rafmacalaba/datause-encoder-data huggingface dataset:
the KHDS, one-parent orphans are significantly less likely to be residing with the surviving parent than children with both parents alive. In the baseline, 86 percent of non-orphans were residing with at least one parent. Among one-parent orphans, however, 67 percent were residing with the surviving parent.

Table 1 shows that out of our sample of 718 children, who at baseline had both parents alive, 133 lost either one or both parents before the age of 15. The sample includes 52 people who lost their mother and 98 people who lost their father before the age of 15. Only 17 people became double orphans before age 15.

Table 2 shows that, starting from a sample of non-orphaned children aged 6-15 at baseline, those that subsequently lose a parent will, by adulthood, be on average 2 cm shorter and have 1 year less schooling than those who do not. Paternal and, especially, maternal orphanhood at childhood are statistically significant predictors of lower adult height, while only paternal orphanhood is statistically associated with lower education in adulthood. These correlations, however, do not control for other socioeconomic differences in these two groups of children, differences that could in part or in full explain the height and education gaps observed.

Before modeling the link between human capital indicators and orphanhood, we look at the household characteristics and socio-economic status during the baseline survey of those who became an orphan relative to their non-orphan counterparts, using simple significance tests of the differences in the means. As shown in table 3, those who had experienced the death of one or more parents before the age of 15 are, on average, less likely to live with either their mother or father at baseline. The heads in the households in which they were living during the baseline were older, less educated and more likely to be female. Further, they do not appear to be wealthier in terms of household consumption per capita or quality of flooring in the dwelling. The height of their mothers is also not significantly different. This would suggest that socio-economic differences determining selection into orphanhood are possibly not as strong as in some other studies.<sup>11</sup> Nevertheless, these descriptive statistics suggest the need to carefully control for baseline characteristics of children, their parents, and the households in which children reside to make inference about the impact of subsequent orphanhood.

> orphanhood using longitudinal household data from Uganda with information on child fostering. Yamano et al _._ (2005) conclude that these results will overestimate the negative impacts for orphans.

> 11 In the panel data examined by Case and Ardington (2003), they found that paternal orphans had lower socioeconomic status. Their work concludes that the death of the father does not cause lower socio-economic outcomes but that poverty may have contributed to these deaths.

7
Figure 7: Average Job Quality Ratio over Time, by Country <!-- Start of picture text --> Argentina Bolivia Brazil Chile<br>.5 .3 .65 .5<br>.45 .6 .45<br>.25<br>.4 .55 .4<br>.35 .2 .5 .35<br>Colombia Costa Rica Dominican Republic Ecuador<br>.29 .445 .32 .45<br>.285.28 .44 .3 .4<br>.275 .435 .28 .35<br>.27 .43 .26 .3<br>El Salvador Guatemala Honduras Mexico<br>.4 .2 .28 .4<br>.35 .195.19 .26 .35<br>.3 .185 .24 .3<br>.25 .18 .22 .25<br>Panama Paraguay Peru Uruguay<br>.345 .4 .25 .7<br>.34<br>.335 .35 .2 .65<br>.33<br>.325 .3 .15 .6<br>2004 2006 2008 2010 2012 2000 2005 2010 2015 1995 2000 2005 2010 1995 2000 2005 2010<br>2008 2009 2010 2011 2012 20102010.5 20112011.5 2012 2000 2005 2010 2015 2000 2005 2010 2015<br>2004 2006 2008 2010 2012 2000 2005 2010 2006 2008 2010 2012 1995 2000 2005 2010<br>2006 2008 2010 2012 1995 2000 2005 2010 2004 2006 2008 2010 2012 2006 2008 2010 2012<br>Average Job Quality<br><!-- End of picture text --> _Notes_ : Authors’ calculations using SEDLAC data.

not show the change in average job quality across time for the different employment sectors, the change in job quality does not seem to be associated with a substantial structural change in the type of work. With the exception of the mining industry which witnessed a small decline in job quality in all years, the rise in job quality was uniformly distributed across the different job sectors.

The growth of good jobs across the different countries reveals similar patterns. Figure 7 displays the job quality trends across each of the Latin American countries in the sample. Nicaragua is omitted since there is only one year of data. For most of these countries, the overall trend is unambiguously positive. There is a slight decline 22
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45
# **Table S18. Relationship between the OECD Better Life Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.056***<br>(0.01)|0.000<br>(0.01)|0.001<br>(0.00)|||||
|Open Data Barometer<br>||||0.050***|-0.008**|-0.007*||
|Score||||||||
|||||(0.01)|(0.00)|(0.00)||
|Global Data Barometer||||||||
|Score|||||||0.064***|
||||||||(0.02)|
|Trade (% of GDP)|||-0.001|||-0.002||
||||(0.00)|||(0.01)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||0.077|||0.017||
||||(0.06)|||(0.05)||
|Manufacturing value<br>added (% of GDP)|||-0.066**|||-0.032||
||||(0.03)|||(0.02)||
|School Enrollment,|||0026|||0009||
|Primary (% gross)|||.|||.||
||||(0.02)|||(0.01)||
|Year 2014|||||0.014|-0.017||
||||||(0.04)|(0.04)||
|Year 2015|||||0.084*|0.041||
||||||(0.05)|(0.05)||
|Year 2016|||||-0.229***|-0.287***||
||||||(0.05)|(0.06)||
|Year 2017||-0.122***|-0.178***||-0.317***|-0.400***||
|||(0.03)|(0.04)||(0.07)|(0.08)||
|Year 2018||-0.217**|-0.313***|||||
|||(0.09)|(0.08)|||||
|Year 2019||-0.217**|-0.359***|||||
|||(0.09)|(0.08)|||||
|Year 2020||-0.217**|-0.262***|||||
|||(0.09)|(0.08)|||||
|Year 2021||-0.217**|-0.368***|||||
|||(0.09)|(0.09)|||||
|Year 2022||-0.218**|-0.420***|||||
|||(0.10)|(0.11)|||||
|Constant|3.174***|||4.396***|||3.295***|
||(0.94)|||(0.49)|||(1.07)|
|Sigmau||1.12|0.48||0.99|0.37||
|_<br>Sigmae||0.23|0.22||0.24|0.24||
|_<br>R2|0.239|0.977|0.981|0.397|0.986|0.986|0.230|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|41|41|41|38|38|38|30|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|287<br>05, *** p<0.<br>(WDI), Open|287<br>01. Standard er<br>Data Watch (O|287<br>rors are cluster<br>DIN), Global|163<br>ed at the count<br>Data Baromete|163<br>ry level. Data<br>r (GDB), and|163<br>from the Worl<br>Open Data Bar|30<br>d Bank's World<br>ometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

67
Policy Research Working Paper 8927

# **Abstract**

This paper analyzes the long-term health impacts of Nepal’s 1996–2006 civil conflict. It exploits the heterogeneity in conflict intensity across villages and birth cohorts to document long-term health and intergenerational impacts. The analysis finds that childhood exposure to conflict and, in particular, exposure starting in infancy, negatively impacts attained adult height. Each additional month of exposure decreases a women’s adult height by 1.36 millimeters. The impacts are not limited to first-generation. The analysis also finds that a mother’s exposure to conflict in her childhood is detrimental to her child’s health. Mothers exposed to conflict during their childhood have more children and live in less wealthy households, likely reducing their ability to invest during their children’s critical period of physical development. The finding points to a potential trade-off between the quantity and quality of children. The paper uses information on monthly conflict incidents at the village level, which allows identifying identify the effects of exposure to conflict more accurately than prior studies.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at lphadera@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 2.4. Privatization processes in Latin America airports.**

|**Country**|**Privatization Plans**|
|---|---|
|ARGENTINA|Privatization in progress<br>*<br>33 airports concessioned as a group to Argentina 2000.<br>*<br>Concession to 30 years with a 10 year possible extension.<br>.<br>Bidding variable: annual payment.<br>*<br>Total investment: Aprox. US$ 2000 m.<br>*<br>Excluded: Ramp services, cargo and duty free shops.<br>*<br>ATC: Fuerza Aerea Argentina.|
|BOLIVIA|The three largest airports have been already privatized<br>*<br>El Alto, Viru Viru and Cochabamba concessioned to Airport Group International.<br>.<br>Concession to 25 years, started March 1997.<br>*<br>Bidding variable: % of revenues - minimum: 14%.<br>*<br>A fund is created for maintenance and operation of 34 remaining airports.<br>*<br>Adaptation to FAA II rules and IATA level B.|
|BRAZIL|Strategy under consideration.|
||Privatization in progress|
||<br>*<br>Concepci6n, Punta Arena, Temuco and Copiac6 to be concessioned in 1998.<br>*<br>Investment requirements: US$ 150 m.|
|CHILE|.<br>Concession to 15 years.<br>*<br>Bidding variable: lowest charge per epax. Minimum revenues guaranteed.<br>*<br>Excluded: Aircraft fuel services.<br>*<br>ATC: DGAC.|
||Airports already concessioned: Iquique, Calama, La Serena, Puerto Montt, Santiago.|
||*<br>Bogota: El Dorado second runway concession to Ogden-Dragados-Conconcreto.|
|COLOMBIA|*<br>Cartagena: Awarded to Schiphol (30%) to 15 years. Fixed annual payment US$ 24.5<br>million.<br>*<br>Barranquilla: Awarded to AENA (50%) to 15 years. Fixed annual payment US$ 9<br>million.<br>*<br>Medellin and Cali: next in line.|
|COSTA RICA<br>COSTA<br>RICA|Privatization in progress. OD contract for San Jose International Airport is being<br>prepared.|
|ECUADOR|Privatization in progress. BOOT contract for new airport development at Quito and<br>Guayaquil. Required investment of US$ 700 m.|
|SALVADOR|Privatization under study.|
|GUATEMALA|Privatization under study for La Aurora and Tikal.|
|HONDURAS|Privatization under study for Tegucigalpa, San Pedro Sula, La Ceiba and Roatan.|
||Privatization for Montego Bay-Sangster|
|JAMAICA|.<br>BOO for passengers terminal.<br>*<br>49 years term.|
||<br>*<br>The concessionaire will also operate actual terminal and airside activities.|
|PANAMA|Privatization under study.|
|PERU|Privatization plans for five national airports in the first half of 1999 under a master<br>concession.|
||Privatization at initial stage.|
|MEXICO|.<br>58 airports to be concessioned groupedinthree sets. Mexico D.F. excluded.<br>*<br>Southeast Airport Group (Cancun) awarded to the consortium formed by<br>Copenhagen airport, GTM, Cintra and Tribasa.|
|REPUBLICA<br>DOMINICANA|Privatization in progress. OD contract for Las Americas, Puerto Plata, Samana and<br>Barahona.|
|URUGUAY|Concessions plans:<br>.<br>Laguna del Sauce, Punta del Este.<br> <br>|
||*<br>Carrasco, Montevideo|
|VENEZUELA|Privatization under study for Simon Bolivar airport at Caracas.|
|Source: Anuario del|Transporte 1997 (adapted).|

22
We do not consider self-medication because in our data we only have people who indeed sought care.<sup>7</sup> The patient will choose the facility that maximizes her (expected) utility. The patient will choose provider j over provider k if (1) the patient can afford facility j, and (2) the income value of the differential gain in recovered health stock from visiting provider j instead of k outweighs the utility loss incurred because of the higher total costs involved in choosing provider j, and (3) condition (2) must be satisfied for all facilities in the patient’s LHM when compared to facility j.<sup>8</sup> The patient engaged into bypassing if her LHM is not restricted to the facility they visited. Although we know the number of facilities a patient bypassed, we can not identify those specific facilities. We resort to assigning to each patient a LHM which coincides with the medical district to which the chosen facility belongs. Therefore, conditional on the information that the patient has bypassed some facilities, we consider that the patient has bypassed the “average” facility in the district i.e.

# 8  Econometric Specification and Analysis

This section presents the econometric specification used to implement our bypassing model. The data requirements for implementing such a model are impressive. Indeed, for each patient, we must be able to identify all providers in her LHM but also explicitly link the providers to the health facility survey. Unfortunately, the survey did not collect information on each provider in the patient’s LHM. Instead, for each type of provider, the survey collected information on the number of such facility bypassed and the distance of the closest such facility to the patient’s home. Therefore, we cannot attach a provider from the facility survey to any particular patient’s LHM. However, since the survey provides a census of facilities within each

> 7 Because we observe only individuals who went to the health provider and thus decided to seek care, we will only consider utilities conditional on falling sick and seeking care to a provider. Self-medication is a dominated strategy for the patients in our sample.

> 8 Note that no patient would visit a more expensive provider whose expected quality if care is also lower than another facility in the patient’s local health market.

19
|**Patients Characteristics**|||||||
|---|---|---|---|---|---|---|
|Male|0.011|-0.002|-0.034|0.011|-0.034|**0.1**|
||-0.34|-0.04|-0.34|-0.43|-1.04|**(2.29)****|
|Age|0.001|0.001|0.002|0|0|-0.005|
||-0.25|-0.96|-0.7|-0.41|-0.11|-1.38|
|Education|**0.031**|0.005|0.018|0.016|**0.021**|-0.005|
||**(2.01)****|-0.07|-1.17|-1.08|**(2.02)****|-0.11|
|Reason visit|-0.101|0.063|-0.092|0|-0.01|-0.092|
||-1.53|-1.26|-0.56|-0.01|-0.25|-0.59|
|Multiple Problems|0.037|**-0.139**|0.059|**0.054**|0.027|**0.153**|
||-0.64|**(2.21)****|-0.48|**(3.65)*****|-1.45|**(2.71)*****|
|Number of days sick|0|-0.003|0.001|**0.001**|**0.001**|0.002|
||-0.05|-0.93|-0.82|**(2.71)*****|**(2.29)****|-0.76|
|Time to reach facility|**0**|**0**|**0.01**|0|**0**|**0.002**|
||**(2.63)*****|**(2.41)****|**(2.74)*****|-0.39|**(2.07)****|**(2.07)****|
|Visited another provider|**0.209**|0.246|**0.184**|**0.068**|0.054|0.004|
||**(3.43)*****|-1.43|**(1.73)***|**(2.33)****|-1.38|-0.04|
|Observations|530|110|99|805|139|170|
|Pseudo R-squared|0.2|0.28|0.32|0.23|0.31|0.27|
|LogLikelihood|-286.05|-43.85|-45.2|-257.09|-36.93|-77.24|

Robust z-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 30
The literature seems to suggest that women tend to be more ethical and less adapted or tolerant to corruption. Indeed, women are more likely to be reciprocal in gift exchanges (Croson and Buchan 1999, Buchan et al. 2008), and therefore less involved in corruption. Another reason for lower corruption for women is their weaker access to the personal networks traditionally dominated by men (Goetz 2007). Furthermore, since women tend to be more risk averse (Croson and Gneezy 2009) and corruption entails risk of being detected and punished, women are naturally more reluctant to participate in corrupt activities. Women, including female top executives, also tend to have lower overconfidence (Lundeberg et al 1994, Barber and Odean 2001, Huang and Kisgen 2013), which makes them less likely to underestimate the probability of being caught, again reducing their susceptibility to corruption. Thus, it is not surprising that firm-level data (from Georgia) and cross-country individual data (World Value Survey) show that women are less involved in bribery and are less likely to condone taking bribes, while cross-country evidence shows that corruption is less severe in countries with higher presence of women in the parliament, government, and the labor force (Dollar et al. 2001; Swamy et al. 2001). The negative effect of corruption on the level of technical efficiency is found to be particularly strong for female-headed firms (relative to male-headed firms) in 14 Central and Eastern European countries (Hanousek et al. 2017).

Since women business leaders are likely to be risk averse and apprehensive of the risks of expropriation associated with corruption, we suspect that in corrupt environments they are more reluctant to invest in innovations and long-term investment, and the effects of corruption on productivity performance should be especially pronounced and more negative.

_The corruption hypothesis_ : the effects of corruption on productivity performance should be more adverse for female than for male business leaders _._

# **Access to personal networks and female business leader effects**

Business leaders organize various inputs into outputs that are sold to customers. A key function for business leaders is to obtain or to help obtain scarce inputs, such as bank credit, trade credit, key suppliers, and key customers, all of which likely requires personal networks. For instance, firms need to establish and maintain trade credit relationships 9