rafmacalaba/datause-encoder-data huggingface dataset:
nominal rates of assistance” using a database created by Anderson and Nelgen (2013), although this database applies only to agricultural products, where distortions are most prevalent.

Assessing the drivers of participation in GVC sectors then hinges on identifying so‐called “GVC products” based on a list of traded products most likely to be included in cross‐border production networks. The specific lists utilized, described below in the data section, are isolated to manufacturing sectors, where a predominant set of opportunities for developing countries exists. Being a snapshot of GVC activities at a point in time, agri‐food and services cross‐border value chains are not included, even though these chains constitute smaller, but increasingly important, opportunities for developing countries.

The utilization of gross exports from standard trade data stands in contrast with other studies on GVC participation, which utilize value‐added data from input‐output (I‐O) tables. These I‐O databases, including the World Input‐Output Database (WIOD), UNCTAD’s EORA database, and OECD‐WTO Trade in Value Added (TiVA), cover only a subset of countries participating in GVCs. Some studies on drivers of GVC participation utilize an I‐O based “GVC participation index” by De Backer and Miroudot (2013), which sums the import content of exports and the exports of intermediate inputs (goods and services) used in third countries’ exports, taken as a share of gross exports. The use of trade data in this study allows for measurement of GVC participation at a higher level of disaggregation and country coverage. The use of gross exports also puts the focus in this study on the extent of participation in global value chains rather than the amount of value created in global value chains, an equally important issue associated with upgrading in production networks.

# III. Framework and data

## A. Capabilities that matter for GVC participation

Employing the above methodology, we focus on a set of capabilities that are most common in the theoretical, policy, and empirical literature on GVC trade. These are summarized in Table 1, along with the specific indicators chosen and their data sources. It is worth noting that because this analysis is carried out at the global level, some capabilities that may have a big impact on GVC trade and investment at a bilateral level – such as preferential trade agreements, bilateral investment treaties, common language, and time zone – are not included. The selection of capabilities to include in the analysis, and most importantly the specific indicators chosen to represent them, were also dependent on country coverage of the source data. Our final data set covers 87 countries, which together represent 81 percent of world trade in 2012.

The capabilities fall into three categories:

- **<u>Fixed</u>** :  capabilities that cannot be changed by a country.

- **<u>Long‐term Policy Variables</u>** : capabilities that can be changed gradually over a relatively long time horizon.

- **<u>Short‐term Policy Variables</u>** <u>: capabilities that can be changed directly through a policy shift or</u> negotiations.

5
Table 8: Differential effects of SPEI shocks by level of irrigation

|||_Dependent _|_variable:_||
|---|---|---|---|---|
|||current em|ployment||
|Gridcell Area Equipped for Irrigation (AEI):|AEI = 0||AEI>0||
||(1)|(2)|(3)|(4)|
|Dry shock|-0.0144|-0.0323**|-0.0298*|-0.0251|
||(0.0226)|(0.0164)|(0.0179)|(0.0219)|
|... _×_ % Area Equipped for Irrigation|||-0.00115<br>(0.00477)||
|... _×_ ln(% Area Equipped for Irrigation)||||-0.0131<br>(0.0246)|
|Wet shock|0.00446<br>(0.0399)|0.0770**<br>(0.0318)|0.0586<br>(0.0396)|0.0725<br>(0.0476)|
|... _×_ % Area Equipped for Irrigation|||-0.00115<br>(0.0257)||
|... _×_ ln(% Area Equipped for Irrigation)||||0.0110<br>(0.0767)|
|Controls|Yes|Yes|Yes|Yes|
|Full set of FEs|Yes|Yes|Yes|Yes|
|Observations|137,441|435,466|435,466|435,466|
|R<sup>2</sup>|0.242|0.232|0.232|0.232|

**_Note:_**<sup>_∗_</sup> **p** _<_ **0.1;**<sup>_∗∗_</sup> **p** _<_ **0.05;**<sup>_∗∗∗_</sup> **p** _<_ **0.01** . This table reports the how the effects of the occurrence of extreme weather conditions (wet and drought shock) vary by the the presence of irrigation. Column 1 shows the effects in regions without irrigation, column 2 - 4 shows the impacts in regions where irrigation is present. Standard errors clustered at the gridcell-month-year level are reported in parenthesis.

35
estimating both equations simultaneously as a system. We tested our model using a three-stage least squares (3SLS) system and obtained similar results.<sup>10</sup> Table A. Log Ratio of Human Capital (HJ;00=HJ;90), controlling for age of

||J = 12||entry<br>J = 18||J = 22||
|---|---|---|---|---|---|---|
||OLS|IV|OLS|IV|OLS|IV|
|ln(mJ;90)|0.032**|0.077**|0.033**|0.077**|0.033**|0.077**|
||(0.015)|(0.036)|(0.016)|(0.036)|(0.016)|(0.036)|
|ln(HJ;90)|-0.273***|-0.279***|-0.273***|-0.277***|-0.273***|-0.277***|
||(0.038)|(0.040)|(0.038)|(0.040)|(0.038)|(0.040)|
|ln(�90)|0.121**|0.116**|0.121**|0.116**|0.120**|0.115**|
||(0.049)|(0.053)|(0.048)|(0.051)|(0.048)|(0.051)|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|
|F-statistic|41.45|40.11|41.29|40.28|41.37|40.24|
|N. Obs|104|104|104|104|104|104|

Between brackets, Standard errors. Reported heteroskedastic-robust.

> � p<0.1, ** p<0.05, *** p<0.01

> 10These results can be obtained upon request to the authors.

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- [15] Engle, Patrice, Fernald, Lia, Alderman, Harold, Behrman, Jere, O’Gara, Chloe, Yousafzai, Aisha, Cabral de Mello, Meena, Hidrobo, Melissa, Ulkuer, Nurper, Ertem, Ilgi Iltus, S., and the Global 13 Child Development Steering Group (2011). ”Strategies for Reducing Inequalities and Improving Developmental Outcomes for Young Children in Low and Middle Income Countries.” _The Lancet_ 378(9799), 1339-53.

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- [17] Havnes, Tarjei and Mogstad, Magne (2011). ”Money for nothing? Universal child care and maternal employment.” _Journal of Public Economics_ , 95, 1455–1465.

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- [19] McCrary, Justin (2008). ”Manipulation of the running variable in the regression discontinuity design: A density test.” _Journal of Econometrics_ , 142, 698-714.

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- [21] Paxson, Christina and Schady, Norbert (2007). ”Cognitive development among young Ccildren in Ecuador: The roles of wealth, health, and parenting.” _Journal of Human Resources_ , 42, 49–84.

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Okoboi, Geoffrey, and Mildred Barungi. 2012. _Constraints to Fertiliser Use in Uganda: Insights from Uganda Census of Agriculture 2008/9_ . Research Series 88. Kampala: Economic Policy Research Centre.

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64
relative to their pre-migration jobs. Accordingly, we restrict the sample to Venezuelan migrants (living in Ecuador) that report information on both their current job and their last job prior to migration.

The findings are collected in Table 9. Among Venezuelan migrants to Ecuador, the three most popular occupations _prior_ to migration were private-sector employment (about 116,000), government employment (about 30,000) and self-employment (21,000). In comparison, their shares of employment in Ecuador as self-employed, day laborers, domestic employees or outsourced employees have experienced large increases. In contrast, the shares of managers and government employees are sharply lower than prior to migration. More specifically, when we focus on Venezuelan migrants employed as day laborers in Ecuador, we find that their main occupations in Venezuela were private-sector and government employment. Both of these occupations are characterized by much higher wages than what day laborers typically earn and highlight the large occupational downgrading experienced by Venezuelan migrants.

# **6 Policy Simulations**

This section carries out a number of simple, yet informative policy simulations. We focus on two policy actions: legalization (in the sense of providing legal work and residence permits to all migrants) and measures aimed at improving the quality of employment for highly educated Venezuelan migrants.

## **6.1 The Effects of Legalization**

Based on the EPEC survey, approximately 90% of Venezuelan migrants in Ecuador lack legal status, in the sense that they are not authorized to work. This has important consequences for Venezuelan workers. As shown in Section 4.3, lack of legal status is associated with a higher likelihood of informal employment (column 5 in Table 7).

Inspired by Clemens et al. (2018), the goal of this section is to use the estimates of these effects to simulate the consequences of providing legal work permits to Venezuelans on the quality of their employment (measured by informality) and, through this channel, on their productivity (measured by wages).

The relevant information is collected in Table 10. We classify Venezuelan workers that _lack legal status_ on the basis of their education level (primary, secondary or tertiary) and the quality of their employment (formal or informal). Based on the data in column 3 15
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as revealed, for example, by domestic resource cost calculations (UNIDO, 198619. It would do little good, for example, to expand the supply of technicians careful trained in the details of aluminum production, when the economic rate of return on this industry is -20% (compared to a financial rate of return of +12%). The stress at all levels of education should be on developing problem-solving ability, n_1 on developing excessively specialized skills.

## 63.

63. In the final analysis, however, the olismatch between the labor market and the higher education system is the result of non-educational labor market policies. The failiure of adequate job creation in the Egyptian econorry is fundamentally the fault o. the capital-intensity bias of policy and the failure of growth, in turn caused by the accumulation of debt, by the excessive reliance on petroleum and remittances, and by macroimbalances and microdistortions.

# Equlity

64. The fundamental equity problem of the higher educaticiaal system is a common one: the inequitably (and probably also inefficiently) excessive allocation of public funds to higher education at the expense of lower levels. This problem was especially acute during the 1970s: while university enrollments exploded, the school participation rate for children between the ages of 6 and 11 remained approximately stationary at just over 68%. (The rate actually fell for boys during the decade, from 81.6% in 1970/71 to 77.8% in 1980/81). The situation improved during the 1980s. The increased real resources allocated to primary education during the 1980s probably has increased the overall equity of the educational system. It is noteworthy that the absolute number of unenrolled primary school children decisively declined during the 1980s; the decline was especially notable for girls, falling by roughly one-third.

## _65._

_65._ However, a comparison of the budgets of the Ministries of Higher Education and of Education show a rather different picture (see Table 9). If one assumes that the sum of the two Ministries' budgets is a reasonable proxy for "educational spending", then the share of higher education in the total is approximately one-third; an alternative analysis, combining official data with cohort analysis, suggests that the 10% of the cohort of 1970 in higher education received _50%_ of all educational expenditure. It is disturbing to note that, if anything, the share of total educational expenditure (defined as the sum of the two Ministries' budgets) allocated to higher education has increased during the 1980s.

66. The reason why such allocations are inequitable is the usual one: university students rarely come from the lowest i-icome -brackets. As is true world-wide,the subsidy to higher education is a subsidy for the middle-class. An analysis of the educational cohort of 1970 showed that those in 'l Domestic resource cost calculations measure comparative advantage by comparing the DRC ratio (the r3tio of value. added to non-traded inputs) to the (shadow) exchange rate.
imputed share was not substantially different from the observed one, with the average absolute difference between observed and imputed shares in Panama and Nigeria being around 7 percentage points.

In the short-run, incomes of self-employed farmers will increase in proportion to the increase in prices of their produce. The lack of household-level information on rural income sources, implies that, as a result of higher food prices, _all_ rural households experience an increase in nominal income equal to  ˆ _hYh p_  . Therefore, as long as  ˆ _h_   ˆ _h_ , household “ _h_ ” will experience a reduction in real income as a result of higher food prices. For the same increase in price, given the higher value of  ˆ estimated by specification _h_ (4), rural households in Sub-Saharan Africa experience a higher increase in nominal income compared with rural households in Latin America.

**Figure 4: Observed and Imputed Share of Agricultural SE Income**

<!-- Start of picture text --> Nigeria<br>All Countries<br>Panama<br>0 20 40 60 80 100<br>Percentiles of Per-Capita Consumption<br>(1) Using data from RIGA; (2) the percentiles are country-specific<br>100<br>80<br>60<br>40<br>20<br>Self-employment Agricultural Share, %<br>0<br><!-- End of picture text --> The rural poverty effects of a simulation accounting for the consumption and income ˆ effects assuming  _h_   _h_ are presented in Table 3. Despite the fact that we are allowing for positive income effects in the relatively poorer rural areas, indicators in all regions show deterioration in terms of the incidence and depth of poverty. Notice that, although the initial poverty headcount is much higher in rural areas, the increase in this poverty indicator is smaller than in urban areas capturing the offsetting income effects of higher food prices taking place in rural households. For each region except for Latin America, the change in the rural poverty headcount ratio is smaller than the change taking place in urban areas. At the global level, the headcount ratio in rural areas increases by 2.06 percentage points representing an additional 87.19 million individuals falling below the poverty line. The rural poverty deficit, i.e. the resources needed to alleviate extreme 12
in their migration journey.<sup>2</sup> While migrants who do and do not use coyotes differ in systematic ways, such as indigeneity and age (see Online Appendix Table A3), we focus our analysis exclusively focuses on the majority of respondents who did use a coyote, of whom 17% experienced extortion. Over half of these people (10%) were extorted en route to the U.S., while the remaining 7% were extorted after crossing the border.

To measure perceptions of economic difficulty, we primarily use a 5-point scale where respondents rated their current economic situation (“Bad Econ”), collected in the 1- and 6-month post-arrival survey waves. For this measure, higher values indicate more negative assessments of one’s economic situation. We use two other variables as robustness checks in the main tables detailing our results. First, in the initial survey wave, respondents reported the degree to which needing to pay outstanding debts poses a challenge to their reintegration in Guatemala on a 10-point scale (“Debt Barrier”).<sup>3</sup> In addition, respondents in waves 2 and 3 rated their level of economic hardship as low, moderate, or high (“Econ Hardship”). We also examine more objective indicators of economic hardship, monthly income and unemployment (see Table A6).

To conceptualize fear, we use a set of questions asked in the second and third survey waves about actions taken by respondents since their deportation out of fear of being a crime victim. The behaviors include: avoiding leaving their homes by themselves, avoiding using public transit, preventing children from leaving the house, feeling the need to move to a different neighborhood, changing their job or place of study, or obtaining a weapon for personal security. For this analysis, “Fear Any” is a binary variable which indicates whether the respondents answered any of these six questions affirmatively. “Fear Count” is a 0-6 count variable summing how many of the items the respondent selected. Finally, as a robustness check, we use a measure of perceived threat of violence to the respondent and their family (“Violence”), which is measured as low, medium, or high.

To measure political behavior, we ask respondents their likelihood of taking different types of action in the coming year (5-point scale). We focus on four key types of behavior indicative of civic engagement: protest, community meetings, and volunteering. We analyze these measures separately and as a 3-item “Civic Index.” Voting, a more conventional measure of political behavior, is not included in the index because national elections would not occur until 2023. Therefore, asking about voting intent within the coming year was not a meaningful question for respondents. In addition to our civic action items, we also measure

> 2Thus, we cannot include in our analysis those respondents who did not use a coyote to enter the U.S., since we should not uniformly assume that they did not experience extortion from some other criminal actor during the journey.

> 3Our sample is larger for this analysis since this dependent variable was asked at the arrival baseline survey.

13
After controlling for firm age, Table 10 indicates that our core results hold: issuing firms start out larger and grow faster than non-issuing firms. Moreover, the results in Panels A and B for the three proxies for firm size (assets, sales, and the number of employees) are quantitatively similar to those reported in the last columns of Tables 3 and 5, respectively. These patterns hold on average for both equity and bond issuers in bank-based and market-based developed countries and in emerging economies.

Our findings regarding the FSD are qualitatively robust to the split of the sample by firm age (Table 11). The estimated coefficients on the issuer dummy show that issuing firms are ex ante larger than non-issuing firms for firms in all age groups. The results are statistically significant for all estimates across all deciles. Issuing firms are not only larger to start with, but they also grow faster than non-issuing firms across the three age groups. Also consistent with our previous findings, the regressions show that while non-issuing firms of all ages expanded between 2003 and 2010, their growth rates increased with firm size, especially for young and mature firms. In contrast, among issuing firms, smaller firms typically grew faster than larger firms. Hence, these results indicate that there is some divergence in the FSD for non-issuing firms and convergence for issuing firms. These patterns are observed across the three sub-samples of young, mature, and old firms.

In addition, the growth differential between issuing and non-issuing firms declines with firm age. The growth differential for younger firms is larger than that for mature firms, which in turn is larger than that for old firms. For example, the growth differential between issuing and non-issuing firms in the 1<sup>st</sup> decile of the distribution of young firms is 36 percentage points per year between 2003 and 2010, whereas the differential in the same decile of the distribution of mature and old firms is 10 and 4 percentage points per year, respectively. The estimations show that, controlling for firm size,

> in 2011); (3) 15 years and younger, 16-25 years, more than 25 years in 2011; and (4) 10 years and younger, 11-20 years, more than 20 years in 2011.

26