rafmacalaba/datause-encoder-data huggingface dataset:
In the case of Ghana, there are some indications that corruption may have been on the rise since the discovery of oil. The Afrobarometer Survey conducted in 2014 by the Ghana Centre for Democratic Development (CDD 2014) found that public perceptions of corruption had significantly increased, and there was a similar perception of an increase in corruption in the runup to the 2008 elections under the NPP government (CDD 2008). According to the 2014 survey, three quarters (75 per cent) of respondents said corruption had increased over the previous year.

# **11 Ghana’s political system in the context of oil**

Ghana practises what has been described as a hybrid system of governance. The president is chosen directly by the people (unlike in the parliamentary system), and is head of government and head of state with full executive powers. But unlike the system operating in the United States, for example, the president can appoint members of parliament to the executive branch—even while they continue to function as legislators. Indeed, the Fourth Republican Constitution, under which Ghana operates, makes it mandatory for the president to select a majority of ministers from parliament.

The fact that the president is mandated to choose members of parliament as ministers—and that, as in the case of all previous sitting presidents, these legislators are likely to be members of the president’s political party—means that power is significantly skewed towards the executive branch of the government. The executive branch also plays a large part in judicial duties. Not only is the executive responsible for framing and presenting all bills sent to parliament, but the president wields the power to appoint justices to the High Court and Appeals Court (acting on the advice of the Judicial Council) as well as Supreme Court, and to appoint the chief justice (in consultation with the Council of State and with prior approval of parliament). In practice, therefore, Ghana’s political system is a presidential one (Banful 2011).

Ghana is a multiparty democracy, but of the 23 political parties registered in 2014, very few are active across the nation. The strongest, most vibrant parties are the NDC, the NPP, the Convention People’s Party, the People’s National Convention, and the Progressive People’s Party. Of these, all but the latter have had at least one member in parliament since 1996. That said, the relative dominance of the NDC and NPP in various general elections since 1992 has turned Ghana’s democracy into a virtual two-party system.

24
change depending upon the scenarios. However, forecasting the load and energy is beyond the scope of the study; they are exogenous to the electricity planning model used.

# **4.3. Economic and Technical Characteristics of Power Generation Technologies**

The data on the economic and technical characteristics of power generation technologies are presented in Tables 4a to 4e. Economic data include costs (capital costs, O&M costs, fuel costs/prices) and fuel prices (subsidized/local and unsubsidized/international). Technical data include heat rates, natural gas availability constraints, and availability factors for renewable energy technologies.

**<u>Table 1. Peak load and energy demand forecasts (Annual Average, %)</u>**

|**Economy**||**Peak load**||**E**|**nergy deman**|**d**|
|---|---|---|---|---|---|---|
||2020-25|2025-30|2030-35|2020-25|2025-30|2030-35|
|Algeria|4.0%|3.8%|4.0%|4.2%|4.0%|4.0%|
|Bahrain|3.1%|3.4%|3.9%|3.9%|3.7%|3.9%|
|Egypt, Arab<br>Rep.|5.7%|5.4%|5.5%|6.2%|5.5%|5.5%|
|Iraq|1.2%|0.6%|3.9%|4.3%|3.8%|3.9%|
|Jordan|5.0%|4.8%|5.9%|5.3%|5.1%|5.9%|
|Kuwait|4.9%|2.1%|2.6%|5.4%|2.6%|2.6%|
|Lebanon|2.8%|2.8%|2.7%|3.2%|2.7%|2.7%|
|Libya|2.9%|2.5%|2.7%|3.3%|2.8%|2.7%|
|Morocco|5.0%|4.5%|4.8%|5.6%|4.8%|4.8%|
|Oman|5.2%|5.0%|5.0%|5.9%|5.4%|5.0%|
|West Bank and<br>Gaza|5.1%|4.4%|4.4%|-2.2%|4.4%|4.4%|
|Qatar|2.6%|1.8%|1.8%|2.6%|1.8%|1.8%|
|Saudi Arabia|3.9%|2.1%|2.8%|4.0%|2.2%|2.8%|
|Sudan|12.8%|13.0%|13.1%|12.9%|13.4%|16.1%|
|Syrian Arab<br>Republic|4.0%|3.7%|3.8%|4.1%|3.8%|3.9%|
|Tunisia|4.8%|3.8%|4.6%|5.5%|4.6%|4.6%|
|United Arab<br>Emirates|6.0%|5.0%|3.7%|4.2%|3.7%|3.7%|
|Yemen, Rep.|5.0%|4.8%|5.9%|5.3%|5.0%|6.1%|
|Total|4.2%|3.5%|4.4%|4.7%|4.0%|4.5%|

Source: World Bank (2020) The capital cost, also called overnight construction cost, includes the costs of site preparation, construction, and interest during construction of power plants. While costs of solar technologies are rapidly decreasing, we considered current costs for all technologies as it is difficult to predict the capital costs of any technology. We considered both local (or subsidized) 12
derestimation of the effect of the program, due to endogenous effects on mortality, correlated with cognitive skills. Finally, section 9 concludes.

# **2 Motivation: Open defecation and cognitive achievement in the IHDS**

Section 1.1 reviewed evidence in the literature that early-life health and net nutrition shape later human capital and cognitive achievement. Is exposure to open defecation indeed associated with less cognitive achievement? As a motivation for this paper’s evaluation of the impact of the TSC on children’s learning outcomes, this section uses the rural sample of the 2005 India Human Development Survey (IHDS) to document a robust association between sanitation externalities and children’s test scores.

The IHDS interviewed about 40,000 households in India and gave children aged 8 to 11 learning tests that were modeled closely on the ASER tests that this paper primarily studies. Section 6.3 uses this data to document that TSC program intensity was not correlated with trends in test scores in children whose early life developmental period was before the program. Although the main objective of this paper is to exploit heterogeneity in program timing to identify an effect of the TSC, there are two reasons the IHDS provides additional useful motivation. First, the IHDS includes detailed socio-economic data, specifically including household consumption per capita. Second, the IHDS allows us to match children’s learning outcomes to the local area open defecation to which they are exposed in their own village. Additionally, this result has the advantage of being representative of rural Indian households.

Figure 1 documents that children living in villages where most of their neighbors defecate in the open are less likely to be able to read paragraphs than children living in villages with better sanitation.<sup>3</sup> The sample of rural children is split into those who live in local

> 3The rest of this paper, studying younger children, will use reading words rather than reading paragraphs as a dependent variable.

6
- Auxiliary nurse midwife: a dummy variable that takes a value of one if the village has any Auxiliary Nurse Midwives (ANMs).

- Female doctor: a dummy variable that takes a value of one if the village has a female doctor.

- Share of constituencies won by women in a district: The fraction of state assembly constituencies in a district in which a female candidate won in the most recent state election before the woman-level outcome variable survey.

- Share of close male-female elections won by women in a district: Fraction of state assembly constituencies in a district where a woman won in a close election against a man in the most recent state election before the woman-level outcome variable survey.

- Share of male-female close elections in a district: Fraction of state assembly constituencies in the district that had a close male-female election, in the most recent election before the woman-level outcome variable survey.

- Whether the district had at least one male-female election: A dummy for whether a district had at least one male-female election, in the most recent election before the woman-level outcome variable survey.

- Whether the district had at least one male-female close election: A dummy for whether a district had at least one male-female close election, in the most recent election before the woman-level outcome variable survey.

- Whether the district had at least one male-female close election won by a woman: A dummy for whether a district had at least one male-female close election in which a woman was the winner, in the most recent election before the woman-level outcome variable survey.

- Age: The age of the respondent.

- Rural: A dummy variable equal to one if the respondent lives in a rural location.

- Hindu: A dummy variable equal to one if the respondent reports that her religion is Hindu.

- Muslim: A dummy variable equal to one if the respondent reports that her religion is Muslim.

- Christian: A dummy variable equal to one if the respondent reports that her religion is Christian.

- Sikh: A dummy variable equal to one if the respondent reports that her religion is Sikh.

- SC/ST: A dummy variable equal to one if the respondent reports that her caste is a scheduled caste or tribe (SC/ST).

- OBC: A dummy variable equal to one if the respondent reports that her caste is other backward caste (OBC).

- Years of schooling: The number of years of schooling that the respondent reports having completed.

26
_Figure 2: Comparison of area poverty estimates by method for Sri Lanka (left) and Tanzania (right):_ <!-- Start of picture text --> 1 331 1 159<br>Sub-district District<br>Direct survey estimates Fay-Herriot model estimates Direct survey estimates Fay-Herriot model estimates<br>Household model estimates Census value Household model estimates Census value<br>.5 .8<br>.4<br>.6<br>.3<br>.4<br>Poverty rate .2 Poverty rate<br>.2<br>.1<br>0 0<br><!-- End of picture text --> _Notes: Figures show predicted asset-based poverty rates generated by household-level model, Fay-Herriot model, and Direct Survey estimates, in comparison with actual asset-based poverty rates calculated from the census_ _Figure 3: Comparison of estimated Mean Square Error by area and method for Sri Lanka (left) and Tanzania (right)_ <!-- Start of picture text --> 1 331 1 159<br>Sub-district District<br>Direct estimates Fay-Herriot estimates Direct survey estimates Fay-Herriot model estimates<br>Household model estimates Household model estimates<br>150 150<br>100 100<br>50 50<br>Mean Squared Error * 10000 Mean Squared Error * 10000<br>0 0<br><!-- End of picture text --> _Notes: Figures show mean squared errors of predicted asset-based poverty rates generated by household-level model, FayHerriot model, and Direct Survey estimates. Direct survey estimate MSEs top-coded at 0.015._ The significantly lower mean squared errors generated by the household level model is associated with a moderate reduction in coverage. This begs the question of whether the household level model remains more precise than others after equalizing coverage rates. To shed light on this, we perform a subsequent adjustment. This adjustment artificially inflates the estimates MSEs from each method by multiplying them by a constant scale factor greater than one, with the scale factor manually selected to achieve a 95 percent coverage rates. This occurs after the benchmarking and is therefore an additional adjustment to the mean squared error. Table 10 presents the results. The first column shows the scale factor that was multiplied by the mean squared error to achieve a 95 percent coverage rate, while the 27
to their origin regions (e.g., World Bank 2017b; D’Souza et al. 2022; etc.).

The escalation of the conflict in 2015 also led to a dramatic worsening in living standards and precipitated a humanitarian crisis, which might also have forced displacement. There was an immediate and widespread loss of livelihoods (e.g., EFSNA 2017; Tandon 2019; etc.); transportation costs and the prices of key goods and services escalated (e.g., World Bank 2018; ACAPS 2020); and there was a breakdown of access to basic services such as health care, sanitation, and electricity (OCHA 2018). However, the regions that experienced the most violence were not the regions suffering from the worst of the humanitarian crisis, likely due in part to specific instances of violence having had impacts far beyond the location where they occurred and other conflict-related factors having had a larger impact on the humanitarian situation (e.g., Tandon and Vishwanath 2020).

Given all of these challenges, estimates of poor food access, which are the most reliable welfare indicators that have been measured since the conflict began, increased precipitously. The 2015 Integrated Phase Classification (IPC) estimated a 21 percent increase in the share of the population classified as IPC 3 (Crisis) or worse relative to 2014— only three months after the conflict escalated; and the Emergency Food Security and Nutrition Assessment (EFSNA) in November 2016 estimated that 65 percent of Yemenis had poor or borderline food consumption in 2016, a 57 percentage point increase relative to 2014 (EFSNA 2017; World Bank 2017). Importantly, there was an affordability crisis where households were not able to afford either food or basic services available for purchase (e.g., World Bank 2018; ACAPS 2020; Favari et al. 2021). Given the desperate humanitarian situation immediately following the escalation of the conflict, there was and continues to be an unprecedented scale-up of humanitarian and development assistance, on which much of the population relies (e.g., OCHA 2021).

Furthermore, numerous shocks—many a direct result of conflict—continue to worsen the already severe crisis. Shocks include intermittent air and sea blockades, natural disasters, widespread disease outbreaks, and currency crises (OCHA 2017; OCHA 2019; OCHA 2021). Importantly, the severity of the emergency has varied along with variation in the frequency and types of shocks that have occurred over time. Shocks that have 7
|**Scenario**|**World Total Hig**|**h Income**<br>**Table 6**|**US**<br>**: Impact on**|**EU**<br>**Welfare, Man**|**Low and**<br>**Middle**<br>**Income**<br> <br>**ufacturing Outp**|**China**<br>**ut, and Expo**|**India**<br> <br>**rts**|**Brazil**|**SSA**|
|---|---|---|---|---|---|---|---|---|---|
|||||**%Change i**|**n Welfare**|||||
|NBTA17|-0.5|-0.6|-0.6|-0.7|-0.3|-0.2|0.0|-0.1|-0.5|
|BTAFU|-1.0|-0.4|-0.4|-0.4|-2.4|-3.7|-1.4|-0.5|-1.2|
|BTADU|-0.6|-0.5|-0.5|-0.5|-0.8|-0.6|-0.3|-0.4|-1.0|
|BTADE|-0.5|-0.5|-0.6|-0.5|-0.5|-0.3|0.1|-0.3|-0.7|
|BTAFR|-0.6|-0.5|-0.6|-0.6|-0.8|-0.7|-0.3|-0.3|-0.9|
|BTADR|-0.5|-0.5|-0.6|-0.6|-0.5|-0.3|-0.1|-0.2|-0.7|
||||**%Change**|**inOutput of**|**Total Manufac**|**turing**||||
|NBTA17|-0.4|-0.8|-1.2|-0.8|0.1|-0.2|0.1|0.4|0.8|
|BTAFU|-0.8|0.8|-0.2|1.9|-3.0|-3.6|-3.3|1.5|-0.9|
|BTADU|-0.5|-0.6|-1.2|-0.3|-0.4|-0.5|-0.4|0.3|0.4|
|BTADE|-0.4|-0.3|-0.5|0.0|-0.6|-0.5|-0.5|0.0|-0.1|
|BTAFR|-0.3|-0.4|-1.0|-0.1|-0.2|-0.3|-0.3|0.2|0.1|
|BTADR|-0.4|-0.7|-1.2|-0.6|0.0|-0.2|0.0|0.1|0.5|
||||**%Change**|**in Exports of**|**Total Manufac**|**turing**||||
|NBTA17|-1.0|-1.8|-2.3|-2.1|-0.1|-0.9|-0.3|1.0|2.7|
|BTAFU|-12.9|-11.3|-10.1|-23.2|-14.8|-20.8|-16.0|1.9|-8.8|
|BTADU|-4.0|-4.8|-6.5|-6.6|-3.2|-3.4|-3.2|-2.5|-1.8|
|BTADE|-1.2|-0.5|0.0|0.5|-2.0|-1.8|-2.1|-0.6|-2.0|
|BTAFR|-3.2|-3.5|-3.7|-5.8|-2.9|-3.2|-3.0|-1.8|-3.6|
|BTADR|-1.9|-2.6|-3.4|-3.3|-1.2|-1.5|-1.3|-1.8|-0.1|

_Notes:_ **_NBTA17:_** _Industrial countries alone reduce emissions by 17% and take no trade policy action;_ **_BTAFU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in imports;_ **_BTADU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in domestic production;_ **_BTADE:_** _Industrial countries alone reduce emissions by 17% with import tariffs on all merchandise imports and rebates on all merchandise exports based on carbon content in domestic production;_ **_BTAFR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in imports;_ **_BTADR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in domestic production_ 29
prior to the baseline survey and 0 otherwise), _siblings_ (equals 1 if the student has siblings and 0 otherwise), _parents at home_ (equals 1 if both parents stayed at home between January 2011 to August 2011 and 0 otherwise), _parents did not finish junior high_ (equals 1 if neither parent finished junior high school and 0 otherwise), and _low-income_ (equals 1 if students are in the bottom 33% of the distribution of our family asset value variable and 0 otherwise). Importantly, we also control for baseline computer and math scores. Finally, we control for social, economic, and political differences in local context by adding a fixed effect term �� to indicate the prefecture where the student went to high school.<sup>11</sup>

# _2.3.2 Instrumental Variables_

For our second type of analysis, we conduct an instrumental variables (IV) analysis. We conduct the IV analysis because, in contrast to OLS, it can in theory produce causal estimates of the impact of vocational versus academic high school on student outcomes. In particular, whereas OLS fundamentally relies on the assumption of ignorability (that after controlling for observable pretreatment covariates, treatment assignment is independent of the outcome of interest), the IV analysis relies on two different assumptions (Murnane and Willett 2010). The first assumption is that of exogeneity: the IV should influence student outcomes only through the treatment variable (attending vocational versus academic high school) and not through any other channel. The second assumption is that the IV should be strongly correlated with the treatment 11 The estimates of the impact of attending vocational schools on dropout in tables 2 and 3 are from a linear probability (OLS) model. Since dropout is a binary outcome, as a robustness check, we also estimated the impact using a logit model. The results from the logit model are substantively identical to the OLS results and are available upon request.
Policy Research Working Paper 10541

# **Abstract**

Shocks and seasonality may have profound effects on poor households’ wellbeing, especially in contexts like the Sahel where livelihoods depend on rainfed agriculture and pastoralism. Understanding how seasonal variation affects Sahelian households is therefore essential for guiding policies that jointly seek to address chronic poverty, seasonality, and unexpected shocks. This paper uses harmonized household survey data from Burkina Faso, Chad, Mali, Niger, and Senegal, collected in two distinct waves in 2018 and 2019, to examine the extent of seasonal deprivation in the Sahel. These data reveal significant seasonal variation in poverty and wellbeing. Mean real monetary consumption is around 10.5 percent lower in the lean season. Moreover, rather than representing a reduction in dietary diversity, this drop is concentrated in staple foods (especially cereals), implying that seasonality brings about extreme forms of deprivation. Welfare losses may begin early in the lean season, even as early as April. When the data were collected in 2018/19, the climatic conditions were relatively benign and the security situation was more stable than today, so the effects of seasonality shown in this paper likely represent a lower bound. On policy, although initiatives currently focus on responding to unpredictable shocks, seasonal food insecurity could be better tackled by expanding social protection and providing regular transfers early in the lean season, when prices are lower and fewer households have succumbed to extreme deprivation. Seasonal variation happens every year and more can be done to support Sahelian households if there is information on how it will perennially threaten their wellbeing.

This paper is a product of the Social Protection and Jobs Global Practice and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at jlain@worldbank.org and sbrunelin@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
15 suggests that other factors offset the advantages associated with lower<sup>labor</sup> costs in developing countries. In an attempt to identify these factors, production information was collected on the following variables<sup>which other</sup> studies (Baldwin (1971), Helleiner (1976) and Hufbauer (1970) indicate<sup>have</sup> influenced the level and structure of trade:

- (1) Two Human Capital Variables (a). The share of the labor force accounted for by professional or technical personnel, and (b). average<sup>per</sup> capita wages in the industry. Average per capita wages is part of the<sup>NBER</sup> measure; the rest is non-wage value added that represents physical capital. The assumption is that most developing countries would not have a comparative advantage in sectors requiring high human capital inputs.

- (2) A Market Size Variable Developed market economy imports measured in 1985 US dollars. The purpose is to determine whether developing countries "target" items with larger markets, ceteris paribus, when establishing a production base for exports.

- (3) Capital Requirements The value of fixed plant and equipment per capita immediately employed in making the commodity. Developing countries comparative advantage should be inversely related to this variable.

- (4) A Product Differentiation Variable Measured by the coefficient of variation in unit values of the industry's goods destined for different countries. To determine if developing countries are less likely to have a comparative advantage in more differentiated products.

- (5) Consumer Orientation (Goods) Ratio Percentage of industry output (and imports) directly purchased by final consumers. Has developing countries' comparative advantage differed in consumer as opposed to producer goods?

- (6) Resource Based Production Variable UNIDO (1982) identified goods whose production location is normally based near raw material supplies. A dummy variable takes a value of one for these items or zero otherwise. Do natural resource requirements significantly influence patterns of developing countries' revealed comparative advantage?

- (7) A Product Cycle Variable The approximate date that the item was first traded internationally according to Hufbauer (1970). Is developing countries comparative advantage weighted towards older more established products?