rafmacalaba/datause-encoder-data huggingface dataset:
# **5 HIV infection among couples**

In the previous sections we focused on the determinants of HIV infection at the individual level. The Lesotho Demographic and Health Survey also allows studying HIV/AIDS infection at the level of cohabiting couples. This section exploits this feature of the data, focusing on understanding the dynamics of HIV/AIDS with the couple as unit of observation. This study sheds light on how and through which partner -male or female- the virus is more likely to enter in a couple. For prevention purposes, it is also important to figure out the likelihood that the other partner is infected if one partner is infected.

We define as a couple all formally married couples as well as couples in unions, i.e. living together for at least 12 months. A couple is described as concordant negative when both partners are HIV negative and concordant positive when both are HIV positive. A discordant couple is a couple where one partner is HIV positive and the other is HIV negative. We define “discordant male” as a couple where the male is HIV positive and the female is HIV negative and “discordant female” a union where the female is HIV positive and the male man is HIV negative. Once an individual is infected, he or she remains HIV positive for life. Anti-retroviral therapies treat the disease, but do not cure the infection.

[Insert table 15] Table 15 reports the fraction of couples in each of these four categories in Lesotho. The second column includes the fraction of concordant positive, discordant male and discordant female among HIV infected couples, i.e. couples with at least one HIV positive partner. The very simple statistics in table 16 include important findings which have important consequences for prevention polices. The first point is that more than 40 percent of the infected couples are discordant couples, i.e. couples where only one of the two partners is HIV positive. This means that there are opportunities for prevention among couples, even though this is rarely mentioned as a priority in prevention efforts. For example, a recent policy position paper by UNAIDS (2005) mentions the following groups as being the vulnerable groups to which prevention programs should be specifically targeted: women and girls, youth, men who have sex with men, injecting and other drug users, sex workers, people living in 20
<!-- Start of picture text --> Figure S1.2h: Residuals with Time Trend, PM 2.5 at ITO<br>Blue Line (second extension)<br>Source: Based on data described in the text<br>1<br>.5<br>0<br>-.5<br>-1<br>8Oct2006 12Oct2006 16Oct2006 20Oct2006 24Oct2006 28Oct2006 1Nov2006 5Nov2006 9Nov2006 13Nov2006 17Nov2006 21Nov2006 25Nov2006 29Nov2006 3Dec2006 7Dec2006 11Dec2006 15Dec2006<br><!-- End of picture text --> <!-- Start of picture text --> Figure S1.3: Monthwise Power Generation by Thermal Plants in Delhi, 2004-2006<br>Mar2004 Dec2004 Jul2005 Dec2005 Apr2006 Nov2006<br>700<br>600<br>500<br>400<br>Year Month<br>Note: Dashed vertical lines indicate Delhi Metro expansion episodes<br>Source: Based on data from the Central Electricity Authority<br>Gigawatt Hour<br>Jan2004 Apr2004 Jul2004 Oct2004 Jan2005 Apr2005 Jul2005 Oct2005 Jan2006 Apr2006 Jul2006 Oct2006 Jan2007<br><!-- End of picture text --> 55
human development categories, from which four PUCs were randomly chosen. From each of the 12 PUCs, we randomly selected 12 GPs, for a total of 144 GPs from 12 PUCs from three districts. The three districts from which the 144 GPs for this survey were selected are Coimbatore, Pudukottai, and Vellore.

Data were collected using several surveys. For this analysis, the data come from individual interviews with each GP president, interviews with 270 randomly selected citizens, and a secondary data schedule used to gather current and historic data on GP characteristics and levels of public goods. Summary statistics for all data are given in Tables 4a and 4b.

# **III. Empirical Strategy**

This study aims to examine the treatment effect of reserving the GP president’s position for women by comparing reserved and unreserved GPs. However, because we can never observe a reserved GP in the absence of a reservation, estimates of the effect of reservations can be biased. Additionally, one may be concerned about the use of GP female-population ratios to determine women’s reserved seats. One technique to control for potential bias caused by unobservable factors is a difference-in-differences (DID) estimation strategy.

DID estimation uses a control group and an experimental group and observations in at least two time periods: one before the ‘change’ and one after. The benefit of using DID is that even if there are unobservable factors that influenced a GP’s reservation status and could affect public goods provision, our estimate of the effect of reservation on public goods provision will be unbiased if those factors do not change differently over time in reserved and unreserved GPs.

In its most general form, the DID estimator compares the change over time between the two groups. Consider unreserved (U) and reserved (R) GPs and the time periods 1991 (before the 73<sup>rd</sup> Amendment policies were instituted in Tamil Nadu) and 2005 (the fourth year of a Tamil 11
manufacturing exports in 2018 were 4.5 times larger than Indonesia’s, despite a population less than half that of Indonesia.

# **6 Conclusion**

Most of the literature on energy prices and plants’ performance has focused on electricity. However, distinguishing between electricity and fuels is important because they power capital equipment of different types. In this paper, we test for the impact of both electricity and fuel prices, finding an asymmetric impact: fuel price increases have a positive effect on plants’ performance while electricity price hikes worsen performance in Mexico and have a more muted effect in Indonesia. We contend that this result is consistent with fuel powering old technology capital vintages, while new technology tends to be embodied in electric capital vintages. An increase in fuel prices induces plants to scrap old fuel-powered machinery and purchase new electric equipment. Since electric machinery is more productive and energy-efficient, the static negative effect of fuel price increases is more than compensated by the positive effect on technological upgrading, with a potentially positive net effect on performance.

Our analysis is based on large nationally representative samples of manufacturing plants in two of the largest developing countries (Indonesia and Mexico). As such, threats to external validity which typically affects single country empirical analyses are less of a concern in our framework. Our approach also implies that the results are not specific to a particular setting or identification strategy.

These results suggest that besides the environmental and fiscal costs, subsidization of fuel prices may also lead to firms using sub-optimal technologies to the extent that a decrease in subsidy may actually improve performance. This reinforces the arguments in favor of fuel subsidy reform and carbon taxes, particularly in developing countries.

Much as in the case of other technology, such as management practices (Bloom et al., 2013), the findings also hint at some (information or otherwise) frictions that prevent firms from adopting more efficient electricity powered technology despite the positive returns of adoption. This calls for further research on the barriers to adoption in developing countries, which is needed to inform policy intervention in 29
the frequency with which they use computers at work—never, a couple of times a week, or more than three times per week—followed by a description of the computer-related tasks required in their job. These computer tasks are used to define binary variables indicating different levels of complexity of computer use on the job. Four levels of complexity have been defined: level 1 includes using _browser-based tasks_ (for example, use of email and Internet); level 2 involves use of _basic Microsoft Office functions_ like the word processor, presentations, and graphics; level 3 involves use of _basic programming tasks_ (involving spreadsheets and databases); and level 4 captures _advanced programming tasks_ (for instance, designing websites, using computer-aided software, programming software, and/or managing networks).

Noncognitive skills measures in the STEP surveys come from self-reported responses to various items measuring personality traits and behaviors. The personality traits include the Big Five traits—openness, conscientiousness, extraversion, agreeableness, and emotional stability (which is the obverse of neuroticism)—grit, and behaviors such as decision-making.<sup>12</sup> Items measuring these traits and behaviors are rated on a four-point Likert scale from “Almost never” to “Almost always _._ ” The STEP household surveys include extensive modules on education and employment history that provide information on educational and labor market outcomes. Further, the household roster of the survey gathers information on several individual and household characteristics used in this study. These include information on the number of children in the household, the marital status of the respondent, and retrospective information on the economic status of the household when the respondent was 15 years of age. The Appendix includes descriptive statistics on some of these key variables.

# **_Analytic Sample_**

To address the research questions posed in this study, the analytic sample for each country was limited to wage workers and self-employed adults between ages 25 and 64 who were in full-time employment. The self-employed group constitutes a large proportion of the sample in the countries examined in this paper (see Table 1) and is included in the analytic sample to enable policy-relevant inferences. The age range was selected to include adults mostly likely to have completed their education and who were below retirement age. Those currently enrolled in an educational or training program were excluded from the sample. Observations missing

> 12 The STEP surveys also gather information on preferences through measures of risk preference and hostile attribution bias. These are not used in the current analysis.

11
debt ratio rises slower across different scenarios as the growth rate of GDP increases. Under the High Growth Scenarios II that features a 1.5 percent annual increase of TFP, the debt ratio rises slowest and reaches just above 63 percent by 2030. In contrast, the debt ratio rises fastest under the Baseline scenario and closes on the 80 percent mark by 2030. Yet, the government debt situation remains sustainable across all scenarios.

The discussion above suggests that the sustainability of government debt is a foregone conclusion as it remains sustainable even if the economy experiences tepid growth going forward. However, such a conclusion overlooks the fact that the debt situation is closely tied to government operations and is not only determined by economic growth. To test whether the debt sustainability faces risks if the underlying conditions of government operations are not met, I conduct a simple counterfactual. Specifically, I analyze the risks of government not meeting the tax revenue targets as laid out in the Five Year Plan with regards to the debt situation. Keeping all else the same, I assume that the tax revenue to GDP ratio fails to improve going forward and stays at 9.3 percent. The figure below illustrates the sharp contrast to the findings above. Like the figure before, the government debt to GDP ratio is increasing under each scenario but is rising at a much faster rate. The government debt surpasses GDP by 2030 even under the most optimistic High Growth II scenario and lies just below 125 percent of GDP under the Baseline scenario.

<!-- Start of picture text --> Government Debt to GDP (%)<br>130<br>110<br>90<br>70<br>50<br>30<br>Baseline E + P HG I HG II<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> The debt ratio reaches alarming levels if the revenue targets are not met. Such high debt ratios might bring sustainability of government debt under serious threat. The exercise shows that the government debt situation is sensitive to government operations and that meeting tax revenue targets is essential in keeping the government debt to GDP ratio in check. The government debt sustainability will face additional risk if the government overshoots its expenditure without making much progress on the revenue front.

27
|.<br>Guantile<br>o2|o4|0.6|0.8|diff.<br>in coets<br>o8-o2|
|---|---|---|---|---|
|F<br>-42.19"*<br>emale<br>(12.63)|-36.47*<br>(12.09)|-40.42°"<br>(13.73)|-471.49"*<br>(12.44)|74<br>(15.6)|
|.<br>14.3<br>Some Tracking forMath Classes<br>(11.94)|6.6<br>(10.25)|3.66<br>(6.59)|3.57<br>6.24)|10.73<br>(9.52)|
|Female x Same Trackin<br>22.6<br>3<br>(14.62)|afar<br>(13.65)|24.76"<br>(14.66)|20.00°*<br>(12.57)|6.29<br>(15.65)|
|Other Controls<br>Yes|Yes|Yes|Yes||
|Constant<br>ao9.62""<br>(41.24)|466.967"<br>(39.05)|Aga<br>(40.24)|570.57<br>(37.21)|180.95"<br>(30.677)|
|Number of Observations<br>2970|2970|2970|2970||
|Average R-Square<br>ta|ra|ra|ra||
|;<br>“4.59<br>Female +Some Tracking x Female<br>8.18)|“11.2<br>6.82)|-10.66<br>G64)|-13.01*<br>6.53)|3.42<br>(7.86)|
|.<br>36.69"<br>Some Tracking +Some Tracking x Female<br>(13.52)<br>Note: Standard errors are reported in parentheses and signific<br>* respectively. Estimates were calculatetd using 5 plausible v<br>Repreated Replication with Fays(0.5) adjustment were used to<br>Source data: PISA 2OOS Korea, General School Strata, Grade|33.67"<br>(13.18)<br>ance at the 1%<br>alues for esti<br> estimate the <br> 10 Students|33.41"<br>(14.13)<br>, 5%, 10% le<br>mates of achiev<br> standard error|a2.45**<br>(13.13)<br>vels is denote<br>ernent and Ba<br>s.|-4.44<br>(13.39)<br>d ***, **, and<br>lanced|
|Variables|Allocation to<br>nontraditional<br>assets|Number of<br>eligible<br>currencies|Number of<br>eligible assets|Duration –<br>Total portfolio<br>(months)|Duration –<br>Liquidity<br>tranche<br>(months)|Duration –<br>Investment<br>tranche<br>(months)|
|---|---|---|---|---|---|---|
|Independent investment committee|-0.233*|-0.102|-0.148|-0.282**|-0.108|-0.14|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.9 Reporting to the Board and Investment Policies**

|Variables|Allocation to<br>nontraditional<br>assets|Number of<br>eligible<br>currencies|Number of<br>eligible assets|Duration –<br>Total portfolio<br>(months)|Duration –<br>Liquidity<br>tranche<br>(months)|Duration –<br>Investment<br>tranche<br>(months)|
|---|---|---|---|---|---|---|
|Middle office reports to the board|0.227*|0.193*|0.345***|0.114|0.153|-0.006|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.10 Obligation to Cover Negative Equity and the Broader Governance Environment**

|Variables|Independence of<br>central bank|Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|
|---|---|---|---|---|
|Obligation to cover negative equity|-0.194*|-0.479***|-0.357***|-0.483***|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.11  Obligation to Cover Negative Equity and Investment Policies**

|Variables|Allocation to<br>nontraditional<br>assets|Number of<br>eligible<br>currencies|Number of<br>eligible assets|Duration – Total<br>portfolio (months)|Duration –<br>Liquidity tranche<br>(months)|Duration –<br>Investment<br>tranche (months)|
|---|---|---|---|---|---|---|
|Obligation to cover negative equity|-0.200*|-0.201*|-0.251**|-0.172|-0.309**|-0.067|
|No obligation to cover negative equity and transfer of<br>realized income (partial and full)|0.221*|0.063|0.211**|-0.004|0.193|0.06|
|Obligation to cover negative equity and transfer of realized<br>income (partial and full)|-0.201*|-0.217**|-0.230**|-0.153|-0.202|0.011|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.12 Obligation to Cover Negative Equity and Country Risk**

22
include many other possible impacts (for example through tourism and energy prices) and looks only at the short‐term (during which there will be small changes in climate conditions compared with what unabated climate change could bring over the long‐term). Indeed, our overall results show that between 3 million and 122 million additional people would be in poverty because of climate change in our main _prosperity_ and _poverty_ scenarios (Table 7).

- In the _poverty_ scenario, the total number of people living below the extreme poverty line in 2030 is 1.02 billion people in the high‐impact scenario; this represents an increase of 122 million people compared to a scenario with no climate change. For the low‐impact scenario, the additional number of poor people is 35 million people.

- In the _prosperity_ scenario, the increase in poverty due to a high‐impact climate change scenario is “only” 16 million people, suggesting that development and access to basic services (like water and sanitation) is effective in reducing poor people’s vulnerability to climate change. For the low‐ impact scenario, the additional number of poor people is 3 million people.

_Table 7 Climate change can have a large impact on extreme poverty, especially if socio‐economic trends and policies are not supporting poverty eradication._

|||Climate<br>|change scenario<br>|<br>||
|---|---|---|---|---|---|
||No climate change|Low‐imp|act scenario|High impa|ct scenario|
||Number of people in<br>extremepoverty|Additional|number of peop<br>to climate|le in extreme p<br>change|overty due|
|Socio‐<br>economic<br>Prosperity<br>scenario|142 million|+3<br>Minimum<br>+3 million|million<br>Maximum<br>+6 million|+16<br>Minimum<br>+16 million|million<br>Maximum<br>+25 million|
|scenario<br>Poverty<br>scenario|899 million|+35<br>Minimum<br>‐25 million|million<br>Maximum<br>+97 million|+122<br>Minimum<br>+33 million|million<br>Maximum<br>+165<br>million|

Note: The main results use the two representative scenarios for prosperity and poverty. The ranges are based on the 60 alternative scenarios for each category (10‐90 percentiles). These simulations are performed using 2005 PPP exchange rate and the $1.25 extreme poverty line, but results are not expected to changed significantly under the $1.90 poverty line and using 2011 PPP.

Note that the large range of estimates in our results—3 to 122 million—may incorrectly suggest that we cannot say anything about the future impact of climate change on poverty. The reason for this rather wide range is not just scientific uncertainty on climate change and its impacts. Instead, it is predominantly policy choices—particularly those concerning development patterns and poverty reduction policies between now and 2030. While emissions‐reduction policies cannot do much regarding the climate change that will happen between now and 2030 (since that is mostly the result of past emissions), development choices can affect what the impact of that climate change will be.

30
Type B analysis has two potentiil problems. The multiplying that with the projected tariff is highly questionable (unless the pri.-e elasticity of water demand is calculated into the estimates of incremental revenues.

opportunity cost of capital, the project is justified." forthe sake of simplicity). Foradditionaljustification, the benefits.<sup>12</sup> quantified, those benefits would have made the projects only. economically viable. In these cases, without additional information, the FIRR calculations did not give enough the non-quantified benefits need to be.

function"3 were determined and then connected by a linear Accuracyof thewholeproceduredependsonthe households use piped water for drinking, cooking, bathing, washing, and sometimes even for gardening. Water (1/c/d). Bathing, washing, and other water demand is from vendors, other than the price and quantity, consisThe third type C analysis tried to place both calculation of consumer surplus to the first 15 VIc/d of piped water."<sup>4</sup> However, that may actually have underesThe three type C analyses tried to come up with timated the benefits of piped water supply. When piped the additional information by estimating the value of water costs less than other nonpiped sources, consumers consumer surplus. To do that, the water demand function will enjoy a surplus on their water for other use also. had to be estimated in the relevant range. The same Despite this discrepancy, such a conservative approach procedure was followed in all three cases: the coordinates might be useful when the FIRR (based on existing tariff) of two points on the ordinary (uncompensated) demand is close to the opportunity cost of capital.

1 See OMS 3.72, para. 34.

12 One SAR, without presenting a demand analysis, used the assumed tariffasa proxy forbenefitsofproposed small "Additional" benefits were mentioned without quantifiefits are usually the reason why consumers might be double counting.

If a second point on the demand curve represents the full quantity of piped water consumption, the first 13 For the definition of ordinary and compensated demand

**14** While the method of estimating part of the consumer onatariff increaseof morethan 500 percent (in realterms) effect on water consumption.

5
Despite the progress, the inequalities are still substantial in the case of ANC4+and SBA, although, in both cases, the curves moved closer to the line of equality.

The test of dominance between concentration curves finds significant improvements for measles vaccination, contraception, ANC4+, and SBA. The shift was not significant for full immunization. In addition, only the improvement in SBA is found to be significant when the stricter rule, iup, is used.

<!-- Start of picture text --> Figure 2c. Concentration curves of selected MNCH services<br>(2000-2014)<br>Contraception ANC4+ SBA<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>cumulative proportion: children under 5 cumulative proportion: last birth in previous 5 years cumulative proportion: births in last 5 years<br>Earlier Survey Latest Survey E arlier Survey L atest Survey Earlier Survey Latest Survey<br>Line of Equality Line of Equality Line of Equality<br>Measles Vaccination Full Immunization<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>cumulative proportion: children 12-23 months cumulative proportion: children 12-23 months<br>E arlier Survey Latest Survey E arlier Survey L atest Survey<br>Line of Equality Line of Equality<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>cumulative proportion: underweight cumulative proportion: received ANC cum. proproportion: attended by skilled personnel<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>cumulative proportion: with full vaccination<br>cumulative proportion: with measles vaccination<br><!-- End of picture text --> Note: Computed from DHS 2000 (Earlier Survey), DHS 2011 (Latest Survey for immunizations) and mini-DHS (2014-Latest Survey for Contraception, ANC4+ and SBA). Values in the horizontal axis are ranked by wealth 29