rafmacalaba/datause-encoder-data huggingface dataset:
Haiti has become far more urbanized in the last two decades because the highest population growth has been in urban areas. In 2003, 40.4 percent of Haitians lived in urban areas, up from 24.5 percent in 1982. Rural Haiti is now home to some 4.7 million people (59.6 percent of the population). The urban population increased from 1.2 million to 3.2 million between 1982 and 2003. In other words, 115,000 people have been added to Haiti’s cities every year for the past 21 years. Among the 1.97 million people added to urban areas between 1982 and 2003, 1.3 million (or two-thirds) went to the West region. The metropolitan area has received an average of 75,000 migrants a year in the past 20 years, in addition to a natural growth of nearly 40,000 people a year, bringing its total annual population growth to 115,000 people.

The rural population represented 59.6 percent of the total population in 2003; down from 75.5 percent in 1982. Hence, rural Haiti is currently home to around 4.7 million dwellers (Figure 2.1 and Table 2.1). The regions that have the largest share of rural population – all have close to 85 percent – are Center, Grand-Anse, South and Southeast. The regions with the lowest share of rural-dwellers are the North, West, and Northeast with 61.8, 33.1, and 62.5 percent respectively. Moreover, demographic developments in rural areas have not been homogeneous in the last decade.

Table 2.2: Degree of urbanization in Haiti and its regions, 1982 and 2003 (percent)

|Region|1982|2003|
|---|---|---|
|Artibonite|15.6|26.0|
|Center|10.7|16.1|
|Grand-Anse|10.6|14.9|
|North|21.1|38.2|
|Northeast|18.2|37.5|
|Northwest|11.3|23.0|
|West|49.1|66.9|
|South|11.7|15.7|
|Southeast|7.2|14.6|
|Haiti(whole country)|24.5|40.4|

_Source:_ IHSI 2003.

During 1982-2003 the rural population increased by 910,000, but it lost ground to urban Haiti. The rural population share fell from 75 to 60 percent in this period. Not only did the country as a whole experience relative population loss to urban areas and emigration, but all the nine regions followed the national trend (Figure 2.2). The West region that was already the region with the lowest share of rural-dwellers (51 percent) in 1982 experienced the largest relative reduction of rural population during 1982-2003, reaching 33 percent in 2003. However, in 2003, the West was still the region with the largest rural population in absolute terms of around 1.0 million people, more than the second most populous region, Artibonite, which had 792,107 people in rural areas in 2003. The South, Center, and Grand-Anse regions lost the least population. The easier access to Port-au-Prince and the imports and other goods and markets it provides the country may explain this.

6
INDONESIA

- 2. Projections NRR-1 by 2005

|**Te r**|Paputltion|Op.<br>rftio|Perlod|Growth<br>rate|CR|CDR|TFR|*o<br>**0**<br>**lotes**|*o<br>**Fmles**|**It**|**xlO00**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|181580|67.3|190-94|1.72|25.4|8.2|3.00|61.3|64.7|55|73|
|1995|<br>197878|60.9|1995 99|1.52|22.6|7.4|2.62|64.0|67.3|43|55|
|2000|213458|56.5|2000-04|1.30|19.8|6.8|2.28|66.5|69.7|34|42|
|2005|227756|51.4|200509|1.18|18.6|6.8|2.16|67.7|71.1|29|36|
|2010|241568|47.7|2010-14|1.12|18.0|6.8|2.14|68.9|72.6|25|30|
|2015|255492|45.6|2014-19|1.04|17.2|6.8|2.13|70.2|74.2|20|24|
|2020|269088|45.7|2020-24|0.93|16.2|6.9|2.11|71.6|75.8|15|18|
|2025|281910|46.6|2025-49|0.63|14.2|8.0|2.08|74.8|79.5|8|10|
|2050|329595|57.7|2050-74|0.23|12.7|10.4|2.07|78.4|83.7|4|5|
|2075|i49352|65.4|2075-99|0.12|12.2|11.1|2.06|80.2|86.1|3|4|
|2100|359854|70.2|2100-24|0.07|11.9|11.2|2.06|81.6|88.0|2|3|
|2125<br>2150|366507<br>369852|72.2<br>73.5|2125-49|0.04|11.8|11.4|2.06|82.2|88.8|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75+<br>70-74<br>65-89<br>80-64<br>55-59<br>50-54<br>45-49<br>40-44<br>35-39<br>3C-34<br>(>§iDg 2U§§m22 25-29 54 Wm<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 810<br><!-- End of picture text --> <!-- Start of picture text --> 10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-90

<!-- Start of picture text --> TFR e0 Growth rate<br>Coun~try Region Income group Coun~try Region Income group Country Region Income grou.p<br><!-- End of picture text --> 59
subsidies (Metropolitan Buenos Aires, Santa Fe, Santiago del Estero), while others depend on fiscal transfers (Chaco, Mendoza, Salta, Tucuman). Notwithstanding the number of schemes, it is estimated that the total number of beneficiaries is less than 100,000 (less than 1% of the population) and the total cost of these schemes is less than US$10m.

In Paraguay, eligibility for the social tariff is assessed on the basis of the characteristics of the customer’s dwelling (Robles, 2002). In order to be considered eligible for the social tariff scheme households must meet four out of five of the following conditions: earth floors, mud or wood walls, straw or zinc roofs, no more than two rooms, no internal tap and no sewerage. However, the scale of this scheme is very small affecting no more than 5,000 beneficiaries and costing less than US$0.1m.

It is interesting to compare the targeting performance of each of these different schemes. Notwithstanding the various selection criteria used, all three schemes present relatively high errors of inclusion, meaning that 30-50% of subsidy beneficiaries do not fall below the poverty line. As a result, 40-60% of subsidy resources are captured by the non-poor. At the same time, the Argentine and Chilean schemes with their tightly defined eligibility criteria erroneously exclude 70-80% of the poor from receiving the subsidy. The Colombian scheme, on the other hand, being virtually universal in reach excludes only 1% of those living below the poverty line. Finally, the overall distributional incidence of these subsidy schemes is slightly regressive in the Colombian case (given the positive value of the concentration coefficient), but reasonably progressive in the Argentine and Paraguayan schemes. However, it should be noted that the evaluation of these social tariffs is based on simulated rather than observed eligibility and hence is likely to overstate the targeting performance of these schemes.

To put these results in perspective, it is interesting to compare them to those found in other social welfare programs. The Coady statistic takes a value of one for social programs that do no better than to allocate resources randomly across the population, with no particular bias towards rich or poor. Based on a review of 122 targeted welfare programs in 48 countries, Coady and Grosh (2002) find that three quarters of these programs score more than one indicating that resources are concentrated towards low income household. However, the median score was 1.25 indicating that the program allocates 25% more resources towards poor households than would a random allocation of resources. These authors set a benchmark level of 1.50 for a well functioning targeted welfare program. Thus, the Chilean water subsidy scheme performs better than the typical targeted welfare program and falls just short of the best practice benchmark. However, the Colombian cross-subsidy scheme performs worse than typical welfare programs and is only slightly better than a random allocation of resources, while the Paraguayan scheme comes close to a random allocation of resources.

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<!-- Start of picture text --> LAND REGISTRATION<br>Land titles and tenure security Efficient land markets<br>Increased Greater P Reduced increased Greater<br>labor lavectinent Creation of collateral value transattionicostsin iseah ivactiment<br>mobilitywe Fincentives2 for landcreat;leadingdit Suppytof greater landmarkets and creditF mobility oincentives2<br>allocativeEnhanced efficiency cost and Greater Transformation of landa<br>in resource use investment into a liquid asset Greater<br>investment<br>Enhanced cost and<br>allocative efficiency<br>in resource use<br>FINANCIAL DEVELOPMENT<br>ECONOMIC GROWTH<br><!-- End of picture text -->
shows sustainable debt issuances in percentages of total debt issuances over the same years as obtained from Bloomberg.

Ensuring additionality requires two critical elements: a proactive allocation of resources through the design of securities explicitly focused on additionality, and a thorough post-issuance monitoring process to prevent 'greenwashing' practices.<sup>15</sup> In response to these challenges, the market is undergoing a transition from a voluntary framework to a more regulated approach. Furthermore, there is an evolving proliferation of product offerings that address distinct market frictions and segments.

The market for sustainable debt can be divided into two broad categories (red and green groups in Figure 2). The first category includes Green, Social, and Sustainable (GSS) bonds and loans. The second (more recent) category includes Sustainability-Linked (SL) bonds and loans. GSS contracts can be classified as “labeled” debt contracts which earmark proceeds for a particular project which has been assigned a green, social, or sustainable (i.e., a mix of green and social) label according to a reference taxonomy. SL contracts are more flexible, result-based contracts which do not earmark proceeds for specific projects but instead make the security payoff contingent on the issuer’s achievement of a sustainability performance target. The two categories of contracts currently co-exist in the sustainable debt market and seem to capture different issuance groups across various geographical areas as described below.

> 15 Greenwashing is “the act of providing the public or investors with misleading or outright false information about the environmental impact of a company’s products and operations.” <u>https://www.investopedia.com/terms/g/greenwashing.asp</u>

30
-54 -

# VI. REFERENCES

1. U-S. Enviromnental Protection Agency, Regulatory Impact Analysis on the National Ambient Air Quality Standards for Particulate Matter, Research Triangle Park, NC, February (1984).

2. Chestnut LG. Ostro BD and Rowe RD (1987) Sana Clara Criteria Air Pollutant Benefit Analysis. Prepared for the U.S. Environmental Protection Agency, Region 8, San Francisco, CA.

3. Chestnut LG and Rowe RD (1987) 'Ambient particulate matter and ozone benefit analysis for Denver," prepared for U.S. Enviromnental Protection Agency, Region 8. September.

4. Krupnick A and Kopp R (1989) Appendix: The Health and agricultural benefits of reductions inambient ozone in the United States, in Catching our breath: Next steps for reducing urban ozone. Office of Technology Assessment, U.S. Congress, Washington, D.C.

_5._ Hall JV, Winer AM, Klceman M et a[. (1992) Valuing the health benefits of clean air. Science 255, February 14.

_6._ Krupnick _A!r_ and Porney PR (1991) Controlling urban air pollution: A benefit-cost assessment. Science 252:522-528.

7. U.S. Enviromnental Protection Agency (1982). Review of the National Ambient Air Quality Standard for Particulate Matter: Assessment of Scientific and Technical Infornation. Research Triangle Park, NC 27711.

- 8 Reilly WK forward to Friedman RD (1981) Sensitive populations and enviromnental standards. Washington: Conservation Foundation, p- viii-

9. Ozkaynak H, and Thurston, GD (1987). Associations between 1980 U.S. mortality rates and alternative measures of airbome particle concentration. Risk Analysis 7:449-461.

10. Cummings BT and Waller RE (1967). Observations from a ten-year study of pollution at a site in the city of London. Atmos Environ 1:49-68.

11. California Air Resources Board (1982). California ambient air quality standard for particulate matter. Decerber.

12. Martin AE and Bradley WH (1960). Mortality, fog and atmospheric pollution: An investigation during the winter of 1958-1959. Mon Bull Minist Health Lab Serv. 19:56-73.

13. Mazumdar S, Schimnmel H, and Higgins ITT (1982). Relationship of daily mortality to air pollution: An analysis of 14 London winters, 1958/59-1971fl2. Arch Environ Health 38:213-220.

14. Ostro BD (1984). A search for a threshold in the relationship of air pollution to mortality: a reanalysis of London winters. Environ Health Perspect 58:397-399.
Table 20: Pooled estimates of the effect of ICT use on regression share of white-collar/blue-collar workers and wages: Manufacturing

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Dependent variable: Number white/**|**blue-collar **|**workers**||||
|Share of labor that uses computers|0.0009***|0.0009***|0.0010***|0.0009***|0.0010***|
||(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|
|Share of labor that uses Internet|0.0009***|0.0009***|0.0010***|0.0008***|0.0010***|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|ln(stock of computer equipment/worker)|0.0448***|0.0397***|0.0380***|0.0371***|0.0356***|
||(0.0011)|(0.0013)|(0.0013)|(0.0012)|(0.0012)|
|**Dependent variable: Wages white/bl**|**ue-collar w**|**orkers**||||
|Share of labor that uses computers|0.0023***|0.0023***|0.0012***|0.0024***|0.0013***|
||(0.0003)|(0.0003)|(0.0003)|(0.0003)|(0.0003)|
|Share of labor that uses Internet|0.0022***|0.0022***|0.0013***|0.0024***|0.0014***|
||(0.0003)|(0.0003)|(0.0003)|(0.0003)|(0.0003)|
|ln(stock of computer equipment/worker)|-0.0144**|-0.0098|-0.0092|-0.0136*|-0.0107|
||(0.0068)|(0.0075)|(0.0073)|(0.0074)|(0.0074)|
|Controls||||||
|ln(capital/worker)|No|Yes|Yes|Yes|Yes|
|Age dummies|No|No|Yes|No|Yes|
|Size dummies|No|No|Yes|No|Yes|
|Sectoral efects|No|No|No|Yes|Yes|
|Observations|22,876|22,876|22,876|22,876|22,876|

Robust standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Source: Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI.

49
3) respond adequately to any gains made by citizens. This allowed local elites to reassert their interests and set back the gains made by citizens.

Variation in the quality of facilitation: Only two of the five treatment GPs had high quality, intrinsically motivated facilitators. RPs in the other GPs were less than diligent in their work with a lot of evidence that they missed obvious opportunities to make a difference. In one GP in our qualitative sample RPs were rotated to such an extent that each set of new RPs had to start from scratch and was unable to build on the RPs that preceded them. Even good facilitators acted more as community leaders rather than as development workers whose main task was to build local capacity. This was probably because of the lack of a local educated class who could be called on to become community mobilizers.

# 4)

Poor application of Kerala design: While the design of the program showed many positive outcomes time in the qualitative data, the program was poorly implemented due to the lack of public support. Thus, one could argue that the “treatment” that was applied was well short of its potential – a fact that would not had been observed without the ethnographic analysis.

However, the nature of this complex intervention also made it less amenable to an RCT. Effective facilitators were able to introduce positive changes because they were able to modify the intervention to suit the context. In other words this was not one, but several interventions, resulting in heterogenous and often unexpected outcomes. As other scholars have pointed out (Woolcock 2009, Mansuri and Rao 2013) these variations in the intervention were compounded by unpredictability in knowing about when they would produce an outcome; outcomes were sometimes observed two years after the RPs had left the villages and the final survey round had been conducted. Outcomes observed in the qualitative work were often subtle, gentle rather than dramatic in their cadence of change. These processes have to be tracked with frequent visits to fully understand, and are very difficult to capture in a survey instrument.

All this suggests that the approach to evaluating complex, contextualized, heterogenous, interventions needs to be rethought. Given the high variability and unexpected nature of outputs, surveys may require larger samples and more rounds in order to account for this heterogeneity and track unexpected outcomes. It also raises questions about how pre-analysis plans should be used in assessing interventions of this kind. Complex interventions also, in our view, require that we take qualitative analysis just as seriously as quantitative data and methods to address the problem of understanding mechanisms of 26
<u>Gender Parity Index.</u> Gender Parity Index (GPI) is defined as the ratio of the number of female students enrolled at a given level of education to the number of male students at the same level. A gender parity of less than one indicates higher share of male students, whereas a gender parity greater than one indicates higher share of female students. Consequently, a gender parity value close to one indicates parity. To calculate the GPI, we use the Gross Enrollment Ratio (GER) by gender. UNESCO defines GER as the total enrollment as a percentage of the population of the age group for the education level. It also defines the population used for GER at the tertiary level is that of the five-year age group after the secondary school leaving age. GPI is the ratio of GER of female students at tertiary level to GER of male students at tertiary level.

To create one measure of access, we weigh the proposed four indicators into one overall indicator. As with the case of the overall affordability indicator, this weighing involves a degree of subjectivity when assigning weights. We follow the weighing of Usher and Cervenan, 2005 to ensure comparability between the results for Latin American countries and the high-income countries. Equal weight (and importance) is given to Type I (quantity of access) and Type II access (composition of access). For Type I access, participation rate and education attainment rate are assigned equal weights. For Type II access, socio-economic equity is assigned greater weight than gender equality (Table 3).

|**Table 3. Accessibility Indicator Weights**||
|---|---|
|**Indicator (per GDP/capita)**|**Weight**|
|Participation Rate|25%|
|Education AttainmentRate (between 25-34years old)|25%|
|Education EquityIndex|40%|
|Gender ParityIndex|10%|
|Total|100%|

# **2.2. Description of Data**

This section describes the data for estimating affordability and accessibility indicators. We have resorted to several types of information: household survey data including data on expenditures and several kinds of administrative data.<sup>15</sup>

# **2.2.1. Affordability**

To estimate affordability, we rely on cost information in the following prioritized sequence: (i) household surveys with data on expenditures (Table 4), (ii) administrative data from government sources, (iii) data-collection from the websites of educational institutions, and (iv) academic papers. Estimating affordability was the most difficult task in producing this paper due to the data limitation.

> 15 The data sources for the high-income countries can be found at Usher and Cervenan, 2005. Expansion factors are applied for household and expenditure surveys for the weighted estimation of affordability and accessibility. The annex provides further detail on data sources.

9
sort of deadline: since peace processes often are designed to resolve persisting problems of divided sovereignty gradually, we would have to decide how we would treat cases where sovereignty was still divided a few months into the peace process, but there was gradual improvement until there was undivided sovereignty a year or two into the peace process. These considerations illustrate the usefulness of a short-run cutoff point that allows us to ask: has the UN missions helped resolve these issues in a given time frame? The supplement presents survival analysis estimates that use a definition that is consistent with the participatory peace measure, and we explain the judgment calls that we made in our coding rules. The results are highly consistent with the results of the short-term participatory peace analysis. But since the long-term measure of war recurrence is both conceptually and empirically clearer, we focus on war recurrence here.

In the single-record, single-failure dataset, out of 138 “subjects” (peace processes), we have 73 failures ( _peacend_ ) with mean peace duration of 53 months and the longest peace duration in the dataset is 634 months. Failures cannot occur at time t = 0, but there are several failures of the peace in the first month. Our main concern is to explore the impact of UN operations on war recurrence.

We estimate the core model from Table 1 using a Cox Proportional Hazards (PH) model. Given its greater versatility, the Cox model is a better initial choice than the more frequently used Weibull model or other parametric hazard models. The Cox model gives the hazard rate for the i’th individual as _hi_ (t)= _h0_ (t) exp( β _’_ **x** ), where _h0_ is the baseline hazard rate and β _’_ **x** are the estimated coefficients and covariates. It assumes a proportional hazard rate and utilizes the ordered duration times to derive estimates for the regression covariates. We test the proportional hazard assumption and, if it is not satisfied, we shift to another model, such as the Weibull model, which is appropriate if the hazard rate is monotonically increasing or decreasing.

Model 1 (Table 3) is the core model, with added controls for real per capita income, the rate of growth of real income at the end of the war, and the level of ethnic fractionalization. These three variables did not have a significant association to participatory peace in the short run, so they were not included in previous Tables, but since we are now using a different concept of peacebuilding and since there are several arguments in the literature linking them to civil war onset, we add them to the model.

18