rafmacalaba/datause-encoder-data huggingface dataset:
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE hydropower plant in the context of the West African Power Pool. It is expected to provide a good complement to the overall power mix.

The institutional structure of the power market between Benin and Togo imposes a significant financial burden on CEB. Because CEB is, by law, the producer of energy for both Benin and Togo, all power produced by SBEE and CEET (Togo’s power utility) must be bought by CEB, which then resells it to these utilities at a price lower than its purchase price. As of 2009, the average effective tariff charged by CEB was $0.11 per kilowatt-hour, against an average cost of $0.19. This situation has resulted in important losses to CEB, which has seen the losses associated with underpricing increase from $33 million in 2005 to $50 million in 2009 (table 12, see ―hidden costs from sales to Benin‖<sup>6</sup> ). Furthermore, SBEE’s liquidity problems have created arrears to CEB, which are slowly being paid by Benin’s government. These payments were as as high as $23 million in 2007, equivalent to almost 4 percent of 2007 Benin’s national budget (World Bank 2009a).

**Table 12. CEB’s hidden costs**

|**Year**|**Power**<br>**billings to**<br>**Benin and**<br>**Togo**|**Share of**<br>**Benin's**<br>**billings**|**System**<br>**losses***|**Average**<br>**cost**|**Average**<br>**effective**<br>**tariff**|**Hidden costs**<br>**from sales to**<br>**Benin**|**Hidden**<br>**costs as a**<br>**share of**<br>**revenue**|**Hidden**<br>**costs in**<br>**Benin as a**<br>**share of**<br>**CEB revenue**|
|---|---|---|---|---|---|---|---|---|
||(GWh/year)|(%)|(%)|($/kWh)|($/kWh)|(US$ million/ year)|<br>(%)|(%)|
|2005|1,274|40|7|0.16|0.10|33|67|27|
|2006|1,309|38|6|0.17|0.10|36|75|28|
|2007|1,294|39|3|0.17|0.11|31|58|23|
|2008|1,418|41|5|0.19|0.11|44|66|27|
|2009|1,538|50|5|0.18|0.12|50|55|27|

_Source_ : Briceno-Garmendia, Smits, and Foster 2009; World Bank 2009a, SBEE annual report. * Refers only to distributional losses.

The high cost of production, mainly thermal, compounds the financial problems of SBEE and CEB. At $0.19 and $0.20 per kilowatt-hour, the average costs of power production by CEB and SBEE are among the highest in Africa (figure 15). In both utilities, the high costs of power production are driven by expensive oil-based generation. SBEE incurs costs of between $0.20 and $0.23 per kilowatt-hour when using rented generation infrastructure. However, the way forward looks more optimistic, as prices of inputs are expected to drop between 30 to 60 percent when Nigerian gas becomes available through the West Africa pipeline to run the turbines.

> 6 When the mispricing of services, the distributional losses and collection inefficiencies are expressed as percentage of the utility’s revenues one is able to calculate how much of the revenue is lost due to these inefficiencies

29
**Table 4.6.1a: Business services which are most critical for firms’ ability to innovate**

||Percentage of responses|
|---|---|
|R&D/design/ technology|30.8|
|Marketing/PR/communications|24.4|
|IP rights/legal|7.7|
|Consulting|7.7|
|Financial/ accounting/funding|6.4|
|Logistics|6.4|
|ICT|5.1|
|HR/mindsets|3.8|
|Education/RICs|2.6|
|Industry specific|2.6|
|Business innovation|1.3|
|Contract manufacturing|1.3|

_Note_ : Includes multiple responses

**Table 4.6.1b: Business services which are most critical for firms’ ability to innovate**

||**Availability**|**Quality**|
|---|---|---|
|R&D/design/ technology|2.8|3.4|
|Marketing/PR/communications|2.9|3.3|
|IP rights/legal|2.7|3.0|
|Consulting|2.8|2.8|
|Financial/ accounting/funding|2.8|3.0|
|Logistics|4.8|4.6|
|ICT|4.3|3.8|
|HR/mindsets|2.3|4.0*|
|Education/RICs|3.0|3.5|
|Industry specific|2.5|2.5|

Note: Availability rated on a scale of 1(very difficult) to 5 (very easy) Quality rated on a scale of 1(very bad) to 5 (very good) 48
This analysis has examined the relationship between the stock of emigrants a country has and its current passport cost. The advantage of the emigrant stock measure is that it is based on national census and household survey data in many countries around the world. It therefore captures most of the world’s legal and illegal migration. The disadvantage of this measure is that if passport costs change at different rates across countries over time, then today’s passport prices may not be reflective of the relative prices prevailing at the time of emigration. Detailed migration flow data is even rarer than migration stock data<sup>10</sup> , and so we restrict our analysis to legal emigration flow into the four traditional settlement countries of the United States, Canada, Australia and New Zealand.<sup>11</sup> The total migration flow into these four countries in 2004 is then expressed as a percentage of the sending countries 2004 population. The correlation between the emigrant stock of a country and the flow into these four countries for our sample is 0.833.

Table 5 then repeats Table 4, using emigrant flow rather than emigrant stock as the dependent variable. The median flow into the four traditional settlement countries in 2004 was only 0.02% of the sending country population. Passport costs as a share of per capita annual income are found to be significantly related to emigration flows, in a nonlinear fashion. The size of the estimated effect is quite large: among countries with passport costs less than 20% of per capita annual income, dropping from the 75<sup>th</sup> percentile of passport costs (2.5% of income) to the median (0.77% of income) is associated with a 0.024% increase, or doubling, of the emigration flow.

> 10 Many countries do not break down country of origin beyond the top ten or so sending countries.

> 11 Migration flow data collected from the Office of Immigration Statistics, Department of Homeland Security in the United States; Citizenship and Immigration Canada in Canada; the Department of Immigration and Multicultural and Indigenous Affairs in Australia; and the New Zealand Immigration Service in New Zealand.

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**Table 2: Discrete choice models (Probit) for satisfaction with health services**

||Full sample||Poor distri|cts|Rich distri|cts|
|---|---|---|---|---|---|---|
|VARIABLES|(1)|(2)|(1)|(2)|(1)|(2)|
|**Dimensions of facility qual**|**ity**||||||
|Index for coverage|-0.006|-0.006|-0.084*|-0.067|0.012|0.010|
|Index for support|0.059***|0.060***|0.081*|0.078*|0.052**|0.055***|
|Index for quality of inputs|0.058***|0.064***|0.071*|0.110***|0.024|0.020|
|Index for infrastructure|0.007|0.001|0.001|-0.010|0.022|0.018|
|Level of discrimination|-0.153|-0.176|-0.363|-0.302|-0.074|-0.086|
|Speed of service|0.223***|0.202***|0.209*|0.105|0.239***|0.232***|
|Waiting time|-0.003***|-0.003***|-0.003|-0.003|-0.004***|-0.003***|
|**Institutional and governanc**|**e environme**|**nt**|||||
|Provider responsiveness|0.307***|0.325***|0.414***|0.454***|0.186|0.224*|
|Index for participation|0.073***|0.073***|0.139***|0.142***|0.033|0.030|
|**Components of expectation**|||||||
|Age of the household head|0.006**||0.006||0.005||
|Gender: female|0.185***||0.233*||0.099||
|Level of education:<br>Primary|-0.053||-0.244||-0.079||
|Junior High|-0.287**||-0.676***||-0.181||
|Higher education|-0.222*||-0.143||-0.304*||
|Religion: Catholic|-0.212*||-0.200||-0.303||
|Christian|-0.079||-0.441**||0.062||
|Other|0.211||-0.016||0.178||
|Log per capita monthly<br>exp.|0.032||-0.040||0.066||
|Elite/Association with<br>elites|-0.153*||-0.326**||-0.059||
|Index for sources and info|-0.036||-0.138***||0.001||
|Index for expectation||-0.089***||-0.150***||-0.055**|
|**Location dummies**|||||||
|Urban area|0.279***|0.354***|0.270|0.267|0.261***|0.303***|
|Region: Kalimantan|-0.013|-0.041|-0.351|-0.299|-0.002|-0.081|
|NTT|0.212|0.075|-0.024|-0.079|0.274|0.205|
|Sulawesi|0.093|0.024|-0.194|-0.161|0.170|0.124|
|Sumatra|-0.063|-0.081|-0.386|-0.264|-0.073|-0.136|
|Constant|-0.792|-0.234**|0.453|-0.071|-1.108|-0.191|
|Observations<br>Note: *** 1% level of signific|1786<br>ance, ** 5% le|1786<br>vel of signific|682<br>ance, * 10% l|682<br>evel of signif|1104<br>icance|1104|

38
sample but, as discussed above, this is simply driven by the fact that 60 of the 62 firms in manufacturing operate in the Garment and Leather sector, which is the sector with the highest propensity to export in the region. In fact, among all sectors, Garments represent the largest share of exports in Cambodia but also in Malaysia, Philippines and Vietnam. In China and Republic of Korea, the highest propensity to export is in Textiles, in Indonesia it is Wood and Paper Products, while in Thailand it is in Food Products and Agro-Industry.

Similarly, table 3 reports the share of firms with at least 10 percent of foreign capital across countries and sectors. As expected, there is a higher share of firms that export than ones reporting having some foreign capital. The findings in table 3 are similar to the ones reported for exports in table 2. Cambodia and Malaysia lead the penetration of FDI with 82 percent and 32 percent of the firms in the sample reporting at least 10 percent of foreign capital, respectively. Unlike the case for exports, Electronics is the sector in the region with the highest FDI penetration (39 per cent of the firms). More than 60 percent of the firms in this sector in Indonesia, Malaysia, Philippines and Thailand report at least 10 percent of FDI penetration. A closer examination also shows that this is the sector where the share of foreign capital is highest in the region reaching 30.8 percent of total capital. This contrasts with the Food and Agro Industry sector where foreign penetration is the lowest in the region reaching 6 percent of total capital (these shares are not reported in table 3 but available on request).

Finally, table 4 reports the mean share of firms adopting new technology across countries and manufacturing sectors. We define that a firm has adopted new technology when it reports having “introduced a new technology that substantially changed the way the main product was produced in the three years prior to the survey” ( _Enterprise Surveys_ ). On average, 52 percent of the firms in the sample report having adopted a new technology that has significantly changed the way the main product is produced. Technological innovation was greater for Cambodia and Thailand but again the findings for Cambodia are driven by the selected set of industries included in our sample. Electronics and Metals and Machinery are the sectors with the greatest technological innovation according to this definition.

13
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# Table 2.1: 1986 COST COWPONENTS OF RETAIL CHARODAL PRICES IN KHARTOUM, SUDAN FOR CHARCOAL PRODUCED IN THE BLUE NILE PROVINCE

|---------UnitU|S S-----------||
|---|---|---|
|Line ItemCost<br>CostItems(Including<br>Profit)<br>Per Sacka/|Cumulative<br>Per Sack|Total<br>PerTonne|
|Marketing Cost In Khartoum<br>0.79|3.50|100.00|
|Transportanddistribution<br>costs (400km oneway)<br>C,70|2,71|77.50|
|Charcoal depot costs Including transport<br>to|||
|depot (50 km bush roads)<br>0.30|2.01|57.50|
|Charcoal production costs (including<br>felling)<br>0.87|1.71|49.00|
|Residual price for charcoal wood<br>0.84|0.84|24.00|
|ResidualStumpagePrice|||
|Cost per tonne of wood used for production b/<br>4.80|||
|Cost of wood per**m3**used for production b/<br>4.00|||
|Cost per**m3**standing (residual<br>stumpage fae)_cl_<br>3.20|||

- _8i/_ Average weight of sock In Khartoum market assumed to be 35 kg, 80% (28 kg) of which Is lump charcoal.

- b/ On average five tonnes of air dry wood (15% m.c.d.b.) required to produce one tonne of fully carbonized charcoal sold In the market place. Actual production, accounting for losses, would be 1.2 tonne per fIve tonne of air dry wood. Also 1.2 _m3_ a 1 tonne air dry wood; therefore six cubic metors are used per tonne of charcoal sold.

- _cl_ Assu-se that 20% of each selected tree Is not suitable for charcoal productlon. Also because not all species, and not all sizes of trees are desirable for charcoal production the actual stumpage price for the average tree Irrespective of species or size may be between 52.1 and 53.2 per m3 standing.

Source: Sudan Forestry Sector Review: Eastern and Southern Africa Region 1986.

2.5 The charcoal production process has been studied in detail in Sudan. There, charcoalers range from casual producers, who generally use inefficient techliques, to large-scale, efficient producers who run wellorganized 'bush camps' for four to six months at a time. In the camps, most of the charcoal is first produced in small kilns, using unseasoned wood, in a quick-production process. As the season progresses, the wood raw material is dried properly, larger kilns are built, and the production cycle takes longer. All these steps increase the efficiency of production. Because efficiency improves as the season progresses,
|**Dimensions**<br>**of Food**<br>**Security**|**Food Security Indicator**|**Description**|
|---|---|---|
||Household Food Insecurity Access<br>Scale (HFIAS)|Subjective indicator based on perceptions and experience. The measure<br>is based on statements such as: (1) You were worried you would run<br>out of food because of lack of money or other resources; (2) You were<br>unable to eat healthy and nutritious food because of lack of money or<br>other resources; (3) You ate only a few kinds of food because of lack of<br>money or other resources; (4) You had to skip a meal because there<br>was not enough money or other resources to get food; (5) You ate less<br>that you thought you should because of lack of money or other<br>resources; (6) Your household ran out of food because of lack of money<br>or other resources; (7) You were hungry but did not eat because of lack<br>of money or other resources; (8) You went without eating for a whole<br>day because of lack of money or other resources. Later modified into<br>the Household Hunger Scale (HHS)|
||Child Dietary Diversity Score (CDDS)|fl h f fd d b h h d hld d|
||Mother’s Dietary Diversity<br>Score(MDDS)|Reects te variety o oo consume y te moter an ci an is<br>based on individual level intake information|

# **2.1.2 Available Indicators for Food**

Table 3 lists the various indicators of food security each capturing different dimensions of the ideal food security measure and the indicators that can be constructed from the surveys (mainly DHS) in the countries studied.

**Table 3: Components of Food Security Measured**

|**Ideal Indicators**|**AVAILABLE**|
|---|---|
|Children's DietaryDiversityScore(CDDS)|Yes|
|Mom’s DietaryDiversityScore(MDDS)|No|
|Minimum Acceptable Diet(for children 6‐24 months)|Yes|
|Food Insecurity Access Scale (HFIAS)|No (Only in Helen<br>Keller)|
|Food Consumption Score(FCS)|No|
|Relativeprices of different foodgroups|No|
|**PROXIES IF IDEAL INDICATORS ARE NOT AVAILABLE**||
|Household DietaryDiversityScore(for child/mom)|Helen Keller|
|Starchy Staple Ratio or the Fraction of household Calories<br>Derived from Starchy Staples|No (since surveys<br>in this study do<br>not contain<br>detailed<br>household<br>consumption<br>module)|

12
boundary,” which governs their (lack of) access to public spaces alone as well as their social interactions with individuals outside the household, similar to the Pakistani context examined in Cheema et al. (2019). Our BAF voucher intervention addresses these mobility barriers not by changing the social norm (although we observe a small but significant increase in women’s ability to visit clinics alone), but by enabling women to “bypass” these normative conditions by visiting clinics with their female peers, who may be less likely to constrain their choices than husbands or mothers-in-law.

Finally, prior research based on collective models of the household (Chiappori and Mazzocco, 2017) has mainly focused on spousal interactions, particularly when examining fertility and FP decisions (e.g., Ashraf et al. (2014); McCarthy (2019)). However, a growing body of work in economics (Anukriti et al., 2020; Gupta et al., 2021; Khanna et al., 2015; McKelway, 2020) and a large literature in other disciplines has emphasized the importance of other household members, such as the mother-in-law, in household decision-making (Gram et al., 2018; Rassam, 1980). Our study is unique in showing that vouchers and peer connections can be leveraged to improve women’s reproductive autonomy by overcoming the constraints imposed by mothers-in-law in patrilocal societies.

# **2 Experimental Design and Data**

In this section, we describe our study sample, the experimental design, the interventions that were implemented as part of the randomized experiment, the data, and the landscape of FP service provision in our study area.

## **2.1 Sample**

Our study is based in 28 villages of Jaunpur district in the state of Uttar Pradesh (Figure A.1). According to the 2015-16 Demographic and Health Survey (DHS) of India, the total fertility rate in rural Uttar Pradesh (3.8) is high relative to other states; 20 percent of women have an unmet need for FP;<sup>14</sup> and the share of demand satisfied by modern methods is only 47 percent. Therefore, our intervention is highly relevant for this context.

Figure A.2 describes our sample selection process. We began by conducting a listing exercise for all households (N = 2,781) that were located within a 10-kilometer radius of our partner clinic, the Arogyaneer Diagnostic Clinic (ADC), situated in the village of Chandwak. The ADC was chosen based on its proximity to our sample women (many of whom lived in Chandwak), more reliable supply of a wide range of FP methods, and higher quality of service, relative to other clinics in the area. A total of 698 households were identified to have

> 14These statistics from the 2015-16 DHS calculate unmet need for FP as the proportion of women who: (1) are not pregnant and not postpartum amenorrhoeic, are considered fecund, and want to either postpone their next birth for two or more years or stop childbearing altogether, but are not using a contraceptive method; (2) have a mistimed or unwanted current pregnancy; or (3) are postpartum amenorrhoeic and their last birth in the last two years was mistimed or unwanted (NFHS, 2016).

5
**-** **<u>Table13: Generalized Entropy Indices Inequality Indicators</u>**

||<br>**Inequalit**|<br>**y Indicat**|<br>**ors**|<br>**Variation **|<br>**from Base**|**Difference**|
|---|---|---|---|---|---|---|
||**Base**|**Sim1**|**Sim2**|**Sim1**|**Sim2**|**Sim2-Sim1**|
|**1. National Level**|||||||
|General Entropy (0)|1.22|1.19|1.19|-0.02|-0.02|0.00|
|General Entropy (1)|1.20|1.18|1.19|-0.02|-0.02|0.01|
|Gini Coefficient|0.72|0.71|0.72|-0.4|-0.4|0.1|
|**2. Regional Decomposition**<br>|||||||
|**Urban**|||||||
|General Entropy (0)|1.02|1.00|1.00|-0.02|-0.02|0.00|
|General Entropy (1)|1.01|0.99|1.00|-0.02|-0.02|0.01|
|Gini Coefficient|0.67|0.67|0.67|-0.5|-0.5|0.3|
|**Rural**|||||||
|General Entropy (0)|1.13|1.12|1.11|-0.01|-0.01|-0.01|
|General Entropy (1)|1.46|1.43|1.43|-0.02|-0.02|0.00|
|Gini Coefficient|0.71|0.71|0.70|-0.5|-0.5|-0.2|
|**3. Population Deciles (Gini Co**|**efficient)**||||||
|Poorest|0.55|0.57|0.57|2.0|2.0|0.0|
|2|0.52|0.55|0.55|3.2|3.2|0.0|
|3|0.52|0.53|0.53|1.5|1.5|0.0|
|4|0.43|0.45|0.45|1.8|1.8|0.0|
|5|0.37|0.39|0.39|2.0|2.0|0.0|
|6|0.39|0.39|0.39|0.4|0.4|0.0|
|7|0.40|0.40|0.40|0.3|0.3|0.0|
|8|0.34|0.35|0.35|0.8|0.8|0.0|
|9|0.32|0.32|0.32|-0.1|-0.1|0.0|
|Richest|0.44|0.44|0.44|-0.2|-0.2|0.0|
|**4. Level of Education of head**<br>|**of the ho**|**usehold (**|**Gini Coeff**|**icient)**|||
|No Schooling|0.62|0.62|0.61|0.00|-0.4|-0.4|
|Grade 0-6|0.61|0.61|0.60|-0.01|-0.6|-0.4|
|Grade 7-9|0.62|0.61|0.60|-0.01|-0.8|-0.2|
|Grade 10-12|0.60|0.59|0.60|-0.01|-0.7|0.3|
|NTC Level|0.50|0.46|0.47|-0.03|-3.3|0.5|
|Degree andPost Graduate|0.53|0.53|0.53|0.00|-0.4|0.3|

Notes: Simulation 1: 10 percent wage subsidy given to employers for low- and semi-skill in formal sector. Simulation 2: Adding social security tax to simulation 1.

In addition to overall poverty, rural poverty also decreases about 1.5 percent from 72.1 percent to 70.6 percent; urban poverty declines 1.6 percent. Poorer households gain more than richer households. The decomposition of headcount poverty ratio by population deciles shows that poorer households on average gain more than richer households (see Table 12). For instance, the poverty rate falls on average by more than 2 percentage points for lower deciles of

- 24 -
explicit development programs – using Brazilian data to implement the initial stages of the analysis, and plotting a course for continued research.

# **_Regional Disparities in Brazil_**

Brazil has a long history of regional disparities. The Brazilian Northeast has historically been the poorest region in the country, with regional per capita incomes being about one half of those in the prosperous Southeast (Lall and Shalizi, 2003). Figure 1 describes the 25<sup>th</sup> , 50<sup>th</sup> , and 75<sup>th</sup> percentiles of the Brazilian monthly income distribution by state based on 1991 demographic census data. The highest median per capita household income ($79.93 per month in Goias) is nearly four times larger than that of the lowest median per capita household income state ($20.49 per month in Maranhao). Eight states report median per capita household incomes of less than $1 per day, seven of which are located in the country’s Northeast region. Of the ten poorest states in the country, eight are in the Northeast, and two in the North region (Azzoni et. al, 2002). These large per capita income differences across regions are, moreover, surprisingly stable over very long periods. Per capita income in the Southeast was 2.9 times that of the Northeast in 1939 and 2.8 times in 1992 (World Bank, 1998).

Beyond purely economic indicators, social indicators in the Northeast region are also considerably worse than the national average. The illiteracy rate is at least three times higher than that of São Paulo, the child mortality rate is twice as large as that of the Southeast (54.5 per thousand in the Northeast compared to 26.3 per thousand in the Southeast), life expectancy four years shorter, and income inequality is much worse (Ferreira, 2004). Inequality, measured by the Theil coefficient is 0.80 for Ceará, Bahia and 3
# **4. Estimation results**

# **4.1. Main results**

Tables 2–6 report the main estimation results for the predicted poverty rates in each of the experimental survey treatment arms, using either the NPS 2020/21 round (in Tables 2–4) for across-year predictions or the TA 1 round (in Tables 5–6) as the base survey round for within-year predictions.

Model 1 is the most parsimonious model and consists of household size, household heads’ age and gender, household heads’ highest completed levels of schooling (as binary indicators), the shares of household members in the age ranges 0-14, 15-24, and 60 years old and older (with the reference group being those 25-59 years old), binary indicators indicating whether the head worked in the past 7 days or was self-employed, and a binary variable indicating urban residence.

Model 2 adds household asset variables and house (dwelling) characteristics to Model 1. Household assets include variables indicating whether the household has a motor vehicle, bicycle, mobile phone, video/DVD player, television set, computer, refrigerator/freezer, air conditioner/fan, radio, and mosquito net. Additional controls include the number of rooms in the house, construction materials used for the house’s roof, wall and floor, and access to drinking water from a pipe/truck, and a flush/VIP toilet.

Models 3-8 additionally control for one observed expenditure category, respectively: total food consumption, total non-food consumption, furnishing and other expenses, health expenditures, education expenditures, and utility expenditures. Finally, Model 9 controls for utility expenditures, as in Model 8, but does not control for household assets and dwelling characteristics. The list of 17