marcelo fuentes capstone - final version

Multilevel analysis of the effects of non-educational factors on

educational attainment (PISA scores) in Latin America & Brazil

A Capstone Project Submitted in Partial Fulfillment of the Requirements of

The Renée Crown University Honors Program at Syracuse University

Marcelo M. Fuentes

Candidate for B.S. Degree in Economics, B.A Degree in International Relations,

and Renée Crown University Honors

May 2016

Honors Capstone Project in International Relations

Capstone Project Advisor: ___________________________________ (Don Dutkowsky)

Capstone Project Reader: __________________________________ (John Burdick)

Honors Director: ___________________________________________ (Stephen Kuusisto)

Date: __________________________________


ACKNOWLEDGEMENTS

This capstone is dedicated to my parents, Cesar and Isabel Fuentes, who have always cheered

for me granting me unconditional support. To my wife Andreacarola Urso for her continuous

patience and support during the long nights of work this paper demanded, and to my sister

Oriana Fuentes, who has always inspired me to strive for perfection. Last but not least, I would

like to thank Dr. Don Dutkowsky who gave me guidance and the much needed direction required

to nurture this project from beginning to end.


ABSTRACT

This paper examines the different determinants of educational attainment in Latin America and

Brazil, by using PISA scores (an exam created by the OECD) as a metric for educational

attainment, and an OLS multivariable regression to assess causality. In this paper Brazil has been

used as a base case of analysis from which conclusions were drawn for the Latin American region

as a whole. Results show that, at a local scale, dependency, analphabetism, economic inequality

and unemployment are the strongest determinants of PISA scores in Brazil. At a macro level, PISA

scores appear to be affected mainly by urbanization, dependency, and GDP per capita. Results also

indicate that, within Brazil, a one percent increase in the level of dependency lowered students’

scores on PISA examinations by an average of 2.5 points. While, at a macro scale, a one percent

increase in urbanization increased students’ PISA scores by an average of 8.4 points. After careful

analysis, we suggest Latin American countries need to pay closer attention at their widening

demographic pyramids, and urbanization models to improve adolescents’ educational

performance.


EXECUTIVE SUMMARY

Latin America is a region which has undergone unparalleled economic growth and development

in the last two decades; however, not the same can be said in matters of educational attainment.

Therefore, the purpose of this paper is to evaluate what the main determinants of educational

attainments in Latin America are, using PISA scores (a triennial exam taken by adolescents

worldwide) as the dependent variable of this study. Seeing that this is a macroeconomic issue, this

study focuses on using a statistical model (ordinary least squares) to determine what the main

macroeconomic determinants of educational attainment are (some of the variables explored were

unemployment, GDP per capita, divorce rate, etc.). After testing several models and using Brazil

as the base case for this analysis, the main results indicate that Latin America as a region should

pay closer attention to their changing demographics as well as their investment in infrastructure if

they have hopes of raising their average educational attainment levels. Furthermore, this paper

suggests other Latin American economies follow suit with Brazilian welfare policies, since these

will prevent a hard landing effect in the region once the demographic dividend is exhausted.

Interestingly, yet contrary to what was expected, GDP per capita and inequality effects on PISA

scores were negative and positive, respectively. Lastly, this study suggests that Latin America

should put more emphasis on their policies geared towards educational attainment if they wish to

be competitive in the future, given their low endowments of skilled labor.


TABLE OF CONTENTS

I. Introduction ____________________________________________________________ 6

II. Background Information __________________________________________________ 8

II. A. PISA and Secondary Education ____________________________________________ 8

II. B. Latin America ________________________________________________________ 10

II. C. Brazil _______________________________________________________________ 15

III. Literature Review: Educational Attainment ___________________________________ 17

III. A. Income & Income inequality ____________________________________________ 17

III. B. Urbanization _________________________________________________________ 18

III. C. Dependency _________________________________________________________ 20

III. D. Analphabetism _______________________________________________________ 20

III. E. Unemployment _______________________________________________________ 22

III. F. Public Expenditure ____________________________________________________ 22

III. G. Infant Labor _________________________________________________________ 23

III. H. Divorce Rate _________________________________________________________ 24

IV. Model, Theory & Hypotheses _____________________________________________ 26

IV. A. Methodology and Data _________________________________________________ 26

IV. B. Estimated Models _____________________________________________________ 29

IV. C. Table of Expected Results ______________________________________________ 41

V. Findings ______________________________________________________________ 42

V. A. Cross-Country Model __________________________________________________ 42

V. B. Regional Model _______________________________________________________ 43

V. C. Inter-State Model ______________________________________________________ 45

VI. Discussion ____________________________________________________________ 47

VII. Conclusion ____________________________________________________________ 53

Bibliography ________________________________________________________________ 55

Annexes____________________________________________________________________ 58

Tables _____________________________________________________________________ 62


I. INTRODUCTION

This paper uses multivariable ordinary least squares (OLS) regressions to assess what

variables should be of major concern to education policy makers in both Brazil and Latin America,

using educational performance data (PISA scores) collected from this past decade. For the

purposes of this study PISA scores were chosen as our dependent variable. GDP per capita,

urbanization rates, dependency, analphabetism, unemployment rates, Gini indexes, public

expenditure, infant labor rates, and divorce rates were chosen as its independent variables.

This study adds to the present literature on education policy since it focuses on education

policy in Latin America, which is a geographic region that has been much understudied.

Furthermore, given that PISA scores are relatively new metrics in education policy, this paper also

gives more clarity into what some of the variables are which affect adolescents’ educational

performance. Additionally, this study’s layered macro-to-micro analysis is innovative given that

few examples in present literature exhibit use of this approach.

For the reader’s convenience, this paper has been broken down into seven subsections: this

introduction where the features of this study are briefly discussed; a background information

section where some context on PISA, Latin America and Brazil is given to the reader; a literature

review, where previous contributions to this topic by different authors is summarized; a

theoretical framework where this study’s hypotheses and models are described; a findings

section where the empirical results of this study are highlighted; a discussion section with an

exhaustive analysis of the study’s results; and finally a conclusion with closing remarks for this

study, and suggestions for future ones.


So what exactly are PISA scores? PISA (Programme of International Student Assessment)

is a triennial exam, which was tailored by the OECD (Organization for Economic Co-Operation

and Development) to objectively assess high school students worldwide on their proficiency in

mathematics, reading, writing and science. However, for the scope of this study, all the analysis

performed uses uniquely PISA scores for the mathematics portion of the exam. The decision to

use this specific portion was made on the assumption that marginal increases in proficiency in

mathematics should translate into skills yielding the highest future return for Latin Americans.

South America and Brazil were the geographies of choice for this study, primarily due to

their rising economic status in the western hemisphere. The region also has high potential for

growth given its large stocks of human capital. Therefore, considering South America’s recent

strides in the economic arena, understanding what factors are both promoting and hindering

academic performance in the region is crucial to enable these countries to pursue meaningful

economic growth.1 Since globalization will soon be pushing the supply for skilled labor even

further, if Latin America chooses not to embrace better public policies, it is likely that these

economies future will continue to be trapped in a spiral of low human capital gains and an over-

demand for skilled labor.

Therefore, this paper uses Brazil as a base case of analysis for the rest of the region,

principally due to Federative Republic’s avant garde public policies, but also due to its pivotal

position in the South American economy. It is also worth noting that Brazil has outperformed all

the countries in this study in educational attainment (given its PISA scores). Therefore, more than

1 Meaningful economic growth is driven by gains in human capital. Latin American over the past couples of decades has been notable for their export led-growth. Therefore, policies to diverge from this source of income are crucial for the region’s future.


a few lessons might be learned from analyzing Brazilian data. In addition, finding what variables

affect proficiency in mathematics is important because technical skills are the main drivers of

progress, research and development. And considering the major brain drain2 Latin America has

suffered from in the past couple of decades, understanding how to better equip the region’s

adolescents (pre-adults) for the future is of the essence to stop and reverse this trend.

II. BACKGROUND INFORMATION

II. A. PISA AND SECONDARY EDUCATION

The Programme for International Student Assessment (PISA) is an ongoing triennial

survey that assesses the extent to which 15-year-old students near the end of compulsory education

have acquired key knowledge and skills essential to participate fully in modern societies. PISA

does not simply ascertain whether students can reproduce knowledge; rather, it examines how well

students can extrapolate from what they have learned and apply that knowledge in unfamiliar

settings, both in and outside of school. One of PISA’s main goals is to measure skills that students

will find applicable later on in life. Furthermore, PISA offers insights for education policy and

practice, and helps monitor trends in students’ acquisition of knowledge and skills across countries

and in different demographic subgroups within each country. The findings allow policy makers

around the world to gauge the knowledge of students in their own countries in comparison with

those in other countries, set policy targets against measurable goals achieved by other education

systems, and learn from policies and practices applied elsewhere (Avvisati, 10).

2 Brain Drain is defined as the movement of high skilled human capital from one geography to a different one, thus the emigration of this demographic ‘brain drains’ a country.


In a study performed by the OECD in 2012, it was found that secondary education in Latin

America had a major gap relative to an average OECD country. Performance differences of Latin

American students and that of students in the OECD countries were equivalent to more than two

years of schooling according to 2012 PISA results.3 This relationship can be appreciated to a

greater extent in graph 1 (below) where the relationship between GDP per capita and PISA scores

for selected countries has been graphed (ECLAC, 27).

Graph 1. PISA scores vs. GDP per capita for the year 2012.4

However, in spite of these dim results, in recent years several Latin American countries

have started paying greater attention to secondary education, as they acknowledge its intrinsic

value for future generations. Especially considering that Latin America is about to start undergoing

a severe process of ageing, dealing an increasing burden on those who work, as they will need to

provide for the consumption of not only their young, but also their retired. This being the case, if

Latin America has hopes of alleviating this issue it will be by investing heavily into future

generations’ human capital. Given that an economy will only sustain growth if either human capital

or resources increase in the future, and since Latin America’s model of export-led growth is

3 Two years of schooling is on average about 80 PISA points. 4 Data for this graph was retrieved from an OECD report on 2012 PISA results and the World Bank Databank.

270

320

370

420

470

520

570

0 20000 40000 60000 80000

PIS

A S

co

res

GDP Per Capita (In 2005 US $)

PISA Scores vs. GDP per capita

GRULAC

Asia-Pacific

CIS

WEOG

OECDAverage


unsustainable in the future, without proper investments in human capital, an increase in production

and development will be hard to achieve (Moura Castro, 17; Glover, 99).

The changing characteristics of today’s and tomorrow’s economies require a labor force

with stronger mathematic, scientific, and communications skills. As a result, in developed

countries these demands yielded virtually universal secondary education, revised curricula, and

higher learning standards. In contrast, in Latin America and the Caribbean, school enrollment rates

have been historically lower, mainly due to schools’ subpar quality of teaching and curricula

poorly matching labor market demands. Moreover, considering that most students in Latin

America decide to delve into the labor force right after completing secondary school, the fact that

this institution is achieving poor results has a direct effect on the productive capacity of the

economy (Moura Castro, 1; Gropello, 44).

Lastly, it is a fact that education’s social returns surpass its costs, given that several studies

quote both educational attainment and learning being tied to a number of social and developmental

outcomes that generate greater social welfare in the long run, such as reducing infant mortality,

decreasing criminality, raising civil participation, decreasing risky behaviors, reducing the age of

marriage and reducing fertility rates. Therefore, governments should be driven to be stronger about

their efforts to improve educational attainment (Vegas, 11).

II. B. LATIN AMERICA

Latin America is a region where few students have access to higher education. Most young

people enter the work force right after finishing secondary school, or in some cases right before

graduating. This being said, how prepared will Latin Americans be as members of the future world,


if the sole institution capable of giving them the tools needed to integrate into the economy is not

achieving the desired results? (Bassi, 7)

During the mid to late 1990s, several Latin American economies which had recently

stabilized after suffering massive economic recessions started implementing significant reforms to

improve the coverage, equity, and quality of their secondary education systems. This was primarily

accomplished by implementing comprehensive educational reforms based on efficient public-

private partnerships and enhancement in the level of relevance of curricula as well as vocational

education. In the past, studies on educational attainment had typically been done with data from

countries that belong to the European Union or NAFTA (mainly due to data availability). But

given that Latin America, as a region, has only experienced significant economic gains as of fairly

recent (1990’s onwards) the fruits of this growth can only be studied nowadays after some years

have passed and data has been collected (Gropello, 8).

Graph 2. Inequality in Latin America over the past three decades.5

5 Data for this graph was retrieved from the World Bank Databank

40

45

50

55

60

65

1980 1985 1990 1995 2000 2005 2010

GIN

I (In

eq

ual

ity

Ind

ex)

YEARS

Gini coefficient (inequality) in Latin America 1980-2010

Chile

Brazil

Colombia

Peru

Argentina

Uruguay


Latin America has severe issues of social and economic inequality; as can be seen in graph

2, Gini coefficients6 for the region range from 45 to 55 on average (the United States’ Gini index

for the year 2009 was 47). Therefore, inequality in education is also a major topic in Latin America.

However, the good news is that across the region income distribution has been improving in recent

years while poverty is declining. Interestingly, the two reasons for this increase in social equality

lie in improved relative earnings for low-skilled workers, as well as a drop in the earnings

premiums associated with education, which shows that as education increases in the region, social

and economic gaps are destined to close (Aedo, 6) (Manacorda, 308).

However, this optimism might need to be delayed for some years, since several countries

in the region appear to be trapped in a low-level equilibrium of low standards for entry into

teaching, low-quality candidates and relatively low (and undifferentiated) salaries – not to mention

poor education results. Hence, moving to a new equilibrium will be a difficult task to achieve,

since no Latin American school system today, with the exception of possibly Cuba, is even close

to having high academic standards (Bruns, 11).

Furthermore, another key issue in Latin America is that very few countries possess

programs and policies that orient young people in the areas of occupational placement. Since most

youth in Latin America do not see the returns of pursuing tertiary education (due to a lack of

employment opportunities where such skills are needed), there is an overall tendency to undervalue

education, which creates an oversupply of unskilled labor that ultimately foments the expansion

of industrial sectors where unskilled as opposed to skilled labor is needed. While this is negative

6 The Gini Coefficient is an index used to model inequality in a country; a value of 1 implies all the income of a country is held in the hands of one person and a value of 0 or total equality implies all income is distributed evenly within the population.


for Latin America’s youth, it must be stressed that the overall impact on the economy is a lack of

diversification and expansion of sectors related to research, innovation and development (Barth,

6).

There are also signs that Latin America’s underperformance might be due to poor resource

allocation at the secondary education level, evidenced by Latin America’s low PISA scores

compared to those of other PISA participants in the OECD with similar capital endowments. Most

countries in Latin America, for which we have data, exhibit a relatively low per-student investment

in secondary education and an inability to turn that investment into learning achievements (as

reflected in PISA scores). This finding is suggestive of what seems to be a low quality equilibrium

where poor performance makes it hard to justify increased (or appropriate) funding, thus keeping

the system in mediocrity (Aedo, 13).

Another key issue to highlight regarding social relations and education in Latin America is

household relations. In most countries in the region, above 70% of children and grandchildren

reside with their elderly for a prolonged period of time (which is well above 20% co-residence

seen North America and Europe). Therefore, this high level of co-residency with younger

generations translates into a high intensity of intergenerational family transfers. However, most of

the transfers are directed towards younger generations, implying that younger generations tend not

to transfer much to their elderly but they do reap most of the benefits of households (Bixby, 156).

This being said, taken as a whole, Latin America is currently at the optimal stage of the

demographic dividend7, in which the dependency ratio has already reached relatively low levels

7 Demographic dividend is the notion that whenever an economy has fewer people who are economically dependent (children and elders), the workforce has extra income to spend, since they have fewer people to maintain/support.


and continues to fall. However, since this period started at the beginning of this century and is

expected to last until the end of the next decade, it is likely that right now positive economic

outcomes might be a product of this dividend, and mishandling this surplus accordingly might

imply that, in a near future, older age people will start becoming a burden for the workforce.

Therefore, by increasing the quality of education of younger generations, higher productivity will

yield higher economic returns and safeguard the quality of life which Latin Americans have been

enjoying of in these past years. In essence, Latin America has a demographic window of

opportunity right now, in which betting for better secondary education might be optimal (Saad,

16-18).

Another key issue to highlight is that, in terms of foundational education, only some

countries in South America have compulsory secondary education cycles of school. These include

Venezuela and Ecuador, where some or all of secondary schooling is mandatory, and Brazil and

Bolivia, where the basic education cycle (including lower secondary) is compulsory. Yet, in the

remaining eight countries of the region, governments only demand that children complete primary

schooling (Gropello, 12).

Due to these aforementioned relations, nowadays there is a strong movement in several

countries in the region to pay further attention to secondary education. Chile, Argentina, Colombia,

Uruguay, Barbados, Paraguay, and El Salvador have recently undertaken major efforts to expand

and improve secondary education, while El Salvador, Costa Rica, Dominican Republic, Mexico,

and others have sought to expand and improve lower secondary education. In the case of Brazil,

we see the federative republic has sought out to make secondary education its priority over the

next four years (Moura Castro, 3).


II. C. BRAZIL

Brazil’s case has been a particularly interesting one, not only because it represents a large

share of the Latin American economy and population, but also because of the rapid ageing process

its population has undergone. Due to this, towards the end of the 1980s Brazil introduced several

economic reforms to expand the coverage of their pension system to both poor and rural sectors,

but also for those working in the informal sector, thus providing benefits to all. However, it was

not until the late 1990’s that we saw Brazil making large investments in education with programs

such as FUNDEB8 and FUNDEF9 aimed at promoting the educational attainment of the country’s

most critical demographics. Therefore, several experts argue these systems of pensions and

subsidized education have been the trigger for Brazil’s social improvements in this past decade

(Donehower, 12).

It is also worth noting that in 1996 Brazil passed the Lei de Directrices e Bases de

Educaçao Nacional (the National Education Law), redefining the roles of both state and municipal

governments in education provision, though charging the central government with standard setting,

ensuring equity, monitoring, evaluation, and partial responsibility for education funding. Thanks

to this policy, Brazil was able to cope with the issue of mismanagement of government funds in

the education sector, but also giving more responsibility to local governments (Gropello, 64-65).

8 FUNDEB: Fundo de Manutenção e Desenvolvimento da Educação Básica e de Valorização dos Profissionais da Educação (Fund for the Maintenance and Development of Secondary Education and Valorization of Professional Educators) – this fund oversees the secondary education system, assessing professors’ performance and awarding better salaries to schools where professors are most needed. (Gropello, 64) 9 FUNDEF: Fundo de Manutenção e Desenvolvimento do Ensino Fundamental e de Valorização do Magistério (Fund for the Maintenance and Development of Elementary Education and Valorization of School Faculty) – this fund is analogous to FUNDEB, since it also oversees the salaries of teachers in the region; however, it also deals with professionals who are part of the schools’ management. (Gropello, 64)


Another critical point to highlight is the structural changes Brazil’s labor market has

undergone in recent history. As seen in annex #1, nowadays more than 60% of Brazilians in the

labor force have completed at least secondary education. This fact should not be taken lightly, as

it sheds light onto the level of educational attainment currently being sought after by Brazilian

companies, but also by Latin American companies in the region. Thus, it is a reality that in a near

future this increasing demand for skilled workers will force those who decide not to pursue higher

education into equilibriums of low skill and low wages, since companies will look for skills

elsewhere, leaving only the most underserving jobs in the domestic market (do note, this applies

to Brazil, but also to any other Latin American economy with a similar situation) (Annex #1).

Lastly, in terms of income distribution, Brazil is one the most unequal countries in the

world. Anecdotally, education has historically played an import role in explaining this fact, since

about 50% of the income distribution in Brazil can be associated with education. The explanation

behind this is that returns to education in Brazil are very high and only a small proportion of the

population has access to higher levels of education. Moreover, although access to the first year of

schooling in Brazil is almost universal, children from poor backgrounds tend to drop out of the

school system early on. Unfortunately, one the reasons behind this high drop-out rate is typically

associated with the quality of education children receive in the public education system. For all

these reasons, this study considers it crucial to evaluate an education reform is aimed at changing

the government’s funding structure of the public school system, in order to redistribute resources

to the poorest regions and those who need them the most (Menezes-Filho, 12).


III. LITERATURE REVIEW: EDUCATIONAL ATTAINMENT

This section introduces some of the different opinions and conclusions several academics and

international organizations have regarding the effect each one of the factors detailed below have

on educational attainment. Therefore, these previous findings are the ones guiding this paper’s

expectation of what each parameter’s effect might be on educational attainment.

III. A. INCOME & INCOME INEQUALITY

The relationship between income and educational attainment suffers from issues of

endogeneity, as pointed out by several authors such as Bruns in their 2015 World Bank report,

where it is stated that gains in educational attainment reflect large gains in economic growth. The

relationship between income and income inequality with educational attainment is far more

complex (Bruns, 3). However, in a report by World Vision it is stated that while there is a positive

relation between income and educational attainment, this relationship becomes weaker for

countries which have reached levels of income far exceeding those of the median, such as countries

in the European Union (World Vision, 11).

This particular study highlights that wealthy countries where there is large government

participation to implement education reform tend to display much higher results that those that

remain idle. This study does stress, however, that increased national income allows for higher

private spending on health and education, which in turn reflects into higher child well-being

(World Vision, 12-15). It is also important to highlight that countries’ incomes and their levels of

inequality are typically uncorrelated; however, Latin America as a region suffers from severe

income inequality, something which deeply disturbs the analysis of income and income inequality

and their effect on PISA scores. (Gropello, 52)


In a study performed by Paul Glewwe and Hanan Jacoby in Vietnam (which by income

and geography is socioeconomically comparable to equatorial Latin American countries), they find

evidence that higher family incomes lead to more children attending school and/or attending for

longer. It must be noted that this study controlled for factors such as quantity and quality of schools

and yet arrived to the same conclusion. It is also crucial to highlight that this correlation is defended

by the Asian Development Bank as well (World Vision, 11; Glewwe, 49).

One last study performed by Catalina Gutierrez and Riuchi Tanaka suggests that countries

with high income inequality tend not to endorse public education nor any public institution

whatsoever, which translates into further restrictions for children to receive a basic education due

to high costs associated with receiving public education. As a result, due to low income families

being able to afford only low quality/price schools, the overall quality of education that children

from poorer backgrounds receive is overall lower, translating into low performance and low PISA

scores (Gutierrez, 75; Tanaka, 32).

III. B. URBANIZATION

In a study performed by Fafchamps in Nepal, where the effects that distance from the rural

portions of a city to the urban portions of cities were analyzed, the study found that children born

in households found far away from urban centers tended to work much more than their urban

counterparts. The main reason for this is that rural children assist their parents on the farm and in

house chores, while urban children do not. It was also found that children who lived in the

proximity of cities or urban centers were far more likely to attend school (Fafchamps, 29).


In a paper by Bent, where he analyzes the main reasons behind an increase in school

enrollment in the American education system, he points out that when taking the example of the

United States and tracing it back 80 years ago, the key determinants of school performance and

attendance laid on issues regarding transportation of students from rural to urban areas (where

schools were located) and compulsory school-attendance laws. However, due to rural areas’

remoteness, enforcement was much harder to apply to children living in areas far away from the

city centers, thus lowering the opportunity cost of child labor and increasing that of education

(Bent, 16-18).

Looking into some of the other effects which are caused by rapid urbanization, Zhang

found that one of the major effects of urbanization is a considerable decline in fertility rates, which

translates into rising investments per child relative to the output per worker, given that parents’

future expenditure expectations become dramatically lower. Zhang concludes saying that a key

issue to tackle if the education of rural citizens is to be improved is for there to be better

infrastructure bridging gaps between urban and rural communities (Zhang, 115).

In a study performed by Bertinelli, he finds that cities provide incentives for investments

in education by their residents, by arguing that urban areas provide higher returns to education

than suburban areas or even rural areas. He also shows that literacy rates and educational

attainment overall tend to be higher in urban areas in comparison to rural areas, arguing that urban

agglomerations have a positive externality effect on the educational attainment of children

(Bertinelli, 82).


III. C. DEPENDENCY

Dependency represents the ratio of the number of people who are dependent on the

workforce, over the number of people who are part of the workforce. Therefore, its impact on

educational attainment is purely derived from the shape each country’s population pyramid is. In

light of Latin America as a whole tending towards a more ‘dependent society’ it is important to

understand the different impacts each demographic composition has. As Lutz explains, whenever

birth rates start to decline and there is a decrease in the young-age dependency ratio, this translates

into a demographic bonus, where families are able to invest more in healthcare and education due

to the low level of dependents (Lutz,14-16).

In 1974, in a theoretical study carried out by Becker, he formalized a theory on the shadow

price of children with respect to their number and quality. Becker states that the higher the quality

of children, the higher their marginal costs to families. Also, the higher the number of children,

the higher their overall costs to families, given that their marginal cost would then be multiplied

by a scalar quantity. Therefore, in the case of dependency for this study, what would be expected

is that the total cost of children to families would rise with increases in the level of dependents, as

budgetary constraints would tighten. (Becker, 81-82).

III. D. ANALPHABETISM

Parental schooling is typically the way intergenerational externalities are measured. In the

Latin American context, seeing that it was only as of recent that governments were able to achieve

literacy rates above 90%, it would be prudent to analyze analphabetism or its antagonist literacy

as equivalents of ‘years of parental schooling’. One of the main examples explaining the positive


externality effect of parents’ schooling is Behrman’s paper on intergenerational mobility in Latin

America. As Behrman defends, the intergenerational returns to schooling become incredibly large

in the Latin American context, and on average the expectations to pursue further/more degrees in

the future increases by close to 25% solely due to the positive externality effect. Therefore,

children who are in high school feel far more motivated to excel when they see that their parents

had a higher level of educational attainment (Behrman, 17-19).

In a paper by Gropello, it is suggested that by promoting mass literacy and access to

primary education countries are far more likely to ensure that their citizens will be capable to

engage in 1) more efficient economic activities and 2) ensuring future generations will be able to

secure a secondary education. Furthermore, it is stressed that a top-to-bottom approach towards

education, such as early tracking, does not promote the overall academic performance of a

community given that it focuses too much on punctual issues as opposed to overarching issues. It

must be noted, however, that this study was carried out in Tanzania and Tunisia (Gropello, 47).

In a study performed by Chevalier, findings from ordinary least square regressions suggest

that there is strong evidence of intergenerational transmission of education from parents to children

after performing an ordinary least squares regression. Furthermore, this paper finds that the effects

are most significant for maternal over paternal education, and stronger on sons than on daughters.

Therefore, under this framework it would be safe to assume that in households with divorces where

mothers as opposed of fathers keep custody of the child (controlling for education), there should

be an overall positive effect on academic achievement (Chevalier, 14).


III. E. UNEMPLOYMENT

According to Kieselbach, long-term unemployment is highly linked with social exclusion,

which is typically in the form of institutional isolation. As Kieselbach explains, whenever someone

becomes unemployed there is an increased dependence on the welfare state and the ability to

become financially independent decreases dramatically. At the same time, with unemployment

comes institutional exclusion, where one stops having access to both financial and health services

in the private sector, either due to an inability to pay or a lack of insurance. Thus, long-term

unemployment ultimately promotes further decline in the overall ability citizens have to improve

their social condition due to institutional and social exclusion (Kieselbach, 70).

III. F. PUBLIC EXPENDITURE

As reported by Avvisati, PISA results show a positive relation between the resources

invested in education and performance, but only up to a certain point, before the effect of these

plateau. Moreover, in this study it is also shown that regardless of the level of expenditure

performed by countries, those which were top performers tended to display a far more equitable

distribution of resources to both the socioeconomically advantaged and disadvantaged. Therefore,

there is an element of governance which also comes into play, when considering the investments

in education made by countries (Avvisati, 7).

In a report drafted by Menezes-Filho, it was seen that the mathematical proficiency of

students in public schools increased after teachers’ relative wages improved (thanks to localized

investment - FUNDEF). However, Menezes-Filho also points out that this effect is mainly

concentrated in municipal schools in the poorer neighborhoods of Brazil in both the Northern and


North-Eastern regions of the country. This result suggests that localized investment in areas of

critical need is probably the most impactful policy to improve academic performance (Menezes-

Filho, 16).

In Lee’s papers on determinants of public expenditure, he finds that typically political

variables such as weak democracy, natural resource endowments and ethnic fractionalization will

tend to obstruct a government’s ability to make public expenditure in education have a significant

impact on adolescents’ educational outcomes. As he details, whenever a country has weak

democratic institutions the effectiveness of public expenditure on education as tool for improving

educational attainment will be low. Therefore, under this framework, considering the weak

institutional framework of Latin America, it would be reasonable to see public expenditure having

weaker effects on PISA scores for less developed countries (Lee, 110).

III. G. INFANT LABOR

A study performed by Levine details that infant labor, when performed in excess of twenty

hours a week, starts to negatively impact children’s overall academic performance. In his study, it

is highlighted that the cause of this relationship lies in that children with sleep debt will typically

suffer from brief lapses of attention, impaired memory and low creativity. All of this ultimately

translates into low performance and parental frustration, making household dynamics far tenser,

and making children perform at even lower levels (Levine, 175).

Levine also notes that while infant labor does indeed impact school attendance and

academic performance, in many cases adolescents who choose employment over education are

typically less engaged in school even before they enter the labor force. Levine also found that these


children were far more likely to exhibit developmental dysfunctions than their ‘educated’

counterparts. Lastly, Levine also states that when minors enter the labor force, their educational

expectations lower, they cut class more often, delinquency and drug abuse increase, their overall

investment in education diminishes, and autonomy from parental control increases, which results

in far more disengaged students (Levine, 176-178).

On the other hand, in a study performed by Seref Akin, he suggests that school attendance

is not a substitute for child labor, since he found that children either combine both of these activities

or do neither and remain idle. Therefore, an increase in the overall level of child labor might not

necessarily have such a strong impact on the overall level of school enrollment. This study was

carried out in Sub-Saharan Africa, however, which is the region with the highest rate of child labor

worldwide (Seref Akin, 20).

III. H. DIVORCE RATE

As seen in annex #2, in cases of divorce the custody of children pass to the hands of women

in more than 85% of the cases, which implies that women in Brazil (and speculatively in the rest

of South America) have historically been and persist to be the ones who gain custody of children

in the event of a divorce. This fact should not be taken lightly, given that it is in these same

countries where we see a rather severe degree of institutional patriarchy which directly affects the

status and income of women. Furthermore, an added externality to this relationship is that

prospects of future gains in education decrease for women who belong to this category, given that

a vast majority will choose to stay home and opt for lower wages as opposed to pursuing higher

education (Annex #2; World Bank).


In a study performed by Bernal, he finds maternal employment, which becomes far more

prevalent in families when couples divorce, typically has a negative effect on children’s

performance. Controlling for other factors, Bernal shows that an additional year of full-time work

is associated with a reduction of test scores of about 0.8%. This means that if a mother were to

work full time during the most critical years of her children’s childhood (0 to 10 years) we could

expect to see an overall decrease of test scores of close to 8% (Bernal, 46).


IV. MODEL, THEORY & HYPOTHESES

IV. A. METHODOLOGY AND DATA

The central focus of this paper revolves around a series of models which will be estimated

using an ordinary least squares (OLS) multivariable estimator. However, before delving into the

specifics of every variable that is being used in this model, it is important to mention that while

analyzing this data one ought to know that PISA scores (this study’s dependent variable) are

diagnostic tests (pre-tests) given that there is no teaching involved to prepare students to take this

exam. Therefore, in this regard PISA scores measure purely the state of the students at time zero

without taking into account any kind of outside influence or treatments, since they are not being

purposely trained or prepared prior to taking the test (Wellington, 13-14).

Furthermore, this study will be measuring the impact each variable has on educational

outcomes but only from the point of view of the student, as opposed to the educators. The

underlying assumption here is that in spite of teacher quality having a considerable impact on the

quality of education children receive, many factors affecting child development and their

performance lie outside the reach of the school environments.

This project is based around Latin America and Brazil mainly due to their economic

relevance in the region, but also to a large extent, due to the availability of data for Brazil at the

more micro level and Latin America at the macro level. Given that, PISA scores are fairly young

in the world of education policy, and data availability for them is indeed scarce. Also, this analysis

tries to group Brazilian states and Latin American countries based on wealth, development and

geography, to have a better understanding of what the determinants of education attainment are in

specific latitudes or regions, regardless of their wealth and/or development.


Having said this, for the purpose of this project, three multivariable OLS regressions have

been estimated. One uses cross-country data from six different countries: Argentina, Brazil, Chile,

Colombia, Peru and Uruguay. One uses regional data from the five regions of Brazil: Center-West,

North, Northeast, South and Southeast. And lastly, one uses state-wide data from all twenty-six

Brazilian states. This quantitative method of analysis was chosen as focal point of this study due

to the linear behavior that the independent variables of this study exhibit. Moreover, to reach

certain conclusions about each one of the variables, a set of mathematical models and functions

has been designed to help make sense of the economic forces which might be underlying each one

of the variables analyzed.

Also, in this regression there have been a series of controls installed to prevent issues of

geography and development from offsetting certain trends in the overall data. These can be seen

in the tables annexed in the last portion of this paper. Briefly, the controls used were as follows,

with their specific reasons:

South Cone dummies: the cross-country OLS model used controls for the countries of the

southern cone of Latin America (Argentina, Uruguay and Chile) due to their vastly higher

levels of wealth and development comparable to Peru, Colombia and Brazil as a whole.

Recession dummies: all the OLS models used here controlled for the recession, given that all

samples included observations for the year 2009; therefore, it is conceivable these observations

showed skewedness from their normal trend due to the 2008 recession.

Southern Regions Dummies: both the regional and state-wide OLS regressions control for the

southern regions of Brazil (the South and South-east regions of Brazil). Because these two

regions have been historically wealthier, more industrial and populated than the rest of the


country, they would be expected to show very different statistics from those of the rest of

Brazil.

Metropolis dummies: only the state-wide OLS regression uses this variable to account for the

differences in development that states with large urban centers have. Thus, any state which has

a city with a metropolitan population larger than one million inhabitants was put under this

dummy variable (i.e. Curitiba, which has 1.87 mill inhabitants, is in the state of Parana;

therefore, all of Parana’s observation received a ‘1’ for the Metropolis dummy).

Regarding data concerns, this study has tried to perform (within what is possible) the fewest

number of manipulations of data for these analyses. This was possible since most observations

were drawn from either the World Bank Data Bank, for the cross-country regressions, or the IBGE

(Brazilian Institute of Statistics) for the regional and statewide regressions. Thus, very few

interpolations were carried out to find data for specific years. The variables for which minor

interpolations were performed were:

Public expenditure: there were some discontinuities for the Uruguayan and Argentinian

samples.

Unemployment: there were some discontinuities for Argentinian data.

Regarding the divorce rate per every 1000 citizens, to use a uniform metric, the gross

number of divorces per region/state was divided by the overall population of the region and then

multiplied times 1000 to eradicate the excessive number of decimal numbers present in the series.

For the logarithm of GDP per capita, a natural logarithm of the GDP per capita of the respective

Country/Region/State is used, as opposed to the gross GDP per capita, due to the exponential rate

of growth this variable exhibits.


IV. B. ESTIMATED MODELS

Systems assumption: in a society where there is interdependence, every societal component is

juxtaposed onto one another of the institutions and factors that comprise the educational system as

one single unit to be analyzed, which is a valid approach as long as we always referred to a system

of education. Furthermore, doing cross-country analyses of systems makes much more sense under

this underlying assumption. (Bhatta, 30-32).

MODEL FOR CROSS-COUNTRY REGRESSION:

F( XGDP_Cap, XUrban, XDependency, XAnalphabetism, XUnem, XGINI, XPublic_Exp )

F(Xi) = Yi = α + β1 Log(X1i) + β2 X2i + β3 X3i + β4 X4i + β5 X5i + β6 X6i + β7 X7i + εi

Where:

Yi = PISA scores for the mathematics test for the ith year

α = Coefficient for the intercept

β1 = Coefficient for the slope of the Logarithm of GDP per capita

β2 = Coefficient for the slope of the proportion of population living in urban areas

β3 = Coefficient for the slope of the dependency ratio

β4 = Coefficient for the slope of the proportion of illiterate in the population above 15 years of age

β5 = Coefficient for the slope of the percentage of workers unemployed

β6 = Coefficient for the slope of the GINI ratio. (inequality index)

β7 = Coefficient for the slope of the proportion of public expenditure as % of GDP

εi = residuals (independent random error)


Yi = YPISA_Math (Dependent Variable): PISA scores are used as the dependent variable of this study,

since PISA scores (derived from a homonymous exam designed by the OECD) aim to collect data

in the most objective way possible. Aggregate PISA scores typically factor in students’

performance in reading, writing and mathematics. However, for the purpose of this study and

seeing that the scope of this paper is to find the factors that affect the most impactful aspects of

learning (mathematics skills), only the mathematics portion of the PISA scores was chosen. The

basic intuition used here is that capital gains from reinforcing students’ skills in mathematics will

be greater than those from reading and writing. It is important to note that PISA scores are collected

every 3 years in different countries around the world.

* PISA scores were collected from a 2012 OECD Report

Log(X1i) = Log(XGDP_Cap): logarithm of GDP per capita, or the logarithm of the average economic

gains per year (GDP) for every citizen of the nation/state/region. (To account for changes in the

currency value, all the series have been deflated using either the value of 2005 US Dollars ($) for

the cross-country model or 2008 Brazilian Reais (R$) for the Brazilian models). Logarithm of

GDP per capita and not GDP per capita, has been used to account for the exponential growth rate

both GDP and populations experience. For the purpose of this study the logarithm of GDP per

capita is used a proxy/estimate of the average level of income citizens of a country/region/state

enjoy. Moreover, this variable is defined in these models as the budgetary constraints of the

students’ families. Therefore, given that PISA scores are dependent on students’ families’ ability

to pay for education, we will argue that Log(xGDP_Cap) is positively correlated to PISA scores and

will have a positive beta. A higher budget implies a higher ability to pay for health and education

and a lower budget vice versa. * *Data

was collected from the World Bank Databank

https://www.oecd.org/pisa/keyfindings/pisa-2012-results-volume-I.pdf

http://databank.worldbank.org/data/reports.aspx?source=2&country=BRA&series=&period=


X2i = XUrban : this variable is defined by the World Bank as the proportion of the population that

lives in urban areas as a percentage of the total. Urbanization was chosen as a variable due to the

positive externality effect that urban societies have on the upbringing of children. As several papers

mention, there are stark difference between the quality of life and education in urban areas vis-a-

vis the quality of education in rural areas; this assumption has been called urban advantage.

‘We are used to thinking of urban children as being better off than rural children in every way –

better fed, better educated, with better access to health care and a better chance of succeeding in

life. For many children, this is true’ (World Vision, 14).

In formulating this advantage we ought to think about not only the marginal cost of attending

school for children/adolescents that live in rural areas, but also the benefits that children receive

from living in urban areas. Also, what might be some relationships between these variables

allegedly:

GAttend_School= QDays*CRural (XTrans, XHours Lost, XServices .... Xi)

BAttending_School = PQDays

UAttend_School = B(P,QDays)– G(C,QDays)

Where ↓G = ↑ UAttend_School

And MUAttend_School > 0

∴ (𝜕𝑈

𝜕𝐵) (

𝜕𝐵

𝜕𝑄𝑑) + (

𝜕𝑌

𝜕𝐺) (

𝜕𝐺

𝜕𝑄𝑑) = P – C = βUrban

G is the function for the cost of attending school. QDays is the quantity of days children have to

attend school. CRural is the marginal cost of attending school. XTrans, represents the transportation


costs for children in rural areas (which are considerably higher than those of their urban

counterparts) as they are closer to urban schools. XHours Lost represents the number of hours lost due

to travelling from rural to urban areas. XServices shows services in rural areas tend to be not only

scarcer but also much more expensive than those in urban areas. (Xi represents any other shadow

costs implicit in rural living).

B is the budget allocated to attending school, consisting of the financial, physical and

psychological endowments adolescents require to attend school and increase their human capital

while doing so. Thus, P is the marginal budget allocated to school attendance. MU must be strictly

greater than zero every day for children not to miss class.

Therefore, the expected beta of XUrban would be positive, given that with higher levels of

urbanization the cost of education should be lower (vis-à-vis those living in rural areas) and the

utility obtained from going to school is higher. Therefore, more urbanized societies should display

higher PISA scores.

* Data was collected from the World Bank Databank

X3i = XDependency: dependency as defined by the World Bank and the IBGE is a ratio of the number

of people over the aged 65 or older, plus those ages 15 or younger, divided by the number of people

who are those who are ages 16-64. Dependency is a variable that will be used to measure the

impact of demographic changes on Latin American families, focusing particularly on the

proportion of income which can be allocated to children’s development from both families and

governments. The underlying assumption is that higher levels of dependency imply tighter

budgetary constraint on families (as wage-earners need to support not only their young but also

the elderly). But also, higher levels of dependency translate into higher pressure on governmental



budgets. This is because retirees typically depend on subsidized healthcare and pensions; children

depend on public education, and taxes are only paid by the workforce. Thus, in formal theory,

dependency could be interpreted as the cost of supporting children and the elderly combined

allegedly:

FDependency (QChildren,QElderly) = QChildren*Cc + QElderly*Ce

∴ (𝜕𝑌

𝜕𝐹) (

𝜕𝐹

𝜕𝑄𝑐) + (

𝜕𝑌

𝜕𝐹) (

𝜕𝑦

𝜕𝑄𝑒)= Cc + Ce = βDependency

Where CC represents the overall cost to families and the state of supporting one child, and QChildren

is the quantity of children in a particular country or locality. While Ce represents the cost to families

and the state of supporting one more retiree and QEy represents the number of people who are

retired. This being said, with higher levels of dependency, PISA scores should be lower due to less

time & income being devoted to a single child (in the case of large families), while at a macro

scale, higher dependency should imply that governments run on deficits to maintain dependents.

In this case, the expected sign of beta for dependency would be negative as higher dependency

should lower PISA scores.


X4i = XAnalphabetism: analphabetism or illiteracy is defined as the proportion of the population ages

15 or older that hasn't been alphabetized yet. Analphabetism has many different ways of being

computed; however, throughout this study, to ensure consistency between samples, only data

which reflected the used of the equation specified below was used.

XAnalphabetism = PopAges 15 or older – % of population ages 15 or older that is literate

X4i = 100 - %Alphabetized



Thus, adult analphabetism gives us an index of how instructed family members and parents of the

high school children being used for this study really are, especially given that analphabetism uses

data of the population ages 15 or older. Now, for the case of dependency we will be assuming that

there is an intergenerational educational transmission, which implies that when parents are

educated, children will typically receive a positive externality from their education; therefore, in

this case we could expect that with lower levels of analphabetism there should be higher PISA

scores. Therefore, since lower levels of analphabetism should translate into higher

intergenerational transmissions of education, we can expect this variable to have a negative beta.

It must be noted, however, that although both analphabetism and PISA scores are variables that

measure educational attainment, PISA scores are only based on adolescent mathematics

performance and our analphabetism variable is solely based around adults’ ability to read and

write,. Thus, this variable is not perceived to be endogenous to the model.


X5i = XUnem: unemployment rate is defined by the World Bank as the number of people unemployed

divided by the total number of people in the workforce. Thus, working under pre-established

frameworks it is understood that higher unemployment should lead to higher social and

institutional exclusion of those who are unemployed, making it increasingly more difficult to find

a future job, but also for the unemployed to find any kind of benefits or access to certain

institutions. Furthermore, higher unemployment implies a great restriction on both the short term

as well as long-term budgetary constraints at the micro level. Also, unemployment might drive

children to be motivated to work to expand the family budget, but in doing so negatively impact

their academic performance.



Children of unemployed parents’ short term expected return on education

↑UUnemployment = ↓ EShort-Term(rEducation)

Parents’ budgetary constraint becomes tighter

↑UUnemployment =↓ IParents

Therefore, for XUnem what would be expected is that with higher levels of unemployment there

should be an overall decrease in the PISA scores observed - negative Betas.


X6i = XGINI: is a variable which measures the economic inequality present in a region, state or

country. It is assumed that with higher levels of economic inequality the quality of education of

the country or region will decrease, given that there will be fewer resources devoted to the general

population. This is a result of people’s expectation of the quality of public education being

mediocre at best, given that few resources are devoted to equalize the overall state of inequality

(this is particularly true for the Latin America case).

Higher inequality translates into lower expectations of the value of public education

↑GINI = ↓ E(VPublic Education)

Lower expected value of education translates into low expected return on education – thus,

lowering attendance and academic performance

↓ E(VPublic Education) = ↓ EShort-Term(rEducation)



Therefore, from seeing this relationship, it can be said that the expected beta should be negative

given that with higher inequality, PISA scores should be lower.


X7i = XPublic_Exp: this variable measures the level of investment federal governments make towards

children/infrastructure in the secondary education system relative to the overall GDP per capita

of the country. This variable aims to measure the overall level of investment a nation is making

towards children and adolescents that are part of the secondary education system. Therefore, with

higher levels of public investment in education at the national level we could expect to see higher

PISA scores in the respective country. This being the case, we could expect to see a positive beta,

given that higher endowments should translate into higher PISA scores.





MODEL FOR INTERREGIONAL REGRESSION:

F( XGDP_Cap, XUrban, XDependency, XAnalphabetism, XUnem, XGINI, XPublic_Exp, XInfant_Labor, XDivorce )

F(Xi) = Yi = α + β1 Log(X1i) + β2 X2i + β3 X3i + β4 X4i + β5 X5i + β6 X6i + β7 X7i + β8X8i + εi

Where:






β4 = Coefficient for the slope of the proportion of population that is illiterate


β6 = Coefficient for the slope of the GINI ratio. (inequality index)

β7 = Coefficient for the slope of the quantity of infant laborers

β8= Coefficient for the slope of the divorce rates per 1000 citizens.


The variables for this model (X1i … X6i) are the same ones as those used in the previous model for

the cross-country regression. In this model the only two differences are the addition of the infant

labor rate and the divorce rates, which are denoted by X7i and X8i respectively. Additionally, the

expected signs for the coefficients (β1i … β6i) were explained in the previous section. PISA scores

for the interregional model were drawn from OECD reports for Brazil generated in 2012, 2009

and 2006. The observations for variables (X1i … X8i) in this model were drawn from the IBGE

(Brazilian Institute of Geography & Statistics)

http://download.inep.gov.br/acoes_internacionais/pisa/resultados/2014/relatorio_nacional_pisa_2012_resultados_brasileiros.pdf

http://download.inep.gov.br/acoes_internacionais/pisa/resultados/2009/brasil_relatorio_nacional_PISA_2009.pdf

http://download.inep.gov.br/download/internacional/pisa/Relatorio_PISA2006.pdf

http://seriesestatisticas.ibge.gov.br/series.aspx?no=4&op=0&vcodigo=PD384&t=taxa-analfabetismo-funcional


X7i = XInfant_Labor: infant labor rate is defined as the proportion of people between the ages of 10 to

15 who are currently working/employed in the labor force either formally or informally. It is

possible that up to a certain degree XUnem would show correlation with this variable. However, the

goal of using this variable is to show the impact children’s decisions have on their academic

performance (since working and attending school are typically mutually exclusive). It is

understood that for child labor the expected utility of going to school is lower to that of going into

the labor force. However, understanding the impact this decision has on educational attainment

has to be considered particularly since policy should aim to increase the quality of children’s

educational institution, but also increase their expected utility of studying vs. working. With this

said, literature indicates that working children typically devote less time to academics, lowering

academic performance:

↑TWorking = ↓TStudying ∴ ↓PAcademics

Since, as children work more, their investment in education decreases, lowering the marginal cost

of missing class & lowering the expected return to education (trapping them in a spiral of low

attendance, low investment and low performance)

↑TWorking = ↓MCMissing_Class & ↓E(rEducation)

Therefore, using infant labor as a variable of study in relationship to children’s educational

attainment has two elements, one of which is shedding clarity on what is keeping students from

attending school, and a second one being understanding the relationship between adult

unemployment and infant labor. Hence, under this analysis, with a higher infant labor rate (and

lower school attendance) we should be seeing lower PISA scores and negative betas.

* Data was collected from the IBGE (Brazillian Institute of Geography & Statistics)



X8i = XDivorce : divorce rate per one thousand people is defined as the gross number of divorce

filings that are performed in a year, divided by the overall population of the region/state these

occurred in, times one thousand. Several studies have proven that divorces tend to harm children’s

psychological and emotional development, potentially leading them to underperform in school.

Moreover, due to divorces pushing mothers to become part of the workforce, their investment in

their children lowers, which ultimately translates into neglected children who underperform. Also,

to understand the importance of this issue it is helpful to know that custody of children in divorce

cases is won by women 85+% of the time, and unless the receive alimony, it is most likely mothers

will go back to the workforce. Therefore, with higher divorce rates we should be seeing lower

PISA scores and negative betas.

* Data was collected from the IBGE (Brazilian Institute of Geography & Statistics)



MODEL FOR INTER-STATE REGRESSION:

F( XGDP_Cap, XUrban, XDependency, XAnalphabetism, XUnem, XGINI, XPublic_Exp, XInfant_Labor, XDivorce )

F(Xi) = Yi = α + β1 Log(X1i) + β2 X2i + β3 X3i + β4 X4i + β5 X5i + β6 X6i + β7 X7i + β8X8i + εi

Where:






β4 = Coefficient for the slope of the proportion of population that is illiterate


β6 = Coefficient for the slope of the GINI ratio. (Inequality index)

β7 = Coefficient for the slope of the quantity of infant laborers

β8= Coefficient for the slope of the divorce rates per 1000 citizens.


The variables for this model (X1i … X8i) are the exact same ones as those seen in the previous model,

and have the exact same expected betas as the previous models. Please reference both the regional

and the cross-country model for further specifications of the nature of these variables.

Additionally, PISA scores for the inter-state model were drawn from OECD reports for Brazil

generated in 2012, 2009 and 2006. *For this model, data was collected from the IBGE (Brazillian

Institute of Geography & Statistics)

http://download.inep.gov.br/acoes_internacionais/pisa/resultados/2014/relatorio_nacional_pisa_2012_resultados_brasileiros.pdf

http://download.inep.gov.br/acoes_internacionais/pisa/resultados/2009/brasil_relatorio_nacional_PISA_2009.pdf

http://download.inep.gov.br/download/internacional/pisa/Relatorio_PISA2006.pdf



IV. C. TABLE OF EXPECTED RESULTS

This table provides a summary of the variables used in the estimated models and their expected

parameter signs.

Table 1. Table of Expected Results for Regression Model

Parameters Description of Variable Expected

Results

Y PISA_Math

PISA examinations are taken by high school students every 4 years. They are

designed by the OECD and aimed to be objective. The mathematics portion of

the exam was used as an independent variable in this case.

-

Log(X GDP_Cap )

GDP per capita is the average annual income of every citizen within a country

or state. This variable should reflect the overall level of income that can be

attributed to the average citizen.

β > 0

X Urban

Urbanization is defined as the proportion of the total population of a country or

state that lives within urban areas. The urban advantage effect implies that

higher urbanization should raise PISA scores.

β > 0

X Dependency

Dependency is the quotient of the total number of people aged 15 and below,

and 65 and above over those who are in the workforce. Higher dependency

implies less resources available.

β < 0

X Analphabetism

Analphabetism is defined at the proportion of the overall population that is

illiterate (unable to read nor write at the most basic level of proficiency) for

adults ages 15 and above.

β < 0

X Unem

Unemployment is defined as the percentage of the population that is part of the

workforce yet is currently unemployed. (Those unemployed for more than 18

months are not accounted for)

β < 0

X Gini

Gini coefficient (income inequality), measures the relative level of income

inequality in a country. The higher the inequality, the higher the economic

constraints on the majority of the population

β < 0

X Public_Exp

Public expenditure measures the percentage of the federal budget allocated

towards the secondary education system. With higher levels of public

expenditure we should expect to see higher performance.

β > 0

X Infant_Labor

Infant Labor rate is a variable which measures the proportion of adolescents

ages 10 to 15 that are currently working. With higher rates we expect less

children to be pursuing secondary education

β < 0

X Divorce_Rate

This variable is defined as the number of divorces a year a region/state has per

every 1000 inhabitant. Divorces are associated with negative household

environments and proven to have a negative impact on children's upbringing.

β < 0


V. FINDINGS

This section presents the findings from the multiple regressions performed (which can be found in

the tables subsection). Note that throughout this analysis, Table 1 is referenced continuously (Table

1), since it is being used as the base reference for the findings presented in this section.

V. A. CROSS-COUNTRY MODEL

On Table 2a, we have the cross-country model’s table of summary statistics, where some

of the characteristics of the model’s raw data have been conveniently displayed for the reader’s

ease. The results for the regression for the cross-country model are displayed in Table 2b.

Analyzing the results for the complete regression model (1), it can be seen that the most supportive

results are given by the variables urbanization and dependency. The findings for these effects

reveal statistically significant t-statistics and betas that are in agreement with the theoretical

framework aforementioned as well as previous findings made by Fafchamps, 2004, Zhang, 2002,

and Lutz 2013. Further evidence of these relationships was plotted in a scatter graph found under

annexes #3 and #4 to shed more clarity on the relationship between these variables and PISA

scores.

Furthermore, these results also check for robustness as their significance remains when the

model is subjected to fewer controls, as can be seen in (4). Taking a closer look at the best fitted

lines plotted in annexes #3 and #4, it can be seen that more than half of the variability in PISA

scores at the international level may be explained by changes in urbanization and dependence, as

their graphs exhibit R2‘s of 0.175 and 0.546 respectively.

Additionally, (1) also finds that analphabetism rates also share a statistically significant

negative relationship with PISA scores, which supports the theory of intergenerational returns to


schooling. However, this relationship dissipates when the model is run without the recession and

south cone variables, as can be seen in models (3) and (4). Interestingly, both unemployment rate

and public expenditure appeared to hold little to no relationship with PISA scores, as can be seen

in all four regressions. The results for these explanatory variables oscillate between positive and

negative betas which are statically insignificant.

Also, (1) shows that logarithm of GDP per capita affects PISA scores in a way which

contradicts the theory, as can be seen by comparing the value of the betas with those from Table

1. Gini coefficients show an unstable, insignificant, yet undeniably positive relationship with PISA

scores, which also goes against what was expected, as specified in Table 1. However, it is

important to note that when comparing (2), (3) and (4) to the results in (1) the statistical

significance of logarithm of GDP per capita as a determinant of PISA scores remains, which poses

a major issue under the framework of this study.

V. B. REGIONAL MODEL

On Table 3a, we have the interregional model’s table of summary statistics, where some of

the characteristics of the model’s raw data can be seen for the reader’s ease. For this model,

findings are being drawn from Table 3b of regression results. Starting with the results from model

(1), the best results obtained from this regression were those for the relationship between

unemployment rate and PISA scores. Given that, we find this relationship to be both negative and

statistically significant, which is in accordance to what had been suggested by the theory presented

before. For further clarity, this relationship has been plotted in a scatter graph found in annex #5,

under the annex section of this paper.


Looking at (1), both divorce rates and infant labor rate appear to have a negative

relationship with PISA scores, which is in agreement to what was expected, as seen in table 1.

However, only (2) shows evidence of a significant relationship for divorce rates with PISA scores,

and only (1) shows a semi-significant relationship between infant labor rates and PISA scores. The

lack of robustness from other regressions to substantiate this analysis places some doubt on any

claim regarding the relationship between these variables and PISA scores.

On the other end, (1) shows that GDP per capita, urbanization and analphabetism hold little

to no relation with PISA scores at the regional level. However, in spite of the base model indicating

otherwise, results from regressions (3) and (4) would indicate urbanization might have a significant

positive relation with PISA scores at the regional level, which is in agreement with what had been

plotted in table 1. Similar assumptions can be made for GDP per capita, when looking at the results

obtained from (2).

This being said, some problematic results were obtained from two variables, which show

significant relations that are in contradiction to what the theory stipulated. While dependency

shows a significant effect in (1), this relationship to PISA scores appeared to fade away when the

model was estimated without specific controls in (2), (3) and (4). Thus, this result suggests a lack

of robustness, and a weakening argument in defense of this explanatory variable, going against

what had been expected. Gini coefficients on the other end appear to have strong and contradictory

results for models (1) and (2). However, this relationship fizzled in models (3) and (4) ultimately

showing a lack of robustness.


V. C. INTER-STATE MODEL

Lastly, on table 4a we have the inter-state model’s table of summary statistics, where some of the

characteristics of the model’s raw data are conveniently showcased for the reader’s ease. The table

of regression results, which pairs with this model, is table 4b.

Considering the results for (1), the most supportive results obtained from this regression

were those for unemployment, dependency and analphabetism rate. The latter two display the most

uniform results throughout the five different regression rounds performed. These three variables

gave both statistically significant and signs that support what had been outlined in table 1.

Moreover, it is important to highlight that both dependency and analphabetism have results

significant at the 1% level for all regression runs, which goes to show that this relationship is

particularly strong and robust. Having said this, unemployment also shows highly significant

results, which are in agreement with theory throughout all the runs (1) through (5). Scatter graphs

were plotted for dependency, analphabetism and unemployment rates against PISA scores in

annexes #6, #7 and #8 respectively, to serve as visual guidance of the distribution and behavior of

the sample.

Infant labor rate is also a variable which showed significance in (1) and (2), and had signs

that matched expected results in Table 1. However, once the control for the southern regions of

Brazil was taken away, results show the relationship between this variable and PISA scores fizzled,

as can be seen in runs (3), (4) and (5). However, considering that the betas obtained are consistently

positive throughout all the runs, it could be said that infant labor does hold a negative yet weak

relationship to PISA scores. In this model, we also see that urbanization does not have a significant

relation with PISA scores, with t-statistics approaching zero at times, as seen in (3), deeming these

results statistically insignificant.


Lastly, (1) gives us results which are consistent with the results seen in both Tables 2b and

3b, yet (once again) problematic considering that they are opposite in sign to what the theory

indicates. In Table 4b, Gini coefficients and GDP per capita show significant relations to PISA

scores, yet they contradict the theory stipulated earlier. Furthermore, in this model, we find

logarithm of GDP per capita was not robust when the model was run without controls, as can be

seen in (3), (4) and (5). However, when looking at the results given by Gini coefficients, looking

at runs (2) to (5), results are significant and betas are positive, which is troubling considering the

scope of this paper, yet valuable given that it supports the overall trends which have been seen in

previous models.


VI. DISCUSSION

In summary, the most supportive results from this analysis were those for dependency,

urbanization, unemployment and analphabetism. Starting with dependency, it is incontrovertible

that Latin America has issues of both decreasing mortality rates and high birth rates. This implies

that if trends continue as they are, dependency in the region will be destined to increase in

following years. Therefore, something to pay attention to is the fact that dependency had a far

stronger impact on PISA scores at the international level than it did at the Brazilian inter-state

level, something which sheds light on a major issue present in the region.

As long as Latin American countries choose not to expand their social retirement plans and

public education systems, demographic changes will persist as major determinants of educational

attainment in the region. In Brazil’s case, due to its policies of social inclusion and welfare, we see

educational outcomes being less susceptive to fluctuations in the demographic structure of the

country, something which might not be said of the rest of Latin America. With this said, findings

suggest a one percent increase in dependency at the international level represents a decrease in

PISA scores of close to 5.5 points, while at the Brazilian inter-state level a one percent increase in

dependency translates into a mere decrease in PISA scores of close to 2.5 points. Therefore, seeing

these results it could be argued that Latin American policy makers should pay closer attention to

their public pension plans, as well as increasing both the quality and the funding of their public

education systems (ideally, using the same techniques used by FUNDEF and FUNDEB).

Regarding unemployment, we can see that across the board, unemployment in Latin

America and in Brazil impacts PISA scores negatively. However, when looking at international


regression results we see unemployment impacts PISA scores in an insignificant manner. Still,

when looking at Brazilian regional and state level results we see unemployment has far more

significant and pronounced effects on PISA scores. It is also worth noting that changes in

unemployment have at the regional level have a much higher impact than those seen at the state

level, implying that there is probably a compounding effect present. Seeing that, a one percent

increase in unemployment rates decreases PISA scores by an average of 2.4 points at the Brazilian

state, and 19.3 points at the regional level. This stark different might be justified when considering

Brazilian regions as economic clusters which may be analyzed with the eyes of a macroeconomist.

The reason behind unemployment’s strong impact on PISA scores at the region level might

lay in that, while unemployment in certain states is probably associated with industrial cycles

experienced homogenously within regions, regional unemployment is probably driven by forces

that are far more immutable and powerful in nature (i.e. loss in competitiveness in the agrarian

sector, for example). Therefore, changes in unemployment at the regional level are likely to have

a strong impact on PISA scores at the regional level because they reflect the economic outlook of

a region as a whole. On a side note, it is important to note that unemployment regression results at

the international level might have not yielded the expected results because natural levels of

unemployment are determined domestically by each country. Hence, variations from mean or

historical unemployment levels for the cross-country regressions might have been a better option

in this case (as opposed to using raw unemployment statistics).

Regarding urban development, it seems to be that urbanization is a major determinant of

educational attainment at the international level. However, these results do not appear either at the

regional or at the interstate level. Thus, what this relationship suggests is that while major

infrastructural developments probably do contribute to the overall educational performance of


countries as a whole (i.e. roads, public hospitals, schools, public transportation, etc. improve the

quality of life citizens of a country enjoy at a macro scale), at a more micro scale, increases in

urbanization might be less impactful on the overall quality of life citizens of a specific locality

might enjoy, since improvements at a micro level will tend to develop at a slower pace in

comparison to those where the federal government is involved (i.e. improved local lighting will

not have the same impact as the implementation of a full-fledged road bridging two localities).

What these results suggest is that when it comes to urban development, federal and not

local governments should be taking more action to bridge the gap between isolated, rural and

impoverished communities and those urban ones where educational centers are. Also, to explain

this lack of correlation at the micro level, it could be argued that slums/barrios/favelas also find

themselves to be secluded from the metropolitan areas of major cities. Therefore, changes in

urbanization under this context might be ultimately trivial in matters of improving educational

attainment, simply because urban advantages are typically felt whenever strong interventions from

the federal government bridge these isolated communities (something which local governments

fail to do).

When it comes to analphabetism, results suggest that at the inter-regional level, adult

literacy has little to no impact on the overall educational attainment of children. However, when

taking a look at inter-state-level regression results, the story being told by these results is quite

different. Since it seems that while results show that at a macro scale overall adult illiteracy has no

major impact on the aggregate performance of adolescents in mathematics, at the inter-state level

results indicate this relationship to work otherwise.


What is observed is that analphabetism’s significance increases when we pass it through

data that is more granular in nature. Therefore, it could be argued that analphabetism may be seen

as less of a federal concern, considering the results obtained at the macro level (since PISA scores

are not affected by changes in the overall alphabetization of a country’s citizens). However, given

that the state level puts in evidence that there is a strong relation (even if the slope is small), it

could be argued that addressing illiteracy should be proposed as a long-term goal of federal

governments, given that national PISA score performance will not increase dramatically if federal

governments draw most of their attention to illiteracy. However, these results suggest that

empowering local governments to improve literacy within their localities should be pursued.

Regarding infant labor and divorce rates, further study might be required to make stronger

claims on their relevance as determinants of educational attainment within Brazil, given that results

were not as salient as those for variables previously explained. While infant labor displayed

coefficients in agreement with what had been expected in models (1) and (2) at the interstate level

and in model (1) at the regional level, there was a lack of robustness in subsequent regression runs

at both the regional and the state level, suggesting infant labor might be a significant determinant

of educational attainment when controlling for other confounding factors; however, arguing it is

the root cause of decreasing educational attainment in Brazil would be excessive. Furthermore, the

fact that coefficients for this variable are considerably higher at the regional level in comparison

to the state level would indicate that this issue should be addressed by regional governments

instead of local ones.

On the end of divorce rates, considering the fact that at the regional level only regressions

(2) and (4) displayed significant and supporting results and that, at the inter-state level, only

regressions (1) and (2) displayed significant results (even though they contradicted what theory


stipulated) sheds led on a series of issue, divorce rates in southern states appear to have significant

effects on children’s educational attainment. But also, results indicate that a one unit increase in

divorce rates appears to have severe consequences in adolescent educational attainments, since the

effects seen are negative 25 PISA scores at the state level and positive 100 PISA scores at the

regional level. However, this duplicity only sheds light on the magnitude of the effect this variable

has on educational attainment, but not the true nature of its effect, since results are unclear.

However, it could be possible that with more trials and larger datasets this study could find

evidence of clearer relations between these two variables. But for now, this is all that we may

claim.

Some of the results which were conflicting with the aforementioned theory were those for

GDP per capita and income inequality. Originally it had been proposed that increasing GDP per

capita would have a significant positive impact on PISA scores, and that inequality was going to

have a significant negative impact on PISA scores. However after testing several models at both

the macro and the micro level, the results were completely opposite to what had been originally

proposed. One theory for these contradicting results might be that income inequality ultimately

pushes the general population to have a much stronger drive to rise and perceive education as a

safe long-term investment given their uncertain futures. GDP per capita’s contradicting results

might be the result of a GDP growth being driven not by human capital gains but rather due to

resource exports, which might explain the lack of human capital gains with increasing GDP per

capita.

Lastly, regarding public expenditure, due to the large amount of inconsistency in results

and statistical insignificance, it is clear that little to no conclusions can be drawn from these results

and most importantly that public expenditure per student in the secondary education system might


not be the best explanatory variable to understand what drives educational attainment.

Furthermore, this study suggests that, whenever public expenditure in education will be used as an

explanatory variable, future researchers should be wary of what kind of ‘public expenditure’ they

are using. In this study, public expenditure on education in a country’s secondary education system

is used, since considering solely what is devoted to students in the secondary education system

might not be the best iteration of this variable for a study of this nature. Furthermore, it is possible

public expenditure effects on education attainment might suffer of a slight degree of delay, which

is not being accounted for in this study. Caveats aside, our study provides evidence that public

expenditure in the secondary education system does not drive adolescents’ educational attainment.

Furthermore, in the future, Latin American nations might want to use models such as FUNDEF’s

or FUNDEB’s when in search of better results, since Brazil appears to have a good record in this

regard so far.


VII. CONCLUSION

This paper evaluates the different impacts a set of social and economic variables have on

the overall educational attainment of adolescents in Latin America, by using PISA scores as the

study’s dependent variable. This study finds that urbanization, dependency and unemployment

appear to be significant determinants of educational attainment in Latin America, while estimated

effects that support the theory and are statistically significant variables, such and infant labor and

divorce rates, generated mixed results. However, further studies would need to be carried out in

order to completely discard these two variables as issues that should concern education policy

makers in the region. Furthermore, throughout every regression model estimated in this study, it

was found that both GDP per capita and the Gini inequality coefficient displayed behaviors that

went against what theory stipulates (GDP per capita had a negative effect on PISA scores and Gini

had a positive one).

This study does find, however, that when it comes to domestic policy in Latin America,

two major issues should be addressed in order to gain traction in matters of education deficits:

countries should pay closer attention to their changing demographics and over investment into

public education. These two issues need to be addressed if Latin America hopes to reap the benefits

of it in less than a decade from now. An interesting finding particular to this study is that Brazilian

educational attainment appears to be less susceptive to changes in the country’s demographic

structure in comparison to its South American peers, primarily due to their embracing and

widening welfare state. A lesson South America must take from the Brazilian economy is that the

future benefit of investing into a country’s human capital reflects in the long run sooner than one

might expect.


For future reference, some adjustments and improvements I would make to this study

would probably be regarding the variables that were chosen. For example, choosing a variable

such as divorce rate, which in theory seemed sound, ended up displaying little to no results once

the model was estimated. Furthermore, in the future something interesting would be to look for

series in alternative and/or untapped databases from non-traditional sources, especially

considering how much survey data exists nowadays (i.e. number of books in households, average

number of TV’s in households, etc.). Also, given how significant and relevant a determinant’s

dependency was in relationship to PISA scores, performing a study solely on the impact that

demographics have on educational attainment would probably be incredibly interesting (i.e.

running regressions solely with old age dependents vs. PISA, and some with only young age

dependents vs. PISA). Lastly, an expansion to be performed on this paper would probably happen

by the end of this year, once PISA scores are released for the year 2015, since these observations

would imply an expansion of the sample period of this study, allowing for more precise estimates

and stronger conclusions, but until then this study will have to wait patiently.


BIBLIOGRAPHY

Aedo, Christian. Directions in Development: Human Development. Skills for the 21st Century in

Latin America and the Caribbean. 2012. PDF.

Avvisati, Francesco. Programme for International Student Assessment (PISA) Results from 2012,

Brazil, OECD. 2012. PDF

Barth, Espen. Bridging the Skills and Innovation Gap to Boost Productivity in Latin America The

Competitiveness Lab: A World Economic Forum Initiative. World Economic Forum. 2015. PDF

Bassi, Marina. Disconnected: Skills, Education and Employment in Latin America. Inter-American

Development Bank. 2012. PDF.

Becker, Gary. Economics of the Family: Marriage, Children, and Human Capital. Interaction

between Quantity and Quality of Children. University of Chicago Press. 1974. PDF.

Behrman, Jere. et. al. Intergenerational Mobility in Latin America. Inter-American Development

Bank. 2001. PDF.

Bent, Rudyard K. Principles of Secondary Education. McGraw-Hill Book Company. Six Edition.

1970. Print.

Bernal, Raquel. Employment and Child Care Decisions of Mothers and the Well-being of their

Children. New York University. 2003. PDF.

Bertinelli, Luisito. Urbanization and growth. Journal of Urban Economics. 2002. PDF

Bhatta, Ganesha. Secondary Education: A Systems Perspective. Ashish Publishing House, New

Delhi. 1990. Print.

Bruns, Barbara, et.al. Great Teachers: How To Raise Student Learning in Latin America and the

Caribbean. World Bank Group. 2015.

Chevalier, Arnaud. The Impact of Parental Income and Education on the Schooling of Their

Children, IZA. Discussion Paper Series. 2005. PDF

Donehower, Gretchen. Population ageing, intergenerational transfers, and economic growth: Latin

America in a global context ECLAC. 2011. PDF


ECLAC. Latin American Economic Outlook 2016: Towards a New Partnership with China, OECD

Publishing, 2015. PDF.

Fafchamps, Marcel. Child Labor, Urban Proximity and Household Composition. Institute for the

Study of Labor. 2004. PDF.

Glewwe, Paul. Economic growth and the demand for education: is there a wealth effect? Journal

of -Development Economics. Elsevier. 2004. PDF

Glover, Ian, et. al. Ageism in Work and Employment. Stirling Management Series. University of

Sitrling. 2007. Print

Gropello, Emanuela. Meeting the Challenges of Secondary Education in Latin America and East

Asia: Improving Efficiency and Resource Mobilization. The International Bank for Reconstruction and

Development/ The World Bank. 2006. PDF.

Gutierrez, Catalina. Inequality and Education Decisions in Developing Countries. Journal

Economic Inequality. 2009. PDF

Kieselbach, Thomas. Long-Term Unemployment Among Young People: The Risk of Social

Exclusion. American Journal of Community Psychology. 2003. PDF.

Lee, Jeonghee,.et.al. Essay on the Determinants and Effects of Public Education Expenditure in

Developing Countries. American University. 2008. PDF

Levine, Marvin J. Children for Hire: The Perils of Child Labor in the United States. Praeger

Publishers. 2003. Print

Lutz Wolfgang, et.al. Demography and Human Development: Education and Population

Projections. International Institute for Applied Systems Analysis (IIASA). United Nations Development

Programme. 2013. PDF

Manacorda, Marco. Changes in Returns to Education in Latin American: the role of Demand and

Supply of Skill. Industrial and Labor Relations Review. 2010. PDF

Menezes-Fliho, Naercio. Evaluating the Effects of FUNDEF on Wages and Test Scores in Brazil.

University of Sao Paulo. Brazil. PDF


Moura Castro, Claudio. Secondary Education in Latin America and the Caribbean the Challenge

of Growth and Reform. Inter-American Development Bank. Sustainable Development Department:

Technical Paper Series. 2000. PDF

Rosero-Bixby, Luis. Generational Transfers and Population Aging in Latin America. Population

and Development review. 2011. PDF.

Saad, Paulo. Demographic Trends in Latin America and the Caribbean. Workshop on Demographic

Change and Social Policy. ECLAC 2009. PDF

Seref Akin, Mustafa. Three Essays on Economics of Education in Developing Countries.

Department of Economics Southern Illinois University. 2004. PDF.

Tanaka, Riuchi. Essays on Education, Growth and Income Distribution: Inequality as a

Determinant of Child Labor. Chaper 2. Department of Economics. New York University. 2004. PDF.

Vegas, Emiliana. Raising Student Learning in Latin America. The Challenge for the 21st Century.

The World Bank. 2008. PDF

Wellington, Jerry. Secondary Education: The Key Concepts. Routledge Taylor and Francis Group,

2006. Print.

Wolff, Laurence. Money Counts: Projecting Education Expenditures in Latin America and the

Caribbean to the year 2015. UNESCO. Montreal. 2005. PDF

World Vision. Rapid Urbanization, Economic Growth and the Well-being of Children. Cities for

Children, Centre for Expertise for Urban Programming. World Vision International. 2014. PDF

Zhang, Jie. Urbanization, Population Transition, and Growth. Oxford Economic Papers. 2002.

PDF. pp.91-117.


ANNEXES

Annex #1: Labor force composition broken down by years of education (Brazil National Statistics)

– Source IBGE

Annex #2: Proportion of women who held custody of children post-divorce in both Brazil and its

main regions. – Source IBGE

0%

20%

40%

60%

80%

100%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2011

% o

f to

tal L

abo

r Fo

rce

Years

Labor Force broken down by years of schooling

1 yr or less 3 yrs or less 7 yrs or less

10 yrs or less (High School) 14 yrs or less (University) 15 yrs or more (Graduate)


Annex #3: PISA scores against dependency for all South American countries

Annex #4: PISA scores against urbanization for all South American countries

R² = 0.1747

270.0

290.0

310.0

330.0

350.0

370.0

390.0

410.0

430.0

43.00 48.00 53.00 58.00 63.00 68.00

PIS

A s

core

s

Dependency Ratio

PISA scores vs. Dependency for all South American Countries

R² = 0.5488

280.0

300.0

320.0

340.0

360.0

380.0

400.0

420.0

440.0

70.00 75.00 80.00 85.00 90.00 95.00 100.00

PIS

A S

core

s

Urbanization (%)

PISA scores vs. Urbanization for all South American Countries


Annex #5: PISA scores against unemployment rates for all Brazilian regions years 2006-2012

Annex #6: PISA scores against dependency for all Brazilian states years 2006-2012.

R² = 0.2414

320.0

330.0

340.0

350.0

360.0

370.0

380.0

390.0

400.0

410.0

420.0

3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

PIS

A S

CO

RES

UNEMPLOYMENT RATE (%)

PISA scores vs. Unemployment rates for Brazi l ian Regions 2006-2012

R² = 0.6613

250

270

290

310

330

350

370

390

410

430

450

44.0 49.0 54.0 59.0 64.0 69.0 74.0 79.0

PIS

A S

core

s

Dependency Ratio

PISA scores vs. Dependency for all Brazilian States 2006-2012


Annex #7: PISA scores against analphabetism for all Brazilian states years 2006-2012.

Annex #8: PISA scores against unemployment for all Brazilian states years 2006-2012.

R² = 0.355

250

270

290

310

330

350

370

390

410

430

450

2.00 7.00 12.00 17.00 22.00 27.00

PIS

A S

core

s

Analphabetism (Illiteracy) Rate (%)

PISA scores vs. Analphabetism for all Brazilian States 2006-2012

R² = 0.0333

260

310

360

410

460

2.00 4.00 6.00 8.00 10.00 12.00 14.00

PIS

A S

core

s

Unemployment Rate (%)

PISA scores vs. Unemployment Rates for all Brazilian States 2006-2012


TABLES

Tabl

e 2A

. Sum

mar

y St

atis

tics f

or C

ross

-Cou

ntry

Dat

a

Obs

erva

tions

Mea

nSD

Max

Min

PISA

2338

5.18

31.1

342

6.80

292.

00

GDP

per c

apita

2355

63.5

717

41.8

694

77.9

323

45.6

5

Urba

niza

tion

2385

.00

6.85

94.8

073

.04

Depe

nden

cy23

55.1

65.

2664

.58

45.5

6

Anal

phab

etis

m23

5.51

3.75

12.8

71.

17

Unem

ploy

men

t23

8.54

2.49

15.0

03.

60

GIN

I 23

0.51

0.05

0.60

0.41

Publ

ic E

xpen

ditu

re23

14.6

04.

3224

.32

6.50

DSO

UTHC

ON

E23

0.52

1.00

0.50

1.00

D200

923

0.26

0.44

1.00

0.00


Tab

le 2

B. E

ffe

cts

of

Dif

fere

nt

Soci

oe

con

om

ic f

acto

rs o

n M

ath

em

atic

s P

ISA

sco

res

at t

he

Cro

ss-C

ou

ntr

y le

vel.

(1)

(2)

(3)

(4)

Log

(GD

P p

er

cap

ita)

-61.

039

-68.

472

-105

.215

-109

.301

(-2.

49)*

*(-

2.95

)**

(-4.

42)*

**(-

4.96

)***

Urb

aniz

atio

n7.

699

7.96

28.

704

8.86

1

(7.4

7)**

*(8

.03)

***

(7.2

5)**

*(7

.80)

***

De

pe

nd

en

cy-4

.371

-4.6

73-6

.999

-7.1

45

(-3.

53)*

**(-

3.92

)***

(-6.

72)*

**(-

7.28

)***

An

alp

hab

eti

sm R

ate

-5.3

75-5

.133

1.28

71.

295

(-1.

97)*

(-1.

90)*

(0.7

1)(0

.73)

Un

em

plo

yme

nt

Rat

e-0

.389

-0.4

380.

153

0.10

6

(-0.

33)

(-0.

37)

(0.1

1)(0

.08)

GIN

I Co

eff

icie

nt

(In

eq

ual

ity)

158.

681

155.

739

23.7

8824

.971

(1.5

5)(1

.53)

(0.2

1)(0

.23)

Pu

bli

c Ex

pe

nd

itu

re

-0.4

97-0

.298

0.21

90.

341

(-0.

87)

(-0.

56)

(0.3

4)(0

.58)

Co

ntr

ols

fo

r So

uth

Co

ne

?Ye

sYe

sN

oN

o

Co

ntr

ols

fo

r th

e R

ece

ssio

n?

Yes

No

Yes

No

Nu

mb

er

of

dif

fere

nt

Co

un

trie

s6

66

6

Nu

mb

er

of

Ob

serv

atio

ns

2323

2323

R-S

qu

are

d0.

9695

0.96

740.

9498

0.94

76

The

'So

uth

-Co

ne

' du

mm

y ac

cou

nts

fo

r A

rge

nti

na,

Ch

ile

& U

rugu

ay w

hic

h h

ave

sig

nif

icat

ly h

igh

er

leve

ls o

f w

eal

th a

nd

de

velo

pm

en

t

to t

he

re

st o

f th

e c

ou

ntr

ies

pre

sen

t in

th

is s

amp

le.

The

six

co

un

trie

s u

sed

fo

r th

is s

tud

y w

ere

(in

alp

hab

eti

cal o

rde

r): A

rge

nti

na,

Bra

zil,

Ch

ile

, Co

lom

bia

, Pe

ru &

Uru

guay

.

The

val

ue

s in

par

en

the

sis

are

th

e r

egr

ess

ion

's t

-sta

tist

ics

The

ast

eri

sks

ind

icat

e s

tati

stic

al s

ign

ific

ance

: **

*P<0

.01,

**

P<0

.05,

* P

<0.1

0

All

re

sult

s in

bo

ld a

re s

tati

stic

ally

sig

nif

ican

t.


Tabl

e 3A

. Sum

mar

y St

atis

tics

for R

egio

nal D

ata

Obs

erva

tion

sM

ean

SDM

axM

in

PISA

1537

8.99

23.1

040

8.15

333.

00

GD

P pe

r cap

ita

1515

296.

5964

47.7

525

984.

4154

98.0

2

Urb

aniz

atio

n15

83.6

87.

3493

.02

73.8

7

Dep

ende

ncy

1557

.11

5.63

69.0

049

.90

Ana

lpha

beti

sm15

9.68

5.53

22.8

24.

38

Une

mpl

oym

ent

157.

171.

559.

634.

00

GIN

I 15

0.53

0.03

0.58

0.46

Infa

nt L

abor

159.

713.

4216

.65

4.16

Div

orce

s pe

r 100

0 15

0.50

0.21

0.93

0.22

DSO

UTH

ERN

150.

400.

491.

000.

00

D20

0915

0.33

0.47

1.00

0.00


Tab

le 3

B. E

ffe

cts

of

Dif

fere

nt

Soci

oe

con

om

ic f

acto

rs o

n M

ath

em

atic

s P

ISA

sco

res

at t

he

Re

gio

nal

leve

l (B

razi

l).

(1)

(2)

(3)

(4)

Log

(GD

P p

er

cap

ita)

16.3

9657

.057

-25.

052

19.5

32

(0.5

8)(1

.97)

*(-

0.47

)(0

.47)

Urb

aniz

atio

n-0

.257

-1.0

783.

862

3.12

3

(-0.

20)

(-0.

64)

(2.3

6)*

(1.9

4)*

De

pe

nd

en

cy1.

742

1.55

82.

545

2.37

4

(3.3

0)**

(2.2

1)*

(2.5

6)**

(2.2

9)*

An

alp

hab

eti

sm R

ate

0.38

21.

336

2.06

43.

242

(0.3

6)(1

.02)

(1.0

4)(1

.77)

Un

em

plo

yme

nt

Rat

e-1

8.31

3-1

7.10

2-2

1.15

3-1

9.90

4

(-3.

77)*

*(-

2.62

)**

(-2.

17)*

(-1.

95)*

GIN

I Co

eff

icie

nt

(In

eq

ual

ity)

808.

013

889.

256

107.

109

165.

642

(3.7

7)**

(3.1

2)**

(0.4

1)(0

.62)

Infa

nt

Lab

or

Rat

e-5

.132

-5.1

53-5

.081

-5.1

03

(-2.

35)*

(-1.

75)

(-1.

15)

(-1.

10)

Div

orc

es

pe

r 10

00 p

eo

ple

-49.

367

-104

.415

-39.

785

-102

.443

(-1.

42)

(-3.

13)*

*(-

0.57

)(-

1.94

)*

Co

ntr

ols

fo

r b

oth

So

uth

ern

Re

gio

ns?

Yes

Yes

No

No

Co

ntr

ols

fo

r th

e R

ece

ssio

n?

Yes

No

Yes

No

Nu

mb

er

of

dif

fere

nt

Re

gio

ns

55

55

Nu

mb

er

of

Ob

serv

atio

ns

1515

1515

R-S

qu

are

d0.

9857

0.96

750.

9265

0.90

27

The

So

uth

ern

& S

ou

the

aste

rn r

egi

on

s o

f B

razi

l we

re u

sed

as

du

mm

ies

du

e t

o t

he

ir v

ast

accu

mu

lati

on

of

we

alth

(48

% o

f G

DP

)

Thh

e f

ive

Bra

zili

an r

egi

on

s ar

e (

in a

lph

abe

tica

l ord

er)

: Ce

nte

r-W

est

, No

rth

, No

rth

eas

t, S

ou

th a

nd

So

uth

eas

t

The

val

ue

s in

par

en

the

sis

are

th

e r

egr

ess

ion

's t

-sta

tist

ics

The

ast

eri

sks

ind

icat

e s

tati

stic

al s

ign

ific

ance

: **

*P<0

.01,

**

P<0

.05,

* P

<0.1

0

All

re

sult

s in

bo

ld a

re s

tati

stic

ally

sig

nif

ican

t.


Tab

le 4

A. S

um

mar

y St

atis

tics

fo

r St

ate

-wid

e D

ata

Ob

serv

atio

ns

Me

anSD

Max

Min

PIS

A79

373.

4027

.50

431.

0027

0.00

GD

P p

er

cap

ita

7913

453.

6388

24.8

658

325.

6836

99.8

4

Urb

aniz

atio

n79

81.1

98.

3496

.76

64.0

3

De

pe

nd

en

cy79

59.1

16.

9376

.30

45.7

0

An

alp

hab

eti

sm79

12.0

77.

0729

.93

2.82

Un

em

plo

yme

nt

797.

382.

1713

.22

2.87

GIN

I 79

0.53

0.05

0.66

0.42

Infa

nt

Lab

or

7910

.76

4.52

19.8

91.

87

Div

orc

es

pe

r 10

0079

0.51

0.29

1.59

0.08

DM

ETR

OP

OLI

S79

0.52

0.50

1.00

0.00

DSO

UTH

ERN

790.

260.

441.

000.

00

D20

0979

0.33

0.47

1.00

0.00


Tab

le 4

B. E

ffe

cts

of

Dif

fere

nt

Soci

oe

con

om

ic f

acto

rs o

n M

ath

em

atic

s P

ISA

sco

res

at t

he

Sta

te-w

ide

leve

l.

(1)

(2)

(3)

(4)

(5)

Log

(GD

P p

er

cap

ita)

-22.

091

-23.

131

-13.

827

-14.

568

-6.6

12

(-2.

24)*

*(-

2.34

)**

(-1.

31)

(-1.

40)

(-0.

64)

Urb

aniz

atio

n-0

.285

-0.3

03-0

.010

-0.2

69-0

.042

(-0.

92)

(-0.

97)

(-0.

03)

(-0.

08)

(-0.

12)

De

pe

nd

en

cy-2

.459

-2.4

32-2

.757

-2.7

35-2

.835

(-6.

35)*

**(-

6.25

)***

(-6.

61)*

**(-

6.62

)***

(-6.

63)*

**

An

alp

hab

eti

sm R

ate

-1.9

23-2

.037

-1.8

97-1

.951

-1.6

63

(-3.

47)*

**(-

3.70

)***

(-3.

11)*

**(-

3.26

)***

(-2.

72)*

**

Un

em

plo

yme

nt

Rat

e-1

.991

-2.2

74-2

.595

-2.7

07-2

.138

(-2.

24)*

*(-

2.61

)***

(-2.

70)*

**(-

2.89

)***

(-2.

26)*

*

GIN

I Co

eff

icie

nt

(In

eq

ual

ity)

205.

646

214.

339

204.

539

208.

614

197.

384

(4.7

4)**

*(4

.97)

***

(4.2

9)**

*(4

.45)

***

(4.0

7)**

*

Infa

nt

Lab

or

Rat

e-1

.058

-1.0

14-0

.778

-0.7

65-0

.519

(-2.

09)*

*(-

1.99

)**

(-1.

41)

(-1.

40)

(-0.

92)

Div

orc

es

pe

r 10

00 p

eo

ple

21.7

5325

.025

12.7

7114

.571

2.09

6

(2.3

9)**

(2.8

4)**

*(1

.31)

(1.6

1)(0

.26)

Co

ntr

ols

fo

r So

uth

ern

?Ye

sYe

sN

oN

oN

o

Co

ntr

ols

fo

r G

reat

Re

cess

ion

?Ye

sYe

sYe

sYe

sN

o

Co

ntr

ols

fo

r M

etr

op

oli

s?

Yes

No

Yes

No

No

Nu

mb

er

of

Stat

es

2727

2727

27

Nu

mb

er

of

Ob

serv

atio

ns

7979

7979

79

R-S

qu

are

d0.

8104

0.80

520.

769

0.76

70.

7438

Stat

ew

ide

ob

serv

atio

ns

for

3 d

iffe

ren

t ye

ars

(200

6,20

09,2

012)

we

re u

sed

. Th

e 'S

ou

the

rn' d

um

my

con

tian

s al

l of

Bra

zil's

So

uth

& S

ou

the

aste

rn

stat

es

du

e t

o t

he

ir la

rge

co

nce

ntr

atio

ns

of

we

alth

(Sa

o P

aulo

, Rio

de

Jan

eir

o, C

uri

tib

a &

Be

lo H

ori

zon

te a

re w

ith

in t

he

sam

e g

eo

grap

hy)

The

me

tro

po

lis

du

mm

ies

con

tro

ls f

or

any

stat

e t

hat

has

a c

ity

wit

h m

ore

th

an 1

mil

lio

n in

hab

itan

ts.

The

val

ue

s in

par

en

the

sis

are

th

e r

egr

ess

ion

's t

-sta

tist

ics

The

ast

eri

sks

ind

icat

e s

tati

stic

al s

ign

ific

ance

: **

*P<0

.01,

**

P<0

.05,

* P

<0.1

0

All

re

sult

s in

bo

ld a

re s

tati

stic

ally

sig

nif

ican

t.

marcelo fuentes capstone - final version

Documents