rafmacalaba/data-use-mentions-extended huggingface dataset:
# **<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
# **Table A12: Fractional regression**

|Dependent variable:|Labor force<br>participation|Employment<br>status|
|---|---|---|
||(1)|(2)|
|Standardized mental health scores|-0.011***|-0.069***|
||(0.001)|(0.006)|
|Dep. Mean|0.418|0.455|
|Other controls|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|7,768|3,893|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. The fractional regression is conducted by using Stata command ‘ _cmp’_ developed by Roodman (2011). Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one.

44
LivingC onditions Survey (LCS) 2014 yes<br>Tanzania ‘National Household Travel Survey (NHTS) 2013 yes<br>Togo EnqurtePopulation and Housing Census 2012 5 dom ains. No com munication.<br>Uganda surla Transiion vers la Vie Actrve (ETV A) m12 5 dom ams. No communicahion.<br>Zambabwe Population Census 2014 4 domains. No self-care or communication<br>Note: * Has the Washington GroupPoverty,Extended Income,Set Consumption and Expendire Survey 2017 yes |<br><!-- End of picture text -->
.132)<sup>_∗∗_</sup>||
|||[0.188]|[0.235]|[0.144]||
|Contributions to clinic index|0.000|0.173|0.292|0.080|320|
||(1.000)|(0.127)|(0.142)<sup>_∗∗_</sup>|(0.148)||
|||[0.205]|[0.143]|[0.488]||
|Water and sanitation index|0.000|0.167|0.137|0.190|3183|
||(1.000)|(0.086)<sup>_∗_</sup>|(0.101)|(0.096)<sup>_∗_</sup>||
|||[0.188]|[0.264]|[0.144]||
|Economic outcomes index|0.000|0.151|0.065|0.219|3183|
||(1.000)|(0.087)<sup>_∗_</sup>|(0.106)|(0.105)<sup>_∗∗_</sup>||
|||[0.188]|[0.425]|[0.144]||

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. Multiple-inference corrected q-values that adjust for the false discovery rate within treatment arm are shown in square brackets. Significance: * is significant at the 10% level, ** is significant at
Figure 2: **Percentage share of free market exports in total world sugar exports** <!-- Start of picture text --> __~~~~~~~~ AI EN WE _<br>70 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~00<br><!-- End of picture text --> Source: See Data _55_ for details Since 1977, total world trade in sugar has fluctuated around 28-30 million MT per annum, in contrast to earlier years during which there were regular increases in the volumes traded . Intemnational trade has stagnated partly due to protection -induced increases in production by importing countries, and partly as a result of the substitution for sugar by lowcaloric sweeteners, especially HFCS in the USA. Declining income elasticities in developing countries and unfavorable economic conditions in a number of the previously centrally planned economies also contributed to lower world trade.

Table 1 shows the principal countries exporting raw sugar. In the 1980s the major exporters were Cuba (3 8%), Australia (1 4%), Thailand _(9.5%)_ and Brazil (7.7%). The relative positions remain unchanged in 1990-93. Together, the total exports of these four countries were around 12 mln MT per annum, out of total world exports of 16.9 million MT during these years. Indian exports have been sporadic and very small if they occurred at all.

Table 1: **Principal exporters of raw sugar, 1990-93**

|Amount|Countries|
|---|---|
|More than_5_mln MT|Cuba|
|1-3 mln MT|Australia, Brazil, Thailand|
|_0.5-0.9_mln MT|Guatemala, Mauritius, South Africa|
|0.2-0.45 mln MT|Colombia, Dominican Republic, Philippines, Swaziland,<br>Guyana, Fiji|

Source: See Data S6 for details Data S6 lists the major exporting countries and their
estimating the impact of price changes on each household‘s real income by multiplying the price change experienced by the household by the quantity of the good produced and by the negative of the quantity consumed by that household (see Deaton, 1989 for a justification of this approach). The first modification involves allowing households to substitute away from commodities whose price rises. We do this by introducing a Constant Difference of Elasticities (CDE) demand system parameters (Hanoch, 1975), estimated for 112 countries and regions following the methodology of Liu, Surry, Dimaranan and Hertel (1997), to allow us to capture the second-order impacts of price changes on households‘ real incomes through changes in the volume of each good consumed. A second modification is to omit modeling the effects of commodity price changes on wage rates on the grounds that commodity price changes appear to take some time to affect wage rates in developing countries (Ravallion 1990). As in Ivanic and Martin (2008)—but with more justification given the speed of the 2010 price increase—we ignore the potential second-order effects of price changes on incomes through increases in output of products whose prices have risen.

Our measures of the poverty headcount and the poverty gap index follow the definition of Foster, Greer and Thorbecke (1984). Hence, our poverty headcount reflects the number of people whose daily expenditure is below the defined poverty-line income while the poverty gap measures the average expenditure shortfall of the poor as a share of the poverty-line income for the entire population.

# **Data**

In this study, we use household survey information on household food production, sales and consumption of thirty-eight food and agricultural commodities for twenty-eight developing countries (Appendix table 1). This is an increase from twenty commodities and ten countryperiods in the 2008 study. The agricultural commodities identified in the surveys include not only basic staples (e.g. wheat, maize and rice), but also various types of animal products (e.g. poultry, eggs, pork,
both men and women, and more specifically couples in Côte d’Ivoire and Mozambique, only 42.7% of the respondents are women. Most studies’ respondents are adults and young adults, in both urban and rural areas, with an average age of 36 years old (23 in the Republic of Congo skills project to 49 in the Future of Business project). Very few studies limit sample selection by education level and the average respondent has completed 9.6 years of education (a minimum average of 2.6 years in Mozambique and a maximum of 13.3 years in Nigeria). Among the respondents, 57.3% are married. Of the respondents, 74.5% report earning a positive income, with some variation across studies (43% in Mozambique up to 98% in Togo’s Private Sector Development Project). Respondents earn on average $138 (USD) monthly with the average income varying between $2 in

> 6 _Self Awareness_ in the framework was renamed _Positive Self Concept_ and _Collaboration_ from the framework was renamed _Teamwork_ , as the latter terms are similar, but better reflected the specific data that was available.

13
also emphasizes that financing (at least to the extent we can measure it) does not account for the majority of the increase in entry. It is clear that more women-owned establishments with financial backing enter after the reforms, but this increase is only part of a general expansion for female entrepreneurship, rather than the sole driver.

The NSSO collects data on external financing of unorganized establishments.<sup>2</sup> A baseline descriptive point is that only 2%-3% of female-owned businesses in a typical year report using external finance (9%-10% for male-owned businesses). This statistic is again informative. While it is possible that many latent women entrepreneurs are precluded from entry due to poor financial conditions, we should also not be surprised if much of the entry occurs in a way that does not display a connection to external finance, at least as our data can measure it.<sup>3</sup> The first econometric evidence is in Table 3 when we segment entrepreneurs into groups based upon whether or not they are using external finance. The group not reporting external financing both accounts for most of the sample and also has the higher elasticity response. While we do observe some tentative evidence of an increase in firms supported by external finance, the bulk of the entry is occurring elsewhere.

A second approach tests whether the entry response is disproportionately stronger in industries that were more dependent on external finance in 1994. This approach has been used frequently since Rajan and Zingales (1998) to test for the importance of external finance channels in economic outcomes. It again has the advantage of not requiring direct observation of loans, just the correct ordering of industries. These regressions are reported in Column 7 of Table 4. There is a positive elasticity of 0.1 with a t-statistic greater than one. There thus appears to be more traction to this story than the previous two, but at the same time, the financing interactions are far less than those for the other industry dimensions
African electricity regulators. The median age was just under 5 years. However, 5 (29%) were 10 years old or more and accounts for 42% of the total number of sample years with an autonomous regulator. The over 13 year-old group of autonomous electricity regulatory agencies comprises Costa Rica, Philippines and Trinidad and Tobago.

# **4.4 Ministry or Autonomous Regulator and Per Capita GDP**

Figure 3 shows, very interestingly, that – at least within this sample - there is little relationship between the existence of an autonomous regulator and per capita GDP. Both autonomous and Ministry regulators are scattered through the income range.

The mean income for countries with an autonomous electricity regulator was $3,500. For those with a ministry regulator it was $3,300. The difference was not significant. However, low income countries with an autonomous regulator have younger regulators e.g.. the two Sub-Saharan African regulators established since 1998 (Kenya and Uganda).

# **Figure 3: Type of Regulator and Per Capita Income (in Real $ 1995)**

<!-- Start of picture text --> Independent regulators and National Income per<br>capita<br>Independent<br>regulator<br>Ministry<br>18000.00<br>16000.00<br>14000.00<br>12000.00<br>10000.00<br>8000.00<br>6000.00<br>4000.00<br>2000.00<br>0.00<br>Rank by GDP<br>GDP per capita<br>1 4 7 10 13 16 19 22 25 28<br><!-- End of picture text --> Source: Domah 2001 survey, supplemented and updated by authors

# **4.1.5 Generation Capacity and per Capita Income**

Figures 4 and 5 plot generation capacity by real GDP for our 28 country panel at
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Railway indicators for Senegal and select other countries, 2005–09**

||**TRANSRAIL**<br>**(Senegal–Mali)**|SITARAIL<br>(Côte d’Ivoire—<br>Burkina Faso)|Camrail<br>(Cameroon)|Madarail<br>(Madagascar)|
|---|---|---|---|---|
|Concessioned (1)/ state run (0)|**1**|1|1|1|
|Freight traffic volume (million tonne-km)|**393**|794|1,061|113|
|Passenger traffic volume (million passenger-km)|**91**|210|377|3|
|Total traffic volume (million TU)*|**429**|878|1,212|114|
|**Efficiency**|||||
|Staff: 1,000 TU per staff|**247**|558|547|118|
|Derailments per million TU|**0.45**|0.01|0.15|2.31|
|Mainline locomotive breakdowns per 100,000 km|**15**|6|9|6|
|**Tariffs**|||||
|Average unit tariff, freight, US cents/tonne-km|**7.0**|6.3|8.1|6.0|

_Source:_ Bullock 2009. Derived from the AICD rail operator database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ *2.5 passenger-km equivalent to 1 TU, 1 tonne-km equivalent to 1 TU. TU = traffic unit.

# Ports

## **Achievements**

The overall safety of maritime traffic
differ from what has been observed historically. Stochastic data sets provide a synthetic view of all events that could occur in a given year, together with their likelihood of occurring. These data sets typically allow for a more reliable analysis of drought risk, especially for events that occur less frequently. Use of a stochastic data set for Malawi showed how such an analysis can be carried out, but further work is needed to produce and use more reliable analyses of spatial and temporal correlations of drought hazard. A further extension of this work would incorporate climate trends into the analysis of welfare impacts in order to estimate the costs of longer-run welfare impacts (Baquie and Foucault 2023).

# References

ABI (Association of British Insurers). 2011. _Industry Good Practice for Catastrophe Modelling_ . London: ABI. - <u>https://www.abi.org.uk/globalassets/sitecore/files/documents/publications/public/migrated/solvency ii/industry-good-practice-for-catastrophe-modelling.pdf.</u> Angrist, J. D., and J. S. Pischke. 2009. _Mostly Harmless Econometrics_ . Princeton, NJ: Princeton University Press. Anttila-Hughes, J., and M. Sharma. 2015. “Linking Risk Models to Microeconomic Indicators.” Policy Research Working Paper 7359, World Bank, Washington, DC. <u><mark>http://hdl.handle.net/10986/22235.</mark></u> <mark>Artuc. E., G. Porto, B. Rijkers. 2023. Crops, Conflict, and Climate Change. World Bank Mimeo.</mark>

- Auffhammer, Maximilian. 2018. “Quantifying Economic Damages from Climate Change.” Journal of Economic Perspectives 32 (4): 33–52. https://doi.org/10.1257/jep.32.4.33.

- Baez, Javier E., Varun Kshirsagar, and Emmanuel Skoufias. 2019. “Adaptive Safety Nets
# 2. Methodological framework and scenarios

To provide a quantitative assessment of the policy scenarios, we rely on a soft linkage between the global recursive dynamic CGE model ENVISAGE (van der Mensbrugghe 2019) and the GIDD microsimulation framework (Bussolo et al. 2010). First, the ENVISAGE model is run to provide an assessment of policy scenarios under consideration. Next, selected indicators from this assessment are transferred to the GIDD model. These are further implemented as an external shock relative to the baseline scenario within the GIDD modeling framework to estimate the distributional impacts of the considered set of policies. Such soft-linking approach does not incorporate any backward linkages from the GIDD to ENVISAGE and can be considered as a decomposition of the aggregate ENVISAGE results using the microsimulation framework.

Key data input to the ENVISAGE model for the reference year representation is the Global Trade Analysis Project (GTAP) 10 MRIO Data Base with 2014 as the reference year (Aguiar et al. 2019; Carrico et al. 2020). Relying on the MRIO data, which distinguishes agent-based demand for imports by region of origin, selected manufacturing sectors in the ENVISAGE model are represented using the MRIO specification, while all other sectors use the standard Armington assumption, treating imports as imperfect substitutes with domestically produced commodities—and sourcing is done at the national level. The model is solved as a sequence of comparative static equilibria where the factors of production accumulate over time. Nested constant-elasticity-of-substitution (CES) functions are used to represent production technologies. 141 regions and 65 sectors of the GTAP 10 Data Base have been aggregated to 21 countries/regions and 27 sectors for the current assessment. Annex A reports sectoral and regional mappings, as well as provides identification of the sectors of the model that are represented using the MRIO specification. Annex B provides technical details of the modeling framework.

The GIDD is a top-down macro-micro simulation framework that exploits heterogeneity observed in household surveys to distribute macroeconomic shocks obtained from the CGE model. Transferred shocks are distributed in a predefined set of linkage aggregate variables that communicate between
, NAS (Nashville), NOR (New Orleans), NYC (New York City), PRT (Portland), SA (San Antonio), SD (San Diego), SF (San Francisco), SLC (Salt Lake City), STL (St. Louis), WPB (West Palm Beach), and WS (Winston-Salem). Cities are identified from inventors' city names using city lists collected from the Office of Social and Economic Data Analysis at the University of Missouri, with a matching rate of 99%. Manual recoding further ensures all patents with more than 100 citations and all city names with more than 100 patents are identified. Panel B presents comparable statistics calculated from the 1990 Census using country of birth for scientists and engineers. Anglo-Saxon provides a residual in the Census statistics. Many US inventors with European names are native citizens.
- Firms in capital cities, not surprisingly, complain less about electricity cuts and more about land access. They are also more likely to face perceived skills shortages, and complain more about the macroeconomic environment and about regulation.

There are also are clear relationships between performance and perceived investment climate constraints:

- High TFP firms perceive higher constraints deriving from regulation, and complain less about finance.

- High employment growth firms face greater constraints from land access, regulation, and complain less about finance.

- Profit-reinvesting firms, and firms that have opened plants in the preceding three years, complain more about infrastructure, more about regulation, more about skills shortages, and more about property rights.

The interpretation of these results is straightforward: firms that have high productivity, are growing and investing, and that have characteristics that are associated with good performance and growth (private/foreign ownership and export orientation) are more likely to report that they face significant investment climate constraints of all sorts. The exception is finance, where poorer performers are more likely to complain they are constrained. The usefulness of these findings from the international PICS-BEEPS surveys is primarily to set a benchmark against which to compare the investment climate constraints perceived by Serbian firms.

**_Serbian evidence._** Do firms in Serbia face the same kinds of investment climate constraints as those types of firms in the rest of the world? The same methodology used above was applied to just the firms in the 2003 Serbian PICS survey. The investment climate variables are those described earlier, plus composite measures for infrastructure, taxation, regulation, macroeconomic conditions, finance, and property rights. We estimated the effects on these investment climate measures of ownership (privatized, new private, foreign-owned), export orientation, size, sector (manufacturing, agriculture, services), and location (operating in Belgrade, Vojvodina, or central Serbia).

The main results from the analysis are summarized below:

- Privatized firms face greater skills shortages and bigger constraints from business licensing (Figure 2)
few remaining ways a government committed to an agenda of low inflation, fiscal responsibility, and a stable exchange rate can attempt to influence the vote in the months leading up to an election.

Moncada presents a simple time line of quarterly FONCODES expenditures and elections between 1991 and 1995. Interpretation of Moncada's graph is not easy, however, because elections do not always fall conveniently at the end of a quarter. Nor does Moncada formally test the relationship between elections and expenditures--perhaps because of the small number of quarters (16 quarters) and the large number of national elections (3 elections) during the period.

Figure 1 graphs monthly data for the amounts approved for community-based and special projects between November 1991 and May 1995. The chart shows that expenditures are highly irregular, especially (but not only) for the special projects. There are at least four distinct spikes in total expenditures. Three of these spikes coincide with the three national elections held during the period-the November 22, 1992 election to the constituent assembly (month=13), the October 31, 1993 referendum (month=24), and the April 9, 1995 presidential election (month=42). Figure 1 thus provides some _prima facie_ evidence that FONCODES expenditures were affected by elections.

Figure 1: FONCODES expenditures, by month <!-- Start of picture text --> Apit 91995<br>40<br>Noenbr192Ocrtober 1993<br>2 0 AX -~community<br>November~~~~Mnt 1992emer191<br>30<br>I I~~~~~~~~~Mnh(-Nvme 91<br>35-<br>a~~~~~~~~~~~~~~~~~~~~~~<br><!-- End of picture text -->
<mark>interaction terms are not statistically significant (and are not shown in Table 7 for lack of space), except for that between childcare attendance with private center (Model 1 in Table 7). This interaction term is strongly statistically significant, and suggests that private childcare attendance has a stronger impact on maternal employment than public childcare. We find a lower effect from childcare attendance in communes which are far from town (Model 2), meaning that the effect is larger for communes closer to town. Possibly, employment opportunities as well as wages are higher in areas near towns, increasing the effect of childcare attendance on women’s employment.</mark> <mark>We also test the interaction of the childcare attendance and commune-level variables (availability of kindergartens in communes and access by car to villages).</mark><sup>18</sup> <mark>VHLSS contain commune-level data for rural areas but not for urban areas. Thus, we use the rural data sample to estimate the models, including interactions with commune-level variables. Both the interactions are negative, but not statistically significant. We also interact childcare and urban residence, but this interaction term is not statistically significant (results not shown).</mark> <mark>The effect of childcare attendance on maternal employment may depend on the opportunity costs of staying at home (i.e., not participating in the labor market) to take care of children. In Model 5, we test the interaction of childcare attendance with the mean wage of districts.</mark><sup>19</sup> <mark>The interaction is positive and highly significant, meaning that the effect of childcare use is greater in areas with higher wages. In Model 6, we include interaction between childcare use and per capita income for the district, but this interaction is not statistically significant at conventional levels. Income includes not only wages but also selfemployed income. Thus, the interaction between recourse to childcare
of Variance Components in PWT9 Growth Dataponents in PWT9 Growth Dataonents in PWT9 Growth Data**

**Table 2: Estimates of Variance Components in PWT9 Growth Dataponents in PWT9 Growth Dataonents in PWT9 Growth Data**

|**SD(leader)**<br>**1**|**SD(CE)**<br>**. Pooled**|**SD(iid)* **|**SD(leader)**<br>**2. Auto**|**SD(CE)**<br>**cracies/Tr**|**SD(iid)***<br>**ansition**|**SD(leader)**<br>**3. Establis**|**SD(CE)**<br>**hed Dem**|**SD(iid)***<br>**ocracies**|
|---|---|---|---|---|---|---|---|---|
|1.33%|0.65%|4.58%|1.35%|0.72%|4.99%|1.04%|0.42%|2.62%|
|(0.13%)<br>|(0.13%)<br>|(0.21%)<br>|(0.15%)<br>|(0.15%)<br>|(0.23%)<br>|(0.18%)<br>|(0.12%)<br>|(0.27%)<br>|

~~Notes: Estimates of variance components using the method described in