rafmacalaba/datause-encoder-data huggingface dataset:
poverty, prisoners, migrant labourers, people in conflict and post-conflict situations and refugees and internally displaced persons. This is a very broad list, but it does not mention HIV negative cohabiting partners of HIV positive individuals as a group that should be specifically targeted for prevention. Table 16 shows that the 14 percent of all infected couples are “discordant female”, where the woman is infected and not the man, and that 27 percent are “discordant male”, where the man is infected and not the woman. This seems to be in line with the common assumption that unfaithful males are the main link between high risk groups and the general population.

This picture is quite different than in five other African countries (De Walque, 2007a) where the percentage of discordant couples is much larger.

## [Insert table 16]

In the other African countries, at least 55 percent and very often more than two-thirds of the infected couples are discordant couples and between 30 and 40 percent of the infected couples are couples where the only female partner is infected. A potential explanation for this result might be that extra-marital sex among women is more common than reported, or that, even if infrequent, women are very vulnerable to infection during extra-marital sex (for example, because they would be less likely to use condoms than single women and than married men).

# **6 Conclusions**

This paper analyzes the socioeconomic determinants of HIV infection and related sexual behaviors using the 2004 Lesotho Demographic and Health Survey. This is the first nationally representative survey in Lesotho to include an HIV test.

We find that education appears to have a protective effect: it is negatively associated with HIV infection (not always significantly though) and it strongly predicts preventive behaviors like condom use, the absence of non marital sex, delayed sexual debut, voluntary counseling and testing and knowledge about AIDS. This adds to the many reasons for supporting the current efforts by the Government of Lesotho in expanding access to primary and secondary education.

21
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE By increasing hydropower’s share of the regional generation portfolio, the trade expansion scenario would lead to substantial savings in carbon emissions. The main impact of trade expansion would be to enable a shift away from thermal generation and relatively dispersed small-scale hydropower toward larger and more cost-effective hydropower resources. Overall, the share of hydropower in the regional generation portfolio would increase from 25 to 34 percent, displacing coal and other thermal options (figure 3.6). Some 47.5 TWh of additional hydropower generation would reduce carbon emissions by 41 million tonnes (table 3.5). The savings in carbon emissions would be the highest among regional power pools, indicating that regional trade permits a much larger volume of hydropower to be harnessed.

**Figure 3.6 Power generation mix**

<!-- Start of picture text --> a. Trade expansion b. Trade stagnation<br>7% 7%<br>0% 2%<br>25%<br>34%<br>Hydro<br>Hydro<br>Coal Coal<br>Gas Gas<br>Other<br>Other<br>59% 66%<br><!-- End of picture text --> _Source:_ Derived from Rosnes and Vennemo 2009.

**Table 3.5 Differences in electricity production and carbon dioxide emissions under power trade scenarios**

||**WAPP**|**SAPP**|**EAPP**|**CAPP**|**Total**|**WAPP**|**SAPP**|**EAPP**|**CAPP**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|
||**_Produ_**|**_ction differe_**|**_nce (TWh)_**|||**_Em_**|**_issions sav_**|**_ings (millio_**|**_ns of tonne_**|**_s)_**|
|Coal||–41.5|0.7||–40.8||–37.8|0.6||–37.2|
|Diesel|–0.8|–0.3|0.3||–0.8|–0.6|–0.2|0.2||–0.6|
|Gas|–9.2|–5.3|–42.4||–56.8|–4.7|–2.7|–21.5||–28.9|
|HFO|0.2||0.4|–4.9|–4.3|0.1||0.3|–3.6|–3.2|
|Hydro|11.5|47.5|43.4|5.1|107|||||0|
|**Total**|**1.6**|**0.5**|**2.4**|**0.3**|**4.7**|**–5.2**|**–40.7**|**–20.4**|**–3.6**|**–69.9**|

_Source:_ Derived from Rosnes and Vennemo 2009. _Note:_ The East African Power Pool is expanded to include key Nile Basin trading partners Egypt, Ethiopia, and Sudan. CAPP = Central Africa Power Pool; SAPP = Southern African Power Pool; WAPP = West African Power Pool; EAPP = East African Power Pool; TWh = terawatt-hour; HFO = heavy fuel oil 46
**Table 5:** The effects of AIA on the adoption of big data and AI

|Regressions|(1)|(2)|(3)|
|---|---|---|---|
|Variables|Big Data|Big data|AI|
||2007/15|2007/19|2007/19|
|AIA Dummy|-0.185***|-0.182***|-0.027***|
||(0.030)|(0.034)|(0.011)|
|Observations|2,262|1,746|1,746|

Note: Regression 1 uses data for 2007 and 2015; regression 2 and 3 use data for 2007 and 2019. The dependent variables big data and AI adoption are dummy variables. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. All regressions use a binary AIA eligibility and are estimated via difference-in-differences. All regressions include year and firm fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age, not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.

analytics use of 7 percentage points and AI use by 1 percentage point. While seemingly small numbers, these should be considered in the context that only 3% of UK firms used AI by 2019<sup>38</sup> . Moreover, big data analytics use would have been 14% higher and AI 30% higher in 2019 in the absence of the AIA policy. Or put another way, the policy slowed down big data analytics by 1.4 years and AI diffusion by 1.2 years.<sup>39</sup> This delay is large, especially for AI, which has only emerged in the US in 2015 (Bloom et al., 2022).

# **5.4 Data workers**

The previous sections found that the AIA policy discouraged firm adoption of data analytics technologies, such as cloud, big data analytics or AI. In this section we examine whether this also materialises in the demand for workers performing data analytics tasks.

We find evidence that the firms treated by the AIA capital incentive reduced their demand for data analytics workers, but that this had no effect on the other types of workers. The wages of workers that perform data analytics fell by 1.5% to 1.6% in firms treated by the

> 38AI use is 3.4% in 2019, and assumed to be zero in 2007, corresponding to the 1.7% in Table 2

> 39In our data, big data analytics and AI diffusion were increasing approximately 5 percentage points and

> 1 percentage points per year, respectively.

26
# Table A2 NADI-ISIC Conversion

|Time Period|Internal Classifcation|Source for match with ISIC|
|---|---|---|
|1974−1979|Matched<br>with<br>new<br>1980<br>classifcation, based on 5-digit<br>NADI|Matched with Revision 2<br>Categories<br>with<br>nonzero<br>imports:<br>Conversion<br>Table<br>published<br>by<br>the<br>National Statistics Agency (INDEC)<br>Categories<br>with<br>no<br>imports:<br>Conversion table produced by Secretaría<br>de Planeamiento (Secretary of Planning,<br>Economic Ministry)|
|1980−1991|Data were kept at original<br>level of disaggregation.<br>(8-<br>or 10-digit, depending on the<br>year) (1)|same as 1974−1979|
|1992−2001|Harmonized System. Original<br>level of aggregation (8 digits)|Matched with Revision 3<br>All<br>categories:<br>Conversion<br>Table<br>provided<br>by<br>Secretaría<br>de<br>Industria<br>(Secretary of Industry)|

Notes: (1) In 1980 and 1981, some tariffs are disaggregated at 8 digits, and other at 10. In these cases, the conversion was done in two stages, starting with the 10 digit categories, and then continuing with the rest.

corresponding to each 4-digit category. To go one step further and obtain the three-digit tariff measure, we repeated this procedure. The final result is therefore the median of the medians of each subcategory.

The analysis in the main text uses v2 as the starting point for the aggregation procedure. However, our results do not change much if we use v1 instead. In fact their correlation coefficient between our tariff measures v1 and v2 is 0.954 at the three digit level. In Figure A1, we plot the time series of tariffs for nine 2-digit categories. It is clear that the series are quite similar (the correlation is 0.682 at this stage).

We used an intermediate nomenclature built by the World Bank to match the Revision 2 and Revision 3 classifications. Once again, several categories overlap and we used the median to take a representative tariff.

As a final step, we adjusted the import tariffs to take into account the differential tariff 19
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# **5.3 Impacts on Income Volatility**

Entrepreneurship may be a way to both raise incomes and lower income volatility. To causally estimate women’s capacity to cope with negative economic shocks, we exploit the unanticipated COVID-19 lockdown in Uganda.<sup>25</sup> Figure 1 shows bi-weekly event study estimates using revenue data from our SMS survey. All effects are relative to the two weeks prior to the first Ugandan lockdown, estimated separately for the control group and treatment group. As such, each estimate is normalized by revenues in each group in the two weeks prior to the lockdown. Doing so allows us to see how each group was affected by the COVID-19 lockdown, but the figure does not show treatment effects. We estimate effects 5 months before the lockdown, during the 6-month lockdown, and for 4.5 months after the lockdown was lifted.<sup>26</sup> Prior to the lockdown, revenues of firms in both the treatment and control group are stable relative to the two weeks prior to the lockdown. While there are fluctuations in reported revenue, most bi-weekly periods before the lockdown exhibit no statistically significant differences for women in either treatment group. Such patterns confirm the validity of our event study design: the COVID-19 lockdown was not anticipated.

The lockdown causes immediate, significant reductions in revenues among women in the control group that persist over multiple months. Women in the control group have lower sales by around UGX 5,000 (USD1.29) per day until the final month of the lockdown. Strikingly, women in the treatment group do not experience any significant reductions in sales: we cannot reject that their revenues are the same as the two weeks prior to lockdown in most periods, and in three lockdown periods average revenues are significantly higher than the two weeks prior to lockdown. The difference in the effect of the lockdown between the control and treatment group is statistically significant up until the last month of the lockdown, when women in the control group begin to recover to pre-lockdown revenue levels.

> 25Note that this analysis was not pre-registered.

> 26The first COVID-19 case in Uganda was detected on March 21, 2020. On March 31, 2020 the president announced a 14-day lockdown effective April 1, 2020 which suspended all public and private transportation, closed all but essential businesses, and suspended all forms of public gatherings. Authorities started lifting select components of the lockdown 7 weeks after. Uganda’s school closure lockdown and curfew were the longest worldwide.

28
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26
# Figure 10. Effects of climate-related infrastructure investment on potential growth

A. EMDEs: potential growth in climaterelated infrastructure investment scenarios <!-- Start of picture text --> Percent Improvement in spending efficiency<br>6 Climate-related investment boost<br>Baseline<br>5<br>4<br>3<br>2011-21 2022-30 Impact<br>C. EMDEs: potential growth in climate-<br>related infrastructure investment scenarios<br>Percent Improvement in spending efficiency<br>8 Climate-related investment boost<br>Baseline<br>6<br>4<br>2<br>EAP ECA LAC<br>2011-21 2022-30 Impact 2011-21 2022-30 Impact 2011-21 2022-30 Impact<br><!-- End of picture text --> C. EMDEs: potential growth in climaterelated infrastructure investment scenarios B. EMDE excluding China: potential growth in climate-related infrastructure investment scenarios Percent Improvement in spending efficiency 6 Climate-related investment boost Baseline 5 4 3 2011-21 2022-30 Impact D. EMDEs: potential growth in climaterelated infrastructure investment scenarios <!-- Start of picture text --> Percent Improvement in spending efficiency<br>8 Climate-related investment boost<br>Baseline<br>6<br>4<br>2<br>MNA SAR SSA<br>2011-21 2022-30 Impact 2011-21 2022-30 Impact 2011-21 2022-30 Impact<br><!-- End of picture text --> Source: World Bank estimates.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. A.-D. GDP-weighted arithmetic averages. Climate-related investment boost assumes an increase in average annual investment between 2011-21 and 2022-30 of 2.3 percentage points of GDP in line with the average of World Bank’s Country Climate and Development Reports of 13 countries (Argentina, China, Egypt, Ghana, Iraq, Jordan, Kazakhstan, Morocco, Peru, Philippines, South Africa, Turkiye, and Vietnam) The regional differences are in line with Rozenberg and Fay (2019). Improvement in spending efficiency assumes that each quartile of the spending efficiency moves two quartiles among emerging market and developing economies (EMDEs).
plot in MAPS I<sup>34</sup> , and (3) edge effects, specifically, the share of the crop cut sub-plot that is within 4 meters of the nearest plot edge (and separately, within 1 meter of the nearest plot edge, specifically in Round 2, given the increase in the crop cut sub-plot area to 8x8m). The inclusion of this vector is motivated by the competing hypotheses on the weakening or disappearing inverse scale-productivity relationship as a result of controlling for plot attributes, such as soil quality (Barrett et al., 2010), and edge effects (Bevis and Barrett, 2017). Appendix V provides more details on the construction of the soil fertility index and the edge effects.

# **5.2. Drivers of Farmer Over-Estimation**

Descriptive analysis is first used to highlight the validity of the hypothesized sources of measurement error in self-reported production estimates, primarily heaping of production figures. Consistency of the error across time is additionally explored, with an eye for examining the potential to correct this measurement error econometrically. Following Desiere and Jolliffe (2017), we subsequently move to explore the correlates of farmer over-estimation through regression analysis of the log ratio of self-reported to crop cutting yields. Since it is feasible that the drivers of farmer over-estimation vary along the distribution of over-estimation, recentered influence function (RIF) regressions are estimated. The econometric method, put forth by Firpo et al. (2009), executes an unconditional quantile regression by first estimating an influence function recentered on a given quantile, and subsequently utilizing the estimated RIF value as the dependent variable in a linear regression. The influence function for the dependent variable, Y, in this case the degree of measurement error in self-reported production-based yields proxied by the log ratio of selfreported to crop cutting yields, is as follows: where ���≤��� equals 1 if the dependent variable is less than or equal to the quantile QT (0 otherwise), QT is the population T-quantile of the unconditional distribution of Y, and _fY(QT)_ is the density of the marginal distribution of Y. The RIF is a function of equation (4), assuming _IF(y;v)_ is the influence function for an observed productivity outcome _y_ :

- (5) ���(�; �) = �(��) + ��(�; �)

where _v(FY_ ) is the distributional statistic for the dependent variable. Therefore,

- (6) ���(�; ��) = �� + ��(�; ��)

> 34 For more information on the MAPS maize variety identification component, please see Ilukor et al. (2017).

20
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35. There are several possible reasons for this. First, the current bus operations may not be provided to the poor, who may need to be served more. As discussed, the current routes may not be extended to suburban areas where many poor people live. In addition, affordability may also be an issue. The current bus fares are MGA 500 to MGA 900 or US$0.14 to US$0.24 per ride, which are not necessarily too expensive. However, taking the level of services (i.e., slow speed, too many stops, too congested and unsafe) into account, the real fares may not be so low. As the result, relatively rich people tend to benefit more from public transport services. This is consistent with the estimated household quantile effects. The dummy variables for the poor households, i.e _., Q1_ or _Q2_ , tend to have greater coefficients in absolute terms.

36. Second, there may be a measurement error in our rent variable. In the sample, many households actually do not pay any rent. They are homeowners and relatively rich. In the above estimation, a conventional approach to avoid the logarithm of zero was used, i.e., adding a small positive value to _RENT_ . However, this may cause significant bias in the estimation results (Battese 1997). Thus, the Battese specification, which introduces dummy variables, _dRENT_ , for the incidence of zero inputs, is used. The results are shown in **Table 4** . The statistical significance of _RENT_ was lost, while the rest of the coefficients remain broadly unchanged: The impact of commuting on wages is significantly positive, and the proximity to taxi-be services is critical to support the commuting decision.

**<u>Table 4. Endogenous treatment effects model with Battese specification for</u>** **_RENT_**

|<br>Dependent var.|<br>ln_W_||<br>_D_||
|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|
|_D_|0.691|(0.150) ***|||
|ln_TAXIBE_|||-0.076|(0.037) **|
|ln_SUBUBUS_|||0.026|(0.041)|
|ln_MINCBD_|||0.038|(0.177)|
|_dRENT_|||0.087|(1.065)|
|ln_RENT_|||0.054|(0.096)|
|ln_AGE_|0.183|(0.089) **|-0.324|(0.151) ***|
|_MALE_|0.495|(0.056) ***|-0.172|(0.091) *|
|_PRIM_|0.194|(0.085) **|0.283|(0.167) *|
|_SECO_|0.286|(0.088) ***|0.591|(0.170) ***|
|_TERT_|0.622|(0.097) ***|0.677|(0.171) ***|
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