Maguire Teas<br>d|July 1993|L. Maningas<br>80380|
|WPS**1**62 The Slovenian<br>Labor Market in<br>Transition: Issues and Lessons<br>Learned|Milan Vodopivec<br>Samo Hribar-Milic|July 1993|S. Moussa<br>39019|
|WPS**1**63 Domestic Distortions<br>and<br>International<br>Trade|James E. Anderson<br>J. Peter Neary|July 1993|D. Gustafson<br>33714|
# 5.3 Accounting for density/crowding externalities

An additional aggregate e¤ect of immigration may stem from its impact on the aggregate scale of production. On one hand, the existence of a ...xed factor in production (such as land) would cause aggregate decreasing returns. On the other hand, the e¢ ciency of production may be increased by an increase in employment density due to "agglomeration externalities" as in Ciccone and Hall (1986). In general agglomeration externalities/crowding e¤ects can be modeled by assuming that the TFP is also as function of the aggregate scale of production (Qt). An extension of expression 10 above can be stated as follows: In this expression, the crowding e¤ect of labor force size on land, assuming a share of land in production of 0:03 in rich countries (see Ciccone and Hall 1986) would give � = �0:03 . On the other hand, incorporation of the positive density externalities estimated by Ciccone and Hall (1986) would lead to a positive value of � = 0:06:<sup>14</sup> We explore the implication of such e¤ects for the impact of immigration, keeping the values of the other parameters at the basic speci...cation.

Figure 8 illustrates the estimates in the benchmark case and compares with either type of externality. Since the values of the estimates for the parameter � and the e¤ect of emigration in changing total employment (and its density) are small, the e¤ects of introducing this externality (either positive or negative) are also minimal. The presence of an agglomeration externality would marginally increase the positive average wage e¤ects of immigrants, while the crowding externality would marginally reduce it but the di¤erences are on the order of a small fraction of a percentage point.

# 5.4 Accounting for employment rates

While the model is based on employment the empirical analysis is based on working-age population data. As reduced labor market participation of immigrants and/or natives may alter the wage e¤ect of immigration (see Angrist and Kugler 2003, or D’Amuri et al 2010) it is important to check whether excluding from the analysis non-employed natives and immigrants a¤ects our predictions.

Using the European Labor Force Survey, we correct the size of each skill group (highly and less educated natives and immigrants) for their employment/population ratio as of year 2000. If these employment rates are identical across the four groups, the results would not change at all. If they are very di¤erent between natives and immigrants, they may imply di¤erences in relative employment e¤ects for given population sizes. While employment/population ratios may di¤er across countries what matters for our wage e¤ects is their within country di¤erence across the four skill groups described above. Figure 9 shows the simulated wage

> 14Notice that we simply model the density e¤ect as depending on average density: total population divided by area. The fact that immigrants move mainly into cities that are already dense should increase even further the e¤ect, if one uses the exact Ciccone and Hall (1996) index that counts density in cities (where most population lives) as more relevant.

19
**Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015**

||**Population end-**<br>**2015 (millions)**|**Urban or**<br>**Rural**<br>**Location**|**Accommodatio**<br>**n**|**Age**|**Sex**|
|---|---|---|---|---|---|
|**Refugees and people in**<br>**refugee-like situations**|16.1|85%|83%|58%|68%|
|**Other people of concern to**<br>**UNHCR**|47.8 (including<br>37.5 IDPs)|70%|47%|25%|39%|
|**UNHCR’s total population of**<br>**concern**|63.9<sup>94</sup>|73%|56%|33%|46%|
|**IDPs monitored by IDMC but**<br>**not included in UNHCR's**<br>**data**|3.3|0%|0%|0%|0%|

_Source_ : UNHCR Global Trends 2015 _Note_ : Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘other’.

# **Overall robustness of current data**

The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are critical for sound decision making, both to inform the allocation of resources and to design effective humanitarian and development responses. However, the available estimates are potentially misleading and should not be referred to without appropriate caveats and qualifiers. They may provide reasonably solid insights on trends at the level of individual crises or countries, assuming no significant methodological change over time, but in most cases, they provide an incomplete picture and are often not comparable across displacement situations. Table 6 provides an estimate of methodological robustness using a simple rating system that takes into account, among other factors, whether data are based on an actual counting of persons (registration) by a national or international institution, whether some or all data are based on an estimation, the coverage of data, and whether data are up to date.

**Table 6: Methodological Robustness**

|**Methodological**<br>**Robustness**||**Refugee**<br>**Situations**<br>**(%)**|**IDP**<br>**Situations**<br>**(%)**|**Refugees**<br>**and IDPs**<br>**(%)**|
|---|---|---|---|---|
|**A**<br>**(highest)**|<br>Individual registration by a national or<br>international institution|88||22|
|**B**|<br>Individual registration by a national or<br>international institution for a portion of the<br>population combined with an estimate for the<br>other portion; or<br><br>Data originate from different sources<br>including statistical ones and a review<br>process is in place|12|2|5|
|**C**|<br>No information on parts of the country||46|34|

> 94 UNHCR’s total population of concern includes refugees and people in refugee-like situations, IDPs protected or assisted by UNHCR, asylum seekers, returned refugees, returned IDPs, person’s under UNHCR’s statelessness mandate, and others of concern to UNHCR. This figure does not include Palestinian refugees, who fall under UNRWA’s mandate.
peer countries in terms of the distributional impact of its fiscal policy? and (d) how can the analysis be used to inform policy reform?

The approach of the paper is largely based on the Commitment to Equity (CEQ) methodology (Lustig 2018). As such, the paper builds a comprehensive incidence analysis by sequentially quantifying the poverty and inequality impact of direct and indirect taxes, noncontributory direct transfers, contributory pensions, and social spending on health and education.<sup>3</sup> To do so, we use the 2016 Household Budget Survey (HBS), and the 2016 Survey of Income and Living Conditions (SILC) collected by Turkey’s Institute of Statistics (TUIK), together with administrative information on transfers and taxes from Turkey’s Ministry of Treasury and Finance (MoTF). The data allow for relatively good coverage of fiscal interventions for this class of studies. The analysis covers about 46 percent of total revenues—including 74 percent of social contributions and 45 percent of tax revenue—and about 50 percent of government spending— including 87 percent of social spending.

The main findings of the paper are that: (a) fiscal policy significantly decreases inequality in Turkey, and (b) among low-income households the magnitude of targeted transfers does not fully mitigate the effect of taxes, resulting in net poverty increases. In driving these changes, social spending on education and health have the largest impact on inequality, complemented by direct taxes and direct transfers that counter the inequality-increasing impact of indirect taxes. Turkey’s relative performance, however, is below median compared to peer countries. In particular, Turkey’s indirect taxation is comparatively more inequality-and-poverty-increasing, while direct transfers and taxes are less inequality-and-poverty-decreasing than in the median upper-middleincome economy. From a forward-looking stand, the diagnostic identifies some potential entry points for policy change. The large-scale Minimum Subsistence Allowance program ( _AGI_ in Turkish) presents an opportunity if its design were more targeted to the poor and vulnerable, while keeping a neutral fiscal position. Overall, the paper offers a platform to simulate the distributional implications of a variety of policy changes and inform the policy debate among stakeholders. Finally, the analysis provides a building block for future policy research to dig deeper into the reasons that could explain Turkey’s performance relative to peers.

> 3 EUROMOD is another initiative that aims to comprehensively analyze taxes and transfers. However, since it is based on household income, it does not consider household spending and therefore does not estimate the impact of indirect taxes like VAT.

3