rafmacalaba/datause-encoder-data huggingface dataset:
8 distillery in Mali was fir.anced with a World Bank loan. A recent Bank paper"' recommended further assessment of proposed ethanol programs for Thailand and the Philippines.

24.

24. In general, the viability of a fuel alcohol program increases when it is possible to use lower economic value molasses rather than cane as feedstock, the sugar mill is far from the coast, thereby reduci.g the value of molasses as an export commodity, and when the distillery can be ann-xed to the sugar mill so that surplus bagasse can provide process energy for ethanol production. As the primary economic benefit of ethanol production is the displacement of imported gasoline, the economic rate of return of an ethanol project rises directly with the economic price of gasoline. Exchange rates a!&' affect economic viability, but less robustly because a strengthening local currency will reduce both gasoline import costs and export values of molasses and sugar. Additional benefits that should be accounted for in a fuli economic analysis include: employment, foreign exchange savings, and security of access to an indigenous transportation fuel.

# 25.

25. To illustrate the impact of lower oil prices on fuel alcohol projects, it will be sufficient to consider the case of anhydrous alcohol procauction from molasses (i.e., if project justification is lost for this case, so would it be for ethanol from cane). As with dendrothermal power above, the economics of ethanol projects are very site specific. Hence, two representative projects will be analyzed: a proposal in Swaziland re-ently reviewed by the Bank and the Brazilian ethanol program.

26.

26. Three proposals for ethanol production in Swaziland were analyzed as part of the Swaziland Energy Assessment. The most promising proposal (by CDC/PEA for 65,000 lpd annexed distillery at the Mhlume sugar mill) is designed to produce ethanol at 21¢/liter to meet 20% of a growing national gasoline demand until i995 (details in Annex). If saving3 on imported gasoline are the only benefits counted in the economic analysis, the 22% drop in the actual landed price of gasoline<sup>81</sup> , from 30e/l in mid-1985 to 24¢/l in July 1986, is enough to cut the project economic rate of return from 28.5% to 18%.

# 27.

27. The approximate minimum real crude oil prices needed to justify the Brazilian anhydrous ethanol program in economic terms are shown in Table 2 below<sup>9</sup> /. Minimum crude oil break-even prices for hydrous import substitu:-on and ex.ort of hydrous ethanol average 25% and 50% higher.

7/ 7/ Ody, **A., ePrects** for Etbanol **ir** Developing Countries, INDD2 Office Memorandum, 21 **Februaiy** 1985, World Bank. S/ The **FOB price** of **gasoline** in the Middle **East** market dropped **50% from** October 1985 to **July** 1986, **while** due to high transportation **cogs** and other levies, the landed cost of gasoline at Matsapha, **Swaziland** dropped only 22% over the same **period.**

> 9/ Draft Public **Sector** Investment **Rcview** Brazi, October 1986, **LC2BR World Bank.**
and (iii) loss of protection (wave attenuation) from mangroves. Hence, in this section the mangroves in WMP categories 3 and 4 were combined with the inundation zone for storm surges and 1 meter SLR to estimate the land area and human resources that will be at risk in a changing climate.

In order to understand the specific impacts of SLR, storm intensification and loss of mangroves on surge inundation, we conducted our computation in two steps. First in step 1, we estimated the impacts of 1‐meter SLR and a 10 percent increase in storm intensity (assuming no loss of existing mangroves) on the surge inundation area. This was calculated using data and methods described in Nicholls et al. (2007), Dasgupta et al. (2011) and Brecht et al. (2012) and is summarized in Box 2. Thereafter in step 2, we estimated the additional impact on inundation area due to loss of mangroves.

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# 2. Drivers of migration

Studies on migration from developing countries have long been debated and tested. In a seminal study aiming to analyze a comprehensive set of factors of migration, Lee (1966) classifies the different factors into four groups: push factors (linked to the origin country), pull factors (linked to the destination country), intervening obstacles, and personal factors. Moreover, standard models of migration showed that rational agents moved from regions in which labor was abundant and wages low to regions in which labor was scarcer and wages high (Harris and Todaro, 1970). The new economics of migration focused on the societal dimension of the phenomenon, in an attempt to reconcile structuralist and neoclassical approaches of migration. Since then, the literature has evolved to reflect the much more complex reality of migration and has shifted a large part of its focus to developing countries (De Haas, 2010). Despite the large set of studies on migration that exist today, ranking the determinants of migration remains a difficult task. Moreover, whether these determinants are gendered is not systematically addressed in the literature.

Migration models focusing on human capital show that an individual’s decision to migrate is guided by the comparison of the returns to human capital in the destination and origin countries (see for instance (Dustmann et al., 2011)). Kahanec & Fabo (2013) point out that generally, the level of education and employment status are not important drivers for youth migration inside the European Union, but individuals with completed education are more likely to report intentions to stay abroad more than five years and less likely to report permanent migration intentions. Van Mol (2016) finds that higher educated youths have a higher likelihood of migrating when they face unemployment in the European context, in line with Bartolini et al. (2017). In developing countries, Dibeh et al. (2019) and Docquier et al. (2020) show that education level is an important driver of migration in the Middle East and North Africa region. Similar evidence is found for the Pacific (Gibson & McKenzie, 2011).

The desire to migrate can also be influenced by institutional contexts, living standards and culture where the destination country becomes more attractive than the origin country. Macroeconomic factors such as slow economic growth, weak job creation, lack of meritocracy and upward mobility are strong push factors especially for highly educated individuals (Bartolini et al., 2017; de Grip et al., 2010; Dibeh et al., 2019). Moreover, perceptions of corruption in governments, discontentment with the political situation and undersupplied public services provision can also increase young people’s intention to emigrate (Etling et al., 2020; Van Mol, 2016). Naudé (2010) finds that among 45 SubSaharan African countries, the main determinants of migration between 1965 and 2005 were armed conflict and lack of employment opportunities.

5
(see Figure 2). In comparison, the domestic content share of China’s non-processing exports stood at a much higher 88.7%, meaning that imported inputs only represented 11.3% of the export value.

# **[Figure 2 about here]**

In this section, we are primarily interested in the geographic characteristics of China’s processing trade regime. To analyze the countries of origin of processing imports and the destination countries of processing exports, an important data issue that needs to be addressed is that 90% of China’s trade with its largest trading partner, Hong Kong SAR, China, are re-exported elsewhere (Feenstra, Hai, Woo and Yao, 1999; Feenstra, Hanson and Lin, 2004; Ferrantino and Wang, 2007). This can significantly affect the analysis since it biases the _true_ source country of processing imports and the _true_ destination country of processing exports that are shipped through Hong Kong SAR, China. To account for these re-exports, we follow Ma, Van Assche and Hong (2009) by linking the processing trade data from China’s Customs Statistics to a data set from Hong Kong SAR, China’s Census and Statistical Office on its re-exports. This allows us to estimate the country of origin of processing imports re-exported through Hong Kong SAR, China and the destination country of processing exports re-exported through Hong Kong SAR, China. A comparison of columns 1-2 and 3-4 in Table 1 illustrates the impact of adjusting for re-exports through Hong Kong SAR, China on China’s processing trade with its major trading partners. It almost doubles the share of processing imports originating from China’s other major trading partners and increases by a quarter the share of processing exports destined to these same economies.

# **[Table 1 about here]**

China’s processing trade regime heavily relies on East Asian inputs. As it is shown in Figure 3, China heavily sources its inputs from neighboring East Asian economies, with 75.1% of its processing imports originating from within East Asia in 2008. By contrast 9
Table E.5: Impacts on crime incidence, in the last two weeks and annualized extrapolation

||Cash + th|erapy ITT,|12–13 mont|h endline|Annualiz|ed impact|
|---|---|---|---|---|---|---|
||Control<br>mean|Coef.|Std. Err.|%<br>change|Control<br>mean|Cash +<br>therapy|
||(1)|(2)|(3)|(4)|(5)|(6)|
|# crimes, past two weeks|2.54|-0.994|[.438]**|-39%|66.1|-25.8|
|# times sold drugs, past two weeks|0.70|-0.266|[.188]|-38%|18.3|-6.9|
|# thefts/robberies, past two weeks|1.84|-0.728|[.363]**|-40%|47.8|-18.9|
|Selling/switching fake goods|0.27|-0.063|[.069]|-23%|7.1|-1.6|
|Stealing unwatched items|0.34|-0.087|[.084]|-26%|8.8|-2.3|
|Overcharging or cheating|0.30|-0.104|[.077]|-35%|7.8|-2.7|
|Burglary|0.09|-0.074|[.035]**|-79%|2.5|-1.9|
|Con artistry/scams|0.12|-0.093|[.036]**|-79%|3.1|-2.4|
|Pickpocketing|0.60|-0.196|[.137]|-33%|15.5|-5.1|
|Mugging|0.09|-0.078|[.050]|-91%|2.2|-2.0|
|Armed robbery|0.03|-0.026|[.026]|-82%|0.8|-0.7|
|Arrested in past two weeks|0.12|-0.033|[.024]|-28%|3.1|-0.8|

_Notes:_ Columns (1) to (4) report the same ITT regression as in Table 3, with robust standard errors in brackets. Columns (5) and (6) simply multiply the two week estimates by 26 weeks to generate an estimated annual impact per person. *** p<0.01, ** p<0.05, * p<0.1 levels of antisocial behaviors at baseline who were assigned to both therapy and cash experienced a 0.19 standard deviation decline in their level of antisocial behaviors 12–13 months later, but men whose initial level of antisocial behaviors was a standard deviation higher than average experienced about double the decline.

Our results also indicate that in the long run, men with high levels of initial antisocial behavior who received a cash grant actually increase their anti-social acts. This is especially interesting given that the effects of cash on occupational choice and income disappeared after a year. One possibility is that this increase in antisocial behavior is a reaction to the failed attempt at legitimate livelihoods, but these results are more speculative than anything else.

# **E.6 Program impacts on occupational choice**

To measure changes in occupational choice, we asked respondents at each endline whether they had engaged in 22 occupations, from farming to petty business, trades, and formal jobs. For each occupation, we collected self-reported earnings and hours in both the last week and the week prior. We use these to calculate the total earnings and hours variables. With two endline surveys, we have four weeks of employment data per person both in the short-term and in the long term.

We can also calculate hours by occupations each week, aggregating our 22 occupations into 5 mutually exclusive categories:

1. Non-agricultural high-school work, which includes trading and office work

2. Non-agricultural low-skill business, which includes selling from a shop, selling at a table, buying and selling, engaging in petty trade, and conducting small business xlv
Since child-rearing is time-consuming and affects women’s allocation of time, fertility is considered inversely related to labor force participation, leading to the joint determination of both variables. While labor force participation is clearly endogenous to fertility in modern societies where women actively manage fertility jointly with labor market decisions, it is less convincing for poor agricultural economies where female labor market participation is high as family responsibilities and agricultural work are usually combined (Mammen and Paxson, 2000). In Rwanda, as in many parts of rural Africa, women tend to their daily occupations, which mainly consist of cultivating the family farm or self-employment in small-scale family businesses, while simultaneously taking care of children. In the data used for this paper, women with an above average number of children are not less likely to participate in the labor market than women with a below-average number of children. To the contrary, labor force participation is higher for the former women in both surveys. Similarly, women who gave birth in the year preceding the surveys were not less likely to work than women without birth in the year preceding the surveys. This holds true even within cohorts (women in a certain age-group who gave birth in the year preceding the survey were not less likely to work than women in the same age-group without birth). As a consequence, we will include labor force participation as a determinant of fertility in a certain number of specifications. We will however also present results excluding women’s labor force participation to see how estimated coefficients change.

Between 2005 and 2010 Rwanda experienced rapid economic growth and poverty reduction. To explore whether the rapid increase in living standards was associated with the drop in fertility, we construct a household wealth index along the lines proposed by Filmer and Pritchett (2001). The wealth index consists of 22 binary asset indicators containing information about the quality of housing of the household, sources of water and energy, asset holdings, etc.<sup>12</sup> To be able to compare the wealth index over time (the wealth index is standardized), the principal component analysis underlying the

> 12 Information about construction of the index available on request.

13
in columns 7 to 12 drop observations from individuals living in communes with no record of past herbicide spraying. The results are comparable to the full‐sample analysis; cancer prevalence is positively correlated with the adopted continuous measure of herbicide exposure. In this case, however, no significant difference is found between pre and post 1971 cohorts and the effect of exposure on post‐1971 can be detected at a 10 percent confidence‐ level , except when health care utilization is controlled for (column 12).

## [TABLE 4 ABOUT HERE]

Tables A1 and A2 in the appendix perform the same analyses as in tables 3 and 4, restricting to _dioxin‐only_ spray applications to construct measures of exposure. Although the results look similar, they are not statistically significant at conventional confidence levels.

# **4.2. Discussion**

A discussion of the results – the existence of a positive dose‐response association between past herbicide exposure and contemporaneous prevalence of cancer, despite a documented attrition bias – attempts to assess the limitations of the adopted approach, and to debate a causal interpretation of the estimated correlations. Three issues need to be addressed: (i) the use of self‐reported cancer status to construct prevalence measures, (ii) the adopted measure of herbicide exposure, and (iii) potential omitted variable biases.

## _4.2.1. Self reported cancer status_

An overwhelming majority of studies that aimed at investigating a potential link between dioxin and cancer were performed on non‐Vietnamese war veterans, for whom hospital records are available or biopsies and X‐ray screenings were performed (with few exceptions that also relied on self‐reports; see e.g. Kang et al., 2006). With regards to Vietnam, such information is not systematically available, and national cancer registries are subject to the caveat that surveillance might be positively correlated with herbicide spraying; the government might have encouraged cancer screening in areas more heavily sprayed, which would then be a source of bias in any attempt to correlate cancer prevalence obtained from registries and the intensity of herbicide spraying.

An alternative is to use a large‐scale survey with the understanding that the analysis needs 12
<!-- Start of picture text --> 2.0%<br>0.0%<br>2.0%<br>4.0%<br>-6.0%<br>“8.0%<br>10.0%<br>e * e & ra ef & & eoa rod<br>@ Push Effect @ Geographical Effect Gi Sectoral Effect @ Deviation from world export growth<br><!-- End of picture text --> <!-- Start of picture text --> Push_Factors_all (BRICS, EU27, MIST US, JP)<br>® SK<br>asg RU MT ye BG cz<br>oa8 3 EE iDLV S| IN<br>8oa BR r KR R<br>2 IE us BESR AT<br>2 JP.za FR iT<br>°58 8 se R LU<br>&<br>425 =.0625 0 0625 125<br>Pre Crisis (2006q1-2008q2)<br><!-- End of picture text -->

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# **Figure 4: FDI inflows/stock to China and 15 CEECs as a percent of world’s total, 1990-2005**

FDI inflows to China & 15 CEECs as a percent of world total, 1990-2005 <!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>China 15 CEECs<br>Percentage (%)<br><!-- End of picture text --> FDI stock in China & 15 CEECs as a percent of world total, 1990-2005 <!-- Start of picture text --> 7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br>China 15 CEECs<br>Percentage (%)<br><!-- End of picture text --> Source: UNCTAD World Investment Report online database and authors’ own calculation.

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To give a concrete idea of what such low levels of energy consumption mean in terms of quality of life, it is helpful to think of a subsistence package of energy requirements that can be used to define a ‘fuel poverty’ line. Based on consultation with local energy experts about the energy needs of low income households, this subsistence package provides enough energy to run two 60 watt light bulbs and one 16 watt radio for four hours each day, and incorporates a cooking requirement of ten kilograms of fuel wood each day. The survey indicates that 92% of households without access to electricity have energy consumption levels that fall below the ‘fuel poverty’ line, compared to only 35% of households with access to electricity (Table 4.5). It is estimated that if these households had access to electricity they would be able to substantially increase their energy consumption, so that the fuel poverty rate would fall from 92% to between 37% and 73%, depending on what assumption is made about the price elasticity of demand.

**Table 4.5: Fuel poverty estimates with and without access to electricity**

||Households|Hou<br>|seholds witho<br>|utaccessto el<br>|ectricity<br>|
|---|---|---|---|---|---|
||with access<br>to electricity|Current<br>situation|After gainin<br>elasticiti<br>|g access, for<br>es ofdemand <br>|ifferent price<br>forenergy<br>|
||||ε= -0.5|ε= -1|ε= -1.5|
|Price er effective kwh (Q)|1.7|39.3|1.8|1.8|1.8|
|p<br>Net consumption (kwh/month)|67.5|.98|16.2|21.6|32.4|
|Fuel poverty<br>Headcount|.27|1.00|.68|.55|.33|
|<br>Povert a|.12|.94|.34|.24|.14|
|y gp<br>Squared poverty gap|.08|.89|.21|.14|.08|

_Note:_ Refers only to energy used for lighting and appliances, based on a poverty line of 200 kwh/year _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala

# **4.2 Water**

The typical tariff structure for water in Guatemala comprises a flat payment up to a relatively high consumption threshold, and a linear unit charge for consumption above this level. This kind of tariff structure has the disadvantage that it does not provide any incentive for households to control consumption below this threshold level.

A recent survey of water tariffs found that in the larger citiesGuatemala and Quetzaltenangothe flat rate charge of $1 to $2 per month entitled households to consume between 15 and 25 cubic meters per month, while further consumption was charged at a rising rate of between $0.10 and $0.30 per cubic meter (Figure 4.6) (ESA Consultores, 2001). The same survey found that water charges in the smaller towns of the interior were substantially lower, with a flat charge of around $0.50 per month entitling the household to around 30 to 40 cubic meters per month, and subsequent consumption being charged at less than $0.10 per cubic meter (Figure 4.6). The implication is that for a typical monthly consumption of 20 cubic meters, households in the larger cities would be paying an implicit tariff of less than $0.10 per cubic meter, while households in the smaller cities would be paying less than $0.05 per cubic meter.

34