rafmacalaba/datause-encoder-data huggingface dataset:
Most importantly for our purposes, including these additional controls has little effect on the magnitude and has no effect on the significance of the coefficients on remittances. We continue to find that remittances have a positive impact on the number of bank branches and accounts and on the ratio of deposits and credit to GDP (compare to Table 3 columns 5 through 8).

# _Potential endogeneity of remittances_

The results reported on tables 2-4 ignore the potential endogeneity of remittances. There are numerous sources of endogeneity, with suggested biases running in either direction. First, the presence of financial institutions may cause higher remittance flows, either because banking development allows people to finance migration, and hence increases migration flows and remittances, or because the presence of financial institutions is associated with lower costs of sending remittances, and hence a greater propensity to do so. Neither of these seems to be a first order concern. Commercial banks in Mexico are an unlikely source of credit to finance migration. While better access to financial networks might facilitate receipt of remittances, the primary channel appears to be from migration flows to banking sector depth and breadth. We check this by using _municipio_ level data on migration rates rather than remittance rates. The 2000 population census asks whether any member of the household has migrated outside of Mexico in the past five years, and if so, to which country. We calculate the percentage of households with at least one emigrant to the United States. Appendix Table A.2 shows that we obtain nearly identical results when we use this variable in place of remittance flows. Thus, the effect appears to be driven by migration flows, which cause remittance flows.

Our findings are likewise unaffected by reverse causation arising from the fact that we measure bank branches and remittances at the same point in time. Data on banking breadth and depth are available from the Banco de Mexico for the 2001-2005 period. These later data exclude some of the _municipios_ ,<sup>21</sup> though the excluded _municipios_ account for less than 3 percent of the Mexican population. When we regress the mean of branches and deposits per capita as well as the average deposits and credit to GDP ratios over the period 2001-2005 against our measure of

> 21 There are more than 2,400 _municipios_ in Mexico. However, the data provided by the Banco de México aggregate the branches, deposits and loans for some of the smaller _municipios_ into a broader category labeled “others”.  There are 29 states which report this “other” category.

16
# **Fig 2 Zambia, 2009 (attending school, ages 7-18)**

<!-- Start of picture text --> Fig 2a Fig 2b<br><!-- End of picture text --> <!-- Start of picture text --> Unitary expenditure on education Unitary expenditure on education<br>by quintiles of consumption (2009) by quintiles of probability (2009)<br>61 64<br>68 69 48 67 70 74<br>77 83<br>55 53 49 37 41 53 50 38<br>-7 -11 -19 -32 -35 -7 -14 -19 -36 -20<br>-112 -102<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>100 100<br>50 50<br>0 0<br>US$ US$<br>-50 -50<br>-100<br>-100<br><!-- End of picture text --> Source: Author’s estimates from Zambia’s Ministry of Education (2010), Zambia’s Ministry of Finance and National Planning (2010), Cuesta, Kabaso, Suarez-Becerra (2012), CSO’s Living Conditions Monitoring Survey (LCMS, VI).

# **Fig 3 Liberia, 2007 (attending school, ages 6-15)**

**Fig 3a                                                         Fig 3b**

<!-- Start of picture text --> Unitary expenditure on education Unitary expenditure on education<br>by quintiles of assets index (2007) by quintiles of probability (2007)<br>9<br>3 3 5 5 6 2 3 4 6<br>-3 -5 -2 -4 -3 -4 -2 -3<br>-6 -6 -5<br>-7 -6 -7<br>-8 -10 -9 -10<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>10 10<br>5 5<br>0 0<br>US$ US$<br>-5 -5<br>-10 -10<br><!-- End of picture text --> Source: Authors’ estimates from LISGIS (2008) and Abras and Cuesta (2011), World Bank (2010) 17
In particular, there have been a limited number of studies of the emissions and health consequences of cook-stoves in use in Africa. In 2000, a study of health effects in the Kiambu District of Kenya demonstrated statistically significant reductions in the prevalence of Acute Respiratory Infections (ARI) and conjunctivitis among women and children under five who used improved stoves compared with those who did not.<sup>10</sup> The study found that the probability of children under five having ARI in households with traditional three-stone fires was 2.6 times greater than for households with improved stoves, and was 2.8 times greater for women aged between 15 and 60. Similarly, the chance of contracting conjunctivitis was 3.3 times higher for children, and 3 times higher for women, in households using three-stone fires rather than improved stoves. More recent evidence from Africa is provided by measurements undertaken in Ghana to test the _Gyapa_ stove compared to a traditional stove, which showed a reduction in kitchen concentrations of fine particulates of 52%, as well as measurements in Ethiopia to test the _CleanCook_ ethanol stove, which showed a reduction of 84% in average kitchen concentrations of PM2.5.<sup>11</sup> Using new air monitoring data, this paper presents a case study conducted in Madagascar and provides evidence on fine particles whose diameter is less than 2.5 microns (PM2.5) and carbon monoxide (CO) concentrations in rural households of Madagascar. Attempts have been made to analyze sources of variation in HAP: fuel type, stove type and structural ventilation characteristics of houses, to inform decision-making regarding the effectiveness of alternate HAP mitigation measures. Given that fuelwood is the primary source of household energy in Madagascar and that consumption is expected to increase with population growth, it is anticipated that national production of wood will no longer be able to meet national demand in a sustainable manner. It is in this context that the Ministry of Environment has proposed the goal of replacing 30% of fuelwood for cooking with ethanol, and that this study considered ethanol as a potential HAP mitigation measure.

The remainder of the paper is organized as follows. Section 2 introduces the household air quality problem in Madagascar. Section 3 discusses determinants of HAP and our stratified sampling strategy. Summary data on the factors affecting HAP are described in Section 4.

> 10 E.M. Wafula, M. Kinyanjui, L. Nyabola and E.D Tenambergen, Department of Paediatrics and Child Health, College of Health Sciences at the University of Nairobi, January 2000 11 Pennise et al., 2009

3
Figure 4. Proportion of Adults with an Account at a Formal Financial Institution <!-- Start of picture text --> 2011 2014<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Rest of High Rest of ECA Turkey BRIC Rest of High Rest of<br>Developing Income Developing Income ECA<br>World World<br>Lowest quintile q2 q3 q4 top quintile<br><!-- End of picture text --> _Source_ : FINDEX 2011 and 2014 The largest gaps in access to finance are due to differences by gender in Turkey. Females have a 49 percentage point lower proportion of having an account at a formal institution in Turkey (Figure 5). This disparity is over five times larger than the difference in the BRICS countries. The discrepancy in comparison with rest of ECA is even more alarming, as males and females use accounts at an almost identical rate. This difference in account usage in Turkey may be resulting from historical social norms, low female labor force participation, and a relatively lower female education level in the country.

The disparity between males and females in terms of account usage decreased between 2011 and 2014. Male use of bank accounts diminished from 82 percent to 69 percent, while female usage increased from 33 to 44 percent. Despite Turkey’s success in expanding access to accounts among women, the gender discrepancy is still significantly larger when compared with the BRICS, ECA and other developing countries. Going forward, specifying the policies that resulted in the improved access to banking for females and ensuring their sustainability can help Turkey in catching up to benchmark country groups.

Figure 5. Account Use in Turkey, by Gender <!-- Start of picture text --> 2011 2014<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Turkey BRIC Rest of High_Income Rest of ECA Turkey BRIC Rest of High Income Rest of ECA<br>Developing Developing<br>World World<br>male female<br><!-- End of picture text --> _Source_ : FINDEX 2011 and 2014 The level of education and age groups also affect account use in Turkey. Remarkably, 99 percent of individuals with tertiary education have an account at a formal financial institution, which exceeds the 7
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 40: Assumed age and gender distribution of new civil servants (% of new recruits)**

<!-- Start of picture text --> 7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49<br>Age<br>male female<br><!-- End of picture text -->

**Source** : WB calculations based on actual data

**Figure 41: Assumed age and gender distribution of new teacher (% of new recruits)**

<!-- Start of picture text --> 7.0%<br>6.0%<br>5.0%<br>4.0%<br>3.0%<br>2.0%<br>1.0%<br>0.0%<br>17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49<br>Age<br>male female<br><!-- End of picture text -->

**Source** : WB calculations based on actual data

- 86 -
-3-

produdltiviiies, Lieri exArd caiories may weili De callea tor as an income generation device for the household as a whole (Pitt, Rosenzweig, and Hassan 1990). Even a household that was egalitarian in terms of its welfare functicn might allocate calories unequally. While there are some tests of the degree of inequality aversion displayed by a household welfare function, as revealed by consumption allocation and other outcomes (Behrman and Deolalikar 1989), a major debate in the literature is on whether such a "common preference" model is valid. An alternative is a view of the outcomes as being determined by intrahousehold bargaining. The empirical implications of this for demand theory are laid out by McElroy (1990), who makes clear that the difference between the common preference and bargaining approaches can be traced to the fact that, in the latter, changes in the external environment can alter the "threat points" and lead to reallocations that are different from those predicted by the household wel-are maximizing model.

Haddad and Kanbur (1990b) have considered the predictions of the bargaining model on intrahousehold inequality as the household gets better ott. they find that the net effect depends on a subtle interaction between changes in the gains from cooperation and in the threat points. Under certain conditions, intrahousehold inequality can first increase and then decrease, thus tracing out a "Kuznets curve." Haddad and Kanbur (1990c) find empirical evidence for such a relationship for data from the Philippines. Thomas (1990) and Altonji, Hayashi, and Kotlikoff (1990) are among the authors who find
<!-- Start of picture text --> No. of IDA-only Project Approvals per FY<br>Ss<br>wv<br>So<br>oO°<br>o<br>oOo<br>N<br>o-<br>oOi=]vl lt<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> <!-- Start of picture text --> IDA-only Project Portfolio<br>by Number of Project Approvals per FY<br>8<br>Soo<br>58<br>Oo<br>a<br>wvo<br>oON<br>Oo<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>EEE AwaysF-CS «(I Partial FCS<br>ME Never FCS<br><!-- End of picture text --> <!-- Start of picture text --> Net Commitment of IDA-only Project Approvals per FY ($ m)<br>So<br>Sooa<br>oO<br>ooS o<br>o<br>=<br>o<br>o<br>So<br>=]<br>So<br>oS<br>N<br>oa 2O ll<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> <!-- Start of picture text --> IDA-only Project Portfolio<br>by Net Commitments of Project Approvals per FY ($ m)<br>8<br>Soo<br>58<br>Oo<br>a<br>owv<br>oON<br>Oo<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>EEE AwaysF-CS «(I Partial FCS<br>ME Never FCS<br><!-- End of picture text -->
Table 5: IV regression: The effect of migration on land tenure arrangements

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|2.607***|2.199***|0.409|-1.787**|-1.041*|0.209|0.459|
||(0.832)|(0.652)|(0.502)|(0.865)|(0.577)|(0.728)|(0.425)|
|bank branches (number)|-0.0252|-0.00298|-0.0222*|-0.000115|0.0347**|-0.000842|0.0233|
||(0.0242)|(0.0204)|(0.0131)|(0.0255)|(0.0174)|(0.0198)|(0.0151)|
|road length (km)|-0.000722|-0.000640|-8.20e-05|0.000606|0.000136|0.00164***|-0.000556|
||(0.000631)|(0.000556)|(0.000316)|(0.000553)|(0.000360)|(0.000473)|(0.000348)|
|2012 year|-5.709*|-3.258|-2.451|-3.936|7.936***|22.50***|3.767**|
||(3.400)|(3.003)|(1.956)|(3.028)|(2.013)|(2.685)|(1.884)|
|observations|878|878|878|878|878|878|878|
|mean dep. var.|45.34|27.83|17.51|34.13|4.92|36.90|11.25|

Note: This table uses the shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
sectors, a lot remains to be done especially in terms of funding mobilization. For instance, in 2013, only 0.6% of public expenditures was allocated for preschool education (Minist`eres en charge de l’ ́education et de la Formation, 2017).

# **2.2 Youth Employment Context**

In 2018, an estimated 37% of young women and 22% of young men aged 16 to 35 in Burkina Faso were not in school or employed outside the home (authors’ calculations using the Harmonized Survey on Households Living Standard 2018/19 data). Additionally, 33% of young women and 38% of young men who were working were still living in poverty as defined by earning less than USD 1.90 a day (International Labour Organization, 2020). Against this backdrop, the Government of Burkina Faso through its Youth Employment and Skills Project (PEJDC) recruited 10,255 young women (85%) and men (15%) in 49 communes to implement labor-intensive public works (“PTRHIMO”, in French) in all regions of the country from mid-July 2019. The eligibility conditions were: i) being a Burkina Faso citizen; ii) out of school or never enrolled in school; iii) not a former beneficiary of the project; and iv) aged 16 to 35. PTR-HIMO participants were selected through a call for applications for the public work scheme in each commune by the government. The public work included the construction of bridges and rural roads, the maintenance of urban roads and administrative spaces, and reforestation. The number of desired workers in each site varied depending on the size of the site and the type of work, with a minimum requirement of 30% women at each site. Sites were defined as a _commune_ (municipality) for smaller urban centers or an _arrondissement_ (neighborhood) in the two largest cities, Ouagadougou and Bobo-Dioulasso. Two rounds of selection procedures were followed: i) registration of interested applicants; and ii) participation in a public lottery – all applicants drew a piece of paper from a box containing papers with either yes or no, where drawing yes implied being selected to participate to the program and no implied not being selected.

Selected participants (“brigadiers”) for the project began their public works assignment in July 2019. They worked from 8am to 2pm Monday to Saturday and received 37,000 FCFA a month (approximately 63 USD at the time of the study, roughly the minimum wage in Burkina Faso), for six months of project participation between July 2019 to February 2020, including a one month 6
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Dulitzky, D. and X. Hou (2007). Health. Fiscal Policy and Economic Growth, Lessons from <u>Eastern Europe and Central Asia. C. Gray, T. Lane and A. Varoudakis. Washington DC, the</u> World Bank Group.

European Observatory on Health Care Systems (2002). Health care systems in transition: Georgia. Copenhagen, Denmark, WHO Regional Office for Europe.

Feldstein, M. S. (1973). "The welfare loss of Excess Health Insurance." Journal of Political <u>Economy</u> **81** : 250-280.

Gotsadze, G. e. a. (2001). Health Service Utilization and Expenditure in Tbilisi - 2000, Report of Household Survey. Tbilisi, Georgia, Curatio International Foundation.

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Harmon, C. and B. Nolan (2001). "Health insurance and health services utilization in Ireland." <u>Health Econominics</u> **10** : 135-145.

20
**Figure 2**

**Figure 3**

**Quality, policy distortion and growth**

**Quality, government consumption and growthb**

<!-- Start of picture text --> 35<br>1.38 Percapita 3.0 | |<br>1.0 /^distorb n<br>distaton<br>0.o > f igh policy<br>high quaIlity<br>low quality<br><!-- End of picture text -->

<!-- Start of picture text --> 3.5 -<br>Percapita ao<br>GDP growth<br>20<br>1.5owsize<br>~~~1.0<br>oo.0 t hgh size<br>qai<br>highuqalityy<br>low quality<br><!-- End of picture text --> Number of years needed to double per capita GDP Panel A Panel B

|Policy Dist|ortion|
|---|---|
|Quality<br>Low|High|
|Low<br>50 years|165 years|
|High<br>23 years|33 years|

||Govermnent Size|
|---|---|
|Quality|Low<br>High|
|Low|46 years<br>239 years|
|High|22 years<br>36 years|

Based on the IV regression in Table 6, column 4.

bBased on the IV regression in Table 6, column 4.

58
**Table S1.5: Alternative dependent variables – using indices of IPV**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|A: Physical Violence|||||
|ln(U<sup>f</sup>)|-0.007|-0.373***|-0.409***|-0.386***|
||(0.029)|(0.120)|(0.109)|(0.119)|
|ln(U<sup>m</sup>)|-0.008|0.347***|0.331***|0.325**|
||(0.042)|(0.108)|(0.115)|(0.125)|
|Observations|381730|381730|381730|348165|
|Mean Violence Index|0.018|0.018|0.018|0.018|
|B: Sexual Violence|||||
|ln(U<sup>f</sup>)|-0.164**|-0.953***|-0.881***|-0.887***|
||(0.074)|(0.185)|(0.270)|(0.278)|
|ln(U<sup>m</sup>)|0.173**|0.847***|0.828***|0.834***|
||(0.084)|(0.158)|(0.242)|(0.244)|
|Observations|275718|275718|275718|268173|
|Mean Violence Index|-0.001|-0.001|-0.001|-0.001|
|C: Any Violence|||||
|ln(U<sup>f</sup>)|-0.088|-0.725***|-0.668***|-0.679***|
||(0.052)|(0.121)|(0.205)|(0.213)|
|ln(U<sup>m</sup>)|0.084|0.642***|0.601***|0.610***|
||(0.066)|(0.111)|(0.206)|(0.212)|
|Observations|274002|274002|274002|266517|
|Mean Violence Index|0.008|0.008|0.008|0.008|
|Year FE||Y|Y|Y|
|Country FE|||Y|Y|
|X controls||||Y|

_Source:_ Authors’ analysis based on data from the Demographic and Health Surveys (DHS).

# _Notes:_

* p<0.10, ** p<0.05, *** p<0.01. Standard errors are robust to arbitrary forms of heteroscedasticity and are clustered at the country level. X is a vector of controls for age and education of respondent, education of her spouse, and urban location. Relative to Table 1, the dependent variables are now indices rather than binary constructs.
<!-- Start of picture text --> Tat% 2 Clbseuiom of Cme Study Episodes.97-1990<br>Coverae of Parsllel Rate<br>Mainly Capital Capital Account and<br>Account Some Current Account<br>Eutope (*) Venezuela ('83-'89)<br>LAgKl/ Mexico ('82-'88) Tanzania (after 1984)<br>Tolrated Argentina ('81 '89) Ghana ('83-'87)<br>Turkey ('80-'84) Zambia ('87-'88)<br>Lepaty of Ghan (after 1987)<br>Truanadow<br>at the<br>Paralid<br>Rate Ghana (before '83)<br>Sudan<br>Illgal Tanzania (to '84)<br>Turkey (1970,s)<br>Zambia (except '87-'88)<br><!-- End of picture text -->

* Includes dual exchange rab experiences in Belgium (1957-90), France (1971-74) and Italy (197374).
<u>Table 3: Cyclical Properties of Transition Rates into of Employment: Mexico and Brazil</u>

|||Mexico<br>|||Brazil<br>||
|---|---|---|---|---|---|---|
|Series|St. Dev|Correlation|Elasticity|St. Dev|Correlation|Elasticity|
|S-O|0.032|0.108|0.128|0.037|-0.298|-0.563|
|I-O|0.035|0.402|0.519|0.039|0.135|0.271|
|F-O|0.081|0.240|0.720|0.051|0.125|0.325|
|S-U|0.167|-0.716|-3.156|0.154|-0.725|-3.485|
|I-U|0.118|-0.665|-4.141|0.093|-0.730|-5.742|
|F-U|0.093|-0.416|-1.440|0.072|-0.162|-0.600|

Notes: The table shows the standard deviation, the correlation with output and the elasticity with respect to output of the logged and HP filter de-trended transition rates from employment (S,I and F) into non employment (O and U) for Mexico and Brazil. Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. O=Out of the Labour Force, U=Unemployment, E=Employment, S=Informal Self-Employed, I=Informal Salaried, and F=Formal Sector, all as proportions of working age population. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2.

42