rafmacalaba/datause-encoder-data huggingface dataset:
program. The ...rst evaluation, a qualitative study of the performance and inner workings of Full Time Schools, was carried out by Equipos/Mori for MECAEP (Spanish acronym for Program for the Improvement of Quality in Primary Education, ...nanced by the World Bank) in 2001 (Estudio de la Evaluación Social de las Escuelas de Tiempo Completo) and showed that, while opinions about the program were generally positive, implementation was incomplete at the time of the study, especially regarding the provision of resources to complement the extension of the school day. This study, however, did not attempt to measure the impact of the program on learning outcomes. The second evaluation was carried out by the Administración Nacional de Educación Pública (ANEP) and showed that the average test score in full-time schools was higher than non-full time schools with similiarly unfavorable socioeconomic characteristics, using the National Assessment of Student Learning (Evaluación Nacional de Aprendizajes), a nation-wide standardized test. While the restriction of the comparison group to schools in similar contexts does control for some socioeconomic characteristics, the analysis did not use the richness of data available to minimize biases from unobservable di¤erences between schools that did and did not participate in the program.

In this paper, we evaluate the impact of the FTS program on student oucomes, trying to shed more light on the e¤ects of the program using student level data and controlling for characteristics of the school that may be driving these di¤erences. The paper is organized as follows: Section 2 describes the main characteristics of the Full-Time School program in Uruguay since its inception, emphasizing implementation issues that may a¤ect our estimation. Section 3 describes the data, Section 4 explains the estimation strategy, Section 5 presents the main results, and Section 6 shows some cost-bene...t considerations. Finally, Section 7 concludes.

# 2 Program Description<sup>1</sup>

The FTS program in Uruguay was introduced in the early 1990s as a means to increase student achievement in disadvantaged schools. The conversion of schools to the FTS was e¤ectively done in three phases, in which both the content of the program and the characteristics of the bene...ciary schools varied notably: before 1996, 1997 to 1998 and 1999 to 2005. The 52 schools that converted to FTS before 1996 served a very disadvantaged part of the population; 25 of them were "open-air" schools (that is, schools with no buildings or with buildings in very bad condition).

> 1 Sections 2 and 3 draw on three documents: Evaluacion Nacional de Aprendizajes, Resultados en Escuelas de Tiempo Completo y Areas Integradas, and the Propuesta Metodologica para Escuelas de Tiempo Completo. These documents and more information are available MECAEP’s website (www.mecaep.edu.uy).

2
The results of the current round of ICP do not differ from the past only in terms of better country coverage but also in the methodology, and the scope of direct price comparisons. In the words of Australian Bureau of Statistics (2006, pp.13), “[The 2005 round] is the most comprehensive and firmly-based ICP round to date.” Prices of more than 1000 goods and services were compared across countries, using the assistance of national statistical offices and regional statistical organizations (Asian Development Bank, Eurostat, Statistics Canada, Economic Commission for Latin America, etc.). The project was organized around six regions (Africa, Asia/Pacific, Commonwealth of Independent States, South America, OECD/Eurostat, and Western Asia). The methodological innovation was the concept of “ring” countries. These are countries (ranging between 2 and 6 from each region, 18 in total) with developed market economies and wide range of goods and services such that direct price comparisons (using the same basket of goods and services) can be made between them. Prices of about 1200 goods and services were directly compared for the ring countries (see World Bank 2008, pp. 160-1). Using the results from the ring countries’ price comparisons, the price levels for other countries belonging to a given region (for which ring countries are representatives) were linked to the rest of the world. The “ring approach” is considered better than the previously used “bridge-approach” (where only one “bridge” country’s prices were directly compared to with those of the “neighboring” region) because it requires direct price comparison of the same bundle of goods and services to be conducted in all “ring” countries.

The most important results concern the new estimates of price levels in China and India. As they are now estimated much higher than previously, the GDPs per capita of these two countries have been correspondingly revised downward by about 38 percent (Table 1). But while these downward revisions are among the largest, they are not the only ones. Indonesia’s GDP per capita was revised downward by 17 percent, Philippines’s by 41 percent, Ghana’s by 50 percent, Argentina’s by 24 percent, South Africa’s by 32 percent.<sup>6</sup> The upward revisions were much less frequent and more

> 6 The attention is sometimes unduly paid to the revisions for China only. It is argued (correctly) that prices in China were collected  more in urban and better-off provinces than in rural and poorer provinces. But, as

4
It is impossible to determine quantitatively the degree of interdependence among sources used in TI and WBI. Many of the cross-country or over-time differences they classify as “statistically significant” undoubtedly would not be, if the appropriate corrections for interdependence could be made.

This unknown but substantial degree of interdependence among many of the sources also obviates any claims regarding the “precision” of estimates. Other things equal, one can have more confidence in a rating based on 9 sources than on a rating for another country based on only 3 sources. It is also important however to identify the sources and to consider the likely degree of interdependence among them. Three sources comprised of a firm survey, a household survey and an expert assessment may provide a richer set of information than 9 sources, if all 9 are expert assessments. Iceland’s 2002 TI index is computed from six sources, which at first glance appears impressively diverse. However, none of them are truly independent: three of them are from WEF surveys for 2000, 2001 and 2002, and the other three are from IMD surveys for the same years.<sup>21</sup> Although the partner institutes in Iceland are different for WEF and IMD, the likelihood of overlapping samples of top executives with international experience in a country so tiny must be very high. Iceland in TI is an extreme example of interdependence, but the problem in more moderate form is endemic to both TI and WBI. Claims of “being precise about imprecision” (Kaufmann, Kraay and Zoido-Lobaton, 2000) ) depend on independence of assessments, hence cannot be supported.

_The Choice of Weights in Aggregation_

> Report” from 1989 through 1995, but went their separate ways in 1996, with the WEF publishing the “Global Competitiveness Report” and the IMD publishing the “World Competitiveness Yearbook.”  Both organizations list their partner institutes on their web sites.

> 21 The TI index uses the most recent three years of data for WEF and IMD.  The WBI index uses only the most recent year.

23
25 Table 7: Contributions to Trade Reduction Index for covered products by different policy instruments, by region, 1972 to 1976 (percent)

# (a) Production side of economy

|||**High-i**|**ncome co**|**untries**|||**Deve**|**loping c**|**ountries**||
|---|---|---|---|---|---|---|---|---|---|---|
||**1972**|**1973**|**1974**|**1975**|**1976**|**1972**|**1973**|**1974**|**1975**|**1976**|
|**All measures**|**24**|**16**|**12**|**22**|**32**|**22**|**20**|**28**|**37**|**25**|
|**Border measures**|**24**|**16**|**12**|**22**|**32**|**22**|**24**|**27**|**33**|**25**|
|Export tax|0|1|0|0|0|15|29|32|26|17|
|Export subsidy|-4|-2|-1|-2|-2|-2|-1|0|-1|-1|
|Import tax|29|20|17|24|35|13|3|2|10|11|
|Import subsidy|-1|-3|-4|-1|-2|-3|-8|-7|-3|-2|
|**Domestic taxes & subsidies**|**0**|**0**|**0**|**0**|**0**|**0**|**-4**|**1**|**4**|**0**|
|Production tax on output|0|0|0|0|0|0|1|1|4|0|
|Production subsidy on output|0|0|0|0|0|0|-5|0|0|-1|
|Farm input net subsidies|0|0|0|0|0|0|0|0|1|1|

# (b) Consumption side of economy

|||**High-in**|**come co**|**untries**|||**Develop**|**ing coun**|**tries**||
|---|---|---|---|---|---|---|---|---|---|---|
||**1972**|**1973**|**1974**|**1975**|**1976**|**1972**|**1973**|**1974**|**1975**|**1976**|
|**All measures**|**28**|**19**|**13**|**25**|**38**|**20**|**30**|**21**|**30**|**23**|
|**Border measures**|**28**|**19**|**13**|**25**|**38**|**21**|**30**|**21**|**30**|**23**|
|Export tax|0|1|0|0|0|10|36|28|22|14|
|Export subsidy|-3|-2|-1|-2|-1|-2|-1|0|-1|-1|
|Import tax|33|23|18|28|41|17|3|3|13|13|
|Import subsidy|-1|-4|-5|-1|-2|-4|-9|-9|-4|-3|
|**Domestic taxes & subsidies**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
|Consumption tax|0|0|0|0|0|0|0|0|0|0|
|Consumptionsubsidy|0|0|0|0|0|0|0|0|0|0|

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008) and Anderson and Croser (2009).
the count of the number of poor in switching to the mixed method—is due to India’s (unusually large) discrepancy between consumption measured in the national accounts and that measured by surveys. Explaining this gap should be a high priority.

While the new data suggest that the developing world is poorer than we thought, it has been no less successful in reducing the incidence of absolute poverty since the early 1980s. Indeed, the overall rate of progress against poverty is fairly similar to past estimates and robust to our various changes in methodology. The trend rate of global poverty reduction of 1% point per year turns out to be slightly higher than we had estimated previously, due mainly to the higher weight on China’s remarkable pace of poverty reduction. The trend is even higher if we use our Bayesian mixed-method. The developing world as a whole is clearly still on track to attaining the first Millennium Development Goal of halving the 1990s “extreme poverty” rate by 2015. China attained the MDG early in the millennium, almost 15 years ahead of the target date.

However, the developing world outside China will not attain the MDG without a higher rate of poverty reduction than we have seen over 1981-2005. The persistently high incidence and depth of poverty in Sub-Saharan Africa are particularly notable. There are encouraging signs of progress in this region since the late 1990s, although lags in survey data availability and problems of comparability and coverage leave us unsure about how robust this will prove to be.

The marked “bunching up” in the global distribution of consumption just above our international poverty line is also notable. There are a great many people who have reached the frugal $1.25 standard, but are still very poor, and clearly vulnerable to downside shocks. Two such shocks have been experienced since 2005. The first is the steep rise in international food and fuel prices and the second is the global financial crisis that emerged in late 2008. Despite the progress in reducing the lags in survey data availability, it will probably not be until 2010 that we can make a reasonably confident assessment of the _ex post_ impacts of these events on the world’s poor. Until then, _ex ante_ assessments will be required, based on pre-crisis data and economic assumptions. Such assessments suggest that at least a few years of the progress reported here have been eroded since 2005.

34
Correlations with a few of the broad perception-based measures strengthen somewhat, but remain far weaker than any of the other administrative-corruption correlations.

Two WEF variables measure business executives’ perceptions of other aspects of corruption that pertain less to state-enterprise interactions, and more to misappropriation of taxpayer funds by government officials. One of these is titled “diversion of public funds,” and the other “public trust in the financial honesty of politicians.” Diversion of funds is most strongly correlated with EIU (.66), among the four expert-based indicators. It is most weakly correlated (.42) with CPIA - despite the fact that of the four only the CPIA explicitly includes diversion of funds in its definition. Public trust in honesty of politicians is correlated at .44 with ICRG, but only at .04 for CPIA.

The dimensions of corruption that present the largest obstacles to doing business are of course likely to vary not only across countries, but also across firms within a country. Gray, Hellman and Ryterman (2004) use BEEPS II data to run country-specific, cross-firm regressions of the “obstacle” measure on several administrative corruption measures (also from the BEEPS), among other regressors. In some countries, they find bribes paid in dealing with courts to be a significant obstacle, while in others bribes paid to obtain business licenses are significant. The 5<sup>th</sup> column of results in Table 6 is a cruder look into what forms of corruption appear most often to represent a serious obstacle, using the BEEPS III data. Unlike Gray, Hellman and Ryterman (2004), it does not disaggregate by country, or control for other variables. With those caveats, the broad perceptions measure of corruption in BEEPS – the “obstacle” measure – is found to be correlated more strongly, on average, with the state capture questions in BEEPS, and to a lesser extent in WEF, than with their administrative corruption questions. These findings 39
**Table 1b: Sample Composition (Kitchens): Airmetrics MiniVol Air Sampler**

|**Fuel Type**||**Kitchen Type**||**Construction Ma**|**terial**|
|---|---|---|---|---|---|
|Gas, Electricity,<br>Kerosene|35|Single room dwelling,<br>no separate kitchen|36|Wall: Thatch|100|
|Firewood, Twigs,<br>Leaves|89|Kitchen with a<br>partition (4 walls and<br>a roof)|37|Wall: Tin|37|
|Cow Dung|42|Separate, attached<br>kitchen (4 walls and a<br>roof)|43|Wall: Mud|39|
|Rice Husks, Straw|16|<br>Separate, detached<br>kitchen (4 walls and a<br>roof)|89|Wall: Brick/ Mud,<br>Roof: Other than<br>Concrete|9|
|Jute Sticks,<br>Bagasse, Sawdust|54|Outside/ open kitchen<br>(0 walls, no roof)|31|Wall: Brick/ Mud,<br>Roof: Concrete|20|
|Total number of<br>households|236|Total number of<br>households|236|Total number of<br>households|205|

air at 5 liters/minute through PM10 and PM2.5 particle size separators (impactors) and then through filters. The particles were caught on the filters, and the filters were weighed preand post exposure with a microbalance. We operated the air samplers at 236 locations.

# **3.  Sample Evidence: PM10 vs. PM2.5**

We focus on PM10 in this paper because our monitoring sample is much larger.

However, we have also monitored PM2.5 at over 80 sampling points, to assess the stability of the relationship between the two measures. Overall, we find an extremely stable relationship: The mean ratio (PM2.5/PM10) is .51, with a standard error of .02. This implies a 95% confidence interval of .47 - .55. As Table 2 shows, the ratio does not vary significantly across biomass fuels for which we have information. Our results suggest 10
# **Vulnerability and Livelihoods before and after the Haiti Earthquake**

Damien Échevin<sup>1</sup>

**Keywords:** Vulnerability; Poverty; Asset-Wealth; Earthquake; Haiti.

**JEL Codes:** D12; D31; I32; O15.

> 1 Université de Sherbrooke. Email: <u>damien.echevin@usherbrooke.ca. I gratefully acknowledge Andrea</u> Borgarello, Carlo del Ninno, Nancy Gillespie, Francesca Lamanna, Philippe Leite, Ana Maria Oviedo and Ludovic Subran as well as participants at workshops in Port-au-Prince and Washington DC (World Bank) for their useful comments and suggestions. I also acknowledge Gary Mathieu from CNSA-Haiti for providing some of the data used in this paper. All errors and opinions expressed in this paper remain mine.
|**Table A24: International Research and Exchanges**<br>**Data Provider**<br>International Research and Exchanges B<br> <br>|**Board** **Media Su**<br>oard<br>|**stain**<br>|**ability Index (M**<br>|**SI)**<br>|
|---|---|---|---|---|
|**Description**<br>International nonprofit organization<br>specializing in education, independent<br>programs.|headquartered i<br>media, Internet|n W<br>devel|ashington, United<br>opment, and civil|States,<br>society|
|**Website**<br>www.irex.org<br>**Data Source**<br>Media Sustainability Index|||||
|**Type**<br>Expert assessment|||||
|**Respondents**<br>Panel of local respondents in each countr<br>**Frequency**<br>Annual since 2002<br>**Coverage**<br>Developing country sample<br>**Public Access**<br>Yes<br>**Description**<br>Index rates countries on a variety of sub<br>of media available to citizens, professio<br>media, and the efficacy of institutions t<br>year we have carried forward score fro<br>(up to two) but not in current year.|y, subject to centr<br>components relati<br>nal journalism st<br>hat support indep<br>m those countries|alized<br>ng to<br>andard<br>enden<br>that w|review<br>freedom of speech,<br>s, business sustaina<br>t media. Note that<br>ere covered in earl|plurality<br>bility of<br>for each<br>ier years|
|2008<br>**Voice and Accountability**<br>Media Sustainability Index<br>X<br>**Political Stability and Absence of Violence**<br>NA<br>..<br>**Government Effectiveness**<br>NA<br>..|2007<br>2006<br>2005<br>X<br>X<br>X<br>..<br>..<br>..<br>..<br>..<br>..|2004<br>X<br>..<br>..|2003<br>2002<br>2000<br>X<br>X<br>..<br>..<br>..<br>..<br>..<br>..<br>..|1998<br>1996<br>..<br>..<br>..<br>..<br>..<br>..|
|**Regulatory Quality**<br>NA<br>..|..<br>..<br>..|..|..<br>..<br>..|..<br>..|
|**Rule of Law**<br>NA<br>..|..<br>..<br>..|..|..<br>..<br>..|..<br>..|
|**Control of Corruption**<br>NA<br>..|..<br>..<br>..|..|..<br>..<br>..|..<br>..|
|**Country Coverage ***<br>**76**<br>**Year of publication**<br>**2009**|**76**<br>**63**<br>**38**<br>**2008**<br>**2007**<br>**2005**|**20**<br>**2005**|**20**<br>**20**<br>**..**<br>**2004**<br>**2002**<br>**..**|**..**<br>**..**<br>**..**<br>**..**|
|* includes countries carried forward from previous years|||||

63
Incidence of COVID-19 and Connections with Air Pollution Exposure <!-- Start of picture text --> Normal Q−Q Plot for standardized residuals of Model 5b Normal Q−Q Plot for standardized residuals of Model 5c<br>Meierijstad G Boekel G G<br>G G<br>GG<br>G<br>Landerd G<br>Zwartewaterland G<br>Berg en Dal G<br>GG G<br>G G<br>G GGGGGGGGGGGGGGGGGGGGGGGGG GGGGGG GGGGGGGGGGGGGGGG GGGG G GG GGG GGGGGGGGGGGG G GGGG G GGG G G G GGGGGGGGG G GG G GGGG G G G G GG GGG GG GG G GGGG G GGGGG G GG G GGG G GGGGGGGG G GGGG G GGGGGG GGG GG G GG G GG GG GGGGG GGGGGGGGGGGGGG GGGGG GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG<br>G GGGGGG<br>G GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG G GGGGG G GGGGGGGGGGGGGGG GGGGGGG G GG GGGGGGGGGGGGGGGGGGGGGGGGGGGG G GGGGGGG GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG GG G GGGG<br>GGG G<br>G G G GG GG GGGG G Rhenen<br>G Zoeterwoude G Renswoude<br>G Baarle−Nassau G Montfoort<br>−3 −2 −1 0 1 2 3 −3 −2 −1 0 1 2 3<br>norm quantiles norm quantiles<br>2<br>4<br>1<br>2<br>0<br>0 −1<br>Quantiles of Standardized Residuals Quantiles of Standardized Residuals<br>−2<br>−2<br>−3<br><!-- End of picture text --> Figure 9: Comparison of residuals to theoretical quantities.

Table 5: Dependent Variable: Confirmed COVID-19 cases <u>per</u> 100,000 inhabitants.

|**Variable**|**Model 5c**|**Model 5d**|
|---|---|---|
|(Intercept)|-91.40**|-10.75***|
||(43.93)|(2.41)<br>|
|Population density|-0.83|-0.16**|
||(1.27)|(0.07)|
|Share from 25 to 44|-0.16|0.05|
||(0.73)|(0.04)|
|Share above 65|0.67|0.10**|
||(0.68)|(0.04)|
|Share of unmarried|1.61***|0.10***|
||(0.52)|(0.03)|
|Share of single households|-0.56*|-0.05***|
||(0.30)|(0.02)|
|Share of non-western immigrants|-0.55**<br>(0.25)|-0.02<br>(0.01)|
|Share of water surface|9.33*|0.68**|
||(5.31)|(0.31)|
|Share with long-term illness|0.29<br>(0.37)|0.01<br>(0.02)|
|Mean PM2_._5|2.67***|0.45***|
||(0.89)|(0.04)|
|_λ_|-0.22|-0.05|
||(0.16)|(0.19)|
|_ρ_|0.73|-0.06|
||(0.07)|(0.17)|
|R2|0.54|0.31|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 19
Canning and Pedroni (2008) use a simple empirical model in the spirit of Barro (1990) to compare physical infrastructure stocks with their growth-maximizing levels in a panel of countries. Their finding is that infrastructure is under-provided in some countries and overprovided in others, and the verdict shows no clear correlation with countries’ per capita income. On average, the level of infrastructure is ‘just about right’ from the point of view of growth maximization, so there is no evidence of a generalized infrastructure shortage. Using a similar framework, Kamps (2005) likewise concludes that there is no shortage of public capital in EU countries. In turn, Eden and Kraay (2014) assess public capital shortages in low-income countries, using a Ramsey-type framework that highlights the degree of substitutability between public and private capital. Their estimate of the marginal return on public capital exceeds the user cost, given by the rate of depreciation plus the world real interest rate (thus implicitly assuming non-distortionary taxation). They conclude that, on average, public capital is underprovided in their sample countries.

# **3. Infrastructure and income inequality**

The preceding discussion refers to the effects of infrastructure on the economy’s average income. But does infrastructure also have an effect on the distribution of income -- specifically, through its differential impact on the income of the poor? For example, if an enhanced infrastructure network helps connect lower-income segments of the population to markets for their inputs and outputs – by reducing transport and logistic costs to affordable levels -- their incomes may rise more than the average, as may the value of their assets (land or human capital).

The theoretical literature on the linkages between infrastructure and inequality is not as vast as that on infrastructure and growth. It has examined the distributional effects of infrastructure development under various assumptions about income distribution dynamics, economic distortions – notably in capital markets – and infrastructure-driven externalities. Empirical research has likewise employed a variety of approaches, from cross-country and time-series regressions using macroeconomic data, to micro-level studies assessing the impact of infrastructure-related interventions on the incomes of the poor, especially in rural areas.

# _3.1 Analytical approaches_

10