rafmacalaba/datause-encoder-data huggingface dataset:
reasons. First, interviewed women and men were asked if they consented to be tested for HIV. Further, the HIV results obtained can be merged with the socio-demographic data collected in the individual questionnaires. Compared to previous surveys including information on HIV/AIDS, the LDHS has the great advantage of including this bio-marker and not relying exclusively on self reported behavior. In addition, the survey questionnaire includes relevant sections on both health and sexual behavior. Finally, the data is nationally representative and the variables are defined similarly in DHS implemented in different countries, so it is possible to perform useful international comparisons.

The remainder of the paper is organized as follows: Section 2 gives some insights about the background of HIV/AIDS diffusion in Lesotho, Section 3 describes the data and summary statistics, Section 4 shows the empirical analysis and reports the main findings, Section 5 investigates HIV infection among couples, and section 6 concludes.

# **2 Background of HIV/AIDS in Lesotho**

Lesotho is a small mountain state, located in the southern part of Africa and completely surrounded by the Republic of South Africa. The country is divided into ten administrative districts: Butha-Buthe, Leribe, Berea, Maseru, Mafeteng, Mohale's Hoek, Quthing, Qacha's Nek, Mokhotlong, and Thaba-Tseka. The population of Lesotho is estimated at 2.1 million with a population growth rate of 0.144% (Central Intelligence Agency, 2007). The population estimates for Lesotho explicitly take into account the effects of excess mortality due to AIDS, resulting in lower life expectancy (49.9 years) (Central Intelligence Agency, 2007), higher infant mortality and death rates, and lower population and growth rates. The number of single and dual orphans has risen to 100,000 children aged between 0 and 17 years old and 40 percent of households have at least one orphan (DHS 2004).

The principal mode of transmission of HIV in Lesotho is heterosexual contact and specifically multiple concurrent partnership (UNAIDS 2006). In the past decade, 60% of the total Lesotho workforce was employed in the mines in South Africa. The mines host a large sex industry, and, anecdotal evidence suggests that, once away from their families, men might be more likely to have multiple sexual partners. Once 4
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33
survey took place throughout the month of April, interviewing students in weeks 3, 4, 5 and 6 after schools had closed, with final attempts ending May 3. We successfully interviewed 1,552 students for an overall survey response rate of 64.3%. Only 10 students (0.4%) refused the survey, while the rest had phone numbers that were not answered (11.0%), were non-existent (5.4%), or went straight to voicemail (16.8%). Appendix Table 1 compares the characteristics of students who respond to the survey to those who did not. While response rates are lower for those from the bottom wealth quartile, students with less-educated mothers, indigenous students, and students in the two more remote provinces, we still have good coverage of students from all these groups. Nevertheless, we caveat that to the extent that students who do not respond have less technology and are more isolated, we may overstate the share of students who are engaging in remote learning. We see few differences in the characteristics of those students we reach on first attempt, versus those who it took multiple attempts to interview.

# **3. Access to and Use of Remote Learning Technologies**

Table 1 summarizes the remote learning technologies that students have in their homes. Two-thirds of students have a computer or tablet in their home, 74 percent have internet, and 59 percent have both internet and a computer or tablet. An alternative to online learning is educational programs broadcast by television or radio: 92 percent of students have television in their homes, and 59 percent radio. Access varies with socioeconomic level. We measure wealth quartiles on the basis of household assets measured in our baseline survey.<sup>2</sup> Only 57 percent of students in the bottom wealth quartile have internet in their homes, compared to 87 percent of students in the top wealth quartile. Likewise, access is lower for students whose mothers have at most high school education, for students of indigenous ethnicity, and for students from the more remote Napo and Orellana regions.

Table 2 then examines usage of different forms of remote learning. We first asked whether students have used any form of online or tele-educative content in the past seven days, and if so, which platforms they had used. As a check against students giving what they think to be socially desirable answers, we included the name of a made-up platform “ _Leer Ahora_ ” (Read Now). Only 4.7 percent

> 2 We take quartiles of an asset index formed as the first principal component of the following seven assets measured in our baseline survey: washing machine; air conditioning/dryer; flat screen television; family car; domestic employees; number of bathrooms; number of bedrooms.

5
71

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22
**Table 4.1**

**Correlates of the skilled-unskilled wage premium. 16 LA countries, 1989-2009**

||**Simple**|**Country**<br>**fixed**|||**Co**|**untry andy**|**ear fixed eff**|**Cl**<br>**ects**|**El VEN**|<br>**L**|
|---|---|---|---|---|---|---|---|---|---|---|
|||||||||**ontros,**|**xc.**|<br>**ong**|
||**OLS**||||||**Controls**||||
||(1)|(2)<br>**effects**|(3)|(4)|(5)|(6)|(7)|**min w.**<br>(8)|**CHL**<br>(9)|**series**<br>(10)|
|**Relative supply**|-0.2195|-0.1435|-0.203|-0.1671|-0.1646|-0.1729|-0.1474|-0.2689|-0.1608|-0.2062|
||[0.0260]***|[0.0619]**|[0.1849]|[0.1616]|[0.1573]|[0.1596]|[0.1556]|[0.1337]*|[0.1564]|[0.2252]|
|**Log Net Barter Terms**||||-0.1824|-0.1911|-0.1858|-0.1751|-0.1633|-0.2525|-0.2086|
|**of Trade**||||[0.0544]***|[0.0533]***|[0.0492]***|[0.0580]***|[0.0539]***|[0.1088]**|[0.0698]**|
|**Aggregate**|||||0.6656||1.0884|0.5989|0.7705|1.1363|
|**unemployment rate**|||||[0.5213]||[0.5434]*|[0.5388]|[0.4837]|[0.4445]**|
|**Unskilled**||||||0.8390|||||
|**unemployment rate**||||||[0.4748]*|||||
|**Skilled unmeployment**||||||-0.2548|||||
|**rate**||||||[0.5658]|||||
|**Log minimum wage**<br>**index (1995+)**||||||||-0.0396<br>[0.0654]|||
|**Constant**|0.9062<br>[0.0215]***|0.9551<br>[0.0398]***|0.8694<br>[0.1892]***|1.7065<br>[0.1939]***|1.7162<br>[0.1816]***|1.6855<br>[0.1623]***|-0.6757<br>[1.5420]|1.627<br>[1.6355]|1.9858<br>[0.4587]***|1.7083<br>[0.2541]***|
|**Observations**|209|209|209|209|209|209|209|178|186|141|
|**R-squared**|0.03|0.37|0.48|0.49|0.50|0.50|0.51|0.44|0.52|0.58|
|**Year effects**|No|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Country effects**|No|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Additional controls**|No|No|No|No|No|No|Yes|Yes|No|No|

Source: own computations based on SEDLAC (CEDLAS and World Bank) and WDI (World Bank). Notes: Robust standard errors in brackets, clustered at the country level for models 3-10. * significant at 10%; ** significant at 5%; *** significant at 1%. The additional control variables include the log of GDP per capita, the log of the real exchange rate against the US Dollar (both from WDI), and the log of a tariff index. Column 8 includes the log of a minimum wage index compiled by ILO (available only for 1995-2009). The regressions in column 10 only include the nine countries for which there is a relatively long, continuous and homogeneous data source: Argentina, Brazil, Chile, Costa Rica, Mexico, Panama, El Salvador, Uruguay and Venezuela.

32
Table 8: Effects of forecast errors and forecast growth on different wage components, high vs low earners, according to managerial skill (defined as top 5% earners)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dep. variable:<br>|log ba|se wage|log over|time pay|log o|ther pay|
|High versus low earners|high|low|high|low|high|low|
|Definition|15%|85%|15%|85%|15%|85%|
|A. Firms with high-skilled|managers||||||
|Weighted forecast error|0.0011<br>(0.0084)|-0.0042<br>(0.0038)|0.1185***<br>(0.0342)|0.0539*<br>(0.0296)|0.0681<br>(0.0478)|0.0608**<br>(0.0298)|
|Weighted forecast growth|-0.0040<br>(0.0044)|-0.0046*<br>(0.0025)|0.0719***<br>(0.0239)|0.0636***<br>(0.0212)|-0.0294<br>(0.0386)|-0.0025<br>(0.0177)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|9299|9299|9299|9299|9299|9299|
|<br>N (firms)|3682|3682|3682|3682|3682|3682|
|Adj. R<sup>2</sup>|0.0323|0.0808|0.0361|0.0404|0.0215|0.0280|
|RSS|272|59|10771|7595|11250|5042|
|B. Firms with low-skilled|managers||||||
|Weighted forecast error|0.0109*|0.0097***|0.0301|0.0308|-0.0175|0.0067|
||(0.0062)|(0.0028)|(0.0225)|(0.0217)|(0.0368)|(0.0286)|
|Weighted forecast growth|0.0012<br>(0.0039)|0.0049**<br>(0.0021)|0.0299*<br>(0.0164)|0.0154<br>(0.0145)|0.0088<br>(0.0262)|0.0038<br>(0.0181)|
|Period x region FE|Y|Y|Y|Y|Y|Y|
|Period x industry FE|Y|Y|Y|Y|Y|Y|
|Period x export share|Y|Y|Y|Y|Y|Y|
|Firm FE|Y|Y|Y|Y|Y|Y|
|N (obs.)|11585|11585|11585|11585|11585|11585|
|<br>N (firms)|5063|5063|5063|5063|5063|5063|
|Adj. R<sup>2</sup>|0.0377|0.0719|0.0265|0.0217|0.0164|0.0185|
|RSS|423|85|8736|6446|15292|8820|
|Notes: In each column, the|dependent va|riable is the|change betw|een the averag|es of each|3-year period.|
|Standard errors are clustered <br>significance.|at firm-level.|*10% level of|significance,|**5% level of si|gnificance,|***1% level of|
POVERTY-SPECIFIC PURCHASING POWER PARITIES (PPPs) IN AFRICA <!-- Start of picture text -->
1.100<br>Below IPL Uniform Kernel (bw 1.0)<br>Uniform Kernel (bw 0.5) Uniform Kernel (bw 0.25)<br>Bi-weight Kernel (bw 1.0) Bi-weight Kernel (bw 0.5)<br>Bi-weight Kernel (bw 0.25)<br>1.050<br>1.000<br>0.950<br>0.900<br>PPP Ratios<br>Burkina Faso Burundi Cameroon Cape Verde Congo, Rep. Congo, Dem. Rep. Côte d'Ivoire Ethiopia Gabon Ghana Guinea Kenya Liberia Madagascar Malawi Mali Mozambique Namibia Niger Nigeria Rwanda Senegal South Africa Swaziland Tanzania Togo Uganda Zambia<br><!-- End of picture text -->

FIGURE 8. EFFECT OF MOVING FROM SNA-BASED PPPS TO VARIOUS POVERTY-WEIGHTED PPPS, 28 COUNTRIES

# **5.** **_Poverty-specific PPPs: Removing irrelevant items_**

## _5.1 Method and approach_

We now turn our focus away from the basic heading level and toward the item level. The povertyspecific PPPs in the previous section attenuated the influence of basic headings irrelevant to the poor through reweighting. However, if we want to produce PPPs untainted by any possible influence of items irrelevant to the poor, then the reweighting process is by and large effective, but by no means sufficient. Items seldom consumed by the poor, such as extra virgin olive oil, would still be present within basic headings like “other edible oils and fats”; which, overall, have an important role in the consumption basket of both poor and non-poor.

As mentioned before, the inclusion of items that are arguably irrelevant to the poor in ICP consumption PPPs has often raised doubts on their applicability for poverty analysis. However, the effect (if any) of constructing consumption PPPs that exclude the price of, say, extra virgin olive oil or Kellogg's Cornflakes is not immediately evident.

In light of this, we produce new consumption PPPs with items deemed irrelevant to the poor removed from the calculation. We refer to these poverty-specific PPPs as reduced-list PPPs and produce three scenarios: (1) after removing items priced only in supermarkets, (2) after removing clothing and footwear items belonging to a _medium_ or _high_ brand stratum (for brevity, we name them “branded garments & footwear”); and (3) after removing food and nonalcoholic beverage items that we categorized as _premium_ beforehand.

Within each reduced-list scenario, we computed two sets of PPPs for each of the 50 African countries. First, a full-list set, based on the full basket of items used for collecting prices for the 2011 ICP in Africa, and, second, a counterfactual reduced _-_ list set after removing items deemed irrelevant according to each scenario.<sup>13</sup> Basic heading PPPs for the two sets, in each of the three scenarios, were estimated using the

> 13 Full-list PPPs were only estimated for expenditure categories for which counterfactual PPPs were also produced. Otherwise, published 2011 ICP PPPs were used. Hence, the full-list PPPs for the ‘supermarket only’ and ‘premium food and nonalcoholic’ items are equivalent, but differ slightly from the full-list PPPs for the ‘excluding branded clothing & footwear’ scenario.

11
by 0.4 percent. Because the Internet penetration rate amounted to 63.5 percent in 2008 (the final year in our sample) in the absence of the Internet the counterfactual paper consumption in 2008 would have been 25.5 percent (0.635 times 0.4) higher than observed.

Our estimates thus suggest that the increase in Internet adoption caused a large decrease in paper consumption. In order to evaluate the environmental consequences from this decrease in paper consumption it is useful to measure the decrease in paper consumption in kilograms. The most recent data on global paper consumption from 2011 (from the Food and Agriculture Organization of the United Nations) indicate that the global consumption of paper amounted to 400.335 million kilograms. In turn, according to the ITU Global Internet penetration amounted to 32.7 percent in 2011.<sup>15</sup> Combining these statistics and using our estimates from the previous section suggests that absent the Internet paper consumption in 2011 would have been 52.362 million kilograms greater than observed (multiplying 400.335 times 0.327 times 0.4).

The environmental effects from paper production vary by both the technology employed in its production and paper type. We can use as an approximation the production of 1000 kilograms of printing and writing paper using the Kraft pulping technology and EFC bleaching technology (see Section 3a).<sup>16</sup> According to the Environmental Paper Network (a network that brings together more than 100 NG0s and whose objective is to quantify the environmental impact of the paper industry) producing these 1000 kilograms of paper uses 3.47 tons of wood, 38.7 million BTUs of energy, 20,520 gallons of water, generates 2,283 pounds of solid waste, generates

> 15 Note that our statistics regarding Internet penetration in Table 2 are higher than 32.7 percent. The explanation is that our statistics in Table 2 do not include data on Internet penetration from many countries with large population and very low penetration rates (See note Table 2).

> 16 The Kraft pulping technology is employed in approximately 80 percent of the total paper production (Technical Association of the Pulp and Paper Industry “Fundamentals of the Kraft Recovery Process Technology”). The EFC bleaching technology is employed in approximately 75 percent of the Kraft total pulp production (Ensis/CSIRO (Australia) joint research "Frequently Asked Questions on Kraft Pulp Mills").

21
15 land-use planning. Because no market in land developed to signal scarcity value, clusters of high-rise apartments buildings would typically be built outside the city center, often on prime farm land, leading to inefficient use of land and high transportation and infrastructure costs. Although Slovenia suffered less from this syndrome than more highly-centralized socialist systems, its construction and zoning rules5' had some of the same shortcomings. Furthermore, existing Slovene construction regulations require a long list of required permits that are likely to be overly-restrictive, illdesigned, or redundant in a private market economy. If the past is an indicator, they are also likely to impose an expensive and time-consuming burden that will further hamper the emergence of a private construction sector.<sup>59</sup>

# Rights to Intellectual Property

Until recently there was little need for intellectual property protection in Slovenia or other socialist countries. Not only was there less incentive for invention within the relatively rigid and static socially-owned enterprises, but the basic concept of individually-owned intellectual property was anathema to the socialist system. Intellectual property, like other industrial property, was owned by enterprises rather than individuals and ultimately under the control and discretion of the state. Foreign intellectual property was nominally protected under existing intellectual property laws (as discussed below), but the protection was weak and inconsistent. Without extensive foreign involvment in the economy, however, intellectual property protection was not an important issue. It is becoming so only now, as foreign investment is more eagerly sought and as the domestic private sector begins to grow.

# Patents and Trademarks

After declaring independence, Slovenia recognized all federal Yugoslav laws in the field of intellectual property as applicable in the new republic. These include the law on the protection of industrial property of 1981 (covering patents and trademarks)60 , the copyright law of 1978 (as amended), 6 and the statute setting up the patent office.'2 Slovenia is now

**58** Zakon o stavbnih zemljiibih [Law on Buidtng Land], Official Gazette SRS, No 18 (1984), most recent amendment, Official Gazette SRS, No 33 (1989).

59 Obtaining a permit for building contruction typically takes at least 1 year.

60 Zakon o varstvu izumov, tehnibnih izboljlav in znakov razlikovanja (Law on Protection of Inventions, Tecnical improvements and Signs of Differentiation], Yugoslav Official Gazette, No 34 (1981), most recent amertment Yugoslav Official Gazette, No 20 (1990).

**"** Zakon o avtorski pravici (Copyright Law], Yugoslav Official Gazette, No 19 (1978), most recent amendment Yugoslav Official Gazette, No 21 (1990).