rafmacalaba/datause-encoder-data huggingface dataset:
on the same day, to avoid changes in family circumstances or birth rank that bias the estimation. By identifying twins in our large data set, we are able to estimate 2. To account for the effects of all observable and unobservable family characteristics on education and labor market outcomes, we run our model on a sample of twins with family fixed effects. The fixed effects specification on the sample exploits just the within-family (between twins) variation on 6th grade ENLACE, identifying, therefore, the relationship between test scores and education and labor market outcomes net of family characteristics. We interpret these estimated coefficients as the composite of the effect of skills at Grade 6, as captured by ENLACE, on future outcomes and the (within-family) individual-level variation in factors that produce better outcomes both in Grade 6 and later grades (e.g. intelligence, motivation, and health). We also follow a more conventional approach in which we substitute the vector _τf_ for a vector _Xi_<sup>_′_ofkstudent,andhouseholdobservable</sup> characteristics. In the ENLACE panel, this last specification uses the sample of students who answered the ENLACE context questionnaire to control for differences in household characteristics with respect to both maternal and paternal education (graduation from lower secondary school or not) and parents’ occupation (blue- versus white-collar). In the ENILEMS-ENLACE panel regressions, _Xi_<sup>_′_includesuppersecondaryschoolgradepoint</sup> average (GPA), a dummy variable indicating graduation from a private upper secondary school, residence in an urban area, sex, and age.

ENLACE test scores are likely not independent of other student characteristics which are, in turn, important determinants of education and labor market trajectories. For instance, math scores might be correlated with socio-emotional skills such as motivation or perseverance that produce higher test scores today and better attainment, achievement and labor market outcomes in the future. This type of omitted variable bias is not necessarily a limitation of our empirical strategy since the objective of our exercise is not to identify the effect of an exogenous change in, say, math skills at the end of primary school holding constant all other student characteristics. Instead we want to understand what drives the predictive power of test scores for future wellbeing. Test score outcomes will surely be the result of a combination of previous subject-specific knowledge and 14
They also found that the regulators often aggressively target their discretionary inspections, to the degree that half of the plants receive fewer than one inspection per year, while plants expected to be the dirtiest may end up receiving up to ten or more. More importantly, they found that discretion in targeting helps enforcement. Inspections that the regulator assigns cause almost three times more abatement than would the same number of randomly assigned inspections. At the same time, the regulator’s information on plant pollution is often found to be poor, and improvements in monitoring can significantly reduce emissions.

In this paper, a unique firm-level data set from India is used to compare and analyze the compliance levels of firms that are inspected versus the ones that self-report. Attempt is also made to identify other factors that may be influencing compliance levels such as size, sector, age, location, etc. The main objective of the paper is to broadly examine the drivers of compliance of firms with regards to environmental regulations. The analysis is based on unique information available on compliance as well as firm and location characteristics of 150 firms and inspection data for 5 years. As India is increasingly moving to encouraging self-reporting, the paper also compares the measurements of air and water pollution to see how these measures differ as reported in the inspection data and when they are self-reported by the firms.

While enforcement is seen as crucial for environmental quality, it is often found to be ineffective and wanting. Regulators are quick to blame resource constraints for not being able to carry out what is on their books. Others offer less charitable explanations, for example, that regulators with wide discretion choose not to enforce the given standards due to corruption, laziness, or incompetence (Stigler (1971), Leaver (2009)). In 1996, the High Court of Gujarat ordered the Gujarat State Pollution Control Board (GPCB) to install a third-party audit system wherein plants from polluting sectors must provide an annual audit report to GPCB. Duflo et al. (2013) evaluated a reform of this audit system and found that the third-party auditors were far more accountable to the regulator and less beholden to the plants and audits improved compliance and lowered pollution levels.

The rest of the paper is structured as follows: Section 2 presents the history of environmental regulation in India. Data and descriptive statistics are presented in detail in Section 3. Section 4 outlines the empirical approach and Section 5 presents the results. The conclusions are presented in Section 6.

4
# **Table 22: Piecemeal Sensitivity of impact on Armenia of a DCFTA with the EU**

# **Results are estimated changes in welfare (Hicksian EV) as a percent of consumption**

||Pa|rameter value||Re<br>Piecemea|sults for EV<br>l sensitivity a|nalysis|
|---|---|---|---|---|---|---|
|Parameter|Lower|Central|Upper|Lower|Central|Upper|
|σ(qi, qj) – services sectors|2|3|4|1.39|1.44|1.51|
|σ(qi, qj) – goods sectors||See below||1.76|1.44|1.30|
|σ(va, bs)|0.625|1.25|1.875|1.39|1.44|1.52|
|σ(D, M)|2|4|6|1.42|1.44|1.46|
|σ(M, M)|4|8|12|1.44|1.44|1.44|
|σ(L, K)|0.5|1|1.5|1.44|1.44|1.44|
|σ(A1,...An)|NA|0|0.25|NA|1.44|1.45|
|σ(D, E)|2|4|6|1.44|1.44|1.44|
|εARM|Central va|lues of all 4 s|ets of ε|1.46|1.44|1.43|
|εEU|parameters|are listed in|table 6B.|0.96|1.44|1.77|
|εCIS|Lower and|upper value|s are 0.5|1.44|1.44|1.45|
|εROW|and 1.5 t|imes central|values.|1.53|1.44|1.39|
|θr|NA|0|1|NA|1.44|1.37|
|θr - CRTS model|NA|0|1|NA|0.73|0.67|
|θm|0.025|0.05|0.075|1.44|1.44|1.45|
|σ(qi, qj) – goods sectors|||||||
|Mining & quarrying|8.5|17.00|25.5||||
|Manufacturing|4|8.00|12||||

## **Key:**

σ(qi, qj): Elasticity of substitution between firm varieties in imperfectly competitive sectors σ(va, bs): Elasticity of substitution between value-added and business services σ(D, M): Elasticity of substitution between domestic production and imports σ(M, M): Elasticity of substitution between imported varieties σ(L, K): Elasticity of substitution between primary factors of production in value added σ(A1,...An): Elasticity of substitution in intermediate production between composite Armington aggregate goods σ(D, E): Elasticity of transformation (domestic output versus exports) εARM: Elasticity of national service firm supply with respect to price of output εEU: Elasticity of EU service firm supply with respect to price of output εCIS: Elasticity of CIS service firm supply with respect to price of output εROW: Elasticity of Rest of World service firm supply with respect to price of output θr: Share of rents in services sectors captured by domestic agents θm: Shares of value added in multinational firms due to specialized primary factor imports Source: Authors‘ estimates.

63
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of output growth or instability has led to GMT to argue that "having an independent central bank is like having a free lunch; there are benefits but no apparent costs in terms of macroeconomic performance." Debelle and Fischer (1994) have revisited this issue, and conclude-by comparing US and Germany, and then extending the analysis to a group of OECD countriesthat countries with more independent central banks tend to have greater output losses during disinflations and their sacrifice ratios (output loss per percentage point of inflation reduction) are larger. Walsh (1994) and Fischer (1995) have also presented evidence showing that the sacrifice ratio is higher the higher is the degree of independence of the central bank. He argues that independent central banks produce lower inflation, and, most theories predict, the lower the inflation rate the flatter the Phillips curve. Therefore, in low inflation economies it is more costly to reduce inflation. In this respect, there would not be such a free lunch and independent central banks would not have a credibility bonus that would allow them to fight inflation without increased output losses. Excessive weight in inflation prevention could be detrimental.

This evidence, however, is based on sacrifice ratios-that is the output loss associated with a reduction of _on percentage point_ of inflation-, and analogously to the discussion on inflation and growth, it should not be surprising that it is less costly to reduce inflation from 10 to 9 percent than to reduce it from 3 to 2 percent. It would be more appropriate to use some form of standardized sacrifice ratios. For example, as reported by Fischer (1995), using the sacrifice ratios from Ball (1993) for 28 disinflations in OECD countries for the period 1960-90 and using the GMTAS index of central bank independence, it is found that a regression of the sacrifice ratio on the GMTAS index yield a positive coefficient with a t-statistic of 3.7. This indicates that an increase in central bank independence increases (in a statistically significant sense) the sacrifice ratio. However if the sacrifice ratio is redefined as the output loss per one percent decline in the inflation rate<sup>2 6</sup> , the regression on the GMTAS index yields a positive coefficient, but statistically insignificant with a t-statistic of 1.17.

Furthermore,-even when sacrifice ratios could be greater in countries with inde-

> **2 6 The usual sacrifice ratio is defined as S** = **A(-y)/Anr, where the numerator is the sum of output deviations from full employment, and the denominator the decline in inflation. The redefinition proposed in the text is S'** Et - **y)/(A7r/,ro), or analogously S'** = **S x 7o.**

29
8 proposed to cancel all military obligations for the same groups and the law was definitively introduced in February 1982. The population targeted by the laws included all males eligible for draft in 1981, 1982 and 1983 and living in the quake region at the relevant date. This included youths turning 18 in those years (i.e. those born in 1963, 1964 and 1965) who thus were exempted before becoming formally eligible and still in high school and their older peers who had deferred service.

# 3 Research design

Our sample frame is designed so as to replicate an experimental setting in which the exemption is as good as randomly assigned. This allows us to exclude that variation in the outcomes of interest associated with exemption status is due to confounding factors, such as complementary measures undertaken in the aftermath of the quake or unobserved characteristics.

For this purpose, we focus on individuals from towns that lie on the border of the quake region (fig. 2). Towns along the border but within the quake region were exposed to the exemption as well as to the quake itself and possibly to other compensatory measures. Towns just outside the quake region, on the other hand, were excluded from all quake-related measures. Overall, the sample consists of 117 towns (57 within the quake region and 60 just outside). The average town size is quite small, 7,700 people and the median is about 2,700. Inner towns are somewhat smaller: the median size is about 2,500 against a median size of outer towns of 2,900<sup>7</sup> . The distance between neighboring towns lying across the border is also small, about 6 miles on average and always below 16 miles.

This design has at least two appealing features. First, towns along the inner border of the quake region suffered minor damages or none at all, as reported by the government agencies assessing the consequences of the quake. Specifically, of the 57 towns, 18 did not record any damage, 15 towns ranked at the very lowest level of the damage scale, meaning only very mild and limited damage was

> 7 Town sizes are computed as of 1979 from Istat population records.
_<u>Table 11: Decomposing factor price changes due to CAFTA</u>_

||<br>**Nicara**|<br>**gua Unila**|<br>**t. Lib**|<br>**US**|<br>**Unilat. L**|<br>**ib**|
|---|---|---|---|---|---|---|
||Δ**P**|Δ(**P/CPI)**|**% of**<br>**CAFTA**|Δ**P**|Δ(**P/CPI)**|**% of**<br>**CAFTA**|
|**_Non-Farm Segment:_**|||||||
|**Skilled Labor**|0.0|1.1|86|0.6|0.2|14|
|**Unskilled Labor**|0.5|1.5|94|0.6|0.1|6|
|**Capital**|0.2|1.2|81|0.8|0.3|19|
|**Sk/Unsk wage gap**||-0.4|||0.1||
|**_Farm Segment:_**|||||||
|**Skilled Labor**|-3.2|-2.2|160|1.3|0.8|-60|
|**Unskilled Labor**|-5.2|-4.1|106|0.7|0.3|-6|
|**Capital**|2.1|3.1|67|2.0|1.5|33|
|**Sk/Unsk wage gap**<br>**_Price indexes:_**||2.1|||0.6||
|**Food price index**|-2.0|||0.6|||
|**Non food price index**<br>**CPI**|-0.4<br>-1.0|||0.4<br>0.5|||

Sources: Author calculations from model results. Notes: The first column, Δ **P** , represents the percent variation of the price of each factor with respect to the initial levels, and Δ **(S/CPI)** is the percent variation of the price deflated by the Consumer Price Index. The column, **% of CAFTA** , shows the percent ratio of the real price changes in the unilateral liberalizations with respect to the bilateral CAFTA case.

As far as factor market effects are concerned, Table 11 shows that the nonreciprocal removal of Nicaragua’s tariffs causes factor prices of the non-farm segment to vary almost as much as with the CAFTA scenario, leaving a small contribution to the full price change to the U.S. unilateral response. Interestingly, the two unilateral liberalizations have contrasting price effects for factors in the farm segment. In the case of the U.S. liberalizing its tariffs, factor prices go up due to the increased export demand and this inflationary effect is not counterbalanced by inflows of cheaper imports. However, these inflows explain why factor prices tend to contract with the unilateral liberalization of Nicaragua. This shows that increasing market access mitigates the potentially negative shocks to farm incomes associated with the liberalization of Nicaraguan agricultural markets.

# **_<u>Beyond a short term impact analysis: Full liberalization and CAFTA with fully flexible factor markets</u>_**

The model used here to evaluate trade policy reform is a static model, which only accounts for the allocative efficiency gains originating from trade liberalization. It does not measure the potentially vastly larger dynamic gains. For instance, in the current 32
**<u>Table 5: Gender Heterogeneity in Impacts on Formalization</u>**

||<br>Z score|BRC|TPIN|Citycouncil|
|---|---|---|---|---|
||D|atapooled for a|ll follow upsur|veys|
|**Panel B: Results by Gender**|||||
|Treatment 1: BRC|0.728***|0.543***|0.020**|0.025|
||(0.039)|(0.021)|(0.009)|(0.023)|
||0.000|0.000|0.024|0.267|
||_0.000_|_0.000_|_0.057_|_0.42_|
|Treatment 2: BRC + TPIN|0.796***|0.607***|0.01|0.024|
||(0.050)|(0.031)|(0.011)|(0.029)|
||0.000|0.000|0.388|0.418|
||_0.000_|_0.000_|_0.388_|_0.511_|
|Treatment 3: BRC + IS + BBA|0.881***|0.667***|0.015*|0.026|
||(0.032)|(0.017)|(0.008)|(0.020)|
||0.000|0.000|0.05|0.201|
||_0.000_|_0.000_|_0.101_|_0.377_|
|Treatment 1: BRC * Female|-0.131**|-0.071**|-0.02|-0.021|
||(0.063)|(0.033)|(0.014)|(0.039)|
||0.037|0.033|0.144|0.594|
||_0.041_|_0.047_|_0.192_|_0.654_|
|Treatment 2: (BRC + TPIN) * Female|-0.334***|-0.191***|-0.026|-0.079|
||(0.084)|(0.049)|(0.017)|(0.050)|
||0.000|0.000|0.129|0.117|
||_0.000_|_0.000_|_0.192_|_0.321_|
|Treatment 3: (BRC + IS + BBA) * Female|-0.144***|-0.077***|-0.017|-0.028|
||(0.051)|(0.026)|(0.012)|(0.034)|
||0.005|0.003|0.165|0.418|
||_0.006_|_0.006_|_0.198_|_0.511_|
|Control group mean: Male|0.031|0.091|0.051|0.68|
|Control group mean: Female|-0.047|0.065|0.064|0.583|
|p-value: Treatment 1 = Treatment 2 for males|0.219|0.065|0.425|0.96|
|p-value: Treatment 1 = Treatment 3 for males|0.000|0.000|0.572|0.962|
|p-value: Treatment 2 = Treatment 3 for males|0.092|0.062|0.671|0.93|
|p-value: Treatment 1 = Treatment 2 for females|0.076|0.208|0.211|0.145|
|p-value: Treatment 1 = Treatment 3 for females|0.008|0.000|0.842|0.824|
|p-value: Treatment 2 = Treatment 3 for females|0.000|0.000|0.239|0.161|
|p-value test of equality for males|0.000|0.000|0.098|0.596|
|p-value test of equality for females|0.000|0.000|0.575|0.493|

34
Kaplan, D. S., Piedra, E., & Seira, E. (2011). Entry regulation and business start-ups: Evidence from Mexico. Journal of Public Economics, 95(11), 1501-1515.

Karlan, D., Knight, R., & Udry, C. (2014). Consulting and Capital Experiments with Micro and Small Tailoring Enterprises in Ghana. Working Paper, October.

Leea, E. Y., & Cin, B. C. (2010). The effect of risk-sharing government subsidy on corporate R&D investment: Empirical evidence from Korea. Technological Forecasting & Social Change, 77, 881–890.

Lopez-Acevedo, G., & Tan, H. W. (2011).Impact Evaluation of Small and Medium Enterprise Programs in Latin America and the Caribbean. World Bank Publications, The World Bank, 2298, March.

Lopez-Acevedo, G., & Tan, H.W. (2005). Evaluating Training Programs for Small and Medium Enterprises Lessons from Mexico. World Bank Policy Research Working Paper 3760, November. Lopez-Acevedo, G., & Tinajero, M. (2010). Mexico: impact evaluation of SME programs using panel firm data. Policy Research Working Paper Series 5186, The World Bank.

Machado, L., Parreiras, M. A., & Peçanha, V. R. (2011). Avaliação de impacto do uso do Cartão BNDES sobre o emprego nas empresas de menor porte. Revista do BNDES 36, December.

Mano, Y., Iddrisu, A., Yoshino, Y., & Sonobe, T. (2012). How Can Micro and Small Enterprises in Sub-Saharan Africa Become More Productive? The Impacts of Experimental Basic Managerial Training. World Development, Elsevier, 40(3), 458-468.

Martincus, C. V., Carballo, J. and Garcia, P. M. (2014). Public Programmes to Promote Firm’s Exports in Developing Countries: Are There Heterogeneous Effects By Size Categories? Applied Economics, Vol. 44, 471-491.

Martincus, C. V. and Carballo, J. (2010). Beyond The Average Effects: The Distributional Impacts of Export Promotion Programs in Developing Countries. Journal of Development Economics, vol. 92, 201-214.

Martincus, C. V. and Carballo, J. (2008). Is Export Promotion Effective in Developing Countries? Firm-level Evidence on the Intensive and the Extensive Margins of Exports. Journal of International Economics, vol. 76, 89-106.

33
rural areas. High school and vocationally educated have considerably higher unemployment rates in rural than in urban areas. It is the educated labor that contributes the bulk of unemployed both in urban and rural areas, 87 percent in urban and 84 percent in rural areas. This is, of course, partly because educated labor now is the bulk of the labor force, 90 percent in urban and 71 percent in rural areas. Yet, even in rural areas with 29 percent of the labor force uneducated, these supply only 16 percent of the unemployed and at the national level the picture is much the same, the uneducated representing 21 percent of the labor force but only 14 percent of the unemployed. The general conclusion would seem to be that the labor markets for uneducated clear better than those for educated which again would mean that wages for uneducated labor are more flexible than those for educated labor.

These findings immediately raise the question whether new entrants to the labor market, school leavers in particular, dominate amongst the unemployed so that we should expect relatively high unemployment rates simply because the educated population increases relatively fast. Of the Turkish LFSs two provides breakdown of unemployed by work experience. Of the unemployed seeking job 1969, 71 percent had had a job before, 29 percent were seeking job for the first time. For 1985 these figures were 64 and 36. Age distributions of unemployed registered at the placement offices are available back to 1979.

- 10 -
**Joint Group** . 2004. _“Report of the Joint Group on Crop Insurance.”_ (Department of Agriculture & Cooperation). New Delhi: Government of India, Ministry of Agriculture

**Ibarra, H** ., 2004. _“Self-Insurance Funds in Mexico,”_ in E.N. Gurenko, ed., Catastrophe Risk and Reinsurance: A Country Risk Management Perspective, 2004, chapter 18.

**James P.C., and R. Nair.** 2009. _A Study of Yield based Crop Insurance in India: A Performance Review_ . New Delhi: Agriculture Insurance Company of India

**Hazell, P.** , 1992. _The appropriate role of agricultural insurance in developing countries_ , Journal of International Development, 4 (6), 567-581.

**Mahul O. and C. Stutley.** 2010. Government Support to Agricultural Insurance: Challenges and Options for Developing Countries. Washington, D.C.: World Bank.

**Nair, R.** , 2010. _“Crop Insurance in India: Changes and Challenges”_ Economic and Political Weekly, February 6th: 19-22.

**Raju, S.S., R. Chand** , 2008. _“Agricultural insurance in India: problems and prospects,”_ Indian Council of Agricultural Research, New Delhi, NCAP working paper

**Skees, J.R., P.B.R. Hazell, and M. Miranda** , 1999. “ _New Approaches to Public/Private Crop Yield Insurance in Developing Countries.,"_ Environment and Production Technology Division (EPTD) Discussion Paper 55, International Food Policy Research Institute (IFPRI)

**Smith, V. and M. Watts** , 2009. _“Index Based Agricultural Insurance in Developing Countries: Feasibility, Scalability and Sustainability,”_ Gates Foundation.

**World Bank** . 2007. _“India National Agricultural Insurance Scheme: Market-based Solutions for Better Risk Sharing”_ Report No. 39353. Washington DC: World Bank.

**World Bank** . 2011a. _“Enhancing Crop Insurance in India”_ Washington DC: World Bank.

**World Bank** . 2011b. Data retrieved February 20, 2011, from World Development Indicators Online (WDI) database.

20