rafmacalaba/datause-encoder-data huggingface dataset:
LACE exam, we measure the impact of the information treatment on a proxy for completing upper secondary on time and standardized test scores in math and language (Spanish). We shed light on some of the behavioral responses induced by the intervention using information from a survey that was administered at the time of the test to a random sample of exam takers.

Our results show that, almost three years after the treatment was implemented the information package had no impact on the proxy for completing high school on time. On average, the intervention had a large and statistically significant impact on math test scores (0.29 standard deviations), and a positive but not statistically significant impact on language scores. A more detailed analysis shows that the treatment had a large and significant effect on the learning outcomes of female students and those who belong to relatively better off households. Both boys and girls update their expectations as a result of the intervention, but only girls report a higher level of effort and switch to more demanding upper secondary subtracks, with higher expected returns in the labor market. Neither the initial level of information about returns to education nor differences in time preferences seem to explain much of the differential effect for boys and girls. There is also no evidence that the gender related heterogeneity is driven by gender-specific parents’ or teachers’ responses. We conjecture that at least in the sample we consider, boys have lower scope to increase their effort. The stronger impact on better-off students is consistent with the hypothesis that the increase in effort can translate into better learning outcomes only if complemented with sufficient initial conditions as proxied by household income.

The contribution of this paper is twofold. First we contribute to a better understanding of the effects of information on different educational outcomes. The very large and significant effects on school attainments in rural Dominican Republic (Jensen 2010) and Madagascar (Nguyen 2008) contrast with the zero impact found for the US (Fryer 2013), China (Loyalka et al. 2013) and Mexico in this paper. In low income countries, providing information about school returns might be sufficient to induce at-risk students to stay in school since the alternative option may be particularly low. However, in medium and high income countries, the higher probability of finding a paid job might induce at-risk students to drop out, irrespective of how well informed they are.<sup>5</sup> Unlike previous work, we provide evidence on the impact of information on learning at the end of high school, an outcome that is likely to be correlated with youth labor market outcomes (Murphy and Peltzman 2004). The aver-

> 5Atkin (2012) finds for Mexico that the employment opportunities for low-skill workers in the manufacturing sector generated by trade liberalization had significant consequences on school drop-out: for every twenty new jobs created in the manufacturing sector, one student dropped out at grade nine rather than continuing on through grade twelve.

3
This approach allows us to link Peri and Sparber’s conceptual framework directly with measures of labor market vulnerabilities created by COVID-19 related risks.

# 3. Data and Descriptive Statistics

## 3.1. Data

The primary data source used throughout this paper is the European Labour Force Survey (EULFS), which is a harmonized dataset composed of nationally representative labor force surveys conducted in all of the 28 EU countries, in addition to Switzerland and Norway. The sample size of the EU-LFS ranges from 4.5 million to 5.2 million observations depending on the year. Data are available on a yearly basis between 2001 and 2018, the latest available year prior to COVD19. The data provide information on whether the individuals in the sample were born in the same country in which they work. While the specific origin countries if migrant workers are not available in the data, broad world regions of origin are observed. These are as follows: the 15 original member states of the European Union (EU15)<sup>4</sup> , the 13 new member states of the EU as of 2013 (NMS13)<sup>5</sup> , non-EU European countries, Latin America, Asia, the Middle East and North Africa, Sub-Saharan Africa, and other non-European high—income countries that are members of the Organisation for Economic Co-operation and Development (OECD).<sup>6</sup> The dataset includes detailed information on various labor market outcomes of the individuals sampled, along with their demographic characteristics such as gender, age, and educational attainment. In terms of labor market status, observations are labelled as (i) out of the labor force, (ii) seeking employment, or (iii) currently employed. Detailed information on the characteristics of the jobs of employed workers are classified according to (1) the sector of activity at the one-

> 4 EU15 comprises the higher-income EU countries that were members before accession of the 10 new members in 2004. These are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and United Kingdom.

> 5 The New Member States (NMS13) and their accession dates are the following: Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovak Republic, Slovenia (2004); Romania, Bulgaria (2007); Croatia (2013).

> 6 These high-income OECD members are Australia, Canada, Japan, New Zealand and the United States.

8
**Table 3. Long-Term Income Mobility Patterns, RLMS 1994-2015 (percentage)**

||**Unconditional**|**Conditional**|
|---|---|---|
|Upward mobility|34.5|45.7|
|Immobility|44.6|44.6|
|Downwardmobility|20.9|36.7|

**Note** : Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 2478 panel individuals from the 5th and 24th round of the RLMS.

36
Table F.2. Heterogeneous Treatment Effect on Sex Ratio by Age Groups

|Sex Ratio (Women/Men)|Sex Ratio (Women/Men)|
|---|---|
|(1)|(2)|
|_Post × Riot × Age_||
|0-9<br>-0.114***<br>|-0.0537**<br>|
|(0.0136)|(0.0219)|
|10-14<br>-0.117***|-0.0515**|
|(0.0137)|(0.0208)|
|15-19<br>-0.0861***|-0.0141|
|(0.0213)|(0.0192)|
|20-24<br>-0.0898***|-0.0294|
|(0.0259)|(0.0190)|
|25-29<br>-0.117***|-0.0693***|
|(0.0226)|(0.0210)|
|30-34<br>-0.0748***|-0.0627***|
|(0.0186)|(0.0194)|
|35-39<br>-0.0846***|-0.0469**|
|(0.0164)|(0.0214)|
|40-44<br>-0.00877|0.0194|
|(0.0130)|(0.0213)|
|45-49<br>-0.0300***|-0.00814|
|(0.00923)|(0.0198)|
|50-54<br>-0.0137**|0.0270|
|(0.00662)|(0.0203)|
|55-59<br>-0.0477***|-0.0842***|
|(0.0131)|(0.0251)|
|60-64<br>-0.0393***|-0.0712*|
|(0.0128)|(0.0402)|
|65-69<br>0.0144|-0.0302|
|(0.0121)|(0.0184)|
|70-74<br>0.0767***|0.0831***|
|(0.0214)|(0.0274)|
|75-79<br>0.137***|0.0745**|
|(0.0209)|(0.0335)|
|80+<br>0.192***|0.119**|
|(0.0298)|(0.0463)|
|Constant<br>0.723***|0.879***|
|(0.0149)|(0.0282)|
|Observations<br>1,190|136|
|R-squared<br>0.501|0.882|
|Num. Clusters<br>35|-|
|Sample<br>Complete|Neighboring States|
|Robust SE<br>No|Yes|
|Year FE<br>Yes|Yes|
|State FE<br>Yes|Yes|
|Age Group FE<br>Yes|Yes|

_Note:_ This table reports coefficients from specification 4. The first column reports the results using the rest of India as control, and the second column reports results using only the neighboring states as controls. While column (1) standard errors clustered at the state level, column (2) reports the robust standard error since, with neighboring states, there are only 4 clusters. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

45
Figure 1: Comparisons of Distributions of Real Wages by Sector of Employment <!-- Start of picture text --> Argentina, _1988 -1997 Brazil, _1989 -1995 Costa Rica, _1989 -1995<br>All workers All workers All workers<br>3. 0< < 41 °0<br>logwage logwage logwage<br>formal formal formal<br>0. .00<br>-t ~ ~ 2 ~ ~ . O 0 2 0 2 .0 2<br>o 0<s I<br>-2 2 4 6 . -4 -2 0 2 4 6 0 2 4 6 8<br>logwage logwage logwage<br>nformal informal iformal<br>-2 - 6 0 -2<br>logwage logwage Iogwage<br>co<br>-2 0 2 4 6 .4 -2 0 2 4 6 0 2 4 6 6<br>logwage logwage logwage<br><!-- End of picture text --> 2~~~~~~~~3
19 Meade, James E. (1951), _The Theory of International Economic Policy. Volume 1: The Balance of Payments_ (Oxford: Oxford University Press, 1951).

Montiel, Peter J. (1994), "Capital Mobility in Developing Countries: Some Measurement Issues and Empirical Estimates," _World Bank Economic Review,_ 8(3), September 1994, 311-350.

- Odaki, Mitsuhiro (1986), "Tests of Granger Causality by the Selection of the Orders of a Bivariate Autoregressive Model," _Economics Letters,_ 22(2-3), 1986, 223-227.

- Turner, Philip (1991), _Capital Flows in the 1980s: A Survey of Major Trends_ (Basel: Bank for International Settlements, Monetary and Economic Department, BIS Economic Papers No. 30, 1991).

- Urbain, Jean-Pierre (1989), "Model Selection Criteria and Granger Tests: An Empirical Note," _Economics Letters,_ 29(4), 1989, 317-320.

World Bank (1993), "Global Economic Prospects and the Developing Countries" (Washington, D.C.: World Bank, 1993).
<!-- Start of picture text --> Average annual rate of stunting change (%)<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>e Malaysia<br>1 ; 2006-2016<br>Pakistan<br>0 ° 3001-2012 an<br>Burundi Egypt, Arab Philippines Timor-Leste<br>©2000-2016 Rep. 2000-2014 @ 2003-2015 2002-2013<br>-1 © Jamaica Yemen, Rep. 2000-2013Indonesia e<br>2000-2014 2003-2013<br>-24 Haiti2000-2012 Guinea-Bissau Azerbaijan<br>ee emp0.2014 © Myanmar 2000-2013<br>Mexico. 2000-2016<br>3 2006-2015<br>© Thailand ® Vietnam<br>-4 United States 2006-2016 2000-2015<br>2001-2012 ® Equatorial<br>5 Guinea<br>® 2003-2014 El Salvador 2000-2011<br>-6 Cote d'Ivoire @Peru 2000-2016<br>e©* 2006-2016 West Bank and<br>-7 2000-2012Algeria Gaza 2002-2014<br>© sao Tome @China 2000-2013<br>“8 and2006-2014Principe ® Mongolia<br>2000-2013<br>“9 kazakhstan2006-2015<br>-10 ©@ Paraguay<br>2005-2016<br>-1 0 1 2 3 4 5 6 7 8 9 10 11<br>Average annual rate of GDP per capita change (%)<br><!-- End of picture text --> Other ® Outliers © Philippines ® Regional comparator Source: World Bank World Development Indicators. Notes: Only countries with at least 3 stunting estimates between 2000 and 2016 are considered. Labeled countries are either Philippines' regional comparators or 'outliers' - countries with the 5 highest and 5 lowest growth elasticities of stunting as well as those with average annual reductions in stunting of below -6% per year and average annual increases in per capita growth of 8% per year. The line indicates the average relationship between changes in growth and changes in stunting based on ordinary least squares (OLS) fit of the data points, the shaded area represents the 95% confidence interval of the OLS line.
_Table 13: Dependent variable – The firm innovates_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|GOB|-0.1327|-0.1044|0.077|0.090|0.153|0.168|0.057|0.096|
||(0.0813)|(0.0703)|(0.213)|(0.220)|(0.226)|(0.233)|(0.215)|(0.212)|
|GOBx(1-IQ)|||-0.483|-0.460|-0.675|-0.657|-0.504|-0.534|
||||(0.430)|(0.470)|(0.481)|(0.519)|(0.468)|(0.488)|
|ROA||-0.0263||-0.035||-0.037||-0.034|
|||(0.0244)||(0.024)||(0.024)||(0.023)|
|LTA||-0.0436||-0.033||-0.031||-0.039|
|||(0.0314)||(0.036)||(0.035)||(0.033)|
|ln(SIZE)|0.0461|0.0443|0.044|0.043|0.044|0.043|0.045|0.044|
||(0.0342)|(0.0338)|(0.035)|(0.034)|(0.035)|(0.034)|(0.035)|(0.034)|
|ln(AGE)|0.0634**|0.0640**|0.064**|0.064**|0.064**|0.064**|0.064**|0.064**|
||(0.0281)|(0.0277)|(0.028)|(0.028)|(0.028)|(0.028)|(0.028)|(0.027)|
|SOE|-0.2431|-0.2557*|-0.205|-0.229|-0.159|-0.185|-0.193|-0.210|
||(0.1506)|(0.1553)|(0.157)|(0.160)|(0.163)|(0.166)|(0.159)|(0.162)|
|FEM|0.0421|0.0457|0.050|0.051|0.051|0.052|0.044|0.047|
||(0.0837)|(0.0836)|(0.083)|(0.083)|(0.083)|(0.083)|(0.083)|(0.084)|
|Const|-0.5738***|-0.2077|-0.567***|-0.288|-0.567***|-0.303|-0.571***|-0.238|
||(0.1259)|(0.2491)|(0.128)|(0.271)|(0.127)|(0.267)|(0.127)|(0.254)|
|N. Obs|11,127|11,127|11,127|11,127|11,127|11,127|11,127|11,127|
|R2|0.0889|0.0925|0.092|0.095|0.094|0.097|0.090|0.094|
|CY FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IQis|||Control of|Corruption|Rule o|f Law|Government|Effectiveness|

*** p<0.01; ** p<0.05; * p<0.1

30
<!-- Start of picture text --> Standard deviation<br>35<br>30 e<br>55<br>20 °<br>e<br>15 e<br>e e<br>105 een,2 e<br>oe<br>nied<br>010 20 30 40 50<br>TaxtoGDP<br><!-- End of picture text --> <!-- Start of picture text --> Standard deviation<br>18<br>e<br>16<br>14 e<br>10 a ° e<br>ee<br>8 # e e<br>6<br>4<br>2<br>0 20 40 60<br>Government consumption volatility<br><!-- End of picture text --> <!-- Start of picture text --> \— —_—<br><!-- End of picture text -->
Policy Research Working Paper 7421

# **Abstract**

This paper examines the phenomena of high rates of youth that are out of school and out of work in Latin America. The analysis pursues a dynamic approach by constructing a pseudo-panel from 234 household surveys for 18 countries in the region that allow tracing the life cycle trajectories of different cohorts over time. The trajectories are associated with a series of variables characterizing the household, community, and macro environment in which schooling and labor market participation decisions take place. The most important result obtained is that the persistently high rates of being out of school and out of work among males are strongly associated with greater labor force participation by women, which can be generating a “crowding out” effect against men, given slow job creation rates across the region. The analysis also explores the possibility of scarring effects, and finds that higher shares of out of school and out of work youth at ages 15020 years are associated with lower wages for the same cohorts later in life, at ages 35–40 years, for males and females. As for employment prospects, the analysis finds scarring effects only for females, with greater out of school and out of work youth shares being related to lower proportions of women in the labor market later in the life cycle.

This paper is a product of the Education Global Practice 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 mszekely@ prodigy.net.mx.

_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
21 investment effect, and as additional independent variables are introduced on the right hand side of the equations the former rises more and the latter falls. The results for the pooled subperiod averages are quite similar, and also the investment effect loses significance.24

# TABLE 3

## EFFICIENCY EFFECTS AND INVESTMENT EFECTS

||dYY/<br>dRR|<br>dIOCR<br>dRR|Efficiency<br>Effect|dIGDP<br>dRR|<br>Investment<br>Effect|
|---|---|---|---|---|---|
|Overall Period<br>1965-85||||||
|Simple Model<br>2'|.26*|<br>.96*|.22|.39*|.09|
|Complex Model<br>26|.26*|<br>.1.21*|.28|.24|.05|
|Pooled Periods<br>1965-73,74-1985|_|||||
|Simple Model|.20*|<br>.99*|.22|.25*|.06|
|Complex Model|.19*|1.11*|.25|.17|.04|

* Significant coefficient at 5% level.

We may conclude that most of the estimated rela:ionship between growth and interest rates is effected through the output-investment component, and that, once the influence of other variables, notably the level of income per head, is taken into account, interest rates have little relationship with the overall rate of investment.27 24 It will be seen that the sum of the investment and efficiency effects exceeds the total effect of RR on DYY. This is because of the negative correlation between IOCR and IGDP which is evident in Table 2. The weakening of the investment effect is mainly due to the inclusion of GDP per head as an explanatory variable.

- 25 Simple Model - right hand side variables - RR and shift group for pooled regression. - -

- 26 Complex Model right hand side variables RR, INF, POPGP GNPPE.

27 Balassa, 1989, reviews evidence on the relationship between interest rates and saving.
– Table A.14 Treatment effects on household asset index (disaggregated results)

||||Eq (1)||Eq (2)|||
|---|---|---|---|---|---|---|---|
||(1)<br>Control<br>Mean|(2)<br>Control<br>SD|(3)<br>Treatment|(4)<br>T1:<br>Cash &<br>Training<br>Alone|(5)<br>T2:<br>Cash &<br>Joint<br>Training|(6)<br>p-value<br>T1 = T2|(7)<br>N|
|**Asset index (stock)**|0.000|1.000|0.127***<br>|0.148***<br>|0.105**<br>|0.424|1,824|
||||(0.038)|(0.049)|(0.043)|||
|Rooms|2.274|1.352|0.124**|0.134**|0.115**|0.793|1,824|
||||(0.049)<br>|(0.062)<br>|(0.058)|||
|Mattress|4.012|1.976|0.140*<br>|0.231**<br>|0.048<br>|0.134|1,824|
||||(0.085)|(0.103)|(0.106)|||
|Radio|0.232|0.523|0.011<br>|0.004<br>|0.018<br>|0.597|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Regular cell-phone|1.729|1.302|-0.008|-0.032|0.017|0.476|1,824|
||||(0.053)|(0.065)|(0.062)|||
|Smart-phone|0.564|1.019|0.058|0.092*|0.023|0.251|1,824|
||||(0.043)|(0.055)|(0.050)|||
|Refrigerator|1.285|11.081|-0.367|-0.384|-0.349|0.542|1,824|
||||(0.362)|(0.376)|(0.350)|||
|Bicycle|0.052|0.232|0.006|-0.009|0.022|0.048|1,824|
||||(0.011)|(0.012)|(0.015)|||
|Motorcycle|0.050|0.217|0.019*|0.010|0.029**|0.229|1,824|
||||(0.011)|(0.013)|(0.014)|||
|Chair, bench|3.758|2.330|0.158|0.182|0.134|0.722|1,824|
||||(0.097)|(0.116)|(0.121)|||
|Generator|0.002|0.047|0.011***|0.009*|0.013**|0.622|1,824|
||||(0.004)|(0.005)|(0.006)|||
|Ventilator|0.101|0.407|0.029*|0.023|0.035**|0.520|1,824|
||||(0.015)|(0.018)|(0.017)|||
|AC|0.037|0.195|0.005<br>|0.011<br>|-0.001<br>|0.386|1,824|
||||(0.010)|(0.013)|(0.010)|||
|Mat|0.845|0.846|0.095***|0.062|0.128***|0.208|1,824|
||||(0.036)|(0.047)|(0.043)|||
|Head lamp|0.280|0.462|-0.012|0.004|-0.028|0.261|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Table|1.171|0.822|0.143***|0.161***|0.126***|0.497|1,824|
||||(0.034)|(0.045)|(0.041)|||
|Equipped living room (table, armchairs)|0.330|0.529|0.053**|0.061**|0.046*|0.636|1,824|
||||(0.022)|(0.029)|(0.025)|||
|Library|0.267|0.445|0.034*|0.058**|0.010|0.089|1,824|
||||(0.020)|(0.024)|(0.024)|||
|Dresser|0.761|0.729|0.023|0.041|0.004|0.359|1,824|
||||(0.029)|(0.036)|(0.035)|||
|Electric iron|0.114|0.467|0.005|0.017|-0.007|0.222|1,824|
||||(0.017)|(0.021)|(0.018)|||
|Sewing machine|0.015|0.129|0.014*|0.016|0.012|0.739|1,824|
||||(0.008)|(0.012)|(0.008)|||
|TV|0.840|0.385|0.042**|0.029|0.054***|0.272|1,824|
||||(0.017)|(0.022)|(0.019)|||

Notes: This table reports the intent-to-treat effects of the interventions on a household asset index, disaggregating the results from Table 3. Columns 1 and 2 report the mean and standard deviation of the outcome variables in the control group. Column 3 reports the effect of the cash grant and training program ( _β_ 1 in Equation 1). Columns 4 and 5 report the effect of the cash grant and training program with and without the male partner ( _β_ 1 and _β_ 2 in Equation 2). Column 6 reports the p-value for the null hypothesis that the effect of the cash grant and training program is the same with and without the male partner ( _β_ 1 = _β_ 2 in Equation 2). Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

56
|Access to Finance|Microcredit|Evidence shows<br>no effect on<br>business<br>outcomes for the<br>average borrower|Banerjee et al. 2015<br>(review), Cai et al.<br>2021 (review),<br>Banerjee et al. 2019<br>(RCT), Field et al.<br>2013 (RCT), Barboni<br>and Agarwal 2018<br>(RCT), Battaglia et<br>al. 2023 (RCT)|No significant<br>average effects<br>have been found,<br>but there is growing<br>evidence of more<br>positive effects<br>when targeted and<br>when introducing<br>variations of the<br>traditional model<br>(e.g., flexible<br>repayment).|
|---|---|---|---|---|
||Reducing<br>collateral<br>requirements|More causal<br>evidence focused<br>on women-led<br>businesses is<br>necessary|Alibhai et al. 2022<br>(RCT), Bari et al.<br>2024 (RCT), Gertler<br>et al. 2021 (RCT),<br>Cordaro et al. 2022<br>(RCT)|Some evidence<br>suggests that it can<br>increase access to<br>finance, but more<br>evidence is needed<br>on the effects on<br>business outcomes<br>for women-led<br>businesses, with<br>various promising<br>new products not<br>yet evaluated.|
||Grants or Loans|Evidence shows<br>no effects of cash<br>grants or loans to<br>micro/small firms,<br>with evidence of<br>positive effects<br>from in-kind<br>grants or larger<br>grants targeted at<br>growth-oriented<br>women-led<br>businesses|Fafchamps et al.<br>2014 (RCT), James<br>et al. 2022 (RCT),<br>McKenzie 2017<br>(RCT), Crépon et al.<br>2024 (RCT), Bryan<br>et al. 2022 (RCT),<br>Bertrand et al. 2019<br>(non causal), Jakiela<br>and Ozier 2016<br>(RCT)|The literature<br>highlights the<br>importance of<br>targeting support to<br>growth-oriented<br>women and offering<br>solutions to prevent<br>resource capture by<br>husband and<br>kinship networks.|
||Blended Finance|A lack of causal<br>estimates is noted|Aydin et al. 2023<br>(quasi-<br>experimental),<br>Liaplina and Sierra-<br>Escalante 2022<br>(review), Cusolito et<br>al. 2021 (RCT)|More causal<br>evidence is needed<br>on the effect of<br>blended finance<br>instruments, one<br>RCT shows the<br>potential of<br>preparing firms to<br>be ready for equity<br>investments.|
|||Positive evidence<br>exists for products<br>with low<br>transaction costs<br>and features that|Dupas and<br>Robinson 2013<br>(RCT), Dupas et al.<br>2018 (RCT), Riley<br>2024 (RCT), de Mel|The literature<br>highlights the<br>importance of<br>offering convenient<br>savings instruments|

32