rafmacalaba/datause-encoder-data huggingface dataset:
surveys (averaging over the various surveys when is there are more than one) and as reported in the World Bank's World Development Indicators (WDI) (World Bank, 1999) database which is based on UNESCO data (averaging over all available data between 1990 and 1999).'3 Overall the two data sources tell a similar story. The primary net enrollment rate for girls averaged across all the countries is very similar from the two sources: 58.6 percent based on the DHS surveys and 58.3 based on the WDI statistics (when restricting the sample to countries that have number from both surveys - the average over all DHS surveys is 63.4 percent). The average male/female ratio is similar when using the two sources as well (1.14 from DHS and 1.22 from WDI). Other characteristics of the distribution (standard deviation, maximum, minimum and median) are very similar as well.

The overall similarity, however, masks some large discrepancies at the country level. The difference between the enrollment rate based on the two sources ranges from -22 percentage points (Turkey where the DHS implies a rate of 71 percent and the WDI 93 percent) to 47 percentage points (Haiti where the DHS implies a rate of 70 percent and the WDI 23 percent). After Haiti, the next largest discrepancy is 19 percentage points (Mozambique where the DHS implies a rate of 54 percent and the WDI a rate of 35 percent).

In most countries, the two datasets tell a sirnilar story with respect to gender differences as well. The main difference occurs in the Western and Central African countries where (except for Cote d'Ivoire and Senegal) the WDI numbers imply a male/female ratio that is substantially larger than what the DHS show. For example, in Chad the WDI imply a male/female ratio of 1.80 whereas.the DHS imply a ratio of 1.53. Relying on the WDI one would overstate the male "advantage" by almost 30 percentage points. Outside of this region, the data for Bangladesh,

> **13** The primary net enrollment rate is defined as the percentage of children of primary school age who are indeed in primary school. Unlike the 6 to 11 years cutofl used in Table 3 the definition of primary school age varies across countries. In 8 of the 41 countries the range in 6 to 11, in another 8 it is 7 to 12, in another 5 it is 6 to 10, in another 4 it is 6 to 12, and another 4 it is 7 to 13. The rest are somewhere around a similar range.

13
# **Figure 12. Marginal Contributions to Child Poverty (AROP), 2020**

<!-- Start of picture text --> Direct Transfer: Family Other (means-tested) 5.4<br>Direct Transfer: Monthly child allowance (means-tested) 2.5<br>Direct Transfer: Pregnancy & childbirth benefit 2.2<br>Indirect subsidies: Electricity direct effects 2.1<br>Other Family Contributory transfers 1.7<br>Direct transfers: disability 1.5<br>Direct transfers: unemployment 1.4<br>Direct Transfer: Benefit for bring up child up of 2 1.2<br>Indirect subsidies: Electricity indirect effects 0.9<br>Direct Transfer: Family Other (non means-tested) 0.8<br>Indirect subsidies: Gas indirect effects 0.3<br>Direct transfers: other 0.2<br>Direct Transfer: Family Birth child grant (non means-... 0.2<br>Direct transfer: Heating allowance 0.1<br>Direct Transfer: School children enrolled in 1st grade... 0.1<br>Indirect subsidies: Gas direct effects 0.1<br>Direct transfers: non-contributory pensions 0.0<br>Direct transfer: Social Monthly Assistance GMI 0.0<br>Direct Transfer: Family child benefit for twins (non... 0.0<br>Personal Income Tax - interest 0.0<br>Direct Transfer: Benefit for raising a child under 1... 0.0<br>Direct Transfer: Family mothers in tertiary education... 0.0<br>Indirect taxes: Excise Alcoholic Beverages -0.1<br>Indirect taxes: Oil derivates direct effects -0.5<br>Indirect taxes: Oil derivates indirect effects -1.2<br>Indirect taxes: Excise Tobacco -1.2<br>Personal Income Tax -3.1<br>Indirect taxes: VAT -6.7<br>-8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 6.0<br><!-- End of picture text --> _Source:_ Authors’ estimates based on 2021 EU-SILC, 2021 HBS and CEQ Methodology.

_Note:_ MT and NM refer to non-contributory means-tested and non-means-tested programs.

How does the overall fiscal system impact poverty among households with different socioeconomic profiles?

**Fiscal policy favors families with children: households with children display notably higher pre-fiscal income poverty rates than those without; however, after fiscal policy measures, households lacking children see a pronounced upswing in child poverty rates, ultimately resulting in elevated poverty levels in contrast to households with children (figure 13, panel a).** Fiscal support is relatively limited for childless households compared to those with children, partially due to a lack of family and child social protection benefits, along with other factors. The elevated VAT payments and fewer electricity subsidies for households without children contribute to their heightened poverty rates, as the reduced VAT rate on baby food, books, and baby-related items results in lower VAT payments for households with children. Additionally, electricity subsidies fail to offset the increased VAT payments for childless households fully **.**

35
# Figure 6 Minimum Wages in Sahel Countries (at constant 1980 prices)

<!-- Start of picture text --> CFA Francs per day<br>160 -<br>140 -<br>120 Senegal (-0.84%<br>100 | Niger (3.21%)<br>80<br>Burkina (-0.65%) '<br>60<br>40<br>Mali (-1.31%)<br>20 -<br>0-* l l l I l l l l l l li l li ll ii 7<br>1960 1965 1970 1975 1980 1985 1990 1995<br><!-- End of picture text -->

Annual growth rates over 1977-1992 in parentheses 36
14 Figure 4: Change in Vulnerability between 2009 and 2011 by Oil Trade Status <!-- Start of picture text --> 70 66 50<br>45<br>Net importers 45 Net exporters<br>60<br>40<br>50 35<br>30<br>40<br>25 23<br>20<br>30 24 20<br>20 15<br>10 8<br>5<br>10 3 5 2 1 5<br>0 0<br>Below 0 0–2.5 2.5–5 5–7.5 7.5–10 Above 10 Below -10 -10 to -5 -5 to 0 0 to 5 Above 5%<br>Vulnerability as % of GDP Vulnerability as % of GDP<br>% of countries<br><!-- End of picture text --> _Source_ : Author’s calculations based on data from EIA (2013b).

# **4. Changes in End-User Oil Product Prices**

This study collected information on retail prices of gasoline, diesel, kerosene, and LPG in January 2012, July 2012, and January 2013, and calculated the degree of pass-through to consumers of world price increases since January 2009. Because prices at different stages in the supply chain are not available for most developing countries, a simplified methodology, described in the next paragraph, is used, yielding meaningful results only if the price difference between the two time periods is large. January 2009 was selected as the initial period for two reasons: first, the oil product prices in January 2009 were as low as in the latter half of 2004, and second, intervals of three to four years were deemed long enough to provide sufficient time to adjust to world price movements.

The pass-through coefficient for each fuel is based on the ratio of the difference in domestic retail prices to the difference in free-on-board (FOB) prices of the same fuel in one of the four major refining centers relevant to the domestic market. As in Kojima (2013), this paper makes one adjustment to the methodology used in Kojima (2012): FOB prices in the denominator are taken from one month earlier to account for the findings in Meyler (2009). That detailed study of the transmission of world oil price increases in major 12 EU countries found that the increases were passed on to consumers within three to five weeks, or about a month. These countries can be safely assumed to be passing on the changes in world oil prices to their domestic markets in full. This paper therefore takes FOB benchmark prices from the previous month. As an illustration, the coefficients between January 2009 and January 2013 are calculated by taking the following ratio: (Retail fuel priceJan 2013 – Retail fuel priceJan 2009 ) (Benchmark FOB fuel priceDec 2012 – Benchmark FOB fuel priceDec 2008) .

It is important to recognize the limitations of this simplified approach, as discussed at some length in Kojima (2012, 7–8), and not to regard a pass-through coefficient of 1.0 as the threshold level below which a market failed to pass through the increase in world oil prices; because of various factors, a fully deregulated and competitive market may pass through less than or more than 100 percent of the increase in the benchmark FOB prices. However, a pass-
- 17 -

School Organizational and Ouality Factors 24. Our first hypothesis was that differences in the management, organization or quality of the schools accounted for the differences observed. Four school organization and quality variables were examined: the number of hours of mathematics or Portuguese instruction offered, the average teacher's salary, and average class size. Table 7 presents the results of these analyses.

Table 7: School organization and quality effects on secondary achievement and social class differentiation, Brazil 1988

||Mathem|atics|Portug|uese|
|---|---|---|---|---|
|Independent<br>variable|Coeff.|t-stat|Coeff.|t-stat|
|Fixed|||||
|Female|-1.51***|<br>-7.24|0.57**|2.99|
|Age|-0.37***|<br>-5.90|-0.48***|-8.24|
|Working hours|-0.03***|<br>-4.64|-0.02**|<br>-3.58|
|Mean achievement|||||
|Intercept|2.73|1.01|12.52***|<br>8.14|
|Average SES|5.43***|<br>5.66|3.79***|<br>7.93|
|Federal technical|8.87***|<br>4.73|3.34*|2.66|
|Private|1.66|1.08|-||
|Organizational<br>complexit|y 0.72|1.53|0.63*|2.54|
|Class size|0.15*|2.57|0.05|1.52|
|Hours of math/Port.|2.21**|3.73|0.72|1.91|
|Teacher salary|0.05|0.69|0.05|0.88|
|SES achievement gap|||||
|Base|0.08|0.08|0.23|0.29|
|Federal Technical|-0.10|-0.15|-|-|
|SENAI|-1.14|-1.49|-|-|
|Organizational<br>complexit|y 0.15|0.84|- 0.09|-0.63|
|Teacher salary|0.05|1.69|0.01|0.41|
|Class size|-0.01|0.47|- 0.18|-0.91|
|Hours of math/Port.|-0.11|-0.40|0.01|0.46|

* p < .05, ** p < .01, *** p < .001

25. Average achievement. For mathematics, two school organization and quality factors were associated with higher achievement: class size and the
Portuguese as PNAD. These two surveys underpin most empirical work on poverty and inequality in Brazil.

The main features of our “pseudo-samples” are:

- a) Using the same selected enumeration areas as from POF 2002/2003 scheme<sup>5</sup> , IBGE selected 20 different samples with approximately 13 households per enumeration area, generating samples of 2,800 households on average.  The samples here comprise about 240 enumeration areas within 151 municipalities;

- b) Based on the POF sample design, IBGE selected one “pseudo-sample” from the beginning to the end.  In this case, new enumeration areas were first selected, and then 13 households were selected from each enumeration areas.

- c) Using the enumeration areas that had been selected for the PNAD 2005<sup>6</sup> survey, IBGE selected a further 20 “pseudo-samples”. The PNAD sample comprises 123 municipalities and 779 enumeration areas. On average, 16 households per enumeration area were randomly selected generating samples of around 12 thousand households.

# **IV. Descriptive Statistics**

# **1. Why Minas Gerais?**

The ELL method assumes that the coefficients β estimated from model (1) at the level of region _R_ , should be the same for each small area _A_ within _R_ . Tarozzi and Deaton (2007) suggest that this assumption is unappealing when the region is characterized by much heterogeneity.

> 5 One feature of the POF sample is that unlike standard techniques of sampling, IBGE stratified the state according to a combination of geographic stratum and a socio-economic status of the household head instead of only geographic stratum. Then, using systematic sampling proportional to size of each enumeration area, different enumeration areas were selected. Finally, random sampling without replacement in each enumeration area was applied to select on average 13 households per enumeration area.

> 6 Unlike the POF, the PNAD sample is only based on geographic stratum, i.e., it is geographically stratified. The primary sampling units (PSU) are the municipalities, which are stratified by size (population), and selected proportional to population size. In the second stage, the enumeration areas are also selected proportional to population size (number of households). Then, a simple systematic and representative sample of households is drawn in the third stage.  Note however, that the sample is not representative at the enumeration area level or at the PSU level other than those corresponding to metropolitan regions.

8
Table 5.1: Main results of aid and government efficiency simulations

||2004|Base||MDG sce|narios||
|---|---|---|---|---|---|---|
|||scenario|aid-hd|aid-infra|eff-hd|eff-infra|
|Macroeconomic aggregate|Bn. YR(1)|_Percent ave_|_rage annu_|_algrowth ra_|_te, 2010-1_|_5_|
|Consumption -private|1,691|4.3|5.5|5.3|4.4|4.3|
|Consumption -government|326|6.0|11.1|6.4|6.4|3.5|
|Fixed investment -private|274|4.6|6.3|6.0|4.7|4.6|
|Fixed investment -government|263|7.5|11.3|19.1|6.7|10.8|
|Exports|931|2.2|-0.3|1.3|2.0|2.7|
|Imports|909|3.7|6.2|7.2|3.6|4.1|
|GDP at factor cost|2,635|5.2|5.7|6.1|5.2|5.3|
|Total factor employment(index)|100|2.9|3.7|4.2|2.9|3.0|
|Total factorproductivity (index)|100|2.3|1.9|1.9|2.3|2.3|
|Real exchange rate(index)||2.2|-0.1|0.7|2.2|2.5|
|Government consumption|Bn. YR|_Percent ave_|_rage annu_|_algrowth ra_|_te, 2010-1_|_5_|
|Water and sanitation|0.1|5.2|22.8|5.2|15.7|5.2|
|Other infrastructure|0.2|5.6|5.6|18.3|5.6|11.1|
|Health|19.6|5.4|23.0|5.4|15.9|5.4|
|Basic education(grd1-grd6)|55.8|4.7|22.3|6.2|15.3|5.3|
|Basic education(grd7-grd9)|16.0|6.9|9.7|7.8|8.2|7.2|
|Secondaryeducation|19.5|7.0|7.5|7.7|7.1|7.3|
|Tertiaryeducation|18.0|7.0|8.1|7.4|7.3|7.0|
|Othergovernment|196.3|6.2|6.2|6.2|1.7|1.7|
|Total|325.4|6.0|11.1|6.4|6.4|3.5|
|MDG indicator(2)|Value|_Value in 20_|_15_||||
|MDG 1: Povertyrate|34.8|34.1|31.3|31.4|33.9|33.9|
|MDG 2: Net enrolment rate in basic education|16.8|55.2|88.7|70.4|78.9|62.4|
|MDG 4: Under-five mortalityrate|93.4|64.6|52.1|59.3|57.8|63.2|
|MDG 7w: Access to safe water|43.9|48.2|49.0|49.3|48.4|48.5|
|MDG 7s: Access to improved sanitation|15.9|24.1|25.5|26.9|24.4|24.9|

(1) YR = Yemeni rials.

(2) Units: percent for MDGs 1, 2, 7w, and 7s; per 1,000 for MDG 4. Source: Authors’ estimates based on results from simulations with MAMS and the microsimulation model.

# _Aid scenarios_

In the scenarios aid-hd and aid-infra, foreign transfers are increased to around 10 percent of GDP in 2011-2015. In the aid-infra scenario, GDP growth gains 0.9 percentage points and is accompanied by expansion, not only in government demands, but also in private consumption and private investment as additional infrastructure permit private incomes and savings to grow more rapidly with a positive feedback into the growth process (see Table 5.1). Moreover, an increase in the infrastructure capital stock raises total factor productivity. The appreciation of the real exchange rate strengthens this process by 33
# 4. Empirical Strategy and Data

## 4.1 _Identification of Sample_

Selection of the sample has been guided by the need to identify schemes that are very similar in characteristics but under different types of institutional arrangements so that the differences in performance across scheme types can be solely attributed to their respective institutional arrangements. Accordingly, Jalanidhi schemes have been matched with KWA-BM schemes and other community managed schemes based on four characteristics – water source, age of the scheme, size of the schemes defined by population coverage, and distance between the treatment and control schemes. Since geographical coordinates were not available for the majority of the schemes, latitude and longitude data for the GPs where the schemes are located have been used to calculate the distance between the schemes. In the first step of the matching exercise, schemes from different institutional arrangements that use similar type of water source (river, open dug well, bore well, ponds, lakes) and have similar age profile have been identified. In the next step, matched pairs have been identified by selecting schemes that are closest to each other with a similar population coverage Using the matching exercise, a total of 200 similar and comparable water schemes from the above mentioned three groups have been identified which formed the final matched sample. The final sample consists of 90 Jalanidhi schemes, 44 KWA-BM schemes and 66 other community managed schemes. The distribution of the final matched sample by institutional arrangement is presented in the table below.

**Table 1: Distribution of Final Matched** **<u>Sample by Scheme Type</u>**

|**Scheme**|**Identified**|**Out of**|**Built**|
|---|---|---|---|
|Jalanidhi-I|90|3,710|2001 to 2009|
|KWA-BM|44|395|2004 & 2005|
|Other CommunityManaged|66|750|1999 to 2010|
|**Total**|**200**|**4,855**||

## 4.2 _Data_

Data collection for the selected sample was undertaken using household surveys of beneficiaries, technical and financial audits of water schemes, and institutional assessment of BGs for the Jalanidhi and other community managed schemes. The following is a brief description of the data collection tools used in the study.

_Technical and Financial Audit:_ The technical audit was focused on assessing the current state of water supply infrastructure such as working of the pumps, condition of the reservoir, functioning of the water treatment plan, frequency of breakdowns, frequency of water quality testing, total daily supply of water with respect to the design criteria, water source reliability, quality, and household service level. The 9
# **IV. Data Description**

# **A. Data**

As youth departments and governments become more sophisticated in their efforts to understand youth, several LAC countries have developed specialized youth surveys. We use two surveys in this paper: the 2003 National Youth Survey ( _Encuesta Nacional de Juventud_ ) from Chile and the 2000 National Youth Survey from Mexico. Youth are defined as being between the ages of 12 and 24 for this analysis, consistent with World Bank (2006). Chile first implemented their National Youth Survey in 1994 and has subsequently repeated it every three years. This paper uses the most recent data set that could be accessed, which was collected in 2003. A nationally representative sample of youth aged 15 to 29 is surveyed, reaching all regions (urban and rural) in the country, with a sampling error of 2.1% and a 95% confidence level. The sample size for youth aged 15 to 24 used in the analysis is 5321<sup>16</sup> .

The survey questions cover socioeconomic status, youth behaviors and opinions, and family backgrounds. It includes many questions about risk factors such as family cohesion, neighborhood violence, and social exclusion. Many protective factors are not covered, but there is information about trust in institutions, connectedness, and relationship with parents. The behaviors and outcomes are limited to employment, schooling, sexual health, victimization, discrimination and participation in activities. It does not include questions about whether the respondents take drugs or commit violent acts. There are very limited health and health access data.

Mexico carried out its National Youth Survey in 2000, through the National Institute for Statistics, Geography, and Information ( _Instituto Nacional de Estadística, Geografía e Informática_ , INEGI).<sup>17</sup> The survey sample is nationally representative and was performed in two stages: the entire household was surveyed and asked basic household characteristics, and, at a later date, youth aged 12 to 29 were asked a set of youth-specific questions. Only those aged 12 to 24 were included in this analysis, resulting in a sample of 37,979 respondents<sup>18</sup> .

> 16 For this analysis, 195 observations out of 7,189 (2.7% of sample) for youth aged 15 to 29 were dropped due to missing data.  We then restricted the sample to youth aged 15 to 24 dropping another 1674 observations.  In many cases, missing responses could be coded based on responses to related questions so as to maintain a larger sample size.  For instance, if someone does not respond as to whether they attend church, after they have already indicated that they do not believe in God, then we assume that they do not attend church.  It would be useful to do analysis using censored data tools.

> 17 The survey was repeated in 2005, but the data was not available at the time this paper was under preparation. 18 Of the almost 60,000 youth respondents, about 10,000 youth aged 12-29 were not surveyed the second time, and were dropped from the sample used for the analysis. The reasons for not interviewing these youth were

10
problematic. To address any latent heterogeneity problems that might hinder interpersonal comparisons of subjective welfare, we adjust the subjective measures of well-being using hypothetical vignettes that provide an explicitly standardized reference point for all respondents’ comparisons in order to bring objective and subjective assessments into alignment (van Soest, Delaney, Harmon, Kapteyn, & Smith, 2011; Krueger & Stone, 2014).<sup>18</sup> Interpersonal comparisons using SWB data can be challenging due to potential unobserved heterogeneity in respondents’ reference points, which may depend on socio-economic conditions, and other observable and unobservable characteristics. Such latent heterogeneity in subjective well-being measures may render interpersonal comparisons meaningless and invalidate inference from subjective welfare regressions (King, Murray, Salomon, & Tandon, 2004; van Soest et al., 2011; Beegle, Himelein, & Ravallion, 2012; Ravallion et al., 2013).

King et al. (2004), King and Wand (2007), van Soest et al. (2011) and Beegle et al. (2012) suggest an approach for correcting latent heterogeneity problems that involves measuring the interpersonal incomparability of responses itself. Respondents are asked to assess their own circumstances relative to a set of hypothetical individuals described by short vignettes on the same scale. Responses to the hypothetical vignettes are then used to construct an interpersonally comparable welfare measure as respondents’ reference points have been exogenously standardized. The validity of this approach relies on two key assumptions: response consistency, and vignette equivalence. Response consistency requires that each respondent use response categories for a particular concept in the same way when selfassessing as when assessing hypothetical individuals. Vignette equivalence is the assumption that each respondent perceives the level of the variable represented by a particular vignette on the same unidimensional scale. That is, the variable being measured by vignettes should have a consistent meaning among respondents (King et al., 2004).

Following King et al. (2004), the reported SWB measures are corrected using a simple nonparametric approach. For notational ease, we momentarily suppress the village and time dimensions of the data. Suppose _SWBi_ is the categorical self-assessment for respondent _i_ ( _i_ = 1 _, ..., n_ ), and _Vij_ is the categorical survey response for respondent _i_ on vignette _j_ ( _j_ = 1 _, ..., J_ ). For respondents with identical vignette ordering (i.e. _Vi,j−_ 1 _< Vij_ ) the vignette adjusted measure of subjective well-being is given as:<sup>19</sup>

> 18As discussed further below, we test the robustness of our core results by re-estimating our model for direct (unadjusted) SWB responses and for responses to a similar SWB question that asks people about their well-being relative to other Borana pastoralists. The core findings prove stable.

> 19In our data, rescaling of self-assessments relative to vignettes does not generate vector responses, which

16