rafmacalaba/datause-encoder-data huggingface dataset:
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<!-- Start of picture text --> 50<br>4 § Wife listed as main DM over pknting, harvesting = Wife not mainDM<br>40<br>2 30 Ee<br>85 25 Beee teoe<br>£ 1 5 basae aoe<br>10 i<br>o ad rome oom em _va<br>Neither Husband Wife Joint H:Husb., H:Husb., H-Joint, H:Joint, H:Noown,<br>W:Wife W-oint W:Noown W:Wife W: Joint<br>Agree onrights Within-couple discrepancy<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>45 (@ Wife listed as main DM over planting, harvesting m= Wife notmainDM<br>40<br>2 30<br>2 ee . Be<br>. Pl| e ae Bg.<br>Neither Husband Wife Joint H-Husb.. H:Husb. H:Joint. H-Joint, H:Noown,<br>W-Wife W-Joint W:Noown W-:Wife W:Joint<br>Agree on ownership Within-couple discrepancy<br><!-- End of picture text -->

<!-- Start of picture text --> 50<br>45 Wife listed as main DM over planting, harvesting | Wife notmainDM<br>40<br>2 30 8 Lu<br>zg© aae eded<br>2 etcd oestd<br>10 eat<br>oS 3 a |<br>Neither Husband Wife Joint H:Husb., H:Husb.| H:Joint.all H-Joint,Be H:Noown.om<br>W-Wife W-Joint W-Noown W-:Wife W-:Joint<br>Agree on rights Within-couple discrepancy<br><!-- End of picture text -->
**TABLE 4: Impacts on Child's Behavioral Problems - 18-Month Follow-Up**

|||**Depende**<br>**Total Di**<br>**(Inve**|**nt variable: S**<br>**Question**<br>**fficulties**<br>**rted)**|**trengths and**<br>**naire Score**<br>**Pros**|**Difficulties**<br>**ocial**|
|---|---|---|---|---|---|
|**Variables**||**(1)**|**(2)**|**(3)**|**(4)**|
|T2 (caregiver training)||-0.039|-0.063|0.098|0.114|
|||(0.065)|(0.061)|(0.079)|(0.077)|
|T3 T2 + rir innti||0.098|0.088|0.018|0.026|
|( caegve ceves)||(0.062)|(0.062)|(0.083)|(0.081)|
|T4 (T2 + arentin trainin)||0.105*|0.072|0.261***|0.252***|
|pg g||(0.056)|(0.053)|(0.080)|(0.078)|
|Laed Deendent Variable (Baseline)|||0.317***||0.142***|
|gg p|||(0.026)||(0.027)|
|Missing Lagged Dependent Variable (Baseline)|||-0.076<br>(0.099)||0.032<br>(0.086)|
||T2=T3|0.037**|0.019**|0.325|0.292|
|F-test for Equality of Parameters (p-value)|T2=T4|0.019**|0.020**|0.028**|0.056*|
||T3=T4|0.897|0.777|0.003***|0.006***|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|
|Lagged Dependent Variable and Age Dummies?||No|Yes|No|Yes|
|Number of observations||1,938|1,938|1,938|1,938|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at the child level using Baseline and 18-month data on the Strengths and Difficulties Questionnaire. Standard errors in parentheses are clustered at the CBCC level. Strengths and Difficulties Questionnaire: Total Difficulties Score had its score inverted so that a higher score is better. We weight observations using sampling weights (and tracking weights for the 42 randomly tracked observations at the 18-month follow-up). We replaced the missing values of lagged dependent variable with its (weighted) average for the overall sample at baseline. For each round, indices are stardardized using the (weighted) mean and standard deviation for the control group in that round.

46
in the literature as being important for trade in general, or for GVC trade in particular.<sup>1</sup> We focus on widely-used measures of backward GVC participation, mainly drawing on the EORA database, which capture the import content of exports, that is, how much imported materials are used in countries’exports. We consider seven broad types of determinants emphasized in the trade literature: (i) factor endowments, (ii) geography, (iii) domestic industrial capacity, (iv) trade policy and foreign direct investment (FDI), (v) institutional quality, (vi) connectivity, and (vii) macroeconomic factors.

However, establishing causality is challenging in the cross-country setting as some of the potential determinants, such as tari¤s and FDI in‡ows, are endogenous to GVC participation. To this end, we rely on instrumental variables (IVs) estimation in the cross-country regressions, and a di¤erence-in-di¤erence framework following Rajan and Zingales (1998) in the cross-country cross-sector regressions. The use of IVs also mitigates biases in the construction of the GVC participation variables, which hinges on the use of international input-output tables that may have measurement problems. Our IV selections are guided by existing trade theories and strong empirical evidence.

The results from our cross-country decadal panel and our cross-country IV regressions show that the key determinants of backward GVC participation are, in order of importance, factor endowments, geographical location, political stability, tari¤s and FDI in‡ows, and domestic industrial capacity. We show that our IVs have the expected coe¢ cients and signi...cant explanatory power with high ...rst-stage F-statistics. Our second-stage IV coe¢ cient estimates are larger in magnitude than the corresponding least squares coe¢ cient estimates suggesting that measurement errors and reverse causality are important in biasing the least squares results. Our ...ndings are robust to the inclusion of alternative determinants and controls. Moreover, broad sector analysis suggests that the overall ...ndings are largely driven by manufacturing. Cross-country cross-sector panel regression analysis in a di¤erence-indi¤erence framework further con...rms that institutional quality, factor endowments, trade policies, FDI, and connectivity matter for GVC participation.

Finally, we extend our analysis to show that the most determinants have larger impacts

> 1For theoretical models on GVCs and international production fragmentation, please refer to Feenstra and Hanson (1997), Feenstra (1998), Antràs and Helpman (2004), Romalis (2004), Nunn (2007), Antràs and Helpman (2008), Chor (2010), Antràs (2016), and Antràs and De Gortari (2020).

2
Blomstrom, M., Kokko, A., (1996): Multinational Corporations and Spillovers. CEPR Discussion Paper, No. 1365.

Carstensen, K, and Toubal, F. (2004): ―Foreign direct investment in Central and Eastern European countries: a dynamic panel analysis‖, _Journal of Comparative Economics_ , (32) 3–22 Caves, R. E. (1971): ―International corporations: The industrial economics of foreign investment‖, _Economica_ , 38(149), 1–27.

Caves, R.E., (1996): Multinational Enterprise and Economic, Analysis, 2nd ed. Cambridge University Press, Cambridge.

Cimoli, M., and Katz, J. (2003): ―Structural reforms, technological gaps and economic development: a Latin American perspective‖, _Industrial and Corporate Change_ , (12) 2, 387-411.

Co, C. Y., (2001): ―Trade, foreign direct investment and industry Performance‖, _International Journal of Industrial Organization_ , (19)163–183.

Coelli, T. J., Rao, P., O‘Donnell, C. J., and Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). Springer.

Cheung, K.-Y., and Lin, P. (2004): ―Spillover effects of FDI on innovation in China: Evidence from the provincial data‖, _China Economic Review_ , 15(1), 25–44.

Coelli, T. J., Rao, P., O‘Donnell, C. J., and Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). Springer.

Damijan, J. P, Knell , M, Majcen, B., and Rojec, M. (2003): The role of FDI, R&D accumulation and trade in transferring technology to transition countries: evidence from firm panel data for eight transition countries, _Economic Systems_ , (27) 189–204.

Das, S. (1987): ―Externalities and technology transfer through multinational corporations‖, _Journal of International Economics_ , 22(1–2), 171–182.

Demsetz, H. (1973): ―Industry structure, market rivalry, and public policy‖, _Journal of Law and Economics_ , 16(1), 1–9.

Dunning, J.H., (1993): Multinational Enterprises and the Global Economy. Addison-Wesley, Reading.

Farrel, M.J. (1957), ―The Measurement of Productivity Efficiency‖, _Journal of the Royal Statistical Society_ , Series A, CXX, Part 3. 253-290.

Findlay, R. (1978): ―Relative backwardness, direct foreign investment, and the transfer of technology: A simple dynamic model‖, _Quarterly Journal of Economics_ , 92(1), 1–16.

13
# **<mark>III. Brief Overview of HIV and AIDS in Colombia</mark>**

## **1. Country Context**

Colombia is located in South America; it has a population of 44,977,758 (estimated for 2009).<sup>15</sup> Table 1 below shows the population distribution by age and sex:

**Table 1: Population of Colombia, 2009**

|**Totalpopulation**|44,977,758|(100%)|**Totalpopulation**|44,977,758|(100%)|
|---|---|---|---|---|---|
|**Men**|22,203,234|(49,4%)|**0–14**|13,073,904|(29,1%)|
|**Women**|22,774,524|(50,64%)|**15–49**|23,651,446|(52,6%)|
||||**> 50**|8,252,408|(18,3%)|

**<mark>Source:</mark>** <mark>Censo General de Colombia (www.dane.gov.co). Población proyectada. 2009.</mark>

The country extension is 1,141,748 Km<sup>2</sup> , which implies a population density of 39 inhabitants by Km<sup>2</sup> .

The country is organized in the following manner:

- 32 departments and 5 districts (including the _Distrito Capital de Bogotá_ ).

- 20 sub-departmental entities ( _corregimientos departmentales)_

- 1,101 municipalities

- 8,059 villages (smaller than municipalities)

The GDP in Colombia in 2009 (last year for which this information is available) was $234,045 million (current international $ or 392,446 million $ PPP).<sup>16</sup> This represents a per capita income of $4,990.<sup>17</sup> The life expectancy at birth in 2009 was 73 years;<sup>18</sup> the adult literacy rate (15-years-old and above) was 93%.<sup>19</sup>

> 15 Censo General de Colombia (www.dane.gov.co). 2009.

> 16 World dataBank. World Development Indicators (WDI) & Global Development Finance (GDF) (http://databank.worldbank.org).

> 17 The World Bank Open Data. GNI per capita, Atlas method (current US$) (2009)

> (http://data.worldbank.org/indicator) (Atlas method).

> 18 The World Bank Open Data. Life expectancy at birth, total (years) (2009)

> (http://data.worldbank.org/indicator).

> 19 The World Bank Open Data. Literacy rate, adult total (% of people ages 15 and above) (2009)

> (http://data.worldbank.org/indicator).

19
Table 1: Effect of zonal classification on change in nightlight intensity

||Night<br>light in|-time<br>tensity|Border<br>districts|No metro<br>districts|Unchanged<br>zones|Shuffled<br>zones|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Red zone district*Post|-0.043<sup>∗∗∗</sup>||-0.035<sup>∗∗∗</sup>|-0.031<sup>∗∗∗</sup>|-0.041<sup>∗∗∗</sup>||
||(0.011)||(0.011)|(0.005)|(0.011)||
|Orange zone district*Post|-0.0094<sup>∗∗∗</sup><br>||-0.011<sup>∗∗∗</sup><br>|-0.0094<sup>∗∗∗</sup><br>|-0.0093<sup>∗∗∗</sup><br>||
||(0.003)||(0.003)|(0.002)|(0.003)||
|Post|1.16<sup>∗∗∗</sup>||1.17<sup>∗∗∗</sup>|1.14<sup>∗∗∗</sup>|1.16<sup>∗∗∗</sup>|1.17<sup>∗∗∗</sup>|
||(0.134)||(0.134)|(0.129)|(0.134)|(0.132)|
|Red zone district*May||-0.060<sup>∗∗∗</sup><br>(0.020)|||||
|Orange zone district*May||-0.010<sup>∗∗</sup>|||||
|||(0.005)|||||
|Post * May||1.07<sup>∗∗∗</sup><br>(0.139)|||||
|Red zone district*June||-0.040<sup>∗∗∗</sup><br>(0.011)|||||
|Orange zone district*June||-0.011<sup>∗∗∗</sup><br>(0.004)|||||
|Post * June||1.14<sup>∗∗∗</sup><br>(0.133)|||||
|Red zone district*July||-0.026<sup>∗∗</sup><br>(0.012)|||||
|Orange zone district*July||-0.0066<br>(0.005)|||||
|Post * July||1.15<sup>∗∗∗</sup><br>(0.132)|||||
|Shuffled Red zones*Post||||||-0.0027<br>(0.003)|
|Shuffled Orange zones*Post||||||-0.00041<br>(0.005)|
|Per-capita COVID Infections|-0.067|-0.071|-0.073|-0.024<sup>∗∗∗</sup>|-0.065|-0.076|
||(0.051)|(0.052)|(0.060)|(0.009)|(0.052)|(0.050)|
|_Standard Controls:_|||||||
|Previous year nightlights|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|State*Months (2020) fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean of dependent variable|6.76|6.76|6.76|6.76|6.76|6.76|
|Std. dev. of dependent variable|26.32|26.32|26.32|26.32|26.32|26.32|
|R-squared|0.997|0.996|0.996|0.992|0.996|0.992|
|Number of observations|3632|3632|2972|3552|3557|3632|

Notes: The unit of observation is a district-month. Asterisks denote significance: ∗ _p_ < 0.10, ∗∗ _p_ < 0.05, ∗∗∗ _p_ < 0.01. Standard errors clustered at state level.

13
**Table A.14. Differences by ethnicity in association between social norms indices and women’s paid**

**work outcomes**

||**HH Roles**|**Public Space**|**Both themes**|
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
||**Paid work**|**Paid work**|**Paid work**|
|PNB<br>Index_HH<br>Roles<br>#<br>Madheshi**(**β)|0.04<br>(0.07)||-0.04<br>(0.08)|
|PNB Index_HH Roles**(**α)|-0.09** (0.04)||-0.07* (0.04)|
|SNE<br>Index_HH<br>Roles<br>#<br>Madheshi**(**β)|-0.09<br>(0.12)||0.01<br>(0.12)|
|SNE Index_HH Roles**(**α)|-0.03<br>(0.06)||-0.04<br>(0.05)|
|PNB Index_Public Space #<br>Madheshi**(**β)||0.10* (0.05)|0.09<br>(0.06)|
|PNB Index_ Public Space**(**α)||-0.04<br>(0.03)|-0.03<br>(0.03)|
|SNE Index_ Public Space #<br>Madheshi**(**β)||-0.12***<br>(0.04)|-0.13*** (0.04)|
|SNE Index_ Public Space**(**α)||0.01<br>(0.03)|0.02<br>(0.04)|
|Madheshi|-0.01<br>(0.13)|-0.04<br>(0.07)|0.05<br>(0.13)|
|Obs.|2,000|2,000|2,000|
|Adjusted R2|0.03|0.02|0.03|
|Controls|YES|YES|YES|
|Regional FE|YES|YES|YES|

_Notes:_ (1) Controls include a binary variable for belonging to the Madheshi ethnic group, respondents’ age, years of education, having children younger than 5 years, log of household’s total income, at least one among mother-in-law or father-in-law living in the household, being a migrant household, living in an urban area, and provinces. (2) Interaction term (β) can be interpreted as the difference between the two subgroups (in this case, Madheshi vs Madheshi respondents) in the association between conservative beliefs and women’s paid work status. If the interaction term is negative and statistically significant, it means that the association is stronger for Madheshi compared to non-Madheshi respondents. If the interaction term is positive and statistically significant, the association is stronger for non-Madheshi compared to Madheshi respondents.

49
The fact that these explanations are potentially overlapping was clearly evident in the qualitative work. One non‐crossover woman captured both the role of norms and ability when she said: _“Such women are ‘superwomen’. They have the characteristics of men”_ . On the other hand, a crossover foundry owner provides some insight into the importance of information and psychosocial development when she says: _“No, we are not different. It just takes a mindset and the necessary information to do this business.”_ For this analysis, we use the mixed methods data set, which has a larger sample of crossovers and was designed fully for the purpose of this study. Hence, the depth of the information collected is much more detailed on this topic than the KASSIDA data set, which was meant to achieve other objectives.

# _Information_

Before we take an in‐depth look at the groups of factors, there is one potential market failure that could explain why some women do not cross over: information.

As shown in figure 1 (above), women who cross over have significantly higher profits than those who do not. It is possible that women who remain in female‐dominated sectors simply do not know that they are making less. In our mixed methods interviews, we asked about this.<sup>16</sup> A tabulation of the answers shows that most of the women who do not cross over believe that they make the same or more as those in the crossover sectors.<sup>17</sup> As Figure 1 above shows, some of them are right and some are wrong ‐‐ the distributions of profits across crossover and non‐crossover sectors overlap. Hence, in order to examine the potential information barrier, we divide the non‐crossovers into those whose profits are below the mean in the crossover sectors and those whose profits are above the mean. Figure 3 shows the results.

The difference in size of the bars confirms that the majority of the non‐crossovers have lower profits than the mean profits for crossovers. Moreover, this figure shows that non‐crossovers are unaware of the potential profitability of crossing over. Looking at the bar on the left for non‐crossovers with lower

> 16 The questions used in the survey were the following: “[If crossover] Do you think you are making more, the same, or less money than you would if you were in a sector where women typically operate?” / “[If non‐crossover] Do you think you are making more, the same, or less money than you would if you were in a sector where men typically operate?”. In both cases, the respondent had three options: 1‐ Making more money; 2 – Making the same money; and 3 – Making less money.

> 17 It is also true that most of the crossovers think that they make more than the non‐crossovers, even those crossovers that make less. However, this is only a major issue for non‐crossovers as they are the ones in general not taking advantage of the opportunities in the male‐dominated sectors.

15
As expected more countries are net food importers, and overall there is little change during this price increase.<sup>21</sup> Net food importing countries actually decreased from 123 to 122. 5 low income countries switched from a net food exporter to a net importer. As was the case with earlier findings of Ng and Aksoy (2008b), the changes are reversed for middle income countries. In that group six countries switched from being a net importer to a net exporter.

**Table 15: Trade Balance in Food by Selected Country Group in 2000/01-2006/07**

||NetExports|($million)|NetExp as %|Total Imports|NetExports|as % ofGDP|
|---|---|---|---|---|---|---|
|CountryGroup (No. of Country)|2000-01|2006-07|2000-01|2006-07|2000-01|2006-07|
|Industrial Countries (33)|12,268|9,893|0.29|0.13|0.05|0.03|
|Middle Income, all (97)|-1,164|3,431|-0.07|0.09|-0.02|0.03|
|Oil Exporters (20)|-6,950|-15,339|-3.46|-2.40|-0.66|-0.56|
|Civil Conflict States (4)|-595|-706|-3.63|-1.96|-1.25|-0.73|
|Small Island States (23)|-120|-239|-1.20|-0.90|-0.69|-1.47|
|Other Middle income (50)|6,501|19,715|0.44|0.60|0.12|0.19|
|Low Income, all (55)|396|-2,484|0.31|-0.61|0.05|-0.13|
|Oil Exporters (7)|-828|-2,167|-4.72|-4.12|-0.90|-0.75|
|Civil Conflict States (8)|-206|-656|-2.09|-2.81|-0.92|-1.33|
|Other Low income(40)|1,430|338|1.40|0.10|0.19|0.02|

Notes: Food is defined as raw food products, including meats and dairy products, grains and cereals, fruits and vegetables, but excluding cash crops and feeds, processed food, and seafood. Number of countries is showed in the parentheses that based on countries reporting GDP data. Data is computed as two-year average of trade values in SITC Revision 3. Source: Based on mirror data from UN COMTRADE Statistics.

Table 15 shows the net food trade balance of same groups of countries. Trade surplus of industrial countries have declined. Middle income countries have become net exporters. And low income countries have become a large net importer. In terms of vulnerability, either in terms of trade or GDP, most vulnerable groups are either oil exporters or countries in conflict. The reversal in the balances of low income countries have also come primarily from increased imports of oil exporting and civil conflict countries.

> 21 For most commodities or groups of commodities, there are more importers than exporters. This is because there is specialization by countries where selected products are produced by few countries and consumed by many.

25
Combined with the fiscal constraints, these problems have encouraged many governments to privatize public enterprises. Although the privatization of public enterprises operating in competitive markets appears to have improved efficiency and many other outcomes of interest,<sup>31</sup> it is less clear whether the privatization of regulated monopolies has worked. Studies of public and private water and power monopolies in the United States, for example, have found that private firms are no more efficient.<sup>32</sup> On the other hand, studies of privatization in developing countries have often found that privatization was associated with lower costs and higher investment.<sup>33</sup> Knowing what would have happened in the absence of privatization is difficult, however. Moreover, even when privatization seems to have been helpful, it has often been unpopular—in Latin America perhaps more than anywhere else.<sup>34</sup> It seems likely that government will continue to experiment with both privatization and commercialization of public enterprises. Options for improving the success of privatization include the following:<sup>35</sup>

- Improving governments’ capacity to write concession contracts, regulate the firms, and avoid opportunistic renegotiation

- Better protecting firms from the risks of losing money not because of poor performance or bad luck, but because of unexpected, adverse government decisions

- Making greater use of subsidies when cost‐covering prices are undesirable or infeasible.

Options for improving the success of the commercialization of public enterprises include the following:

- Legislating that the public enterprise’s goal is to be profitable

> 31 Megginson and Netter (2001) survey this evidence.

> 32 See Kwoka (1996).

> 33 Harris (2003) reviews some of this evidence.

> 34 McKenzie and Mookherjee (2003).

> 35 See, for example, Brook and Smith (2001), Guasch (2004), World Bank (2004, chapter 6),

28
particularly important for conflicts of low or medium intensity, wherein markets do not necessarily break down. These policies should be complemented by short-relief programs, as adjusting the labor supply is not able by itself to fully counter the impact of conflict shocks. Additionally, providing support in order to rapidly boost agricultural production is important, as off-farm work is not a perfect substitute for on-farm work.

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32