rafmacalaba/datause-encoder-data huggingface dataset:
educational achievement have limited opportunities to access post-school training (Christiaensen and Premand, 2017). In 2018, just 2% of 15-24-year-olds participated in technical and vocational programs in Cˆote d’Ivoire. This low representation is partly due to insufficient capacity in public sector training institutions, which can only admit around 35,000 students annually (International Labour Organization, 2019). Moreover, the infrastructure and equipment in these institutions are in poor condition. To improve the quality of Technical and Vocational Education and Training (TVET), there is a need for revised and updated curricula, modernized equipment and facilities, teacher training with mandatory immersion courses, and better management of course information, anticipation of job market needs, and certification. Currently, TVET programs do not align with industry requirements. Although connections with the private sector have been established, it remains challenging for graduates and school leavers to secure employment or internships. Frequent changes in governance and a lack of coherent long-term policies have further hampered the development of TVET in Cˆote d’Ivoire.

The TVETs offered by PRO-Jeunes differ from most other programs in that they offer quality training focused on the technical skills needed for the job market.

# **2.3 PRO-Jeunes**

## **2.3.1 Enrollment in the PRO-Jeunes program**

We rely on baseline surveys collected in urban Cˆote d’Ivoire (Abidjan, the country’s capital and Grand Bassam, the historic capital situated at around 33 km from Abidjan). These surveys were conducted as part of the impact evaluation of the PRO-Jeunes ( _Pro-Youth_ ) program, a six-year program (2017-2022) implemented by the International Rescue Committee (IRC). The program provides flexible (self-) employment support services to youth aged 15 to 30 years, in both rural and urban Cˆote d’Ivoire.<sup>5</sup> In addition, to be eligible for the PRO-Jeunes program, applicants must be unschooled or have dropped out of school, and show proof of identity. The program was designed to accommodate a wide range of beneficiaries’ needs and knowledge levels, with one cohort targeted approximately every six months for a total of eight cohorts. For this study, we focus on two of the eight cohorts.

The Pro-Jeunes initiative was promoted through a comprehensive communication strategy coordinated by the IRC team, aimed at optimizing beneficiary recruitment and enhancing the program’s visibility. This strategy employed a blend of public relations, direct marketing, media outreach, and

> 5The age range of youth targeted by the project was expanded from 15-24 years old for the first three cohorts to 15-30 years old to take into account context specific constraints to youth recruitment into the project.

5
the technology in their sectors. In this section, we examine spillovers of sanctions to other (non-sanctioned) firms within the same technological field.

We identify the IPC fields affected by the Entity List sanctions through the following procedure. First, we classify firms into technology fields based on their patent applications’ main IPC code at the 4-digit level. We then compute each sanctioned firm’s number of patent applications in each IPC code before being added to the Entity List. A firm’s technology field is defined as the IPC field hosting most of its patents. Second, we match each sanctioned firm in the entity list to PATSTAT and identify its technology field, using the method in the first step. We then define these IPC codes as “sanctioned IPC field” if (1) the sanctioned firms are among the top 10% of Chinese innovating firms in their primary IPC field, or (2) the sanctioned firms patent more than 10% of total patents in their primary IPC field.<sup>17</sup> We have 40 sanctioned IPC fields in our sample, with 22 added after 2018.<sup>18</sup> Most of the sanctioned IPC fields are under the electric communication technique (H04) and measurementtesting technique (G01). Firms patenting in sanctioned IPC fields file significantly more patents overall, and in high-tech fields than other firms in our sample.

We define our treatment group as the set of firms whose primary IPC field is being sanctioned. In our sample, firms can switch among different IPC fields over the years. To eliminate potential bias caused by firms that switch their treatment status, we drop firms that switch in/out of the sanctioned IPC field after the IPC field was sanctioned. Figure 5 shows the total number of firms patenting under the sanctioned IPC field each year. The majority of firms are in the technology field of Computing and Information, Electronics, and Electric Technology.

> 17For example, consider firm ABC: it was added to the Entity List in 2017, and its primary IPC field is G08B. If ABC is not among the top 10% of Chinese innovating firms whose primary IPC fields are G08B and its granted patents are less than 10% of total granted patents in G08B up to the year 2017, we do not consider G08B as a sanctioned IPC field. Conversely, if ABC is among the top 10% of Chinese innovating firms, or its granted patents are more than 10% of the total granted patents in G08B, we treat G08B as a sanctioned IPC field.

> 18Our classification method differs slightly from Han et al. 2020, as they classify a sanctioned IPC field as the primary technology field of the entity sanctioned. The classified IPC field is at the 3-digit level. The defined sanctioned IPC fields using our method are thus much stricter.

21
regions using the IALS and ALL datasets.<sup>**</sup> We show that: (1) skills acquired via schooling account for a larger part of the return to schooling, compared to what is implied by earlier methodologies (as summarized in Bowles et al. 2001) and (2) there is heterogeneity and a dichotomy between two groups of countries. For a subgroup of educationally advanced countries, the labor market predominantly rewards observable skills compared to non-observable. In the rest of the countries, only a small part of the reward can be attributed to skills acquired in school, as was the case when using earlier methodologies.

# **2. Methodology**

In the basic Mincerian human-capital model (Mincer 1974), schooling is assumed to be independent of ability and the return from schooling investments is equal for all individuals. However, in the contemporary literature it is acknowledged that the return to schooling should differ for different skill levels. Intuitively, an estimate of the average return to schooling probably overestimates the return for less-skilled workers and underestimates the return to the highly skilled. One should, therefore allow skill to affect the rate of return to schooling investments.

Attempts to account for basic skills in earnings functions include, for low-income countries: Jolliffe (1998) for Ghana, Boissiere et al. (1985) for Kenya and Tanzania, Behrman et al. (1997) for Pakistan; for lower-middle-income countries: Psacharopoulos and Velez (1992) for Colombia, Angrist and Lavy (1997) for Morocco; for upper-middle-income countries, Case and Yogo (1999) and Moll (1998) for South Africa; Patrinos and Sakellariou (2011) for Chile; and for high-income countries, Finnie and Meng (2001) and Green and Riddell (2003) for Canada, Vignoles et al. (2011)

> ** These countries are, in IALS: Denmark, Norway, Finland, Czech Republic, Hungary, Slovenia and Chile; in ALL: Switzerland-German, Switzerland-French, Switzerland-Italian, Italy, Korea, Bermuda and the Mexican region of Nuevo Leon.

5
<mark>Table 7: Transition Matrices - Synthetic vs. Actual Panel Data</mark> <u><mark>Using Different Number of Replications for the Upper Bound Estimates</mark></u>

|<br>Status in Year[1], Year[2]<br>L|<br>ower Bound<br>Estimates|<br> <br>Truth<br>Confidence<br>Interval|<br>Upper<br>Estim|<br>Bound<br>ates|
|---|---|---|---|---|
||[1]|[2]|[3]|[4]|
|_Panel A: Chile 1996 and 2006_|||||
|Poor,Poor|5.35|(3.19, 6.07)|2.52|2.49|
|Poor, Non-poor|11.09|(16.87,22.29)|21.50|21.64|
|Non-poor,Poor|2.25|(1.79,4.11)|5.08|5.10|
|Non-poor, Non-poor|81.31|(69.77, 75.85)|70.91|70.76|
|<br>_Panel B: Nicaragua 2001 and _|<br>_2005_||||
|Poor,Poor|37.34|(32.08, 39.26)|30.76|30.77|
|Poor, Non-poor|3.31|(2.00,4.70)|10.58|10.57|
|Non-poor,Poor|24.46|(22.82,29.41)|31.04|31.03|
|Non-poor, Non-poor|34.89|(31.27, 38.42)|27.62|27.63|
|<br>_Panel C: Peru 2008 and 2009_|||||
|Poor,Poor|28.70|(21.89,25.31)|17.56|17.57|
|Poor, Non-poor|4.87|(8.544, 10.93)|17.21|17.21|
|Non-poor, Poor|5.40|(9.251, 11.72)|16.53|16.52|
|Non-poor, Non-poor|61.03|(54.16, 58.16)|48.70|48.70|
|Numberof Repetitions|.|.|150|300|
|Observations on Panel A|823|823|823|823|
|Observations on Panel B|684|684|684|684|
|Observations on PanelC|2,368|2,368|2,368|2,368|
|Data source: Chile CASEN,|1996-2006;|Nicaragua EMNV|2001-20|05; and|
|<br>Peru ENAHO 2008-2009.<br>Note: R-squared is calculated|<br>for opposit|<br>e halves of the tot|<br>al 1996|<br>sample.|

<mark>Note: R-squared is calculated for opposite halves of the total 1996 sample. Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Controls are the same as in columns [3] and [7] of tables 2a, 2b, and 2c. Results in column [3] are based on 150 repetitions, while results in column [4] are based on 300 repetitions. Results in column [2] show actual panel mobility. 95 percent confidence interval between parentheses.</mark> 30
# References:

Arora, Seema and Timothy N. Cason., 1995, "An Experiment in Voluntary Environmental Regulation: Participation in EPA's 33/50 Program ". _Journal of Environmental Economics and Management 28,, 271-_ 286, 1995.

Barbara, A.J. and V. D. McConnell, 1990, "The Impact of Environmental Regulations on Industry Productivity: Direct and Indirect Effects," _J of Environmental Economics and Management,_ vol. 18.

Berman, Eli and L. T.M. Bui, 1998, "Environmental Regulation and Productivity: Evidence from Oil Refineries," NBER Working Paper Series, #6776.

Burrows,P. 1981. "Controlling the Monopolistic Polluter: Nihilism or Eclecticism?", _Journal of Environmental Economics and Management,_ Vol. 8, pp.<sup>3 72</sup> -<sup>3 8 0</sup> .

Conrad, Klaus, 1993, "Taxes and Subsidies for Pollution-Intensive Industries as Trade Policy," _J. ofEnvir. Econ. Management,_ Vol. 25.

Dasgupta, S. and D. Wheeler, 1997, "Citizen Complaints as Environmental Indicators: Evidence from China," World Bank Policy Research Working Paper, # 1704.

Farber, S.C., and R.E. Martin. 1986. "Market Structure and Pollution Control under Imperfect Surveillance ". _Journal of Industrial Economics,_ Volume 35, Issue 2. Dec. 1986,147-160.

Gray, W. B and R. J. Shadbegian, 1995, "Pollution Abatement Costs, Regulation, and Plant-Level Productivity," NBER Working Paper Sries, #4994.

Gray, W. B and R. J. Shadbegian, 1997, "Enviromnental Regulation, Investment Timing, and Techmology Choice," NBER Working Paper Sries, #6036.

Harford, Jon D., 1989 "Efficient Scale of the Pollution-Abating Firm: Comment." _Land Economics, 65,_ 413-16, November, 1989.

Hartman, R., M. Huq and D. Wheeler, 1997, "Why Paper Mills Clean Up: Determinants of Pollution Abatement in Four Asian Countries," World Bank Policy Research Report #17 10, 1997.

Hettige, H., M. Huq, S. Pargal, and D. Wheeler, 1996, "Determinants of Pollution Abatement in Developing Countries: Evidence from South and Southeast Asia," _World Development,_ Vol. 24, No. 12 (1996).

Kohn, Robert E., 1991, "Porter's Combination Tax and Subsidy for Controlling Pollution," J. _of Environmental Systems,_ Vol. 20(3).

Kohn, Robert E., 1998, Pollution and the Firm, Cheltenham, UK, Northhampton, MA, USA Konar, Shameek and Mark A. Cohen, 1997, " Information As Regulation: The Effect of Commurnity Right to Know Laws on Toxic Emissions. " _Journal ofEnvironmental Economics and Management 32,_ 109-124, 1997.

Lovei, Magda. 1995. "Financing Pollution Abatement: Theory and Practice " Environment Department Papers No. 028. World Bank, Washington, D.C.

Rozelle, S., X. Ma, L. Ortolano, 1993, "Industrial Wastewater Control in Chinese Cities: Determinants of Success in environmental Policy," Natural Resource Modeling, vol. 7.

22
Policy ReseaRch WoRking PaPeR 4535

# **Abstract**

Using detailed geographical and household survey data from Nepal, this article investigates the relationship between isolation and subjective welfare. This is achieved by examining how distance to markets and proximity to large urban centers are associated with responses to questions about income and consumption adequacy. Results show that isolation is associated with a significant reduction in subjective assessments of income and consumption adequacy, even after controlling for consumption expenditures and other factors. The reduction in subjective welfare associated with isolation is much larger for households that are already relatively close to markets. These findings suggest that welfare assessments based on monetary income and consumption may seriously underestimate the subjective welfare cost of isolation, and hence will tend to bias downward the assessment of benefits to isolation-reducing investments such as roads and communication infrastructure.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the impact isolation on subjective measure of economic welfare. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fshilpi@worldbank.org.

_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
## 3.2.3 Starving the beast

In a paper closely-related to Talvi and Vegh (2005), Alesina et al. (2008) endogenize the political distortion that is taken as given by Talvi-Vegh by providing a compelling political-economy story. Specifically, in good times voters seek to “starve the beast” to reduce political rents appropriated by corrupt politicians. Voters observe the state of the economy but not the rents appropriated by corrupt politicians. Therefore, when voters observe a boom, they optimally demand more public goods and/or lower taxes, which leads to procyclical fiscal policy. The more corrupt the government and close political allies, the higher the rents that they will appropriate, and the higher the public demand for public goods and/or lower taxes. From an empirical point of view, the key variable to capture Alesina et al.’s story is some measure of corruption. We will use the variable “corruption”from the International Country Risk Guide (ICRG).<sup>24</sup>

# 3.3 Regressions

Table 1 reports the results of cross-country regressions for commodity exporters. The dependent variable is the country correlation between the cyclical components of real government spending and real GDP measured over the entire time interval. The explanatory variables are the four independent variables discussed above that capture each of the four explanations for the existence of procyclical fiscal policy: the financial openness index; the political constraint index; the standard deviation of income (SD(y)); and control of corruption.

Columns (1)-(4) report the regressions results with each variable individually. We can see that each variable is significant at least at the five percent level. In light of the discussion above, three of the four coeffi cients have the expected signs: higher financial openness reduces procyclicality (Column (1)); more control of corruption decreases procyclicality (Column (2)); and larger volatility of output increases procyclicality (Column (4)). In contrast, Column (3) shows a negative coeffi cient, indicating that the higher the political constraint index, the less procyclical fiscal policy is. Based on

> 24ICRG views corruption as a threat to foreign investment by introducing distortions in the economic environment, reducing government effi ciency, and rendering the political system more unstable. Since a higher number implies less corruption, we refer to this index as “control of corruption.” For further details, see ICRG’s website.

15
# **3.2 Measures and Empirical Model**

Measures of domestic violence and women’s status used in the analysis come from the survey module administered to the wife of the household head (or the head if the household is female-headed). Because of the sensitive nature of questions about a woman’s status within the household and domestic violence, we used female enumerators for this module and instructed them to politely inform other household members, including the husband, that these questions should be answered in private. Furthermore, we asked enumerators to record if, despite this request, the husband insisted on being present in the interview, which occurred in 17 percent of interviews. There was no statistically significant difference, however, in reported domestic violence in interviews in which the husband was present.

The key outcome, domestic violence, came from the woman’s response to the question “Has your husband ever beaten you?” In response, 63.6 percent of women responded “no”, 8.8 responded “once”, 26.9 percent responded “more than once”, and 0.8 percent responded “regularly”. I convert the response to a binary outcome that is equal to one if the wife has ever been beaten, which 35.4 percent were. This rate is somewhat lower than the rates found in other areas of Bangladesh,<sup>7</sup> although it is difficult to know whether this difference is due to the specific population and the fact that the data are more recent than in most previous studies or due to underreporting.

The most direct test of the theories in section 2 that relate work opportunities and domestic violence would be whether domestic violence is currently occurring, given the current work status of the woman, rather than the cumulative measure of exposure that I am able to use. Of course, these two measures are correlated, but since the only measure of domestic violence I have is a cumulative measure of lifelong exposure, in section 4.2 I explore the possibility of reverse causality: perhaps women who suffered domestic violence in the past are prompted to enter the labor force to help escape the bad situation.<sup>8</sup> To help differentiate between the different mechanisms behind the correlation between work and domestic violence, I also examine the relationship between labor force participation and a woman’s reported control over resources in the household. Specifically, I use answers to the following questions:

> 7Forty-two percent of women in two areas of rural Bangladesh in 1993 (Koenig et al., 2003) reported physical abuse and 67 percent reporting any type of violence (33 percent reporting major violence) in 2001/2002 (Bates et al., 2004).

> 8Note that if the only measure I had was current exposure, there would still be reverse causality concerns if domestic violence exposure was serially correlated. In potential follow-up studies, I would plan to ask about both current and past incidence, which would yield valuable information on the timing of domestic violence relative to work exposure.

11
# **2.4. Testing for reverse causality**

To test directly for reverse causality, we use a hazard model that assesses whether changes in the distribution of income spurred branch deregulation. Following the procedure in Kroszner and Strahan (1999), Table 5 reports tests of whether income inequality influences the likelihood that a state deregulates in a specific year given that it has not already deregulated. We only include the 37 states that deregulated after 1976.<sup>6</sup> As in Kroszner and Strahan (1999), we find that states with a larger share of small banks and better capitalized small banks deregulate later, while states with a higher share of small firms deregulate earlier. States where banks are allowed to sell insurance products deregulate later if the insurance sector is relatively larger. Finally, states where the Democratic Party holds a larger share of political power deregulate later.

Consistent with our interpretation, the Gini coefficient does not enter significantly in any of the Table 5 regressions. The likelihood that a state deregulates does not change with higher or lower income inequality. This also confirms that our finding of a negative association between deregulation and income inequality is not driven by reverse causation.

> 6 While the Kroszner and Strahan sample period starts in 1970, we do not have Gini data available before 1976. Also, since we use the original Kroszner and Strahan dataset, our sample period ends in 1994, when there were three states that had not deregulated yet – Arkansas, Iowa and Minnesota.

18
**Table 21. Significance of legal and regulatory obstacles: Minimum investment requirements**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0134|0.00797|||0.0589|0.0498|||
||(0.0570)|(0.0564)|||(0.109)|(0.112)|||
|Export % of sales|0.000207|0.000136|0.000188|0.000162|0.000456|0.000539|0.000542|0.000615|
||(0.000729)|(0.000742)|(0.000734)|(0.000743)|(0.00140)|(0.00147)|(0.00144)|(0.00152)|
|Share of sourcing via imports|0.000273|-0.000544|0.000333|-0.000616|0.000667|-0.000791|0.000708|-0.00102|
||(0.000867)|(0.000889)|(0.000878)|(0.000887)|(0.00161)|(0.00169)|(0.00167)|(0.00174)|
|High-income source country|-0.0755|-0.00265|-0.0745|0.00144|-0.106|0.0228|-0.107|0.0325|
||(0.0856)|(0.0846)|(0.0850)|(0.0837)|(0.162)|(0.162)|(0.165)|(0.165)|
|>250 employees|-0.145**|-0.113*|-0.176**|-0.145**|-0.254*|-0.203|-0.326**|-0.277**|
||(0.0681)|(0.0676)|(0.0701)|(0.0697)|(0.130)|(0.133)|(0.138)|(0.141)|
|Cumulative inv. >US$10M|-0.0699|-0.0742|-0.0756|-0.0781|-0.149|-0.168|-0.168|-0.182|
||(0.0689)|(0.0703)|(0.0695)|(0.0706)|(0.134)|(0.141)|(0.138)|(0.146)|
|Years in country|0.000270|-0.00166|0.000594|-0.00149|0.00137|-0.00268|0.00178|-0.00245|
||(0.00231)<br>|(0.00236)|(0.00220)|(0.00228)|(0.00468)<br>|(0.00480)|(0.00452)<br>|(0.00474)|
|Level of foreign ownership (%)|-<br>0.00319***|-0.00187|-0.00287**|-0.00144|-<br>0.00622***|-0.00454*|-<br>0.00571***|-0.00372|
||(0.00114)|(0.00127)|(0.00112)|(0.00122)|(0.00203)|(0.00240)|(0.00205)|(0.00232)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.017|0.051|0.043|0.077|||||

Standard errors in parentheses

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

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Minimum investment requirements]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

38