rafmacalaba/datause-encoder-data huggingface dataset:
- Hanson, G. H. (2006). Illegal migration from Mexico to the United States. _Journal of Economic Literature_ , _44_ (4), 869–924.

- Kemnitz, A., & Mayr, K. (2012). Return migration and illegal immigration control. _Norface Migration Discussion Paper No. 2012-40_ .

- Kossoudji, S. A., & Cobb-Clark, D. A. (2002). Coming out of the shadows: Learning about legal status and wages from the legalized population. _Journal of Labor Economics_ , _20_ (3), 598–628.

- Krafft, C., Assaad, R., & Rahman, K. W. (2019). Introducing the Egypt Labor Market Panel Survey 2018. _Economic Research Forum Working Paper No. 1360_ .

- Lacuesta, A. (2010). A revision of the self-selection of migrants using returning migrant’s earnings. _Annals of Economics and Statistics/Annales d’Economie_<sup>_ ́_</sup> _et de Statistique_ , 235–259.

- Lavy, V. (1984). The economic embargo of Egypt by Arab states: Myth and reality. _Middle East Journal_ , _38_ (3), 419–432.

- Mann, J. (2012). King Faisal and the challenge of Nasser’s revolutionary ideology. _Middle Eastern Studies_ , _48_ (5), 749–764.

- Massey, D. S., Pren, K. A., & Durand, J. (2016). Why border enforcement backfired. _American Journal of Sociology_ , _121_ (5), 1557–1600.

- Massey, D. S., & Singer, A. (1995). New estimates of undocumented Mexican migration and the probability of apprehension. _Demography_ , _32_ (2), 203–213.

- OAMDI. (2016). Labor Market Panel Surveys (LMPS). Retrieved from `http:// www.erfdataportal.com/index.php/catalog/161`

- OECD. (2018). _International migration outlook 2018_ . Retrieved from `https:// www.oecd-ilibrary.org/content/publication/migr` ~~`o`~~ `utlook-2018-en` doi: https://doi.org/https://doi.org/10.1787/migr outlook-2018-en

- Orrenius, P. M., & Zavodny, M. (2003). Do amnesty programs reduce undocumented immigration? Evidence from IRCA. _Demography_ , _40_ (3), 437–450.

- Orrenius, P. M., & Zavodny, M. (2005). Self-selection among undocumented immigrants from Mexico. _Journal of Development Economics_ , _78_ (1), 215–240.

- Ortega, F., & Hsin, A. (2018). Occupational Barriers and the Labor Market Penalty from Lack of Legal Status. _IZA Discussion Paper No. 11680_ .

- Patler, C., & Pirtle, W. L. (2018). From undocumented to lawfully present: Do changes to legal status impact psychological wellbeing among latino immigrant young adults? _Social Science & Medicine_ , _199_ , 39–48.

- Phillips, J. A., & Massey, D. S. (1999). The new labor market: Immigrants and wages after IRCA. _Demography_ , _36_ (2), 233–246.

- Reinhold, S., & Thom, K. (2013). Migration experience and earnings in the Mexican labor market. _Journal of Human Resources_ , _48_ (3), 768–820.

- Reyes, B. I. (2001). Immigrant trip duration: The case of immigrants from Western Mexico. _International Migration Review_ , _35_ (4), 1185–1204.

31
Where _Treat_ is an indicator for being assigned to the group that will be matched, _Male_ is a dummy variable indicating the youth is male, and _Si_ are the randomization strata dummies. Robust standard errors are then reported. We likewise run a similar regression for salary conditional on being employed. This is a potentially endogenous outcome, so should be viewed as providing a descriptive statement of whether employed treated individuals are earning more or less on average than employed control individuals.

Table 4 reports the resulting ITT estimates. We find small and statistically insignificant impacts on both the likelihood of being employed, and on the monthly salaries graduates earn conditional on being employed. This is consistent with the lack of direct effect from the matching, and suggests that there are no strong effects occurring through these other channels. Note that 44 percent of the control group are employed by the October 2012 survey, and 56 percent by the May 2013 survey, so the lack of effect on employment is not because nobody is able to find a job – youth are finding jobs, just not through the matching process.

Designing, validating, and computerizing the psychometrics assessments cost $28,800. The management and implementation cost of enrolling and testing the job candidates, and matching them to firms cost $146,000 over 14 months. This excludes the cost of the firm listing exercise and survey of 2000 firms, which was used to generate a list of firms to match with, but which was also used to ask more detailed questions on the firms. An approximate cost for the listing itself would be $30,000. The total implementation cost is thus approximately $204,800. If we consider just the 9 jobs in which graduates lasted beyond one month, the cost per job directly matched is $22,755, or $19,556 if we ignore set-up costs and consider only the steady-state variable costs. This is approximately 39-45 months of income for a youth earning the average of 350 JD ($500). Matching therefore seems very expensive in terms of cost effectiveness.<sup>4</sup>

# **3.4 Explaining the Lack of Results**

The matching intervention had very little success in helping job candidates find jobs. We examine several possible explanations for this lack of effect.

# _3.4.1. Did the testing process not reveal any new information about the graduates?_

> 4 Note that we can’t rule out that these youth would have found jobs in the absence of the treatment, in which case the cost effectiveness is even worse.

15
enterprises show that enterprises in developing countries are badly managed (Bloom et al. 2010). This study provides the answers to the following questions. How critical are business services and management innovation for rural entrepreneurs? How could business services and management innovation promote business entry and performance if they are critical?

# **3. Assessment of the Rural Investment Climate**

The RIC surveys were conducted in three Governorates. However, sampling data are representative of each Governorate with sampling weights. Sample sizes of household enterprises in industrial sectors are listed in Table 3.1.

|**Table **<br>|**3.1 Samp**<br>|**le Size**<br>|**of Hou**<br>|**sehold **<br>|**Enterp**<br>|**rises**<br>||||
|---|---|---|---|---|---|---|---|---|---|
|**Governorate**|**San**|**aa**|**Lah**|**j**|**Dha**|**mar**|**Tot**|**al**||
|**Male or Female Manager**|**M**|**F**|**M**|**F**|**M**|**F**|**M**|**F**||
|Agriculture|42|3|30|1|24|0|96|4|100|
|Mining & Construction|8|0|8|0|21|4|37|4|41|
|Food Processing|19|2|5|0|13|0|37|2|39|
|Other Manufacturing|25|10|6|0|24|13|55|34|89|
|Retail-wholesales|47|2|56|0|55|3|158|5|163|
|Other services|5|0|1|0|0|0|6|0|6|
|Total|146|17|106|1|137|20|389|38|427|

_Source:_ 2010 Yemen RIC Survey The rural investment climate (RIC) is assessed based on major obstacles perceived by entrepreneurs, major constraints assessed by percentile ranking of RIC indicators, entry and performance regressions, and quantitative survey data.<sup>6</sup> Major obstacles perceived by entrepreneurs are defined as the obstacles that entrepreneurs perceived as major or very severe obstacles for their businesses. The objective of this assessment is to determine which RIC components are critical to influence entry and performance of rural enterprises.

# **3.1 Major Obstacles Perceived by Entrepreneurs**

Major obstacles perceived by rural entrepreneurs are compared with urban small enterprises to identify what investment climate components are different from each other. Farm and nonfarm comparison and gender comparison are included.

> 6 The assessment using RIC indicators is newly developed to assess the RIC more objectively using quantitative data, whereas major obstacles perceived by rural entrepreneurs may be biased due to limited knowledge and experience. The percentile ranking methodology of “DOING BUSINESS” was applied for assessment by RIC indicators.

5
Rama 1994] reports a set of unemployment figures for many countries around the world. Some of the data reported are relatively old, and some are not credible (for example 9.45% for India in 1986 or 0.09% for Nigeria in 1986) hence these data are supplemented from other sources. Second, the OECD's _Employment Outlook_ [OECD 1994] reports estimates of 1995 unemployment rates for OECD countries (excluding Mexico). Third, the ILO's 1994 _World Labor Report_ [ILO 94] reports 1991 unemployment rate estimates for many countries. Fourth, the EBRD's _Transition Report_ [EBRD 1994] estimates are used for the countries of the former Soviet Union except for the Russian Federation. Fifth, country publications or WVorld Bank sources are used in special cases for data which are still missing, old, or not credible. These are described below.

- **2.2.1.** Special cases:

- Country statistical publications are used for Bangladesh [Bangladesh 1992], India [India 1992].

- l A staff estimate is used for the Russian Federation.

- For South Africa, an estimate based on the _Project for Statistics on Living Standards and Development_ data is used.

- For Ghana, an estimate based on the _Ghana Living Standards Survey 1988-89_ is used.

- For Nigeria, an estimate reported in _Adjustment in Africa: Case Studies_ [Faruqee 1994] is used.

4
and quantitative dimensions. A considerable, although quite incomplete body of evidence suggests that the type, quantity and quality of transactions influences the success of programs as measured in terms of contraceptive prevalence. The NAS reviewers felt that the transaction dimension of programs was relatively neglected both in the day to day contact of programs and in research.

Both demand generation and service provision need to be organized and supported as any other programs. Thus an important component of any project is the set of organizing activities that facilitate the necessary transactions between the provider and the potential client. These organizing activities are themselves complex. In the NAS framework, a useful distinction is made between the family planning organization narrowly defined and the complex of other organizations that make up its environment. Internal management concerns include the organizational structure used for delivering services (e.g., the role of the private sector' patterns of staff recruitment, training and compene.tion; supervision; monitoring and evaluation; etc.. Management concerns relating to the environment i-nclude the generation of political and financial support for program activities, relationships with other agencies undertaking activities that impact on the population sector, etc.. There is considerable evidence that effective family planning programs are characterized both by good internal management and by effective relationships with and support from the environment.

In our analysis of Bank population activities we will use this framework to examine different aspects of sector work and projects and how they have contributed to making national population programs more effective. Bank projects are not designed to support all of the activities in the population sector, and it is to be expected that they will differ in the amount of attention they have given to different aspects of population program implementation. The HAS evidence suggests that national programs which take a systematic view of the factors influencing program effectiveness are better able to increase and sustain higher levels of contraceptive prevalence over time. While it is not always possible or desirable for a project to be comprehensive, selective interventions must be chosen after careful diagnosis of the nature of the client population, transaction activities, organizing or management activities, and the political and administrative environment of the program. The sector review, project identification and formulation activities provide opportunities for such analysis, while project design and monitoring help adaptations over time to the changing needs of the clients and the changing nature of the environment.

10
**<mark>Table 4. Reduced-form regression of maternal employment on the instrument</mark>**

|Dependent variables|1-month<br>bandwidth|2-month<br>bandwidth|3-month<br>bandwidth|
|---|---|---|---|
|_Probit model (marginal effects)_||||
|Working|-0.016|0.037|0.146|
||(0.110)|(0.060)|(0.124)|
|In a wage-earning job|0.377***|0.477***|0.333***|
||(0.024)|(0.038)|(0.087)|
|In self-employed nonfarm work|0.043|-0.004|0.089|
||(0.108)|(0.150)|(0.145)|
|In self-employed farm work|-0.419***|-0.384***|-0.297***|
||(0.032)|(0.078)|(0.103)|
|In skilled work|-0.055|0.187|-0.239|
||(0.384)|(0.143)|(0.157)|
|In a formal job|0.149|0.382|0.017|
||(0.206)|(0.349)|(0.296)|
|_2SLS_||||
|Log of monthly working hours|0.293|0.489|0.206|
||(0.312)|(0.470)|(0.463)|
|Log of hourly wage|-0.275|-0.104|-0.421|
||(0.478)|(0.511)|(0.842)|
|Log of wage for the last month|-0.078|0.071|-0.286|
||(0.523)|(0.580)|(0.895)|
|Log of total wage for the past 12 months|-0.068|0.397|-0.527|
||(0.678)|(0.733)|(1.183)|

Note: This table reports the coefficient of the instrument variable in regressions of maternal employment on the instrument variable. The 1-month bandwidth sample includes women with children born in December and in January of the following year, and the instrument is a dummy indicating a child born in December.

The 2-month bandwidth sample includes women with children born in November-December and in January-February of the following year, and the instrument is a dummy indicating a child born in November-December.

The 3-month bandwidth sample includes women with children born in October-December and in JanuaryMarch of the following year, and the instrument is a dummy indicating a child born in October -December. Heteroskedasticity-robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation at the commune level. *** p<0.01, ** p<0.05, * p<0.1.

Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.

36
priorities are distorted in this fashion, the average quality of projects with high funding priorities is relatively closer to the average quality of projects with low funding priorities. As the aggregate marginal return to funds is equal to the average productivity of projects with marginal funding priority (equation 2), this implies compliance with Property 1.

# **7.1 Empirical interpretation**

How plausible are Assumptions 1 and 2? In this section I present empirical evidence that is consistent with Assumptions 1 and 2, and is hence supportive of Property 1 (by Theorem 1) .

As illustrated in the previous sections, there are many general equilibrium implications of Property 1 that are consistent with empirical regularities. However, there are obvious problems with testing Property 1 directly. Essentially, it would require an exogenous source of variation in the supply of funding that does not also affect the productivity of funding. This is problematic, as we would expect that periods of high productivity would imply both that the returns to funding are higher and that the supply of funding is larger. Using the standard small open economy trick and restricting attention on global variation in the supply of funding is not helpful here, as it may capture variation in global productivity that also affects the domestic marginal return to funds.

Appendix A presents an empirical investigation of Assumptions 1 and 2. I use the AMADEUS data set<sup>11</sup> , that includes balance sheet data for all formal sector firms for several European and Eastern European countries, between the years 1990-2011. For each firm, I construct a measure of the marginal productivity of funding ( _Ai_ ). In the appendix I illustrate how this measure maps into the productivity of projects ( _Ag_ ( _x_ )).

I divide the set of countries into “distorted” and “efficient” based on the crude measure of GDP per capita. I choose the cutoff to be the GDP per capita in Cyprus. After removing countries with insufficient data, the list of “efficient” economies includes Austria, Belgium, Finland, France, Germany, Iceland, Italy, Luxembourg, Netherlands, Norway, Spain, Sweden and Switzerland. The list of “distorted”

> 11The AMADEUS database (Analyze Major Databases from European Sources) is published by Bureau van Dijk Electronic Publishing.

30
64

|COUNTRY|PROJECT|**LEGAL COMPONENT**|FINANCIERS|
|---|---|---|---|
|Mexico (Cont.)|Agricultural Technology Project<br>(Loan No. 3465 ME), June 17, 1992|Review and improvement of the legal, financial and<br>administrative aspects of resource mobilization in the<br>agricultural sector.|IBRD$150m.|
||Forestry Resources Development and<br>Conservation Project<br>Japanese Grant Agreement, May 21,<br>1993|Analysis of the current policy, legal and regulatory<br>framework for forestry development and conservation.|Japanese Grant T65.6 m.|
||Second Solid Waste Management<br>Project (Loan No. 3752 ME),<br>October 2, 1994|Strengthening environmental management of solid waste<br>through assistance in the preparation of model regulations for<br>States and municipalities for solid waste management and the<br>provision of technical assistance to three states in the<br>introduction and implementation of such regulations.|IBRD $200 m.<br>l|
||Northern Border Environment<br>Project<br>(Loan No. 3750 ME), June 10, 1994|Support for preparation of eavironmental regulations and<br>standards for border States and their municipalities in border<br>area; comparative study of industrial health, safety and<br>environmental pollution legislation in Mexico, USA and<br>Canada.|IBRD $368 m.|
||Water Resources Management<br>Project<br>Japanese Grant Agreement<br>(proposed)|Review of legal and regulatory framework related to the<br>National Water Rights and Users' Register. Preparation of a<br>program for the issuance of water rights titles.<br>_|Japanese Grant 172.8 m.|
|_________|Financial Sector Technical<br>Assistance Project (Loan, proposed)<br>_____<br>_______________________________|Review of regulatory and supervision processes of the<br>financial sector (National Banking Commission, National<br>Securities Commission, National Insurance and Bonding<br>Commission, National Commission of the Retirement Savings<br>System).|IBRD $19 m.|
11 <!-- Start of picture text --> Finished Goods/Sae AAA<br><!-- End of picture text --> <!-- Start of picture text --> Finished Goods/Sae AAA Finshd Goods/Sobs AA<br>-Q .<br>lM~~~~<br>og-io)(8L15<br>4" r ~~~~gI<br>NornE Worhe Cpeiod Decmbr 99 - NorneiMnd asormrkbe Working A<br>(D-s os oly tOO) s* AAA f b faf o A t tOtt) suges;<br><!-- End of picture text --> <!-- Start of picture text --> tt"<br>out<br><!-- End of picture text -->

**se4**

<!-- Start of picture text --> Fintshed Goods/Soes A<br>'4. IAIVM* , M<br><!-- End of picture text -->

> **loutp,(o** -10

<!-- Start of picture text --> a "i<br>capedLan oital loakns Cito l AAA e<br>tha th comruiaon<br><!-- End of picture text --> Toiable pta Lerioans Ace Nomine W992i alio reLans AA Napital ooamns Caosto LoAnsA A

|~~|~ ~ | Dec-89||Mar-90|Dec-90|Jun-91|Dcc*91|UJun-92|
|---|---|---|---|---|---|---|
|AAA|49.5|84.3|96.0|116.0|86.8|89.7|
|AA<br>|24.5<br>|26.4|34.2|50.9|70.8|45.4|
|A|93.3|73.7|142.4|15740 .<br>.|176.|207.4|

Table 7 shows tbat this more than doubled for A film between March 1990 and December 1991t
.73 -

# TABLE 8

# Conformity with Loan Covenants for ComPleted Projects 1966-1982

|Coveriant|N|umbe<br>C|r of P<br>onfor|rolec<br>mitv|ts Ac<br>Ratina|cordin<br>|a to|
|---|---|---|---|---|---|---|---|
||1|2|3|4|5|N/A|Total|
|Rate of return on<br>historical assets|3|1|0|0|6|1|11|
|Rate of return on<br>|8|2|2|5|8||25|
|revalued assets||||||||
|Debt service coverage|29|2|0|5|4|1|41|
|<br>Self-financing ratio<br>|3<br>|4<br>|0<br>|2<br>|1<br>||10<br>|
|Other financial ratios|3|5|0|0|1||9|
|Tariff Increase, or<br>|15|2|0|5|4|1|27|
|no reductlons, change<br>In tariff structure||||||||
|Days receivables,<br>|2|2|0|1|5||10|
|govL pay arrears||||||||
|<br>Economical/financial<br>|18|1|0|3|1|1|24|
|planning, Bank||||||||
|<br>concurrence re changes<br>||||||||
|<br>Government to provide<br>adequate fUnds for<br>for Investment program<br>If shortfall|16|1|0|4|0||21|
|Powersectorchanges<br>|14<br>|0<br>|1<br>|1<br>|3<br>|2|19<br>|
|<br>Tariff or marginal cost study|6|0|1|0|0||7|
|<br>Submission of audits<br>|20|2|1|6|0|2|31|
|Appointments to<br>|16|0|1|0|1||18|
|senior management||||||||
|<br>Asset revaluation|10|1|1|3|2||17|
|Improve efficiency of|13|1|1|1|4|1|21|
|<br>utility||||||||
|Umt on Investment|7|0|0|0|0||7|
|In other activitles||||||||
|<br>Other|22|1|0|8|8|4|43|
|Total|205|25|8|44|48|13|343|

a/ Total of 50 projects. Each project had more than one covenant. Rating key: I = 100% conformity 5 = no conformity whatsoever n/a = Information not available
study may give biased results (as they are based on linear extrapolations away from a common support—King and Zeng 2006).

In this paper, we examine whether vocational high school students are learning specific and/or general skills. We seek to accomplish three goals. First, we seek to assess the impact of attending vocational versus academic high school on the dropout rates, math, and computing skills of the _average_ student. Second, we seek to estimate the _heterogeneous impacts_ of attending vocational versus academic high school on the dropout rates and skill levels of disadvantaged (low-income or low-ability) students. Third, we aim to establish whether vocational high school leads to any absolute gains in math and computing skills.

To accomplish these aims, we conduct analyses using longitudinal data on more than 10,000 students in China. Estimates from instrumental variables and matching analyses show that attending the most popular major in Chinese vocational schools (computers) relative to attending academic high school substantially reduces math skills without improving computing skills. Attending vocational high school also increases dropout, especially among disadvantaged (low-income and low-ability) students. We also use comparable (equated or scaled) baseline and follow-up test scores to measure students’ absolute gains in math and computing skills. We find that computing major students who attend vocational high school experience absolute reductions in math. Taken together, our findings indicate that the promotion of vocational schooling as a substitute for academic schooling may be detrimental to building human capital in developing countries such as China.