rafmacalaba/datause-encoder-data huggingface dataset:
|Coastal Resources for Sustainable Development Project|Binh ThangCao|
|---|---|
|Tamil Nadu Road Sector Project|PratapTvgssshrk|
|Levelingthe PlayingField forQuilombola Communities in North|eastAlberto Coelho Gomes Costa|
|Levelingthe PlayingField forQuilombola Communities in North|eastAlberto Coelho Gomes Costa|
|Levelingthe PlayingField forQuilombola Communities in North|eastAlberto Coelho Gomes Costa|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India Low-Income HousingFinance|Anuradha Ray|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|India: ScalingUpSustainable and Responsible Microfinance|Subrahmanya Pulle Srinivas|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Financial Sector Rapid Response Project|AndrejPopovic|
|Support to IAVI-Sendai Vector|Robert Oelrichs|
|Support to IAVI-Sendai Vector|Robert Oelrichs|
|Support to IAVI-Sendai Vector|Robert Oelrichs|
|Support to IAVI-Sendai Vector|Robert Oelrichs|
|Support to IAVI-Sendai Vector|Robert Oelrichs|
|Support to IAVI-Sendai Vector|Robert Oelrichs|
treatment group as the _first_ batch of schools that received SBM funds in 2003-2004. The treatment group consists of the schools that did not receive SBM funds and training in 2003-2004. Therefore, for this study, we operationalized SBM batch one schools as the _treatment group_ (n=1666) and all other schools (batches that received the SBM intervention later) as the _control group_ (n=3501).<sup>11</sup> In our analysis, the treatment group (batch one) therefore had exposure to SBM for a period of two years, 2003-04 and 200405.

Because selection into SBM was not voluntary, there is likely to be placement bias in our control and treatment groups. We therefore use two non-experimental evaluation techniques to estimate program impact: difference-in-difference estimators and difference-in-difference with propensity score matching estimators. Using nonparametric kernel matching techniques, we correct for potential sources of bias.

Specifically, we tested whether the composite test scores and test scores individually in three subject areas (math, science and English) were higher after SBM was introduced for schools in the treatment group compared to those in the control group.

# **4.  Data and Statistical Specification**

We use school administrative data from all 5,167 schools in the 23 TEEP districts to examine the effect of SBM program on student test scores. The dataset includes information on school personnel, students, facilities, and enrollment and dropouts for all schools in the district over a 3-year period, 2002-2003 to 2004-2005. The data were collected by the Philippines Department of Education as part of the management of the Third Elementary Education Project (TEEP) of which SBM was one component.

Student achievement is measured using National Achievement Tests conducted in 2002-03 (year one – pre-intervention) in Grade 4, 2003-04 (year two) in Grade 5, and 2004-05 (year three, post-intervention) in Grade 6 in English, mathematics, science. However, only the school-level Mean Percentile Score (MPS) data are available in the dataset, limiting the analysis to the school level. We utilize the composite MPS computation (based on tests in all subjects), as well as the English, mathematics, and science MPS for 2002-03 (pre-intervention) and 2004-05 (post-intervention).

> 11 The number of schools in the analysis is not the same as the number of schools included in the SBM program due to missing information.  The difference is 728 schools.

5
# **1. Introduction**

With over 100 million primary school age children not in school worldwide (UNESCO 2005) the target of universal education, endorsed by over 180 countries as a part of the Millennium Development Goals, remains elusive. Children with disabilities face particular hurdles to attend, and complete, school in developing countries. While there has been much policy discussion about interventions to increase access to schooling for children with disabilities (for example see Peters 2003, World Bank 2003), there has been little systematic empirical analysis on which to base this policy. A large part of this is due to the lack of appropriate and comparable data. Despite Elwan’s (1999) description of the more general lack of empirical work on the association between disability and poverty in the developing world, such work is still missing.<sup>1</sup> This study aims to start filling some knowledge gaps using existing data on the prevalence of disability and its association with poverty and schooling among youth in 8 developing and 1 transition country.

Defining disability is complicated—and controversial. Purely medical definitions used in the past are giving way to definitions that incorporate continuous measures of the activities that people can undertake, the extent of participation in society and social and civic life, as well as the role of adaptive technologies. The World Health Organization’s International Classification of Functioning, Disability and Health (ICF) describes disability as an umbrella term for impairments, activity limitations, and participation restrictions as a part of a broader classification scheme covering three main domains: body functioning and structure, activities and participation, and environmental factors.<sup>2</sup> The interaction of aspects of all three of these domains determines individual welfare and social policy choices facing governments.

The main goal of this paper is descriptive. Many of the basic facts about disability, poverty and schooling in developing countries are unknown, or not systematically addressed. In order to contribute to the foundations of policy development, this paper analyzes available data to investigate the interactions between physical impairment and participation in schooling, and the intermediary relationship with poverty. The analysis finds that disability among youth is sometimes, but not always, associated with household poverty, but that it is systematically and significantly related to lower school participation.

The paper proceeds as follows. Section 2 compares definitions and the prevalence of disability across the household surveys covered. Section 3 investigates the association

> 1 An early exception to this is Afzal (1992) who analyzes disability and its correlates in Pakistan. Yeo and Moore (2003) review some of the literature on poverty and disability but the literature they refer to is typically not based on large-scale surveys.

> 2 An online guide to the ICF is available at http://www3.who.int/icf/.

1
program mandates not driving one day during the week (except the weekends) and two days during serious pollution episodes. During the weekends, odd and even license plate numbers are used, which forced one-half of the fleet to be parked. By removing 20 percent of the vehicles from the streets in its first few months of operation, it did contribute towards the betterment of ambient air quality (Goddard, 1997). The gains made, however, were temporary. The program did not yield the desired level of success for several reasons. First, the city lacked sufficient public transport systems to meet the travel demand resulting from the ban on personal vehicles. Second, the driving public intelligently subverted the existing regulation. For example, many drivers adjusted to the restriction by purchasing additional autos in order to have at least one vehicle available on any given day. Many of the second vehicles were older and released more emissions. Some studies (e.g., Eskeland, 1994; Eskeland and Feyzioglu, 1995; Goddard, 1997) even argued that the program actually may have led to an increase in the number of vehicles and total emissions from road transport.

In addition to Mexico City, the traffic restriction (restricción vehicular) policy has been implemented in three different Latin American cities: Santiago (Chile), São Paulo (Brazil), and Bogotá (Colombia), with varying degree of success. The traffic restriction policy in Santiago implemented to reduce congestion and air pollution has limited the circulation of 20% of buses, taxis, and cars. In order to combat free rider problem, the schedule for the restriction is changed every few months. In São Paulo, the effects of the traffic ban have been undermined by growing car ownership. In order to meet air quality targets, Mexico City authorities are planning modifications to the existing scheme to ensure stricter enforcement with fewer exemptions (Mahendra, 2007).

Many Latin American countries have imposed complete or partial bans on used vehicles imports. Despite a huge market for used vehicles, countries such as Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Uruguay, and Venezuela have completely banned used vehicle imports (Pelletiere and Reinert, 2002).

37
Approved projects also receive technical assistance from local institutions -universities, technical institutes or NGOs. This assistance is given by agents ( _tutores_ ) and covers general management practices and more specific technical assistance.<sup>21</sup> The objective of providing these services is to ensure the financed projects are sustainable. During the six month period, the agents are supposed to visit the project at least four times (for general purposes) and one time for a specific technical tutoring activity. An in-depth review of a sample of projects highlights some shortfalls in the design and implementation of the technical assistance (Etchegaray 2005).<sup>22</sup>

# **3. Survey Design and Methodology**

## **3.1 Survey Design**

We use data from the survey implemented in the Greater Buenos Aires (hereafter GBA) area, which included _Capital Federal_ and _Conurbano_ . The survey restricted the attention to this geographical area for different reasons. First, within GBA there was geographical variation in the incidence of the program (some municipalities implemented the program while others did not). Second, GBA had a relatively homogenous labor market and, municipalities conducted similar MEP promotional activities. Third, there was a reliable list of beneficiaries interested in the program which could be used as a sampling frame for the survey. Finally, the GBA area accounted for almost 1/5 of the total financing as of 2004.

The sample of MEP participants was drawn in July 2004 from a list of approved projects just after the deadline for the submission of proposals. Since the program assignment was not

> 21 This technical assistance was very wide ranging, covering management, administration and commercialization techniques. The _tutores_ are experts from local universities, technical institutes or NGOs that were previously selected by the Ministry of Social Development based on their experience and academic background..

> 22 Some of these shortfalls highlighted in the qualitative work (sample collected in November 2004) might have been typical of the early stages of program implementation and presumably should have improved over time, some might be more structurally inherent to the program design.

12
Table 6: Comparing Coefficients for Wages and Consumption

|dependent variable:<br>sample:|(1)<br>hourly wage<br>full|(2)<br>consumption<br>full|(3)<br>consumption<br>main earner|(4)<br>consumption<br>not main|
|---|---|---|---|---|
|IMR in birth year|-0.00195*|-0.00152*|-0.00173*|0.000211|
||(0.000796)|(0.000646)|(0.000780)|(0.00105)|
|district fxed efects|✓|✓|✓|✓|
|year of birth fxed efects|✓|✓|✓|✓|
|state _×_ urban fxed efects|✓|✓|✓|✓|
|social group _×_urban indicators|✓|✓|✓|✓|
|state-specifc linear time trends|✓|✓|✓|✓|
|_n_ (adult men)|12,783|12,716|8,438|4,278|

Note - _p_ values: _†_ = 0 _._ 1 _, ∗_ = 0 _._ 05 _, ∗∗_ = 0 _._ 01 _, ∗∗∗_ = 0 _._ 001. Standard errors clustered at the district level.

Table 7: Regression of Wages on IMR and Sanitation for Stayers

|sample|(1)<br>full|(2)<br>non-migrants|(3)<br>full|(4)<br>non-migrants|
|---|---|---|---|---|
|IMR in birth year|-0.00174*|-0.00162**|||
||(0.000706)|(0.000602)|||
|sanitation in birth year|||0.00296***|0.00255***|
||||(0.000509)|(0.000528)|
|district fxed efects|✓|✓|✓|✓|
|year of birth fxed efects|✓|✓|✓|✓|
|_n_ (adult men)|12,783|11,017|6,134|5,533|

Note - _p_ values: _†_ = 0 _._ 1 _, ∗_ = 0 _._ 05 _, ∗∗_ = 0 _._ 01 _, ∗∗∗_ = 0 _._ 001. Standard errors clustered at the district level.

43
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4 Figure 2: Role of the United States in trade and foreign direct investment

- (A) Global trade (B) U.S. trade

<!-- Start of picture text --> Constant 2010 US$, trillions US$, trillions<br>30 Actual 2000-07 trend 4 Actual 2000-07 trend<br>25<br>3<br>20<br>15 2<br>10<br>1<br>5<br>0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

- (C) U.S. share of global trade and FDI (D) Countries for which the United States is the largest export destination and import source

<!-- Start of picture text --> Percent of total Percent of countries<br>25 25<br>Total EMDE<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>Merchandise trade FDI stocks Exports Imports<br><!-- End of picture text -->

- (E) Share of U.S. multinationals in (F) U.S. firms participation in global largest 100 non-financial multinationals value chains

<!-- Start of picture text --> Percent of total Percent of exports<br>35 45 Domestic VA sent to third economies<br>30 40 Foreign VA content of exports<br>35<br>25<br>30<br>20 24.9<br>25<br>15 20 19.4<br>10 15<br>10<br>5 15.0<br>5 11.4<br>0<br>0<br>Assets Sales Employment 1995 2011<br><!-- End of picture text --> Sources: UNCTAD, World Bank, WTO. A.B. Includes merchandise and services imports. C. Total of merchandise exports and imports and total of inward and outward FDI stocks. D. The sample includes 190 countries, of which 139 EMDEs, for exports and 189 countries, of which 139 EMDEs, for imports.

- F. VA refers to value added.
_Figure 1 Average Monthly Earnings by Year and Gender_ <!-- Start of picture text --> 1600<br>1500<br>All<br>Men<br>1400<br>Women<br>1300<br>1200<br>2012 2014 2016 2018 2020<br>year<br>Average Monthly earning<br><!-- End of picture text --> The results suggest that after a small decline in average real monthly earnings from 2013 to 2016, there was a slight improvement in the following two years, with a small decline in 2019, with average wages remaining at stable levels in 2020, despite the Covid-19 pandemic.<sup>7</sup> The results also suggest that the gender earnings gap has shown a somewhat increasing trend between 2013 and 2019, although it is predicted to decline a little in 2020.

7 This estimate does not take into account the decline in labor force participation observed during the pandemic.
Chart 4:Total Incidence of Transport Costs, Insurance Costs and Tariffs<sup>on Exports</sup> to the US: India and Other Countries <!-- Start of picture text --> 4A: Total Incidence: Clothing and<br>Apparel<br>2512 tU~~~~~~~~~~~~npOlt Cost<br>I a _ _ Ind S.niprwre All C..fti.<br><!-- End of picture text --> <!-- Start of picture text --> 4B: Total Incidence: Iron/Steel<br>Articles<br>12 lR<br>India Singapore All Countries<br><!-- End of picture text --> <!-- Start of picture text --> 4D: Total Incidence: Organic<br>Chemicals<br>India Singapore All Countries<br><!-- End of picture text --> <!-- Start of picture text --> 4C: Total Incidence: Electric 4D: Total Incidence: Organic<br>Machinery Chemicals<br>4S f<br>India Singaporm All Countries India Singapore All Countries<br>4E: Total Incidence: Trade 4F: Total Incidence: Median Levels<br>Weighted Avg.<br>10<br>4 U 4- 4 -<br>India SIngapore All Countries 0<br>l________________________________________ lIndia Singapore All Countnes<br><!-- End of picture text --> _Note:_ Ad valorem transport and insurance costs are stacked above advalorem<sup>tariff. Total Incidence is measured as the sum of these</sup> costs for each of the above mentioned categories.

Source: Research in progress by Fink and Mattoo, World Bank, 1999 19
10 life.<sup>16</sup> In fact, Lature was to be remade according to man's image, transformed so as to be more suitable to humans' needs and desires. The relationship between human activity and nature under this management paradigm thus can be seen as _unilaterally oriented (anthropocentric)._ From "nature's perspective," the relationship may have been characterizable as _zero-sum,_ or _negadve;_ humans benefitted at the expense of other species and natural ecosystems.

17. This type of relationship between society and nature is common to relatively decentralized, capitalist economies and to centrally-planned, Marxist economies. They differ in tactics, such as in the type of property regime promoted as most efficient and/or desirable (private property versus state property), responsibility for governance and design of activity, and in how the income from production is to be distributed, but the underlying wxrldviews about the roles of people and nature, and their ultimate goals, are mu. h the same. Their visions are of infinite economic growth and human "progress." 18. Many technologies that have been used for "development" could thus be seen, with a minor adjustment in view, as technologies or strategies for managing the environment, since they were developed for the purpose of increasing man's power to extract resources and production from nature, and/or to reduce the negative impacts of nature's variability on society. A prime example is modern, industrial agriculture, which in order to solve the basic problem of hunger, replaced natural nutrient cycles and pest control with man-madc chemicals, irrigation, and fossil fuel energy. Another example is the "tall smokestacks" strategy of waste dispersal, based on the idea that if pollution is spread thinly enough, it will go unnoticed, by people or by nature.

19. Most developing nations have emulated this basic approach to economic and environmental management in one way or another. They have been in no small way encouraged by not just the example and teachings, but also the direct policies prescribed for them by the leaders and policymakers of industrialized nations and international development and financial institutions.<sup>17</sup> This approach was sometimes jusdfied as a minor evil, "necessary" during the pre- and early-industrial stages of development, as was rapid population growth, in order to achieve a more advanced state. This population growth then became a reason for yet more resource consumption and pollution. It is believed that damage can easily be repaired, where necessary, after development has proceeded

> 16 Bnn, Momris, 1981. _The Reenchantment of the World,_ ComneD University Press, Ithaca, NY, pp. 14-18. 17 It should be noted that such prsiptions were not necessary intentionally harmful; they arose due to the implicit, often unconscious assunptions made about the relationship and intedendence between hnman activity and nature. Unfortu"aely, the hidden effects were built into the policies. Many of these institutions and leaders are now trying to change this.
31 Both male and female educated workers gain if they migrate to urban areas. The gains are more substantial for men with a primary and secondary education if they migrate to other urban areas rather than to Lima. Similarly, women with a secondary education gain if they migrate to other urban areas from rural areas. The returns to schooling aie, therefore, likely to be overestimated if the effect of interregional migration is not controlled for in the regional estimates. This is evident in the all-Peru wage estimates. When gains from interregional migration are excluded and sample selectivity bias is corrected, the private rates of return decrease for men and women.

The differences in returns to schooling for men and women are worth noting. In Lima the private rates of return at the primary school level are low, but higher for women than for men (7 percent compared to 4 percent).= At the secondary and postsecondary levels, however, the returns to schooling are high, and again, higher for women than for men: 13 and 10 percent respectively, compared to 8 percent in each case for men. In other urban areas the returns for men are 10 and 12 percent respectively at the primary and secondary levels, compared to 8 and 7 percent res~pectively for women. The returns drop at the postsecondary level for both geniers in other urban areas -- to 7 percent for men and 9 percent for women. In conzrast, the return to schooling is an increasing function of the level of education for both men and women in rural areas. If we exclude from the wage estimates gains due to migration, the private rate of return to schooling drops for men at the primary level and for both men and women at the secondary level, while increasing for both men and women at both postsecondary levels.

The returns to schooling are relatively higher for women than for men, XThis pattern is different from that of Bogota (Mohan 1986).