rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4679

# **Abstract**

This paper attempts to quantify the impact of the HIV/ AIDS epidemic on social capital with cross-country data. Using data from the World Values Survey, the authors estimate reduced-form regressions of the main determinants of social capital controlling for HIV prevalence, institutional quality, social distance, and economic indicators. The results obtained indicate that HIV prevalence affects social capital negatively. The empirical estimates suggest that a one standard deviation increase in HIV prevalence will lead to a decline of at least 1 percent in trust, controlling for other determinants of social capital. Moving from a country with a relatively low level of HIV prevalence, such as Estonia, to a country with a relatively high level, such as Uganda, there is a more than 11 % point decline in social capital. These results are robust in a number of dimensions and highlight the empirical importance of an additional mechanism through which HIV/AIDS hinders the development process.

This paper—a product of the Finance and Private Sector Development Unit, Africa Region—is part of a larger effort in the department to support the drivers of growth and foster an enabling environment for business in Africa. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at adavid2@ worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
||**India**|**Pakistan**|**International**<br>**Benchmark**|
|---|---|---|---|
|**Generation Planning**|57%|29%|56%|
|**Transmission Planning**|75%|25%|72%|
|**Procurement of Generation**|95%|100%|85%|
|**Transmission Procurement**|83%|100%|64%|

Source: Note: Scores based on index developed for the Rethinking Power Sector Reform Project. For more details go to project website at http://www.esmap.org/rethinking_power_sector_reform On procurement for both generation and transmission Pakistan gets the maximum score but this conceals a different reality. Despite allowing for international competitive bidding Pakistan has struggled to attract investment in generation—there is still a large deficit in generation (about 4GW in 2017). The China-Pakistan Economic Corridor is now bringing large generation capacity (according to their website 14 projects worth over $18 billion and with 11.1 GW capacity are at various stages of development/construction) but none of these have been awarded through international competitive bids or auctions—all have been awarded through direct negotiations between the Government of Pakistan and the Chinese Government/Project company. Thus, while the country allows for good practices in procurement these are hardly followed in practice.

# **_3.2 Access and affordability_**

Access and affordability are key variables when considering the benefits of the electricity system to the poorer members of society. Governments in developing countries often find it difficult to increase access while keeping electricity affordable. First, the households most eager to be connected have the highest incomes and may well live in the same area making it easier on a per household basis to supply this group who tend to purchase larger amounts of electricity. Second, the costs of connection are higher to more remote areas and these tend to have lower incomes and consume less electricity. Thus, the last households to be connected will tend to be those of least interest to the utility, so that some government support may be needed to ensure access moves towards the 100 percent mark.

Access is defined by the existence of a connection to the grid by a household—all members of the connected household are counted as having access. Affordability is usually defined by the household having to spend less than a given fraction of income to purchase a given amount of electricity. When the household spends more for this quantity, or purchases less for the given expenditure, then at the given prices electricity is said to be “unaffordable”. In practice such a calculation tends to be carried out only when a large-scale expenditure survey is available.

# _3.2.1 Performance_

**Access** data are usually derived from household expenditure surveys, but these are not available annually and are typically based on modest sized samples. Only in census years can a solid estimate of the total population and average household size be obtained. However, a population census does not usually enquire about items such as access to mains electricity. For this, the

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second case, the practice is to provide some amount of compensation. The justification for compensation is that if there is none, the government might be seen as enjoying windfall gains unfairly and would have a hard time attracting lenders and investors for PPP projects in general (EPEC 2013; World Bank 2019). Even in the case of its own default, the private partner may legally allege government responsibility, so the government becomes liable to compensate the private party or incur additional legal costs (World Bank 2019). In the case of force majeure, because the distress event is outside both parties’ control, both parties should share the risk. The government is liable for less than full compensation and has the right to take over the relevant asset, while the private partner loses any return on its invested equity and possibly some of the invested equity itself (EPEC 2013; World Bank 2019).

Limited data are available on losses incurred by governments in cases of early termination of PPPs. The data on the recovery rates of bank loans to PPP projects collected by the Data Alliance Project Finance Consortium, mainly in developed economies, show that the average ultimate recovery rate is 79.3 percent (Moody’s Investor Service 2019).<sup>14</sup> Data on compensation of private equity is even scarcer than data on recovery rates of bank loans. The sample of contracting terms from the countries of interest that could be accessed provide generous early termination clauses.<sup>15</sup> For example, in an airport project in Albania, if the project company defaults, the government will take on the liabilities of the company. In road PPPs in Kenya, if the project company defaults, the government is liable only for the debt due. In road PPPs in Argentina, even if the company defaults, the government has to compensate the private party for the total accumulated unamortized project cost net of penalties charged to the private sponsor for failing to fulfill the contract.

In the case of early termination of the contract by the government, all the countries for which some information could be gathered stipulate full compensation for the total accumulated investment and some measure of return on the invested equity. In Bulgaria, this type of compensation is written into the PPP law. In Albania and Kosovo, the private party is entitled to its debt, share of capital, and additional accumulated equity. In Kenya, the private party is due the net present value of the equity it invested in the project. In road PPPs in Argentina, on top of the unamortized investments, the private sponsor receives a termination payment determined as the expected loss of the company as a result of loss of the concession contract.

> 14 A study by Moody’s of project defaults between 1983 and 2017 shows that 209 of the 1,970 PPP projects included in the dataset were in emerging markets and developing economies (Moody’s Investor Service 2019).

> 15 Contracts and other contracting terms have been examined in Albania, Argentina, Bulgaria, Kenya, and Kosovo.

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simulation study by Thome et al. (2014), integrating input subsidies with SCTP in Malawi has larger multiplier effects on income than implementing the programs separately. Specifically, even though SCTP targets households with fewer productive assets, its benefits for production are amplified. Similarly, households receiving subsidies found it easier to obtain inputs with reduced liquidity constraints when they took part in both programs. A review by Tirivayi et al. (2016) concurs that combining social protection and agricultural programs builds synergies for increased food security, incomes, and crop productivity to a greater extent than when implemented in isolation. Development practitioners are also made aware of the importance of harmonizing these programs by comparing and contrasting their features that can be utilized to enhance food and nutrition security in Malawi (Ellis & Maliro, 2013; Matita & Chirwa, 2014). This is particularly relevant because, depending on the context, the effects frequently vary and involve trade-offs between different policy instruments (Qureshi, Dixon, & Wood, 2015). For instance, public investments in irrigation and roads lift more individuals above the poverty line in India than input subsidies (Akber, 2020), in contrast to Uganda, where agricultural research and education was more effective (Pauw & Thurlow, 2015).

Figure 1 conceptualizes likely impact pathways from various public interventions supporting crop production and enabling exchange entitlement and their influence on food and nutrition security outcomes. For production-based investments such as input subsidies and irrigation infrastructure, the anticipated results might include increased agricultural productivity and potentially higher incomes for farmers (Hemming et al., 2018), but with trade-offs in terms of environmental impact (Slater, Baker, & Lawrence, 2022). In contrast, transfer-based policies are expected to directly improve household purchasing power and food access, potentially leading to immediate improvements in food security and dietary diversity (Baird, McIntosh, & Özler, 2019; Brugh et al., 2017; Miller et al., 2011; Tiwari et al., 2016). However, the fungibility of cash transfers may have unintended consequences if a person chooses to buy unhealthy foods or non-food items that do not improve food and nutrition outcomes. Production and transfer-based policies may interact and be optimized together for a synergistic effect on food and nutrition security. For example, increased agricultural productivity (a result of production-based policies) could be complemented by enhanced market access and purchasing power (outcomes of transfer-based policies). This combination could lead to a more substantial and sustainable improvement in food and nutrition security, beyond what might be achieved by either stream in isolation.

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**Figure 2. Gasoline Taxes in Selected Countries, 2008**

<!-- Start of picture text --> 3.5<br>2.5<br>1.5<br>0.5<br>-0.5<br>-1.5<br>Egypt Mexico US Peru Brazil Argentina Chile Canada Australia Poland Czech Republic Japan Austria Ireland Belgium Italy Norway France Netherlands Germany UK<br>US $/gallon<br><!-- End of picture text --> Source. IEA (2008) and Parry and Strand (2010).

# **2. Conceptual Framework**

This section briefly describes the assumptions underlying our model of passenger transportation for the GCMA, discusses the determination of optimal pricing policies implied by the model, and then comments on some limitations of the analysis.

# **Model Description**

We assume essentially the same analytical model as that in Parry and Timilsina (2010) that was developed to assess optimal prices for the Mexico City passenger transport system. This model provides a simplified representation of an urban passenger transportation system that is meant to capture, in a parsimonious way, the most important underlying determinants of optimal 5
becomes negative and significant in Western and Central Africa and insignificant in Eastern and Southern Africa (Table 6). After excluding Madagascar, the relationship is positive but insignificant in Eastern and Southern Africa. The relationship with average education of mothers is statistically significarnt in the expected direction: higher education is associated with a reduction in the reported incidence of fever.

**Table 6. OLS regressions of average incidence of fever in sub-region on average poverty and average mothers' education**

|||_Fever_||_Fev_|_erwithouta _|_cough_|
|---|---|---|---|---|---|---|
||_Western_<br>_and_<br>_Central_<br>_Africa_|_Eastern_<br>_and_<br>_Southern_<br>_Africa_|_Easternand_<br>_Southern_<br>_Africa(excL_<br>_Madagascar)_|_Western_<br>_and_<br>_Central_<br>_Africa_|_Eastern_<br>_and_<br>_Southern_<br>_Africa_|_Easternand_<br>_Southern_<br>_Africa(excL._<br>_Madagascar)_|
|Proportionin|||||||
|poorestquintile|-0.134|-0.058|0.059|0.008|-0.019|0.083|
||(2.25)*|(0.35)|(0.33)|(0.20)|(0.22)|(0.88)|
|Averageyearsof<br>schoolingof|||||||
|mothers|-0.027|-0.021|-0.020|-0.011|-0.010|-0.009|
||(4.19)**|(2.70)**|(2.51)*|(2.38)*|(2.46)*|(2.16)*|
|Constant|0.479|0.504|0.484|0.196|0.189|0.169|
||(16.91)**|(8.17)**|(7.67)**|(9.96)**|(5.77)**|(5.08)**|
|Observations|81|76|70|81|76|70|
|R-squared|0.18|0.13|0.17|0.11|0.12|0.18|

_Note:_ Absolute value of t-statistics in parentheses; * significant at 10%; ** significant at 5%. _Source:_ Author's calculations from pooled, and weighted and aggregated DHS data.

_Bivariate relationship between fever and poverty at the individual level_ Abstracting from geography, Table 7 reports the percentage of children under three who have had a fever in the past two weeks by quintile. This bivariate relationship conforms to the geographic results: although the incidence is generally larger in the poorest quintile than in the richest quintile, there is not a strong relationship between fever and poverty in either region. In Western and Central Africa, 28 percent of children in the richest quintile are reported to have had a fever in the past two weeks, whereas almost 3 8 percent of children in the poorest quintile reported fever. In Eastern and Southern Africa the percentage is 36 percent for the richest quintile and 42 percent for the poorest.

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## <u>Variable details:</u>

- Migration: indicator equal to one for respondents that report living in a different location outside the baseline district. Movers between Grama Niladharis but within the same baseline district are not considered as migrants. We exclude respondents who are deceased, whose location is unknown, or who did not complete the survey.

- Operating business: for the 10-year endline, the operating business indicator equals one if the respondent is running the business or engaged in another/new business according to question F.5 of the March 2016 survey. It also includes respondents who have moved residence to another city or gone overseas (question F.5) and report non-missing business income during the previous month (question 6.6a). For the 5-year endline, we consider the variable provided in the replication file of DeMel et al. (2012) as it is complemented with observational data not included in the surveys.

- Any employment: for the 10-year endline, we check whether the respondent is (i) still operating a business, (ii) engaged in wage work (according to question F.5), or (iii) currently engaged in any economic activity (question 3.1). For the 5-year endline we consider respondents who are still running a business or employed as wage workers.

# **C.5 Sri Lanka Start-and-Improve Your Business (SIYB)**

We use data from the randomized experiment designed by DeMel et al. (2014), which tested whether business training alone has a different impact on microenterprises than a package of training and an unrestricted grant of about $130. The training component is modeled according to the Start-and-Improve Your Business (SIYB) program, and includes a three day training for potential entrepreneurs (Generate Your Business), a five day course covering the main aspects of starting a business (Start Your Business), and a five day course for existing business owners that want to develop their business (Improve Your Business).

The study follows two samples of female entrepreneurs: a first group includes 628 women already involved in self-employment activities, working more than 20 hours per week and having monthly profits of 5000 Rs or less (less than $2 per day). The authors refer to this as ”current business owner” sample. The second group is instead comprised of 628 women currently out of the labor force, but who express interest in starting a business activity within a year. The authors refer to this sample as ”potential 62
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improved, leading to better subsequent returns from investments in transport vehicles or businesses relating to transportation. The service and the trade sectors fare the worst in terms of profit margin, which is also revealed in the rate of return earned on assets in all three survey years, indicating that there is room for performance improvement. The return on assets in the service sector is 61.3 percent in 2000, 62.5 percent in 2005, and 50 percent in 2010. The corresponding figures for the trade sector are 59.9 percent, 54.2 percent, and 52.6 percent respectively. The manufacturing sector seems to be exposed to high risk, as reflected in its relatively low profit margin, and the rate of return in this sector also tends to be around the average for all enterprises in all activities.

Our estimates of average rates of return to assets are consistent with findings from other countries. For example, Kremer, Lee, and Robinson (2008) take advantage of the characteristics of the retail industry in rural Kenya to create estimates and bounds on the rate of return to inventory capital in a set of retail firms. Using administrative data on whether firms purchased enough to take advantage of quantity discounts from wholesalers, they estimate a lower bound on rates of return for the median shop of greater than 100 percent per year. McKenzie and Woodruff (2006) similarly find large returns to small entrepreneurs. Exploiting county-level variation in credit supply due to the Community Reinvestment Act, Zinman (2002) estimates gross rates of return to capital in the US on the order of 20-58 percent per year. In a recent study, Anagol and Udry (2006) take the elegant approach of using data on prices of used car parts of varying expected lifetimes to estimate a lower bound to the opportunity cost of capital of 60 percent for taxi drivers in Ghana. Banerjee and Duflo (2005) compute the rate of return to capital in the economy to be about 22 percent in India, and Caselli and Freyer (2007) calculate the marginal product of capital to be at most 19 percent for Sri Lanka.

<u>5. Rates of return using a profit function approach</u> 16
7 the survey and the head we ide-itified is interesting. If the income criterion is applied, 17 percent of those classified in the survey as female-headed would move to the male-headed classification, but only 7 percent of those classified as male-headed would be classified as female-headed. This shows that, in Brazil, the presence of an income-earning male does not a priori exclude a household from Leing designated female-headed in survey data.

The middle section of Table 2 shows the distribution of households in metropolitan areas according to the "hours worked" criterion. Overall, this criterion turns up roughly the same number of female-headed households as the survey classification. However, the number of households classified as jointly-headed rises to almost one-fourth of the sample. About the same number of reported female-headed households move to male-headed under the hours-worked criterion as unider the income criterion, but more reported male-headed households move to the female-headed column under the hours-worked than under the income criterion.

Putting the two criteria together in the bottom section of Table 2, we see that of the 20 percent of households classified as female-headed according to the income criterion, less than two-thirds would classified as female headed if both criteria were used. Twenty-five percent would be classified as jointly-headed, the rest as male-headed. Of the 74 percent classified as male-headed under the income criterion, almost three-fourths would be classified as male-headed under the hours-worked criterion. If we consider that jointly-headed households have a head of the same gender as the reported head, then the concordance among the criteria is quite strong. Thus, the reported and the hours-worked criteria misclassify, relative to each other, 9 percent of all households and the income and the working criteria misclassify, relative to each other, only 7 percent of all households.

Who are the alternate heads in these households? We investigated this question for those households where the income criterion turned up a different gender of head than the one reported in the survey (Table 3). In the case of reported male-headed households, the alternate head is most likely to be a spouse, especially if the reported head is himself a labor-
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