rafmacalaba/datause-encoder-data huggingface dataset:
# **VI Tables**

Table 1: Complete ROCKS Database for Low and Middle Income Countries

||N|Percent|
|---|---|---|
|Low income|780|23.48|
|Lower middle income|1,352|40.70|
|Upper middle income|1,190|35.82|
|Total|3,322|100|

_Notes_ : Income classification based on World Development Indicators 2012. _Source_ : ROCKS data and World Development Indicators 2012.

Table 2: Unit Costs per km of Asphalt Overlays 40 to 59 mm

|Country|Cost per km<br>in $1000|Number|Year|Country<br>|Cost per km<br>in $1000|Number|Year|
|---|---|---|---|---|---|---|---|
||Work activ<br>|ities under<br>|taken bet<br>|ween 1996–1998<br>||||
|Dominican Republic|33.5|1|1997|Argentina|69.7|1|1997|
|Ghana|42.9|5|1998|Brazil|74.4|1|1998|
|Lithuania|44.4|1|1996|Argentina|74.9|1|1996|
|Indonesia|48.5|1|1996|Cameroon|76.8|4|1997|
|Lithuania|49.7|1|1998|Bangladesh|79.1|26|1998|
|Mexico|50.7|1|1997|Vietnam|79.6|2|1998|
|Ghana|52.7|1|1996|Bangladesh|83.6|1|1997|
|Costa Rica|57.9|1|1996|Panama|84.1|1|1997|
|Armenia|60.7|1|1997|Nigeria|95.1|1|1997|
|Brazil|62.5|2|1996|El Salvador|102.2|1|1998|
|Bolivia|67.4|1|1997|Pakistan|105.0|1|1997|
|India|68.1|3|1997|Tanzania|111.7|1|1996|
||Work activ<br>|ities under<br>|taken bet<br>|ween 2005–2007<br>||||
|Paraguay|31.2|1|2005|Botswana|68.0|1|2006|
|India|35.9|2|2006|Nigeria|73.0|1|2007|
|Bulgaria|40.7|1|2006|Argentina|76.2|3|2006|
|Ecuador|41.6|1|2005|Georgia|82.6|1|2006|
|India|45.6|1|2005|Brazil|82.9|2|2005|
|Burkina Faso|48.0|1|2007|Georgia|84.9|1|2005|
|Brazil|55.2|3|2006|Vietnam|85.4|1|2005|
|Brazil|58.2|1|2007|Macedonia|85.7|1|2007|
|Thailand|59.5|1|2005|Rwanda|90.6|1|2006|
|Philippines|60.8|1|2006|Philippines|94.8|1|2005|
|Bosnia and Herzegovina|61.9|2|2006|Chile|98.9|1|2006|
|Nepal|63.1|1|2006|||||

_Notes_ : Costs per km of asphalt overlays 40 to 59 mm; all costs are in 2000 US$; number denotes the number of work activities in a given country over which a simple average is taken. _Source_ : ROCKS data.

31
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Policy Research Working Paper 6807

# **Abstract**

A randomized experiment is used to evaluate a large-scale, active labor market policy: Turkey’s vocational training programs for the unemployed. A detailed follow-up survey of a large sample with low attrition enables precise estimation of treatment impacts and their heterogeneity. The average impact of training on employment is positive, but close to zero and statistically insignificant, which is much lower than either program officials or applicants expected. Over the first year after training, the paper finds that training had statistically significant effects on the quality of employment and that the positive impacts are stronger when training is offered by private providers. However, longer-term administrative data show that after three years these effects have also dissipated.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dmckenzie@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
Table 5: ITT Effects on Savings and Re-Investments Outcomes

||Savings|Business Assests|Investments in<br>Other Businesses||
|---|---|---|---|---|
||(1)<br>Levels|(2)<br>Levels|(3)<br>Levels|(4)<br>Index|
||_Pan_|_el A: Midline (6 m_|_onths)_||
|ITT|1193.12|3558.45|11562.89|0.09|
||(21159.68)|( 21010.23)|(15108.80)|(0.08)|
||[0.970]|[0.970]|[0.832]|[0.634]|
|Observations|529|547|547|547|
|Control Mean|166976.10|119889.56|47501.17|0.06|
|Adj. R<sup>2</sup>|0.290|0.325|0.087|0.351|
||_Panel _|_B: Endline (18–24 _|_Months)_||
|ITT|26805.09|17021.32|27773.52<sup>_∗∗_</sup>|0.20<sup>_∗∗_</sup>|
||(25150.95)|( 24990.66)|(12058.10)|(0.08)|
||[0.455]|[0.455]|[0.050]|[0.040]|
|Observations|532|545|545|545|
|Control Mean|162703.16|125942.20|29836.05|0.00|
|Adj. R<sup>2</sup>|0.194|0.188|0.029|0.212|

_Note:_ We winsorize all savings and investment measures at the 99th percentile. Coefficients are ANCOVA estimates that control for the outcome at baseline, the respondent’s location, marital status, household size, number of children, age, network size and level of education. ITT shows traditional intent to treat effects. Savings is the total amount held in all financial savings instruments. Business assets is the estimated monetary value of all assets held in the main business. Investments in other businesses is the total estimated monetary value of all investments in businesses other than the main business in the last 6 months. Values in columns marked with heading _Levels_ are values in UGX. Index is an Anderson index of all outcomes using the swindex command in Stata. We report White robust standard errors in parentheses. Romano-Wolf multiple hypothesis test q-values are presented in brackets. Q-values include the same controls as above and are computed within pre-specified families of outcomes.

30
- 20 -

One might be concerned about the magnitude of the estimated marginal effect of OPRC. One possible reason is that the estimated coefficient may include the effect of agro-climatic potential. Recall that the OPRC roads were selected based on agricultural potential. In addition, the underlying crop production patterns may have changed significantly during the project implementation period.

To investigate this, the simple average DID estimator is considered. Note that our data are cross-sectional at two periods of time, not panel data. The double difference statistics need to be adjusted with the weight, _w_ , given by kernel matching (Heckman, Ichimura and Todd, 1997): While maize production in the sample area increased between 2009 and 2012, other crops, including groundnuts, which is another important crop produced in the survey area, were harvested much less ( **Table 7** ). This is consistent with the national agriculture statistics: Zambia experienced a significant drop in agriculture production in 2012/2013. Particularly, cotton production in 2012/13 decreased by nearly 50 percent because of unfavorable market prices in the Southern African region. In our data, no cotton production was recorded in the follow-up survey.

Together with these drastic changes in production patterns, the significant increase in maize production is likely to explain the measured large impact of OPRC on the total production value. Maize production in the OPRC areas increased from 1,385 kg to 2,208 kg, which is more than 50 percent growth. Although production in the control group also increased, the net impact is significant at 209 kg per household. The net impact on groundnuts production is also significant at 40 kg per household, a more than 10 percent increase.
11 In Table 3 we examine whether budget composition in these states continues to be systematically different even after including in the specifications available measures of state income, population, poverty and inequality. The first two columns of Table 2 include controls for state population, per capita domestic product in constant prices, and the share of agriculture in state income, variables for which data is available from 1980-2003.<sup>11</sup> Even after the inclusion of these state income variables, the variation in the share of capital spending across states and within states, with changes in voter turnout, remains as above.

The second two columns of Table 3 include measures of rural poverty and inequality within states, for which annual data is available from 1960 to 1992.<sup>12</sup> After controlling for poverty and inequality, the share of capital spending in the five states characterized as having weaker governance is higher than in the other states at the 10 percent level of significance; greater turnout within these states continues to be associated with a greater shift away from capital spending than in other states. Greater turnout is associated with a shift towards recurrent social spending in all states, but this turnout correlation is not statistically different between the two groups of states when income, poverty, and inequality are included as controls. This could be because poverty and inequality explain part of the difference between the two groups of states in the correlation between turnout and budget composition, or simply because the sample available for this estimation is smaller.<sup>13</sup>

> 11 The state income and population series is taken from the data available at the World Bank’s Development Data Pltaform, and is reported to be compiled from the statistics provided by the Central Statistical Office in India.

> 12 The rural poverty and inequality data, and the state income and population data for the period 1960-1992 is taken from the Indian states poverty database compiled by the World Bank’s development research group (Ozler et al, 1996).

> 13 Data on spending in the category of social programs is available from 1972 onwards, and on poverty and inequality only until 1992. Hence, for this category of spending estimates including poverty and inequality cover only the period 1972-1992.
However, one may still worry about time varying unobservables that could potentially bias the estimates; perhaps the most important time-varying factor in our context is dynamic learning effects that vary across households.<sup>29</sup> For example, ability to learn, and deal with “disequilibria” may depend on the education level and experience as emphasized by Schultz (1975). We thus include a set of household characteristics from the 2000 round of the survey including the household head’s education and age (as a proxy for experience) to allow for differential learning across households. The specification thus becomes: Bit = θ0 + θ1d07 + θ2Ti + θ3(d07 ∗Ti) + X00,iΠ + eit (6) Where X00,i is a vector of household characteristics from the 2000 round of the panel, thus determined prior to the treatment. Note that our treatment group consists of all of the households that joined MFI programs in any year after 2000 and before the second round survey in 2007.

We also provide evidence from an approach that combines the DID approach with matching in the spirit of Heckman et al. (1998) (in addition to household fixed effects). The combination of matching with DID is called MDID by Blundell and Costa-Dias (2011). The MDID-FE approach utilized here matches treatment and comparison groups on the basis of pre-intervention characteristics after household fixed effects. Matching can improve upon the linear conditional DID-FE model in equation (6) above in two ways: (i) it allows for nonlinear effects of the pre-treatment observable characteristics in the DID-FE model which would be able to capture the dynamic learning effects more faithfully without imposing any functional form assumption and (ii) it imposes the common support condition. In addition to a standard matching estimator, we also use the MB estimator in the implementation of the MDID approach in a household fixed effect model (henceforth called MBDID-FE). As noted earlier, the MB estimator minimizes the bias due to potential failure of conditional independence assumption. As before, we assume the radius of the neighborhood to be 0.25 which means that at least 25 percent of the both the treatment and control groups have a propensity score in this interval used in the estimation of causal effect.<sup>30</sup> The progressively richer and more flexible empirical models from DID-FE to MDID-FE to MBDID-FE allow us to understand the sensitivity of the estimates due to violation of the CIA, possibly because of dynamic learning effects. It is important to appreciate that if the main sources of unobserved heterogeneity are innate entrepreneurial ability and attitude toward risks which are arguably time-

> 29 Note, however, that this requires that the households are aware of their differential learning capacity and estimate it with reasonable accuracy _before_ they apply for the MFI loans.  Otherwise, such learning differences may affect the decision to take informal loans conditional on becoming an MFI member, but would not affect the self-selection into MFI membership.

> 30 Note also that the heteroskedasticity based IV estimator is not applicable here, because the dependent variable is binary.

21
**Table 4**

Dependent Variable: Wage Index

|VARIABLE<br>S|(1)<br>OLS-FE|(2)<br>OLS-FE|(3)<br>IV-FE|(4)<br>IV-FE|(5)<br>IV-FE|(6)<br>IV-FE|(7)<br>IV-FE|(8)<br>IV-FE|(9)<br>IV-FE|(10)<br>IV-FE|
|---|---|---|---|---|---|---|---|---|---|---|
|POST<br>Time|-2.99<br>(6.79)<br>-1.52|5.31<br>(10.70)<br>-1.67|269.48***<br>(63.98)<br>-39.48***|202.83***<br>(45.63)<br>14.13***|118.60***<br>(39.02)<br>-23.40***|188.90***<br>(3.59)<br>14.81***|10.64<br>(18.01)<br>-11.90***|59.44***<br>(12.54)<br>21.17***|61.69***<br>(18.06)<br>-17.34***|117.88***<br>(8.06)<br>18.30***|
||(2.19)|(1.72)|(5.99)|(2.95)|(3.14)|(1.62)|(1.11)|(1.40)|(0.98)|(1.52)|
|Timesq|0.28*|0.19|2.06***|-0.63***|1.20***|-0.68***|0.59**|-1.12***|0.88***|-0.92***|
|WorkingAge|(0.14)|(0.14)<br>-0.00***|(0.37)|(0.20)<br>-0.00***|(0.23)|(0.06)<br>-0.00***|(0.29)|(0.10)<br>-0.00***|(0.32)|(0.08)<br>-0.00***|
|Pop|||||||||||
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|rGDP||0.00*||0.00***||0.00***||0.00***||0.00***|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|LFPRate||1.74||4.60**||4.59**||4.46**||4.51**|
|||(1.44)||(2.13)||(2.12)||(1.97)||(2.01)|
|Observation|274|220|47|47|47|47|47|47|47|47|
|s|||||||||||
|Number of|20|17|8|8|8|8|8|8|8|8|
|Countries|||||||||||
|R-squared|0.11|0.45|||||||||
|Partial R-<br>squared|||0.15|0.18|0.35|0.33|0.44|0.41|0.58|0.56|
|Hansen J p-<br>value|||0.31|0.29|0.29||0.46|0.68|0.34||
|Kleibergen-<br>Paap F<br>statistic|||16.52|2.27|161308.16|48175.53|25.20|51.11|100353.35|39253.04|

Note: Robust and clustered standard errors at the country level are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. POST is instrumented with the debt-to-GDP ratio, terms of trade, and five-year change in democracy score in columns (3) and (4). In columns (5) and (6) the instruments also include lagged POST. POST is instrumented with external debt-to-GDP ratio interacted with the change in world interest rates, terms of trade, and five-year change in democracy score in columns (7) and (8). In columns (9) and (10) the instruments also include lagged POST.

30
|P160665|2017|2020|12/19/2019|5 Active|Indonesia|
|---|---|---|---|---|---|
|P160665|2017|2020|6/24/2020|6 Active|Indonesia|
|P160667|2017|2018|11/22/2017|1 Closed|Guatemala|
|P160667|2017|2018|4/13/2018|2 Closed|Guatemala|
|P160667|2017|2019|8/21/2018|3 Closed|Guatemala|
|P160672|2019|2020|8/6/2019|1 Active|Lesotho|
|P160672|2019|2020|10/8/2019|2 Active|Lesotho|
|P160672|2019|2020|2/28/2020|3 Active|Lesotho|
|P160672|2019|2021|7/22/2020|4 Active|Lesotho|
|P160674|2018|2018|11/15/2017|1 Active|West Bank and Gaza|
|P160674|2018|2018|5/16/2018|2 Active|West Bank and Gaza|
|P160674|2018|2019|12/10/2018|3 Active|West Bank and Gaza|
|P160674|2018|2019|3/20/2019|4 Active|West Bank and Gaza|
|P160674|2018|2020|8/12/2019|5 Active|West Bank and Gaza|
|P160674|2018|2020|1/8/2020|6 Active|West Bank and Gaza|
|P160674|2018|2021|7/14/2020|7 Active|West Bank and Gaza|
|P160678|2019|2019|12/20/2018|1 Active|Guinea-Bissau|
|P160678|2019|2020|7/5/2019|2 Active|Guinea-Bissau|
|P160678|2019|2020|2/11/2020|3 Active|Guinea-Bissau|
|P160678|2019|2021|7/22/2020|4 Active|Guinea-Bissau|
|P160680|2018|2018|6/26/2018|1 Active|Colombia|
|P160680|2018|2019|12/14/2018|2 Active|Colombia|
|P160680|2018|2019|6/13/2019|3 Active|Colombia|
|P160680|2018|2020|12/16/2019|4 Active|Colombia|
|P160680|2018|2020|3/3/2020|5 Active|Colombia|
|P160696|2020|2020|12/19/2019|1 Active|Nepal|
|P160696|2020|2020|6/30/2020|2 Active|Nepal|
|P160699|2017|2018|10/15/2017|1 Active|Rwanda|
|P160699|2017|2018|1/12/2018|2 Active|Rwanda|
|P160699|2017|2018|5/7/2018|3 Active|Rwanda|
|P160699|2017|2019|8/8/2018|4 Active|Rwanda|
|P160699|2017|2019|12/27/2018|5 Active|Rwanda|
|P160699|2017|2019|5/7/2019|6 Active|Rwanda|
|P160699|2017|2020|11/11/2019|7 Active|Rwanda|
|P160699|2017|2020|5/11/2020|8 Active|Rwanda|
|P160703|2018|2021|7/5/2020|1 Active|Western Africa|
|P160708|2019|2020|9/19/2019|1 Active|Western Africa|
|P160708|2019|2020|3/20/2020|2 Active|Western Africa|
|P160717|2017|2018|7/6/2017|1 Active|Central African Repub|
|P160717|2017|2018|2/2/2018|2 Active|Central African Repub|
|P160717|2017|2019|8/21/2018|3 Active|Central African Repub|
|P160717|2017|2019|2/22/2019|4 Active|Central African Repub|
|P160717|2017|2020|9/16/2019|5 Active|Central African Repub|
|P160717|2017|2020|6/3/2020|6 Active|Central African Repub|
|P160719|2018|2018|2/14/2018|1 Active|Sierra Leone|
|P160719|2018|2019|9/17/2018|2 Active|Sierra Leone|
|P160719|2018|2019|4/14/2019|3 Active|Sierra Leone|
# Table A6: Social norms survey

||Women<br>Mean/SE<br>(1)|Men<br>Mean/SE<br>(2)|p-value<br>(1)-(2)<br>(3)|
|---|---|---|---|
|Women in public space are more likely to accept advances|0.408<br>(0.041)|0.486<br>(0.042)|0.190|
|Women invite advances then change their minds|0.314<br>(0.037)|0.347<br>(0.041)|0.553|
|Women on reserved space are less likely to invite advances then change their minds|0.294<br>(0.037)|0.362<br>(0.040)|0.210|
|Physical harassment is frequent on public space|0.472<br>(0.038)|0.472<br>(0.039)|0.997|
|Ever chose not to go somewhere due to lack of safety or harassment on Supervia|0.440<br>(0.037)|0.510<br>(0.039)|0.191|
|Bystanders rarely intervene when witnessing harassment on public space|0.671<br>(0.035)|0.458<br>(0.039)|0.000|
|Bystanders rarely intervene when witnessing harassment on reserved space|0.641<br>(0.036)|0.506<br>(0.039)|0.0110|
|Women are partly at fault if harassed on public space|0.217<br>(0.031)|0.199<br>(0.030)|0.675|

_Notes_ : Unit of observation is one respondent. Survey modules were randomly allocated to subsets of platform survey participants to minimize survey length. The number of women respondents for each question varies between 250 and 303. The number of men is between 235 and 302. Sampling weights applied. Robust standard errors in parentheses.<sup>1</sup> Women were asked about their past experiences, while men were asked about their women relatives.

Table A7: Test for order effects in on screen presentation of public / reserved space

||Dependen<br>Chose rese<br>(1)|t variable:<br>rved space<br>(2)|
|---|---|---|
|Reserved space shown frst|0.005<br>(0.010)|-0.008<br>(0.020)|
|Positive opportunity cost||0.014<br>(0.021)|
|Reserved space shown frst _×_ Positive opportunity cost||0.013<br>(0.022)|
|Constant|0.068***|0.054***|
||(0.011)|(0.018)|
|Observations|6001|6001|
|Riders|273|273|

_Note:_ Unit of observation is one ride. Sample includes revealed preference rides of riders recruited in the first wave. Observations weighted by the inverse of the number of rides taken by the individual rider. Standard errors in parentheses, clustered at rider level.
<!-- Start of picture text --> Figure 6: Elasticities of Contraceptive Prevalence with<br>respect to Female Enrollments, 1975, FP Services & GDP pc<br>Elasticity with respect to CP<br>2<br>_- |O Female Sec. Enroll.<br>Access to FP Service<br>1.5 *<br>GDP per capita .. . .<br>0L<br>0 10 20 30 40 50 60 70 80 90 100<br>300 600 900 1200 1500 1800 2100 2400 2700 3000<br>Note: Elasticitle computed from eq. Levels of Enroll., Service & GDP pc ($) 1 Table S. holding all other variables constant at their mean values<br>(Moan.s: Fer. So.. Enroll., (1976) 20.4%. Access to ServIoos (1982) 28%, GOP per capita (1988) $2324. CP 30.5)<br><!-- End of picture text -->
VFs where more committee members have accounting experience, and where the accounts are maintained on computers – both good indicators of managerial competence.

For on-lending VFs, those that require (rather than just allow) their members to save have higher credit ratings – see Appendix 1. VFs that would, if funds were short, cut the size of loans across the board, also had higher credit ratings. More surprising, perhaps, is the finding that when the committee has more members with some university education, the credit score is lower. This is surely related to our earlier result that links the higher education variable with lower capital availability (Table 20), holding other effects constant.

# **Outreach**

Two other measures of performance are of importance. The first looks at the proportion of individuals in a village who borrow from the VF – a measure of outreach that we refer to as “lending density.” Theoretically, we expect altruistic village funds to favor a wide outreach. The second measures the number of loans going to the “poor” (defined as households with expenditure per capita below the village median) relative to the number of loans going to the non-poor. We expect, again theoretically, loans to go to both groups, but probably with a tilt toward the poor, and indeed this is what we find, with a ratio of 1.68 (poor borrowers per non-poor borrower) in rural Thailand. This latter measure was obtained by matching the 2009 SES data with the data from the 2010 VF survey and the 2009 Community Survey, which was possible only in rural areas.

A first cut at the data may be seen in Table 23, which uses household data from the socio-economic surveys of 2009 and 2010 to map VF borrowing by expenditure per capita quintile. Poorer households are more likely to borrow, pay lower interest rates, and get somewhat smaller loans than their richer neighbors. Table 24 breaks down VF borrowing by household (financial) assets, and shows that VF borrowing is particularly important for households with limited assets; for them, it is often their first source of borrowing, rather than a supplementary source of credit. The importance of VF loans for the poor is confirmed by the numbers in Table 25, which show that although VF loans are the main source of credit for 12% of households, this

**<mark>Table 23.  Borrowing terms, by expenditure per capita quintile, 2009 and 2010</mark>**

||Amount bor<br>Village Fund|rowed from<br> (baht/loan)|Times borrow<br>(for those sti|ed since 2002<br>ll borrowing)|Average inte<br>(%|rest rate paid<br>p.a.)|
|---|---|---|---|---|---|---|
|Expenditureper capitaquintile|2009|2010|2009|2010|2009|2010|
|1 (poorest)|14,080|15,954|5.1|5.9|5.9|5.9|
|2|15,398|16,000|5.0|6.0|5.9|5.9|
|3 (mid)|15,881|17,011|5.1|5.9|6.0|6.0|
|4|16,957|18,292|4.8|5.8|6.1|6.0|
|5(richest)|17,781|18,634|4.8|5.7|6.0|6.1|

_Source:_ Thailand Socio-Economic Surveys of 2009 and 2010.

35