rafmacalaba/datause-encoder-data huggingface dataset:
In addition, variables on resource rents are also used to proxy for commodity windfalls. The data on natural resource rents come from the Changing Wealth of Nations data set of the World Bank (2011) available from the World Bank’s World Development Indicators (WDI). Natural resource rents are defined as the difference between the unit price of resources and their unit cost of extraction, multiplied by the volume of resources extracted. Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. The data have been widely utilized in the literature (Klomp and de Haan, 2016; Arezki and Gylfason, 2013). Summary statistics for the sample of analysis are provided in Table A1.

# _2.3 Tax Data_

Tax data are obtained from the UNU-WIDER ICTD government revenue data set (Prichard et al, 2014). The data set combines several sources of tax data compiled from IMF Article IV reports, thereby ensuring extensive coverage. These include IMF Government Finance Statistics (GFS), World Bank World Development Indicators (WDI), OECD Tax Statistics, OECD Revenue Statistics in Latin America dataset, CEPAL Tax Statistics, and the AEO African Fiscal Performance. The data set includes a separate category for resource tax revenues, in addition to several other tax breakdowns. Data are available from 1980 to 2017. Summary statistics for the sample of analysis are provided in Table A1.

# **3. Estimation Strategy**

In this section we present our empirical strategy.

To explore the effect of commodity price shocks on billionaire net worth, we estimate the following equation: L 0<sup>+ ββ</sup> 1<sup>D</sup> (2) ii,tt<sup>= αα</sup> cc,tt<sup>+ γγ</sup> ZZ<sup>DDLLIICCDDLLDDCC</sup> cc,tt<sup>+ ττ</sup> tt<sup>+ υυ</sup> ii<sup>+ εε</sup> ii,tt Where L LLIIL is the log of billionaire net worth in USD for individual<sup>DDIIDDLLDDPPD</sup> _i_ at time _t_ ; D is the log difference of the commodity price index in country _c_ , is a vector of country-level controls including structure and size of the economy. LLIIL is the year fixed effects and represents individual billionaire DDIIDDLLDDPPD fixed effects. As a robustness check, we estimate equation (2) using country fixed effects instead of DDLLIICCDDLLDDCC billionaire fixed effects. Alternatively, we also estimate equation (2) using resource rents in place of ττ υυ commodity price shocks.

Our identification strategy allows us to account for several endogeneity issues. The commodity price shock variables are plausibly exogenous considering most countries are price takers in most commodities they trade, hence limiting the simultaneity bias. We limit omitted variable bias in several ways. Billionaire fixed effects are used to account for time-invariant billionaire-specific and country-specific unobservables. This can include education, ability, as well as geographic location and main sector of activity if they do not vary over time. The year fixed effects capture common year shocks. We also include country-level covariates that capture the size and structure of the economy, that could be important predictors of billionaire net worth.

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provision has stronger impacts, with a positive impact on employment and on having had a formal job by the time of survey which are significant at the 10 percent level, and a positive impact on the aggregate employment index which is significant at the 1 percent level. The latter survives adjustments for multiple hypothesis testing. Training results in a 4 to 6 percentage point higher increase in employment after 1 year in private courses than in public courses. This gap narrows to 2.5 percentage points after 3 years, and is no longer statistically significant.

# **6.3 Skill Acquisition, Signaling, or Job Matching**

A third step in the causal chain is for individuals who take courses of sufficient quality to be able to use what they have learned in the course to find jobs. There are three main channels through which vocational education may help in this respect. First, it might increase human capital through teaching new technical skills. Second, it may act to certify skills that individuals already have and act as a signaling mechanism to employers. Third, it may teach individuals new strategies for finding jobs in a certain profession, or better alert them to job opportunities, thereby improving job matching.

To examine the extent to which courses are playing each of these roles, and to which treatment effects vary with them, our follow-up survey asked course participants whether they thought the course had done each of these three things. We interact the percentage of course participants who think that the course taught new technical skills, certified existing skills, taught new strategies for finding jobs, or made them more aware of job opportunities with treatment and show the results in Table 9. We see that on average 84 percent of participants thought the courses certified skills they already had, and 80 percent thought they taught new skills, while 60 percent thought the course helped with job finding strategies and 45 percent with making them more aware of job opportunities. However, we find the point estimates typically to be negative and not statistically significant. Thinking the course made them aware of new jobs has a significant negative interaction for the outcome of having had a formal job at the time of the follow-up survey, but this is not significant after correcting for multiple testing. Thus the heterogeneity in how participants perceive these factors across courses does not seem to drive heterogeneity in treatment outcomes.

# **6.4 Training impacts and unemployment rates**

The last step specified in the causal chain is for individuals who receive training to find jobs that they would not otherwise get. This depends on the labor market they face. On one hand, 21
## **Abstract**

The migration of labor from rural to urban areas is an important part of the urbanization process in developing countries. Even though it has been the focus of abundant research over the past five decades, some key policy questions have not found clear answers yet. To what extent is internal migration a desirable phenomenon and under what circumstances? Should governments intervene and if so with what types of interventions? What should be their policy objectives? To shed light on these important issues, we survey the existing theoretical models and their conflicting policy implications, and discuss the policies that may be justified based on recent relevant empirical studies. A key limitation is that much of the empirical literature does not provide structural tests of the theoretical models, but only provides partial findings that can support or invalidate intuitions and in that sense support or invalidate the policy implications of the models. Our broad assessment of the literature is that migration can be beneficial or at least be turned into a beneficial phenomenon, so that in general migration restrictions are not desirable. We also identify some data issues and research topics which merit further investigation.

# **1 INTRODUCTION**

In developing economies, structural changes often have profound spatial implications. The present survey focuses on labor-related migration from rural to urban areas, a general pattern which plays a central role in the urbanization process and is often viewed as the labor market adjustment to the inter sectoral shift in importance from agriculture to manufacturing and services.<sup>1</sup> Rural to urban migration has historically been an important part of the urbanization process and continues to be significant in scale, even though migration rates appear to have slowed down in some countries. Migration from rural areas accounted for at least half of all urban growth in Africa during the 1960s and 1970s and about 25% of urban growth in the 1980s and 1990s (Brockerhoff, 1995). In Brazil, at the peak of its urbanization process, it is estimated that over 20 million people moved from rural to urban areas between the 1950s and the 1970s. In comparison, 20.5 million people in India (30% of national urban growth) moved from rural to urban areas in the 1990s (Census of India, 2005).

> 1 In this survey, we do not focus on amenity-based migration which explains the migration of students or of retired people. Nor do we focus on forced migration (due to war or civil unrest).

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Trust has a corresponding international dimension. Absence of trust deters foreign trade (Guiso et al., 2009) and foreign investment (Dearmon and Grier, 2009).

Trust is a substitute for missing or ineffective formal institutions (Ahlerup, Olsson, and Yanagizawa, 2009). Trust is accordingly the basis for informal sector activities where recourse to formal payment relations and court-enforced contracts is not available (Annen, 2013). We therefore expect individuals engaged in informal sector activities to have experienced high trust and also trustworthiness.

Trust is also involved in relations between private individuals and government (see Keele, 2007; Bjørnskov and Sønderskov, 2013). Investors confronting potential time-inconsistency problems require trust that governments will not opportunistically adopt appropriative policies after fixed investments are in place. Lack of trust in government decreases willingness to pay taxes. Trust in government also affects investment incentives, through faith in the civil and criminal justice system. Investment is deterred when trust is compromised by absence of the rule of law as indicated by corruption and organized crime (Knack and Keefer, 1997; Blanco, 2013). Investors also need to be able to trust the judicial system to provide fair treatment in disputes involving the government. In disputes between private parties, trust is required that judges are not prone to biased judgments based on personal relationships.

# _1.2 Montenegro_

Against the background of the evidence of the necessity of trust for economic development, we report on a study of trust in Montenegro – a small (population 650,000) post-socialist post-conflict, post-crisis country that became independent in its contemporary form on June 3, 2006. In a referendum on May 21, 2006 on secession from the federation, 55.5 percent of voters, marginally in excess of the required 55 percent majority, supported secession from a Serbian-Montenegrin federation that had been created in the course of the break-up of the former 3
manager/owner (i.e. insiders) may have significant information about the future prospects of the firm but cannot reveal it either due to strategic reasons or due to lack of credibility. In such a situation potential investors (i.e. outsiders) may undervalue the firm. Most of the existing empirical work on the choice between those two equity issuing mechanisms has focused on US based firms (Hertzel and Smith, 1993 and Wu 2004).<sup>1</sup> A major contribution of our paper is to extend this analysis to firms based in developing countries. These firms are more likely to suffer from problems associated with asymmetric information that drive the choice between those two equity selling mechanisms.

Our paper empirically examines potential motivations of why certain firms choose to raise equity in the PIPE market rather than the traditional SEO market in nine developed and developing Asian countries. We use data from the Asian Venture Capital Journal (AVCJ) database that provides data on PIPE financing raised from Private Equity (PE) investors. While many other institutional investors also buy stakes in publicly listed companies in the PIPE market, PE firms are regarded as sophisticated investors who conduct rigorous screening before making their investments. Retail investors who buy equity in the SEO market are typically perceived to be uniformed with limited ability to obtain and process information about firms. The pronounced difference in investor sophistication across these two groups allows us to clearly test the impact of asymmetric information on issuance choice. By focusing on a single class of PIPE investors we also hope to reduce any regulatory variation that may exist across the different countries in our sample.

> 1 The few studies looking at equity raising in international markets have focused largely on other developed countries. For example, Cronqvist and Nilsson (2005) examine the choice between private placement and rights offerings in Sweden. Carpentier and Suret (2010) describe the Canadian PIPE market while Haggard et al. (2009) examine separately the PIPE markets in Canada, Australia, Hong Kong, Speical Administrative Region of China and the UK.

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experienced a dramatic structural change, witnessing a significant decline of its manufacturing output which has shifted to other parts of the world, especially East Asia. The occupational structure has shifted, in parallel, to white color jobs which more intensively use interpersonal and analytical skills.

# **3. Data**

We use labor force surveys and households income and expenditure datasets with national representation. A large part the surveys come from the International Income Distribution Database (I2D2), developed by the Development Economics Groups at the World Bank. The I2D2 is a harmonized database that represents 158 economies from developed and developing countries and it covers a long period in time (1970-2012). It includes a set of standardized variables on employment, education and income. An additional set of surveys used in this study corresponds to the new member states in the European Union and comes from the Labor Force Surveys harmonized by EuroStat.

All surveys include an employment module which classifies occupations according to the International Standard Classification of Occupations version 1988 (ISCO88) developed by the International Labor Office. We also included the United States, using the American Community Survey, to obtain benchmark values.<sup>11</sup> For the thirty countries, we match all occupations contained in the household surveys with their respective skill content from the O*NET database. To achieve this, we apply an equivalency between the ISCO88 and the United States Bureau of Labor Standard Occupational Classification System in its 2000 version (SOC 2000) on which ONET is based. Whenever possible, we matched the occupations and their content at the threedigit level but for most countries, a two-digit match was used.<sup>12</sup> In the countries that had national variations of the ISCO88, we identified those changes manually, and established proper equivalencies.<sup>13</sup> In most of the countries, we were able to obtain the skill structure for approximately 90 percent of the employed labor force.

> 11 Annex 1 includes the list of countries and surveys used.

> 12 A four-digit match was possible for some countries but it was not considered because some occupations lacked scores in the ONET dataset.

> 13 In case a country did not use the ISCO88 scheme we analyzed whether a national adaptation of ISCO88 or even an older version scheme as ISCO68 was available to construct a crosswalk table. If no mapping codebook was available, we explored the distribution of the occupational groups at two and three digits to identify if it was possible

**10**
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Previous work has shown that lending of state banks in India tracks the electoral cycle.<sup>14</sup> Hence, we control for a district’s temporal distance to the next scheduled state election to account for the resulting fluctuations in credit supply. Electoral data come from the Election Commission of India’s publicly available election statistics database. Finally, we use data on district characteristics from the Census of India and the Indian Agriculture Census. These data include the population and land distribution of each district.

# **4 The Credit Market of the Bailout**

Estimating the credit market impact of the bailout poses two main identification challenges. First, program exposure is a function of loan defaults ahead of the program, and therefore potentially endogenous to pre-existing economic trends at the district level. Second, estimating the causal effect of the program requires us to distinguish changes in credit supply from contemporaneous shocks to credit demand.

We address this identification problem in two steps. First, we estimate the credit market impact of the bailout at the district level, using a difference-in-differences specification, in which we instrument for program exposure. Since we observe both program exposure and real outcomes at the district level, we use this approach as our benchmark identification strategy. Second, we verify the robustness of our credit market estimates by replicating the analysis with data at the bank-district level. This allows us to isolate the impact of the bailout on credit supply from any contemporaneous shocks to credit demand using a fixed effects strategy similar to Khwaja and Mian [2008].<sup>15</sup> As we shall see, both identification strategies yield similar point estimates.

> 14Cole [2009b] shows that agricultural lending by state-owned banks in India increases by 5-10 percentage points in election years.

> 15This approach been used in a number of studies that identify the transmission of credit shocks through the bank lending channel. See, for example, Schnabl [2012], Paravisini et al. [2014] and others.

14
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can be used to predict yi. Given an estimate of the functional form of g, one can use predicted values of the vector y, denoted by y, to calculate the predicted value of P(y;z), which can be defined as: Note that P is in general not equal to P(y;z) and is not even necessarily an unbiased predictor of P. This depends on the econometric technique used to estimate the functional form of g and on the functional form of P.10/ Transfers can then be chosen to minimize P subject to the funds available.

The effectiveness of chis method in targeting transfers can be easily evaluated using data from a sample survey by calculating the index of poverty after implementing the transfers derived from the solution of (9). This amount of poverty can be denoted as P(y + t; z)

> 0/ For example, if P were linear in y and r an unbiased predictor.of y, P would be an unbiased predictor of P. Yet if P is convex in y P is biased downwards due to Jensen's inequality (we are grateful to Tim Besley for pointing this out). However, note that t> estimate of poverty when this. scheme is implemented (equation (10)) is .-ot necessarily biased even if P is a biased estimate of P.
# Appendix Table 8: Non-agricultural activities

||In past 7 days,<br>managed hh non-<br>ag. activity<br>(planter)|In past 7<br>days,<br>managed<br>hh non-<br>ag.<br>activity<br>(spouse)|In past 7<br>days,<br>helped hh<br>non-ag.<br>activity<br>(planter)|In past 7<br>days,<br>helped hh<br>non-ag.<br>activity<br>(spouse)|In past 7<br>days, work<br>in paid<br>employment<br>(planter)|In past 7<br>days, work<br>in paid<br>employment<br>(spouse)|
|---|---|---|---|---|---|---|
|Seedling Subsidy|0.03|0.03|0.05**|0.04**|0.01|0.00|
||(0.03)|(0.03)|(0.02)|(0.02)|(0.02)|(0.01)|
|Couples' Training|-0.09***|-0.06**|-0.09***|-0.08***|-0.03|-0.01|
||(0.03)|(0.03)|(0.03)|(0.02)|(0.02)|(0.02)|
|Seedling Subsidy +<br>Couples' Training (beta)|-0.06**|-0.04|-0.04|-0.03*|-0.02|-0.01|
|H0: Seedling Subsidy +|||||||
|Couples' Training = 0 (p-|0.02|0.17|0.13|0.09|0.25|0.64|
|val)|||||||
|Observations|1435|1435|1435|1435|1435|1435|
|Mean for control group|0.2|0.16|0.13|0.1|0.09|0.03|
|Mean for individual training|0.22|0.18|0.17|0.14|0.1|0.03|

Robust and clustered standard errors in parentheses. * p<0.1 ** p<0.05 *** p<0.01 Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

# Appendix Table 9: Action plan tasks without missing values

||All tasks in<br>AP|<br>Family tasks<br>in AP|<br>Planter tasks<br>in AP|<br>Spouse<br>tasks in AP|<br>Hired labor<br>tasks in AP|Fraction of<br>tasks with<br>assigned<br>person in<br>AP|
|---|---|---|---|---|---|---|
|Couples' Training|-0.09|0.30**|0.28|1.46**|0.53|0.13|
||(1.40)|(0.13)|(0.90)|(0.72)|(0.34)|(0.08)|
|Observations|477|477|477|477|477|477|
|Mean for individual<br>training|9.65|0.14|4.07|1.23|0.57|0.68|

Robust and clustered standard errors in parentheses.

* p<0.1 ** p<0.05 *** p<0.01

Note: Family tasks exclude planter and spouse work tasks. Excluded are individuals who did not take part in the training. Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

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