rafmacalaba/datause-encoder-data huggingface dataset:
The following section places the current study in the recent literature evaluating similar programs. We then provide further details on the _Pronatec_ and _Pronatec-MDIC_ programs and describe the program records we used to link the course requests, courses held, and student records to the administrative data. We then discuss our empirical strategy and present results. We discuss why the program likely exhibited greater effectiveness in generating employment among trainees, and provide concluding observations on the program and thoughts for future work.

# **2 Background: Technical vocational education and training around the world**

Evidence of the effectiveness of skill training programs remains quite mixed. In a metaanalysis of nearly 100 studies, Card et al. (2010) conclude that skills training programs can effect employment gains in the medium- and long-term, although many programs are ultimately ineffective in reducing unemployment. A host of studies have found widely varying degrees of effectiveness of publicly-provided skill training. Positive employment effects have been found among programs in Peru (N ̃opo et al., 2007; D ́ıaz and Rosas Shady, 2016), Colombia (Attanasio et al., 2011; Kugler et al., 2015), Liberia (Adoho et al., 2014), Nepal (Chakravarty et al., 2015), Malawi (Cho et al., 2013), and Kenya (Honorati, 2015). Other programs have shown negligible impacts, including those in Argentina (Alzu ́a and Brassiolo, 2006), Germany (Caliendo et al., 2011), Dominican Republic Card et al. (2011), Kenya (Hicks et al., 2013), Jordan (Groh et al., 2016), and in an RCT in Turkey (Hirshleifer et al., 2016).<sup>1</sup> Specifically in Brazil, Reis (2015) provides a comprehensive overview of the history of training programs, which began as early as 1942. He analyzes the impact of _PLANFOR_ ,

> 1For earlier reviews, see Betcherman et al. (2004) and Kluve (2010).

3
Under ESDP, schools will be built by something other than _chika_ , unless there are no local alternatives. _Chika_ schools are considered uneconomical and should be abandoned. But does this make economic sense? CBA was employed to answer this question.

Household survey data was used to estimate the returns to schooling. In 1996, in Ethiopia the returns to schooling are high. Overall, another year of schooling is associated with a 23 percent gain in earnings. Returns are high for both males and females, at 23 and 22 percent; in rural and urban areas, at 21 and 15 percent; and by level, that is, primary, secondary and university, at 25, 24 and 27 percent. So while these estimates might tell us that education overall is a good investment in Ethiopia, they offer no guidance on what level is more appropriate, nor do they give any guidance about the main investment objective, getting more children into school, or more specifically what sort of buildings should be constructed.

_Chika_ schools certainly are a cheap initial investment (Table 4). However, the maintenance requirements are heavy and even a fully maintained _chika_ school will not last much longer than ten years.

30
# DIVERSIFICATION IN RURAL ASIA

# CHAPTER 1. AN OVERVIEW **OF** DIVERSIFICATION

1.01 Nearly all Asian agricultural economies have been rice-based. In the early stages of development in Japan, Indonesia, and Bangladesh, for example, more than half of agricultural GDP was generated by rice cultivation. Successful agricultural development meant successful expansion of rice production, and in most Asian resource settings, this meant raising rice yields. Table 1.1 provides a handful of summary

statistics on the rice economy in East and Southeast Asia, which reveal the dramatic but uneven increases in yields between 1960 and 1984, as well as the relative decline in the importance of rice in these rapidly growing economies. This decline and its relevance for diversification initiatives are discussed at length in this report.<sup>1</sup>

1.02 The techniques for raising yields are now well understood, if not always well implemented. Increasing rice yields through investments in irrigation, use of modern inputs, and incentive prices provided the engine of rural growth for a number of Asian agricultural economies, an engine that provided higher incomes for rural areas as well as food for urban workers. Imports of rice were often reduced or eliminated, thus freeing up foreign exchange for alternative uses. The drive for self-sufficiency in rice in several countries--South Korea, Taiwan, and Malaysia, especially-has been costly in budgetary and efficiency terms relative to average costs of imported rice since the 1960s. But in Indonesia and the Philippines, for example, import substitution was efficient in both social

- 1/ Sections of this report are drawn from C. Peter Timmer, "Crop Diversification in Rice-Based Agricultural Economies: Conceptual and Policy Issues' (Washington, D.C.: World Bank, Agriculture and Rural Development Department, 1987; typescript) and Carol F. Timmer and C. Peter Timmer, 'Patterns of Agricultural Diversification in Asia' (Cambridge, Mass.: Harvard Institute for International Development, 1988; typescript).
at a given level of GDP per capita, implying an upward shift in the SPEC at the lower end. That can be assessed using Figure 3, which augments Figure 1 to provide the nonparametric SPECs for various years. We see that, if anything, the SPECs have tended to shift downwards over time. Among low-income countries, we see little or no change in the SP share at a given level of GDP per capita except for a drop in average levels between 1995 and 2000. In the middle-upper income range, we actually see a decline after 1995. Across all levels of GDP, the 1995 SPEC is unambiguously higher than those for 2005, 2010, 2015 and 2019, while the 2019 SPEC is unambiguously lower than those for all years except 2000 and 2005. The greater emphasis on SP in development policy discussions in the new Millennium does not appear to have shifted the SPEC upwards.

This finding is not inconsistent with the recent expansion in SP programs in developing countries; it is just that these programs have typically had low coverage (on average) in lowincome countries, as documented in Ravallion (2017). The rise in SP spending as a share of GDP in developing countries is coming from higher GDP rather than a change in development priorities in favor of a higher share of national income going to SP at given GDP.

# **3.3 Data on social protection spending during the pandemic**

Our panel data set on SP spending prior to the pandemic is augmented by a database compiled by a team including two of the present authors and documented in Demirgüç-Kunt et <u>al. (2022). This provides estimates of the expenditures incurred in social protection measures that</u> the government implemented in response to the pandemic over 2020 and 2021 in 154 countries. The data draw on information from the Global Database on Social Protection Responses to COVID-19 (Gentilini et al. 2021), budget data from official documents (including IMF Article IV revisions and other international organizations’ related documents), government websites, and news sources. The key criterion for the inclusion is that the component of SP spending must have been designated as a response to the pandemic. In some cases these were new programs while others were expansions to existing programs. To the extent feasible, we estimated the budget corresponding to the expansion of an existing program (for example, the cost of extending the eligibility period of unemployment benefits) and not the whole pre-existing program, though we 15
10 asset-liability management; and (iv) shrinking balance sheets, including by divesting noncore operations in various jurisdictions to focus on core markets, and cutting jobs in certain locations and business units (box 1). Only a few banks have managed to raise capital through rights issues. Market estimates suggested that a worst-case scenario of meeting the requirement only by shrinking balance sheets would imply shedding €3.6 trillion, or 10.5 percent of total bank assets.<sup>6</sup>

# **Figure 9. Aggregate and Individual Bank Capital Shortfalls for European Banks (Billion EUR)**

<!-- Start of picture text --> a. EU Banks' Capital Shortfall after Including Sovereign Capital Buffer,<br>October 2011 (€106.4 bn) vs. December 2011 (€114.7 bn)<br>35.0 30.0<br>30.0<br>30.0<br>26.2<br>26.2<br>25.0 Oct 2011 Dec 2011<br>20.0<br>15.4<br>15.0 14.8 13.1<br>8.8<br>10.0 5.2 7.3 7.87.0 6.3 3.5<br>5.0 4.1 2.9 3.9 3.6 1.3 1.5 0.3 0.3 0 0.2 1.4 0.0 0 0<br>0.0<br>Banks<br>b. Top 20 Banks with Biggest Capital Shortfalls, Dec 2011<br>18.0<br>15.3<br>16.0<br>14.0<br>12.0<br>10.0<br>8.0<br>8.0 6.3 6.3<br>5.3<br>6.0<br>3.7<br>3.3 3.2<br>4.0 2.7 2.6 2.5 2.1 2.1 2.1 2.0 1.8 1.6 1.6 1.5 1.5<br>2.0<br>0.0<br><!-- End of picture text --> Source: European Banking Authority (EBA).

6 Assuming total assets are on average 2.8 times RWAs, based on a sample of internationally active European banks, and the estimated aggregate EBA capital shortage of Є114.7 billion.
Given the difficulty of measuring many aspects of agglomeration and given that the list of possible agglomeration sources is fairly open, considering all sources of agglomeration in one regression is not a feasible option. A more reasonable path forward, following Ellison, Glaeser, and Kerr (2010), is to consider several classes of agglomeration sources in the same approach. Ellison et al _._ (2010) assess how much labor pooling, input-output linkages, and spillovers account for co-agglomeration between industries in the United States. They use a measure of industry co-agglomeration and find more co-agglomeration among (i) industries that buy from each other, (ii) industries that use a similar workforce, and (iii) industries that share a common scientific base. To reduce the possibility that co-agglomerated industries end up buying from each other or using similar workers because of their physical proximity, they instrument their US measures of input-output linkages and labor pooling using corresponding UK data. Of course, if the biases are the same in the United Kingdom as in the United States, these instruments are of limited value.<sup>8</sup> Another caveat is that input-output linkages may be more easily measured using input-output matrices than spillovers using patent citations. This can also lead to biased estimates because a positive correlation with both linkages and spillovers is likely to be picked up primarily by the better-measured variable (i.e., linkages). Ellison et al _._ (2010) confirm that the three motives for agglomeration they consider are at play, with input-output linkages playing a more important role according to their results.

There has been almost no work on the sources of agglomeration effects in developing countries. The main exception is Amiti and Cameron (2007), who use an empirical strategy that resembles that of Ellison et al. (2010) to examine the determinants of the productivity of firms in Indonesia. Their estimations strongly underscore the importance of input-output linkages. This result and the sometimes larger agglomeration effects for developing countries discussed above 12
Freeman, Richard, 2007. Labor Market Institutions Around the World. NBER Working Paper Number 13242.

Galiani, Sebastian and Federico Weischelbaum, 2007. Modelling Informality Formally: Households and Firms. CEDLAS Working Paper 0047, Universidad Nacional de La Plata.

Goldberg, Pinelopi and Nina Pavnick, 2003. The Response of the Informal Sector to Trade Liberalization, Journal of Development Economics, 72, pp. 463-496.

Gruber, Jonathan, 1994. The Incidence of Mandated Maternity Benefits, American Economic Review, 84(3), pp. 622-641.

Gruber, Jonathan, 1997. The Incidence of Payroll Taxation: Evidence from Chile. Journal of Labor Economics , XV, 72-101.

Harberger, Arnold, 1962. The Incidence of the Corporate Income Tax. Journal of Political Economy 70: 215.

Harris, John and Michael Todaro, 1970. Migration, Unemployment, and Development: A Two Sector Analysis. American Economic Review, 40: 126-142.

Heckman, James J. and Carmen Pages, 2004. Law and Employment: Lessons from Latin America and the Caribbean. National Bureau of Economic Research Conference Report Heckman, James J., and V. Joseph Hotz, 1986. An Investigation of the Labor Market Earnings of Panamanian Males: Evaluating the Sources of Inequality. Journal of Human Resources 21(4):507-542.

Imbens, G., and J. Angrist, 1994. Identification and Estimation of Local Average Treatment Effects, Econometrica, Vol. 62, No. 2, 467-476.

Kugler, Adriana, 1999. The Impact of Firing Costs on Turnover and Unemployment: Evidence from the Colombian Labor Market Reform, International Tax and Public Finance Journal, 6(3).

Kugler, Adriana, 2001. Effects of Increased Labor Market Flexibility: Theory and Evidence from Colombia‟s Labor Market Reform. Universitat Pompeu Fabra, Mimeo.

Kugler, Adriana, 2004. The Effect of Job Security Regulations on Labor Market Flexibility: Evidence from the Colombian Labor Market Reform, in James J. Heckman and Carmen Pagés, Eds., Law and Employment: Lessons from Latin America and the Caribbean. Chicago: The University of Chicago Press, 2004.

31
|||Table A1.8 World Economic Forum|
|---|---|---|
|||**Global**Competitiveness Report|
|Yea|r Numbe|r Question|
||Openn<br>|ess<br>|
|1 98<br>|q101<br>|Neaative impact of tariffs onco0tsand availabilitv of equipment and materials<br>|
|2 98|q102|Negative impact of hidden barriers to trade|
||<br>Govem|<br>ment|
|3 98|q201|Likelihood of dramatic change in institutions|
|4 98|q204|Competence of public sector personnel|
|5 98|q205|Time spent with bureaucracv|
|6 98|q208|Political pressure on public services|
|7 98|q209|The tax svstem hinders business competitiveness|
|8 98|q210|Extent of tax evasion|
|9 98|q211|Wasteful govemment expenditure|
||Financ|e|
|10 98|q303|Domestic banks are protected from foreiqncompetition|
|11 98|q311|Barriers to entry in banking sector are very hiah|
|12 98|q312|Interest rates are heavilv regulated|
||<br>Infrastr|<br>ucture|
|13 98|q414|Private sector participation in infrastructure proiects is not permitted|
||Techno|logy|
|14 98|a511|Intellectual propertv protection|
||Institut|ions|
|15 98|a801|Extent of market comPetition|
|16 98|q802|Effectiveness of anti-trust policies|
|17 97|q803|Irregular, additional payrnents connected with import and export permits, business licensees,|
|||<br>exchange controls, tax assessments, police protection or loan applicatons|
|18 98|q803|Irregular, additional payrnents connected with import and export permits, business licensees,<br>exchange controls, tax assessments, Police Protection or loan applications|
|19 98|q805|Independence of the judiciary from interference by the govemment and/or parties to the<br>dispute|
|20 97|q806|Legal system effectiveness at enforcirn commercial contracts|
|21 98|Q806|Leqal svstem effectiveness at enforcinqcommerdal contracts|
|22 97|q808|Private business capacity to file lawsuits at independent and impartial courts against<br>government|
|23 98|q808|Compliance withcourtnilings and /or arbitration awards|
|24 98|q809|Frequency of'irreqular iayments"to officials and iudiciarv|
|25 97<br>25 97|q810<br>q810|Citizens' willingness to accept legal means to adjudicate disputes rather than depending on<br>physical force or illeqal means|
|26 98|<br>q810|<br>Private business has recourse to independent and impartial courts for challenging the legality|
|||<br>of govemment actions|
|27 98|q811|<br>LUkelihood<br>of winning a court case filed against the govemment|
|28 97|q813|The highest power is ahNays peacefuliv transferred|
|29 98|<br>q813|<br>Govemment commitments|
|30 98|q814|Police effectiveness in safeguarding personal security|
|31 98|q815|Costs of organized crimre for business|

45
their children using health facilities on a regular basis and attending school between the third year of primary school and the third year of secondary school (de Janvry and Sadoulet, 2004).

World Bank (2009) finds that CCTs led to large increases in school enrollment, particularly among those with low enrollment rates to begin with. However, evidence on the impact of educational transfer programs (in kind or in cash) on ‘final outcomes’ such as test scores, is not as encouraging (Miguel and Kremer, 2004; Glewwe, Kremer, and Moulin, 2008). Filmer and Schady (2009) argue that the lack of any discernible effect of such programs on learning (despite large impacts on school enrollment) may be due to the fact that they draw lower ability students back to school.

To our knowledge, no CCT program for schooling has been evaluated to assess its possible impact on the sexual behavior of the young people benefiting from the program. This is the case even though there are good reasons to think that the impacts of such programs on the sexual behavior of young people may be substantial. The World Bank (2009) argues that among the areas that should receive high priority in impact evaluations (and, more generally, research) on CCTs is the role they play in reducing the transmission of HIV, most likely through changes in sexual behavior. Both schooling and poverty reduction (especially for women) are seen by many as key components in a comprehensive strategy to combat HIV/AIDS. However, causal evidence that links increased schooling or income to changes in behaviors associated with HIV is very limited. Most of what we know about the relationship between schooling (attendance or attainment) or income and HIV risk factors come from cross-sectional studies.

5
Incorporating even a small number of satellite predictors, with moderate predictive performance, markedly improves the accuracy of the sample estimates. In particular, the correlation with the benchmark official estimates improves from 0.8 to 0.86 when using a household-level “unitcontext” model instead of estimates derived from the sample survey. Precision improves dramatically as well, as the coefficient of variation drops by roughly a factor of two, roughly equivalent to quadrupling the size of the survey. The gain in precision is moderately overestimated, but after adjusting for that to maintain the same coverage rate as the direct estimates, incorporating the small area estimates effectively increases the sample size by a factor of about 2.4.

When using satellite data, there is a large difference in accuracy between estimates for in-sample and out-of-sample municipalities. While the preferred household model generated a correlation of 0.86 with the intercensus benchmark in-sample, this fell to 0.7 for out-of-sample. This difference in performance comes for two reasons. First, empirical best predictor models improve accuracy by efficiently combining the survey data with model predictions for each municipality. Second, outof-sample municipalities are systematically smaller and poorer than sample municipalities, which reduces the accuracy of out-of-sample predictions in this context. These results suggest additional caution when relying on purely synthetic predictions generated from satellite data.<sup>23</sup> An important benefit of this type of evaluation exercise against a credible benchmark is the opportunity to compare different statistical methods for incorporating geospatial data into small area estimation. In the main set of results, using the 2014 survey data, a household-level model gives more accurate predictions than a sub-area model for sampled areas (correlation of 0.86 vs 0.83) and equally accurate predictions for non-sampled municipalities (correlation of 0.7). When using the 2016 survey, the predictions from the household-level model are more accurate than the sub-area model both in and out of sample. The area-level model is generally less accurate than the sub-area model, with in-sample correlations of 0.8 when using the 2014 data and 0.75 when using the 2016 data. The exception is out-of-sample predictions using the 2016 data, where area-level estimates are more accurate than sub-area estimates but remain less accurate than the household model. Both the household-level and area-level models underestimate uncertainty, relative to the sub-area model, in the 2016 data. In artificial simulations, when compared with predictions from

> 23 It is possible that more sophisticated models or methods that allow for non-linearities and interactions may better predict out of sample, especially if the correlations of predictors vary by things like municipality size. This is a topic for further research.

32
# **_4.1. Implementation of transfers_**

The cash payments take place monthly at centrally located and well-known places, such as churches and schools. The cash transfer points were selected so that no recipient has to travel for more than 5 kilometers to the cash payment point.<sup>19</sup> At each meeting some basic information is collected for each sample respondent, such as who is picking up the money (girl or parent/guardian), how far they had to travel, etc. In between payment dates, the NGO collects attendance records for all the students in the program to make sure that they are complying with the program requirements and attending school.<sup>20</sup> Each household receives the transfer only if the young woman attended school for at least 75% of the days that their school was in session in the previous month.

# **_4.2. Survey Instrument_**

The annual SIHR Household Survey consists of a multi-topic questionnaire that is administered to the households in which the selected sample respondents reside. Although it is described as a household questionnaire, the primary goal of the SIHR Household Survey is to collect detailed information from the individual respondents selected for the survey. The survey consists of two parts: Part I is administered to the head of the household, while Part II is administered to the core respondent, i.e. the

> 19 Some recipients who still live in locations that are remote are visited door-to-door by the NGO implementing the transfer scheme.

> 20 The total cost of the program consists of the cash transfers themselves, as well as the administrative costs of running the program. For every $1 that is transferred to a program beneficiary, approximately $0.50 is spent on administrative costs. The main items under the administrative costs are delivering the cash payments and monitoring attendance, both of which are underlined by large costs of transportation. We estimate that a similar program implemented by the government itself would spend significantly less on administrative costs. This is because the cash transfers could be conducted at schools and the program administers could rely on school records (with spot checks) to monitor attendance, significantly reducing transport costs and producing scale economies. Furthermore, the government would benefit from collecting less research-oriented data during cash transfers, which takes significant time to collect and enter.

13