rafmacalaba/datause-encoder-data huggingface dataset:
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28
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_19_
online at `http://www.cepii.fr/anglaisgraph/bdd/distances.htm` . The details on PTA names and membership are provided in Appendix E of Medvedev (2006). Table 10 lists the names and codes of the WDI and CEPII data series that were used in the analysis.

<u>Table 10: Variable names and codes</u>

|Variable|Name in source data|Code|
|---|---|---|
|GDP|GDP (current US$)|NY.GDP.MKTP.CD|
|Exports|Merchandise exports (current US$)|TX.VAL.MRCH.CD.WT|
|Imports|Merchandise imports (current US$)|TM.VAL.MRCH.CD.WT|
|CPI|Consumer price index (2000 = 100)|FP.CPI.TOTL|
|FDI|Foreign direct investment, net infows (BoP, current US$)|BX.KLT.DINV.CD.WD|
|REER|Real efective exchange rate index (2000 = 100)|PX.REX.REER|
|Distance||dist|

# **A.3 Data transformation**

This section provides the STATA code used to construct the dataset and conduct the analysis.

``` clear setmoreoff setmemory50m setmatsize100 cdv:\fdi
*Createadistancevariable
localEUN"AUTBELDNKFINFRADEUGRCIRLITALUXNLDPRTESPSWEGBR" useV:\Trade\dist_cepii,clear foreachioflocalEUN{ replacepartner1="EUN"ifpartner1=="‘i’" replacepartner2="EUN"ifpartner2=="‘i’" } dropifpartner1==partner2 collapse(mean)dist,by(partner1partner2) replacepartner1="ADO"ifpartner1=="AND" replacepartner2="ADO"ifpartner2=="AND" sortpartner1partner2 savetemp,replace
*TransformPTAmembershipdata
insheetusingR:\Denis\RTA\RTA2.csv,clear dropifpartner1==partner2
*Removeduplicatecountrypairs
sortpartner1partner2startagreement bypartner1partner2:gendup=cond(_N==1,0,_n) dropifdup>1 dropduptypenotifiednum dropifagreement=="GSTP"|agreement=="PTN"
sortpartner1partner2
```

``` mergepartner1partner2usingtemp dropif_merge==2 drop_merge genyear=.
sortpartner1 savetemp,replace ``` ```
*Addatime-seriesdimension
``` 46
**Figure 4. Marginal contribution to reduction in the Gini index, percentage points**

<!-- Start of picture text --> 55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>Pensions Means-tested transfers Non means-tested transfers Direct taxes SIC<br><!-- End of picture text --> Note: Incomes are equivalized using the (modified) OECD equivalence scale. Source: EUROMOD-2017 statistics and authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

The effect of the tax-benefit system on poverty reduction in Russia is moderate compared with the EU countries. The relative poverty rate, defined for comparability with the EU method as a share of population with equivalized disposable incomes below 60 percent of the median income, drops by a half after fiscal interventions. While this is a less-than-average performance for EU standards, it is still better than nine of 28 EU countries (Figure 5). Again, this effect is almost entirely driven by pensions, whereas in the EU other benefits (means-tested and non-means-tested) have typically a significant effect on poverty reduction.

**Figure 5. Marginal contribution to reduction in poverty, percentage points**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>Pensions Means-tested transfers Non means-tested transfers Direct taxes SIC Total<br><!-- End of picture text --> Note: Relative poverty is defined as the share of population with income per adult-equivalent below 60 percent of the median disposable income. Incomes are equivalized using the (modified) OECD equivalence scale. Source: EUROMOD-2017 statistics and authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

12
- 17 -

Table 1: INTEGRATION UNIT ROOT TEST RESULTS<sup>FOR TROPICAL TIMBER PRICES</sup> (prices ir levels)

|Price Series|CRDW|DF|ADF|Lags /b|
|---|---|---|---|---|
|Meranti|0.08<br>|-0.85<br>|-0.75<br>|1<br>|
|(t) /a|0.53|-2.29|-2.51|1|
|Teak|0.15|-0.84|-0.76<br>|2<br>|
|(t)|0.64|-1.98|-2.56|2|
|Sapelli|0.08|-0.90|-0.91|1<br>|
|(t)|0.42|-2.17|-2.62|2|
|Samba|0.12|-0.16|-0.45<br>|1<br>|
|(t)|0.54|-2.29|-2.83|1|
|Niagon|0.12|-0.49<br>|-0.71<br>|1<br>|
|(t5)|0.57|-2.58|-2.53|1|
|Sawnwood|0.07|-0.38|-0.48<br>|1<br>|
|(t)|0.55|-2.08|-2.83|1|
|Plywood|0.16|-1.14|-0.86<br>|1<br>|
|(t)|1.24|-3.54|-3.43|1|
|Critical values at 90%<br>confidence internal|0.32|3.03|2.84||

/a The line (t) below each price series<sup>refers to the regression</sup> Xt = a + bt + ut.

/b Lags refers to the number of lags in<sup>the ADF test.</sup>
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20
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50
Regional Disparities, Targeting, and Poverty in India* by Gaurav Datt and Martin Ravallion

# Table of Contents

1. Introduction 1 2. Regional Disparities and National Poverty 4 3. Average Consumption Levels, Inequality, and 12 Poverty in India: A Regional Profile

4. Regional Disparities and Redistribution: Some 18 Implications for Poverty in India 5. Conclusion 24 Appendix: Relevant Analytical Results 26 Notes 31 References 33

> * Paper presented at the IFPRI/World Bank Poverty Research Conference, October 1989. The paper draws on results of an ongoing World Bank research project, "Policy Analysis and Poverty: Appli;^able Methods and Case Studies" (RPO 675-04), based in the Agricultural Policies Division of the Agriculture and Rural Development Department, Policy.. Plenning, and Research. The authors wish to thank Michael Lipton, Eric Tho:Jecke, and conference participants for useful comments.
practice, the resulting list was not different from the original list produced by the VVC, suggesting that few names were added or subtracted. The list, however, always contained more individuals than the actual number of voucher packs each village ended up receiving. This means that only a subset of farmers in the list of eligible farmers would become new beneficiaries.

**Beneficiary Selection** : To increase transparency and fairness in the selection of beneficiaries, in half of the study villages, the intervention team facilitated a public lottery to select Xi<sup>2010</sup> new voucher beneficiaries from the list of eligible farmers. In the presence of the Village Assembly, the names of all eligible farmers were placed in a basket and beneficiaries were randomly selected. This list of beneficiaries was then ratified by the Village Assembly and displayed publicly in a prominent place in the village. In practice, it took close to two hours to conduct the lottery.

Figure 1 describes the 2x2 factorial design that divides study villages into four bins according to the interventions implemented in each village. While the public meeting with the Village Assembly allows us to assess whether the identification of eligible households is done following the program eligibility criteria, the lottery assesses whether the farmers with highest marginal returns to inputs are selected as beneficiaries among the eligible households and the scope of elite capture. These interventions answer the following questions: Do VVC members identify all potential eligible households, regardless of their productivity? And among the eligible households, do they select only productive households, or their friends and kin?

# _3.2 Data_

This paper uses survey data from 4 different sources. First, in every study village we conducted a _villager_ household survey of 16 households drawn at random. This survey asked basic socioeconomic characteristics, beneficiary status and whether the household met the NAIVS eligibility criteria. Second, we conducted a survey with one male and one female member of the VVC to assess their understanding of the eligibility criteria, the process of beneficiary selection and their socioeconomic characteristics. Third, we collected village level data related to access to services, prices and the implementation of the NAIVS program. The respondent for the village survey was the Village Executive Officer or VEO, a government-appointed official in the village. Finally, we administered surveys to 10 _eligible_ households that had not received vouchers in the 2009/10 season. Five of these eligible households would receive vouchers in the 2010/11 season while the other five would not. To draw this sample, we used the VVC 10
to those reported in DM06<sup>6</sup> .

- Third, general emigration rates may hide important occupational shortages (e.g. among engineers, teachers, physicians, nurses, IT specialists, etc). In poor countries shortages are particularly severe in the medical sector where the number of physicians per 1,000 inhabitants is extremely low. Clemens and Pettersson (2006), and Docquier and Bhargava (2006) provided data on the medical brain drain. The elasticity of medical brain drain rates (as measured by Docquier and Bhargava) to DM06 general rates amounts to 0.44 (R<sup>2</sup> = 0:39). Many observations are far from the overall trend. This suggests that the general brain drain may not reveal important aspects of occupational heterogeneity.

In this literature, the gender dimension has been largely disregarded. An exception is a paper by Dumont, Martin and Spielvogel (2007) which relies on a similar methodology than the one used here and analyzes emigration rates by gender and educational level from about 75 countries. Compared to this study, we use a slightly di¤erent de...nition of high-skill migration (including all post-secondary levels, even those with one year of US college), and rely on plausible estimates of the structure of the adult population in countries where human capital indicators are missing. We repeat the exercise for 1990 and 2000, thus shedding light on the recent feminization of the brain drain. We provide emigration stocks and rates for 195 countries in 1990 and 2000. Our data set can be used to capture the recent trend in women’s skilled migration, as well as to analyze its causes and consequences for developing countries.

# 3 Emigration stocks by education level and gender

This section describes the methodology and data sources used to compute emigration stocks by educational attainment and gender for each source country in 1990 and 2000. Subsequently, we discuss the main insights from the data.

## 3.1 Methodology and data sources

It is well documented that statistics provided by source countries do not provide a realistic picture of emigration. When available, which is very rare, they are incomplete and imprecise. Whilst detailed immigration data are not easy to collect on an homogeneous basis, information on emigration can only be captured by aggregating consistent immigration data collected in receiving countries, where information about the birth country, gender and education of natives and immigrants is available from national population censuses and registers (or samples of them). More speci...cally,

> 6Regressing corrected rates on uncorrected rates gives R2 of 0.9775, 0.9895 and 0.9966 for J=22,18,12.

6
1

# **1. Introduction**

Human capital - regarded as the “stock of skills and productive knowledge embodied in people” (Rosen, 2008) - is the main determinant of modern economic development (Lucas, 1988; Romer, 1990; Jones and Romer, 2010; Jones, 2019). Work is a form of school - it teaches new skills, updates old ones, and gives purpose. However, its contribution to human capital is underappreciated. Since human capital accumulation has a long-lasting impact, a pertinent query is whether returns to work could serve as a measure of current and/or future economic development, especially given that it does not require tracking quantities and prices as GDP per capita does. Returns to work may focus on aspects of welfare improvements that may capture whether an economy is “truly” developing instead of, for example, seeing its GDP increase as a result of natural resource exploitation. We explore this by first measuring the returns to potential work experience and then by investigating how they behave across several dimensions. These include the level of development, economic transitions, and recessions.

Since the seminal works of Schultz (1961), Becker (1964, 1975) and Mincer (1974), there is a broad consensus that human capital is obtained during childhood, at school, _and_ at work (World Bank, 2018b). Heckman (2019) hypothesizes that the returns to early childhood investments are likely higher than the returns to education, which are in turn possibly higher than the returns to work experience. Subsequently, the literature has focused on estimating the _returns to education_ or the _returns to early childhood investments_ . While a substantial literature exists on the _returns to experience_ , the contributions of experience to human capital and economic development have attracted comparatively less attention (for exceptions, see Lagakos et al. (2018b,a)).<sup>1</sup> However, the respective contributions of ECIs, education and experience to the total stock of human capital, and thus economic development, are a function of both their respective returns _and_ the period over which these returns are accumulated. Indeed, most children in the world do not attend primary school before age 6, and the average person in the world accumulates schooling for 10 years _but_ works up to 50 years. In addition, schools and firms may “teach” different skills (e.g., more theoretical skills vs. more applied skills), and the skills learned at work may be more directly relevant

> 1Banerjee and Duflo (2005) survey the literature on the returns to human capital but do not mention experience. Likewise, excellent surveys provided by Caselli (2005), Flabbi and Gatti (2018) and Rossi (2020) focus on education.