rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 60<br>90<br>50 80<br>70 =<br>60 g<br>.& 40 50 5oS<br>40 a3<br>30 &<br>30 20<br>10<br>20<br>0 Daily2 mean consumption4 6 (2011 bpp) 10<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>90<br>50 80<br>70 =<br>60 g<br>.& 40 50 5oS<br>40 a2<br>30%<br>30 20<br>10<br>20<br>0 Daily2 mean consumption4 6 (2011 bpp) 10<br><!-- End of picture text -->
BTK and ZZKs, and the total production costs from these kilns are about NRs 17 per brick, much lower compared to efficient TKs (Table 3). This is mainly due to the higher capital investment (chimney) cost of TK, about 14 times more than the investment cost of ZZK (Figure 2). Despite lower production costs and the technology promoted by the government in the 2010s, the use of VSBKs in the country is limited because the quality of bricks is poor, and the capital investment costs are higher compared to FC BTKs and ZZKs. Although the government has banned CKs, a small number of these kilns are in operation, and these kilns are the most energy-intensive. For example, the energy (coal) cost of producing a brick from CK is more than twice the cost of producing a brick from ZZK (Figure 3). The lowest energy cost of producing a brick is from VSBK, followed by ZZK, FC BTK, HK, TK, and CK, in increasing order across all the provinces (Figure 3). The VSBKs utilize waste heat from cooling bricks to preheat incoming raw bricks, which significantly improves energy efficiency. This heat recovery mechanism reduces the overall fuel consumption. Also, the vertical design of the kiln ensures a more uniform temperature distribution, leading to more efficient combustion and reduced fuel usage. The traditional kilns (e.g., CK and FC BTK) have poor heat transfer characteristics and require higher fuels than modern kilns (ZZK, VSBK, HK, and TK). For example, the specific energy consumption (SEC), a measure of energy efficiency of brick kiln technology measured in MJ of energy needed to produce 1 kg of brick, is much higher for traditional kilns when compared with modern kilns (Table A2.4). Higher energy requirements for traditional kilns contribute to higher energy costs. Almost three-fourths of all brick kilns are concentrated in three provinces (Madesh, Lumbini, and Bagmati) (Figure 4). The brick sector statistics, such as kiln technology types are not well documented, however, half of all operational kilns are large in size by the number of people engaged, and FC BTK and ZZK are commonly used technologies by these large kilns.

14
# RELATIVE PRICE OF INVESTMENT GOODS

<!-- Start of picture text --> (INVESTMENT DEFLATOR/GOP DEFLATOR)<br><!-- End of picture text --> <!-- Start of picture text --> 0.22 -<br>0.2-<br>0.18<br>0.16<br>0.14<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02-<br>0 -a<br>-0.02<br>-0.04<br>-0.06<br>-0.08<br>-0. 1<br>-0.12<br>-0.14- *<br>1960 62 64 66 68 70 72 74 76 78 80 82 84 86<br>YEAR<br>0 ICOSTS<br><!-- End of picture text --> t 04,-
the cholera Case Fatality Rate should be below 1%. Nonetheless, in 2020, in Sub-Saharan Africa the annual Case Fatality Rate was 1.6% - the highest in the world (WHO 2020) and in 2009, for instance, 98% of the 221,226 notified cases worldwide were from Africa. Further, in recent years, remarkable epidemics struck various African regions located far from the coast. For instance, in 2008–2009, Zimbabwe experienced the largest cholera outbreak ever recorded in Africa, with more than 100,000 cases and more than 4,300 deaths (World Bank 2016). These examples stress the need to better characterize cholera outbreaks in non-coastal regions of Africa and how to use various measures to reduce the cholera risk in susceptible environments.

The analysis presented in this paper uses cholera case location data and geospatial covariates to include the location of networked and non-networked WSS infrastructure as well as groundwater data to generate a high-resolution map of cholera risk across the city. It has sought to explain patterns of risk in relation to known risk factors for the disease from the literature, identify priority areas and compare alternative strategies for improved infrastructure. The analysis presents scenarios of standalone or combined investments across sewerage coverage and maintenance; on-site sanitation improvements; piped water network coverage and quality; and ensuring the safety of point source water and the relative costing estimates for each, and number of population it would affect (i.e. cost efficiency). It identifies the investment most strongly correlated with the largest reduction in cholera risk would be the provision of flush to sewer infrastructure citywide. However, such an investment would also be disproportionately the most expensive. It identifies the next most impactful stand-alone investment would be the expansion of piped water city-wide, followed by addressing water quality in the existing network. Finally, the analysis was carried out in the context of a considered restructuring of an existing World Bank investment called the Lusaka Sanitation Program (LSP), and identifies the most efficient combined initiative was partial sanitation investment scale-up and investment in piped water in 10 priority wards, costing US$134 million, and reducing the risk of cholera city-wide by some 48%.

In the following sections, this paper outlines the relevant data for Lusaka that was assembled to complete the analysis, such as case location data on cholera cases, water supply and sanitation infrastructure and other potential predictors of cholera to conduct a geospatial analysis for addressing three important objectives:

- (i) use cholera case location data and geospatial covariates to generate a high-resolution map of cholera risk across the city;

- (ii) seek to explain patterns of risk in relation to putative causal factors;

- (iii) compare current water and sanitation infrastructure and access to the pattern of cholera risk, identify priority areas and compare alternative strategies for improved infrastructure.
higher savings from being informal draws more firms to informality, resulting in a bigger informal sector (Djankov et al 2002, Schneider 2000). While the size of the informal sector is an important factor in determining the competitive effects on formal firms—more firms in a market generally means more price competition—regulation is most importantly a major determinant of the intensity of competition from the informal sector.

The government’s capacity to enforce regulations also matters in the assessment of the cost of regulatory obligations firms face. An informal firm’s chances of getting caught for not complying with laws and regulations are a direct function of the government’s capacity to enforce these.

The two points above on the determinants of the size and intensity of informal competition are the central focus of this paper. We investigate for which firms and in what environment competition from the informal sector affects formal manufacturing firms most. We find that firms most concerned about competition from the informal sector are those that resemble informal firms the most. Moreover, we find that in economies where the government’s ability to enforce rules is high and business entry costs, and labor and tax regulations are relatively onerous, firms in industries with low costs to entry are more likely to cite informal competition as a constraint difficult to overcome.

We reach the findings by analyzing data from the 2006 Latin American regional roll-out 3 of the World Bank’s Enterprise Surveys. The data covers over 6,400 formal manufacturing firms from 14 countries in South and Central America. The data provide information on the business environment in each economy, details of firm-level operations, and specifics on the quality of services and infrastructure that these firms use. We also use the _Doing Business_ 4 indicators to obtain measures of the regulatory burdens in each country. We use the World _5_ Bank Institute’s _Worldwide Governance Indicators_ (Kauffman, Kraay and Mastruzzi, 2007) index to assess each government’s ability to effectively enforce laws and regulations on their books. Together, the _Doing Business_ and the _Worldwide Governance Indicators_ give us a measure of effective regulation.

The paper is organized as follows. In Section 1, we introduce the hypothesis as to which formal, manufacturing firms will be most affected by informal competition. We describe our data, the econometric model and the variables used to test our hypothesis. Section 2 examines the features of the business environment that may explain in which countries informal competition is

> 3 Data can be downloaded at: <u>www.enterprisesurveys.org.</u>

> 4 Data can be downloaded at: <u>www.doingbusiness.org.</u>

> 5 Data can be downloaded at: <u>http://info.worldbank.org/governance/wgi2007/</u>
3

## (1) Wr = U(Wo) + (b-u)Wm

where Wm is the legally enforced minimum wage which applies to the urban formal sector, WO is the wage in the urban informal sector, Wr is the rural wage, and (1-u) is the fraction of workers employed in the modem urban sector. Differentiating Wr with respect to the minimum wage yields (where Em is modern sector employment):

## (2) dWr/dWm = (Wm-WO) dEm/dWm + (1-u)

and thereby highlights the key parameters of interest. From (2) it is easy to see that a higher minimum wage will have an ambiguous effect on rural wages. The minimum wage will be more likely to depress rural wages (i) the larger is the elasticity of demand for labor in the urban formal sector, (ii) the greater is the gap between wages in the formal and informal sectors, and (iii) the smaller is the urban formal sector. Although data are not available from which to analyze the impact of the minimum wage changes on rural sector wages directly, the three parameters at least provide a gauge for speculating on the broader impact of minimum wage policies in Mexico and Colombia.

# _The Data_

Table 1 summarizes the behavior of wages and the national weighted minimum wage over the period 1984 to 1990 in Mexico.<sup>1</sup> Panels A and B of the table use information on monthly wages paid to workers from Mexico's Annual Industrial Survey of manufacturing firms over the period 1984-1990.2 Panel C supplements this information using data from the Mexican Ecuesta Nationale de Empleo for 1988, a household survey covering workers in both urban and rural Mexico.<sup>3</sup> 'Minimum wages in Mexico are set regionally and may differ by occupation. Table 1 presents weighted averages based on these data..

2The Industrial Survey is of predominantly large firms in the formal sector. Data were provided by Mexico's Secretary of Commerce and Industrial Development (SECOFI). 3A11 of the tabulations and regression results from the 1988 Mexican Household data were performed and provided by Ana Revenga.
decline in poverty. This growth came in the form of higher wages and a demand-led growth of farm and off-farm self-employment incomes.

Between 1995 and 2010, the share of labor income in total income remained mostly unchanged, while the share of remittances increased significantly. Figure 9 summarizes changes in income component shares between 1995 and 2010, where total income is classified into four categories: agricultural income, non-agricultural income, remittances and capital income.<sup>22</sup> The remittance share nearly tripled from 6 percent to 16 percent for the whole population and a similar magnitude of increase is observed when analyzed by urban/rural, income levels, and geographical regions, which underscores the widespread incidence of remittances in 2010 as seen in Figure 8.

**Figure 9: Income component shares between 1995 and 2010**

<!-- Start of picture text --> Remittances Capital Income Non-Agricultural Income Agricultural Income<br>1995 2010<br>National 23% 23% 48% 16% 14% 34% 36%<br>Urban 31% 56% 10% 13% 21% 54% 12%<br>Rural 23% 20% 51% 17% 12% 29% 41%<br>Bottom<br>22% 19% 55% 13% 13% 30% 45%<br>40%<br>Middle 40% 24% 25% 45% 19% 13% 37% 30%<br>Top 20% 9% 27% 34% 29% 24% 16% 42% 18%<br>Kathmandu 31% 63% 10% 26% 63%<br>Other Urban 30% 52% 15% 15% 19% 50% 16%<br>Rual Western<br>9% 26% 21% 43% 19% 12% 27% 42%<br>Hill<br>Rural Eastern<br>30% 21% 46% 12% 13% 29% 46%<br>Hill<br>Rural Eastern<br>17% 22% 54% 18% 11% 31% 40%<br>Terai<br>Rural Western<br>17% 15% 64% 20% 13% 30% 37%<br>Terai<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br>Income Share Income Share<br><!-- End of picture text --> Source: World Bank Staff Calculation based on Nepal Living Standard Surveys 1995/96 and 2010/11

> 22 Agricultural income includes agricultural wages and net farm income. Non-agricultural income is a sum of nonagricultural wages and self-employment income. Capital income includes housing and non-agricultural rental income and also subsumes other miscellaneous incomes.

17
On the import side, the United States is by far the largest single market, accounting for more than _50%_ of world imports. The bulk of such imports are eventually marketed as a snack food under three main brand names--Planters, Eagle Snacks, and Fisher. Cashews are the only major dessert nut for which U.S. domestic production is negligible and the U.S. is in fact a major exporter of almonds, walnuts, hazelnuts, and other varieties. As Table 4 indicates, the other major import markets include Japan, Australia, Canada, the former USSR, and several EEC countries. With the major exception of the former USSR (which had previously been a large importer of lower quality nuts for confectionery use), import levels increased in these major markets during the 1980s.

|Tabl|e 4: Cashew Nut Kern<br>(00|el Inports into Major<br>0 Tons)|Markets<br>l|
|---|---|---|---|
|Country|1980-82 Average|1984-86 Average|1989-91 Average|
|United States|30,917|43,073|48,372|
|(Former) USSR|20,816|3302|3328|
|Netherlands|3080|2301|3669|
|Germany|2796|2736|3661|
|Canada|2666|3235|4309|
|United Kingdom|2638|2934|4919|
|Japan|2371|2717|4520|
|Australia|1535|3011|2930|
|Sub-Total|66,819|63,309|75,708|

Source: Gill and Duffus Edible Nut Market Report, various issues.

Long-term trends in international prices in kernels are shown in Graph 1 below. The data refer to U.S. CIF prices per ton for benchmark size 320 whole kernels. Current prices have been deflated by the G-5 MUV index to obtain real prices. The graph shows that since the early 1970's, sharp increases in (nominal and real) prices were recorded during three periods--1976 to 1977, 1979 to 1982, and 1984 to 1987. Through most of the 1980s, real prices were higher than those which prevailed during the early 1970s.

10
equality in terms of wealth assets or income. This result is robust to different measures.<sup>31</sup> Furthermore, we also find no difference in whether farmers belong to a cooperative in a former FMLN territory relative to areas outside (see Table D.20). This result also aligns with the evidence in Table 1 showing no baseline differences in the 1980 state-implemented land reform across the boundaries that transformed many large plots into cooperatives. Although the FMLN redistributed land to peasants, most of its agricultural programs targeted subsistence crops. They did not teach peasants how to grow and commercialize export crops.

# **VI.B Ruling out differences in land tenure**

We previously posited that enduring distrust of elites and the state in former guerrilla areas prevented the entrance of large landowners and private investment in general, isolating these communities from improvements in productivity. Another possibility is that individuals in former guerrilla areas remained attached to the land since they did not have the right to sell it. This is unlikely since a major aspect of the Peace Agreements was to respect the land tenure that formed during guerrilla territorial control.<sup>32</sup> Indeed, using the Agriculture Census, we do not observe differences between treatment and control areas in land ownership or land property rights.<sup>33</sup>

# **VI.C Ruling out migration**

This section explores potential differences in migration patterns in former FMLN areas compared to those outside the boundary. The guerrillas promoted changes that might have induced different patterns of worker selection. For example, high-ability workers could have migrated from these areas due to fear of expropriation of their income (out-migration). But there could have been adverse selection of workers if guerrilla-held areas attracted less productive peasants or individuals with more egalitarian preferences into the areas (in-migration).

We explore these migration patterns empirically in Table 8 using data from the 2007 census. Columns 1–5 examine impacts on international migration. In particular, we estimate equation (1) for the share of international emigrants during the period of FMLN territorial control and afterward, the number of years since the international emigrant left the household, and the share of households receiving remittances. Unfortunately, the 2007 census does not include questions

> 31We also checked the robustness of these results using the Wealth Index from the 2007 Census at the census tract level (Table D.19). We do not find any difference across the boundary when using this measure to assess inequality. 32As mentioned in Section II.D, the agreement established that individuals occupying land in “conflict zones” could keep it. The state was in charge of administering land to anyone who claimed ownership; it gave current occupants a land title and 30 years to pay for the land.

> 33Results are available upon request.

37
– _SERIEs_ 4(3): 309 331.

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- _Applied Economics Letters_ 28 (12): 1017 20.

- Elgin, C. and E. Iyidost. 2021. “Velocity of Money and Shadow Economy.” _Applied Economics Letters_ , DOI: 10.1080/13504851.2021.1961117

- Elgin, C., C. C. Williams, G. Oz-Yalaman, and A. Yalaman. 2022. “Fiscal Stimulus Packages to COVID-19: The Role of Informality.” _Journal of International Development_ , https://doi.org/10.1002/jid.3628.

- Farazi, S. 2014. "Informal Firms and Financial Inclusion: Status and Determinants", Policy Research Working Paper 6778, World Bank, Washington, DC.

- Fergusson. 2006. “Institutions for Financial Development: What Are They and Where Do They Come From?” _Journal of Economic Surveys_ 20 (1): 27-70.

- Feyen, E. N. Fiess, A. C. Bertay, and I. Zuccardi Huertas. 2020. “Cross-Border Banking in Emdes: Trends, Scale, and Policy Implications.” Policy Research Working Paper 9393, World Bank, Washington, DC.

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- Granda-Carvajal, C., and D. García-Callejas. 2022. “Informality, Tax Policy and the Business Cycle: Exploring the Links.” _International Tax and Public Finance_ , https://doi.org/10.1007/s10797-021-09717-7.

- Head, K., and J. Ries. 2008. "FDI as An Outcome of the Market for Corporate Control: Theory and Evidence." _Journal of International Economics_ 74(1): 2-20.

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- International Monetary Fund. 2015. “Chapter 2 International Banking After the Crisis: Increasingly Local and Safer?” Global Financial Stability Report 2015/April. International Monetary Fund, Washington, DC.

32
A number of existing studies (e.g., USAID; 2009; FAO, 2008a; BNDES and CGEE, 2008; OECD, 2008) have attempted to present an overall picture of the current status of biofuels. These studies, however, have focused on specific issues. For example, USAID (2009) focuses on the sustainability of biofuels in Asia, while FAO (2008a) concentrates on the relationship of biofuels with food prices. On the other hand, BNDES and CGEE (2008) discuss the Brazilian experience with ethanol in detail, and OECD (2008) assesses the impact of biofuel support policies. A succinct but broad assessment of biofuels is still lacking, particularly addressing some crucial issues, such as promises of biofuels, their economics, investment trends and potential economic, social and environmental impacts. This paper aims to fill this gap.

This paper is organized as follows. Section 2 discusses the status of biofuel technologies, followed by a look at production, consumption and trade patterns in recent years in Section 3. It then reviews biofuel policies and mandates around the world in Section 4. The cost of production and investment trend are presented in Section 5. Section 6 discusses impacts of biofuels on food prices followed by presentation of environmental impacts in Section 7 and land use impacts in Section 8. Finally we draw key conclusions in Section 9.

# **2. Technologies**

Based on the feedstock used for production and the technologies used to convert that feedstock into fuel, biofuel technologies can be classified into two groups: first and second generation biofuels. Technologies that normally utilize the sugar or starch portion of plants (e.g., sugarcane, sugar beet cereals and cassava) as feedstock to produce ethanol and those utilizing oilseed crops (e.g., rape seed, sunflower, soybean and palm oil) to produce biodiesel are known as first generation biofuels (Rutz and Janssen, 2007; OECD-FAO, 2008). On the other hand, biofuels produced using technologies that convert lignocellulosic biomass (e.g., agricultural and forest residues) are called second generation biofuels, as are biofuels produced from advanced feedstock (e.g., jatropha and micro-algae) (Worldwatch, 2007). Whereas first generation biofuels have already been in commercial production for several years in many countries, second generation technologies have yet to begin commercial production, with some exceptions (e.g., 3