rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5076

# **Abstract**

Understanding the demographic and socioeconomic patterns of the prevalence and incidence of HIV/AIDS in Sub-Saharan Africa is crucial for developing programs and policies to combat HIV/AIDS. This paper looks critically at the methods and analytical challenges to study the links between socioeconomic and demographic status and HIV/AIDS. Some of the misconceptions about the HIV/AIDS epidemic are discussed and unusual empirical evidence from the existing body of work is presented. Several important messages emerge from the results. First, the study of the link between socioeconomic status and HIV faces a range of challenges related to definitions, samples, and empirical methods. Second, given the large gaps in evidence and the changing nature of the epidemic, there is a need to continue to improve the evidence base on the link between demographic and socioeconomic status and the prevalence and incidence of HIV/AIDS. Finally, it is difficult to generalize results across countries. As the results presented here and in other studies based on Demographic and Health Survey datasets show, few consistent and significant patterns of prevalence by socioeconomic and demographic status are evident.

This paper—a product of the Poverty and Inequality Team, and Human Development and Public Services Team, Development Research Group—is part of a larger effort in the group to understand the determinant of the HIV/AIDS epidemic. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kbeegle@worldbank.org and ddewalque@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
# **Appendix 1.**

# **Breaking the power of landowning elites through development programs: a case study from Uttar Pradesh, India**

The socioeconomic hierarchy of the village is apparent even before the bus arrives at the village: there is one large brick whitewashed mansion, standing out among a sea of mud huts. The mansion is the home of the _talukdar_ family, the erstwhile large landowner whose duty it was to collect land revenues for the colonial power. They lost some of their holdings when land ceilings were imposed in the 1950s, although they evaded much of this through fictitious division.

> For the next couple of decades, the talukdars consolidated their relationships with the

> new power structures of the state. In a typical pattern of diversifying family networks, the father arranged for one son to be in the police service, while another managed the land.

> They continued to be the critical source of credit and employment for the villagers, and their overwhelming social superiority was acknowledged by the villagers as they prefaced every interaction with the greeting "Touching your feet, Lord..."

For this family, well-educated and well-connected, it was easy to divert development funds to their own benefit and the other villagers would typically not even come to know of the entitlements of which they were being deprived. Even if they did come to know of it, they could hardly protest about it when the talukdars had guns and were known to rape and maim at will.

> In the mid-1970s, information on tubewells reached some middle-level peasants, who pooled resources to sink a tubewell to irrigate their contiguous plots. Eager to maximize their profits, they began to sow cash crops and raised the wages they offered agricultural labor. The talukdar's son responded by striding around at the weekly market with a gun slung over his shoulder, threatening that he would shoot anyone who offered laborers more than the "going rate." This temporarily thwarted the middle peasants' efforts.

> However, new opportunities offered by tubewells and the opening of a milkcollection center in the village made it increasingly difficult for the talukdars to retain their position in the village society and economy. Over time, the middle-level peasants were successful in raising their incomes, and offered a greater multiplicity of sources of employment and credit for the poor. The bi-polar polity of the village changed into one with a broader distribution of power.

In a study of another Uttar Pradesh village, Dreze, Lanjouw, and Sharma (1998) noted similar tensions between the same middle peasant group and the ex-talukdars. There, the talukdars had tried methods such as arson and election-rigging to maintain their power, but the middle peasants' determined use of the new agrarian opportunities was eventually successful in challenging the hegemony of the talukdars. By the 1990s, the middle peasants had become prosperous and educated, and formed a serious challenge to the talukdars in the village polity.

> _Source:_ Vinod Jairath and Monica Das Gupta, fieldnotes from Hardoi district.

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**Table 8: Cross Country Comparison of Infrastructure Outcomes, 2000**

||_Improved sanitation_<br>_facilities(% of population_<br>_with access)_|_Road paved(%of_<br>_total roads)_|_Electric power_<br>_consumption(kwh_<br>_per capita)_|
|---|---|---|---|
|log(real GDP per capita)|65.28***|-4.17|-4969***|
|log(real GDP per capita)^2|-3.23**|1.08|430.78***|
|India|-20.72***|10.25|142.08|
|China|-1.64|33.46|277.897|
|Bangladesh|-6.80|-21.9*|-135.54|
|Pakistan|-2.8|20.46|29.34|
|Srilanka|16.78***|-|-368.54|
|Control for size|Yes|Yes|Yes|
|Observations|57|44|61|

_Source_ : WDI

*** represents significance at 1%, ** represents significance at 5%, * represents significance at 10%. Country size is measured by population.

One way to get around this problem is to look at how infrastructure outcomes vary across the countries when we control for the stage of development, non linearity in development and country size. We looked at several outcome variables (% of population having access to improved sanitation, roads paved as % of total roads, and per capita electric power consumption) to ascertain if South Asia differs from rest of the world. The results are presented in Table 8.

As we can see India is a big and significant negative outlier on access to sanitation after controlling for stage of development, non linearity in development and size. Sri Lanka is a large positive outlier amongst the South Asian countries. South Asian countries are no different from the norm on paved roads as a share of total road. The exception is Bangladesh. It is a significant negative outlier. All South Asian countries are no different than the norm on energy power consumption per capita. Both India and China have positive coefficients, although they are not significantly different from zero. Only Bangladesh and Sri Lanka have negative coefficients.

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**Figure 5: Growth and changing shares of income in 34 countries** .

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Using _Povcalnet_ at the World Bank, we then searched for the average household income for the countries in the above sample, based on household survey data. This reduced the sample to 27, but we reproduced the decomposition graph above. The general pattern is fairly similar, as can be seen in Figure 6 below. Note that some country data points with slower income growth according to household surveys drop into the quadrant where sharing has a larger relative role than pure growth.

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11 Figure 3. Ireland: Central Parity Against the ECU. Source: European Economy.

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Financial Regulation and Government Revenue

- Cochrane, John H. (2011). ”Understanding policy in the great recession: Some unpleasant fiscal arithmetic.” European Economic Review 55, no. 1: 2-30.

- Davig, Troy, Eric M. Leeper, and Todd B. Walker. (2011). ”Inflation and the fiscal limit.” European Economic Review 55, no. 1: 31-47.

- Demetriades, Panicos O., and Kul B. Luintel. (2001). ”Financial restraints in the South Korean miracle.” Journal of Development Economics 64, no. 2: 459-479.

- Espinosa-Vega, Marco A. (1995). ”Multiple reserve requirements.” Journal of Money, Credit and Banking 27, no. 3 : 762-776.

- Feldstein, Martin, and Charles Horioka. (1979). ”Domestic Savings and International Capital Flows.” Economic Journal 90, no. 358.

- Fischer, Stanley. (1982). ”Seigniorage and the Case for a National Money.” The Journal of Political Economy : 295-313.

- Giovannini, Alberto, and Martha de Melo. (1993). ”Government Revenue from Financial Repression.” American Economic Review 83, no. 4 : 953-63. 953–963.

- Hilscher, Jens, Alon Raviv, and Ricardo Reis. (2014). Inflating away the public debt? An empirical assessment. No. w20339. National Bureau of Economic Research.

- Huang, Yiping, and Xun Wang. (2011). ”Does Financial Repression Inhibit or Facilitate Economic Growth? A Case Study of Chinese Reform Experience*.”Oxford Bulletin of Economics and Statistics 73, no. 6: 833-855.

- International Monetary Fund. (2013). “The Federal Democratic Republic of Ethiopia, 2013 Article IV Consultation,” IMF Country Report No. 13/308 (October 2013).

- Limodio, Nicola and Francesco Strobbe, F. (2016)“The Development Impact of Financial Regulation: Evidence from Ethiopia and Antebellum USA,” Mimeo.

- McKinnon, Roland I., (1973). _Money and Capital in Economic Development_ (Washington, DC: Brookings Institution Press).

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interest rates, to identify the better bargain among two different savings and loan products, and to demonstrate their understanding of how inflation affects their savings. Lastly, one question aimed to evaluate the respondent’s know‐how needed to successfully interact with financial institutions (awareness of required documentation for being able to open an account).

Since single questions provide a rather incomplete picture of respondents’ levels of financial knowledge, an arithmetic financial knowledge score ranging from 0‐6 was calculated by summing up the correct answers to these 6 questions.

To reflect the level of difficulty associated with each question, an alternative financial knowledge score has been developed, which weights every question with the inverse of the proportion of respondents who were able to provide a correct answer. Therewith, larger weights are given to questions that fewer people answered correctly.

# **_Baseline Balance_**

Table 1 reports summary statistics for the entire sample, as well as for each of the 5 assignment groups for all exogenous variables including information from the baseline listing, and time‐invariant variables measured at follow‐up. Results are thus reported on balance for business owners who were included in both the baseline and follow‐up survey (n=2,358). The micro‐entrepreneurs comprising the total sample are on average 38 years old, predominantly female (71%), married (84%), Christians (64%), are able to speak English (70%), completed high school as their highest level of education (50%), and live in households with an average size of 4.5 individuals. They are experienced in running a business (on average around 11 years of experience and 8 years in the current operation), and more than half of the sample (57%) already holds a savings account.

Given that treatment was randomly assigned, the 5 assignment groups are expected to have similar characteristics. Columns (4), (6), (8) and (10) in Table 1 show the mean baseline characteristics of all micro‐ entrepreneurs surveyed at the baseline by treatment group (including the pure control). Columns (5), (7), (9) and (11) report the p‐values of the t‐test for equality of each of these mean baseline characteristics against those in the (placebo) control group. No characteristics are significantly different from the control (placebo) group at the 5% level for the three treatments, except for the proportion of Igbo business owners in the MOVIE/MFB group. The expectation of balance on observable baseline characteristics also holds between treatment groups, which supports our claim that the randomization worked well. We see for the Pure Control group, however, that 3 of the 26 characteristics are significantly different at a 5% 10
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# **<u>Appendix S4: Selection to Attend Clinic under C800</u>**

||Attend<br>|Not Attend<br>|Difference<br>|
|---|---|---|---|
||(1)|(2)|(3)|
|_Baseline Characteristics:_||||
|Age|25.799|23.637|2.162 (0.782)**|
|Muslim|0.461|0.623|-0.162 (0.056)**|
|Any education|0.529|0.497|0.032 (0.053)|
|Marital status = Single|0.155|0.107|0.048 (0.049)|
|Currently pregnant|0.203|0.148|0.055 (0.047)|
|Have children|0.782|0.685|0.097 (0.063)|
|Has paid work|0.437|0.376|0.061 (0.076)|
|Monthly HH earning per capita (naira)|5753.1|8107.1|-2353.9|
|Distance to clinic (km)|1.722|1.777|-0.055 (0.036)|
|Transport to clinic (naira)|130.95|153.82|-22.88 (20.99)|
|Opportunity costs to clinic (naira)|4.161|3.825|0.336 (0.888)|
|_Baseline behaviors:_||||
|Ever used clinic|0.72|0.654|0.066 (0.043)|
|Received tetanus vaccine before|0.392|0.449|-0.057 (0.062)|
|_Baseline perceptions:_||||
|Likely to contract tetanus|0.36|0.408|-0.048 (0.074)|
|Number of people who die of tetanus|30.737|27.840|2.897 (2.906)|
|Very worried about Tetanus|0.351|0.369|-0.018 (0.062)|
|Tetanus is very bad|0.456|0.502|-0.046 (0.064)|
|Very important to be protected from tetanus|0.497|0.531|-0.034 (0.047)|
|Vaccine efficacy|23.043|19.466|3.577 (2.699)|
|Vaccines protect from disease|0.915|0.907|0.008 (0.034)|
|Needles are scary|0.602|0.678|-0.076 (0.050)|
|Vaccines have side effects|0.629|0.739<br>|-0.110 (0.051)**|
|Vaccines give diseases|0.253|0.336|-0.083 (0.054)|
|Vaccines give HIV|0.167|0.173|-0.006 (0.062)|
|Heart rate (beat/min)|86.856|86.829|0.027 (1.686)|

_Source:_ Authors estimate _Notes:_ These are based on the analysis sample of 867 women who were offered C800. 751 women visited the assigned clinic and 116 did not. Transportion and opportunity costs to clinic are the costs for one-way. "Likely to contract tetanus" is a binary variable; "Number of people who die of tetanus" is the number of people out of 100; "Very worried about tetanus", "Tetanus is very bad", and "Very important to be protected from tetanus" are binary variables. "Vaccine efficacy" is the difference between the hypothetical number of unvaccinated people whom each respondent thinks get tetanus and the number of vaccinated people who get tetanus (range: - 100 to 100). 150 naira = $1 approximately. Standard errors are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
electronics industries. There are no innovative firms in the automobile industry in Nepal, and a small proportion of firms are innovative in Pakistan.

Figure 2. Technological and Non-Technological Innovations in Key Sectors <!-- Start of picture text --> Technological and Non Technological Innovation Rates by Specific Sectors<br>Bangladesh (% All Firms) India (% All Firms)<br>Food Apparel Electronics Automobiles Food Apparel Electronics Automobiles<br>Nepal (% All Firms) Pakistan (% All Firms)<br>Food Apparel Electronics Automobiles Food Apparel Electronics Automobiles<br>Product & Process Organizational Product & Process Organizational<br>1 1<br>.75 .75<br>.5 .5<br>.25 .25<br>0 0<br>1 1<br>.75 .75<br>.5 .5<br>.25 .25<br>0 0<br><!-- End of picture text --> Source: Author’s elaboration from Enterprise Survey (2014)

# 5.4 Degree of Innovativeness or Novelty

Although innovation rates are relatively large for some countries in the region, this does not necessarily mean that the degree of innovativeness is high. Indeed, one pattern found for several developing countries is that a limited number of firms engage in disruptive innovative activities such as introducing new products to the country or even better new products to the world, while the majority of them conduct incremental innovations or pure imitation - either by upgrading the quality of existing goods or introducing new products to the firm (Cirera and Sabetti, 2019).

When firms decide among alternative innovation choices, several factors affect this decision. The market structure is one important determinant. When product market differentiation is low and the degree of competition is high, the entrance of new firms in a market reduces incumbents’ market shares by much more than in the case where products are imperfect substitutes like in the case of monopolistic or oligopolistic competition. As a result, firms escape competition by upgrading the quality of their existing goods. In other words, the marginal value of adding one extra unit of quality to an already supplied variety increases with the degree of product market competition (Cusolito, 2009).

Another determinant is access to credit. If breakthrough innovations are related to long-term investments, while incremental innovations are associated with short-term investments, then tight credit constraints can create a bias towards the latter. This is because lack of credit increases the probability that long-term investments will be interrupted by a liquidity shock. Ex-ante, the anticipation of this risk reduces firms’ 17
can express the autocorrelations of the first differences in terms of the autocorrelations of the levels arvey (1985, p. 219), see also Appendix 2)

# _(15)_ PI p ( - 1)) 2(l -PO ())

The correlograms of LPV and DILPV are shown in Table 1. As we can see, the sample autocorrelation function of DILPV shows significant negative values at lags 2 and 16 and a positive one at lag 10. We have chosen an AR(16) process as a possible candidate able to pick those features. An AR(16) is a process of sufficiendy high order (p is almost T/5) that can capture the significant correlation at lag 16. Thus, - we have estimated eq. (13) with W, AR(16), getting the following values for the j =.716; 02= -.164; 03 =.089; 04= -.019; 05 = -.014; 06= -.014; 07 =.085; 08= -.030,; 9 =.018; ¢10 =.174; I= -077; 012 = -. 138; ¢X3 =.121; 014 =.016; 015 = -.234; *-= -. 039.

From the estimated coefficients we have computed the theoretical autocorrelations and, from these, the theoretical autocorrelation function p, (:) for the first differences of y. Values are listed in Table 1. Interestingly, the three peaks at lags 2, 10, and 16 are picked rather well by the theoretical autocorrelation function of the first differences of y, with a deterministic trend and cyclical AR(16) disturbances (Figure 2D), and it can be seen that the pattern is not dissimilar to that of the observed correlogram in Figure 2C In sum, the conclusion is that first differencing is not necessarily needed and that a difference-stationary model of the ARIMA type is probably not the best description of the actual piocess since it would require a positive autocorrelation at lag 1 and zero autocorrelations at higher lags for the first differences. On the contrary, the 12
# <u>Overview of the Sample</u>

Table 3 below presents key descriptive statistics from our sample of 1,300 households. These are the variables we use as potential carbon supply function shifters. Respondents were mainly male, but with a significant minority (34%) of respondents being female. The sample includes a variety of ethnic groups, including _Dalit_ (i.e. lowest) castes (16%) and 38% of households are classified as poor or ultra-poor. Virtually all households use fuelwood, but 9% also use biogas and 15% each use LPG or improved biomass cookstoves. Virtually all households own some land and 42% say that REDD+ will very likely or extremely likely benefit them personally.

Table 3: Descriptive Statistics of Sample and Potential Carbon Supply Shifters

|Variable|Mean|Standard Deviation|
|---|---|---|
|Member of CF (1 = yes, 0 otherwise)|0.50|0.50|
|Respondent age (years)|50.61|13.76|
|Respondent is female (1 = yes, 0 otherwise)|0.34|0.48|
|Female-headed households (1 = yes, 0 otherwise)|0.16|0.37|
|Household size (number of people)|5.96|2.52|
|Household is classified as poor or ultra-poor (1 = yes, 0 otherwise)|0.38|0.48|
|Dalit ethnic group (1 = yes, 0 otherwise)|0.16|0.37|
|Indigenous or Newar ethnic group (1 = yes, 0 otherwise)|0.41|0.49|
|Brahmin or Chetri ethnic group (1 = yes, 0 otherwise)|0.40|0.49|
|Madheshi ethnic group (1 = yes, 0 otherwise)|0.02|0.14|
|Respondent migrated to site from another location (1 = yes, 0 otherwise)|0.25|0.44|
|Uses biogas (1 = use, 0 otherwise)|0.09|0.29|
|Uses LPG (1 = use, 0 otherwise)|0.15|0.36|
|Uses improved biomass cooking stove (1 = use, 0 otherwise)|0.15|0.36|
|Uses firewood (1 = use, 0 otherwise)|0.95|0.22|
|Household owns land (1 = yes, 0 otherwise)|0.94|0.24|
|Walking distance from respondent’s house to road < 2 hours (1 = yes,<br>0 otherwise)|0.70|0.46|

fuelwood collections. The May 2013 exchange rate of the Nepali Rupee with the $US was RS. 85/$US. As no observations meet this criterion, the lognormal results are not trimmed.

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