rafmacalaba/datause-encoder-data huggingface dataset:
maximum measure captures large, non- or semi-anthropogenic sources of pollution that may spike in certain months (e.g. seasonal forest burning).

We also examine whether our results are sensitive to alternative data on wealth, using data from DHS clusters, or pollution, using ground based monitor data. In both cases the sample we are able to analyze using the alternative datasets is smaller than our global sample because of a lack of comprehensive data covering all 103 countries in our sample.

When we estimate the country-specific correlation using DHS wealth data and our global pollution data set we find the same general pattern of correlation with roughly half the countries exhibiting a positive relationship between wealth and pollution (Figure SI-7). Nigeria and Nepal remain the countries with the most negative and most positive correlations respectively. There are some countries where the sign of the correlation flips when using the DHS data relative to RWI but in general there is broad agreement in sign and magnitude across the two data sets (Figure SI-8).

We also examine the trend in pollution within the seven countries for which we have pollution data from ground monitors that represent at least 10 distinct RWI grid-cells. Figure SI-9 shows that in most of these countries, including all of those for which we have the largest number of ground monitors, the relationship between pollution and wealth is qualitatively similar when calculated using satellite measures of pollution or ground monitors. In the same figure we highlight which ground monitors are in urban or rural settings to underline the point that restricting analysis to those places with ground monitors will often result in the omission of most rural locations and so will fail to capture the urban-rural gradient that appears to be at the heart of our primary results. In several of the countries included in this sample there is only data from one or two rural ground monitors for the entire country. There is also often substantial temporal gaps in ground monitor data, on average ground monitors only report measurements in 45% of the months over which we have satellite measures of pollution. This can also lead to substantial bias when using ground monitor data if the ground monitor data is missing observations during particular seasons when pollution spikes (or is only available in those seasons).

In our city level results the choice of city boundaries is an important determinant of the sign and magnitude of the within-city correlation. The overall pattern of heterogeneity remains regardless of which precise city boundary is chosen but estimates for individual cities do change. In contrast, the threshold for the minimum level of RWI points within a city does not change our results in the range from 10 to 50.

In Figure SI-14 we show how the point estimates change for three cities in our data for which we have both the URCA designated boundaries as well as administrative boundaries. In some cases (e.g. Delhi) the URCA and administrative boundaries align relatively closely but in 24
permit.<sup>18</sup> Unfortunately, the law of 1998 is not clear about this issue and we cannot observe in our data if that was the case or not. Figure C.2 in the appendix shows that in 1999 the formalization rate was small but positive among the control cohort and null among the eligible cohort. In one of our placebo tests, we use December 1997 as cutoff and find statistically significant difference in formalization rates between children who turned age 14 before and after December 1997.<sup>19</sup> To check robustness, eq. (1) is estimated with two different bandwidth sizes, six and nine months. We use nine-month bandwidth to perform analysis when only one cross-sectional data is used. Estimates with six-month bandwidth are provided when several waves of the survey data are pooled. We perform two placebo tests in the short term and one in the long term to check robustness.

# **5. Data**

We use several years of the Brazilian household survey PNAD. Data from 1999 and 2001 are used for descriptive statistics and short-term estimates. Data from 1998 are used in one of the placebo analyses. We use all PNADs from 2001 to 2014 to look both at persistency effects in the outcomes used in the short-term analysis and at trajectories of the outcomes used in the long-term analysis.

IBGE has annually conducted the PNAD since the late 1970s. It covers around 100,000 households and 320,000 individuals. The survey is conducted between October and December each year and constitutes one of the main sources of microdata in Brazil.<sup>20</sup> The PNAD is nationally representative, containing information on household socio-economic characteristics, demographic data, household sources of income, and labor-force status.

The sample of interest is given by two cohorts of individuals aged 14 by the time the law passed. The first cohort, which we consider the comparison group, includes individuals who turned 14 before the ban of December 1998. The second cohort, defined as the eligible group, consists of individuals who turned 14 after the change in the minimum legal age. These two cohorts are compared from ages 13 to 29.

> 18 We would like to thank an anonymous referee for pointing this out.

> 19 In a very short paper Piza and Souza (2016) combined RDD with difference-in-differences methods to control for cohort effects. The results are almost identical to the ones we found for incidence in paid work activities.

> 20 The survey documents provide the month (September), week (last of the month) and day (usually 27th) as the period of reference for the Brazilian PNAD.

14
# **References**

- Azevedo, Joao Pedro, Minh Cong Nguyen, and Viviane Sanfelice. 2012. “Shapley Decomposition by Components of a Welfare Aggregate.” Washington, DC: World Bank. Mimeo.

- Azevedo, Joao Pedro, Minh Cong Nguyen, and  Viviane Sanfelice. 2012. "Adecomp: Stata <u>module to estimate Shapley Decomposition by Components of a Welfare Measure,”</u> _Statistical Software Components_ S457562, Boston College Department of Economics.

- Barros, Ricardo Paes,  Mirela de Carvalho, Samuel Franco, and  Rosane Mendoça. 2006. “Uma Análise das Principais Causas da Queda Recente na Desigualdade de Renda Brasileira.” In: Revista Econômica 8 (1): 117–147. Rio de Janeiro: Universidade Federal Fluminense. http://www.uff.br/revistaeconomica/V8N1/RICARDO.PDF

- Bourguignon, François, Francisco H.G. Ferreira, and Nora Lustig. 2005. _The Microeconomics of Income Distribution Dynamics in East Asia and Latin America_ . Washington, DC: World Bank.

-

- Bourguignon François, Francisco H. G Ferreira., and Phillippe G. Leite 2008. Beyond Oaxaca Blinder: Accounting for Differences in Households Income Distributions _. Journal of Economic Inequality_ 6 (2): 117–148

- Bruno, Michael, Martin Ravallion, and Lyn Squire. 1998. "Equity and Growth in Developing Countries: Old and New Perspectives on the Policy Issues." Ed. Vito Tanzi and Ke-young Chu, _Income Distribution and High-quality Growth_ . Cambridge: MIT Press.

- Chen and Ravallion. 2008. “The Developing World is Poorer Than We Thought, But No Less Successful in the Fight Against Poverty” World Bank Policy Research Working Paper 4703.

- Datt, Gaurav, and Martin Ravallion. 1992. Growth and Redistribution Components of Changes in Poverty Measures: A Decomposition with Applications to Brazil and India in the 1980s. _Journal of Development Economics_ , 38: 275–296.

- Dollar, David, and Aart Kraay. 2002. “Growth is good for the poor” _Journal of Economic Growth_ 7: 195–225.

- Essama-Nssah, B. 2012. “Identification of Sources of Variation in Poverty Outcomes.” World Bank Policy Research Working Paper 5954.

- Ferreira, Francisco H.G. 2010. “Distributions in Motion: Economic Growth, Inequality and Poverty Dynamics”.  World Bank Policy Research Working Paper No. 5424.

17
# Reference List

Arcand, J. L., C. Ai and F. Ethier. 2007. "Moral Hazard and Marshallian Inefficiency: Evidence from Tunisia." _Journal of Development Economics 83_ (2): 411-45.

Battese, G. E. 1997. "A Note on the Estimation of Cobb-Douglas Production Functions When Some Explanatory Variables Have Zero Values." _Journal of Agricultural Economics 48_ (2): 250-2.

Bell, C. 1977. "Alternative Theories of Sharecropping: Some Tests Using Evidence from Northeast India." _Journal of Development Studies 13_ (4): 317-46.

Bellemare, M. 2009. "Sharecropping, insecure land rights and land titling policies: A case study of Lac Alotra, Madagascar." _Development Policy Review 27_ (1): 87-106.

Bourguignon, F., M. Fournier and M. Gurgand. 2007. Selection Bias Corrections Based on the Multinomial Logit Model: Monte Carlo comparisons." _Journal of Economic Survey 21_ (1): 174-205.

Cheung, S. N. S. 1969. "Transaction Costs, Risk Aversion, and the Choice of Contractual Arrangements." _Journal of Law and Economics 12_ (1): 23-42.

de Janvry, A., G. Gordillo, J. P. Platteau and E. Sadoulet. 2001. _Access to Land, Rural Poverty, and Public Action_ . Oxford: Oxford University Press.

Deininger, K. 2003. _Land Policies for Growth and Poverty Reduction. A World Bank Policy Research Report_ . Oxford and New York: World Bank and Oxford University Press.

Deininger, K., D. A. Ali and T. Alemu. 2008. "Assessing the Functioning of Land Rental Markets in Ethiopia." _Economic Development and Cultural Change 57_ (1): 67-101.

Deininger, K. and S. Jin. 2005. "The potential of land markets in the process of economic development: Evidence from China." _Journal of Development Economics 78_ (1): 241-70.

Deininger, K. and S. Jin. 2006. "Productivity and equity effects of rental markets: Evidence from China." The World Bank, Policy Research Working Paper Series.

Deininger, K. and S. Jin. 2007. "Land rental markets in the process of rural structural transformation: Productivity and equity impacts in China." Policy Research Working Paper 4454. Washington: The World Bank.

DeSilva, S. 2000. "Skills, Partnerships and Tenancy in Sri Lankan Rice Farms." _Yale Economic Growth Center Discussion Paper: 819_ .

Dubin, J. A. and D. L. McFadden. 1984. An Econometric Analysis of Residential Electric Appliance Holdings and Consumption." _Econometrica 52_ (2): 345-362.

Feder, G. 1985. "The Relation between Farm Size and Farm Productivity: The Role of Family Labor, Supervision and Credit Constraints." _Journal of Development Economics 18_ (2-3): 297-313.

Fuller, W. A. 1977. "Some properties of a modification of the limited information estimator." _Econometrica 45_ (4): 939-54.

Holden, S. T.and M. Bezabih. 2009. "Gender and Land Productivity on Rented Land in Ethiopia." In Holden, S. T., K. Otsuka and F. M. Place, eds., _The Emergence of Land Rental Markets in Africa: Impact on Poverty, Equity, and Efficiency_ . Washington, DC, USA: Resources for the Future.

Holden, S. T., K. Deininger and H. Ghebru. 2011. “Tenure Insecurity, Gender, Low-cost Land Certification and Land Rental Market Participation.” _Journal of Development Studies_ 47(1): 31-47.

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Campbell, M. K., G. Piaggio, D. R. Elbourne, and D. G. Altman. 2012. “Consort 2010 Statement: Extension to Cluster Randomized Trials.” _British Medical Journal_ 345 (7881): 19–22.

Casey, K., R. Glennerster, and E. Miguel. 2012. “Reshaping Institutions: Evidence on Aid Impacts Using a Preanalysis Plan.” _The Quarterly Journal of Economics_ 127 (4): 1755–812.

Clark, J. D., G. A. Wells, C. Huët, F. A. McAlister, L. R. Salmi, D. Fergusson, and A. Laupacis. 1999. “Assessing the Quality of Randomized Trials: Reliability of the Jadad Scale.” C _ontrolled Clinical Trials_ 20 (5): 448–52.

Council of Economic Advisors. 2014. Economic Report of the President. US Government Printing Office.

Danzon, P. M., S. Nicholson, and N. S. Pereira. 2005. “Productivity in Pharmaceutical– biotechnology R&D: The Role of Experience and Alliances.” _Journal of Health Economics_ 24 (2): 317–39.

Deaton, A. 2010. “Instruments, Randomization, and Learning about Development.” _Journal of Economic Literature_ 48: 424–55.

DiNardo, J., and D. S. Lee. 2011. “Program Evaluation and Research Designs.” _Handbook of Labor Economics_ 4: 463–536.

Duflo, E., R. Glennerster, and M. Kremer. 2007. “Using Randomization in Development Economics Research: A Toolkit.” _Handbook of Development Economics_ 4: 3895– 962.

37
Romanian (35 on average at the time of the survey against a population average of 39<sup>8</sup> ). No information was gathered on ethnicity of the migrants.<sup>9</sup>

**Table 1 Descriptive Statistics**

|**Variables**|**Mean**|**Std Error**|
|---|---|---|
|Intention to Return home|0.426|0.010|
|Earnings Gap (logs)<br>|6.730|0.012|
|Probability of being employed if return to Romania – based on 2012<br>Household Budget Survey|0.444|0.005|
|Average Earnings of migrant workers(Euros)|1333.39|21.902|
|Predicted Earnings in Romania (Euros) – based on 2012 Household<br>Budget Survey.|320.57|1.7889|
|Predicted earningsgap (Logs)|6.70|0.013|
|Predicted earnings(Logs)<br>|5.74|0.0054|
|Assistance|0.487|0.010|
|ManyRomanians nearby|0.434|0.011|
|Some Romanians nearby<br>|0.356|0.010|
|No Romanians nearby|0.210|0.009|
|Speak well or fluently|0.899|0.006|
|Fluent and Language Trainingabroad|0.115|0.007|
|Fluent and no Language Trainingabroad|0.784|0.009|
|Not Fluent and Language Trainingabroad|0.003|0.001|
|Not Fluent and no Language Trainingabroad|0.098|0.006|
|Other trainingwhile abroad|0.258|0.011|
|Skills and experienced effected investment interest|0.644|0.010|
|Invested in host country|0.068|0.005|
|Legal Migrant|0.844|0.008|
|Spouse in Romania|0.173|0.008|
|Child <18years|0.160|0.008|
|Child >18years|0.119|0.007|
|Parents in Romania<br>|0.686|0.010|
|Remittance|0.653|0.010|
|Share of Remittance on necessities|0.338|0.009|
|Share of Remittance on savings|0.108|0.005|
|Share of Remittance on investment|0.021|0.003|
|Share of Remittance on house improvement|0.187|0.007|
|Invested in Romania|0.181|0.008|
|Return to Romania several times ayear or more|0.254|0.009|
|Tenure as Migrant|6.424|0.096|
|Same occupation|0.555|0.011|
|Better occupation|0.084|0.006|
|Worse occupation|0.362|0.010|
|Agriculture|0.061|0.005|
|Construction|0.277|0.010|
|Hotel|0.112|0.007|

8 Population data available from Eurostat: http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database 9 This is a pity given the importance of the Roma population in Romania, and the fact that demographic changes mean that they represent an increasing share of the working age population. Future research is required in this area.

12
Suppose that _σ_ s and _σ_ d are the standard deviations of supply and demand fluctuations, respectively. Then, as a rule of thumb, a system operator requires reserves equal to three 2 2 standard deviations of all potential fluctuations, or reserves = ±3  _s_   _d_ (see Gross et al., 2006, 2007; DeCarolis and Keith, 2005). If wind farms are added to an existing grid, required 2 2 2 reserves must be increased to ±3  _s_   _d_   _w_ , where _σ_ w is the standard deviation associated with wind intermittency. If _σ_ w > _σ_ s and wind replaces other generation that is more reliable, then reserves must increase; if _σ_ w < _σ_ s, reserve capacity would decline. How large must the additional reserves be? According to Gross et al. (2006, 2007), assuming no correlation between demand and variable supply from wind, additional reserve requirements would be small. Based on empirical wind data, they estimate that the standard deviations of wind fluctuations amount to 1.4% of installed wind capacity for a 30-minute time horizon (regulating or fast-response reserve) and 9.3% of installed capacity over a four-hour time period (contingency or standing reserve). Assume 10 GW of installed wind capacity, _σ_ w = 140 MW for regulating and _σ_ w = 930 MW for contingency reserves, and total generating capacity of 24.3 GW. Then, if _σ_ s+ _σ_ d = 340 MW, regulating reserves would need to equal 1020 MW (= 2 2 2 3 × 340 ) without wind and 1181 MW (=3× 340  140 ) with wind, while respective contingency reserves would need to be 6780 MW and 7332 MW. Thus, wind intermittency requires increases in regulating reserves of 15.8% (161 MW) and contingency reserves of 8.1% (552 MW).<sup>6</sup> Although this might be considered a small addition to overall reserves, the financial implications are significant In addition to the need for greater system reserves, there is a second cost associated with the need to retain system balance, the added cost of managing the grid (Lund, 2005). How the grid is to be managed depends on the policy implemented by the authority. If the grid operator is required to take any wind power that is offered, wind power is then nondispatchable, or ‘must run’. In that case, existing generators may need to operate at below optimal capacity, while ready to dispatch power to the grid in the event of a decline in wind

> _6 These are the current authors’ calculations using values from Gross et al (2007). Although not given, total generating capacity is approximately 24.3 GW. However, there is no discussion in Gross et al. (2006, 2007) as to whether wind generating capacity simply replaces conventional generating capacity, yet this seems to be the logical assumption based on the discussion found in these sources. Our analysis suggests that this is a highly optimistic analysis of wind power._

21
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The emphasis in this paper so far has been on changes in the quantity of labor demanded or supplied as a consequence of adopting ‘green growth’ policies. But the skills needed in the labor force are likely to be affected as well. If the availability of the skills required along green growth trajectories is inadequate, that could place a major obstacle in the way of the transition to green growth.

Fankhauser et al. (2008), in their review of the literature on climate policy and jobs, note that there is relatively little information on the productivity of jobs created and destroyed by climate-change policies. There is even less on their pecuniary and non-pecuniary attributes. CEDEFOP (2009) complains that “We have not paid enough attention to the social dimension of sustainable development: its implications for employment, training and skills.” Even in countries with relatively good labor market data, it is difficult to identify the jobs the content of which is most likely to be affected by green growth policies. For example, Hatfield-Dodds et al (2008) note that in Australia “current information on green skills and workforce capabilities is very poor;” there are some data for renewable energy and construction trades but little for transport and agriculture.

Perhaps the most thorough study of green growth and skills so far is ILO/CEDEFOP (2011), which reports the results of 21 country reviews and provides a synthesis of key findings. The study notes that the demand for skills is being affected in three ways by the transition to green growth.

First, there is induced structural change across industries, increasing the demand for the skills specific to expanding industries such as renewable energy and reducing the demand for skills such as those associated with coal-mining. This ‘green restructuring’ brings with it the usual challenges to policy-makers trying to facilitate restructuring and to reduce the labor market adjustment costs, including those resulting from a changing skill mix. Many of the expanding industries are likely to be using new products and processes, reflecting the transition to low-carbon technologies, so the generic skill requirements of many of the new jobs created are likely to be higher than average as they have to allow for assimilation of unfamiliar tasks and working methods and ‘learning by doing.’ It would appear, however, that a larger proportion of jobs in the renewable energy sector and in energy efficiency are low skilled than in the fossil-fuel energy sector (Pollin et al., 2009). The oil and gas industries 27
WPS4183 <mark>B</mark> ACKGROUND <mark>P</mark> APER TO THE <mark>2007 W</mark> ORLD <mark>D</mark> EVELOPMENT <mark>R</mark> EPORT

# **Youth Unemployment, Labor Market Transitions, and Scarring: Evidence from Bosnia and Herzegovina, 2001-04**

Jean Fares and Erwin R. Tiongson The World Bank Abstract Relatively little is known about youth unemployment and its lasting consequences in transition economies, despite the difficult labor market adjustment experienced by these countries over the last decade. This paper examines early unemployment spells and their longer-term effects among the youth in Bosnia and Herzegovina (BiH), where the labor market transition is made more difficult by the challenges of a post-conflict environment. This paper uses panel data covering up to 4,800 working-age individuals over the 2001 to 2004 period. There are three main findings from the analysis: First, youth unemployment is high, about twice the national average, consistent with recent findings from the BiH labor market study. Younger workers are more likely to go into inactivity or unemployment and are also less likely to transition out of inactivity, holding other things constant. Second, initial spells of unemployment or joblessness appear to have lasting adverse effects on earnings and employment (“scarring”). There is no evidence, however, that the youth are at a greater risk of scarring, or suffer disproportionately worse outcomes from initial joblessness, compared to other age groups. Third, higher educational attainment is generally associated with more favorable labor market outcomes. Skilled workers are less likely to be jobless and are less likely to transition from employment into joblessness. However, there is evidence that the penalty from jobless spells may also be higher for more educated workers. We speculate that this may be due in part to signaling or stigma, consistent with previous findings in the literature.

JEL Classification number: J13, J64, J31 Keywords: youth unemployment, transition probability, wage scarring World Bank Policy Research Working Paper 4183, April 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._