rafmacalaba/datause-encoder-data huggingface dataset:
should have completed 1 year of education. However, 13% of them have not completed any education at this point. This problem aggravates as children become older. For example, 26% of the children are lagging behind at age 9. The first row of the table also illustrates rapidly growing drop out rates from age 12 onwards. While, until age 11, school attendance is almost universal (99%), at age 12 this is 98% and by age 15 this is in the order of 90%.

In Table 3 we document the rapid rise in preschool attendance across subsequent birth cohorts. Here we report the coefficients of a regression of a dummy for preschool attendance on birth cohort dummies. In the first column we include no additional controls while in the second column we condition on household fixed effects. In practice, the latter investigates the growth in preschool attendance of siblings born in different years. Standard errors are heteroskedasticty consistent. The OLS estimates in column (1) show a pronounced trend in preschool attendance across cohorts. Preschool attendance grows by 12 percentage points between those born in 1986 (the omitted group) and those born in 1998. Results are qualitatively similar if one examines the fixed effect estimates in column (2). If anything, point estimates are slightly larger in magnitude.

Although these results show a secular rise in preschool attendance in the population at large, they also mask substantial heterogeneity across different households. As already mentioned the reform was apparently extremely successful in incorporating children from more disadvantaged backgrounds. Columns (3) and (4) check for this by reporting the same regressions as in columns (1) and (2) where now the cohort dummies are interacted with a dummy for mother's low education.

We define a low-education mother as one with at most compulsory education (9 years of education). Around 50% of children in the sample have mothers with at most compulsory education so by this criterion we split the sample into two approximately equally sized groups. Column (3) shows that children of low-educated mothers start from lower enrollment. For the 1986 cohort this difference is in the order of 12 percentage points. As time goes by, an increasing proportion of domestic employees). By age 20 this proportion rises to around 12%.

9
# **1 Introduction**

In the 1990s advances in transportation and communication technologies led many commentators to believe that geographic distance between countries would soon no longer encumber international transactions (e.g. Cairncross 1997). Despite some anecdotal evidence in support of the “death of distance” hypothesis (e.g. Friedman 2005), a large number of academic papers suggests that distance is “thriving”, not “dying”. Disdier and Head (2008), using a meta-analysis based on 1,000 gravity equations, found that the estimated coefficient on distance has been slightly on the rise since 1950. Chaney (2011) argues that the need for direct interactions between trading partners, resulting from information frictions first highlighted by Rauch (1999), explains why distance still matters for international trade today. Similarly, Allen (2011) suggests information frictions account for 93 percent of the distance effect. This would suggest that advances in technology in recent decades have failed to reduce information frictions. Is this the death knell for the “death of distance” hypothesis?

In this paper we breathe new life into the “death of distance” hypothesis. We argue that the right place to look is in online markets which, as opposed to “offline” markets, make full use of technologies that can reduce information frictions. Indeed, as argued by Horta ̧csu et al. (2009) and Goldmanis et al. (2010), the main benefit of the internet as a trade facilitator is to reduce search costs, and it is reasonable to think of online marketplaces as “frictionless” in this regard. Exporters no longer need to make multiple phone calls, send faxes, write emails, attend trade fairs and networking events. And while importers still incur some search costs, these are typically brought down to a simple internet search. In any event, online search costs are not necessarily correlated with how remote markets are.

The heart of our paper is a dataset on cross-border transactions conducted over eBay, the world’s largest online marketplace. This dataset allows us to examine the effect of distance on international online trade. Our approach is similar to that of Horta ̧csu et al. (2009) who, using a sample of within-US eBay transactions, showed that the coefficient on distance on trade was much smaller online than offline. However, as noted by the authors, several caveats make their comparison with offline trade imperfect. One is that the products traded on eBay are mainly household durables, and thus comparison with total offline trade 2
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- **Demirg ̈u ̧c-Kunt, Asli, Leora Klapper, Doroth ́e Singer, and Peter van Oudheusden** , “Measuring Financial Inclusion and Opportunities to Expand Access to and Use of Financial Services,” _World Bank Economic Review_ , 2017, _31._

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30
# The Relationship between Conflicts, Economic Shocks, and Death with Depression, Economic activities, and Human Capital Investment in Nigeria<sup>*</sup>

_Julian Jamison, Kevin Robert McGee, Gbemisola Oseni, Julie Perng,_ _Ryoko Sato, Tomomi Tanaka, Renos Vakis_ _The World Bank_ .

<u>Keywords: conflict, depression, welfare measurement, survey methodology, data collection. labor</u> productivity, neuroeconomics; cognition; decision-making; mental health <u>JEL codes: D74, D87, D91, E24, E71, F63, I10, I30, J24</u>

* The authors thank Abul Azad, Biljana Bogicevic, Oscar Calvo-Gonzalez, Andrew Dabalen, Gabriela Farfan, Indira Konjhodzic, Juan Diego Luksic, Emily Hewlett, Patricio Marquez, David McKenzie, Muna Meky, Serrana Mujica, Emmanuel Skoufias, and seminar audiences at the University of Tokyo, Keio University and National Graduate Institute for Policy Studies (GRIPS) for helpful inputs. Remaining errors are our own. The findings expressed here represent the views of the authors and not necessarily those of The World Bank Group.
31 managers and the direct political intervention in the operations of such firms.<sup>51</sup> Various studies in China have also concluded that the incentive-based contracts for managers in SOEs have often failed to reward profitability efforts – an indication of serious principle-agent problems;<sup>52</sup> of course, similar problems can be found in other countries. It is often also believed (realistically, I think) that managers of privately owned firms are usually more concerned about boosting the work efficiency of their employees than are managers of public-sector firms. In fact, this observation is a general “folk theorem” as regards services to households, since private service providers have a stronger interest than public-sector providers that the customer are interested to come back.<sup>53</sup> For similar reasons, we would expect that TVEs tend to be more effective when privately owned than when owned by towns and villages. This may help explain why truly collective TVEs have lost ground over time, as compared to private TVEs.

When judging the efficiency of China’s growth path, it should also be kept in mind that GDP statistics represent a _gross_ measure, since there is no deduction for capital depreciation when old buildings, infrastructure and machines are scrapped. On the contrary, work on such scrapping (for instance, demolition of houses) is included as a positive contribution to GDP. While such measurement issues emerge in all countries, they could be expected to be particularly important in China owing to the gigantic demolition of old physical structures. The hard wear and tear on the natural environment (soil, water and air) in China is another important example of capital decumulation that is not recorded in GDP statistics (indeed, not for any country). For instance, the depletion of water resources harms not only the quality of life of Chinese citizens but also the country’s production capacity in the future.

> 51 Xu et al.(2005) conclude from an empirical study that the economic efficiency of SOEs is negatively affected by more political control. Chang and Wong (2004) found that the profitability performance of listed firms in China is negatively associated with the power over decision-making in firms by local party authorities (outside intervention) relative to the managers of firms.

> 52 See, for instance, Choe and Yin (2000). Similar explanations, in terms of principal-agent relations, for the low productivity of SOEs (as well as their profitability before its recovery from about year 2000) have been used, for instance, by Mi and Wang (2000).

> It remains to be seen whether the new government agency designed to manage physical government assets, the state-owned Asset Supervision and Administration Committee, will create some arm’s-length distance between politicians and managers of state enterprises.

> 53 Chow (2002, Chapt. 19) reports subjective impressions, by himself and others, of poor service in government-operated service institutions in China.
# Annex. Measurement of Explicit and Implicit Subsidies

This Annex presents the methodology for calculating explicit and implicit subsidies from the budget and the two energy monopolies: RAO Gazprom and RAO UES.

Explicit budget subsidies to enterprise sector (lines l.i and 1.ii in the Table A-1 below) are taken directly from official MoF reports on federal and subnational budget execution. Since most of the budget expenditure category titled "national economy" represents input subsidies and investment grants, this category was taken as a proxy for explicit subsidies.

Gross budget lending to enterprise sector also includes a subsidy component to the extent of: (i) difference between market interest rate and discounted interest rate on budget lending; and (ii) amount of overdue loans. Measurement of this component is problematic, especially at the subnational level, due to lack of data. Hence, this has been excluded from our calculations.

# We now discuss implicit subsidies.

In case of offsets, which are basically swaps between overdue receivables and payables, the subsidy component is represented by 30% overpricing of procured goods and services in case of the government.<sup>4 9</sup> Thus, for instance, entry "2.ii.a" in Table A-1 in 1996 is calculated as: Implicit subsidy = Offsets amount*0.3/1.3=121.3*0.3/1.3 = Rb 28 bn.

Implicit subsidy as percent of GDP = Implicit subsidy / GDP = 28/2200*100 = 1.3%.

The net increase in tax arrears to each of budgetary level and EBFs (lines 2.i.b, 2.ii.b and 2.iii.b in Table A-1) is a difference between outstanding tax arrears at the end of period and of its beginning. Fines and penalties are excluded.

Subsidies embedded in non-equivalent barter<sup>50</sup> are evaluated under the assumption that prices of goods or other non-cash instrument used to pay RAO Gazprom and RAO UES in NCS transactions are inflated by 30%, while energy prices are not, because the latter are fixed by the energy commissions. Example: the entry "II.i.b" in Table A-1 in 1995 is calculated as: Implicit subsidy = Amount of non-equivalent barter*0.3/1.3 = 62.0*0.3/1.3 = Rb 14.3 bn.

Implicit subsidy as percent of GDP = Implicit subsidy / GDP = 14.3/1585*100 = 0.9%.

The change in net inter-industry receivables of Gazprom (line II.i.a) is calculated as the increase of stock of overdue unpaid deliveries of gas to other industries after barter and before tax offset operations during each year of the period multiplied by the average annual domestic price of gas deliveries:<sup>51</sup> Line II.i.a. Change in net inter-industry receivables of Gazprom = change in stock of overdue unpaid gas deliveries to other industries after barter and before tax offsets * average annual domestic gas price. Change in net inter-industry receivables of Gazprom as percent of GDP = Change in net interindustry receivables of Gazprom / GDP.

> 49 30% is a sort of average overpricing reported in interviews with subnational authorities.

50 Data on RAO Gazprom and RAO UES are taken from companies' financial reports. **51** Gazprom's own overdue payables to other industries are negligible.
With the objective of measuring different aspects of adult farmers’ cognitive ability, we selected five different cognitive tests: i) The Raven Colored Progressive matrices, measuring visual processing and analytical reasoning; ii) The digit span forwards and backwards, measuring shortterm memory and executive functioning; iii) A written and timed test of basic math skills; iv) An oral 9-item test containing short math puzzles relevant for agriculture and increasing in level of difficulty; and v) A reading comprehension test. Table A1.A provides a detailed description of each of these tests.

# _<u>Noncognitive skills</u>_

The noncognitive part focuses on testing instruments derived from commonly used scales in noncognitive domains that the literature has emphasized as potentially predictive of success in life and that are potentially relevant for smallholder farmers. We use a subset of items from the 44-item BFI, a commonly used instrument for the Big Five personality traits. We also test commonly used instruments for lower-order constructs such as scales for locus of control, selfesteem, perceptions about the causes of poverty, attitudes towards change, organization, tenacity, meta-cognitive ability, optimism, learning orientation, and self-control. The majority of these subscales are derived from a set of questions asking the respondent the level at which he agrees or disagrees with general statements about himself, with answers on a Likert scale from 1 to 5.<sup>9</sup> In addition, we asked a set of locus-of control questions with visual aids in which people are asked to attribute success to effort & good decisions, luck or endowments. We also included the CESD, a commonly used depression scale, that has been used and validated in many developing countries, and which relates to some of the noncognitive domains also captured in other scales (such as neuroticism and optimism). A standard risk aversion game and time preference questions were also added, mostly for comparison and completeness.

Table A1.B in the appendix presents all items, and the first column indicates the sub-scale each of the items belongs to. As is the case in the original scales, some of these questions are positivelycoded, indicating that a higher likelihood to agree with the statement indicates a higher score on the noncognitive trait of interest, while others are reverse-coded. The last column in Table A1.B

> 9 The causes-of-poverty subscale does not ask directly about the respondents themselves but uses a Likert scale to ask about reasons for why poor people are poor.

8
**Table 2: Balance**

# **Panel A: Household characteristics, by survey assignment of household**

||H<br>Detailed<br>Self‐ reported|ouseholds by<br>Short<br>Self<br>reported|survey assignment<br>Detailed<br>Proxy response|Short Proxy<br>response|F‐test of<br>equality of<br>coefficients<br>across groups|
|---|---|---|---|---|---|
|Head: female (%)|20.4|26.7|22.3|19.3|0.544|
|Head: age|48.4|48.7|47.3|48.4|0.882|
|Head: years of schooling|4.2|4.6|4.5|4.7|0.778|
|Head: married (%)|74.3|71.6|76.2|81.5|0.277|
|Household size|6.8|6.2|6.0|6.6|0.046|
|Share of members less 6 years|16.7|15.4|15.5|16.3|0.876|
|Share of members 6‐15 years|41.2|42.1|41.9|41.0|0.915|
|Month of interview (1=Jan, 12=Dec)|6.4|6.1|6.3|5.9|0.711|
|Number of households|113|116|130|135||

Notes: The F‐test tests the equality of coefficients across the groups in a regression of each of the household characteristics on group indicators with clustered household standard errors.

**Panel B: Individual characteristics, by survey assignment of household**

||Detaile<br>d<br>Self|Short<br>Self|Detaile<br>d Proxy|Short<br>Proxy|F T<br>for equ<br>coefficie<br>gro<br>|est<br>ality of<br>nts across<br>ups<br>|
|---|---|---|---|---|---|---|
||||||between<br>all four<br>treatment<br>s|between<br>detailed<br>and short|
|Male|0.46|0.50|0.47|0.47|0.21|0.09|
|Years of schooling|4.5|4.5|4.3|4.3|0.26|0.79|
|Age|33.9|34.4|28.9|29.5|0.00|0.43|
|Married|0.55|0.57|0.48|0.45|0.00|0.86|
|Number of children below age 6|1.1|1.1|1.1|1.2|0.05|0.86|
|Number of children age 6‐15|1.6|1.5|1.8|1.9|0.00|0.85|
|Number of old hh members (65+)|0.2|0.2|0.3|0.3|0.68|0.30|
|Mean community distance to primary school|2.3|2.3|2.3|2.3|0.99|0.87|
|Mean community distance to secondary<br>school|7.5|7.5|7.7|7.7|0.89|0.96|
|Mean community distance to all weather<br>road|3.5|3.6|3.7|3.7|0.86|0.94|
|Number of observations|687|909|723|501|||

29
We use an appropriately-specified econometric model to estimate the effects of these variables on the cumulative percent of forest cleared in 2016. Our estimation exercise uses a small random sample to minimize potential spatial autocorrelation problems. The resulting parameter estimates are extremely robust, with the expected signs and very high statistical significance in all cases. Our result for legal protection status is particularly strong, indicating that forest conservation in protected areas has been robust in Lao PDR.

We focus on distance from the nearest road link and its Delorme primary, secondary or tertiary status, which we use as a proxy for road quality. We find an important role for road quality, which reflects the effects of average vehicle speeds and differential maintenance costs in transport to market.<sup>12</sup> Econometric measurement of road quality effects enables us to estimate the deforestation impact of programs that upgrade secondary and tertiary roads to primary status. To illustrate our methodology, we explore the impact of upgrading all secondary and tertiary roads to primary roads in Lao PDR. We find a highly non-uniform spatial distribution, with several large impact clusters in northern Lao PDR and a few smaller clusters in southern Lao PDR.

With our model-based predictions in hand, we measure biodiversity risk in each 500 m cell using ecoregion information from WWF International and thousands of species range maps from IUCN and Birdlife International. This information is summarized in a new World Bank database that provides georeferenced indicators for ecoregions and species intensity, extinction risk and

> 12 We cannot completely discount simultaneity bias in this estimation exercise, since anticipated returns from future forest clearing might have provided one motivation for past road construction in Lao PDR.  However, the relative paucity of forest clearing beyond established urban centers and principal road corridors as recently as 2000 (Figure 2) strongly suggests that other factors were the primary drivers of historical road construction in Lao PDR.  Any remaining upward bias from simultaneity will produce some tendency for our methodology to overestimate the potential impact of road improvement on forest clearing.  In any case, since “extinctions are forever”, we believe that invocation of the precautionary principle in conservation is more than sufficient to accommodate some potential overestimation.

33