rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4: Correlation of Vote**<br>**_(Barangay_**|**Buying with Ho**<br>**_clustered stand_**<br>(1)|**usehold Reported**<br>**_ard errors in paren_**<br>(2)|**Access to Heal**<br>**_theses)_**<br>(3)|**th Services**<br>(4)|
|---|---|---|---|---|
||Births with<br>Trained<br>Assistance<br>|Received<br>Prenatal Folic<br>Acid|<br>Received<br>Prenatal Iron<br>|<br>Children<br>Receiving Iron|
|Vote Buying<br>(% respondents reporting it in<br>bgy)|-0.35<sup>~~***~~</sup><br>(0.12)<br>|-0.01<br>(0.10)|-0.20<sup>~~*~~</sup><br>(0.10)<br>|0.02<br>(0.09)|
|Mayor’s family reported as major<br>employer or landowner|-0.12<sup>~~***~~</sup><br>(0.04)|-0.06<br>(0.05)|-0.15<sup>~~***~~</sup><br>(0.04)|0.03<br>(0.04)|
|Indicator variable = 1 if no clan<br>dominates municipal politics|-0.15<sup>~~***~~</sup><br>(0.06)|0.11<sup>~~***~~</sup><br>(0.04)|0.04<br>(0.05)|0.03<br>(0.04)|
|Municipal dependency on<br>national revenue share|0.30<br>(0.35)|0.32<br>(0.34)|0.58<sup>~~*~~</sup><br>(0.33)|-0.05<br>(0.29)|
|Barangay poverty estimate|-0.004<sup>~~**~~</sup><br>(0.002)|-0.0002<br>(0.002)|0.004<sup>~~**~~</sup><br>(0.002)|0.001<br>(0.002)|
|Barangay location (travel time to<br>_centro_)|-0.003<sup>~~***~~</sup><br>(0.001)|-0.0001<br>(0.001)|-0.002<sup>~~**~~</sup><br>(0.001)|-0.001<br>(0.001)|
|Barangay roads need repair|-0.07<br>(0.05)|-0.11<sup>~~**~~</sup><br>(0.06)|-0.08<br>(0.06)|-0.08<sup>~~*~~</sup><br>(0.04)|
|Family member holds public<br>office|0.05<br>(0.05)|-0.02<br>(0.05)|-0.13<br>(0.05)|0.07<sup>~~*~~</sup><br>(0.04)|
|Membership in community<br>assoc.|-0.05<br>(0.04)|-0.05<br>(0.05)|0.08<sup>~~*~~</sup><br>(0.05)|0.09<sup>~~**~~</sup><br>(0.04)|
|HH participates in bayanihan|0.11<sup>~~***~~</sup><br>(0.03)|0.05<br>(0.04)|0.04<br>(0.03)|0.07<sup>~~**~~</sup><br>(0.03)|
|District FE: District1|-0.10<br>(0.07)|-0.04<br>(0.06)|0.02<br>(0.07)|-0.10<br>(0.08)|
|District 2|-0.10<br>(0.07)|0.03<br>(0.06)|0.11<sup>~~**~~</sup><br>(0.05)|0.10<sup>~~*~~</sup><br>(0.06)|
|District 3|-0.15<sup>~~*~~</sup><br>(0.08)|0.14<sup>~~**~~</sup><br>(0.06)|0.09<sup>~~*~~</sup><br>(0.06)|0.08<br>(0.0)|
|N, R-sq|1159<br>0.13|1159<br>0.08|1159<br>0.07|1159<br>0.095|

_Baseline control variables include: household size, number of children, length of time residing in the area; age and education of respondent and household head; remittances; whether the family experienced hunger in the last 3 months; whether the family owns a TV, cell phone, washer, and stove; whether the household has electricity; frequency of listening to the radio and watching TV, village population_ 35
Despite this very complex environment, reforming subsidies remains an important question for the Libyan government. In February 2013, the Ministry of Economy conducted a survey on a sample of 931 adult citizens aged 18 to 95 living in 25 cities. The University of Tripoli analyzed results and found that about 70% of the respondents were in favor of a policy that would eliminate subsidies and replace them with cash transfers, although only 28% thought that compensation via cash subsidies should be targeted to the poor only. Libyans believed that they are entitled to subsidies as a means to distribute national wealth to most citizens but they would trade low subsidized prices for a cash benefit.

The government announced several times the intention to reform subsidies. In April 2014, it made public the intention to introduce smart cards for the purchase of fuels and also stated the intention to eliminate subsidies within three years and in July 2014 it committed to substitute goods and fuel subsidies for cash subsidies by January 2015. According to the Libya Herald,<sup>24</sup> this is the first time in Libya’s history that this step is taken and this in spite of the political instability. Yet, at the time of writing this paper, no substantial reform had been implemented and political instability was deteriorating further.

# **7. Summary and recommendations**

This paper provided a food and energy subsidy incidence analysis as well as an impact analysis for two alternative reform scenarios for Libya. The results provide information for each subsidized good in terms of the subsidy’s impact on household welfare and on poverty. Below we review briefly the key findings and discuss the main issues that would still need to be addressed for a more comprehensive picture of subsidy incidence and reform analysis.

Food subsidies save households some 10% of annual expenditure and eliminating them would have a significant effect on poverty. Table 27 summarizes the results of the food subsidy analysis. Household expenditure loss would reach 3.1% under scenario 1 and 10.2% under scenario 2. The incidence of subsidies would drop from 10.2% in the pre-reform scenario to 7.4% under scenario 1 and zero under scenario 2. Subsidy reform would reduce government spending by about 1% under scenario 1 and 2% under scenario 2 (but additional savings from lower administrative costs and less waste/smuggling would also materialize). The poverty impact would be particularly stark: depending on the poverty line used, poverty would rise from 8.5% (or 14.4%) to 10.5% (or 17.3%) under scenario 1 and to 16.6% (or 24%) under scenario 2. Inequality would also rise.

**Table 27: Summary of Aggregate Results for the Case of Food Subsidies**

||Pre-<br>reform|Scenario<br>1 (30%<br>reduction<br>in<br>subsidies)|Scenario 2<br>(Elimination<br>of<br>subsidies)|
|---|---|---|---|
|Total real household expenditure (bn. LYD)|12.53|12.15|11.25|
|Household expenditure loss in real terms (% of pre-reform)|n.a.|-3.1|-10.2|
|Total subsidies (bn. LYD)|1.28|0.9|0|

> 24http://www.libyaherald.com/2014/07/06/2014-budget-commits-government-to-subsidy-reform-by-jan-2015/#axzz3OmulcpVM

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**Table 4. Income and the choice of migration destination**

|**District difference in:**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|**coef**|**t-stat**|
|---|---|---|---|---|---|---|---|---|
|**Income**|||||||||
|Average log income|-0.185|-0.946|-0.188|-0.965|||||
|Differential in log income due to education and high caste|||-0.035|-0.359|||||
|Relative log income controlling for education and high caste|||||-0.089|-0.087|||
|Average consumption adequacy index: total income|||||||-0.958**|-2.078|
|Differential due to education and caste: total income|||||||0.479***|3.041|
|**Prices and amenities**|||||||||
|Log of rice price|-1.909**|-2.001|-1.883**|-1.977|-1.849**|-1.973|-1.921**|-2.163|
|Housing price premium (log)|0.188***|3.005|0.188***|2.993|0.182***|2.916|0.182***|2.924|
|Time travel to nearest paved road|-0.951***|-9.579|-0.955***|-9.451|-0.920***|-8.893|-0.950***|-9.268|
|Time travel to nearest bank|0.107|0.430|0.118|0.473|0.146|0.639|-0.033|-0.119|
|Elevation in meters|-0.575**|-2.359|-0.579**|-2.386|-0.630***|-2.855|-0.457*|-1.857|
|**Population**|||||||||
|Population density|0.828***|5.967|0.823***|5.744|0.791***|5.579|0.797***|5.837|
|Log(population)|0.372**|2.046|0.376**|2.029|0.348*|1.912|0.400**|2.130|
|Ethnicity similarity index|1.685***|7.170|1.686***|7.169|1.701***|7.039|1.668***|7.163|
|Language similarity index|1.519***|10.544|1.515***|10.390|1.496***|10.307|1.483***|10.498|
|Religion similarity index|-0.576|-1.376|-0.588|-1.427|-0.604|-1.468|-0.462|-1.037|
|**Distance**|||||||||
|Distance above 100 Km|-0.842***|-2.726|-0.845***|-2.733|-0.829***|-2.667|-0.779**|-2.414|
|Log-Likelihood|-57,08|9.55|-56,89|8.28|-56,91|0.66|-56,78|6.67|
|Number of observations|1,076,|556|1,072,|804|1,072,|804|1,072|,804|
|Pseudo R2|0.15|5|0.15|5|0.15|5|0.1|57|

The estimator is Fixed Effect Conditional Logit. Standard errors are corrected for clustering across district of origin. *** p<0.01, ** p<0.05, * p<0.1
Table A6: Early Life Exposure to Coffee Mills and Test Scores: Effects by Location

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||||||Rural|||||
|CatchmentArea X Post|0.0459**|0.0505***|0.0447**|0.0342**|0.0353**|0.0306*|0.0493**|0.0557***|0.0495***|
||(0.0187)|(0.0179)|(0.0175)|(0.0164)|(0.0167)|(0.0163)|(0.0197)|(0.0186)|(0.0182)|
||[ 0.029 ]|[ 0.014 ]|[ 0.023 ]|[ 0.049 ]|[ 0.048 ]|[ 0.077 ]|[ 0.026 ]|[ 0.013 ]|[ 0.015 ]|
|R-squared|0.3112|0.3587|0.3588|0.3017|0.3450|0.3450|0.2925|0.3422|0.3423|
|Observations|1314120|1313807|1313807|1314123|1313810|1313810|1314119|1313806|1313806|
||||||Urban|||||
|CatchmentArea X Post|0.0432|0.0369|0.0415|0.0367|0.0045|0.0085|0.0466|0.0545|0.0588|
||(0.0652)|(0.0599)|(0.0603)|(0.0626)|(0.0589)|(0.0594)|(0.0656)|(0.0667)|(0.0666)|
||[ 0.535 ]|[ 0.559 ]|[ 0.525 ]|[ 0.572 ]|[ 0.939 ]|[ 0.898 ]|[ 0.516 ]|[ 0.455 ]|[ 0.42 ]|
|R-squared|0.5513|0.5717|0.5718|0.5355|0.5554|0.5555|0.5253|0.5474|0.5475|
|Observations|172407|172371|172371|172407|172371|172371|172407|172371|172371|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|

Notes: Dependent variable is the standardized aggregate test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. Mills X Post is an indicator variable equal to 1 if a student was born within the vicinity ( _≤_ 5 _km_ ) of an operational coffee mill and 0 if otherwise. Controls include gender of the student, average annual temperature and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at school level in parenthesis. False discovery rate (FDR) adjusted p-values for _MillsX Post_ are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda.

> _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level

45
variables are a set of binary dummy variables used instead of _Post Reform_ , which characterize the nature of market organization post-reform. Three dummies allow differentiating between three types of liberalized markets: _Regulation_ , which implies that firms operate as regional monopsonies or that supply is administratively allocated among firms; _Low Competition_ , which means that two or three firms with large market shares exert price leadership; and _Strong Competition_ , which indicates that many firms compete on prices. Given evidence that the impact of reforms might only show up with delays because of slow reform implementation, we also test the impact of these institutional variables with a lag of one or two periods.

# _3.2.3 Weather conditions_

Our indices of weather conditions comprise a measure of total precipitations and one of the length of the rainy season. Both are based on the dataset provided by the Climatic Research Unit of the University of East Anglia (2011), which reports monthly cumulative precipitations for the years 19612009 on a 0.5 degree grid. Following Schlenker and Lobell (2010), _Rainfall_ is defined as the average cumulative rainfall during the cotton growing season, over all grid cells falling in a country‟s boundaries, weighted by the share of cropland dedicated to cotton cultivation in each grid cell.<sup>14</sup> The onset and offset of the growing season are defined, as in Blanc _et al._ (2008), by fixed percentages of annual rainfall. This _Rainfall_ variable thus represents cumulative rainfall during the cotton season (in thousands of millimeters) within the cotton cultivation areas. It is expected to impact yields mostly in the Sahelian countries of our sample (Mali and Burkina Faso) and in the countries of the South African agro-ecological zone (Malawi, Mozambique, Zambia and Zimbabwe), where drought is a major constraining factor. We therefore use it in interaction with dummy variables for those two zones ( _Sahelian_ and _South_ ). The square of _Rainfall_ is also included for the Sahelian countries where we expect a decreasing marginal effect of precipitations (Blanc _et al._ , 2008).<sup>15</sup> In countries with a Guinean climate (Benin, Cameroon, Chad, Côte d‟Ivoire, Nigeria, Senegal and Togo) and in the eastern zone of

> 14 The shares of cropland dedicated to cotton cultivation are taken from Monfreda et al. (2008). They are based on the potential for cotton cultivation for the year 2000. See Figure 3 for the 5 minutes (~10km) resolution map of the densities used and the annual cumulative rainfall. The major limitation associated to the use of this dataset is the fact that it rests on a static estimation of land use as it is only available for 2000. However the potential for cultivating cotton (estimated with satellite data and agricultural inventories) is little submitted to time variations. This should there therefore affect our estimation only marginally.

> 15 In the sub-tropical countries of Eastern Africa, the effect of cumulative precipitations is much more limited. Identifying the potential non linearity of this effect is thus not possible.

12
and access to water, sanitation, and electricity. The data also contain household and individual identifiers, as well as sampling weights. These dimensions contain most of the required timeinvariant and deterministic variables necessary to apply the LASSO-PMM method for predicting welfare dynamics and poverty mobility.

This paper uses household surveys for 43 countries around the world. The selection of countries is based on the availability of at least two comparable data points and on the availability of the necessary variables to conduct the estimation. We use the most recent surveys for each country at the time or writing, trying to cover a period of five years between both rounds of crosssectional data (i.e., circa 2010-2015). Our sample includes countries in the six geographical regions defined by the World Bank: East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North Africa (MNA), South Asia (SAR) and Sub-Saharan Africa (SSA). While the selected surveys do not cover all countries in the world, they do encompass a large set of nations with different levels of development and welfare. Appendix Table A.2 summarizes the countries and years, as well as the specific household surveys in the GMD that we use in the analysis.

# _3.3 Empirical strategy_

Estimations are carried out for household-level income changes as well as for poverty dynamics defined as the proportion of individuals with a harmonized per capita income lower than a US$5.5 per person per day poverty line, both in 2011 purchasing power parity (PPP) per day.<sup>19</sup> The main difference between the validation and application of the LASSO-PMM method is that we can

> 19 The US$5.5 per person per day in 2011 PPP is the World Bank poverty line used for upper-middle-income countries (UMICs). This line is the median value of UMIC national poverty lines (Jolliffe and Prydz 2016, Ferrerira et al 2016, Castañeda et al. 2018). The only exception is Chile in the validation analysis where we define a relative poverty line equal to 0.5 of median per capita income, because the poverty rate using the US$5.5 threshold is too low in this particular case.

18
Health Survey (DHS).<sup>8</sup> Before 2007, the sibling history was collected only from female not from male respondents. The 2007 DHS surveys all ever-married women who are 15 to 49 years old and every third currently married man who is 15 to 54 years old. I limit the sample to the fertility preferences and histories of women whose partner has also been interviewed (7,792 couples). I exclude all mixed couples formed by one Muslim and one non-Muslim partner (0.79 percent of all observations). Indonesia has legal restrictions for inter-religious marriages and inter-religious couples are usually disqualified from Islamic inheritance (Buchanan 2010). I also exclude polygamous couples (1.3 percent of all observations) because the fertility of men who have multiple wives should be assessed across all wives, but only co-resident wives are interviewed. A final sample size of 7,657 couples and 20,145 child births is obtained.

In order to exploit the single cross-section of data available, I define pre- and postcompilation samples based on the year when the couples initiated child-bearing, and based on the year of birth of each child. I measure men and women’s son and fertility preferences as the ideal proportion of sons and the ideal total number of children. The ideal proportion of sons is a lower boundary of the degree of son preference because “children of either gender” is counted in the denominator but not in the numerator. Further, I calculate the actual proportion of sons and the actual number of children ever born from women’s birth histories. Since only single-marriage couples are included in the sample and only legitimate children can exclude male agnates, women’s birth histories can be confidently used to analyze the effects of the inheritance rule. Finally, I obtain the men’s total number of surviving siblings and an indicator for having at least one surviving brother nine months before the date of birth of each child ever born. If a brother was alive before or at the time of conception, even if he was dead at the time of birth, his presence could have influenced the decision to have a child. Appendix 1 provides summary statistics for the data.

> 8 Information on fertility preferences includes the ideal number of sons, daughters, and children of either gender; ideal total number of children and desire for additional children. Birth histories report the total number of live births; the age, sex, and survival status of each child, and the age and year of death of the deceased children. Sibling histories give a detailed account of the survivorship of all of the live-born children of the respondent’s mother (i.e., maternal siblings), the current age of surviving siblings, and the age at death and year of dead for deceased siblings.

12
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# 6. Appendix

**Table 4 - Studies estimating the cost of electricity disruption for households**

|**Country**|**Method**|**Outage Cost**|**Homogenized**<br>**Outage Cost per**<br>**kWh (in USD)***|**Homogenized**<br>**Outage Cost**<br>**per hour**<br>**unserved (in**<br>**USD)***|**Homogenized**<br>**outage cost per**<br>**hour unserved**<br>**(in % of hourly**<br>**GDPpc (Figure**<br>**4))**|**Source**|
|---|---|---|---|---|---|---|
|Pakistan,<br>2013|Contingent<br>valuation and<br>macroeconomic|PKR 23.94 per<br>kWh|0.41|0.11|40.03|(Pasha &<br>Saleem, 2013)|
|US, 2004|Macroeconomic|USD 2.70 per<br>hour<br>unserved|0.64|2.70|75.43|(Berkeley,<br>2004)|
|US, 1988|Contingent<br>valuation|USD 6 per<br>kWh|6.00|24.96|1029|(Doane Hart<br>man, and<br>Woo1988)|
|Canada,<br>1989|Contingent<br>valuation|CAD 1.40 per<br>kWh|1.08|5.25|465.7|(Wacker &<br>Billinton,<br>1989)|
|Spain,<br>2013|Macroeconomic|EUR 4.39 -<br>EUR 6.35 per<br>hour<br>unserved|3.58|7.25|211.2|(Linares &<br>Rey, 2013)|
|Turkey,<br>2016|Choice<br>experiment|USD 0.24 -<br>USD 0.92 per<br>hour<br>unserved|0.14 – 0.53|0.24 – 0.92|8.674-33.31|(Ozba &<br>Jenkins, 2016)|
|Sweden,<br>2008|Choice<br>experiment|SEK 2.13 - SEK<br>10.00 per<br>hour<br>unserved|0.09 – 0.40|0.32-1.50|7.744-36.63|(Carlsson &<br>Martinsson,<br>2008)|
|Mexico,<br>1999|Macroeconomic|MXN 2.724<br>(1993) per<br>kWh|0.27|0.28||(Jenkins,<br>Henry, &<br>Gangadhar,<br>1999)|
|Italy, 2005|Contingent<br>valuation|EUR 3.75 per<br>kWh|4,95|8,97||(Bertazzi,<br>Fumagalli, &<br>Lo Schiavo,<br>2005)|
|Austria,<br>2013|Contingent<br>valuation|EUR 2.5 per<br>kWh|3,25|8,33||(Reichl,<br>Schmidthaler,<br>& Schneider,<br>2013)|

_Source: authors based on World Bank data and estimates of the WTP found in the literature._ _Conversions are computed in three steps: (1) Computation of the WTP per kWh (hour unserved) in the national currency. (2) Conversion in USD in the year of the study. (3) Conversion into 2018 USD. Hourly electricity consumption data are retrieved from the World Bank Microdata portal. https://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC_

## **Table 5 - List of studies focused on the health impact of water provision interruption and dysfunction***

29