rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 5.2 Percent Change in Mortality Due to Acute Exposure**<sup>**to**</sup> **PM10**

Of all the toxic effects attributed to PM10, death has been the most<sup>thoroughlydocumented.</sup> Death due to the acute effects of air pollution occurs generally between<sup>oneandfivedays</sup> after the hazardous exposure. Since the 1950s studies have recorded increased<sup>mortality</sup> associated with high levels of pollution. This analysis includes the major<sup>studiescarriedout</sup> in the Americas, Europe, Australia and Asia since 1970.

The figure below shows the percent change in general mortality associated with<sup>anin-</sup> crease in air pollution. The percent change, considering all the cases, establishes<sup>anin-</sup> crease in mortality of between 0.06 and 2.82 percent. These data<sup>arefor total,nonaccidental</sup> deaths.

<!-- Start of picture text --> 6<br>5-<br>4<br>3 ~~~~~~~T<br>2 T T~~~~~~~~~~~~<br>E + T<br>-2<br>0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40<br>Author<br><!-- End of picture text --> Note: Percent change in general, nonaccidental mortality for<sup>each10pg/iM3increasein PM</sup> 1<sup>o. The</sup> numbers represent the following studies: 1. Anderson and others 1996 (London),<sup>2. Ballesterand oth-</sup> ers 1996 (Valencia), 3. Borja-Aburto and others 1997 (Mexico), 4. Bremner and others<sup>1999(London),</sup> 5. Dockery and others 1992 (St. Louis), 6. Dockery and others 1992 (Tennessee), 7.<sup>Gambleand</sup> Lewis 1996 (Chicago), 8. Gamble and Lewis 1996 (Utah), 9. Ito and Thurston<sup>1996(Chicago),10.Kel-</sup> sall and others 1997 (Philadelphia), 11. Kinney, Ito, and Thurston 1995 (Los<sup>Angeles),12.Leeand</sup> Schwartz 1999 (Seoul), 13. Mazumdar and Sussman 1983 (Pittsburgh),<sup>14.Moolgavkarand Luebeck</sup> 1996 (Ohio), 15. Moolgavkar and others 1996 (Philadelphia), 16. Neas, Schwartz,<sup>and Dockery1999</sup> (Philadelphia), 17. Ostro 1995 (California), 18. Ostro and others 1996 (Santiago),<sup>19.</sup> Pope<sup>andKalk-</sup> stein 1996 (Utah), 20. Pope III 1999 (Ogdem), 21. Pope, Hill, and Villegas 1999<sup>(Provo),22.Pope,Hill,</sup> and Villegas 1999 (Utah), 23. Samet and others 1998 (Philadelphia), 24. Schwartz<sup>1994c(Cincinnati),</sup> 25. Schwartz and Dockery 1992a (Philadelphia), 26. Schwartz and Dockery 1992b (Steubenvile),<sup>27.</sup> Schwartz 1993 (Birmingham), 28. Schwartz 1994b (Detroit), 29. Schwartz 1994c (Ohio), 30. Simpson and others 1997 (Brisbane), 31. Spix and others 1993 (Erfurt), 32. Sunyer and others 1996<sup>(Barce-</sup> lona), 33. Touloumi and others 1996 (Athens), 34. Touloumi, Samoli, and Katsouyanni 1996<sup>(Athens),</sup> 35 Verhoeff and others 1996 (Amsterdam), 36. Wordley, Walters, and Ayres 1997 (Birmingham),<sup>37.</sup> Zmirou and others 1996 (Lyon), 38. Castillejos and others 2000 (M6xico),<sup>39.Cropperand others1997</sup> (Delhi), 40. Pooled estimate.

<!-- Start of picture text --> Pooled Estimate of the Effect of PM10 in Total Mortality<br>Mortality Mean C195%<br>Total 1.01 0.83,1.19<br><!-- End of picture text --> _25_
Worldwide, one billion people practice open defecation and an estimated 2.5 billion people do not have improved sanitation facilities, such as flush toilets (UNICEF/WHO, 2014a).<sup>1</sup> The limited availability and use of such facilities, the pervasiveness of fecal material, and inconsistent handwashing all contribute to the transmission of infections with severe negative consequences for child health and educational attainment.<sup>2</sup> India faces particularly daunting sanitation challenges, with 597 million individuals practicing open defection and 450,000 deaths attributed to diarrheal illness per year (UNICEF/WHO, 2014b).

In response to this sanitation problem, India has expended substantial resources to subsidize latrine construction.<sup>3</sup> Government construction of latrines has been central to improvements in sanitation practices, and especially reducing the costs of latrines for poorer households (Gertler et al., 2015; Guiteras, Levinsohn and Mobarak, 2015). There has been mixed evidence on health improvements following Indian sanitation initiatives (Patil et al., 2013, 2014; Spears, 2014), however, which has given rise to an argument that deeply rooted cultural beliefs may limit improvements in sanitation practices.

Recent attention has been drawn to substantial differences in latrine use between Hindus and Muslims in India (BBC, 2012, 2014; The Economist, 2014). Geruso and Spears (2018) attribute a lower infant mortality rate for Muslims, relative to Hindus, to a higher rate of latrine use among Muslims that they conclude is due to differences in religious beliefs. Hindus are more likely to live in rural areas with less access to sanitation infrastructure, however, and so lower rates of latrine use and higher rates of open defecation may reflect differences in household locations rather than religion itself.

> 1“Improved sanitation facilities” refers to facilities that reliably separate human fecal material from human contact, and may include: flush toilets, piped sewer systems, septic tanks, ventilated improved pit latrines, or composting toilets (UNICEF/WHO, 2014a).

> 2See Nandy et al. (2005), Chambers and Von Medeazza (2013), Hueso and Bell (2013), UNICEF/WHO (2014b), and Adukia (2017).

> 3In the 1980s, the Central Rural Sanitation Program (CRSP) was directed towards improving access to sanitation facilities and associated changes in sanitation behaviors. These policy efforts evolved into India’s Total Sanitation Campaign (TSC), implemented in 1999, which included the building of latrines in poorer households (WSP, 2011; Spears, 2014). This Total Sanitation Campaign was absorbed into the broader Swachh Bharat Mission Program, which emphasized an educational component intended to drive behavioral changes in latrine use, handwashing, and other sanitation practices (AFC, 2005).

1
# **Village Political Economy, Land Tenure Insecurity, and the Rural to Urban Migration Decision: Evidence from China**<sup>*</sup>

John Giles Development Research Group The World Bank Ren Mu Texas A&M University JEL Codes: O12, O15, J61, Q15, R23 Key Words: Migration, Land Tenure, Property Rights, China, Village Political Economy

*Our work has benefitted from comments and discussions related to an earlier and very different paper (Giles, 2001) with Dwayne Benjamin, Loren Brandt, Andrew Foster, Steven Haider, Kaivan Munshi, Scott Rozelle and Duncan Thomas. This paper has improved with suggestions of Klaus Deininger, James Heckman, Chang‐Tai Hsieh, Hongbin Li, Hidehiko Ichimura, Ghazala Mansuri, Albert Park, Nancy Qian, Scott Rozelle, John Strauss, Dali Yang and Xiaobo Zhang and participants in the 2011 NEUDC Conference at Yale University, the 2012 PAA Meetings in San Francisco, the 2012 China Economics Summer Institute, the 2012 UChicago‐Renmin Symposium on Family and Labor Economics, and the 2013 ASSA Meetings. The authors gratefully acknowledge financial support from the US National Science Foundation for a survey collecting supplemental household and village data used for this study (SES‐0214702, "Weak Property Rights and the Emergence of Labor Markets in Rural China”), and support for this paper from the Knowledge for Change Program at the World Bank.
have had a discernible effect on the price signals received by farmers, on their supply response, and ultimately on their well-being. We now confront this task in the two related ways mentioned in the introduction, looking for evidence from the household surveys in the first part of Section 3, and turning to simulations in the second part.

# **3. Assessing the effect of reforms**

## **3.1 Prima-facie evidence from household surveys**

We return here to the EPM to assess the evolution of various indicators of rural poverty and income inequality before and after the reforms. Because the small sample of vanilla farmers limits the extent of statistical analysis, annex A1 reports several descriptive statistics on the larger sample of close to 10,000 agricultural households giving background on the characteristics and evolution of poverty and inequality in rural areas. The annex also shows various decompositions attempting to measure the impact of price changes since 1993 for this larger sample. Essentially they show that farmers at the top of the distribution responded better to price shocks. The decompositions also show that exporters faced better price incentives than other farmers over the period and that Madagascar imported strong price volatility during 1999-01.

### **3.1.1. Prices, output and intermediation margins**

Before talking about “the” producer price, it is worth noting that the prices reported by farmers in the EPM show substantial variation across households (standard error over twice the mean). Such variation can come from (i) measurement errors, (ii) individual characteristics such as quality differences in the vanilla produced or the bargaining ability of farmers, or (iii) local rather than individual characteristics such as transport costs or the bargaining power of the intermediary. In order to explore these conjectures we ran OLS regressions of individually-reported unit values on farmer characteristics and dummy variables for small geographical entities (“fivondroanana”).<sup>17</sup> 17 OLS results are likely to have a selection bias since the price received by farmers may be affected by omitted variables (such as the quality of their land) that also affect their decision to be in vanilla production. In order to control for this we also ran a selection model in which the selection equation was identified by the geographical variables but the results were not different (which is of course not surprising since none of the individual characteristics were significant in 21
India Shining _and_ Bharat Drowning: Comparing Two Indian States to the Worldwide Distribution in Mathematics Achievement<sup>_∗_</sup> Jishnu Das Tristan Zajonc Center for Policy Research, New Delhi Harvard University World Bank, Washington DC _“It has long been claimed that everything one can say about India is true—and so is the opposite.” -Devesh Kapur, 2005_

# **1 Introduction**

Net primary enrollment in India has risen steadily over the last several decades and now exceeds 90 percent in most of the country. Large planned increases in the government education budget suggest renewed interest and action on the part of the state, with an emphasis on secondary schooling. Not surprisingly, increasing enrollments and resources have shifted the debate from how many children are in school to what they are learning. A consensus is building that getting children into schools may not be enough. Filmer et al. (2006) go so far as to propose augmenting the Millennium Development Goals with a Millennium Learning Goal that provides international benchmarks on how much children know at a pre-specified age. We ask the following question: Is there a way to place Indian children in secondary schools on an international scale (given India’s

> _∗_ We thank Lant Pritchett for extensive discussions of the paper. Kin Bing Wu who led a World Bank sector study on secondary education in India designed the collection of the data we use here, and we are grateful to her for making the data and her report available to us. Eric Hanushek and Eugene Gonzalez provided invaluable comments on an early version of this paper and their insights have been critical for the current revision. Michelle Riboud and Sam Carlson provided useful comments that pertain to India’s education sector. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Working papers describe research in progress by the authors and are published to elicit comments and to further debate.

1
# **5. References**

Aghion, P. and P. Howitt. 2005. “Appropriate Growth Policy, a Unifying Framework.” The 2005 Joseph Schumpeter Lecture, presented to the 20th Annual Congress of the European Economic Association. Amsterdam, August 25, 2005.

Bhagwati, J. 1968. “The Theory and Practice of Commercial Policy: Departures from Unified Exchange Rates.” _Special Papers in International Economics_ . New Jersey: Princeton University Press.

Blundell, R., R. Griffith, and F. Windmeijer. 2002. “Individual Effects and Dynamics in Count Data Models.” _Journal of Econometrics_ 108: 113-31.

Cadot, Olivier, Celine Carrere, and Vanessa Strauss-Kahn. 2010. “Export Diversification: What’s Behind the Hump?” _The Review of Economics and Statistics_ , forthcoming.

Djankov, Simeoun, Caroline Freund, and Cong Pham. 2010. “Trading on Time.” _The Review of Economics and Statistics_ , forthcoming.

Ganslandt, M, and J.R. Markusen. 2000. “Standards and Related Regulations in International Trade: A Modeling Approach.” In _Quantifying the Impact of Technical Barriers to Trade: Can It Be Done?_ , pp. 95-135. Studies in International Economics, Ann Arbor: University of Michigan Press.

Hausmann, R. and D. Rodrik. 2003a. “Economic Development as Self-Discovery.” _Journal of Development Economics_ 72: 603-633.

Hausmann, R. and D. Rodrik. 2003b. “Discovering El Salvador’s Production Potential.” Mimeographed. Kennedy School of Government, Harvard University, Cambridge, Massachusetts.

Hummels, David, and Peter J. Klenow. 2003. “The Variety and Quality of a Nation’s Exports.” _American Economic Review_ 95(3): 704-723.

Ibeh, Kevin I.N. 2003. “Toward a Contingency Framework of Export Entrepreneurship: Conceptualizations and Empirical Evidence.” _Small Business Economics_ . 20: 49-68.

Imbs, J. and Wacziarg, R. 2003. “Stages of Diversification.” _American Economic Review_ 93(1): 63-86.

Klinger, Bailey, and Daniel Lederman. 2004. “Discovery and Development: An Empirical Exploration of ‘New’ Products.” World Bank Policy Research Working Paper 3450, The World Bank, Washington, DC.

Leamer, Edward E. 1984. _Sources of Comparative Advantage: Theory and Evidence_ . Cambridge MA: The MIT Press.

23
costs, or better infrastructure, of between 37 to 54 percent. In contrast, members and non-members allocate approximately the same share of land to tobacco and report similar unit values.

The paper is organized as follows. In section 2, we describe the tobacco clubs. Based on data from our Tobacco Clubs Survey (TCS), we list the basic set of rules and regulations that build the constitutions of the tobacco clubs and we assess the services provided to fellow members. Also, using data from the Malawi Integrated Household Survey (IHS), we provide prima facie evidence of the differences in farming outcomes across members and non-members. The empirical strategy and the main regression results are discussed in section 3. Section 4 concludes.

# **2 Tobacco Clubs in Malawi**

In an attempt to reduce poverty and foster smallholder tobacco production, the Government of Malawi started in the early 1990s a gradual process of liberalization of the tobacco sector, its main export. The reform of such a complex sector, where farmers, estates and exporters interact along various layers of production and processing, comprised several key steps.<sup>7</sup> For our purposes, the major feature of the reforms was the creation of “burley tobacco clubs,” groups of 10-30 smallholder that gathered together to grow tobacco and jointly perform critical farming activities.<sup>8</sup> The initiative was a success. According to the Tobacco Control Commission (TCC), the number of clubs grew to 23,843 in the 2006/2007 season from a few thousand in the early 1990s. In turn, smallholder production of tobacco grew hand in hand with the spread of burley clubs. Figure 1 shows that both the number of clubs and the volume of smallholder burley production experienced a steady growth after market liberalization in 1992. Instead,

> 7One was the amendment of the Special Crop Act that precluded participation of small farmers by allowing cultivation of tobacco only on leasehold and freehold land, and the allocation of an increasing share of the national burley quota to smallholder farmers. Another was the introduction of a program of intermediate buyers to facilitate the logistics of bringing the smallholder’s output to the auction floors.

> 8At the same time, groups of around 160 clubs gathered in different associations and formed the National Smallholder Association of Malawi (NASFAM). Another important medium-scale institution is TAMA, the Tobacco Association of Malawi.

4
<!-- Start of picture text --> Author(s) Year = Intervention<br>A. Teachers |<br>Muralidharan and Sundararaman 2070 Feedback to teachers ~~<br>Muralidharan and Sundararaman 2013b Contract teachers +><br>Muralidharan and Sundararaman 2011 Group bonuses ~~<br>Duflo, Hanna and Ryan 2012 Attendance verification<br>Banerjee et al. 2007 Balsakhi (year 1) ><br>Muralidharan and Sundararaman 2011 — Individual bonuses —~<br>Banerjee et al 2007 Balsakhi (year 2) —<br>Lakshminarayana et al 2013 Remedial teaching etc. —~<br>Subtotal o<br>B. Schools<br>Linden 2008 Computer-assisted learning 1 ——F—_“=<br>Borkum, He and Linden 2013 Libraries ~~<br>Rao 2014 ~~ Mixing students -<br>He, Linden and MacLeod 2008 English curriculum 1 —<br>Das et al, 2013 School grants aa<br>Sarr et al. 2010 Grants to schools ———<br>Linden 2008 Computer-assisted learning 2<br>He, Linden and MacLeod 2008 = English curriculum 3 —_———<br>He, Linden and MacLeod 2008 = English curriculum 2 ==<br>He, Linden and MacLeod 2008 = English curriculum 4 —<br>Banerjee et al 2007 Computer-assisted learning —<br>Barrera-Csorio et al, 2013 New private schools ———<br>Subtotal o<br>G. Househokis<br>Muralidharan and Sundararaman 2013a School-choice program ——<br>Banerji, Berry and Shotand 2013 Classes for mothers +<br>Banerji, Berry and Shotland 2013 = Training for mothers *<br>Banerji, Berry and Shotland 2013 Classes and training for mothers fd<br>Subtotal ()<br>DB. Community |<br>‘Chaudhury and Parajuli 2010b School-based management (grade 3) —_— OO<br>Aturupane et al 2011 Report cards —_<br>Andrabi, Das and Khwaja 2013 Report cards ~_<br>Chaudhury and Parajuli 2010b School-based management (grade 5) oo}<br>Aturupane et al, 2011 School-based management —-<br>Subtotal o<br>-2 1 0 1 2<br>Intervention reduces math test scores Intervention increases math test scores<br><!-- End of picture text -->
# **T AXATION AND C APITAL S TRUCTURE: E VIDENCE FROM A T RANSITION E CONOMY**

## LEORA KLAPPER * KONSTANTINOS TZIOUMIS

_Development Research Group The World Bank 1818 H Street, NW Washington, DC 20433, USA Email:lklapper@worldbank.org_ _Hellenic Observatory European Institute London School of Economics London, WC2A 2AE, UK Email: k.tzioumis@lse.ac.uk_ _Keywords_ : Capital structure, Taxation, Transition Economies _JEL classification_ : G32, G38, K34

> * Corresponding author.  The authors thank Thorstern Beck, Jennifer Blouin, Valentina Bruno, Stijn Claessens, Asli Demirguc-Kunt, Christian Leuz, Sanja Madzarevic-Sujster, and Vassilis Monastiriotis for helpful comments. The opinions expressed do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.
**Table A.5: Heterogeneity Across Urban vs. Rural and Sons vs. Daughters**

|Mbili CEF|**CHI**|**NA **|**IND **|**IA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|---|
|oty<br>Specification|Linear|Quadratic|Linear|Quadratic|Linear|Quadratic|
|OutcomeVar|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|Res. Sq.|
||||**Ur**|**ban**|||
|Father’s Edu|-0.548***<br>(0.053)|-0.546***<br>(0.054)|-0.772***<br>(0.034)|-0.773***<br>(0.033)|-0.707***<br>(0.065)|-0.707***<br>(0.065)|
|Constant|17.449***<br>(0.586)|17.436***<br>(0.593)|21.478***<br>(0.250)|21.452***<br>(0.251)|16.935***<br>(0.598)|16.930***<br>(0.599)|
|Observations|35,308|35,308|31,216|31,216|5919|5919|
|R-squared|0.013|0.013|0.029|0.029|0.027|0.027|
|Mean ofY|14.66|14.66|17.44|17.41|11.475|13.568|
||||**Ru**|**ral**|||
|Father’s Edu|-0.524***<br>(0.044)|-0.528***<br>(0.044)|-0.022<br>(0.040)|-0.023<br>(0.039)|-0.294***<br>(0.069)|-0.294***<br>(0.069)|
|Constant|17.799***<br>(0.342)|17.810***<br>(0.339)|18.702***<br>(0.177)|18.660***<br>(0.178)|15.967***<br>(0.516)|15.931***<br>(0.515)|
|Observations|58,851|58,851|55,532|55,532|12437|12437|
|R-squared|<br>0.013|<br>0.013|<br>0.000|<br>0.000|0.003|0.003|
|Mean ofY|15.94|15.94|18.64|18.60|14.351|14.314|
||||**So**|**ns**|||
|Father’s Edu|-0.476***<br>(0.048)|-0.478***<br>(0.049)|-0.927***<br>(0.025)|-0.934***<br>(0.025)|-0.522***<br>(0.066)|-0.516***<br>(0.066)|
|Constant|17.033***<br>(0.547)|17.033***<br>(0.554)|20.974***<br>(0.165)|20.960***<br>(0.166)|16.259***<br>(0.516)|16.216***<br>(0.514)|
|Observations|46,791|46,791|46,701|46,701|8558|8558|
|R-squared|0.009|0.009|0.038|0.039|0.012|0.012|
|Mean ofY|15.09|15.08|17.55|17.52|13.03|13.025|
||||**Daug**|**hters**|||
|Father’s Edu|-0.364***<br>(0.052)|-0.365***<br>(0.052)|0.334***<br>(0.040)|0.335***<br>(0.040)|-0.483***<br>(0.061)|-0.477***<br>(0.061)|
|Constant|19.099***<br>(0.347)|19.095***<br>(0.351)|16.306***<br>(0.200)|16.296***<br>(0.200)|16.892***<br>(0.499)|16.824***<br>(0.499)|
|Observations|47,368|47,368|40,047|40,047|9798|9798|
|R-squared|0.006|0.006|0.004|0.004|0.010|0.010|
|Mean ofY|17.58|17.58|17.50|17.49|13.875|13.846|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) Each row uses a subsample of children who were born in the given birth cohort and the given category.

41
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