rafmacalaba/datause-encoder-data huggingface dataset:
represents 40% of the predicted transport costs for coastal countries and 60% for landlocked countries. This is especially relevant for Central Asian countries that have particularly high transport costs over a given distance owing to geographical obstacles, landlockedness and weak infrastructure. Therefore, we go beyond the standard specification by including an index for the quality of infrastructure in period t, _K_ i(j)t, as suggested by Limao and Venables (2001), Brun et al. (2005), and Carrère (2006) The infrastructure measure we use is designed to measure the costs of travel in and through a country<sup>11</sup> . It is constructed as an average of the density of the road network, the paved road network, the rail network, and the number of telephone main lines per person.<sup>12</sup> Higher values of the index indicate a better infrastructure. Note that the cost of oil, which is arguably the main determining factor of the marginal cost of transport, is controlled for by the time dummies in the model (see below). Thus, the transport cost function, assuming the standard multiplicative form, yields: Note that alternative measures to capture different components of landlockedness and to better identify its impact on transport costs and, consequently on trade, are developed in Section 3.2..

We replace the bilateral transport costs θ _ijt_ in equation (1) by its expression in equation (3), and we estimate the following equation: 11 The infrastructure variable was introduced in a gravity model by Limao and Venables (2001). They construct an infrastructure measure as an average of the density of the road network, the paved road network, the rail network and the number of telephone main lines per person. They estimate the effect on bilateral trade of this indicator with a 1990 dataset for a sample of 103 countries. Their analysis of bilateral trade reveals the importance of infrastructure: a deterioration of infrastructure from the median of the sample to the 25<sup>th</sup> percentile reduces trade volume by 28%. This infrastructure indicator is also introduced in a gravity model by Brun et al. (2005) and by Carrère (2006) but extended to cover 180 countries over the 1962-1996 period. These studies confirm the significant positive impact of infrastructure on bilateral trade. However, the estimated elasticity is lower than that found by Limao and Venables (2001). There are two main reasons behind this disparity: (i) the use of both the cross-section and the temporal dimensions of the infrastructure development allows Brun et al. (2005) or Carrère (2006) to capture the impact of this variable on bilateral trade more accurately (while controlling for country-specific effects); (ii) the endogeneity of the infrastructure variable is corrected for in these two studies. In the next section we base our infrastructure analysis on the Brun et al. (2005) and Carrère (2006) method, with particular focus on Central Asian countries. 12 Given the construction of the variable, we used 1+Ki(j)t in empirical work. In fact, when Ki(j)t tends towards 0, ln (1+ Ki(j)t) tends towards 0. Appendix A3 describes in detail how this index was constructed.

14
economy-wide and cross-country surveys. Later, our statistical analysis estimates the broad descriptive strategies that emerge from responses to the question posed on different agents. We regard the agent-based and strategy-based approaches as true alternatives since they could not be combined in a single survey question of the type used in this paper, for reasons we clarify below.

The third element of the economics literature that underpins the conceptual basis of our survey question is the view that the pertinent agents can be identified and arranged on a spectrum, from the narrowest to the broadest. Although the language varies enormously across papers, this view is common, although often implicit and not comprehensive in scope (see, e.g., Dixit 2003, World Bank 2002, Greif 1997). To clarify this point and connect it to the structure of our survey question, we use Greif's (1997, 2005) formulations since the notion of a spectrum of agents is an important feature of his highly influential work.

Greif (1997) differentiates between first-, second-, and third-party enforcement (see also Dixit 2009), any combination of which can be relevant to any specific agreement. First-party enforcement reflects the personal characteristics of agents themselves, characteristics that are often relevant to all of the agent's actions, not only those undertaken in implementing a particular agreement. Problems arise less frequently when that agent is trustworthy, trusting, honest, and has absorbed common social norms. The rest of society can provide background to these purely firstparty motives, since many moral values arise as the result of social interactions (Granovetter, 1985).<sup>14</sup> However, these motives are purely first-party because enforcement occurs without any reference to second or third parties: guilt (as opposed to shame) does not imply direct social interaction (Greif 1994, 1997, Argyres et al. 2020). Problem-solving occurs simply because of "...

> 14 Poppo et al. (2016) describe this as follows: "relational trust arises from social relationships when there are strong beliefs about the goodwill, honesty, and good-faith efforts of others, which mitigate risk by aligning core values" (p. 2). See also Granovetter (1985), Gulati (1995), Zhou et al. (2008), Poppo and Zenger (2002), Susarla et al. (2020).

8
56

# Table III.6

Source of Contraceptives, by Vomenrs Education, for Selected Countriesa

|||Source of Co|ntraceptives||
|---|---|---|---|---|
|Country<br>Education|Government<br>(Percent)|Commercialb<br>(Percent)|<br>NGO<br>(Percent)|Otherc<br> <br>(Percent)|
|Colombia (1986)|||||
|None|64.6|15.4|16.9|3.1|
|< Primary|44.5|34.5|19.9|1.1|
|Primary Comp.<br>|36.6<br>|34.4|27.2|1.8|
|Secondary +|19.2|57.6|22.0|1.2|
|Egypt (1984)|||||
|None<br>|32.2|65.5|1.2|1.0|
|< Primary<br>|32.6|64.7|1.5|1.3|
|Primary Comp.<br>|24.7<br>|72.2|1.3|1.9|
|Secondary +|15.7|82.2|1.4|0.7|
|Jamaica (1984)|||||
|<Primary<br>|90.0|10.0|--|--|
|Primary<br>Comp.|59.3|27.3|__|9<sup>.</sup>4<sup>d</sup>|
|Secondary|50.0|.50.0|__|__|
|College|53.9|46.2|--|--|
|Morocco (1984)|||||
|None|64.2|33.8|--|2.0|
|<Primary|52.1|45.7|--|2.2|
|Primary<br>Comp.<br>|40.3|58.4|--|1.2|
|Secondary +|29.7|70.3|--|0.0|
|Sri Lanka (1982)|||||
|None|94.5|5.5|-|-|
|Primary<br>Comp.|87.8|11.5|--|0.7|
|Secondary +|68.1|30.5|--|1.4|
|Thailand (1984)|||||
|None|77.2|20.5|0.4|1.9|
|< Primary<br>|80.5|17.6|0.6|1.3|
|Primary Comp.|63.1|31.2|2.1|3.5|
|Secondary +|62.8|33.1|0.9|3.1|

# Source: CPS; DHS; Akin (1984)

- a. Sample is married women for all but Jamaica, which is all vomen.

- b. Includes private physicians, hospitals, pharmacies, and any other private, non-NGO.

- c. Unspecified source, may encompass NGOs vhen private, nonprofits are not a. category, and may include commercial where it is not a separate category. Other also includes don't know and not stated.

- d. Includes NGOs.

> **PP: Table 11.6**
landlord, and has, according to Malpezzi and Mayo (1987), been more frequently found to be a significant variable in renter rather than in owner demand equations. In addition, Korboe (1992) suggests that in Ghana, owners are subject to cultural obligations that cause them to accept even distant relatives to live rent-free in their houses. Mabogunje (2007) makes a similar argument for other African cities, and Konadu-Agyemany (1991) has an interesting discussion of how these sorts of obligations affect housing choices in Ghana. Consistent with this result, the most recent Census data indicate that some 22 percent of Ghanaian families do not pay any rent at all. Unlike owners, renters are under no such obligations. Hence, it is more likely that family size has a significant effect on rents paid; consistent with our findings.

These overall results, as well as the coefficients on the dummy variables, support our basic hypothesis: the Accra housing market is, at least for lower income families, much more constrained.<sup>19</sup> In order to get a sense of how much less responsive, we first combine these estimates of income elasticity with previous empirical findings regarding the price elasticity of housing demand to infer an estimate of the underlying overall supply elasticity, as depicted by equation 4.

where α2 is the income elasticity of demand as estimated in equations 3.1 and 3.2, γ1 is the coefficient on income in equations 3.3 and 3.4, and α1 is the assumed price elasticity of housing demand. Plugging in the estimates for income elasticity from our estimated demand equations, and an estimate of a price elasticity of -0.5, based on the literature, yields an overall supply elasticity estimate of about 0.3 for owners and 0.7 for renters. At

> 19 We attempted to examine whether the dummy variables affected the slope of the supply curve rather than shifted it. However, our estimates were limited by the degrees of freedom. In terms of the effects on low income families recall from footnote 10 and the related text that our surveys are of lower income families in each of the cities.

15
or descendant of Natives, which corresponds to 2.4 percent of the total population of Argentina.<sup>5</sup> Table 1: Native Groups

||(1)|(2)|(3)|(4)<br>|
|---|---|---|---|---|
||Number of<br>natives|% of Total<br>Population|Economy before<br>conquest|Approximate year<br>of conquest|
|Atacama<br>|13,936|0.035|Advanced agriculture<br>|1,593|
|Ava Guaran ́ı|17,899|0.045|Incipient Agriculture|1,801|
|Aymara|20,822|0.052|Advanced agriculture|1,533|
|Chan ́e|3,034|0.008|Incipient Agriculture|1,879|
|Charr ́ua|14,649|0.037|Hunter-gatherers|1,752|
|Chorote|2,270|0.006|Hunter-gatherers|1,861|
|Chulup ́ı|1,100|0.003|Hunter-gatherers|1,881|
|Comeching ́on|34,546|0.087|Advanced agriculture|1,632|
|Diaguita-Calchaqu ́ı|67,410|0.170|Advanced agriculture|1,630|
|Guaran ́ı|105,907|0.267|Incipient Agriculture|1,843|
|Huarpe|34,279|0.086|<br>Incipient Agriculture|1,623|
|Kolla|65,066|0.164|Advanced agriculture|1,660|
|Lule|3,721|0.009|Hunter-gatherers|1,889|
|Maimar ́a|1,899|0.005|Advanced agriculture|1,596|
|Mapuche|205,009|0.517|?|1,881|
|Mby ́a Guaran ́ı|7,379|0.019|Incipient Agriculture|1,710|
|<br>Mocov ́ı|22,439|0.057|<br>Hunter-gatherers|1,913|
|Omaguaca|6,873|0.017|Advanced agriculture|1,594|
|Ona|2,761|0.007|Hunter-gatherers|1,893|
|Pampa|22,020|0.056|Hunter-gatherers|1,787|
|Pilag ́a|5,137|0.013|Hunter-gatherers|1,883|
|Quechua|55,493|0.140|Advanced agriculture|1,554|
|Querand ́ı|3,658|0.009|Hunter-gatherers|1,795|
|Rankulche|14,860|0.037|Hunter-gatherers|1,849|
|Sanavir ́on|2,871|0.007|Incipient Agriculture|1,647|
|Tapiete|407|0.001|Hunter-gatherers|1,876|
|Tehuelche|27,813|0.070|Hunter-gatherers|1,890|
|Toba-Qom|126,967|0.320|Hunter-gatherers|1,909|
|Tonocot ́e|4,853|0.012|Incipient Agriculture|1,894|
|Tup ́ı Guaran ́ı|3,715|0.009|<br>Incipient Agriculture|1,831|
|Vilela|519|0.001|Hunter-gatherers|1,891|
|Wich ́ı|50,419|0.127|Hunter-gatherers|1,881|
|Other|5,301|0.013|-|.|
|Total Natives|955,032|2.407|-|.|

Source: REDATAM INDEC Census Argentina 2010 for columns (1) and (2), see text for columns (3) and (4).

There is geographical variation in the distribution of Natives across provinces as shown by Figure 1. The province with the greatest prevalence of Natives is Chubut, with 8.7 per-

> 5 It is important to note that 149,000 respondents self-identified as Afro-Argentine (“afrodescendientes”), which corresponds to 0.4 percent of the population. While the main focus of this paper is on the native population, we will also provide an analysis of their outcomes as a point of comparison.

4
Figure 6. Poverty Impact Change between Income Concepts for Fiscal and Care Groups <!-- Start of picture text --> 30<br>20<br>10<br>0<br>Market Disposable Consumable Final<br>income income income income<br>Poverty (national poverty line)<br>No workers, no dependents No workers, children in school Pensioners<br>One/two worker, no dependents One/two worker, children in school No dependets<br>Single adult, dependents Married couple, dependents Other care categories<br>All<br>Percentage<br><!-- End of picture text --> _Source:_ Rodriguez, Wai-Poi, and Woodham (2022).

In Jordan, income inequalities across the care and fiscal categories are driven largely by inequality within any one category; this is true whether pre-fiscal or post-fiscal income concepts are used to estimate inequality (Figure 7 and Figure 8). The fiscal group with the highest inequality within its members is the _workers, no dependents_ group while the _no workers and children in school_ group demonstrates the least (Figure 9). For care categories, the _married couple with dependents_ group shows the largest within-group inequality while the _single adult with dependents_ category demonstrates the least inequality between its members (Figure 10).

27
16.2% have bank loans, while 40.9% have borrowed from informal sources.<sup>12</sup> These figures are higher than those found for China in the World Bank’s Global Index for Financial Inclusion (Findex), which shows that 36.3% of Chinese respondents had borrowed any money in 2011, while 9.6% had borrowed from a financial institution.<sup>13</sup> The disparities could be due to differences in question wording, though it seems likely to us that the CFHS could simply be a more complete tool for summarizing Chinese household financial usage than the Global Findex.<sup>14</sup> The same pattern is observed for access to long-term finance. Interestingly, roughly 92% of all loans are long-term, under the definition that we use (maturities greater than one year). But maturities differ across formal and informal sources: for bank loans, the share of long-term finance is 78%; for informal loans, 94%. Thus, informal loans are more common among Chinese households, especially for long-term finance.

Rural residents borrow more frequently and rely relatively more on informal sources. While 41.7% of urban residents borrow, 56.7% of rural residents do. Moreover, the difference in the tendency to borrow stems mainly from informal borrowing: the share of residents borrowing from bank sources is roughly the same in rural and urban areas (about 16%), but the share borrowing from informal sources is higher by 20 percentage points in rural areas (i.e., 52% versus 32%). Our interpretation is that greater needs for finance in rural areas, perhaps due to lower wealth, higher risks associated with agricultural production and greater demand for productive inputs, along with worse access to formal financial services and medical insurance, results in stronger reliance on informal finance in rural areas.

> 12 Some households have access to both formal and informal loans, which explains why the share for loans is not equal to the summation of the shares of households with bank and informal loans.

> 13 Figures taken from http://datatopics.worldbank.org/financialinclusion/country/china accessed 8/18/2015.

> 14 Of course, the Global Findex was designed to facilitate comparisons across countries, and thus the survey instrument is shorter and less likely to fully capture the specific financial context of each country. The Findex indicates that China is slightly less financially inclusive than countries with similar income levels. In other upper middle income countries, 37.7% of respondents had borrowed over the past year, 10.4% from a financial institution.

12
84 Brynjolfsson, Erik and Chris F. Kemerer (1996). “Network Externalities in Microcomputer Software: An Econometric Analysis of the Spreadsheet Market.” _Management Science_ . Vol. 42, No. 4, pp. 1627-47.

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The strengths of the survey, from the point of view of this paper, are the use of a consistent survey instrument across virtually all transition countries and range of market economy comparators, and, for the transition countries, over a substantial period of time.<sup>4</sup> The 1999 and subsequent BEEPS surveys included 3-year retrospective questions, and we are therefore able to track developments in transition over a period of 6 to 9 years. These two strengths allow us to benchmark the transition countries against developed market economies, and to track their progress in transition. The main weakness of the BEEPS is the consequence of the wide coverage and finite budgets: the sample sizes for individual countries are relatively small. Even in the biggest BEEPS round in 2005, most country samples have fewer than 400 firms. In the first BEEPS surveys in 1999, a typical country sample had about 150 firms. See Table II.2 for the composition of BEEPS surveys by country and year of implementation. The implication of this is that too great a degree of disaggregation in the analysis would results in systematic differences across countries and over time being swamped by noise in the data. We therefore aggregate across countries in much of our analysis.

Our aggregation scheme separates countries according to position in Europe at the time of the most recent BEEPS surveys in 2005, and according to income as of 1999.

# **Aggregation scheme: By income and position in Europe**

- I. West Germany

- II. Cohesion countries (Greece, Ireland, Portugal, Spain)

- III. EU8 (new members as of May 2004) (Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovak Republic, Slovenia)

- IV. Lower middle income transition countries (Albania, Bosnia and Herzegovina, Bulgaria, Macedonia, Romania, Serbia and Montenegro / Belarus,

- Kazakhstan, Russian Federation, Ukraine)

> 4 Although the BEEPS survey instrument has been modified each time it was implemented, the range of questions that remained consistent across surveys is substantial.

9
# **Appendix 1: Attrition in the KHDS survey**

In comparison with most longitudinal surveys, attrition in the KHDS is low; among baseline households, in 93 percent of households at least one baseline household member was reinteriewed in 2004. Among all surviving members of the baseline households, about 82 percent were reinteriewed in 2004. Among the target group in this paper, non-orphaned children of age 6-15 at baseline, attrition is slightly higher; 79 percent of the non-deceased children were reinteriewed in 2004. About half these were found back due to the extensive efforts to locate people how had physically moved to a new village. Any area (town, district or even neighboring country) to which children were reported to have moved was visited by a specific search and survey team to minimize non-random attrition. Despite these efforts and the low attrition rate, it is still possible that attrition may cause problems for our analysis.<sup>1</sup> Attrition linked to unobservables cannot be addressed in this data set; as in Fitzgerald et al. (1998), we focus on attrition linked to observables.

These results suggest some potential problem of non-random attrition. While it is plausible that the descriptive statistics in this paper are affected to some extent, our focus is on the regression analysis aiming to establish causal links between orphanhood and outcomes at adulthood. First, any attrition linked to particular clusters is not likely to be problematic, as all the regression analysis controls for cluster fixed effects so that all effects are identified using within- cluster variation, while for every cluster in the sample at least 55 percent of the cluster sample was found back. Secondly, we used the approach suggested by Fitzgerald et al. (1998), and use the inverse of the probabilities of an individual to remain in the sample by 2004, as predicted by baseline characteristics and with the probabilities normalised to keep the sample size at 718 observations. These probabilities are based on the same specification as in table A.1 but using the logit model with cluster dummies rather than the conditional fixed effects model, as otherwise some sampled individuals could not be assigned a probability of remaining in the sample. The results of the specification equivalent to table 4 in the main text are given below, and (possibly, not 21