rafmacalaba/datause-encoder-data huggingface dataset:
empirical research is required.

# **References**

- Alstone, P., Jacobson, A., Mills, E., 2010. Illumination sufficiency survey techniques: In-situ measurements of lighting system performance and a user preference survey for illuminance in an off-grid, African setting. Lumina Project Research Note 7.

- Ashraf, N., Berry, J., Shapiro, J. M., December 2010. Can higher prices stimulate product use? Evidence from a field experiment in Zambia. American Economic Review 100 (5), 2383–2413.

- Bacon, R., Bhattacharya, S., Kojima, M., June 2010. Expenditures of low-income households on energy: Evidence from Africa and Asia. Extractive Industries for Development Series 16, World Bank.

- Baird, S., Bohren, A., McIntosh, C., Ozler,<sup> ̈</sup> B., March 2014. Designing experiments to measure spillover effects. Policy Research Working Paper 6824, World Bank, Development Research Group, Poverty Inequality Team.

- Banerjee, A. V., Cole, S., Duflo, E., Linden, L., 2007. Remedying education: Evidence from two randomized experiments in India. Quarterly Journal of Economics 122 (3), 1235–1264.

- Beuermann, D. W., Cristia, J., Cueto, S., Malamud, O., Cruz-Aguayo, Y., 2015. One laptop per child at home: Short-term impacts from a randomized experiment in Peru. American Economic Journal: Applied Economics 7 (2), 53–80.

- Cameron, A. C., Miller, D. L., 2015. A practitioner’s guide to cluster-robust inference. Journal of Human Resources 50 (2), 317–372.

- Cohen, J., Dupas, P., February 2010. Free distribution or cost-sharing? Evidence from a randomized malaria prevention experiment. Quarterly Journal of Economics 125 (1), 1–45.

- Copper, H., Robinson, J. C., Patall, E. A., Spring 2006. Does homework improve academic achievement? A synthesis of research, 1987-2003. Review of Educational Research 76 (1), 1–62.

- Dang, H.-A., December 2007. The determinants and impact of private tutoring classes in Vietnam. Economics and Education Review 26 (6), 683–698.

- Dhaliwal, I., Duflo, E., Glennerster, R., Tulloch, C., 2013. Comparative Cost-Effectiveness Analysis to Inform Policy in Developing Countries: A General Framework with Applications for Education. University Of Chicago Press., Chapter 8 in Education Policy in Developing Countries, edited by Paul Glewwe.

21
high income inequality, leading to the low intergenerational social mobility shown in Section II.5. It should not come as a surprise, then, that we find a significant correlation among educational and income Gini’s in the region.

**<u>Figure 10: Educational Gini in LAC countries vs. others</u>**

<!-- Start of picture text --> Panel A. Gini coefficient of years of education<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>LAC Africa Asia Eastern Developed<br>Europe countries<br>Panel B. Years of education: rich vs. poor<br>Argentina<br>45%<br>40% 30% poorest<br>35% 30% richest<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>years of education<br>Panel C: educational and income gini correlation<br>Brazil Bolivia<br>60 Paraguay Colombia<br>58 Chile Panamá<br>Honduras Guatemala<br>56<br>54 El Salvador<br>Argentina Peru<br>52<br>Mexico<br>50 Costa Rica Venezuela<br>48<br>46 Uruguay Nicaragua<br>Jamaica<br>44<br>42<br>40<br>10 20 30 40 50 60<br>Education gini<br>0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18+<br>Income gini<br><!-- End of picture text --> Source: Panel A. De Ferranti et al. (2004). Panel B, Perry et al. (2006).

15
AL fell and REE increased over this period controling for demographic composition. The analysis also uncovers evidence of household scale economies in TEE. Households with more members, but similar demographic composition and per capita expenditures, have a lower AL that is only partly offset by higher REE. Although household scale economies contribute little to the main findings on population TEE, this supports the conjecture in (Deaton and Paxson 1998) that “caloric overheads” (fixed costs of common household activities in developing countries, such as travel for work, buying/selling goods, or collection of water or fuel) could explain the common empirical finding that larger households have lower per capita food expenditures holding constant demographic composition and per capita expenditures.

Second, the estimates imply that TEE fell by over 100 calories per day over this period for individuals aged 15 or older, which on its own would be enough to account for most of the decline in caloric intake in NSS data during this period. However, over the 1983-2012 period, India underwent substantial demographic change, with the share of the population aged 14 and under falling from 40% to 30%. Children have much (about 40%) lower TEE than adults, due to both lower REE (due to smaller size) and lower AL (particularly in rural areas where primary/middle-school attendance appears much less physically active than adult activities). Thus even though TEE is falling for both adults and (to a lesser extent) children throughout this period, demographic change at the population level is more than enough to offset this “within” effect, except after 2005 when activity levels fell particularly quickly.

The findings of flat population TEE and modest gains in nutritional status over time are discussed in light of the potential contribution of measurement error and changes in the disease environment to the (Deaton and Dr`eze 2009) puzzle of declining measured caloric intake . (Smith 2015) argues that the NSS estimates of caloric intake may be biased down, especially in recent years, due to rising consumption of food away from home.<sup>2</sup> Evidence on the magnitude of recall bias identified using different survey recall periods ((National Sample Survey Organization 2000; NSSO 2013)) and the sensitivity of estimated caloric intake to assumptions about the caloric value of food expenditures is reviewed. The findings are also discussed in the context of (Duh and Spears 2017), who test one of the potential mechanisms dis-

> 2See also (Fiedler and Yadav 2017) for evidence from the 2011-2012 NSS survey and (Smith et al. 2014) for a general review of the reliability of food data collected in consumption and expenditure surveys.

3
A firm as defined here may have many factories/facilities in the same or different locations. Each facility may have various pollution endpoints such as stacks for air pollution or discharge points for water pollution. Although facilities are supposed to be inspected at least once every six months or a year, because of the limited capacity of the OSPCB, there is an element of randomness to how many times a facility gets inspected during the five-year period for which data were obtained. It is not obvious if the inspectors go more frequently to the facilities that do not usually comply or to the most polluting facilities or any other metric such as availability of an inspector in the regional office that determines the frequency of inspections. In addition to the inspected data, the firms are also required to submit self-monitored data. For example, the Air Act requires facilities to monitor pollutants such as sulfur dioxide, carbon monoxide, nitrogen oxides, particulates, and metals. Under the Water Act, facilities are required to monitor parameters such as total coliform, turbidity, pH, color, odor, taste, and pollutants such as phenolic compounds, pesticides, metals, fluoride, nitrate, chlorides, and sulfate. The monitoring frequency and methodology vary for each industry and type of pollutant. But the facilities are generally required to maintain records and report the information periodically to the pollution control board.

The following data were recorded from the OSPCB database (i) company id; (ii) monitoring point id; (iii) year started (age); (iv) industry; (v) products; (vi) company name; (vii) location name; (viii) sample date; (ix) inspection/self-reported; (x) environment category (air/water); (xi) indicator monitored (PM 2.5, Sox etc.); (xii) data value; (xiii) standard; (xiv) minimum; (xv) maximum; (xvi) monitoring frequency; (xvii) compliance ratio; (xviii) compliance status; (xix) district; (xx) employment; (xxi) output capacity; (xxii) output capacity units; and (xxiii) ownership (public/private). The summary statistics are given below.

While the SPCB’s mostly rely on inspected data, increasingly, self-monitoring, recordkeeping and reporting are being recognized by the Central Pollution Control Board (CPCB) as providing essential data to supplement and support inspections. This approach is well established and accepted in addressing environmental compliance and enforcement problems in many countries, such as the United Kingdom, Germany, the United States, Japan, Thailand, and others. In the United States, data gathered through self-monitoring are relied upon extensively to determine compliance and take enforcement actions. To ensure the integrity of the data, EPA requires facilities to self-certify their compliance annually and makes all 8
INTmOOUCfnON Lxxxi

|Infant**nortsllty,**1965-90|**Age-sex**structure, 1985|Most recent<br>contraceptive prevalence|Country,<br>economy, or<br>territory|
|---|---|---|---|
|ESC%A Stat.YearbookforAsia<br>**and**the Pacific<br>**196**|Basedon 1986 census|Pop. Ref. Bureau 1990|WesternSamoa|
|U.N. 1990 revision|U.N. 1988 revision||_Yeen,_**People's**<br>**Dem.**Rap.of|
|**Based**onSankmission report 19|89 Bank est.|WFS 1979|Yemen Arab Rep.|
|UkPVSA 10/ U (official<br>est.)|U.N.1988 revision|U.N. 1988|YugosLavia|
|U.N. 1988 revIsion<br>U.N. 1990 revision|U.N. 1988revision<br>U.N. 1988 revision|IRD/Westinghouse<br>1984<br>Rosset at.<br>1988|Zaire<br>Zambia|
|DNS**196**|Bank est.|DHS 1988|Ziwbabwe|
|CBR<br>Crude birthrate||||
|CDR<br>Crude death rate||||
|DHS<br>Demographic**and**Hea<br>INR<br>Infant mortality ra|lth Survey<br>te|||
|TFR<br>Total fertilityrate||||
|UNOY<br>UnitedNations,<br>Dem<br>UhPVSR<br>UnitAd Nations, PoE<br>USBOC<br>U.S.Bureau of the<br>WFS<br>World Fertility Sur|ographic Yearbook (year is given<br>utstion and Vital Statistics Rec<br> Censw (1985;Jamison,Johnson,<br>vey|in the table)<br>ort (month and yeararegiven in table)<br>andEngels,1987)||
_Annex 4 Table 1: Remoteness and food prices reported by households (in natural logs)_

||Egg Price|Rice Price|Sugar Price|
|---|---|---|---|
|Program village|-0.076|-0.017|-0.033|
||(0.055)|(0.025)|(0.045)|
|Above median saturation|-0.092|0.008|-0.053<br>|
||(0.071)|(0.033)|(0.059)|
|Above Median Distance|-0.172*|-0.068|-0.091|
|to Nearest Market|(0.093)|(0.043)|(0.077)|
|Treated*Saturation|0.195**|0.025|0.063|
||(0.092)|(0.042)|(0.076)|
|Treated*Above Median|-0.053|0.038|0.032|
|Travel Time to Nearest Market|(0.099)|(0.045)|(0.082)|
|Saturation*|0.307**|0.089|0.165|
|Above Median Travel Time to Nearest Mkt|(0.133)|(0.061)|(0.110)|
|Treatment* Saturation *|0.004|-0.047|-0.067|
|Above Med. Travel Time to Nearest Market|(0.150)|(0.069)|(0.124)|
|Program village|0.014|-0.004|0.001|
||(0.020)|(0.009)|(0.017)|
|Fourth quartile of saturation|-0.037|0.021|-0.017|
||(0.060)|(0.027)|(0.050)|
|Above Median Distance|0.013|-0.014|0.008|
|to Nearest Market|(0.024)|(0.011)|(0.020)|
|Treated*fourth quartile of saturation|0.103**|0.006|0.008|
||(0.042)|(0.019)|(0.034)|
|Treated*Above Median|-0.017|0.014|0.003|
|Travel Time to Nearest Market|(0.031)|(0.014)|(0.025)|
|Fourth quartile of saturation*|0.087***|0.020|0.038|
|Above Median Travel Time to Nearest Mkt|(0.034)|(0.015)|(0.028)|
|Treatment*Fourth Quartile of Saturation *|-0.101*|-0.020|-0.036|
|Above Med. Travel Time to Nearest Market|(0.058)|(0.026)|(0.048)|
|Control Observations|65|65|65|
|Treated Observations|65|65|65|
|Control Mean|6.015|33.392|38.977|
|*** p<0.01, ** p<0.05, * p<0.1||||

Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey 83
and also a decrease in productivity due to out-migration. In areas around directly affected market/city _K_ , the autarkic region expands leading to an increase in manufacturing in areas which are farther away from _K_<sup>0</sup> and were specialized in agriculture before the trade cost shock; the home-based manufacturing displacing specialized agriculture in this case. It is clear from equation (5), that an increase in transport cost shifts the SMS inward as well as leading to a decrease in manufacturing employment in areas closer to _K_<sup>0</sup> . The overall impact on the employment shares for the entire area surround city _K_ is thus ambiguous.

The movement of population and changes in manufacturing share of employment affect the proportion of built-up areas in both the routes; the built-up density increases around _J_ and decreases around _K_ (proposition 3).

The propositions 1-3 are based on the assumption of no population growth. With population growth, proposition 3 would need to be modified. With positive population growth, the proportion of built-up areas may not decrease around _K_ but the increase around _J_ would outpace that around _K_ . For tractability, the model considers only two economic centers ( _K, J_ ) _._ In the empirical estimation elaborated later, we utilize a measure of market access proposed by Redding and Turner (2015) that takes account of access to all possible markets.

# **(5) Data**

To estimate the impacts of the trade shock caused by the civil war in Cˆote d’Voire on Mali and Burkina Faso, we utilize population census and satellite data sources. To detect sunk investment, we rely on built-up density from global human settlement data, and the employment data come from population censuses. Agglomeration is measured by concentration of skilled (higher educated) labor, and the change in provision of electricity is captured by average night-light luminosity.

Population censuses are available for 1996 and 2006 for Burkina Faso and for 1998 and 2008 for Mali. The first period in the population censuses corresponds to the pre-CIV crisis period and the last year right at the end of the war. The census data come with digital 16
National Institute of Public Cooperation and Child Development (NIPCCD).1992. _National Evaluation of Integrated Child Development Services_ , New Delhi: NIPCCD.

- Nawani, N.P.. 1994. Indian Experience on Household Food and Nutrition Security, paper prepared for the Regional Expert Consulation, FAO-UN, Bangkok Thailand, August 1994. (http://www.fao.org/DOCREP/x0172e/x0172e00.htm)

- Parikh, Kirit and R.Radhakrishna (eds.). 2002. _India Development Report 2002_ , New Delhi: Oxford University Press.

- Parliament of India, Rajya Sabha. 2003. Department-related Parliamentary Standing Committee on Human Resource Development 104<sup>th</sup> Report (April 2003). <u>(http://parliamentofindia.nic.in/rs/book2/reports/HRD/Report140th.htm)</u>

- Radhakrishna, R. 2002. “Food and nutrition security”, in Kirit S. Parikh and R. Radhakrishna (eds.) _India Development Report 2002_ , Oxford University Press.

- Radhakrishna, R. and K. Subbarao.1997. India’s Public Distribution System : a national and international perspective. Washington DC: The World Bank Discussion Paper no. 380.

- Rosenbaum, P., and D., Rubin, (1983) “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” _Biometrika_ , Vol. 70: 41-55

- Rogers, B.L. and J.Coates. 2001. Food Safety Net Programs and Related Nutrition Intervention Annotated Bibliography (http://www.worldbank.org/wbi/socialprotection/southasia/pdf/readings/day1/rogersbiblio.pd

- <u>f)</u>

- Roy, S.K. 1997. Complementary Feeding in South Asia, in Stuart Gillespie (ed.).1997. Malnutrition in South Asia : a regional profile, Kathmandu: UNICEF Regional Office for South Asia.

- Rubin, D., (1973) “The Use of Matched Sampling and Regression Adjustment to Remove Bias in Observational Studies.” _Biometrics_ Vol. 29: 159–183

- Scrimshaw, Nevin S. (ed.). 1995. Community-based Longitudinal Nutrition and Health Studies: Classical Examples from Guatemala, Haiti and Mexico, Boston, MA USA: International Foundation for Developing Countries (INFDC).

(http://www.unu.edu/unupress/food2/UIN09E/uin09e00.htm#Contents)

- Shrimpton R, CG Victora, M de Onis, R Costa Lima, M Blössner, and G Clugston. 2001.Worldwide timing of growth faltering: implications for nutritional interventions. _Pediatrics_ 107(5).

- Whang, In-Joun, 1981: _Management of Rural Change in Korea_ , Seoul: Seoul National University Press.

- World Bank. 1998. Project Appraisal Document... for a Woman and Child Development Project, Washington DC: The World Bank, South Asia Region, Health, Nutrition and Population Sector Unit.

18
**Figure 3. Direct Expenditure Effect: Direct Effect of Tobacco Taxes**

# **b. Medical expenses**

The yearly medical costs associated with tobacco consumption are estimated, assuming a direct medical impact on health. Although this assumption is unrealistic in the short run, the long-run reduction of tobacco consumption would tend to behave according to this pattern, whereby a reduction in tobacco consumption would be strongly related to a reduction in tobacco-related diseases and thus a reduction in medical costs (table 10). Health care expenditures are estimated using equation (5) and tables 3 and 8.

**Table 10 – Reduction in Medical Costs (%)**

|_Price shock scenario_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|
|---|---|---|---|---|---|---|---|---|---|---|
|Lower-bound elasticity|0.43|0.30|0.24|0.16|0.13|0.10|0.09|0.08|0.04|0.02|
|Medium elasticity|0.64|0.50|0.40|0.28|0.24|0.19|0.18|0.16|0.10|0.06|
|Upper-bound elasticity|0.86|0.70|0.55|0.41|0.35|0.28|0.26|0.24|0.16|0.10|

_Source:_ Proportion of household budget. Based on data of the 2013 household budget survey. _Note:_ The table shows the share of total household budget for each decile.

The reduction in tobacco consumption would have a positive effect on income through reduced medical treatments. The saving in expenditure would vary between 0.43 and 0.02 percentage points of the household income in the case of the lower-bound elasticity assumption, between 0.64 and 0.06 percentage points in the case of the medium-bound elasticity, and between 0.86 and 0.1 percentage points in the case of the upperbound elasticity (figure 4). These results show the importance of the elasticity assumptions; they also stress the relevance of the possible elasticity variations across income groups.

15
household, as compared to, for example, 20 percent in the case of the PROGRESA conditional cash transfer program in Mexico (Fiszbein and Schady 2009; Schultz 2004). The median age of CSP scholarship applicants was 14 at the time they completed the application survey; the 10<sup>th</sup> percentile of the distribution is 12, and the 90<sup>th</sup> percentile is 16.

# B. <u>Data</u>

We make use of three sources of data in this paper. We have access to the composite dropout-risk score, as well as the individual characteristics that make up the score for all 26,537 scholarship applicants. In addition, we collected both household and school-based data. The household data come from a household survey of 3225 randomly selected applicants and their families in five provinces: Battambang, Kampong Thom, Kratie, Prey Veng, and Takeo.<sup>5</sup> The household survey was collected between October and December of 2006, approximately 18 months after children filled out the application forms. For applicants who are enrolled in school and who have not repeated grades, the household survey therefore refers to the first half of 8<sup>th</sup> grade.

We also conducted four unannounced visits to the 100 CSP schools (in February, April, and June 2006, and in June 2007). The main objective of these visits was to physically verify school attendance (among CSP recipients and non-recipients). However, during one of the school visits (in June 2006) all students were also asked to complete a mathematics test—so this test refers to the end of 7<sup>th</sup> grade. Note that a simple comparison of the scores on the test taken _in school_ for CSP recipients and non-recipients is unlikely to provide a satisfactory estimate of the impact of the scholarship program on learning. This is because the impacts of the program on school enrollment will introduce biases associated with selection. We discuss this in more detail below.

> 5 The sample was based on randomly selected schools in these five provinces. The survey was limited to applicants ranked no more than 35 places above the cutoff in these schools.  This restriction was imposed to maximize the number of schools, while maintaining the density of observations “around” the cut-off—an important consideration when estimating program effects based on regression-discontinuity, as discussed below.

8
7 sovereign debt crisis in Europe in fall 2011 (figure 1). While the crisis hit hardest the developed countries and members of the European Union (EU) that were at the epicenter of the global financial turmoil and sovereign debt crisis, the weakening of aggregate demand spilled over to developing and emerging countries through economic, trade, and financial linkages, resulting in a sharp turn in economic growth since 2010. It has affected particularly the countries in emerging Europe and Central Asia, Latin America and the Caribbean, and Middle East and North Africa.

**Figure 1. Average GDP growth by regions and income level**

<!-- Start of picture text --> 10 % 10 %<br>8<br>8<br>6<br>6<br>4<br>4<br>2<br>0 2<br>-2<br>0<br>-4<br>-2<br>-6<br>-8 -4<br>-10 -6<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br>Europe EAP LAC LIC LMIC UMIC<br>MENA NA SA<br> HIC all countries<br>SSA all countries<br><!-- End of picture text --> _Source_ : Authors’ computations based on World Bank’s World Development Indicators database.

Depressed aggregate demand and weak economic activity have severely weakened labor market conditions around the world. About 28 million people have lost jobs since 2007, bringing the pool of globally unemployed to an estimated 197 million in 2012 (figure 2), as deteriorating macroeconomic and financial conditions have dampened economic prospects and opportunities for job creation worldwide. The rise in unemployment rates has been most severe in high- and upper-middle income countries that are either at the epicenter of the crisis (North America and western Europe) or are linked to them (Central and South-Eastern Europe, Latin America and the Caribbean, and advanced countries in Asia) (figure 3). The rate in developed countries rose on average from 5.8 percent from 2007 to an estimated 8.6 percent in 2012, reaching 12.1 percent on average for the Euro Area countries in mid-2013. A wide variation remains within the region, however, ranging from 5 percent in Austria, Germany, and Luxembourg to over 26 percent in Greece and Spain. Employment conditions deteriorated sharply in emerging Europe during the crisis but improved somewhat after 2009, while deteriorating in the Middle East and North Africa.

The youth have been particularly hit hard by the crisis, with an estimated 74 million of young people out of jobs in 2012 around the world. The jump in the youth unemployment rate has been most pronounced again in advanced economies (figure 4), rising from 12.5 percent in 2007 to an estimated average of 17.9 percent in 2012. The rate has reached alarming levels in the peripheral Euro Area countries, to 59 percent in Greece and 56 percent in Spain, compared with the average rate for the Euro Area at 24 percent in mid-2013 and 7.5 percent in Germany. The increase in the youth unemployment rate has also been high in the Middle East and North Africa, rising by