rafmacalaba/datause-encoder-data huggingface dataset:
# **Tables**

Table 1: Age distribution of populations in 2010, % (25+ only),

|||**Natives**||**Im**|**migran**|**ts**|**E**|**migran**|**ts**|
|---|---|---|---|---|---|---|---|---|---|
||25-44|45-64|65+|25-44|45-64|65+|25-44|45-64|65+|
|USA|38.80|40.49|20.71|50.01|35.50|14.49|38.71|40.69|20.60|
|CAN|38.30|42.80|18.90|37.82|39.72|22.46|36.31|38.52|25.17|
|AUS|42.97|37.55|19.48|39.23|37.76|23.01|58.97|30.03|11.01|
|NZL|37.06|40.49|22.44|42.73|36.84|20.43|49.98|39.24|10.78|
|GBR|36.55|38.17|25.28|56.17|29.43|14.40|27.98|42.52|29.50|
|IRL|43.36|36.27|20.37|67.18|26.43|6.39|27.65|32.67|39.68|
|DEU|32.37|37.89|29.75|43.19|40.25|16.55|43.18|33.49|23.33|
|AUT|42.04|43.66|14.30|53.01|37.67|9.33|20.82|39.26|39.92|
|CHE|40.54|44.30|15.15|52.10|38.42|9.48|52.45|33.17|14.38|
|FRA|38.70|36.90|24.40|36.39|41.20|22.41|50.37|31.66|17.97|
|BEL|35.72|38.44|25.84|48.43|35.76|15.81|39.46|38.47|22.08|
|NLD|36.24|40.53|23.22|50.75|38.72|10.53|26.54|41.57|31.89|
|LUX|35.27|37.86|26.87|51.56|36.44|12.00|41.42|35.66|22.92|
|ITA|34.01|36.73|29.26|61.20|31.19|7.61|19.29|40.46|40.25|
|ESP|39.93|35.21|24.86|62.82|28.84|8.33|30.82|36.72|32.46|
|PRT|36.37|36.30|27.33|61.42|30.32|8.26|37.34|46.73|15.94|
|GRC|36.60|34.77|28.63|59.59|31.03|9.38|18.06|43.15|38.79|
|DNK|35.22|39.14|25.63|57.18|32.61|10.21|33.64|34.38|31.98|
|SWE|35.12|36.77|28.12|46.70|35.73|17.57|52.59|30.93|16.47|
|NOR|37.21|38.48|24.32|62.58|30.05|7.37|31.87|33.30|34.83|
|FIN|33.71|40.80|25.50|64.87|28.14|6.98|19.39|46.07|34.54|
|CZE|41.01|37.59|21.39|45.42|32.30|22.28|35.29|32.78|31.94|
|HUN|39.87|37.42|22.71|41.43|28.73|29.84|34.78|26.55|38.67|
|POL|41.56|40.44|18.00|9.18|12.01|78.81|51.76|33.15|15.09|
|SVK|44.70|37.86|17.44|25.24|44.87|29.89|43.21|30.17|26.62|
|JPN|34.55|35.25|30.20|63.06|27.48|9.46|49.58|36.64|13.78|
|TUR|58.93|34.81|6.26|42.77|44.52|12.71|53.05|35.87|11.09|

Table reports the age-group breakdown of all those of age 25 or more 42
Figure 4.3 <!-- Start of picture text --> Returns to nigration: 1999 versus 1995<br>(Northeast to Southeast Migration)<br>0.9<br>0.8<br>0<br>1M0.7<br>M<br>0.5<br>0.4<br>no education priml prirn2 secondary university<br>education<br><!-- End of picture text --> _Note:_ Solid lines mark estimations based on PNAD 1999, dotted lines mark estimates from the PNAD 1995 Returns to migration are expressed as the difference in predicted log-mean wages between movers and movers had they stayed. _Source:_ Author's calculations based on PNAD 1999 and 1995.

Figure 4.4 <!-- Start of picture text --> Returns to migration: 1999 versus 1995<br>(Southeast to Northeast Migration)<br>education<br>no education prirri prirrQ secondary university<br>0.2<br>0.0<br>-0.2 -<br>0<br>~ 0.4 -<br>E -0.6<br>2 -0.8-<br>-10<br>-1.2<br>-1.4<br><!-- End of picture text --> _Note:_ Solid lines mark estimations based on PNAD 1999, dotted lines mark esfimates from the PNAD 1995 Returns to migration are expressed as the difference in predicted log-mean wages between movers and movers had they stayed.

_Source:_ Author's calculations based on PNAD 1999 and 1995.

28
- 18 -

RESULTS 35. In this section we discuss, first,<sup>the observed achievement differences</sup> between private, government and local secondary<sup>schools. Second, we discuss the</sup> observed differences between the three<sup>types of schools in terms of their</sup> available resources and social context. Then<sup>we present the results of the HLIF</sup> analyses, which explore reasons for the achievement<sup>differences.</sup>

# Achievement Differences Between Schools

36. Private, government and local secondary schools<sup>in the Philippines differ</sup> in terms of their average science and mathematics<sup>achievement and in terms of the-</sup> average attitudes of their students.

37. Achievement. Average science achievement<sup>in the Philippines was the lowest</sup> of all countries that participated in the IEA<sup>study, with an average for all</sup> students of 11.5 points on the 30-point core<sup>test (IEA 1988).</sup> The science scores of students in both government public and private<sup>schools in this analysis</sup> are slightly higher than the national<sup>average reported in the IEA report.</sup> However, science achievement in local public<sup>schools is 1.5 points lower than the</sup> national average, and nearly two points<sup>-- approximately one-half standard</sup> deviation -- lower than in private and government<sup>schools. The differential in</sup> mathematics achievement is even greater, with<sup>students in local schools scoring</sup> nearly a full standard deviation below those<sup>in private schools.</sup> 38. Attitudes. Student attitudes towards science<sup>and school are also less</sup>
|

References

-

Atkinson, Anthony (1987), "On the Measurement of Poverty", Econometrica 55:749 64. Chaudhuri, Shubham (1994) "Crop Choice, Fertilizer Use, and Credit Constraints: An Empirical Analysis", mimeo, Department of Economics, Colombia University. Chaudhuri Shubham and Martin Ravallion (1994), "How well do static indicators identify the chronically poor?", Journal of Public Economics, 53, 367-394. Chen, Shaohua and Martin Ravallion (1995), “Data in transition: Assessing rural living standards in Southern China", Policy Research Department, The World Bank. Deaton, Angus (1992) Understanding Consumption, Oxford: Oxford University Press. Foster, James., J. Greer, and Erik Thorbecke (1984), "A Class of Decomposable Poverty Measures", Econometrica 52: 761-765. Foster, James, and A. F. Shorrocks (1991), "Subgroup Consistent Poverty Indices", Econometrica 59:687-709. Gould, W. W. (1992), “Quantile regression with bootstrapped standard errors" Stata | Technical Bulletin, 9: 19-21.

Howes, Stephen and Martin Ravallion (1995), "A rural household panel for four provinces of China”, mimeo, Policy Research Department, The World Bank. Lanjouw, Peter, and Martin Ravallion (1995) "Poverty and household size", Economic Journal.

Lipton, Michael and Martin Ravallion (1995), "Poverty and Policy", in: Jere Behrman and T.N. Srinivasan eds., Handbook of development Economics, Volume III, North Holland, Amsterdam.
the root causes of uneven skills distribution and mobility prospects are likely to reap the full benefits from the potential transformative functions of jobs.

The review produces relatively convincing evidence for many of the postulated pathways between social cohesion and labor markets. For instance, research on intergenerational mobility suggests that parents’ occupational background is of decreasing importance as a determinant of economic opportunity (even though there are contrasting results for other dimensions of family background like education or income). Likewise, ethnographic and survey research on work-related interactions provides strong illustrative examples that shared interests and interactions in the work place can help bridge social divides. However, evidence is much more limited when it comes to demonstrating that these separate effects link up to producing more cohesive societies at an aggregate level. Typically, the transitions from individual work-related experiences to the quality of aggregate social relations are mediated by a multitude of wider social and economic influences. These results suggest that, from a social cohesion perspective, individual work-related experiences are probably best studied in conjunction with aggregate social wellbeing outcomes and institutions. Purely micro-level analysis, based on individual wellbeing effects of jobs alone, may overlook aggregate outcomes and interactions that are crucial for understanding why some societies manage to maintain high levels of social cohesion and others not.

A related lesson is the relatively tentative nature of much of the evidence on the outcomes of social relations around jobs. Research on economic consequences of jobs traditionally has to struggle with well-known selection problems, as better skilled or more motivated people may select into better jobs. These problems are typically magnified in the analysis of social interactions and network effects that tend to dominate the recent debate literature on jobs and social cohesion. To this point relatively few studies have been able to deal conclusively with these endogeneity issues. The review therefore concludes on a cautiously optimistic note: while there are strong indications that jobs can have the postulated positive consequences for social cohesion, typically more careful analysis is required to substantiate these claims with robust evidence.

4
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-32-

# CASE STUDIES OF TWO IRRIGATION SYSTEMS IN COLOMBIA

# Their Performance and Transfer of ManaRement to Users' Associations

# Table 2: Land Tenure in the Coello and RUT Irrigation Districts

||||RUT|||CO|ELLO||
|---|---|---|---|---|---|---|---|---|
|Farm Size|F|arms|Are|a|Far|ms|Area||
|(ha)|No.|(2)|<br>ha|_(X)_|No.|(Z)|ha|_(Z)_|
|0-<br>5|1,380|75|1,892|19|587|35|1,445|5|
|5-<br>10|194|10|1,317|13|350|21|2,448|9|
|10 -<br>20|133|7|1,816|18|300|18|4,204|15|
|20-<br>50|131|7|3,888|38|321|19|10,213|38|
|50-<br>100|19|1|1,197|12|110|6|7,110|26|
|Above 100|-||-||13|1|1,767|7|
||1,857||10,110||1,681||27,187||
|Private<br>farms|1,606||9,236||1,656||26,917||
|Agrarian|||||||||
|reform|106||506||25||270||
|farms|1,712||9,732||1,681||27,187||
|Average farm|size:||5.7 ha||||16.2 ha||
Table 4 presents normalized impact estimates based on propensity score matching. Given that estimates depend crucially on the choice of weights, we compute the estimates using five different kernel functions<sup>18</sup> . All five kernel functions lead to results that are very close to each other. Except for the cost of fertilizer, the normalized estimates confirm the qualitative conclusions derived from the naïve outcome comparison. The normalized comparison reveals the tea growers associated with the private sector do pay slightly more for fertilizer than those in the public sector. They certainly have much higher yield per ha than the public sector households. In fact the normalized impact estimated for yield is roughly twice the naïve one. Yet, the better performing tea growers also use less fertilizer than the comparison group. This result suggests the possibility of inefficiencies in public extension services. It also suggests that the little extra cost for those services that the private sector households are paying per are may be worth it. Finally, it is likely that private sector growers produce better quality leaves given that they take about 12 minutes less than the comparators to carry leaves to the collection point.

Table 4. Accounting for Observable Heterogeneity in Outcome Comparison

||Yield|Fertilizer<br>Use|Fertilizer<br>Cost|Time to<br>Carry|Extension<br>Cost|
|---|---|---|---|---|---|
|Gauss|1768.16|-19.34|34.89|-11.58|576.32|
|Epanechnikov|1746.31|-58.59|32.55|-11.84|565.54|
|Quartic|1667.16|-68.94|31.52|-12.01|571.66|
|Uniform|1823.47|-47.16|32.73|-11.69|550.49|
|Cosinus|1732.39|-60.52|32.39|-11.87|566.70|

Source: Authors’ calculations Table 5. Gender Differences in Yield

||Gauss|Epanechnikov|Quartic|Uniform|Cosinus|
|---|---|---|---|---|---|
|Female|1408.92|1172.29|1016.11|1329.53|1142.99|
|Male|1879.64|1927.58|1872.75|1979.45|1918.51|
|All|1768.16|1746.31|1667.16|1823.47|1732.39|

Source: Authors’ calculations 18 The quartic kernel function is also known as the bi-weight kernel. Also note that the use of the uniform kernel is equivalent to radius matching, a variant of the nearest-neighbor method.

16
# **Table A.2 Evidence on Educational Attainment of Migrants from the China Urban Labor Survey (2001)**

||Source C<br>|ommunity<br>|
|---|---|---|
||All Provinces|4 RCRE Provinces|
|Education|||
|Elementary or Less|0.247|0.220|
|Some Middle School|0.086|0.096|
|Middle School|0.485|0.501|
|Some High School|0.039|0.045|
|High School|0.120|0.120|
|Some Post Secondary|0.009|0.011|
|College|0.010|0.012|
|Number of Observations|2,463|481|

*Source: China Urban Labor Survey (see discussion on note of Table 1).

65
(a) reading, sanitation, and consumption, without controls <!-- Start of picture text --> 5 5.5 6 6.5 7 7.5<br>log of monthly consumption per capita<br>low local open defecation high local open defecation<br>1<br>.8<br>.6<br>.4<br>reads paragraphs or better<br>.2<br><!-- End of picture text -->

- (b) reading, sanitation, and consumption, with controls

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>log of monthly consumption per capita, residuals<br>low local open defecation high local open defecation<br>.1<br>.05<br>0<br>-.05<br>reads paragraphs or better, residuals<br>-.1<br><!-- End of picture text -->

Figure 1: Local open defecation predicts reading conditional on SES, rural IHDS Non-parametric local regressions with 95% confidence intervals. “Low” and “high” open defecation are above and below 50% of households in a child’s survey PSU. Household consumption per capita is per month, in rupees. The IHDS gave tests modeled after ASER tests to children aged 8-11. 50% of rural children can read paragraphs or stories; the rest can read letters or words, or cannot read. Residuals are after controlling for age times sex indicators; 15 indicators for highest female education in the household; 35 15 indicators for highest overall adult education in the household; 13 indicators for the language in which the test was taken; a set of indicators that the child is in school, has been in school, or has never gone to school; and the asset count reported by the IHDS.
achievements, at the end of basic education (8<sup>th</sup> grade). For investigating learning achievements at completion of basic education, we use information from the TIMSS conducted in Egypt in 2003 and 2007. These surveys collected responses, from a representative sample of Egyptian eighth-8<sup>th</sup> graders, to series of items of a test-based evaluation of achievements in Math and Science. 7,095 pupils from 217 schools in 2003 and 6,582 pupils from 233 schools in 2007 were interviewed.<sup>5</sup> The TIMSS surveys collected information on children’s background, in particular, in addition to their gender, mother’s and father’s education, the language spoken at home, whether any of the parents was born abroad, household ownership of durables such as books, tv sets, satellite channels access systems, and phones. The information on the economic status of the parents is limited though, with parental, notably father’s, educational attainments serving as an imperfect proxy. Besides, the surveys indicate the location of the schools, which we take as a proxy for places where children were raised.<sup>6</sup> For the 2007 survey, we could use information on the governorate and area type (urban or rural) of the school location. For the 2003 survey, we could only access the area type information.<sup>7</sup>

# _Methodology for measuring learning opportunity inequality_

Achievements in the TIMSS Math and Science tests are measured using item response theory models, aggregating answers from many test items of varying difficulty and discriminating power. These models treat achievement as a latent variable, and, assuming a given distribution (usually a normal) for the achievements of pupils, their estimation allows deriving its distribution and its predicted value for each individual (see <u>Mislevy, 1991; Mislevy et al., 1992). Importantly, these</u> models recover estimates of individual achievements that come with a scale that is arbitrarily fixed.

> 5 We report the results for Math test-scores. The results for Science are very similar and available upon request.

> 6Migrations motivated by studies should be limited among 8 graders. The SYPE survey provides no information on birth place of children less than 15.

> 7The definition of urban areas is not perfectly consistent across the 2003 and 2007 surveys. The 2003 information relies on a classification of “communities” by population size (communities with more than 50,000 inhabitants are classified as urban), while the 2007 information is the official classification consists in the administrative classification (which was not available in 2003).

13
# **Tables and Figures**

Figure 1: Dropout Rates for Primary and Secondary Education, Girls <!-- Start of picture text --> 100%<br>85% 88% 87%<br>90% 80% 78%<br>80%<br>70% 64%<br>58%<br>60%<br>50% 39% 40% 41%<br>40%<br>30% 25% 24% 25% 27%<br>18%<br>20% 11%<br>5% 6% 7% 7%<br>10% 1% 1% 1% 1%<br>0%<br>Female Primary Dropout Rate Female Secondary Dropout Rate<br><!-- End of picture text --> Source: WB EdStats, derived from the variable ―net enrollment rates‖ at the primary and secondary level Figure 2: Lifetime Cost of Girl Primary and Secondary School Dropout, as % of GDP <!-- Start of picture text --> 80%<br>68%<br>70%<br>60%<br>48%<br>50%<br>40% 34% 32%<br>30% 24% 25%<br>20%<br>18%<br>20% 13% 13% 14%<br>10% 11%<br>10% 3%<br>0% 0% 0% 0% 0% 0% 0% 0% 0% 0%<br>0%<br>Cost of Dropout %GDP Primary Cost of Dropout %GDP Secondary<br><!-- End of picture text --> Source: Authors’ computations based on data from ILO KILM, WDI and WB EdStats 26
health. Nature, 430(6998):419.

- Beard, A. S. and Blaser, M. J. (2002). The ecology of height: the eect of microbial transmission on human height. Perspectives in Biology and Medicine, 45(4):475498.

- Becker, G. S. and Lewis, H. G. (1973). On the interaction between the quantity and quality of children. Journal of political Economy, 81(2, Part 2):S279S288.

- Bhalotra, S. and Rawlings, S. (2013). Gradients of the intergenerational transmission of health in developing countries. Review of Economics and Statistics, 95(02):660672.

- Blattman, C. and Miguel, E. (2010). Civil war. Journal of Economic literature, 48(1):357.

- Bohara, A. K., Mitchell, N. J., and Nepal, M. (2006). Opportunity, democracy, and the exchange of political violence: A subnational analysis of conict in nepal. Journal of conict resolution, 50(1):108128.

- Bozzola, M. and Meazza, C. (2012). Growth Velocity Curves: What They Are and How to Use Them, pages 29993011. Springer New York, New York, NY.

- Bozzoli, C., Deaton, A., and Quintana-Domeque, C. (2009). Adult height and childhood disease. Demography, 46(4):647669.

- Bundervoet, T., Verwimp, P., and Akresh, R. (2009). Health and civil war in rural burundi. Journal of human Resources, 44(2):536563.

- Camacho, A. (2008). Stress and birth weight: evidence from terrorist attacks. American Economic Review, 98(2):51115.

- Case, A. and Paxson, C. (2008). Stature and status: Height, ability, and labor market outcomes. Journal of political Economy, 116(3):499532.

- Central Bureau of Statistics (2012). National population and housing census 2011. National report, Kathmandu, Nepal.

CIA World Factbook (2019). The world factbook - nepal. https://www.cia.gov/library/ 27
firm-year observations when no positive transactions are recorded for a given publicly listed firm in a given year (as is common in the finance and trade literatures that work with transaction-level data). A firm enters the sample since its IPO year. We obtain the aggregate issuance data by adding all the transactions for firms in each country-year.

In both our aggregate and firm-level regression analysis we focus on SEOs for two reasons. First, our firm-level regression analysis employs lagged information about firms, including the lagged market value of equity, which is only available for firms that were publicly traded in the prior period. To ensure comparability with our firm-level estimates, we exclude IPOs from our aggregate analysis. Second, we wish to understand how publicly traded firms’ issuance decisions respond to foreign investor interest. Including initial offerings would mix two different phenomena: the responses of private firms (which choose to become public) with the responses of preexisting publicly traded firms. In the Appendix, we show that our aggregate results remain unchanged if we include IPOs in our sample.

The data include issuances in international and domestic equity markets.<sup>12</sup> Equity issuances are sold to a combination of domestic and foreign investors. We have data on a total of 12,723 firms and a total of 330,798 firm-year observations. We include both financial and non-financial firms. Each group has a significant share of the issuance activity. The issuance activity of financial firms is relevant for the financing of investment by non-financial corporations, although financial firms do not directly engage in capital investment. In the Appendix, we show that the results on issuance activity are robust to excluding financial firms from our sample.

> 12 An issuance is classified as international if the firm’s country of origin is different than the country where the equity is raised. SDC classifies the majority of newly issued shares that are destined to become depository receipts (including American Depositary Receipts and Global Depositary Receipts) as international issuances.

11
large-scale attempt to classify countries by trade orientation was conducted by the World Bank (1987), reaching the same broad conclusion. What followed was a flood of cross-country empirical research linking trade to growth, and broadly supporting the paradigm view.

The consensus view was challenged in important papers by Edwards (1993) and Rodriguez and Rodrik (1999). The criticisms went to the foundations of the prior body of research, and were directed at the conclusions one can safely draw from crosscountry studies. Rodriguez and Rodrik argued that we should not be comforted, but rather worried, by the apparent ability of highly disparate measures to capture the ”same” relationship between openness and growth. Edwards argued that the basic approach to cross-country studies abstracted away from important factors better identified through studies of historical episodes. On the basis of such longer-term historical experience, both the Edwards and Rodriguez and Rodrik papers concluded that the role of trade had been overblown. However, the result has not been a paradigm shift, but rather more careful econometrics. As the dust settles, trade remains standing as a focus of attention.

The more recent body of work on export performance and economic growth has internalized earlier criticisms, and emphasis is now on the role of institutions and the record of experience within individual countries. Dollar and Kraay (2002) find that institutional quality is highly correlated with trade itself. They therefore focus on decadal changes in growth instrumented on changes in trade and institutions, and interpret their results as meaning that institutions and trade both matter in the longrun, while trade growth offers short-term advantages over institutional improvements for fostering growth. In another paper, Dollar and Kraay (2004) examine episodes of 2