rafmacalaba/datause-encoder-data huggingface dataset:
by 2SLS and (potentially biased) OLS.<sup>34</sup> This estimate is robust to the inclusion of controls (column 4), and when decomposed, is driven by the larger and more precisely estimated effect in vocabulary. The reduced form effect, roughly 0.1 standard deviations at the discontinuity, is shown in the upper right panel of Figure 5. To the extent that subsequent outcomes depend on a mixture of human capital and signaling, this is evidence that secondary schooling in Kenya does not play a purely signaling role: students measurably gain skills from schooling.<sup>35</sup> These results contrast with the recent work of Filmer and Schady (2014) in Cambodia, who show that increased secondary schooling has no impact on subsequent test scores, as well as the work of Lucas and Mbiti (2014), who show that increased quality of secondary schooling in Kenya (at higher discontinuities in KCPE score) has no impact on subsequent academic outcomes. This appears to be true even when the marginal student admitted into the school is not the worst student in the higher-quality school. A clue to reconciling Lucas and Mbiti’s findings with mine may lie in the recent work of Urquiola and Pop-Eleches (2010). Using a similar multiple-discontinuity design to estimate the returns to secondary school quality in Romania, they find

> 34Note that this is an ideal OLS specification: it includes the KCPE score as a control, and restricts the sample substantially; more discussion of OLS and 2SLS agreement is provided in Section 5.

> 35A pessimistic interpretation might hypothesize that the longer respondents have been out of school, the worse they perform on tests; since secondary schooling delays exit from school, the apparent positive effect is simply a delayed deterioration of human capital. The data do not support such an interpretation: the longer respondents have been out of school (and thus the older they are), the better they do on the tests administered during KLPS2; the coefficient is too small (around 0.02 standard deviations per additional year out of school) to explain an effect more than an order of magnitude larger; and the effect remains significant and of the same magnitude in both OLS and 2SLS after controlling for duration out of school.

21
# <u>References</u>

Aedo, C. e Walker, I. (2012). Is Labor Demand in LAC Accommodating to Inferior Skills? Skills for the 21st Century in Latin America and the Caribbean, Chapter 5, pp. 101-117, The World Bank, Washington, D.C., 2012.

Almeida, R., Amaral, N. e Felicio, F. (forthcoming). Assessing Advances and Challenges in Technical Education in Brazil. Policy report, The World Bank Group.

<mark>Almeida, R., Behrman, J., & Robalino, D. (Eds.). (2012).</mark> _<mark>The right skills for the job?: rethinking training policies for workers</mark>_ <mark>. World Bank Publications.</mark> Almeida, R., Menezes Filho, N., Anazawa, L. (2014). _A Educação Profissional e Técnica no Brasil: Evolução Recente e Impacto no Mercado de Trabalho Brasileiro_ [Technical-Vocational Education in Brazil: Recent Evolution and Impact on the Brazilian Labor Market].

Caliendo, M. e Kopeinig, S. (2008) Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, Vol. 22, No. 1, pp. 31-72, 2008.

Dehejia, R. H., e Wahba, S. (1999). Causal effects in nonexperimental studies: reevaluating the evaluation of training programs. Journal of the American Statistical Association 94(448): 1053-1062.

Hanushek, Eric A., Woessmann, Ludger e Zhang, Lei, 2011. General Education, Vocational Education, and Labor-Market Outcomes over the Life-Cycle. NBER Working Paper No. 17504, October 2011.

Menezes Filho, N. (2012). _Apagão de mão-de-obra qualificada? As profissões e o mercado de trabalho brasileiro entre 2000 e 2010_ [Lack of qualified labor? The professions and the Brazilian labor market between 2000 and 2010]. _Working paper_ .

Neri, M. (2010). _A Educação Profissional e Você no Mercado de Trabalho_ [Vocational Education and You in the Labor Market]. _Rio de Janeiro: FGV/CPS, 2010_ .

Rosenbaum, P. e Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70(1): 41-50.

Tan, Jee-Pang and Yoo-Jeung Joy Nam. “Pre-Employment Technical and Vocational Education and Training: Fostering Relevance, Effectiveness and Efficiency” <mark>. (Eds.). (2012).</mark> _<mark>The right skills for the job?: rethinking training policies for workers</mark>_ <mark>. World Bank Publications</mark> Vasconcellos, L., Lima, F. C., Fernandes, J. G. & Menezes Filho, N. A. (2010). _Avaliação Econômica do Ensino Médio Profissional_ [Economic Assessment of Secondary Vocational Education]. _Relatório de avaliação no 14, Programa Avaliação Econômica de Projetos Sociais, Fundação Itaú Social, 2010_ .

Wooldridge, J. M. (1995). Score diagnostics for linear models estimated by two stage least squares. In Advances in Econometrics and Quantitative Economics: Essays in Honor of Professor C. R. Rao, ed. G. S. Maddala, P. C. B. Phillips, and T. N. Srinivasan, 66-87. Oxford: Blackwell, 1995.

43
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and final output; however, the estimation results using the LP technique are still closer to a true estimation than either OLS or FE, since it gets rid of simultaneity issues of other factor inputs, such as capital, in estimating the production function.

_Table 6. Innovation Impact on Output, OLS, FE, and LP_

|Dep. Var.|OLS|||FE|||Levinsoh|n and Petri|n (LP)|
|---|---|---|---|---|---|---|---|---|---|
|ln revenue|(1)<br>All|(2)<br>Int.vs.<br>ext.|(3)<br>Synergy|(4)<br>All|(5)<br>Int.vs.<br>ext.|(6)<br>Synergy|(7)<br>All|(8)<br>Int.vs.<br>ext.|(9)<br>Synergy|
|ln_labor|0.393<sup>***</sup><br>(0.000)|0.392<sup>***</sup><br>(0.000)|0.391<sup>***</sup><br>(0.000)|0.206<sup>***</sup><br>(0.000)|0.205<sup>***</sup><br>(0.000)|0.206<sup>***</sup><br>(0.000)|0.352<sup>***</sup><br>(0.000)|0.352<sup>***</sup><br>(0.000)|0.351<sup>***</sup><br>(0.000)|
|ln_capital_d|0.198<sup>***</sup><br>(0.000)|0.197<sup>***</sup><br>(0.000)|0.196<sup>***</sup><br>(0.000)|0.196<sup>***</sup><br>(0.000)|0.196<sup>***</sup><br>(0.000)|0.196<sup>***</sup><br>(0.000)|0.320<br>(0.058)|0.272<br>(0.061)|0.207<br>(0.174)|
|ln_mateiral|0.397<sup>***</sup>|0.396<sup>***</sup>|0.396<sup>***</sup>|0.307<sup>***</sup>|0.307<sup>***</sup>|0.307<sup>***</sup>|0.359<sup>*</sup>|0.402<sup>***</sup>|0.491<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.018)|(0.001)|(0.000)|
|ln_all|0.015<sup>***</sup><br>(0.000)|||0.005<sup>***</sup><br>(0.000)|||0.014<sup>***</sup><br>(0.000)|||
|ln_internal||0.007<sup>***</sup>|0.002||0.005<sup>***</sup>|0.000||0.007<sup>***</sup>|0.004|
|||(0.000)|(0.558)||(0.000)|(0.953)||(0.000)|(0.193)|
|ln_external||0.012<sup>***</sup>|0.010<sup>***</sup>||0.003<sup>**</sup>|0.002||0.011<sup>***</sup>|0.010<sup>***</sup>|
|||(0.000)|(0.000)||(0.001)|(0.098)||(0.000)|(0.000)|
|ln_int×ln_ext|||0.001<sup>**</sup>|||0.001<sup>*</sup>|||0.000|
||||(0.007)|||(0.011)|||(0.180)|
|Eexport_stat|0.198<sup>***</sup>|0.198<sup>***</sup>|0.197<sup>***</sup>|0.055<sup>***</sup>|0.055<sup>***</sup>|0.055<sup>***</sup>|0.178<sup>***</sup>|0.177<sup>***</sup>|0.177<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|FDI_stat|0.253<sup>***</sup>|0.253<sup>***</sup>|0.252<sup>***</sup>|-0.032|-0.033|-0.033|0.207<sup>***</sup>|0.207<sup>***</sup>|0.206<sup>***</sup>|
||(0.000)|(0.000)|(0.000)|(0.089)|(0.083)|(0.079)|(0.000)|(0.000)|(0.000)|
|_N_|15,735|15,735|15,735|15,735|15,735|15,735|15,735|15,735|15,735|
|Sum of coeffi|1.004|1.004|0.996|0.715|0.717|0.711|1.045|1.043|1.064|

_Note:_ Year and industry dummies were included but not reported. Standard errors are clustered and robust (White’s correction for heteroskedasticity); _p_ -values in parentheses: Significance level: * = 5 percent, ** = 1 percent, *** = 0.1 percent As expected, the coefficients of labor get larger when using OLS and smaller when using FE, compared to those in table 6. The coefficient of labor using LP is quite similar to the 45
# **Table A.5a: Sustainability of Reform: Full Difference-in-Difference Estimates** **<u>(Margins Based on Current World Price of Crude Palm Oil)</u>**

||Trading M|argins|
|---|---|---|
||Wholesale-World Price|Retail-World Price|
|**Annoucement Effects**|||
|Announcement Dummy|-4.723***|-5.121***|
||(-7.536)|(-7.831)|
|**Announcement*Palm**|**23.03**|**24.09**|
||(1.527)|(1.248)|
|**Announcement*Palm*World Price**|**-0.199**|**-0.208**|
||(-1.062)|(-0.873)|
|**Effects of Policy Implementation**|||
|**Effects during first 9 months**|||
|Policy Implemt.|-2.679***|-3.435***|
||(-3.785)|(-4.773)|
|**Policy*Palm**|**48.55*****|**57.33*****|
||(9.640)|(12.33)|
|**Policy*Palm*World Price**|**-0.567*****|**-0.655*****|
||(-8.702)|(-10.80)|
|**Effects after first 9 months**|||
|Policy Implement.|-4.638***|-4.906***|
||(-4.542)|(-4.665)|
|**Policy Implement.*Palm**|**10.80*****|**25.83*****|
||(2.869)|(6.998)|
|**Policy*Palm*World Price**|**-0.0722**|**-0.215*****|
||(-1.522)|(-4.733)|
|World Price*palm|0.0167|-0.0670|
||(0.231)|(-0.916)|
|World Price|-0.394***|-0.316***|
||(-5.408)|(-4.258)|
|Diesel Price|28.41***|31.24***|
||(10.44)|(12.63)|
|Dummy if World Price was rising (DW)|0.0753|0.227***|
||(1.278)|(4.226)|
|DW*palm|0.594|0.760|
||(0.439)|(0.571)|
|Palm|-3.916***|-3.858***|
||(-2.938)|(-3.213)|
|Relative world prices of palm-soy/wheat-rice|-3.369**|-4.680***|
||(-2.081)|(-2.818)|
|Relative world prices of palm/wheat|1.168***|1.184***|
||(3.926)|(3.734)|
|Intercept|<br>11.13***|<br>7.178**|
||(3.647)|(2.475)|
|Observations|1312|1312|
|Controls|||
|Year, Quarter, Product Fixed effects|Yes|Yes|
|Ramadan Dummies|Yes|Yes|
analyze the data using the specification that incorporates demographic interactions terms to better understand the heterogeneity in the sample.

The � variable represents socio‐demographic interaction terms. Specifically, we test whether household size, gender composition, the presence of the cook, number of children, or having previously seen an improved stove impact the preferences for an improved stove.

# _4.2 Study site and stove technology_

Data for this study were collected from 504 randomly selected households in 36 sites from Oromia, Ahmara and SNNP, the three largest regions that together represent 80% of Ethiopia’s population and 70% of Ethiopia’s forest cover. The survey was conducted by 20 enumerators and 5 supervisors who went out to the communities in groups of five, four enumerators and one supervisor. The enumerators spoke local languages and were trained in conducting choice experiment surveys (and most had previous experience with collecting choice experiment data in similar field settings).

Several different ethnicities are represented in the study area; the largest groups represented are Oromos (39.04%), Amharas (24.5%), and Wolaytas (13.74%). All respondents live in rural areas, and the average walk time to the nearest road is 63.8 minutes. The average number of children in a household is 3.38, and most households consist of about 6 people. Only 5.38% of the households have a female head of the household.

As seen in Figure 2, which presents the primary sources of fuel used by respondents, using the forest sustainably should be an urgent priority for Ethiopians. Over 91% of respondents use firewood as their main source of fuel, while only 7.2% of people use dung and 1.1% use small branches to fuel their stoves. Just under half of the respondents (44.6%) had seen an improved stove before they took the survey, and nearly all (more than 92%) of respondents said they used a three‐stone stove for cooking. Most households (over 77%) report that children were usually in the kitchen when food is cooked, and nearly all households (88%) belong to forest user groups 12
Policy Research Working Paper 10039

# **Abstract**

Stringent employment protection laws are argued to be a cause of reduced employment flexibility, slower growth and increased reliance on temporary employment contracts in many countries, including India. In 2014, the Indian state of Rajasthan amended labor laws to increase employment flexibility in firms. The most discussed of the amendments lifted the requirement for government approval for retrenching regular workers in medium-size factories. This paper first conducts a synthetic control analysis of the policy change using state-level panel data from 1980 to 2018, finding no evidence of an impact on aggregate manufacturing employment and output. The paper then uses firm-level panel data to conduct a difference-in-differences analysis of the main amendment, exploiting its size-dependent feature for identification. This analysis finds that the amendment reduced the implicit regulatory cost of labor in firms, but there is no discernible impact on their total employment and output. The amendment also led to firms substituting temporary (“contract”) workers for permanent workers. This collateral impact is contrary to the expectation that easing the flexibility of permanent employment arrangements would make them more attractive to firms.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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://www.worldbank.org/prwp. The authors may be contacted at ssharma1@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
with length of program participation, increasing commensurately with enrollment increases as inferred from the stable student-teacher and student-classroom ratios across phases.

The FAS program offers three types of cash benefits to schools. These benefits were introduced at different points in time. First, the program offers a per-student subsidy, which was introduced at program inception. The monthly per-student subsidy amount was fixed at Rs. 300 (US$3.5) for all students, which was roughly half of the estimated per-student cost in the public primary and secondary education system at the time the program was initiated. Starting in September 2008, the monthly per-student subsidy was raised to Rs. 350 (US$4.1) for students in grades 1–8 and Rs. 400 (US$4.7) for students in grades 9–12. The subsidy benefit is provided to schools on a monthly basis for all twelve months in the year. To facilitate timely and regular payments, starting in August 2007, the subsidy benefit amounts are electronically transferred to the bank accounts of program schools.

Second, first announced in December 2006, every academic year, the program offers cash bonuses to teachers in high-performing schools. Details on the eligibility criteria for the bonuses are provided in Section 7 but a maximum of five teachers in program schools that achieve (in part) a minimum student pass rate of 90% (i.e., at least 90% of tested students obtain a minimum percentage of 40%) in the QAT receive a bonus award of Rs. 10,000 (US$118) each. This is a substantial bonus amount for teachers in program schools: using available data on maximum and minimum monthly teacher salaries from applications to the program by phase-3 and phase-4 program schools, we estimate that the bonus amount represents 29% and 59% of pre-program mean annualized maximum and minimum salaries. This bonus size is approximately an order of magnitude larger than the range of sizes observed in most teacher incentive programs (see Glewwe et al. 2003). In practice, the bonuses are offered to teachers who taught the classes and/or subjects that were tested in the QAT. Similar to the subsidy benefits, the bonuses are transferred electronically to the personal bank accounts of the teacher awardees. To date, four rounds of teacher bonuses have been awarded.

Third, first announced in February 2007, every academic year, the program offers a competitive school bonus to the top-performing school in each major program district (there are seven major program districts). The program school with the highest student pass rate in the QAT is awarded Rs. 50,000 (US$588).<sup>6</sup> This bonus size is relatively modest: given a pre-

> 6 Ties between schools in pass rates are broken by looking at mean percentage scores in the QAT.

9
<!-- Start of picture text --> 2000 20102017 2000 20102017<br>Child nutrition outcomes Child mortality outcomes<br>Under-5 stunting prevalence (JME) 4 33 Under-5 mortality rate (UN IGME) 39<br>Under-5 underweight prevalence (JME) 21 22 Infantnfant mortalitymortality raterate ( (UN IGME ) 29 2822<br>17<br>Under-5 wasting prevalence (JME) 6 7 Neonatal mortality rate (UN IGME) 14<br>Adolescent pregnancy Maternal nutrition<br>a Pregnant women (% hb < 110 g/L) (NNS)42,44<br>Livebirths per 1,000 women, ages 15-19 (N) (UN Lactating women (% hb < 120 g/L) (NNS)<br>POP)<br>25<br>Nutritionally-at-risk pregnant women (%) (NNS) 5<br>Adolescent childbearing prevalence (% of women 8 3 y<br>15-19) (DHS)<br>Birth outcomes Anemia in children<br>Low birth weight prevalence (% of livebirths < 19| Children: 6mo - tyr (% hb < 110 g/L) (NNS)<br>2.5kg) (DHS) 14<br>13\/<br>Children 1-5yr (% hb < 110 g/L) (NNS)<br>{<br>Preterm birth rate (% of livebirths) (WHO) 4<br>Diarrhea Breastfeeding<br>Treatment of diarrhea, ORS/RHF (% of children<3yrs) (DHS) 57 54 Ever breastfed (% children < 2 years, last(DHS born ) g—&8<br>57<br>Early initiation of breastfeeding (% children < 2<br>Diarrhea. prevalence (% children. < 3 years) (DHS) bu,4 Exclusive breastfeeding (Sol4tenee-Onisa) 46<br>(NNS)<br>Complementary feeding Health system performance<br>Minimum dietary diversity (% children 6-23 mos) 22 Received all 8 basic vaccinations (% of children 80<br>(NNS) 9 12-23 mos) (DHS) 7<br>Minimum acceptable diet (% children 6-23 mos) 12<br>(NNS) 61<br>Skilled birth assistance (% of livebirths) (DHS)<br>Water & Sanitation Poverty<br>Ps 95 Poverty rate (% of pop, US $3.20 line) (FIES)<br>Improved drinking water (% of pop) (DHS) 7<br>78<br>14<br>Improved, non-shared sanitation (% of pop) (DHS) 67 Poverty rate (% of pop, US $1.90 line) (FIES) NS<br>2000 20102017 2000 20102017<br><!-- End of picture text -->

Source: The source of<sup>eachindicatorisindicatedinparentheses.DHS=Demographic&HealthSurvey,NNS=Philippines</sup> National Nutrition Survey, Food & Nutrition Institute, Department of Science & Technology (FNRI-DOST), WHO = World Health Organization, WB = World Bank. Notes: Risk factors labeled as showing a clear improvement/ clear worsening are those with average rates of change greater than 0.5% per year or less than -0.5% per year, depending on the indicator.
# **Comparison of Replacement Households**

|||**Provinc**<br>**Mass**|**e level inte**<br>**media trea**|**rvention**<br>**tment**|||**Commun**|**ity treatmen**|**t sample**|**District level i**|**nterventio**|**n**<br>**School**|**componen**|**t sample**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Contr|ol mean|Treat|ment mean||Contr|ol mean|Treatm|ent mean||Con|trol mean|Treat|ment mean||
||N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|N|Avg.|N|Avg.|Difference|
|**Average HH size**|153|4.588|132|4.719|-0.131|153|4.588|179|4.587|0.002|122|4.852|115|5.130|-0.278|
|**Percentage of HH heads that are male**|152|0.921|131|0.931|-0.01|152|0.921|179|0.860|0.061|122|0.877|114|0.860|0.017|
|**Percentage of other HH members that are male**|550|0.383|492|0.369|0.014|550|0.384|642|0.399|-0.015|470|0.402|476|0.391|0.011|
|**Percentage of HH heads that ever attended school**|150|0.946|131|0.961|-0.015|153|1.216|179|1.268|-0.052|122|1.320|115|1.209|**0.111****|
|**Highest educational level achieved by HH head**||||||||||0.000|||||0.000|
|Primary|119|0.184|101|0.207|-0.023|150|0.947|179|0.961|-0.014|122|0.975|114|0.921|0.054|
|Secondary|119|0.1|101|0.099|0.001|141|0.333|171|0.380|-0.047|117|0.282|103|0.398|**-0.116****|
|Preparatory|119|0.512|101|0.455|0.057|141|0.553|171|0.497|0.056|117|0.547|103|0.534|0.013|
|Trade school|119|0.193|101|0.237|-0.044|141|0.092|171|0.064|0.028|117|0.120|103|0.039|0.081|
|University|119|0.008|101|0|0.008|141|0.021|171|0.058|-0.037|117|0.051|103|0.029|0.022|
|**% of employed HH heads**|152|0.96|131|0.938|0.022|152|0.961|179|0.939|0.022|122|0.893|114|0.921|-0.028|
|**Source of drinking water**||||||||||0.000|||||0.000|
|**(% HHS)**||||||||||||||||
|Tanker truck|153|0|132|0|-|153|0.000|179|0.000|0.000|122|0.008|115|0.000|0.008|
|Water vendor|153|0|132|0|-|153|0.000|179|0.000|0.000|122|0.016|115|0.000|0.016|
|Surface water|153|0.019|132|0.022|-0.003|153|0.020|179|0.039|-0.019|122|0.016|115|0.043|-0.027|
|Bottled water|153|0.013|132|0|0.013|153|0.013|179|0.000|0.013|122|0.025|115|0.000|0.025|
|Piped water, into dwelling|153|0.281|132|0.333|-0.052|153|0.281|179|0.296|-0.015|122|0.320|115|0.426|-0.106|
|Piped water, into yard, plot|153|0.464|132|0.356|0.108|153|0.464|179|0.486|-0.022|122|0.393|115|0.357|0.037|
|Piped water, public tap, stand|153|0.071|132|0.136|-0.065|153|0.072|179|0.056|0.016|122|0.066|115|0.017|0.048|
|Dug well, protected|153|0.065|132|0|0.065|153|0.065|179|0.011|0.054|122|0.033|115|0.000|0.033|
|Dug well, unprotected|153|0|132|0.015|-0.015|153|0.000|179|0.000|0.000|122|0.000|115|0.017|-0.017|
|Spring water, protected|153|0.032|132|0.068|-0.036|153|0.033|179|0.017|0.016|122|0.008|115|0.043|-0.035|
|<br>Spring water, unprotected|153|0.019|132|0.037|-0.018|153|0.020|179|0.011|0.008|122|0.008|115|0.009|0.000|
|<br>Rainwater|153|0|132|0|-|153|0.000|179|0.006|-0.006|122|0.000|115|0.000|0.000|
|Other|153|0.026|132|0.015|0.011|153|0.026|179|0.050|-0.024|122|0.107|115|0.070|0.037|
|*** p<0.01 ** p<0.05 * p<0.1|*** p<0.01 *|* p<0.05 * p<0.1||||||||||||||

57