rafmacalaba/datause-encoder-data huggingface dataset:
Table C.4 – _Continued from previous page_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|**Dwelling tenure agreement**|||||||||
|No Agreement|||||||ref.|ref.|
|Written Agreement|||||||-0.789***|-0.720***|
||||||||(0.206)|(0.209)|
|Non-written Agreement|||||||0.143|0.268|
||||||||(0.227)|(0.233)|
|**Dwelling tenure status**|||||||||
|Owner|||||||ref.|ref.|
|Rented|||||||-1.049***|-1.049***|
||||||||(0.339)|(0.343)|
|Free|||||||-0.120|-0.131|
||||||||(0.170)|(0.175)|
|Other|||||||-0.598|-0.543|
||||||||(0.552)|(0.560)|
|Affected by food|||||-0.054||||
||||||(0.579)||||
|Affected by food_×_Job density|||||-0.397||||
||||||(0.372)||||
|Flood event is likely||||||0.060|||
|||||||(0.504)|||
|Flood event is likely_×_Job density||||||-0.195|||
|||||||(0.337)|||
|Observations|751|751|751|751|751|751|736|736|
|R2|0.36|0.35|0.43|0.42|0.43|0.42|0.49|0.48|

_Note_ : The dependent variable is the log of rent (combined rent paid and self-estimated rent), measured in 2017 PPP US$by year. Robust standard errors are clustered at the enumeration area level. *** p _<_ 0.01, ** p _<_ 0.05, and * p _<_ 0.1.
Table 13: share of material goods

||Manufacturing|ICT|Retail|Other industries|
|---|---|---|---|---|
|Manufacturing|0.73|0.18|0.09|0.31|
|ICT|0.07|0.04|0.02|0.09|
|Retail|0.01|0.13|0.56|0.07|
|Other Industries|0.18|0.66|0.34|0.53|

_Sources:_ UNCTAD-EORA

## _•_ What is the total number of employees?

These questions have been asked for the years 2018, and 2017.<sup>30</sup> We use the number of square meters owned and rented by a firm as a measure for _Hi_<sup>_j_(</sup><sup>_z_</sup> _i_<sup>_j_).</sup> Table 12 reports the results from this analysis. The third column tests the hypothesis that the input share for the ICT sector is the same as for the other industry groupings, and the last column reports the resulting coefficient. The ICT sector uses significantly less immobile factors than the other industries in the economy, with the exception of the last industry grouping, which is 0.14, compared with an average economy of 0.19. Unsurprisingly, the point estimate for the wholesale and retail trade sector is the largest, given its higher need for storage.

# **6.4 Input-Output Linkages**

We use data from UNCTAD-EORA on input-output tables for the Jordanian economy in 2015 to estimate the relevant parameters for our model. This database measures trade in value added following the procedure described by Koopman et al. (2010). From the same database, we also use data on the compensation of employees for each industry. Using these data we compute the share of value added in gross output, _{γi_<sup>_j}n,j_,andtheinput-output</sup> coefficients _{γjk}j,k_ .<sup>31</sup> Table 13 reports the corresponding parameters for _{γjk}j,k_ . 73% of the material inputs in the production of manufacturing goods come from manufacturing sector itself; 18% are produced by the service sectors; 7% by the ICT sector and only 1% by the retail and wholesale trade sector. The ICT sector buys 66% of its material inputs from the service sector, 18% from manufacturing, 13% from retail and wholesale trade, and 2% from ICT.

> 30The first two questions are also asked for year 2016.

> 31We restrict the analysis to the industries included in our survey. The four industry groupings in our survey are matched to the following industries from the EORA database. Coefficients for manufacturing are estimated using data from the following industries: Food & Beverages; Textiles and Wearing Apparel; Wood and Paper; Petroleum, Chemical and Non-Metallic Mineral Products; Metal Products; Electrical and Machinery; Transport Equipment; Other Manufacturing. Coefficients for the wholesale and retail trade are estimated using data from the following industries: Maintenance and Repair; Wholesale Trade; Retail Trade. Coefficients for IT are estimated using data from Post and Telecommunications. Coefficients for the other industries are estimated using data from: Construction; Hotels and Restaurants; Transport; Financial Intermediation and Business Activities; Education, Health and Other Services.

29
# **1 Introduction**

Coal remains a widely used source of energy supply, but is associated with tremendous societal costs, including air pollution, climate change, acid rain, wastewater discharge, and wastage of financial resources. In the U.S. alone, coal-fired power plants release more than 3.1 million tons of sulfur dioxide (SO2), 1.5 million tons of nitrogen oxide (NO _x_ ), and 0.2 million tons of particulate matter each year.<sup>1</sup> At the same time, they also release substantial lead, mercury, volatile organic compounds, arsenic, and other toxic chemicals. The evidence is unequivocal that these air pollutants are linked with heightened risks of asthma, cancer, heart and lung ailments, neurological diseases, and broader public health impacts (Manisalidis et al., 2020).

In addition to their high environmental and health costs, coal-fired power plants operate with low efficiency and a lack of profitability. More than 40 percent of the world’s coal plants are operating at a loss, not least due to high fuel costs, and this share could rise to 75 percent by 2040 (Gray et al., 2018). However, power plants are still expanding in number and capacity, as their economic viability is propped up by fossil fuel subsidies. Each year, the IMF estimates that governments around the world spend around $13.6 billion to artificially lower the price of coal, thus perpetuating the operation of unprofitable coal-fired power plants and the associated air pollution (Parry et al., 2021).

The existing literature, primarily focused on the U.S., has documented significant and systematic inequality in the exposure to such air pollution. For instance, hazardous waste facilities are disproportionately sited in minority communities (Boer et al., 1997; Graham et al., 1999). Analyzing environmental justice in the fossil fuel industry, Carpenter and Wagner (2019) find that air pollution is higher in counties with higher unemployment levels. This pattern is inconsistent with the fair treatment notion of the U.S. Environmental Protection Agency (EPA), which states that “no group of people should bear a disproportionate share of the negative environmental consequences resulting from industrial, governmental and commercial operations or policies” (EPA, 2020). While such environmental injustice in the context of air pollution is well documented in high-income countries, evidence remains scarce for lower income countries and at the global scale.

This paper addresses this gap by documenting the regressive nature of the air pollution and health burden associated with the continued use of coal power plants. It finds that, globally, 2.32 billion people are exposed to air pollution originating from coal power plants. We find that air pollution from coal plants increases with income levels, though at a diminishing rate at high income levels. In other words, coal power plants are located in richer countries, and richer

> 1Data comes from the 2014 National Emissions Inventory (NEI) compiled by the U.S. EPA.
||O|LS|Multinomial Logit|
|---|---|---|---|
||YRD|Guerrilla|YRD_GuerrillaNo<br>YRD_GuerrillaYes|
|Initial Conditions||||
|Ln_GVOPC_52|0.052|-0.048|0.586<br>0.317|
||(0.565)|(0.651)|(0.579)<br>(0.776)|
|Ln_Pop_52|0.053|0.303***|-0.122<br>1.374*|
||(0.472)|(0.000)|(0.870)<br>(0.100)|
|Geography||||
|Share of Plain (%)|0.009*|0.001|0.062<br>0.084|
||(0.076)|(0.891)|(0.331)<br>(0.183)|
|Distance to Xiamen (KM)|0.001*|-0.001|0.008<br>0.005|
||(0.067)|(0.353)|(0.123)<br>(0.353)|
|Constant|-0.012|0.126|-5.534<br>-7.334|
||(0.987)|(0.897)|(0.558)<br>(0.468)|
|Obs.|58|58|58|
|_R_<sup>2 </sup>(Pseudo-_R_<sup>2</sup>)|0.07|0.253|0.159|

Table 5: Correlations of the Factions of Local Leaders in 1949 and Guerrilla Presence with County Characteristics in 1952.

Note: FA3 is the reference group in multinomial logit regression. White Standard Errors are in parenthesis. ***, **, and * respectively indicate 1%, 5% and 10% statistical significance.

guerilla presence or not, as close to random, especially after we control for the initial conditions and geography variables. In Section 5.4, we will further address this issue by focusing only on bordering counties.

# **5.2 Factions, Local Accountability and Economic Growth**

In this section, we report our regression results on the effect of factions (as captured by YRD vs. FA3), local accountability (as captured by Guerrilla or no Guerrilla) and their interactions on local annual economic growth rates.

In Table 6, we examine the effect of YRD vs. FA3 factions on county level annual growth rates, for the whole period of 1952-1998 and for the post-reform period of 1978-1998 separately. We present the results for the full sample, as well as the results when we trim the 5% of the tail observations. In Columns (1) and (4), we do not control for any variables, and the estimates of YRD coefficients simply replicate the raw differences in annual growth rates between YRD counties and FA3 counties we reported in the left side of Panel A in Table 3: YRD-affiliated counties on average grew by 0.99 percentage points faster than FA3-affiliated counties during the whole sample period of 1952-1998, and by 3.17 percentage points faster in the post-reform period of 1978-1998.

26
27 used successfully in East Africa to measure basic reading competencies among young school-age children (UWEZO). The test was applied to ten children in the second grade in each village, 2100 children in all. The average age of the tested children was 8.75 years.<sup>10</sup> Measured literacy rates vary widely among the 210 village public schools. Nineteen percent of all tested children, across all villages, could read simple sentences and paragraphs in French. In the median village, only 10 percent of tested children could read at this level. In 67 villages, or 32 percent of the sample, none of the tested children was able to read at this level while in 12 percent of the sample, 26 villages, more than half the children could.

The central hypothesis in this paper is that a key public service in villages in northern Benin – the quality of education in the village public school – is influenced by village access to community radio. To test the hypothesis, we estimate (1) where _j_ indexes the 210 villages and _k_ the 32 communes, is a commune fixed effect, and **X** is a vector of village controls. The key coefficient is which, because of exogenous variation within communes in the number of community radios to which villages have access, is identified through the inclusion of commune fixed effects. The dependent variable is the proportion of children tested in the village public school who could read sentences and paragraphs.

All of the variables in Table 2 are controls in **X** . In addition, **X** includes the availability of other public goods (literacy centers, health centers, the availability of a secondary school) and whether the village is one of the few with a functioning private school. The effects of access to community radios depend on the village‘s access to other types of radio, as well. A highly popular 10 We did not administer the literacy test in our household survey because we did not have a reliable sample frame to select those households that had young school-age children.
household, the male, receives the payments but who performs the conservation duties is the woman. Cultural tradition determines who the head of household is. In some areas of the country villages are traditionally matrinial societies while in others the males make decisions according to custom. Pilot projects have taken this in consideration, however a balanced gender approach is proposed when setting up the project in which both females and males participate and are paid equally. Some villages have adopted this policy and women are more likely to participate in village meetings so there is more interaction. In the REDD+ village participants pointed out that nowadays decisions are jointly taken within the family. Female participation seems to be stronger in this village relative to the others. The REDD+ program has a gender component that may be influencing the bargaining balance between husband and wife within the household and also the gender interaction at the village level. Female members of the households have also received REDD+ payments that have given them confidence and empowerment.

Although differentiated payments are not possible within the village, pilot projects are paid according to performance. That implies that they are paid differently depending on the carbon sequestered. The projects also collect data on carbon storage so it can be sold in the carbon market. Furthermore, NGOs are trying to develop the mechanism to monitor and sell carbon through carbon enterprises at the local level (e.g., TFCG in Kilosa).

Finally, there are high expectations about the benefits and the money derived from REDD+. Local people may have high expectation about REDD+ payments but they may not receive what they are expecting. This has important implications for the sustainability of the program: if expectations do not match what they receive, villages may stop participating in forest conservation initiatives.

31
<u>Table 3: Determinants of the use of maternal healthcare</u>

|Variables|At least four A|NC|Institutional|delivery|
|---|---|---|---|---|
||OR|CI|OR|CI|
|Women’ and hhs’ characteristics|||||
|Head|0.673|(0.209 2.170)|2.614|(0.620 11.02)|
|Spouse|1.217*|(1.018 1.455)|0.914|(0.762 1.096)|
|Age|1.056***|(1.041 1.072)|1.048***|(1.033 1.064)|
|Number children|0.741***|(0.661 0.831)|0.552***|(0.488 0.623)|
|Squared number children|1.011*|(1.001 1.021)|1.037***|(1.026 1.048)|
|Job outside|1.286***|(1.117 1.481)|1.125|(0.970 1.305)|
|Secondary edu|1.664***|(1.404 1.972)|1.881***|(1.600 2.213)|
|Access information|1.260**|(1.071 1.483)|1.130|(0.935 1.366)|
|Permission|0.839|(0.654 1.076)|0.718*|(0.530 0.973)|
|Bottom 40 (B40)|0.755*|(0.605 0.943)<br>|0.609***<br>|(0.486 0.762)<br>|
|No transportation|0.856|(0.678 1.082)|0.755**|(0.616 0.926)|
|Cluster characteristics|||||
|Mountainous|0.868|(0.733 1.028)|0.910|(0.722 1.146)|
|Share adults w/ secondary edu is|||||
|Medium|0.719|(0.501 1.032)<br>|1.084|(0.723 1.625)<br>|
|High|0.734|(0.467 1.153)|1.027|(0.647 1.630)|
|Share hhs in bottom quintile is|||||
|Medium|1.163|(0.926 1.461)|0.630***|(0.481 0.824)|
|High|1.266|(0.985 1.626)|0.449***|(0.329 0.612)|
|Share women w/ 4 ANC visits is|||||
|Medium|3.120***|(2.652 3.671)|1.601***|(1.257 2.040)|
|High|11.10***|(8.874 13.88)|2.368***|(1.816 3.089)|
|Health centers characteristics|||||
|Dum<br>~~A~~NC18dys|0.803|(0.482 1.338)|1.995|(0.886 4.490)|
|Dum<br>~~i~~nstDEL|0.941|(0.710 1.246)|1.131|(0.834 1.532)|
|Share facilities w/ ANC with|medications|available is|||
|Medium|1.005|(0.830 1.217)|0.721**|(0.562 0.924)|
|High|1.061|(0.898 1.255)|0.837|(0.668 1.047)|
|Share facilities w/ ANC with|equipment|available is|||
|Medium|0.924|(0.727 1.173)|0.956|(0.684 1.337)|
|High|0.940|(0.759 1.166)|1.019|(0.750 1.385)|
|Share facilities w/ delivery with|equipment|available is|||
|Medium|1.048|(0.883 1.243)|0.968|(0.773 1.212)|
|High|1.126|(0.927 1.366)|1.109|(0.857 1.435)|
|Interaction factors|||||
|Head + dum<br>ANC18dys|1.547|(0.477 5.017)|0.355|(0.0837 1.504)|
|Perm + dum<br>~~i~~nstDEL|1.090|(0.516 2.300)|1.509|(0.700 3.252)|
|B40 + dum<br>~~i~~nstDEL|0.991|(0.655 1.500)|1.788*|(1.108 2.885)|
|Access + C<br>~~s~~ecedu=MEDIUM|1.517|(0.984 2.338)|1.146|(0.750 1.749)|
|Access<br>~~i~~nfo + C<br>~~s~~ecedu=HIGH|1.032|(0.634 1.680)|1.503|(0.952 2.375)|
|Constant|0.803|(0.470 1.373)|0.319**|(0.143 0.713)|
|Random intercept|2.97e-08|(0 .)|0.552***|(0.451 0.675)|
|N|4896||4899||

Note= ***p _<_ 0.001; **p _<_ 0.01; *p _<_ 0.05. Reference categories are indicated in parentheses after the names of the characteristics being considered. OR: odds ratios; CI: confidence interval. Dum ~~A~~ NC18dys: dummy if ANC available at least 18 days per month. Dum ~~i~~ nstDEL: dummy if institutional delivery available to women in the cluster. C ~~s~~ ecedu: share of adults with secondary education. Source: Authors’ estimates with EMMUS 2017 13
running a simple cross-country regression using the pooled sample of countries that participated in the 1999 or 2003 rounds of TIMSS (47 countries) or participated in the 2000 round of PISA (42 countries).<sup>30</sup> The results strongly support the notion that _LAYS_ captures important aspects of both the quantity and quality of schooling. Average years of schooling of the cohort of 25- to 29-year-olds in 2000 is related to subsequent GDP per capita growth (Column 1 of Table 1), as are average test scores (in mathematics; Column 2). The coefficients on both variables fall substantially when both are included in the model (Column 3), and, unsurprisingly, the explanatory power of the model increases (R-squared of 0.68, compared with 0.65 with average years alone and 0.59 with test scores alone).<sup>31</sup> _LAYS_ is also strongly related to subsequent growth (Column 4). Importantly, the explanatory power of the model that includes just the _LAYS_ measure (R-squared of 0.68) is the same as that of the model that includes years of schooling and test scores as separate variables (R-squared of 0.68), and greater than the model that includes just average years of schooling or just test scores. This means that _LAYS,_ as a single variable, captures key dimensions of its constituent parts that are associated with economic growth. The findings are consistent (although point estimates differ because of smaller and different country coverage) when using just the TIMSS or the PISA samples (Columns 5-12): again, _LAYS_ explains about as much of the variation as its constituent parts do when entered together. The findings are likewise consistent under various other robustness tests—notably, if we use GDP per capita growth between 1990 and 2016, or between 1995 and 2016 (see Annex Tables A1 and A2); if we use the average years of schooling for the whole population aged 15 and over, together with the corresponding _LAYS_ measure (Annex Table A3); and if we use the percent of the student population attaining Level 1 or above on PISA and TIMSS as our measure of learning (Annex Table A4).

Beyond its usefulness for research, another effect of _LAYS_ could be to improve the incentives for policymakers—to help keep their focus on what really matters, which is schooling _with_ learning. The Sustainable Development Goals reflect a shared international commitment to achieve this. SDG 4 commits countries to ensure education quality and learning for all, and SDG

> 30 Countries that participate in both (22 countries) appear twice in this pooled sample.

> 31 All models include initial GDP per capita, and the pooled models include a dummy variable for whether the test score is from TIMSS or PISA.

43
# **1. Introduction**

Much of the literature in public finance focuses on the role of tax reforms in affecting firm behavior. Policy makers in many countries use tax incentives to encourage firm investment, and China is no different. In China, the value-added tax is the major source of fiscal revenue for the Chinese government, generating much more revenues than any other types of tax. In 2002, the revenue from value-added tax was 814.4 billion RMB, accounting for around 48% of the state total tax revenue in that year. In 2009, the Chinese Ministry of Finance estimated that VAT revenue accounted for approximately 31 percent of China’s overall revenue.<sup>1</sup> Beginning in 2004, the Chinese government implemented a value-added tax reform in three northeast provinces which removes fixed asset investment from the value-added tax base. The reform has since been extended to the whole country, beginning in 2009. The objective of the 2004 reform was to encourage firms to raise investment on fixed assets for production (excluding structures) and to upgrade their machinery and equipment. The goals of the 2009 reform were similar, but in addition the government expressed the need to provide additional assistance to domestic enterprises to help them weather the adverse effects of the crisis, as well as to encourage fixed asset investments to promote an industrial policy now focused on more technologically advanced sectors.<sup>2</sup> In this paper, we use a firm-level panel dataset ranging from 1998 to 2007 to identify the effect of the 2004 value-added tax reduction in selected provinces on firm behavior,

> 1 See www.dorsey.com/china_vat_reform

> 2 According to the People’s Daily Online, December 9, 2008, quoting Zheng Jianxin, deputy director general of the taxation department of China’s Ministry of Finance, “The VAT reform would encourage investment and technological upgrading at Chinese companies, boost domestic demand, improve companies’ competitive strength and play a positive role in helping companies tackle the financial crisis”.  The article also states that “The reform was aimed at a shift from the existing production-based to a consumption-based VAT regime, which would enable companies to get tax deductions on spending on fixed assets, Zheng said, adding that this would reduce the tax burden on companies by more than 123 billion Yuan.”

2
- 39 -

proportional amount of coffee--in volume or equivalent value--which exporters must give to the Coffee Fund for every 100 bags of coffee exported. The proportion is set taking into account the present and expected volume and value of coffee exports, exchange rates, and export taxes. To finance an accumulation of stocks and to improve the Coffee Fund's finances, the retention quota was reised from 15% in 1981 to a peak of 68% 1985-87 before being reduced to 35% in December 1987. The retention tax acts like a tax on exports and because its proceeds are directed to the coffee fund it should be viewed as an earmarked tax.

40. Table 9 shows the amount of revenues the Coffee Fund obtained from its 4% share of the ad valorem export tax and the retention quota.<sup>3 4</sup> The former is usually listed in any accounting of central government earmarked taxes, but the latter is not. Because of discounts and bonuses the Fund gives on private exports and because a significant share of exports in any year comes from the Coffee Fund itself, the retention figures in the table are likely to exaggerate the net income accruing to the Fund. Bearing in mind, therefore, that these are upper bound figures,<sup>3 5</sup> they would represent Table 9 Earmarked Taxes for the Coffee Sector. 1980-1987

||1980|1981|1982|1983|1984|1985|1986|1987|
|---|---|---|---|---|---|---|---|---|
|Ad Valorem|||||||||
|Export Tax|3,005.0|2,823.0|3,112.6|3,377.0|4,698.5|7,298.7|14,905.2|11,671.1|
|Retention Quotas|31,116.2|9,596.9|23,531.2|29,262.1|61,742.0|<br>81,607.4|<br>174,143.8|194,199.4|
|In Kind|31,098.2|9,588.8|23,510.5|29,146.5|<br>61,539.9|<br>80,821.1|<br>172,567.8|n.a.|
|In Value|18.0|8.1|20.7|115.6|<br>202.1|<br>786.3|<br>1576.0|n.a.|
|Total|34,121.2|12,419.9|26,643.8|32,639.1|66,440.5|88,906.1|189,049.0|205,870.5|

Source: Statistical Appendix Table A7.

- 34/ For a morc complete picture of the Coffee Funds' revenues and expenditures, see Statistical Appendix, Table A7.

- 35/ They are also an exaggeration of retenticn's contribution to earmarking because at times the Coffee Fund has loaned substantial sums of money to the central government.