rafmacalaba/datause-encoder-data huggingface dataset:
The challenges women face in human capital accumulation also vary across countries. In Costa Rica and Panama, an advantage in acquiring skills partially offsets the negative effects of wage discrimination in many occupations. However, the strength of support varies across the two countries. The median occupation lies very close to parity in the former while sitting firmly in favor of women in Panama. However, not only do women in both countries enjoy a favorable situation in all three educationintensive occupations, the variation in the median barrier among such jobs is negligible across the two Central American economies. The prevalence of barriers in El Salvador is not innocuous with the median gross barrier standing at 1.35 for all occupations and at 1.22 for the education-intensive ones.

**Table 5: Cross-country comparison of barriers in 2015**

||Costa|El<br>||
|---|---|---|---|
||Rica|Salvador|Panama|
||Panel A:|Barriers in labor|markets|
|No. of occupations in which women are at a disadvantage|8|4|7|
|– of which how many are education-intensive|3|1|3|
|Median gross barrier||||
|All occupations|1.30|1.00|1.43|
|Occupations in which women are at a disadvantage|1.32|1.10|1.50|
|Education-intensive occupations|1.33|1.00|1.50|
||Panel B:|Barriers in huma|n capital|
|No. of occupations in which women are at a disadvantage|4|8|2|
|– of which how many are education-intensive|0|3|0|
|Median gross barrier||||
|All occupations|0.97|1.35|0.82|
|Occupations in which women are at a disadvantage|1.32|1.35|2.41|
|Education-intensive occupations|0.78|1.22|0.76|
||Panel|C: Composite ba|rriers|
|No. of occupations in which women are at a disadvantage|9|9|8|
|– of which how many are education-intensive|3|3|3|
|Median gross barrier||||
|All occupations|1.26|1.18|1.24|
|Occupations in which women are at a disadvantage|1.26|1.18|1.24|
|Education-intensive occupations|1.13|1.14|1.13|

Finally, consider the composite barriers. As I discussed earlier, there is not much difference in the prevalence of obstacles faced by women across the three countries. The combined effect almost always works against women. However, three notable observations stand out when I consider magnitudes. First, 21
inclusion in the MDGs and SDGs). A reasonable criterion for a socially acceptable relative poverty line might then be that any person identified as living in extreme poverty must also be identified as poor by the societal poverty line. In accord with this candidate criterion and past proposals, we also consider a variant of our preferred SPL which sets a lower bound for the threshold (Z*) at $1.90. This lower-bound variant of the SPL, expressed as _max_ ($1.90, $1 + 50% of median consumption), is conceptually analogous to the existing Atkinson-Bourguignon and Ravallion-Chen lines.

Our preferred parameterization of the SPL is $1 + 50% of median consumption, but we also view the lower-bound variant of this line as a reasonable alternative parameterization. Our reason for preferring the SPL of $1 + 50% of median consumption is in part due to its simplicity and in part due to finding no empirical evidence to support a flat portion in the SPL schedule. Given this finding, there is then tension in proposing the lower-bound variant as an alternative, but this tension is more of a conceptual concern than an empirical one. Our analysis indicates that the measured differences between these lines is relatively small and will continue to dissipate.

The RMSE of the lower-bound variant of the SPL (when assessed against the full sample of national poverty lines) is slightly more than 6 percent greater than the RMSE of the SPL without the lower bound. The 6 percent difference is substantial though because by construction, differences in errors will only exist for countries where the lower bound is binding. This means that the lower-bound variant performs significantly less well for the poorest of countries based on analysis of the full sample. This unfavorable finding is tempered considerably when contrasted with findings for the circa-2011 sample. When examining the more recent data, the 21
Having tools or material for fishery, agricultural production and handicraft contributes to 12.2% of the inertia explained by the first component of the analysis.<sup>15</sup> Other items contribute to less than 10% of inertia each.

Using retrospective data on assets from the June survey, Figure 5 presents the asset index distributions before the earthquake, in February and in June. Using this index, we can notice that the inequality of household wealth (as measured by the Gini coefficient) has decreased after the earthquake due to higher losses among the wealthiest. This is particularly true among households living in camps (Gini is 0.2446 before the earthquake and 0.1970 in February). Then, between February and June, inequality of household wealth has increased—from 0.3267 to 0.3325 among non-camp households and from 0.1970 to 0.2183 among camp households.

**Figure 5. Distribution of wealth (directly affected areas)**

<!-- Start of picture text --> Asset Index Density, Non-Camp Households Asset Index Density, Camp Households<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Asset Index Asset Index<br>June (Gini=0.3325) June (Gini=0.2183)<br>February (Gini=0.3267) February (Gini=0.1970)<br>Before Earthquake (Gini=0.3346) Before Earthquake (Gini=0.2446)<br>kernel = epanechnikov, bandwidth = 4.6918 kernel = epanechnikov, bandwidth = 3.0994<br>.05<br>.02<br>.04<br>.015<br>.03<br>.01<br>.02<br>.005 .01<br>0 0<br><!-- End of picture text --> Source: Own computations using June 2010 (EFSA II) survey. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. Weights are calculated using pre-earthquake assets.

Figure 6 presents the distribution of wealth losses in percentage of pre-earthquake wealth. The percentage of asset loss among households who lost assets is around 25%. This percentage does not seem to vary a lot according to pre-earthquake wealth quintile.

> 15 Note that households with tools/material (for fishery, agricultural production and handicraft) will be classified as poorer households (asset index weight equals -0.951). This is due to the fact that such asset variable is not well correlated with other asset indicators (e.g., private water, electricity for lighting, WC, oven, television, fan and so forth) that would classify them as richer. In this sense, the asset index can be described as the best regressed latent variable on the assets variables (or primary indicators) since no other explained variable is more informative (Asselin, 2009).

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Isaacman, S., Becker, R., Caceres, R., Kobourov, S., Martonosi, M., Rowland, J., & Varshavsky, A. (2011). Identifying Important Places in People’s Lives from Cellular Network Data. In K. Lyons, J. Hightower, & E. Huang (Eds.), _Pervasive_ _Computing_ (Vol. 6696, pp. 133–151). Springer. Retrieved from http://www.springerlink.com/content/r14x8r7573738143/abstract/ ITU. (2011). _World telecommunication/ICT indicators database_ . International Telecommunication Union.

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The Parable of Google Flu: Traps in Big Data Analysis. _Science_ , _343_ (6176), 1203–1205. https://doi.org/10.1126/science.1248506 Lu, X., Wetter, E., Bharti, N., Tatem, A. J., & Bengtsson, L. (2013). Approaching the Limit of Predictability in Human Mobility. _Scientific Reports_ , _3_ . https://doi.org/10.1038/srep02923 Ng, A. (2011). Machine Learning and AI via Brain simulations.

NPR. (2015). How Cellphone Use Can Help Determine A Person’s Creditworthiness. _Morning Edition_ . Retrieved from https://www.npr.org/2015/08/04/429219691/how-cellphone-usage-can-help-determine-a-person-s-credit-worthiness Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., ... Barabási, A.-L. (2007). Structure and tie strengths in mobile communication networks. _Proceedings of the National Academy of Sciences_ , _104_ (18), 7332–7336. https://doi.org/10.1073/pnas.0610245104 Palla, G., Barabási, A.-L., & Vicsek, T. (2007). Quantifying social group evolution. _Nature_ , _446_ (7136), 664–667. https://doi.org/10.1038/nature05670 Pedro, J. S., Proserpio, D., & Oliver, N. (2015). MobiScore: Towards Universal Credit Scoring from Mobile Phone Data. In _User Modeling, Adaptation and Personalization_ (pp. 195–207). Springer, Cham. https://doi.org/10.1007/978-3-319-202679_16 Soto, V., Frias-Martinez, V., Virseda, J., & Frias-Martinez, E. (2011). Prediction of Socioeconomic Levels Using Cell Phone Records. In J. A. Konstan, R. Conejo, J. L. Marzo, & N. Oliver (Eds.), _User Modeling, Adaption and Personalization_ (pp. 377–388). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_35 Suri, T., Jack, W., & Stoker, T. M. (2012). Documenting the birth of a financial economy. _Proceedings of the National Academy of Sciences_ , _109_ (26), 10257–10262. https://doi.org/10.1073/pnas.1115843109 Totolo, E. (2018). The Digital Credit Revolution in Kenya: An Assessment of Market Demand, 5 Years On. CGAP. Retrieved from http://www.findevgateway.org/library/digital-credit-revolution-kenya-assessment-market-demand-5-years 23
In this instance _x_ does not include the intercept, since _Y_<sup>_∗_</sup> is crossing unknown thresholds for the ordering of alternatives ( _ibid_ ). Thus we define that: where the lower limit for the poor will be _−∞_ , and the upper limit for the middle class is _∞_ . Therefore the probability of belonging to either group is ( _ibid_ ): where _F_ is the logistic distribution function for _ui_ .

In the ordered logit model, the dependent variable is categorical and assumes value 1 for poor households, 2 for vulnerable households, and 3 is for middle class. In this model, a natural ordering is assumed defined by the consumption status going from poor to middle class. The interpretation of the coefficients is that a one unit increase in one of the independent variables will increase or decrease the log odds of being in a higher income category by the amount of the coefficient, holding other variables constant.

# _Survey to survey estimation_

As mentioned earlier, the GHS panel is not directly comparable to the HNLSS 2003/2004 data. There is also evidence of underestimated consumption in the latter (World Bank, 2014). To overcome these problems, we employ survey-to-survey imputation techniques derived from the poverty mapping literature (inter alia Elbers et al., 2003) to re-estimate HNLSS 2003/2004 consumption figures. Specifically, we use Wave 1 of the GHS-Panel data to impute consumption on the 2003/2004 HNLSS data. The process allows a longer trend analysis between 2003 and 2013 using both the GHS-Panel and the HNLSS datasets.

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**Table** **<u>5: Splitting the commodities’ variables into different types</u>**

||(1)|(2)|(3)|
|---|---|---|---|
||anyonset|anyonset|anyonset|
|Sum_Px_point-source|0.018*<br>(0.01)|||
|Sum_Px_diffused|0.013<br>(0.013)|||
|Sum_Px_consumed||0.017*<br>(0.009)||
|Sum_Px_not||0.022||
|consumed||(0.017)||
|Sum_Px_point-source|||0.032|
|consumed|||(0.033)|
|Sum_Px_point-source|||0.013|
|not consumed|||(0.015)|
|Sum_Px_diffused|||-0.019|
|consumed|||(0.015)|
|Sum_Px_diffused|||0.021|
|not consumed|||(0.015)|
|Other trade variables|YES|YES|YES|
|Controls|YES|YES|YES|
|Weight|[t-12; t-2]|[t-12; t-2]|[t-12; t-2]|
|Observations|3,403|3,426|3,104|
|R-sq. (within)|0.070|0.072|0.072|
|Countries|114|114|114|

_Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; all regressions include country fixed effects, year effects and controls (any conflict since 1946 and war border (t-1)); other trade variables include MP and Pm with their three lags and trade with neighbors (t-1).._ 30
# **Annex 1 – Variables’ definitions**

|**Variable**|**Description**|
|---|---|
|**Log reservation wage**|The logarithm of the wage self-reported as the level below which the<br>respondent would not accept a job offer, per assumed work hour of a<br>standard working week. The variable is observed only for unemployed<br>persons.|
|**Log market wage**|The logarithm of the wage the respondent received at the time of the<br>interview, per actual work hour. The variable is observed only for<br>unemployed persons.|
|**Female**|1 if persons is women, 0 otherwise|
|**Age**|Age expressed in years|
|**Married**|1 if persons is married, 0 otherwise|
|**Number of children**|Number of children living in the household|
|**Education**|Three dummy variables for primary, secondary and tertiary education.<br>Primary is the reference category.|
|**Recipient of unemployment**<br>**benefits**|1 if the person received state unemployment compensation in the 4 weeks<br>preceding the interview, 0 otherwise|
|**Unemployment spell duration**|The period, expressed in years, that elapsed since the last time the person<br>was employed.|
|**Log of household’s welfare**|Calculated variable, based on coefficients obtained from SILC. Note that<br>LFS does not contain data on income except for wages. The following<br>equation was used: Log of household’s welfare = 0.151*Log of the wage<br>per hour + 0.237*Average education level in the household +<br>0.022*Average age in the household -0.057*Number of children in the<br>household+6.81|
|**Sources of livelihood**|Five dummy variables created for the following sources: wages/pensions<br>of others, remittances, social assistance, capital income and other income.<br>Wages/pensions of others are the reference category.|
|**Field of studies completed**|Six dummy variables created for the following fields: general programs,<br>humanities and arts, social sciences, STEM, agriculture and health care.<br>General programs used as the reference category.|
|**Skill-specific employment rate**|The employment rate by skill by period.|
|**Field-specific employment rate**|The employment rate by field by period.|
|**Urban**|1 if the person is residing in urban area, 0 otherwise.|
|_Source: Labor Force Survey._||

25
further dropout during the course; 72% of the treatment group completed training and 69% of the treatment group received a certification of course completion in an evaluation course. Compliance does vary substantially by sub-group, with 72% of women and 62% of men receiving certification; people under 25 were also more likely to complete training. Another 3% of the treatment group as well as 3% of the control group received certification in an ISKUR course that was not included in the sample of evaluation courses during the period that evaluation courses took place.<sup>11</sup>

# **4. Data Collection, Baseline Comparisons, and Estimation Methodology**

The MIS system contained basic information about the course and the sex, age, and education level of the applicant. The main data for evaluation then come from surveys administered to the applicants, with some supplementary data from a survey of training providers, and some longerterm information on formal employment from the social security system.

# **4.1. Surveys**

A baseline survey and follow-up survey were both conducted through in-person interviews by a third-party professional survey firm (Frekans) that was not affiliated by ISKUR and which was selected by the evaluation team. The baseline survey took place on a rolling basis between September 13, 2010, and January 31, 2011. The goal was to conduct the surveys before courses began, but given the short window of time between selection of applicants and the start of the course, in practice only one-third of those surveyed were surveyed before the start of the course, while 79 percent of those interviewed were interviewed with 11 days of the start of the course. Applicants were told that the purpose of the survey was to help improve the services offered by ISKUR, and that their participation in the survey had no impact on being accepted into any training course, nor would their data be shared with ISKUR at the individual level. The overall baseline response rate was 90 percent.

The follow-up survey took place between December 27, 2011 and March 5, 2012, which corresponds to a period approximately one year after the end of training. It collected data on employment outcomes, as well as individual and household well-being. The response rate was 94

> 11 An additional 7.3% of the control group and 2.7% of the treatment group took a non-ISKUR course at any time from the beginning of the study period up to the time of the baseline survey.  The majority of these courses were funded by other government entities.  Our power calculations accounted the possibility that at least 15% might ultimately take up training.

9
||(23.19)|(30.64)|(31.95)|(43.91)|
|---|---|---|---|---|
|Chemicals|68.33***|-24.58|155.20***|96.44***|
||(22.19)|(60.20)|(37.76)|(35.55)|
|Medical|272.51|79.85|681.55*|571.31*|
||(190.54)|(108.69)|(364.86)|(345.14)|
|Fiber, rubber & plastic|28.79|-54.47*|41.76|-18.84|
||(28.23)|(28.32)|(73.15)|(17.63)|
|Smelting|15.55|-28.35*|41.75|-21.98|
||(21.69)|(15.67)|(32.05)|(30.01)|
|Machinery manufacture|6.16|-54.18|-26.72|-59.85|
||(12.24)|(47.44)|(21.91)|(41.71)|
|Utilities|20.87**|2.2|0.62|-46.04|
||(10.09)|(63.66)|(48.36)|(70.90)|
|Transportation|144.51*|95.16***|439.38***|379.11***|
||(84.83)|(10.56)|(94.84)|(58.23)|
|Others|40.96***|-10.94|29.16|-20.65|
||(15.68)|(44.81)|(70.07)|(38.23)|
|Time|3.84|6.78|3.14|1.20|
||(8.03)|(7.90)|(18.50)|(17.48)|
|Constant|-3.13|129.43**|70.99|130.67|
||(73.45)|(51.03)|(124.84)|(103.53)|
|No. ofobs|967|914|659|664|
|within_R_<sup>_2_</sup>|0.53|0.01|0.14|0.96|
|between_R_<sup>_2_</sup>|0.11|0.76|0.98|0.95|
|overall_R_<sup>_2_</sup>|0.24|0.57|0.96|0.96|
|Breusch and Pagan Lagrangia|n Multiplier test|for random e|ffects||
|Test statistics: χ<sup>2</sup>(1)|9.05***|8.91***|8.61**|8.52***|

Numbers in parentheses are standard errors of the estimated coefficients. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
# **I. Introduction**

By the most recent estimates, more than 12 million children under 18 in Sub-Saharan Africa are currently orphaned by AIDS (UNAIDS 2008). This overwhelming figure, resulting from a steady increase over the past 10 years, has led to substantial concern for the welfare of orphans and vulnerable children. While there are obvious psychological and social impacts of losing one or both parents at a young age, much policy attention has been paid to the consequences in terms of investments in the health and education of orphans—on the grounds that low levels of investments will lead to increasing household poverty, and contribute to slower overall economic growth and development.<sup>1</sup> Despite these concerns, the empirical evidence of these impacts is mixed (see discussion in Beegle and De Weerdt 2008). Analysis of the association between school participation and orphan status suggests substantial heterogeneity across countries—including countries in SubSaharan Africa (Ainsworth and Filmer 2006). At the same time, longitudinal case-studies reveal worse health and education outcomes for orphans, mainly maternal orphans, in Tanzania (Ainsworth and Semali 2000; Beegle, De Weerdt and Dercon 2007), South Africa (Case and Ardington 2006; Ardington 2008) and Kenya (Evans and Miguel 2007). While there are several potential explanations for this variability in the estimate of impact—including, for example, the national HIV prevalence rate or the characteristics of the health and education systems—one possible mechanism lies in different, and changing, patterns of living arrangements for orphans. While the vast majority of children live with one or both parents, single orphans are less likely to reside with a parent than non-orphans, and double orphans by definition can not reside with a parent.

Living with a caregiver who is not a parent may lead to worse outcomes for several reasons. First, altruism may be dictated by the extent of biological connectedness such that more closely related caregivers provide higher quality care to children than more distantly or nonrelated caregivers (Hamilton 1964). Case, Paxson, and Abelidinger (2004) analyze this in their study of educational achievement of orphans and non-orphaned children in Africa. They find that

> 1 For an example of direct measurement of the psychological distress associated with HIV/AIDS orphanhood, see the evidence from northern Uganda and a review of other studies in Atwine, Cantor-Graae, and Bajunirwe (2005).

2
# **Figures and tables**

**Figure 1: Household incomes and labor earnings in Brazil, 1995-2012.**

<!-- Start of picture text --> Panel A. Levels. Panel B. Inequality (Gini Index).<br>900 893.6 0.60 0.59<br>0.57<br>781.5<br>800<br>0.55<br>0.52<br>700 638.4 670.4 0.50<br>0.50<br>600 637.1 0.46<br>486.7 0.45<br>500 447.6<br>0.40<br>400 445.0 421.5 0.40<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 1995 1997 1999 2001 2003 2005 2007 2009 2011<br>Year Year<br>Average LaborEarnings Median LaborEarnings Average HouseholdPer Capita Income Labour Earnings 95% CI Household perCapita Income 95% CI<br>Panel C. Inequality (Theil Index & P90/P10 Ratio). Panel D. Inequality (P90/P50 & P50/P10 Ratios).<br>1.0 1.4 1.36<br>0.92<br>0.9 1.2<br>1.18 1.02<br>0.8 0.86 1.0<br>1.05<br>0.94 0.81<br>0.7 0.66 0.8<br>0.6 0.63 0.6<br>0.5 0.4<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 1995 1997 1999 2001 2003 2005 2007 2009 2011<br>Year Year<br>Theil Index (1995=0.45) P90/P10 (1995=10.00) log(p90/p50) log(p50/p10)<br>Labor Earinings Labor Earinings Labor Earinings Labor Earinings<br>2005 Reals Gini Index<br>1995=1 Ratio<br><!-- End of picture text --> Notes: All measures are calculated over the estimating sample (formal, informal and self-employed of ages 18-65). Negative incomes and the 99th and 1st percentiles of earnings are trimmed. Labor earnings refer to monthly earnings reported in the main occupation. The household per capita income includes all the incomes perceived by the household members. Source: _Pesquisa Nacional por Amostra de Domicílios_ PNAD.

27