rafmacalaba/datause-encoder-data huggingface dataset:
_Figure 3. Aportaciones- 2002 Distribution (FAEB – basic education; FASSA – health; FISE – state social investment; FISM – municipal social investment; FAM – education infrastructure and Progresa, FORTAMUN – municipalities, FAETA – technical and adult education, and FASP – public safety)_ <!-- Start of picture text --> 4,500<br>4,000<br>3,500<br>3,000<br>FASP<br>FAETA<br>2,500 FORTAMUN<br>FAM<br>FISM<br>2,000 FISE<br>FASSA<br>FAEB<br>1,500<br>1,000<br>500<br>0<br>MxP per capita<br>Chiapas Guerrero Oaxaca Veracruz-llave Hidalgo San Luis Potosí Puebla Campeche Tabasco Michoacán de Ocampo Yucatán Zacatecas Guanajuato Nayarit Sinaloa Querétaro de Arteaga Durango Tlaxcala Morelos Quintana Roo México Colima Tamaulipas Sonora Jalisco Chihuahua Baja California Sur Aguascalientes Coahuila de Zaragoza Baja California Nuevo León Distrito Federal Weighted Average<br><!-- End of picture text --> _Source_ : SHCP and Bank staff calculations.

The majority of _aportaciones_ to the states (75 percent) are for the basic education fund, which is allocated largely in line with the distribution of federal funding and federal teachers in 1993, when the federal education system was transferred over to the states. The states with the lowest _aportaciones_ per capita—EdoMex, Guanajuato, and Puebla<sup>5</sup> —ended up there because they had built up their own state-funded school systems before 1993 and therefore relied less on the federal system. These states continue to finance (with non- _aportaciones_ resources) a large share of their basic education systems—up to one-half in EdoMex — even though these state governments are not rich in resources compared to the others. On the contrary, as figure 2 shows, they often have the lowest total resources as well as low _aportaciones_ . The pattern of education transfers per school-age student is about the same as the transfer per capita (we lack the data to do a calculation on the basis of per student attendance).

In the health area, the picture is somewhat different. In contrast to education, where the vast majority of primary-school-age children attend public schools, now funded by or through the states, a large share of the population is on various health insurance plans instead of the state run program. What the states fund (mostly with _aportaciones_ ) and operate is the system for the “open” population, those outside the insurance system, mostly the poor. Figure 4 shows the

> 5 The Federal District has the lowest level of Aportaciones in the country, because it does not receive any transfers for education and social infrastructure, given that the federal government directly provides these services in the District.

7
school graduates earn 18.8 percent less wages than public school graduates.

4. For males, the private returns to technical secondary education are 10.4%, compared with 11.6% for general education (U track), 12.3% for the general language track, and 11.2% for the general mathematics track. For females, the returns to vocational secondary education are 2.5%, compared with 12.3% for the general language track, and 11.6% for the general mathematics track.

Sources: Author’s based on Tan and Cho (2012). see Annex 1 of Tan and Cho for detailed description of data sources.

Table 1 shows that several studies find positive private returns for technical-vocational education while others find negative premiums, even after controlling for selection and endogeneity. However, these heterogeneous results may also reflect data limitations as there is often no information about the different types of programs and their modalities. In this sense, the data that we used in this study for Brazil provides much more detail on the technical-vocational education provided than in other countries.

This paper is organized as follows. Section 2 presents the methodology used in the quantitative analyses, and the data sources explored are presented in section 3. The main results are described in section 4. Section 5 presents the results on the heterogeneity of technical-vocational courses, and section 6 presents the results on entry in the labor market and individuals who attended tertiary levels of education in addition to technical-vocational education. Section 7 provides an analysis on the maximum costs for technical-vocational education courses and section 8 concludes.

# 2. Empirical Methodology

Our main objective is to examine the effect of completing a certain type of technicalvocational education course on the wages of individuals who work in relation to a similar comparison group.<sup>4</sup> However, this analysis poses a fundamental problem. Students who pursue a technical career are most likely different, in various observable and unobservable characteristics, from other students who opt for a more general education. Moreover, these characteristics are most likely linked to the individual productivity and wages in the labor market, which makes it difficult to quantify the wage impact of this type of education.

We start by estimating a simple Mincerian equation where we relate hourly wages with several characteristics of the students.

> 4 Our group of interest are those individuals who completed a specific type of education (technical-vocational, or regular), had a job during the reference week, and were 14 years of age or older.

7
and the strength of the reference lockdown is chosen based on experiences in seven European countries in the start of the pandemic (Flaxman et al., 2020).<sup>2</sup> We then compare the economic and mortality outcomes in the reference lockdown to a scenario without any government intervention.

Our main quantitative result is that there is substantial variation in health outcomes across countries following the reference lockdown. In our model, the lockdown led to an average of 1.76 child deaths for every COVID-19 fatality averted in low-income countries. The ratio falls to .59 and .06 in the case of lower-middle and upper-middle income countries, respectively. By assumption, there is no mortality tradeoff in high-income countries. As a result, lockdowns lower the total mortality by 6.2 percent in the richest countries, but _raise_ total mortality by 2.6 percent in the poorest ones. The main country characteristics driving the heterogeneity in health outcomes are (i) the semi-elasticities of child mortality with respect to GDP and (ii) demography, as poorer countries are also younger countries.

Finally, we consider a utilitarian approach to designing lockdown policies, in which the social planner trades off COVID-19 deaths averted against loss of life due to reduced GDP and the loss of consumption. The optimal lockdown varies across countries, as marginal costs and benefits are heterogeneous. Poor countries with younger populations generally feature shorter and milder optimal lockdowns, as the governments take into account the impacts on child mortality. Thus, the optimal lockdown significantly reduces the share of the population infected in the rich countries but not in the poorer ones. In the end, the child mortality impact is smaller as well: among the poorest countries, the optimal lockdown would lead to only 0.32 child deaths for every COVID-19 fatality averted.

Our findings hold general lessons for pandemic mitigation, past and future. Empirical evidence has shown repeatedly that in lower- and middle-income countries, infant and child

> 2We take the perspective of the initial months of the COVID-19 pandemic when governments were first confronted with designing pandemic responses in the absence of effective vaccines or therapeutics. We implement only non-pharmaceutical interventions because in the event of a new but similar pandemic, the same tradeoffs will be relevant as new vaccines and/or therapeutics would most likely not be immediately available. According to the Oxford Blavatnik COVID-19 Government Response Tracker, every single one of the 175 countries that it covers imposed at least some lockdown policy. Lockdown severity was remarkably similar across countries at different levels of development. On a scale of 0 to 100, the mean Oxford–Blavatnik lockdown stringency index was 79 in the low-income countries, and 78 in the high-income countries. (These averages are as of April 2020, the most globally synchronized phase of the lockdown. We use the World Bank classification of countries into income groups.)

3
# Figure 9.D. Economic activity during global recoveries, by country group

## A. Output per capita, advanced economies

## B. Output per capita, EMDEs

<!-- Start of picture text --> Index, t = 100 Index, t = 100<br>116 120<br>1975 1982<br>1975 1982<br>112 1991 2009 1991 2009<br>Average<br>Average 110<br>108<br>104<br>100<br>100<br>96 90<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br>C. Trade, advanced economies D. Trade, EMDEs<br>Index, t = 100 Index, t = 100<br>140 140<br>1975 1982 1975 1982<br>130 1991 2009 1991 2009<br>Average 120 Average<br>120<br>110<br>100<br>100<br>90 80<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br>E. Credit, advanced economies F. Credit, EMDEs<br>Index, t = 100 Index, t = 100<br>130 180<br>1975 1982 1975 1982<br>1991 2009 1991 2009<br>120<br>Average 140 Average<br>110<br>100<br>100<br>90 60<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Cooperation and Development; World Bank.

Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to an average of the four global recessions identified. Variables are index numbers equal to 100 in recession years. Aggregates for output per capita are market-weighted. For details, see Appendix 2.

44
Gendall 2005). Smokers assigned to the Cues treatment were offered their choice of the above cards, and encouraged by the marketers to keep them handy and/or post them in locations where the subject tended to smoke. More than 99% of subjects offered the cue cards accepted them.

# **IV. Experimental Design**

Our study sample consists of 2,000 smokers aged 18 or older who reside on the island of Mindanao in southern Philippines. Green Bank marketers identified smokers by approaching people and asking them whether they smoke regularly. If they did, the marketer then asked if they wanted to participate in a short survey on smoking. All subjects received an informational pamphlet on the dangers of smoking, and a tip sheet on how to quit. Since the primary objectives of this study were to determine whether first there was demand for CARES, and second whether CARES increased smoking cessation, the marketers only collected very quick and basic baseline data on age and smoking status (see Section V-A for more details).

The experiment was implemented in three distinct waves of marketing (the econometric specifications will condition for these waves). The first two waves took place in Butuan City from August to December 2006. After completing the baseline survey marketers revealed a sticker on the back of the survey that randomly assigned the subject to one of four groups: (1A) CARES with deposit collection, (1B) CARES without deposit collection, (2) Cues, or (3) Control.<sup>15</sup> The probability of assignment to groups was initially 45%, 45%, 5%, and 5%. After establishing that there was sufficient takeup of CARES, Green Bank changed the assignment probabilities to 15%, 15%, 30%, and 40% for the second wave. 418 smokers were surveyed (and hence drawn into the sample frame) in the first two waves. Of the 266 assigned a CARES offer, 34 took the product. Two individuals from the Cues group also opened an account (after hearing about the product and approaching bank staff). In our analysis we code these individuals in the Cues group, in adherence to the random assignment.

> 15 In the first wave there were 20 situations in which marketers interviewed respondents with either one or two others present; in these cases, marketers were instructed to interview all individuals in the group before disclosing the random assignment.  All respondents in the group received the same assignment as the first interviewee.  Impact results discussed below correct standard errors for any clustering within groups of individuals that received joint marketing.

9
least possible in most communes.<sup>10</sup> The simplest method for testing fungibility with these data is to regress the actual kilometers rehabilitated during the project period on the amounts allocated by RTP1. This gives a regression coefficient of 0.55 (t=2.13), which is well below, but not statistically different from, unity. The constant term is 12.30 (t=9.35), reflecting the road rehabilitation that would be done in the absence of the project (and again more than the allocated project amount). When we test built roads in the same way, there is little correlation (the coefficient is 0.24 with t=1.2). Finally, looking at total built and rehabilitated kilometers, the coefficient is 0.79 (t=1.94), which is closer to one although not significantly different from zero at the 5 percent level. Indeed, we can’t reject either the null hypothesis that it is zero or that it is one.

The above regressions are suggestive of partial fungibility of the project aid and a road-sector flypaper effect. However, as mentioned previously, we have concerns about the quality of the data on kilometers allocated to communes by RTP1 from the project database. The means appear reliable and reflect donor and government decisions but the commune allocations are more questionable. Possibly we are underestimating impacts due to attenuation biases. In addition, there is an endogeneity concern. This can be thought of as having two aspects: the assignment of which communes get the project, and the amounts allocated given the assignment across communes.

In the light of these observations, we will treat the intervention as a zero/one dummy variable, rather than by the kilometers allocated as recorded in the project database, and we will model this dummy as a function of the observed covariates of program placement. So, we aim to estimate the most common evaluation parameter, the average treatment effect on the treated (ATT) as given by the roads built and rehabilitated by the project less the outcomes under the counterfactual of not receiving the project aid. We then compare this to the independent administrative data on the aggregate allocation.

Despite the non-project communes being originally selected to be “similar” to the project communes, one can expect differences between them. For example, this could

> protection and drainage repairs. Building refers to completely new road construction with new vehicle access.  The SIRRV question on road building clearly notes to exclude “rehabilitation." 10  Later, after discussing our estimation methods, we ascertain that this holds for almost all communes when we predict the counterfactual kilometers of rehabilitated roads in the absence of the project.

10
# **Introduction**

Environmental concern and associated social movements in developing countries have risen rapidly over the past decade, and public opinion polls show that levels of environmental concern in developing countries often exceed those of developed countries (Steinberg 2005). One way to translate public environmental concern into improved environmental outcomes is to formally incorporate participatory environmental mechanisms into local governments. As in other spheres of public life, civil society participation is hypothesized to contribute to government’s greater awareness of environmental issues and to the formulation of more effective and implementable responses (Meadowcroft 2004, Ribot 2002). A well-known Brazilian example of successful civil society involvement in public policy is the experience with local participatory budgeting which started in Porto Alegre and expanded to 170 other cities by 2004 (Avritzer 2006, Wampler and Avritzer 2005, Santos 1998).

This hypothesis raises two linked questions. First, what are the determinants of demand for local environmental institutions? Are they strongly linked to income and education, as the theory behind the Environmental Kuznets Curve would suggest? Second, once in place, do these institutions shape local government responses? In practice, these questions are difficult to disentangle, because unobserved factors are likely to influence both the existence of institutions and the nature of municipal perceptions and actions. In this paper, we address both questions through an econometric strategy that attempts to isolate the causal impact of institutions.

Brazil provides a good test-bed for this study, because it has formally-chartered participatory environmental institutions, and because it presents continental-level variation between localities in average income, education, size, rurality, and environmental condition while holding constant national laws and institutions. Since the adoption of a new constitution in 1988, Brazilian environmental policy-making has undergone fundamental changes towards 2
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49
_Note: Children can carry out more than one type of work. Wage work captures children working for income in the past seven days across three sectors; household chores captures cooking, cleaning, taking care of household members, and so on in the past seven days; household production captures farm work, fetching water, collecting wood, and so on for the family; unpaid family work captures unpaid help in a business owned by a household member; any work captures children doing any of these types of work and/or chores. Source: authors’ estimates using LFS 2015_ Table 1 shows that there are gender differences across all the different types of work carried out. Although the wage work participation rates for girls and boys are similar, with slightly more boys than girls (0.6 percentage points difference), girls are more likely to be employed in the agriculture and service sectors and boys in the industry sector. Girls are disproportionately more engaged (~9 percentage points) in household chores than boys, with girls outnumbering boys in every single chore. Even though girls are more engaged than boys in agriculture, when looking at wage work, boys actually carry out more farming activities for the household (0.7 percentage point difference) than girls. However, overall, 1 percent more girls than boys are busy with household production tasks including fetching water, doing craftwork and collecting wood. Overall, more girls (42.2 percent) than boys (36.6 percent) are engaged in some form of work or chores.

The above data should be interpreted with caution, however. Importantly, there are significant time differences across activities. Wage work is more time consuming than other occupations, with children working on average 53 hours per 6-day week in this type of work compared to 9 or 10 hours in household production or chores during a typical week. Further, household chores are more likely to be done at weekends or after school and do not seem to be a barrier to school attendance. Overall, girls spend one hour more a week than boys doing household chores. Boys, instead, spend more time doing household production activities. Despite the gender differences in types of work and overall work participation, there is no difference in school enrollment between boys and girls unless the data is disaggregated by age. At age 15, girls are more likely to be enrolled in school, whereas boys are more likely to be engaged in wage work. Despite there being almost no gender difference between hours spent on wage work during a typical week (53.4 girls and 53.1 boys), girls earn less money. On average a girl employed in wage work earns 17,120 kyats (USD10.4) per week and a boy earns 19,321 kyats (USD11.7), over a dollar more per week.

Participation in any type of work increases with age along with concomitant decreases in school enrollment. Participation in wage work begins at age 11 and involvement in household chores starts at age 5. By the age of 13, more than half of the children are involved in some type of work (Figure 1). Across 11
Appendix III.E Income Sources of the Elderly, United Kingdom 1986 Percentage of households with income from the following sources:

|Age|Earnings<br>only|Public pension<br>only|Private pension<br>only|Earnings &<br>pensions|Public &<br>private pension|
|---|---|---|---|---|---|
|55-64 years|58.4|10.7|5.7|11.7|13.5|
|65-74 years|0.9|30.2|0.1|16.0|52.8|
|75+ years|-|48.4|0.2|6.8|44.6|
|65+ years|0.6|36.9|0.1|12.7|49.8|
|uintile (65+)||||||
|1|0.3|64.1|0.3|0.2|35.0|
|11|0.0|56.4|0.0|3.6|40.0|
|III|1.1|30.2|0.2|3.6|64.9|
|IV|0.0|16.8|0.0|17.6|65.6|
|V|1.4|8.8|0.0|43.9|45.9|
|QV/QI|4.7|0.1|0.0|219.5|1.3|

Source: See table III.A

# Appendix III.F Income Sources of the Elderly, United States 1986

Percentage of households with income from the following sources:

|Age|Earnings<br>only|Public pension<br>only|Private pension<br>only|Earnings &<br>pensions|Public &<br>private pension|
|---|---|---|---|---|---|
|55-64 years|76.8|10.4|1.2|7.0|4.5|
|65-74 years|8.5|46.8|0.2|20.7|23.8|
|75+ years|1.2|68.5|0.1|5.1|25.1|
|65+ years|5.7|55.2|0.2|14.6|24.3|
|uintile (65+)||||||
|1|3.1|87.8|0.5|0.0|8.5|
|II|1.0|70.9|0.2|3.9|26.0|
|III|4.7|48.3|0.0|10.1|36.9|
|IV|6.9|31.7|0.0|25.0|36.5|
|V|15.3|24.9|0.0|43.0|16.9|
|QV/QI|4.9|0.3|0.0|*|2.0|

Source: See table III.A 116