rafmacalaba/datause-encoder-data huggingface dataset:
nificant differences in test scores variance between genders. They report that the variance of boys’ test scores is larger than that of girls in virtually all 40 countries covered by the 2003 Project for Student International Assessment (PISA).<sup>14</sup> They also find that these results hold for both reading and mathematics test scores, two subjects where the mean gender gap in achievement strongly differs. The average male-to-female test score variance ratio in the PISA sample reported by Machin and Pekkarinen (2008) is very similar for mathematics (1.21) and reading (1.20).<sup>15</sup> In Figure II, we illustrate that these findings hold in the most recent available wave of the PISA data from 2015, which consists of a larger sample of countries. PISA tests nationally representative samples of 15-year-old students in mathematics and reading, and test scores are comparable across countries. In the 2015 PISA sample of 67 countries, the average ratio of the male test score variance over the female test score variance is 1.17 for both reading and mathematics test scores, close to the estimates reported earlier by Machin and Pekkarinen (2008) using an earlier and smaller sample of countries. The variance ratio ranges from 0.96 in Algeria to 1.62 in Jordan for reading, and from 0.90 in Algeria to about 1.50 in Argentina and Jordan. The test score variance of males is higher than that of females’ in 65 countries for mathematics and in 64 countries for reading. The gender difference in test score variance is statistically significant in 58 countries for both reading and mathematics.

Understanding the higher dispersion of boys’ test scores is beyond the scope of this paper, but it could be linked to another growing strand of literature suggesting that boys’ scholastic performance is more sensitive to inputs in early childhood and at school. Bertrand and Pan (2013) document that boys raised in single-parent families have twice the rates of behavioral and disciplinary issues as boys raised in two-parent families; Autor et al. (2015) show that the boy-girl gap in kindergarten readiness, test scores, truancy, disciplinary problems, disability, juvenile delinquency, and high school graduations is larger in more disadvantaged families. Fan, Fang and Markussen (2015) report that maternal employment during early childhood reduces boys’ eventual educational attainment relative to that of girls.

> 14Test scores can here be thought as the empirical measure of individual scholastic performance _z_ in our theoretical model.

> 15Although this evidence is recently known to economists, long-standing evidence on the larger variance in the distribution of some traits among males has been reported in the psychology literature, such as Frasier (1919), Hedges and Nowell (1995) or Jacob (2002), among many others.

14
Figure 3: Expenditure Shares of Subsidized Energy Products across Countries and Quintiles <!-- Start of picture text --> Liquefied Petroleum Gas Electricity<br>2.5 4<br>3.5<br>2<br>3<br>1.5 2.5<br>2<br>1<br>1.5<br>0.5 1<br>0.5<br>0<br>Morocco Tunisia Libya Iran, Islamic Djibouti 0<br>Rep. Morocco Tunisia Libya Iran, Islamic Rep.<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Gasoline Diesel<br>3.5 1.2<br>3 1<br>2.5<br>0.8<br>2<br>0.6<br>1.5<br>0.4<br>1<br>0.5 0.2<br>0 0<br>Tunisia Djibouti Iran, Islamic Libya Morocco Morocco Tunisia Libya Iran, Islamic Djibouti<br>Rep. Rep.<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br>Expenditure share (in %)<br>Expenditure share (in %)<br>Expenditure share (in %) Expenditure share (in %)<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

For food products (figure 4), the situation is much simpler. For all products and in all countries, the household budget shares of expenditure on subsidized products is higher for poorer households and progressively lower for richer households, as we should expect. The decrease between quintiles is also very steep in general, particularly for flour and sugar in Djibouti and bread in the Islamic Republic of Iran. These products are evidently very important for the poor in these countries, representing up to 8 percent of total expenditure for the poorest quintile.

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<!-- Start of picture text -->
[__]0-10Poverty rate (%) .<br>[__][yj 10 - 20<br>[iyGl 2030 -- 4030 ,<br>Hl 40-50<br>Hl 50-60<br>HlHg 6070 -- 80 70 .<br>I 80-9090 - 100<br>a<br><!-- End of picture text --> <!-- Start of picture text --> ed<br>[__]0-10Poverty rate (%) ,.<br>[__]10 - 20<br>[yj[iy 2030 -- 4030 A.<br>Gl 40-50<br>Hl 50-60<br>HlHl 6070 -- 80 70 bi.<br>HiI 8090 -- 90100 €4<br>a a]<br><!-- End of picture text -->
Figure 5: Poverty Eradication capacity Index and Poverty Headcount Index <!-- Start of picture text --> EAS<br>ECS<br>LCN<br>MEA<br>SAS<br>SSF<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Poverty Headcount Index Poverty Headcount Index<br>1 1<br>~p) r)<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>Poverty Eradication Capacity Index, I(P Poverty Eradication Capacity Index, I(P<br>0 0<br><!-- End of picture text --> a smaller share of poverty gaps as the index approaches zero. Of the 27 countries in our sample having an _I_ ( _P_<sup>_∼p_</sup> ) below 0.5, 22 are in Sub-Saharan Africa.

Figure 5, right panel, shows a fuzzier relationship between level of headcount poverty and the Poverty Eradication Capacity Index computed following distribution rule (ii), when the rich are considered responsible for poverty eradication, _I_ ( _P_<sup>_r_</sup> ). Our results show that for poverty levels higher than 30 percent (62 countries in our sample), only four countries (Cape Verde, Namibia, Nicaragua and South Africa) have an _I_ ( _P_<sup>_r_</sup> ) greater than 0.5 (respectively, 0.64, 0.72, 0.78 and 0.96), which means that no marginal tax rate levied on the excess-affluence gaps of the rich generates enough revenue to close the poverty gaps.

Figure 6 shows the relation between the two different _Poverty Eradication Capacity Indices_ obtained by applying the underlying two different redistributive rules: _I_ ( _P_<sup>_∼p_</sup> ) and _I_ ( _P_<sup>_r_</sup> ). Given the sample of our countries, it seems that countries with _I_ ( _P_<sup>_r_</sup> ) _≥_ 0 _._ 2 are also those with an _I_ ( _P_<sup>_∼p_</sup> ) _≥_ 0 _._ 9: countries which are able to eradicate (at least some) poverty, by sourcing resources from the affluence gaps of their rich individuals, usually can solve almost all their poverty by sourcing resources from the excess-poverty gaps of non-poor individuals. Contrarily, wherever _I_ ( _P_<sup>_∼p_</sup> ) _≤_ 0 _._ 7, hardly any country is able to raise any resources from those above the line.

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also have been able to postpone marriage. Table 12 shows that the reform had an impact on marriage decision of women with at least KCPE (or secondary degree): an additional year of education reduces the probability of marriage before the age of 19 by 6.7 (or 19.3) percentage points.

# 6. References

M. Ainsworth, K. Beegle, and A. Nyamete. The impact of women’s schooling on fertility and contraceptive use: a study of fourteen Sub-Saharan African countries. World Bank Economic Review, 10(1):85–122, 1998.

S. Amin. Female education and fertility in Bangladesh: the influence of marriage and the family. Girls’ Schooling, Women’s Autonomy and Fertility Change in South Asia, 1995.

A. Ashcraft and K. Lang. The consequences of teenage childbearing. Working Paper, 2006.

S.E. Black, P.J. Devereux, and K.G. Salvanes. Fast times at Ridgemont High? The effect of compulsory schooling laws on teenage births. IZA Discussion Paper Series # 1416, 2004.

L. Breirova and E. Duflo. The impact of education on fertility and child mortality: Do fathers really matter less than mothers? NBER Working Paper # 10513, 2003.

M. Buvinic. The costs of adolescent childbearing: Evidence from Chile, Barbados, Guatemala and Mexico. Studies in Family Planning, 29(2):201–209, 1998.

J.C. Caldwell and P. Caldwell. The cultural context of high fertility in Sub-Saharan Africa. Population and Development Review, 13(3):409–437, 1987.

CBASSE. Risking the future: Adolescent sexuality, pregnancy and childbearing. Commission on Behavioral and Social Sciences and Education - The National Academy Press, 1987.

CBASSE. Social dynamics of adolescent fertility in Sub-Saharan Africa. Commission on Behavioral and Social Sciences and Education - The National Academy Press, 1993.

CBASSE. Critical perspectives on schooling and fertility in the developing world. Commission on Behavioral and Social Sciences and Education - The National Academy Press, 1999.

DHS. Kenya demographic & health survey. DHS Analytical Studies, Calverton, Maryland: ORC Macro, 2003.

DHS. Kenya demographic & health survey. DHS Analytical Studies, Calverton, Maryland: ORC Macro, 1989.

DHS. Kenya demographic & health survey. DHS Analytical Studies, Calverton, Maryland: ORC Macro, 1993.

DHS. Kenya demographic & health survey. DHS Analytical Studies, Calverton, Maryland: ORC Macro, 1998.

J.G. Dryfoos. Review of intervention in the field of prevention of adolescent pregnancy. Preliminary report. Rockefeller Foundation, New-York, NY, 1983.

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2 Data To assess the profound changes in the lives of people and prepare the national plan for post-independence, the transitional government, supported by the donor community, initiated new surveys.° The analysis of this paper draws on the two new principal data sources. The Timor-Leste Living Standard Measurement Survey (TLSS) is a national representative sample of 1,800 households from 100 communities (“Suco”) and 300 villages (“Aldeia”) covering over one percent of the population.” The survey was designed to diagnose the extent, nature and causes of poverty, and to analyse policy options facing the country. It assembles comprehensive information on household demographics, housing and assets, household expenditures and income, agriculture, employment, health and education, and subjective perceptions of poverty, well-being and social capital. Data collection was undertaken between end August and early December 2001. The TLSS was complemented by a census of all Sucos in the country, conducted during February to April 2001. It provides an inventory of social and physical infrastructure and of the economic characteristics of Suco and Aldeias, in addition to population estimates.

Both surveys relied extensively on recall questions to establish a baseline for well-being prior to the referendum in 1999 for three reasons. First, there were concerns regarding the reliability of existing Indonesian data for Timor Leste. Second, little historic information was available on subjective measures of well-being. Third, due to large population displacement and migration, the population as of end 2001 was unlikely to be directly comparable to the population prior to the referendum in 1999.

6 The data was collected by the Poverty Assessment Project, which was a partnership between the Planning Commission of the Timor Leste Transitional Authority with the World Bank, the Asian Development Bank, the United Nations Development Programme, and the Japanese International Cooperation Agency. Benu Bidani managed the fielding and analysis of the TLSS (World Bank (2003)). ” Timor-Leste is divided into 13 major units called Distritos (districts). These are further subdivided into 67 Postos (sub-districts), 498 Sucos (villages) and 2,336 Aldeias (hamlets) (Munoz (2001)).
it is also used as a proxy for household’s proximity to large cities or towns. Households that are closer to big cities are more likely to be near financial institutions.<sup>7</sup> Hence, we expect a positive relationship between electricity and our dependent variables. Based on the complete sample, 65 percent of households have electricity and the percentage of households with at least one member that worked in any agricultural activity equals 87 percent.

We run the three versions of equation 1 considering the likelihood of having an account, applying for a loan, and obtaining a loan as the dependent variables, respectively. We include fixed effects to control for unobserved characteristics that might affect the link between remittances and the use of financial services. In particular, we allow for three different types of fixed effects with decreasing levels of aggregation: (i) department fixed effects, (ii) municipality fixed effects, and (iii) household fixed effects.

An important concern regarding the interpretation of the results of equation 1 is that they could be affected by endogeneity as a result of reverse causation. There are at least two reasons why endogeneity could arise. First, access to financial services from financial institutions might reduce the costs of remittances and, hence, might make migrants more prone to send and households to receive remittances. Second, financial institutions could finance migration, and, consequently, increase the remittance flow towards households with access to credit. We run instrumental variables regressions to deal with the potential endogeneity problem.

Our main instruments are municipality-level weighted average measures of economic conditions in US states where Salvadoran migrants reside.<sup>8</sup> In particular, we focus on real GDP per capita and poverty rates in US states during 1995, 1997, 1999, and 2001.<sup>9</sup> For each municipality in El Salvador, we identify the main US destinations for Salvadoran migrants using data on migration patterns for Salvadorans from the North American Integration and Development Center (NAID).<sup>10</sup> We then construct, for each Salvadoran municipality, a weighted

> 7 Our survey measures the distance to the closest bank office, but only for the last two waves: 1999 and 2001. We find that _Electricity_ has a negative correlation with the distance to the closest bank (-0.13) and is significant at 1 percent.

> 8 We follow McKenzie and Rapoport (2007), who in their study of the impact of migration on income inequality in Mexico, use economic conditions in the US as one of their instruments for migration from Mexico.

> 9 Appendix 1 describes other instruments we tried. Our main findings are not affected by our choice of instruments. 10 The mentioned database is available at http://gis.ats.ucla.edu/naid/. The NAID gathered information on the main destinations in the US of Salvadoran migrants during the last five years. We assume that migration patterns during

9
<!-- Start of picture text --> Table A20: iJET Country Security Risk Ratings (IJT)<br>Data Provider iJET<br>Description Commercial security risk consulting company based in Annapolis, United States<br>Website www.ijet.com<br>Data Source Country Security Risk Ratings<br>Type Expert assessment<br>Respondents iJET staff<br>Frequency Annual since 2004<br>Coverage Global sample of countries<br>Public Access Commercially available<br>Description iJET provides assessments of security risks faced by travelers, coded on a 5-point scale.<br>2006 2005 2004 2003 2002 2000 1998 1996<br>Voice and Accountability<br>NA .. .. .. .. .. .. .. ..<br>Political Stability<br>Security Risk Rating X X X .. .. .. .. ..<br>Government Effectiveness<br>NA .. .. .. .. .. .. .. ..<br>Regulatory Quality<br>NA .. .. .. .. .. .. .. ..<br>Rule of Law<br>NA .. .. .. .. .. .. .. ..<br>Control of Corruption<br>NA .. .. .. .. .. .. .. ..<br>Country Coverage 187 182 167 .. .. .. .. ..<br>Year of publication 2006 2005 2004 .. .. .. .. ..<br><!-- End of picture text --> 58
# **1. Introduction**

The world has become considerably less poor in the past three decades. In 1981, more than half of citizens in the developing world lived on less than $1.25 a day. This rate dropped dramatically to 22 percent in 2010. Moreover, despite a 35 percent increase in global population, there are slightly fewer people living on less than $1.25 a day today (1.3 billion) than there were three decades ago (1.8 billion). Progress is undeniable and most likely the downward trend of poverty has continued after 2010. But 2.5 billion people living in poverty (measured at US$ 2 dollars a day) and 1.3 in abject poverty are still extremely high figures.

It is widely accepted that economic growth has been the main driver of poverty reduction in the past three decades. For example, Kraay (2006) estimates that growth in average incomes accounts for between 70 and 95 percent of the observed poverty reduction. Other estimates find that two-thirds of the drop in poverty is the result of growth, with the other third coming from greater equality.<sup>1</sup> The role of growth is also made evident as countries that experienced rising inequality in the last two decades account for approximately 80% of the decline in world poverty.<sup>2</sup> However, despite the past performance of growth in reducing poverty, can we continue to rely mostly on growth to achieve significant poverty reduction in the future? Or, as countries become less poor, should we increasingly shift the focus to inequality-reducing policies to further accelerate poverty reduction?

To answer these questions, we need to know what happens to the _inequality elasticity of poverty reduction_ (IEPR) relative to the _growth elasticity of poverty reduction_ (GEPR) as countries become less poor. If relative to IEPR, GEPR increases with the level of development, strategies relying almost exclusively on economic growth are probably justified. But if the IEPR to GEPR ratio increases with lower levels of poverty, faster poverty reduction will likely be obtained with a greater emphasis on policies that reduce inequality.

Several studies have aimed at estimating GEPR solely or jointly with IEPR. For instance, Ravallion and Chen (1997) place the GEPR at around 3, while World Bank (2000) provides

> 1 The Economist “Not Always with us” June 1st, 2013

> 2 Excluding China from the group of countries with increased inequality, the share is estimated at 52%. This calculation is obtained from a sample of 75 countries with head count rate (based on $1.25 a day poverty line) and Gini data available for at least one year in 1985-1995 and one year in 2003-2010.

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Table 3: Impact of remittances on consumption

||Log(|Br|eakdown of|Log(consumpt|ion)|
|---|---|---|---|---|---|
||cons)|food|durable|non-durables|education|
||(1)|(2)|(3)|(4)|(5)|
|||**All Hous**|**eholds**|||
|Log(remittances)|0.164***|0.103***|0.618***|0.179***|0.171|
||(0.046)|(0.031)|(0.187)|(0.052)|(0.130)|
|implied MPC|0.494|0.178|0.088|0.104|0.036|
|First-stage F-stat|17.83|||||
|Observations|5988|5988|5988|5988|5988|
|||**Rural Hou**|**seholds**|||
|Log(remittances)|0.146**|0.120**|0.634**|0.120*|-0.078|
||(0.064)|(0.052)|(0.319)|(0.072)|(0.223)|
|implied MPC|0.399|0.205|0.064|0.060|-0.013|
|First-stage F-stat|5.58|||||
|Observations|3900|3900|3900|3900|3900|
|||**Urban Ho**|**useholds**|||
|Log(remittances)|0.168***|0.082**|0.538***|0.210***|0.367**|
||(0.056)|(0.034)|(0.182)|(0.064)|(0.148)|
|implied MPC|0.718|0.149|0.184|0.201|0.167|
|First-stage F-stat|20.89|||||
|Observations|2088|2088|2088|2088|2088|

Source: Author’s estimates using the NLSS-III data.

Note: This table shows the impact of remittances on consumption and expenditures. Reported coefficients are the estimates of _β_ from Equation (1), and the corresponding estimates of the marginal propensity to consume (MPC). The column headings indicate the outcome variables. Dependent variable is the natural logarithm of remittance received by the household, and is instrumented with the village level migration rates to Gulf-Malaysia in 2001. The MPC is computed for the average household. Standard errors, clustered at the ward level, are reported in parentheses. _∗∗∗_ : _p <_ 0 _._ 01; _∗∗_ : _p <_ 0 _._ 05; _∗_ : _p <_ 0 _._ 1 23
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# **Table I**

MHEAS AND STANDAR **D** EVIATIONS OF SELECTED VARTABLES

|----------.-----------------------------------------------<br>VARIABLES<br>------------------------------------------------------------------|TEACHERS|__--------------<br>OTHERS|---__-<br>ALL<br>__---------__|
|---|---|---|---|
|Monthly Base Salaries|194216|130737|141000|
||(115384)|(125245)|(125735)|
|Monthly Benefits|18284|37826|34667|
||(34080)|(73700)|(69193)|
|- Transportation|2517|11'68|9853|
|- Housing|12416|12974|12883|
|- Co.,ission|3170|12046|10611|
|- Other|182|1538|1319|
|Total Monthly Salaries|212500|168563|175666|
||(138441)|(170973)|(166747)|
|Years of Schooling|12.80|9.57|10.09|
||(3.57)|(3.53)|(3.73)|
|Earned Diploma? (yes-i)|1.00|0.93|0.95|
||(0.0)|(0.25)|(0.23)|
|Experience|10.04|9.82|9.85|
||(8.63)|(7.49)|(7.67)|
|Age|33.37|33.30|33.31|
||(7.85)|(8.27)|(8.20)|
|Monthly Hours Worked|121.11|184.79|174.49|
||(53.49)|(55.80)|(60.13)|
|Sex (Females-i)|0.26|0.20|0.21|
||(0.44)|(0.40)|(0.41)|
|Work in Abidjan? (yes-i)|<br>0.30|0.61|0.56|
||<br>(0.46)|(0.49)|(0.50)|
|Nationality (Ivorian-1)|<br>0.93|0.95|0.94|
||<br>(0.26)|(0.23)|(0.23)|
|Work for Govt? (yes-i)|0.81|0.49|0.54|
||<br>(0.39)|<br>(0.50)|(0.50)|
|Number of Observations<br>|54<br>|279<br>|333|
|Standard-------------de-----iations--|---------in----parenthese|s.-----__------__||
|Standard deviations in pa|rentheses.|||
to the vicious cycle of outages and non-payment of electricity bills (Dzansi et al., 2018). Additionally, self-reported measures of outage intensity are plausibly measured with error, hence the possibility of a downward bias (attenuation bias) in the impact from OLS estimation cannot be ignored (Allcott et al., 2016). To address these issues, I utilize the instrumental variable approach and exploit spatial and time variations in lightning intensity as an instrument for power outages.

Lightning is a major cause of power outages around the world particularly within the tropics where thunderstorm activities are prevalent (Andersen et al., 2011, 2012; Andersen and Dalgaard, 2013). Lightning strikes contain about a billion volts of electricity; therefore when it strikes a transmission line or transformer, it induces voltage surge, thereby destroying the transmission lines and equipments<sup>23</sup> and curtailing the flow of electricity. Electrical infrastructure destroyed by lightning induced voltage spikes and dips, could take several days to be repaired and often entail high cost of replacement. As a result, affected communities often go several days without electricity. In South Africa for instance, lightning is estimated to account for nearly 65% of all over-voltage damages to electrical transmission network<sup>24</sup> , with strikes within 40 meters of a transmission (distribution) line causing significant damages (Andersen and Dalgaard, 2013). Similar effects have been recorded in Swaziland<sup>25</sup> (Mswane and Gaunt, 2005), Nigeria<sup>26</sup> and Ghana<sup>27</sup> . In the United States, lightning activities account for about a third of all incidence of power outages (Chisholm and Cummins, 2006). Moreover, the fact that lightning activities are natural phenomena, it induces random variations in the incidence and intensity of outages across space and time conditional on locational and climatic characteristics. Consequently, lightning strikes have increasingly been used as an instrument for the quality of electric-

> 23http://www.liveline.co.za/lightning-stats.php

> 24http://www.liveline.co.za/lightning-stats.php

> 25Lightning accounts for about 50% of outages incidence in the country (Mswane and Gaunt, 2005)

> 26Adepitan and Oladiran (2012) estimates that lightning accounts for nearly 10% of random electricity outages experienced in Ijebu province.

> 27http://www.ghanaweb.com/GhanaHomePage/NewsArchive/Lighting-cuts-electricity-to-SissalaEast-district-453319

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