rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Real income impacts of BRI (change relative to baseline in 2030)

||BR|ILBD|BR|IUBD|BR|IUBBD|BRI|UBBTD|BRI|UBBTPD|
|---|---|---|---|---|---|---|---|---|---|---|
||%|$bn|%|$bn|%|$bn|%|$bn|%|$bn|
|World|0.4|492.3|0.7|928.9|1.1|1405.0|1.1|1465.2|1.1|1515.6|
|BRI Area|0.8|419.6|1.2|651.2|2.0|1062.0|2.1|1135.9|2.1|1133.0|
|Cambodia|4.8|2.2|5.0|2.3|8.6|4.0|8.3|3.8|8.7|4.0|
|China|0.4|97.0|0.7|178.2|1.0|262.9|1.2|299.9|1.1|291.6|
|Indonesia|0.0|0.2|0.5|12.1|0.5|12.1|0.6|13.5|0.6|12.9|
|Lao PDR|0.7|0.2|3.1|1.1|9.2|3.2|9.1|3.2|9.3|3.2|
|Malaysia|6.8|40.6|7.7|46.0|8.1|48.5|8.5|51.0|8.3|50.0|
|Philippines|0.2|1.1|0.7|4.0|0.6|3.5|0.6|3.5|0.6|3.8|
|Thailand|3.0|23.9|8.2|64.6|9.7|76.0|9.9|77.7|9.7|76.6|
|Vietnam|1.1|5.5|1.6|7.6|1.6|7.7|1.7|8.4|1.6|7.7|
|Rest of East Asia|0.3|2.9|1.6|14.9|2.5|23.7|2.5|23.8|2.5|23.4|
|Bangladesh|6.7|31.3|6.9|32.5|7.2|34.0|7.3|34.2|7.4|34.6|
|India|0.2|12.1|0.4|23.4|0.6|30.5|0.8|44.4|1.0|51.3|
|Nepal|0.3|0.1|0.4|0.2|11.3|4.7|11.0|4.6|11.2|4.6|
|Pakistan|10.1|53.8|10.5|56.1|10.8|57.3|10.9|58.0|11.1|59.2|
|Sri Lanka|0.4|0.8|0.6|1.2|0.8|1.5|0.9|1.8|1.1|2.1|
|Rest of S. Asia|1.0|0.6|1.4|0.9|5.4|3.5|5.6|3.6|5.7|3.7|
|EU ECA|0.2|2.4|0.2|3.1|0.5|6.8|0.7|8.8|0.6|8.1|
|Russian Federation|1.1|35.0|1.2|39.2|1.9|59.6|1.9|61.8|1.9|61.7|
|Poland|0.2|1.3|0.2|1.8|0.3|2.3|0.4|2.8|0.4|3.0|
|Rest of E. Europe|0.3|1.2|0.8|3.1|1.6|5.9|1.6|5.9|1.4|5.2|
|Kazakhstan|0.3|1.4|0.5|2.1|15.4|71.5|15.4|71.6|15.5|71.9|
|Kyrgyz Republic|9.9|1.3|10.4|1.3|37.3|4.7|36.6|4.6|36.1|4.5|
|Rest of C. Asia|1.4|6.6|1.5|7.1|6.7|31.4|6.6|31.2|6.6|31.1|
|Iran,Islamic Rep.|2.0|13.4|3.0|20.5|4.3|28.8|4.9|33.3|4.9|33.2|
|Egypt,Arab Rep.|0.2|1.2|0.2|1.9|0.3|2.2|0.3|2.4|0.9|7.2|
|Turkey|3.6|53.2|3.6|53.7|3.9|57.5|4.1|61.2|4.2|61.7|
|Rest of MENA|0.5|24.5|1.3|65.7|4.1|209.4|4.1|211.5|4.1|207.0|
|Kenya|1.2|2.0|1.5|2.5|2.3|3.9|2.6|4.3|2.7|4.5|
|Tanzania|2.2|3.7|2.5|4.1|2.9|4.7|3.1|5.1|3.2|5.2|
|Non-BRI Area|0.1|72.7|0.3|277.7|0.4|343.0|0.4|329.3|0.5|382.6|
|Ethiopia|1.2|1.9|1.9|3.1|5.9|9.9|5.8|9.7|6.0|10.1|
|Rest of SSA|0.2|8.2|0.4|17.2|0.7|26.7|0.7|25.7|0.7|26.8|
|Lat. Am. & Car.|0.0|1.9|0.1|10.4|0.1|12.2|0.1|10.5|0.1|12.4|
|United States|0.0|1.7|0.4|107.2|0.4|110.9|0.4|109.0|0.5|122.5|
|Rest of High-inc.|0.1|14.5|0.5|69.1|0.5|76.9|0.5|71.9|0.6|87.0|
|Rest of W. Eur.|0.2|44.5|0.3|70.7|0.4|106.4|0.4|102.5|0.5|123.8|

- 27 -
# **4. Data and Context**

The survey experiment was implemented in Tanzania, which has different types of labor market surveys, including CWIQs, LFSs and multipurpose household surveys, like the Household Budget Survey (HBS). Figures reported for 2000/1 by the National Bureau of Statistics illustrate the relevance of our research. Whereas the Integrated Labour Force Survey (ILFS) reports labor force participation rates of 90.6 percent for men and 89.5 percent for women (NBS 2003), the Household Budget Survey (HBS) reports 91.1 percent and 82.4 percent respectively for the same year (NBS 2002).<sup>14</sup> The large difference in labor force participation of women between two nation-wide surveys that refer to the same year may reflect genuine differences (attributable to differences in samples or in the timing of the survey implementation), or may reflect differences caused by the use of distinct survey instruments. Our experimental set up intends to shed some light on the role that questionnaire design and respondent type may have on such empirical regularities.

The survey experiment conducted was the Survey of Household Welfare and Labour in Tanzania (SHWALITA), and was implemented by a well-established data collection enterprise, Economic Development Initiatives (EDI). This survey was purposively designed and fielded to study the implications of the alternative survey designs for consumption expenditure measures and labor market indicators. Here we focus on the component that applies to labor market indicators.

> 14 The former is obtained from the tables in the appendix of the ILFS 2000/1 report, available at <u>http://www.nbs.go.tz/labourforce/index.htm, while the latter is reported in the 2000/1 HBS report</u> available at http://www.tanzania.go.tz/hbs/Index_FinalReport_HBS.htm

16
# I. INTRODUCTION

While establishing his theory of human migration, which forms the basis of much of modern research on migration, Ernst Georg Ravenstein (1885, 1889) relied on various data sources from over 20 countries in Europe and North America to back up his assertions with facts. One of his main conclusions was the importance of high-quality primary data, mainly from national sources, for demographic and geographic research. Since then, researchers in numerous fields such as demography, economics, sociology and political science have established various data collection methodologies and principles via censuses, administrative registers, nationally representative or specialpurpose surveys to answer a wide range of academic and policy questions. On one hand, the progress has been quite impressive in terms of the variety and quality of data sources available. On the other hand, some of the basic challenges from the nineteenth century still haunt us with all their vigor.

This paper has two purposes. First, it answers some fundamental questions on who international migrants are and how their numbers as well as various demographic, personal and human capital characteristics are captured via different data sources. The critical issues are the construction of proper sampling frames in censuses, registers, surveys and the design of questionnaires in the relevant surveys. These are among the challenges with which collectors and users of migration data continue to struggle from the days of Ravenstein to date. Second, the paper discusses how these data sources on migration can be used to answer different policy questions in various areas, such as labor markets, education policies or economic welfare. The focus of the discussion will be on 2
oldest of the mashup indices still in use, the HDI. Its weights were set 20 years ago, with equal weight to the (scaled) sub-indices for health, education and GDP.<sup>16</sup> Equality of the weights was, of course, an arbitrary judgment, and it might have been hoped that the weights would evolve in the light of the subsequent public debate. But that did not happen. The weights on the three components of the HDI (health, education and income) have not changed in 20 years, and it is hard to believe that the HDI got it right first go.<sup>17</sup> Setting initial weights and revising them in the light of subsequent debate would also point to the need to know the tradeoffs in the most relevant space for understanding what the weights really mean. Arguably, the fact that MIPs have assigned weights in the deprivation space rather than the attainment space does not make it easy for the debate to proceed on a wellinformed basis. I would conjecture that most people will find it easier to attach a monetary value (or market-good equivalent) to a non-market good than to assess what tradeoff is acceptable between the corresponding two poverty measures. If the welfare dimension for which prices are missing is (say) health status, it is surely easier for people to judge how much money they would pay for better health than what tradeoff they would accept between poverty in income space and poverty in health space. Indeed, given the opaqueness about the tradeoffs in the primary attainment dimensions built into MIPs such as the Alkire-Santos index, it can be argued that users (including policy makers) may end up tacitly accepting, and acting upon, tradeoffs that they would find objectionable when revealed. This can hardly be helpful in advancing open public debate about the weights. The weights need to be transparent. Here again it is far from clear that the deprivation space is superior to the attainment space for this purpose.

A seemingly natural hybrid can be proposed that blends attainment and deprivation aggregation. Suppose that there is a third attainment dimension, _x_ 3, for a non-market good for which the analyst is unwilling to assign a shadow price. We might then measure poverty by:

> 16 The weights on the HDI’s primary dimensions have varied over time due to (often seemingly arbitrary) changes in the bounds used for scaling the indices. However, the weights on the HDI’s core dimensions have never been explicitly identified or discussed in the HDRs. See Ravallion (2010b).

> 17 In switching to a geometric mean in the 2010 HDR, the weights on the three achievement variables changed, though their logs are still equally weighted; see Ravallion (2010b) for further discussion.

15
teenage girls to have children. In the context of the current study, by decreasing the geographic distance necessary to attend secondary school, the Brazilian expansion improved opportunities for secondary school attendance. In this sense, our estimated negative relationship between schools and teenage childbearing is consistent with Kearney and Levine’s findings and improving access to secondary schooling may improve the (perceived) life trajectory for young women.

# **Data availability statement**

The data and code underlying this article can be accessed at <u><mark>shorturl.at/coGY9.</mark></u>

# **References**

Akresh, R., D. Halim and M. Kleemans, 2018. ‘Long-term and Intergenerational Effects of Education: Evidence from school construction in Indonesia’, NBER Working Paper 25265.

Berthelon, M.E. and D.I. Kruger. 2011. ‘Risky Behavior among Youth: Incapacitation effects of school on adolescent motherhood and crime in Chile’, _Journal of Public Economics_ , 95, 41–53.

Black, S. E., P. J. Devereux and K. G. Salvanes. 2008. ‘Staying in the Classroom and out of the Maternity Ward? The effect of compulsory schooling laws on teenage births’, _Economic Journal,_ 118: 1025–1054.

Brunello, G., M. Fort and G. Weber, 2009. ‘Changes in Compulsory Schooling, Education and the Distribution of Wages in Europe’, _Economic Journal_ , 119 (536), 516–539.

Burde, D., and L. Linden, 2013. ‘Bringing Education to Afghan girls: A randomized controlled trial of village-based schools’, _American Economic Journal: Applied Economics_ , 5(3), 27–40.

Chiavegatto, A. and I. Kawachi. 2015. ‘Income Inequality is Associated with Adolescent Fertility in Brazil: A longitudinal multilevel analysis of 5,565 municipalities’, _BMC Public Health,_ 15(1).

Chicoine, L., 2020. ‘Free Primary Education, Fertility, and Women’s Access to the Labor Market’ World Bank Group, Policy Research Working Paper 9105.

De Felizio, F. 2009. ‘O Ensino Fundamental – Desafios desde a Alfabetização até a Transição para o Ensino Médio’, in: _Educação Básica no Brasil: Construindo o País do Futuro_ , ed: F. Veloso et. al. Elsevier, Rio de Janeiro.

Di Gropello, E. 2006. _Meeting the Challenges of Secondary Education in Latin America and East Asia: Improving Efficiency and Resource Mobilization_ . Washington D.C. The International Bank for Reconstruction and Development.

Duflo, E., 2001. `Schooling and Labor Market Consequences of School Construction in Indonesia: Evidence from an Unusual Policy Experiment’, _American Economic Review_ , 91(4), 795–813.

Duflo, E., 2004. `The Medium Run Effects of Educational Expansion: Evidence from a large school construction program in Indonesia’, _Journal of Development Economics_ , 74, 163–197.

23
Policy Research Working Paper 7396

# **Abstract**

A number of developing countries are currently promoting vocational education and training (VET) as a way to build human capital and strengthen economic growth. The primary aim of this study is to understand whether VET at the high school level contributes to human capital development in one of those countries—China. To fulfill this aim, a longitudinal data on more than 10,000 students in vocational high school (in the most popular major, computing) and academic high school from two provinces of China are used. First, estimates from instrumental variables and matching analyses show that attending vocational high school (relative to academic high school) substantially reduces math skills and does not improve computing skills. Second, heterogeneous effect estimates also show that attending vocational high school increases dropout, especially among disadvantaged (low-income or low-ability) students. Third, vertically scaled (equated) baseline and follow-up test scores are used to measure gains in math and computing skills among the students. The results show that students who attend vocational high school experience absolute reductions in math skills. Taken together, the findings suggest that the rapid expansion of vocational schooling as a substitute for academic schooling can have detrimental consequences for building human capital in developing countries such as China.

This paper is a product of the Operations and Strategy Team, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at yihm.ccap@igsnrr.ac.cn.

_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
the two follow-up surveys illustrate this. Midline data show that shortly after the revolution in 2011, many believed that the revolution improved their chances to access a wage job or realize their own project. However, this sense of optimism was not as strong at endline in 2014, with fewer people feeling optimistic about their future prospects with respect to labor market outcomes three years later.<sup>9</sup>

# **4. Empirical identification strategy**

# _Intent-to-Treat estimates_

Identification of the impacts of entrepreneurship education relies on the randomized assignment of applicants to the entrepreneurship track. We focus on intent-to-treat (ITT) estimates, measuring the impact of offering business training and coaching independently of actual take-up. We estimate the following individual-level intent-to-treat regression: where Y� is the outcome of interest for student i at follow-up (separately for midline and endline), Ti is a binary variable for being randomly assigned to the treatment group, Xi is a set of control variables, πis are fixed effects for each randomization strata (by gender and study subject) and εi is a mean-zero error term.<sup>10</sup> We present results for a specification where Xi includes the few baseline variables in Table 1 that are imbalanced between the treatment and control groups.<sup>11</sup> The sample includes 1,452 individuals tracked at follow-up. We calculate robust standard errors clustered by gender and study subject.<sup>12</sup>

# _Heterogeneous treatment effects_

To determine whether the entrepreneurship track has heterogenous effects between subgroups of applicants, we conduct heterogeneity analysis by estimating the following specification:

> 9 There are no differences between the treatment and control groups in perceptions of labor-market opportunities.

> 10 We include a binary variable for each randomization strata to increase power (Bruhn and McKenzie, 2009). 11 The footnote of Table 2 provides the full list of control variables. Given there was some attrition in the baseline phone survey, in this specification we impute the sample mean to missing observations and add a dummy variable for missing observations in order not to reduce the sample size due to missing controls.

> 12 The standard-errors are clustered at the level of the randomization strata, i.e. gender and 14 study subject groups.

11
Table 8. Relationship Between Inequality and Human Capital

|Dependent Variable is:||Share of P<br>|opulation Tertiary Education<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Inequality Variable is:|Market Gini|Net Gini|Market Gini|Net Gini|
|Sample:|Including Tra|nsition Countries|Excluding Tran|sition Countries|
||||Panel A: 2SLS||
|Inequalityit|0.57***<br>(0.16)|0.59***<br>(0.13)|0.49***<br>(0.15)|0.54***<br>(0.12)|
|Inequalityit|-0.06***|-0.08***|-0.05**|-0.08***|
|*ln(yit-1)|(0.02)|(0.02)|(0.02)|(0.02)|
|ln(yit-1)|0.02***|0.02***|0.02***|0.03***|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Country FE|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|
|Observations|768|768|725|725|
||||Panel B: LS||
|Inequalityit|0.41***<br>(0.16)|0.46***<br>(0.16)|0.43**<br>(0.17)|0.48***<br>(0.16)|
|Inequalityit|-0.04*|-0.06***|-0.04*|-0.07***|
|*ln(yit-1)|(0.02)|(0.02)|(0.02)|(0.02)|
|ln(yit-1)|0.02**|0.03***|0.02**|0.03***|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Country FE|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|
|Observations|768|768|725|725|

Note: The method of estimation in Panel A is two-stage least squares; Panel B least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Inequality_ is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

33
Table 1A. Workers (young worker, females and males) in Tanzania (absolute numbers and percentages)

|||Nu|mber of wor<br>Female|kers<br>Male|||Perce|ntage of w<br>Female|orkers<br>Male||
|---|---|---|---|---|---|---|---|---|---|---|
||All<br>Females|All Males|<br>Young<br>workers|<br>young<br>workers|Total|All<br>Females|All<br>Males|<br>Young<br>workers|<br>young<br>workers|Total|
|**Agriculture/Forestry/Fishing**|**7191237**|**6698817**|**1317936**|**1561141**|**13890054**|**42.51**|**39.60**|**7.79**|**9.23**|**82.12**|
|a. Cattle. beef&dairy&small animals|233066|545952|||779018|1.38|3.23|||4.61|
|b. Crop growing|6944679|6023079|||12967758|41.06|35.61|||76.67|
|c. Agricultural & forest services|7446|17165|||24610|0.04|0.10|||0.15|
|d. Fishing|6047|112074|||118121|0.04|0.66|||0.70|
|**Mining &quarrying**|**13771**|**15452**|**0**|**2279**|**29223**|**0.08**|**0.09**|**0.00**|**0.01**|**0.17**|
|**Manufacture**|**83750**|**161699**|**8582**|**5804**|**245448**|**0.50**|**0.96**|**0.05**|**0.03**|**1.45**|
|i. Grainmillproducts&Food canning|25578|47987|||73564|0.15|0.28|||0.43|
|ii. Apparel. spinning. weavingand finishing|47686|30680|||78366|0.28|0.18|||0.46|
|iii. Furniture making&Non-Metallic<br>Minerals|10486|83032|||93518|0.06|0.49|||0.55|
|**Electricity. Gas and water**|**1233**|**13464**|**0**|**0**|**14698**|**0.01**|**0.08**|||**0.09**|
|**Construction**|**4196**|**147494**|**0**|**1977**|**151690**|**0.02**|**0.87**|**0.00**|**0.01**|**0.90**|
|**Trade**|**697473**|**565495**|**50001**|**44877**|**1262968**|**4.12**|**3.34**|**0.30**|**0.27**|**7.47**|
|A. Trade|515042|517069|0|0|1032112|3.04|3.06|0.00|0.00|6.10|
|B. Restaurants & Hotels|182430|48427|||230856|1.08|0.29|||1.36|
|**Transport & Communication**|**7643**|**103929**|**0**|**1261**|**111571**|**0.05**|**0.61**|**0.00**|**0.01**|**0.66**|
|**Finance. Insurance & Business services**|**4339**|**22162**|**0**|**0**|**26500**|**0.03**|**0.13**|**0.00**|**0.00**|**0.16**|
|**Personal services**|**559872**|**622779**|**276404**|**193455**|**1182651**|**3.31**|**3.68**|**1.63**|**1.14**|**6.99**|
|Public services|145094|284151|||429244|0.86|1.68|||2.54|
|Otherpersonal services|414778|338628|||753407|2.45|2.00|||4.45|
|**COUNTRY TOTAL**|**8563514**|**8351291**|**1652922**|**1810793**|**16914803**|**50.63**|**49.37**|**9.77**|**10.71**|**100.00**|

Source: Author's own elaboration based on the ILFS (NBS, 2002a). Tables 34, 12A and 13.7. Note that the categories “All females” and “All males” include “Female young workers” and “Male young workers”, respectively, 36
ing, educational upgrading and international labor mobility. While we assume a world where technology and institutions are not changing in our main analysis, we relax these assumptions and explore how they interact with demographic changes.

The second strand of the literature pertains to the impact of immigration on the labor market outcomes of natives. Papers examining the impact of immigration on the wages of natives using a framework similar to ours include Ottaviano and Peri (2012) in the context of the USA, and Manacorda et al (2012) and Dustmann et al (2013) in the case of the UK. These papers only focus on immigration, although emigration is particularly high in some OECD countries such as Ireland and Poland. Docquier et al. (2014), referred to as DOP hereafter and the closest study to ours, fills this gap by looking at the joint effect of emigration and immigration on the labor market outcomes of high ~~-s~~ killed and low ~~-s~~ killed natives in OECD countries between 1990 and 2000.

Our study differs from DOP along several dimensions. Whereas their focus is entirely on the labor market changes due to immigration and emigration, we analyze the effects of ageing, educational improvements, emigration and immigration jointly. For this purpose, we introduce the age dimension into the model via imperfect substitution between the young and old workers in production. We also exploit recent data from the 2000 and 2010 censuses which contain detailed information on the age ~~-~~ education ~~-n~~ ativity distribution of the workforce across OECD countries.

The rest of the paper is organized as follows. Section 2 presents the aggregate pro ~~-~~ duction and labor supply framework, and analytically derives the wage effects of ageing, education and migration shocks. Section 3 describes the construction of our data set and provides summary statistics of the labor force, migrant data, and changes in their age and educational composition. This section also presents the discussion of the main parameter values used in the analysis. Section 4 discusses the simulated wage effects of ageing, educational changes and migration, using our model and the range of parameters available from the literature. Section 5 examines the consequences of relaxing some of the assumptions we make. Section 6 concludes the paper.

# 2 The analytic model

We construct a static aggregate model that endogenizes the effect of the changes in the education, age, immigrant and emigrant composition of the labor force on the wage levels of native (non ~~-~~ migrant) citizens. Workers differ by their migration status, educational attainment (i.e. skills), and age group and have heterogeneous labor force participation preferences. In terms of migration status, we distinguish between the nativ ~~e-~~ born and the foreig ~~n-~~ born workers, which are denoted by M = (N,F). In terms of educational attainment, we follow the literature in labor economics (Freeman, 1979; Katz and Murphy, 5