rafmacalaba/datause-encoder-data huggingface dataset:
31

# **Table A2.1  Household Energy Use Patterns in Ghana**

||||||Ur|b|an|||||||Rural|||||||Na|tional||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Rank|LPG|El|ec|Kero|Char||Wood|Res|%|Cum %|LPG|Elec|Kero|Char<br>Wood|Res|%|Cum %|LPG|Elec|Kero|Char|<br>Wood|Res|%|Cum %|
|1|**1**|**1**||0|**1**||0|0|30.0|30.0|0|**1**|0|0<br>**1**|0|24.5|24.5|0|**1**|0|**1**|0|0|20.9|20.9|
|2|0|**1**||0|**1**||0|0|26.6|56.7|0|0|0|0<br>**1**|0|22.5|47.0|**1**|**1**|0|**1**|0|0|20.3|41.2|
|3|**1**|**1**||0|0||0|0|18.2|74.8|0|**1**|0|**1**<br>0|0|13.6|60.6|0|**1**|0|0|**1**|0|12.4|53.5|
|4|0|**1**||0|0||0|0|6.5|81.3|0|**1**|0|**1**<br>**1**|0|10.0|70.6|**1**|**1**|0|0|0|0|12.1|65.6|
|5|0|**1**||0|**1**||**1**|0|4.8|86.1|**1**|**1**|0|**1**<br>0|0|7.8|78.4|0|0|0|0|**1**|0|10.6|76.2|
|6|0|0||0|**1**||0|0|3.1|89.2|**1**|**1**|0|0<br>0|0|4.3|82.7|0|**1**|0|**1**|**1**|0|7.1|83.3|
|7|0|**1**||0|0||**1**|0|2.9|92.1|0|**1**|0|0<br>0|0|3.3|85.9|0|**1**|0|0|0|0|5.1|88.4|
|8|0|0||0|0||**1**|0|1.3|93.4|0|0|0|**1**<br>**1**|0|2.8|88.7|0|0|0|**1**|0|0|2.8|91.1|
|9|**1**|**1**||0|**1**||**1**|0|1.2|94.6|0|0|0|**1**<br>0|0|2.4|91.0|0|0|0|**1**|**1**|0|1.8|92.9|
|10|0|0||0|**1**||**1**|0|1.0|95.6|**1**|**1**|0|**1**<br>**1**|0|1.4|92.4|**1**|**1**|0|**1**|**1**|0|1.3|94.2|
|11|**1**|0||0|**1**||0|0|0.8|96.4|**1**|**1**|0|0<br>**1**|0|1.3|93.7|**1**|**1**|0|0|**1**|0|0.8|94.9|
|12|0|**1**||**1**|**1**||0|0|0.7|97.1|0|0|0|0<br>0|0|1.0|94.7|0|0|0|0|0|0|0.7|95.7|
|13|**1**|**1**||**1**|**1**||0|0|0.6|97.6|0|**1**|**1**|**1**<br>**1**|0|1.0|95.7|0|**1**|**1**|**1**|**1**|0|0.5|96.2|
|14|0|0||0|0||0|0|0.5|98.2|0|0|**1**|0<br>**1**|0|0.8|96.5|**1**|0|0|**1**|0|0|0.5|96.7|
|15|0|0||**1**|**1**||0|0|0.5|98.7|0|0|**1**|**1**<br>**1**|0|0.6|97.0|0|**1**|**1**|**1**|0|0|0.5|97.2|
|16|**1**|**1**||0|0||**1**|0|0.3|99.0|0|0|0|0<br>0|**1**|0.5|97.6|0|0|**1**|0|**1**|0|0.4|97.6|
|17|||||||||||0|**1**|0|0<br>0|**1**|0.3|97.9|**1**|**1**|**1**|**1**|0|0|0.4|98.0|
|18|||||||||||0|**1**|**1**|0<br>**1**|0|0.3|98.2|0|0|**1**|**1**|0|0|0.4|98.3|
|19|||||||||||**1**|0|0|0<br>**1**|0|0.3|98.4|0|0|**1**|**1**|**1**|0|0.3|98.7|
|20|||||||||||**1**|0|0|0<br>0|0|0.2|98.7|0|0|0|0|0|**1**|0.2|98.9|
|21|||||||||||0|**1**|**1**|**1**<br>0|0|0.2|98.9|**1**|0|0|0|0|0|0.2|99.1|
|22|||||||||||0|0|**1**|**1**<br>0|0|0.2|99.1|||||||||

_Source_ : World Bank staff calculations using the Ghana Living Standards Survey.

_Note_ : There are 35 distinct combinations of energy sources in urban areas, 43 in rural areas, and 48 nationally. The combinations are listed in order of decreasing frequency for the first 99 percent of all households in each area. Other energy sources not captured in this table cited by respondents were sawdust, animal waste, and other fuels. Elec = electricity; kero = kerosene; char = charcoal; res = crop residues; % = percentage of households; cum % = cumulative percentage of households.
# **Discussion**

The age profile of COVID-19 mortality risk in 2020 was flatter (with relatively higher mortality at younger ages) in countries with lower incomes. This pattern is found using official COVID-19 death data from a large set of countries as well as excess mortality from a smaller set of countries which include high-income countries plus Colombia, Mexico, and Peru. Our analysis concerns the year 2020 and is therefore almost entirely unaffected by the arrival of COVID-19 vaccines and their unequal distribution across countries.

The findings in this paper can provide the basis for a more comprehensive accounting of the impact of COVID-19. For example, the younger mortality profile in developing countries means that in those countries the pandemic has killed large numbers of people who of working age, many of whom were their families’ breadwinners. Earlier global estimates suggest that more than 1.5 million children have experienced the death of at least one caregiver as a consequence of the pandemic.<sup>18</sup> This raises the concern that long-term impacts via lower investments in human capital of orphaned children will be particularly deep in developing countries. This is highly worrisome given the already massive declines in school enrollment and learning losses generated by the pandemic in developing countries.<sup>15–17</sup> A full understanding of age-specific patterns of excess mortality will require country-by-country analysis using cause-of-death data, where available, to pull apart estimates of direct COVID-19 deaths, indirect COVID-19 deaths, and averted deaths. A brief comparison of Colombia and the United States highlights how components of excess mortality can vary widely by country. In Colombia, both vehicular and homicide deaths fell during the country’s lockdown, reducing its excess mortality count. For 2020 vehicular deaths dropped 18.2 percent, and the number of homicides fell 5.4 percent, causing Colombia’s murder rate to reach its lowest level since 1979.<sup>19,20</sup> In contrast, the United States excess mortality estimate for 2020 reflects in part a 7.2 percent increase in motor vehicle deaths, a 29.4 percent increase in homicides, and a 30.0 percent spike in drug overdose deaths.<sup>21–23</sup> Official COVID-19 death reports and excess mortality estimates show broadly similar age patterns country-by-country. This finding, along with the fact that direct COVID-19 deaths account for the large share of excess mortality deaths, suggests that the flatter profile of excess mortality in lower income countries is driven principally by direct COVID-19 deaths. To 14
|**Measure**|**M1**<br>**M3**|**M4**<br>**Sector**<br>**scope**|
|---|---|---|
|Slot trading-Barter trade allowed: Slots can be exchanged on a one-to-one basis|x<br>x|x<br>SS|
|Slot allocation system|x<br>x|SS|
|Slot allocation systems - Grandfathering: Air carriers are allowed to retain already allocated slots from one<br>seasonto thenext|x<br>x|x<br>SS|
|Slot allocation systems - First come first served: There is no administrative system in place and air carriers<br>are allowed to schedule their flights simply taking into account expected delays at the busier airports|x<br>x|x<br>SS|
|Slot allocation systems - Auctioning: Slots are given to the highest bidder in an auction scheme organised by<br>the authorities|x<br>x|x<br>SS|
|Slot allocation system - Discretionary allocation: Slots are allocated at the discretion of airport and other<br>authorities or incumbent carriers|x<br>x|x<br>SS|
|Slot allocation systems - Other allocation: Allocation schemes which cannot be classified under any of the<br>categories above, or combinations of these categories|x<br>x|x<br>SS|
|Slot trading-Monetary trade allowed: Air carriers are allowed to commercially exchange slots|x<br>x|x<br>SS|
|Airlines allowed to self-handle|x<br>x|SS|
|Airlines allowed to mutually-handle|x<br>x|SS|
|Third-party handling allowed|x<br>x|SS|
|Limits on number of third-party handlers (depending on airport's sizes)|x<br>x|SS|
|Interoperability/interlinking required|x|SS|
|Interswitchingmandated<br>|x|SS<br>|
|Terminal running rights in place|x|SS|
|Bottleneck services regulated<br>|x|SS|
|Trackaccessforsupply ofservices to portsmandated|x|SS|
|Track access for supply of services to terminals mandated|x|SS|
|Running rights mandated|x|SS|
|**2.**<br>**Conditions on service supplier**|||
|Sector subsidies (incentives)|x<br>x|x<br>COM|
|Foreign suppliers treated less favourably regarding taxes and/or eligibility for subsidies|x<br>x|x<br>COM|
|Foreign suppliers are treated less favourably regarding eligibility to subsidies<br>|x<br>x|x<br>COM|
|Financial guarantee requirement|x|COM|
|Liability insurance|x|COM|
|Technology transfer requirements|x|COM|
|Export performance requirements|x|COM|
|Restrictions on foreign subsidiaries|x|SS|
|Restrictions on foreign branches|x|SS|
|Foreign bank"branches"subject to endowment capital requirement<br>|x|SS<br>|
|Local operations limited by amount of capital|x|SS|
|Foreignbank "branches"subject toliquidityrequirements|x|SS|

37
Ndikumana Leonce and James Boyce (2008). “New Estimates of Capital Flight from Sub-Saharan African Countries: Linkages with External Borrowing and Policy Options”. Political Economy Research Institute Working Paper No. 166. University of Massachusetts Amherst.

Ndulu Benno (2006). “Challenges of Africa’s Growth: Opportunities, Constraints and Strategic Directions.” The World Bank, Washington DC.

Okubo Sumiye, Carol A. Robbins, Carol E. Moylan, Brian K. Sliker, Laura Schultz and Lisa S. Mataloni (2006). “R&D Satellite Account: Preliminary Estimates”, Bureau of Economic Analysis Working Paper No. BEA 06-44, Bureau of Economic Analysis, US Department of Commerce.

Papageorgiou Chris and Fidel Perez-Sebastian (2002). “Human Capital and Convergence in NonScale R&D Growth Model”, Unpublished Manuscript.

Rebelo Sergio (1998). “The Role of Knowledge and Capital in Economic Growth”, World Institute for Development Economics Research Working Paper No. 149. The United Nations University, WIDER.

Reinhart Carmen and Kenneth Rogoff (2009). “The Aftermath of Financial Crises”. National Bureau of Economic Research, NBER Working Paper No. 14656.

Rodrik Dani (2008). “Second Best Institutions”, The American Economic Review, Papers and Proceedings 2008, 98:2, 100-1004.

Sachs Jeffrey and A. M. Warner (1997). “Sources of Growth in African Economies.” Journal of African Economies, Vol. 6, No. 3.

Stiglitz Joseph (2003). “The Roaring Nineties: Globalization and Its Discontents”. W. W. Norton & Company, New York.

Stiglitz Joseph (2006). “Making Globalization Work”. W. W. Norton & Company, New York.

Summers, Lawrence H. (2006). “Reflection on Global Account Imbalances and Emerging Markets Reserve Accumulation.” L.K. Jha Memorial Lecture, Reserve Bank of India, March 24, 2006.

Sun Yan (2004). “External Debt Sustainability in HIPC Completion Point Countries”, IMF Working Paper, WP/04/160, The International Monetary Fund, Washington DC.

UNESCO (2005). “State of Science and Technology Training Institutions in Africa,” United Nations Educational, Scientific and Cultural Organizations, regional Bureau for Science in Africa, Nairobi, Kenya.

Westholm Gunnar, Bertrand Tchatchoua and Peter Tindemans (2004). “Measuring Progress Towards Knowledge Societies”, A World Science, Vol. 2, No. 1.

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27
and organizational form.<sup>1</sup> However, once we drop the former Socialist countries, organizational form becomes the most important firm-level explanatory variable.

In comparing the different institutional theories, we exploit the World Business Environment Survey (WBES), a major cross-sectional survey conducted in developed and developing countries in 1999 and led by the World Bank. We use survey responses from 7760 firms in 80 countries to questions about property rights and firm characteristics. The survey contains data on small as well as large firms, and on private corporations and partnerships as well as publicly traded firms.<sup>2</sup> In order to compare the different theories and to examine the relative influence of firm effects versus country effects, we use variance decomposition analysis. This methodology is well established in the corporate strategy literature in the context of decomposing profitability into corporate and industry effects (Schmalensee, 1985; Rumelt, 1991; McGahan and Porter, 1997, 2002; Khanna and Rivkin, 2001).<sup>3</sup> The methodology allows us to focus directly on the general importance of these effects in explaining property rights without any assumptions on _causality_ or structural analysis.

Our analysis also uncovers a methodological issue that has not received adequate attention in the literature. The explanatory power of several institutional theories depends on the proxies used to represent these theories. We identify several potentially significant scaling issues that occur if empirical tests do not pay attention to non-linearities arising due to the way the proxies are scaled. These scaling issues have the potential to overturn conclusions drawn from tests.

This paper is closely related to the recent work of Stulz, Karolyi, and Doidge (2004) who investigate variation in the ratings of governance in large firms in a large sample of countries. They find that most of the variation in governance ratings across firms is explained by country characteristics rather than firm characteristics. They attribute this finding to the increased incentives of firms in better legal environments to adopt better governance structures.

> 1 Firms can be organized as sole proprietorships, partnerships, cooperatives, private corporations or publicly traded corporations.

> 2 The WBES has been used by Love and Mylenko (2003), Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2003), Beck, Demirguc-Kunt and Maksimovic (2005), Beck, Demirguc-Kunt and Levine (2005), Beck, Demirguc-Kunt, Laeven and Maksimovic (2005) among others.

> 3 The original application of this methodology was in quantitative genetics to decompose variation in traits into a genetic component and an environment component (Jinks and Fulker (1970)).

4
Drilling down in more detail into evolving patterns of trade, one can see that most of the export growth in dynamic emerging markets is not primarily driven by increasing exports to the traditional economic powers. Instead increasing integration across emerging markets is the driving factor. For example, developing countries in Latin America and the Caribbean (LAC) have rapidly increased their exports to developing countries in East Asia – most notably China (Figure 10a). LAC imports from emerging Asia, and especially China, have increased even faster (Figure 10d). To some extent, there may be some re-importing of primary exports that have then been processed into manufactures in Asia. This is a phenomenon that is discussed in more detail below.

Focusing on Brazil – the largest economy in Latin America—one can see rapid export growth to a wide variety of destinations over the last decade (Figure 10b). Most of that growth was not driven by traditional growth poles like Europe, the United States and Japan. These countries (EU27, US, Japan) absorbed only about a quarter of the increase in Brazil‘s exports during the first decade of the century. Europe still had an important contribution (nearly 20 percent); however, China‘s contribution was larger (Figure 10c). Growth in exports to the United States contributed a mere 4.4 percent to total export growth, and exports to a vast array of countries around the world comprised over a third of the increase in exports. On the side of imports, we see a similar pattern.

# **Figures 10(a)-(f):  Changing Trade Patterns in Developing Countries—LAC and Brazil Focus**

<!-- Start of picture text --> (a) LAC Exports to China & Emerging Asia (b) Brazil's Merchandise Exports, by<br>($ billions) destination ($ billion)<br>60 To 50.0 EU27<br>Emerging 45.0<br>50 China<br>Asia 40.0<br>To China 35.0<br>40 United<br>30.0 States<br>30 25.0 Argentina<br>20.0<br>20 15.0 Japan<br>10.0<br>10 Sub-Saharan<br>5.0<br>Africa<br>0.0<br>0 India<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2000 2002 2004 2006 2008 2010 Russia<br><!-- End of picture text --> 17
year cash payments.<sup>8</sup> Other contemporaneous village level studies document similar features in the economic arrangements between landlords and tenants (Srinivas, 1976; Bliss and Stern, 1982; Walker and Ryan, 1990).

# **3 Data**

We digitize, compile and connect existing data to construct a district-level dataset. We draw measures of land tenancy from the National Sample Survey (NSS) Land and Livestock Survey, a repeated cross section survey of about 35,000 agricultural landholdings across India. We build on custom-made tables from the Population Census of India for measures of migration. We then add information on banking, roads, distances between districts, weather and agroclimate zones from other data sources. The details of these steps are included in the online appendix. Here, we discuss the main sources, the NSS and the Census, and highlight data challenges.

## **Tenancy: NSS Land and Livestock Survey**

To obtain district-level data on land contracts we compiled data from the National Sample Survey Land and Livestock Survey. The survey covers around 30,000 to 35,000 households from all of India (with exceptions in some rounds).<sup>9</sup> We make use of the rural sample of round 70 (conducted in 2013) and 59 (conducted in 2003). We utilize information from schedule 18.1 which lists all 8On page 49, Reddy notes that, ‘The agreements entered into by absentee landlords were more detailed than those of residents.’ On page 52, he notes that, ‘The rent in the case of absentee lessors was generally paid once per year,’ and on page 76 that, ‘Absent landlords preferred cash over grains except in times of sharp rising prices.’ This is followed up, on page 69, with, ‘The shorter the distance, the greater was the preference for receipts in kind.’ Detailed contracts, specifying the crops and often inputs, were standard; and clauses such as: ‘You should cultivate this particular plot of land as well and carefully as the neighbouring owner cultivators of similar field,’ commonplace.

> 9The survey uses a stratified multi-stage design in which a number of census villages are randomly drawn from each district (usually four in two independent samples). Within each village, eight agricultural households are selected, stratified by landholding class. Each sample household is visited twice, once at the end of the kharif season (rainy season) and once at the end of the rabi season (dry season).

9
# **Table A.3a: Definition of Variables**

|Acronym|Definition|
|---|---|
|RPC|Per capita consumption expenditure deflated by regional price index|
|LRPC|Log per capita consumption expenditure deflated by regional price index|
|I|Dummy for integrated region|
|U|Dummy for urban area|
|drs|Dummy =1 if Household Received domestic remittances|
|frs|Dummy =1 if Household Received Remittances from abroad|
|lasset|Log(Total Asset deflated by regional price index)|
|hsize|Household size|
|hhage|Household head's age|
|hhages|Household head's age squared|
|hhfem|Household head Female|
|hhmar|Household head Married|
|hedu2|HH Head has primary education|
|hedu3|HH Head has Secondary education|
|hedu4|HH Head has higher Secondary education|
|hedu5|HH Head has more than higher Secondary education|
|pchi|Percentage of children|
|agri|agri=1 if HH head employed in agriculture|
|manu|manu=1 if HH head employed in manfacutring|
|serv|serv=1 if HH head employed in services|
|self|self=1 if HH head is self employed|
|priv|priv=1 if HH head employed in private sector wage employment|
|pmedu|Number of adult male with Education equal to or more than primary|
|pfedu|Number of adult female with Education equal to or more thanprimary|
water access, is to eliminate the chemical contaminants and microorganisms by using clean water technology and equipment. Few studies so far have been conducted to evaluate this important program.<sup>2</sup> In this paper, by employing the longitudinal data of the China Health and Nutrition Survey (CHNS) from 1989 to 2011, we study the long-term educational benefits of the improvements in drinking water _quality_ .

Our investigations suggest that young people in villages with access to plant water have better education than those without such access: the grade of education completed among youth increases by 1.08 years, and the likelihood of graduating from a middle and high school increase by 19.8 and 89.6 percent, respectively. The results are obtained after controlling for county-year dummies (and therefore local educational policies and resources), household characteristics, and village characteristics including the distances to schools. These results remain qualitatively robust after dealing with the endogeneity of the water treatment program that relies on topographic features of villages, controlling for village fixed effects, considering local labor market conditions, controlling for water access (i.e., whether a household has access to water in its premise), and estimating the heterogeneous effects by income groups and by gender. The main channels through which plant water benefits youth’s education are improved health of the youth themselves as well as increased household income resulted from improved health of other household members and early entry into brawn-typed jobs of their elder brothers if any, but not time saving due to a better _access_ to water.

We also find strong support for the brawn-based theory of gender division of labor in Pitt et al. (2012). In particular, boys gained more body mass than girls from using plant water; girls benefit much more from water treatment than boys in terms of schooling attainment, and youth with an older brother benefit more than youth with an older sister. The program benefited girls so much more than boys such that it completely wipes away the gender gap in educational attainment in treated areas of rural China.

Young people that had access to treated plant water in early childhood (i.e., 0-2 years of age) are found to experience significantly higher gains in education than those who were exposed to treated water after early childhood, consistent with recent literature on the critical importance

> 2 An exception is Zhang (2012), which examines the benefits on health of this program.

4
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