rafmacalaba/datause-encoder-data huggingface dataset:
4 having gener&lly higher mortality),3 high per capita income is by no means a prereq isite to v antries achieving low mortality and<sup>thus better levels of</sup> health. This lesson is aptly ca.tured in a set<sup>of studies entitled Good</sup> Health at Low Cost, which consists of case studies<sup>of a group of countries and</sup> regions that have achieved low mortality despite<sup>low per capita Incomes,</sup> inc'.uding China, Sri Lanka, the state of Kerala in India, and<sup>(at somewhat</sup> higher incomes) Cuba and Costa Rica. (Halstead, 1985).4 Because there are no internationally comparable measures of morbidity, there have been no cross-national studies o. the *:orrelates of morbidity (independent of mortality). ln fact, since the available measures of mortality heavily reflect infant and child. as opposed<sup>to adult mortality, it</sup> is fair to say that existing studies across countries provide<sup>little insight</sup> into the effects of development on adult mortality or on morbidity<sup>at any age</sup> (that does not result in death in the short run). I return to this<sup>general</sup> issue below.

,ompared to studies at the national level, studies at the<sup>individual and</sup> household level have greater potential for revealing underlying<sup>behavioral and</sup> causal relationships. There is a growing literature on the<sup>determinants of</sup> health status at the household level, made possible by the increasing availabili.y of household data for developing countries<sup>that include such</sup> measures of health status as infant and child mortality and<sup>(more rarely)</sup> 3 Income probably has diminishing effects at high levels; additional income probably matters more to health for poorer individuals and<sup>poorer nations. See</sup> Musgrove (1986) for a good illustration.

4 Of course the other side of this coin (good health despite being<sup>poor)</sup> is why these societies, especially the non Communist Sri Lanka, have so<sup>often</sup> received relatively low economic returns to their large investments<sup>in human</sup> capital. Section II is concerned with the effects of<sup>health on development.</sup>
- Getzen TE (1990). Macro forecasting of national health expenditures. In: Rossiter L, Scheffler RM, eds. Advances in Health Economics and Health Services Research. Greenwood, CN: JAI Press: pp. 27–48.

- Hernandez P, Dräger S, Evans DB, Edejer TT, and Poz D (2006).  “Measuring expenditure for the health workforce: evidence and challenges.” Evidence and Information for Policy: World Health Organization, Geneva.

- International Labor Organization. Social security for all: Building social protection floors and comprehensive social security systems. Geneva: International Labor Organization, 2012 http://www.ilo.org/secsoc/lang‐‐en/index.htm (accessed March 11, 2015).

- Jimba, M., Cometto, G., Yamamoto, T., Shiao, L., Huicho, L., and Sheikh, S. (2010), “Health Workforce: The Critical Pathway to Universal Health Coverage”, background paper, First Global Symposium on Health Systems Research, 16–19 November, Montreux.

- Kinfu Y, Poz D, R M, Mercer H, Evans DB. The health worker shortage in Africa: are enough physicians and nurses being trained? Bull World Health Organ 2009; 87: 225–30.

- Kirch, D. G., Henderson, M. K., & Dill, M. J. (2012). Physician workforce projections in an era of health care reform. _Annual review of medicine_ , _63_ , 435‐445.

- Makridakis S, Wheelwright SC, Hyndman RJ. (2008). Forecasting: methods and applications. John Wiley & Sons.

- McPake B, Maeda A, Araujo, E. C., Lemière, C., El Maghraby, A., and Cometto, G. (2013), “Why do Health Labor Market Forces Matter?”, Bulletin of the World Health Organization, 91, 841–6.

- _______, Scott, A., and Edoka, I. (2014), “Analyzing Markets for Health Workers: Insights from Labor and Health Economics”, Directions in Development, Washington, DC, World Bank Group.

- _______, Allison, S., Agya, M., and Araujo, E. C. (2015). “The Economics of Health Professional Education and Careers: Insights from a Literature Review”, World Bank Studies, Washington, DC, World Bank Group.

- Newhouse JP. Medical‐Care Expenditure: A Cross‐National Survey. J Hum Resour 1977; 12: 115– 25.

- Pfaff M. Differences in Health Care Spending Across Countries: Statistical Evidence. J Health Polit Policy Law 1990; 15: 1–67.

14
Where {NEGATIVE}i is a vector of variables conveying negative information regarding borrower _I_ ; {POSITIVE}i is a vector of variables conveying positive information regarding borrower _i_ . Even though the datasets in the three PCRs are somewhat different, the variables are defined in a conceptually consistent manner to allow for comparability of the results. Table 2 describes the actual PCR data used in the regressions for each of the three countries.

The dependent variable is binary and takes a unit value if the borrower defaults, meaning a late payment of 90 days or more, and is zero otherwise. The last year of the data available is the default horizon and is used for defining the binary dependent variable. Data available prior to that, which goes back 18 months in Argentina, 12 month in Brasil, and up to 59 months in Mexico, is used to define explanatory variables and represents the credit history of a borrower with individual banks and the banking system.

Only Mexico provides straight-forward information on the number of days late for loans in arrears, so for Argentina and Brazil<sup>8</sup> borrower ratings were used as a proxy for delinquency status. Ratings are based on a set of criteria including the number of days pastdue. In Argentina default corresponds to a “3” rating, and in Brazil the rating “E” was used as the default threshold. For Argentina, a borrower was considered to be in default for our exercise if their average rating across banks in the system was “3” or worse at any time during the year-long default horizon. In the case of Brazil there are eight ratings categories; the borrower is considered in default if it had a rating “E” or worse with a given financial institution.

The negative variables then include information on past late payments and defaults while positive variables include a borrower’s (non-default) rating, aggregate exposure, the number of open credit lines, and availability of collateral. To ensure consistency across countries, we restrict the dataset to private sector non-financial enterprises. The Mexican public registry only includes loans above US $20,000, this is adopted as the baseline for empirical testing in Argentina and Mexico. For the case of Brazil a higher cutoff, of US $300,000, was adopted.

8
34

# **ANNEXES**

## **Annex Table 1: List of countries covered and status of government confirmation**

|**Region**|**Countries**|**Year policy**<br>**information**<br>**collected**|**Source of**<br>**information**|**Government**<br>**confirmation**<br>**received**<sup>**10**</sup>|
|---|---|---|---|---|
|AFRICA (AFR)|Botswana|2009|WB survey||
|AFRICA (AFR)|Cameroon|2008|WB survey||
|AFRICA (AFR)|Burundi|2010|WB Survey|Yes|
|AFRICA (AFR)|Congo, Dem. Rep.|2008|WB survey||
|AFRICA (AFR)|Cote d'Ivoire|2008|WB survey||
|AFRICA (AFR)|Ethiopia|2008|WB survey||
|AFRICA (AFR)|Ghana|2008|WB survey||
|AFRICA (AFR)|Kenya|2008|WB survey||
|AFRICA (AFR)|Lesotho|2008|WB survey||
|AFRICA (AFR)|Madagascar|2008|WB survey||
|AFRICA (AFR)|Malawi|2008|WB survey||
|AFRICA (AFR)|Mali|2008|WB survey||
|AFRICA (AFR)|Mauritius|2008|WB survey||
|AFRICA (AFR)|Mozambique|2008|WB survey||
|AFRICA (AFR)|Namibia|2008|WB survey||
|AFRICA (AFR)|Nigeria|2008|WB survey||
|AFRICA (AFR)|Rwanda|2009|WB survey||
|AFRICA (AFR)|Senegal|2008|WB survey||
|AFRICA (AFR)|South Africa|2008|WB survey||
|AFRICA (AFR)|Tanzania|2008|WB survey||
|AFRICA (AFR)|Uganda|2008|WB survey||
|AFRICA (AFR)|Zambia|2008|WB survey||
|AFRICA (AFR)|Zimbabwe|2008|WB survey||
|East Asia and Pacific (EAP)|Cambodia|2008|WB survey||
|East Asia and Pacific (EAP)|China|2011|WB survey|Yes|
|East Asia and Pacific (EAP)|Indonesia|2008|WB survey||
|East Asia and Pacific (EAP)|Malaysia|2008|WB survey|Yes|
|East Asia and Pacific (EAP)|Mongolia|2008|WB survey|Yes|
|East Asia and Pacific (EAP)|Philippines|2008|WB survey||
|East Asia and Pacific (EAP)|Thailand|2008|WB survey|Yes|
|East Asia and Pacific (EAP)|Vietnam|2008|WB survey||
|South Asia (SAR)|Bangladesh|2008|WB survey||
|South Asia (SAR)|India|2008|WB survey||
|South Asia (SAR)|Nepal|2008|WB survey||
|South Asia (SAR)|Pakistan|2008|WB survey|Yes|
|South Asia (SAR)|Sri Lanka|2008|WB survey||

10 No entry means government feedback has not been received.
_5_ _On the physical slde, the other Issue often mentioned_ **_Is_** _technology. We all thlnk that technology_ **_Is_** _frightfully Important, but we don't know how to measure_ **_It._** _We don't know whether it is there or not. Casual empiriclsm and journalistic reports will suggest that there_ **_Is_** _plenty of technoloS;' available. We are innovating in robots and flexible manutacturing systems, electronics, telecommunicatlons, etc. But I am not sure that that cuts much Ice._

**_Scientific a-.d Technological Factors..._**

_Whether we would be hittlng problems on the Innovation, In the technology_ **_froutler Is_** _-- to my mind, an open Issue. I am struck by some of the evidence I have seen w'hlch suggests that even technological innovations are strongly Influenced by the pace and the spesd of demand Itself. The application of Inventions tend to follow th. cycle, Investment, and seem to be a function of demand forces._ _So that if we were able to accelerate a bit, technology may well adapt to the acceieration, just as when a relatlvely low output growth may be leading to a relatively low rate of Innovation and structural 4ange._

**_All_** _these are Izally types of physical bottlenecks -- labor, capital, raw materials and technology. But there are some less physical bottlenecks. One such bottleneck is savings. The possibility that even If we did wish to invest, and even if ootimistic expectations materialized, we would be_ **_hitting_** _an availability of savings bottleneck, whLch would drive up interest rates and/or inflation and therefore frustrate the attempt at growing at a faster rate._

_True to my unreconscructed Keynesianism, I consider savings_ **_to_** _be a function of income. And_ **_If_** _income grows, savings grow. But I must admit that the recent U.S. experience has thrown some doubt in my mind about the universal validity of this proposition._ _Looking at past experiences of acceleration in growth rates, virtually anywhere higher savings followed. This seems to have been the case even for the Uniced States through the 1950s and the 1960s. But_ **_in_** _the early 1980s, United States investment grew rapidly, Government expenditure, of course, grew rapidly as well, while savings, despite rapid income growth, did not follow suit, and hence you had the well-known absorption of savings from the rest of the world._ _There are some arithmet._.s in my paper fPPR Working Paper, WPS 9], which suggests that if we wanted to get back to the investment ratios of the early 1970s, the very peak ratios we ever recorded, and if U.S. savings were not to move from the savings achieved at present over the last cycle, over the last few years, then the rest of the world, the rest of the OECD area would have to increase its gross savings ratio to GDP to levels very rarely recorded in the past. Therefore, it wouldn't be totally unfeasible to get back to an investment ratio like that of 1973, but it would be difficult._ WPlPROC/SF:jsq
# **2. Rural Energy Expansion and Economic Development in Sub-Saharan Africa**

Figure 2 shows that Sub-Saharan Africa ranks last among global regions in energy consumption per capita when South Africa is excluded. Figure 3 and Table 2 document access to electricity for urban and rural households during 2003-2007 in three developing regions (DHS, 2009): Latin America and the Caribbean (LAC), South and Southeast Asia (SSEA), and Sub-Saharan Africa (SSA). The urban and rural distributions for SSA are so low that they hardly overlap with those of LAC and SSEA. Median rural access is 3% in SSA, 62.5% in LAC and 55.7% in SSEA. Among SSA countries the maximum rural access, 33.8%, is barely higher than first-quartile access in the other two developing regions.

The available evidence suggests that closing this gap would significantly reduce rural poverty in Africa. The World Bank (1996) has documented the economic and health benefits of switching from biomass fuels to electricity. According to another World Bank report (2001), “Efficient and clean energy supply is central to the reduction of poverty through many and varied linkages, as well as being important for economic growth.” Barnes (2007) and World Bank (2008a) cite several of these linkages, while noting that supporting evidence remains largely anecdotal. Case studies from India highlight income generation potential for women, for example, thanks to nighttime lighting and sewing machines (Hiremath et al. 2009). One recent empirical study by Khandker et al. (2009) estimates income gains from electrification in rural Bangladesh between 9 percent and 30 percent. Small businesses, which rely heavily on family labor, can increase their production hours once electricity becomes available. Electricity access improves health by facilitating longer hours for clinics, and a strengthened cold chain for vaccines. Education levels improve, as electric lighting extends study hours. While empirical evidence from Africa on social benefits remains limited, there is no doubt that the private returns to rural electrification are substantial. Most households that can afford electricity become subscribers as soon as the service becomes available. Highly-valued private benefits include improved lighting and the ability to watch television.

The least-cost mix of centralized and decentralized power will depend on the cost of grid distribution, which is conditioned by geography (Parshall et al. 2009), and on the 4
# **Burundi**

<!-- Start of picture text --> Prevalence among females 15-24 (%) Spectrum estimates<br>National Antenatal Sentinel National Demographic and Incidence in Antiretroviral<br>Surveillance Surveys Health or AIDS Impact Surveys adults 15-49 (%) coverage (%)<br>1990 - - 0.61 -<br>1991 - - 0.77 -<br>1992 - - 0.90 -<br>1993 - - 0.95 -<br>1994 - - 0.87 -<br>1995 - - 0.71 -<br>1996 - - 0.51 -<br>1997 - - 0.34 -<br>1998 - - 0.17 -<br>1999 - - 0.11 -<br>2000 7.1 - 0.07 -<br>2001 - - 0.06 -<br>2002 - 3.8 0.06 -<br>2003 3.9 - 0.08 -<br>2004 - - 0.09 6<br>2005 - - 0.09 13<br>2006 - - 0.09 17<br>2007 - - 0.10 23<br>2008 - - 0.10 -<br>2009 1.3 - 0.08 35<br>2010 0.8 0.8 0.08 45<br>2011 - - 0.08 54<br>2012 - - 0.06 58<br><!-- End of picture text --> <u>ANC data sources: HIV and AIDS epidemiological surveillance report for the WHO African region: 2007 update, Burundi</u> UNGASS Report 2010, Burundi Bilan des Realisation du Plan d'Action National de Lutte contre le SIDA 2011 <u>National DHS and AIS data sources: Burundi HH 2002 and DHS 2010</u>

# **Central African Republic**

||**Prevalence amo**<br>|**ng females 15-24(%)**<br>|**Spectrum**<br>|**estimates**<br>|
|---|---|---|---|---|
||**National Antenatal Sentinel**<br>**Surveillance Surveys **|**National Demographic and**<br>**Health or AIDS Impact Surveys **|**Incidence in**<br>**adults 15-49(%)**|**Antiretroviral**<br>**coverage (%)**|
|**1990**|-|-|1.18|-|
|**1991**|-|-|1.17|-|
|**1992**|-|-|1.18|-|
|**1993**|-|-|1.17|-|
|**1994**|-|-|1.15|-|
|**1995**|-|-|1.13|-|
|**1996**|-|-|1.07|-|
|**1997**|-|-|1.00|-|
|**1998**|-|-|0.91|-|
|**1999**|-|-|0.81|-|
|**2000**|-|-|0.74|-|
|**2001**|-|-|0.67|-|
|**2002**|-|-|0.62|-|
|**2003**|-|-|0.58|-|
|**2004**|-|-|0.53|3|
|**2005**|-|-|0.48|4|
|**2006**|-|-|0.44|6|
|**2007**|8.21|-|0.40|21|
|**2008**|-|-|0.37|-|
|**2009**|7.22|-|0.34|23|
|**2010**|-|-|0.32|24|
|**2011**|4.83|-|0.29|22|

<u>ANC data sources: Central African Republic UNGASS Report 2008 and 2010, Comite National de Lutte Contre le SIDA</u> 2012 Rapport d'Activite au Niveau du Pays Republique Centrafricaine 53
|**Tab**|**le 10: Single-Shift **<br>(1)|**& IDERio Element**<br>(2)|**ary**<br>(3)|(4)|
|---|---|---|---|---|
||<br>IDERio Elem<br>(SD)|<br>IDERio Elem<br>(SD)|<br>IDERio Elem<br>(SD)|<br>IDERio Elem<br>(SD)|
|Single-Shift * Post|0.436|0.276|0.033|0.180|
||[0.063]***|[0.069]***|[0.100]|[0.136]|
|School of Tomorrow|0.189|0.25|0.323|0.349|
||[0.065]***|[0.067]***|[0.121]***|[0.137]**|
|More Education|0.001|0.009|0.161|-0.108|
||[0.034]|[0.035]|[0.090]*|[0.048]**|
|Mean items in the house||0.026|-0.035|0.023|
|||[0.023]|[0.036]|[0.037]|
|% Mothers w/ HE||-0.067|0.113|0.062|
|||[0.138]|[0.252]|[0.199]|
|Participation rate-Prova Rio 3rd<br>grade||-0.433|-0.333|-0.593|
|||[0.118]***|[0.245]|[0.206]***|
|Age-grade distortion||-0.029|-0.018|-0.038|
|||[0.003]***|[0.009]**|[0.007]***|
|Constant|-0.582|0.091|-0.046|0.257|
||[0.023]***|[0.184]|[0.345]|[0.298]|
|Observations|4557|3605|455|1974|
|Number of schools|651|515|91|282|
|R-squared|0.46|0.49|0.38|0.45|
|School FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Restricted Sample<br>Robust standard errors in brackets|No|No|Yes|Yes|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notes: IDERio in Elementary Schools from 2009 to 2015. Column (3) refers to only schools that eventually become Single-Shift until 2015, in years 2009 through 2013. Column (4) refers to only schools with low age-grade distortion in 2010 (prior to the program). The info on number of goods in the house and mother education is from Prova Brasil (and imputed in other years). Single-Shift includes CES in this model. By 2013, there were 77 non-certified Single-Shift schools and 2 CES with complete information. Standard errors clustered in school level.

29
2015 was used for broad classifications into the 5 categories: Agriculture, Water bodies, Forest, Bare soil based on judgement by the local consultant firm, CEGIS. For example, only natural (or reserved) forest was categorized into the Forest category, while agriculture, orchards, vegetation, and other plantation were categorized into the Agriculture category as vegetation areas are mostly used to homestead or cultured vegetation for food and livelihood ran by local communities. The shifting cultivation was categorized under the Human Interventions in addition to roads and settlements. We acknowledge that these classes and scores could be refined, but we used them to be able to compare our results with the results of this previous study.

10
percentage-point drop in neonatal mortality risk. Meanwhile, for children only partially “treated” by the Supreme Court rulings while _in utero_ , we find a positive though statistically _in_ significant effect on mortality risk. This result is consistent with much existing evidence that links mother’s health to the risks of neonatal mortality (Gluckman et al. 2008; Currie and Almond 2011). We note, however, that the difference between being partially and fully affected by the policy _in utero_ and other temporal factors cannot be disentangled. That is, other changes in the treatment region after July 1988 could also explain the result – for instance, relatively weaker policy effectiveness in the initial year following the verdicts.

In column 7, we test whether neonatal mortality impacts of regulation persist downstream of our defined treatment group. We define three downstream groups. ‘Near Downstream’ denotes the nearest group downstream and includes Pratapgarh, Kaushambi, and Allahabad districts. ‘Intermediate Downstream’ is the next nearest group and consists of Sant Ravidas Nagar, Mirzapur, and Varanasi districts. ‘Far Downstream’ collects the remaining 11 Indian districts through which the Ganga River travels beyond Varanasi. We estimate coefficients on the interaction between dummies for each of these groups and the post-ruling dummy. Only the ‘Near Downstream’ coefficient is statistically significant. In fact, it is significantly larger than the main treatment-group coefficient (-0.32 vs. -0.20), which may be explained by the location of Kanpur’s tanneries very near to the downstream district border. Meanwhile, the lack of impacts beyond Allahabad is not surprising, because pollution decay and other inputs to water quality should attenuate the effect the rulings as we move our focus downstream.

# **4.3 The impact of** **_Mehta vs. Union of India_ on river pollution**

The Ganga Pollution Cases therefore seem to have had a beneficial effect on infant health. Did they do so by reducing river pollution? One necessary condition for this explanation to hold is that the verdict is associated with reduced pollution levels. We test this condition in our reduced-form DD framework, estimating equation 2. Panel A of Table 4 reports regression results with our primary pollutant, BOD, while Panel B shows results for other relevant pollutants.

The BOD results displayed in columns 1-6 uniformly imply a significant drop in pollution. Our baseline estimate, shown in column 1 and corresponding to the full time-period of available pollution data, implies a 53.4 percentage-point drop in the likelihood of exceeding the bathing class BOD standard after the Supreme Court rulings. Prior to the ruling, the likelihood of exceedance in treated districts was 100 percent. The drop in exceedance is consistently observed throughout the post-verdict period, as shown in columns 2-4. It is also robust to an expansion of the control group to include all of India (column 5). Meanwhile, downstream impacts in column 6 mirror those from Table 3, column 7: the nearest group of districts downstream of the treatment group is associated with a large and 17
2003). If these differences are correlated with labor intensity in the country, the coefficient on growth composition may be biased. Moreover, the bias could be in our favor if national accounts underreported production from unskilled workers. To account for this possibility, we include mean income growth from household surveys as an additional explanatory variable (see column 4). As expected, this variable carries a negative and significant coefficient, and its inclusion produces both an improvement in the regression fit and a decline in the magnitude of the coefficients on the size and composition of growth. However, both coefficients remain negative and statically significant, confirming our hypothesis.

Fourth is the issue of growth endogeneity. Our analysis has been conducted in _differences_ in order to control for country-specific structural factors that affect poverty and production jointly. Still, it can be argued that improvements in poverty drive production growth --possibly through higher rates of accumulation of human capital and savings-- thus making the analysis in differences also subject to the endogeneity critique. Although this does not apply to the variable on the composition of growth, its coefficient may still be biased if composition and size of growth are correlated. To control for the potential endogeneity of growth, we instrument for it using the average GDP growth of the country’s trading partners as the source of exogenous variation. The instrumental variable procedure (whose results are presented in column 5) renders coefficients on the size and labor-intensity of growth that remain negative and highly significant. Moreover, their magnitudes are even larger that in the benchmark case, indicating that the endogeneity of growth, if any, was playing against the hypothesis advocated in the paper.

# **Alternative poverty measures**

Our analysis has used the headcount poverty index as the benchmark measure of poverty given its prominence in both the empirical literature and policy circles. However, our simple theoretical model builds the case for the importance of the composition of growth by focusing on its relationship not with poverty directly but with labor wages. The connection with poverty is made by assuming that wages affect poverty according to a linear function, which combined with the basic result of the model brings about the paper’s main regression equation. Yet, the linearity of the relationship 19