Latin American Research Network
Registration Form
The Quality of Education in LAC
1. Name of institution: Instituto Futuro Brasil
2. Name of the participants:
Project Director: Naercio Aquino Menezes Filho
Researcher 1:
Creso Franco
Researcher 2:
Fabio Waltenberg
Researcher 3:
3. Name, title, phone number and e-mail of the person responsible for signing the letter of agreement
with the Bank:
Name: Regina Carla Madalozzo
Title:
Director of IFB
Phone Number: 55-11-3168-9560
Fax:
55-11-4504-2350
E-mail: [email protected]
4. Name of institution: Escola de Economia de São Paulo - Fundação Getúlio Vargas
5. Name of the participants:
Project Director: André Portela Souza
Researcher 1:
Aloisio Araújo
Researcher 2:
Gabriel Buchmann
Researcher 3:
Marcelo Neri
Researcher 4:
Paulo Picchetti
Researcher 5:
Vladimir Ponczek
6. Name, title, phone number and e-mail of the person responsible for signing the letter of agreement
with the Bank:
Name: Yoshiaki Nakano
Title:
Director, Escola de Economia de São Paulo – Fundação Getúlio Vargas
Phone Number: 55-11-3281-3350
Fax: 55-11-3281-3357
E-mail: [email protected]
Instituto Futuro Brasil
&
Escola de Economia de São Paulo
Fundação Getúlio Vargas
The Quality of Education in Brazil
Research Proposal
Inter-American Development Bank
June 2007
Research Team
Instituto Futuro Brasil (IFB)
Naercio Aquino Menezes-Filho (Leader) - IFB
Creso Franco - PUC- RJ
Fabio Waltenberg - IETS
Escola de Economia de São Paulo e Escola de Pós-Graduação em Economia –
Fundação Getúlio Vargas
(EESP/EPGE-FGV)
Aloísio Araújo - EPGE-FGV
Gabriel Buchmann – CPS/IBRE-FGV
Marcelo Néri - EPGE-FGV
Paulo Picchetti - EESP- FGV
Vladimir Ponczek - EESP- FGV
André Portela Souza (Leader) - EESP- FGV
Introduction
The human capital is one of the main determinants of the rate economic growth and level of welfare
in a country and formal education is one of the most important components of human capital. The process
of educational attainment in Brazil can be described as backward (even) when compared to less developed
countries and highly skewed in favor of a privileged slice of the population. The debate over the
importance of education as a factor explaining Brazil’s income inequality is intense, with the leading
current of opinion being that the distribution of schooling is the main causal factor explaining this
inequality, by generating productivity differences among individuals that last throughout their lifetimes
[Menezes-Filho (2001)].
Substantial improvements have been observed in Brazilian education in terms of quantitative
indicators, both in terms of flow variables (decreased average delay, decreased fraction of delayed children,
increased gross and net enrollment rates) and in terms of stock variables (increased average years of
schooling, reduced illiteracy rates), as shown in table 1.
However, there is still considerable room for improvements in those respects, given that the country
is clearly still very far from a situation of universal access to all levels of education, with acceptable drop
out rates in each level. Another insufficiency is that recent changes have not led to a homogeneous pattern
across the country. Indeed, there is huge variation across regions, states and metropolitan areas. For
example, illiteracy rate among children (10-14 years old) is 0.4% in the metropolitan area of Curitiba and
3.8% in Fortaleza; the fraction of children (7-14 years old) at school is 98.4% in the state of Santa Catarina
and 89.1% in that of Maranhão; net enrollment rate at secondary school: 66.6% in the state of São Paulo;
22.1% in that of Alagoas. 1
Table 1 – Education Indicators
1
Source: Brazilian household surveys (PNAD) compiled by the Brazilian IETS (www.iets.org.br).
Finally, and more importantly, while the numbers above suggest that a quantitative change has
taken place, they do not indicate it has been accompanied by a qualitative improvement. It is not enough to
make sure pupils go to school; they should be learning something there. Unfortunately, there are reasons to
believe this is not happening in Brazil. Recent evidence indicates the average quality of education in the
country is low and it is unequally distributed among its population, as compared to the situation found in
many countries, both internationally and regionally (Latin America). 2
According to a study conducted by Ministry of Education in 2003, for example, 55% of students
who complete fourth grade have reading performance considered critical or very critical, and in the
Northeast and North regions this percentage reaches 70% and 66%, respectively. This dismal performance
is also observed in nearly 40% of students who complete the third year of high school. Once again the
regional disparities stand out: in the South 29% have a reading level deemed critical or very critical, while
2
See, for example, how (bad) Brazil compares to other countries in: OECD (2001, 2004a, 2004b), Ravela (2004), Waltenberg
(2005), Willms (2006), Hanushek & Wössman (2007).
in the North and Northeast, these percentages are much larger, at 51% and 48%, respectively [SAEB
(2004)].
Therefore, this project intends to examine in more detail the quality of education in Brazil, to
understand its main determinants and to verify the impact of the quality of education on future socioeconomic outcomes, such as wages, inequality and health. The main objectives of the proposal are:
1-
Decompose the factors that contribute to education quality in Brazil (topics 1 and 2)
2-
Compare education quality in Brazil with that of other Latin American and South European
countries (topic 3)
3-
Investigate the impact of education quality on wages and early pregnancy (topics 4 and 5)
4-
Analyze the design of efficient education policies (topic 6)
This proposal consists of six additional sections and 2 annexes. Each of the following
sections below describes the specific topic it intends to cover in the project, together with the main data
sets to be used in each topic. We then present the bibliographic references, the budget and the CVs of all
the researchers participating in the project in the annexes.
1) Decomposition of the Quality of Education
Fundação Getulio Vargas
In this topic we plan to investigate the relative importance of the schools characteristics, the student
characteristics and the teacher characteristics on the quality of education. It will be accomplished by
applying different methods of decomposition on test results on the SAEB and Prova Brasil datasets. The
main question to be answered is the following: Of all the total dispersion of test-results, how much is due to
school, student, and teacher characteristics? This question will be answered by using three different
methodologies.
1.1 - Data Sources: SAEB and Prova Brasil
Tha dataset to be used are the SAEB dataset and, conditional upon availability, the Prova Brasil
dataset. It is collected by the Anísio Teixeira National Institute of Studies and Research (INEP) from the
Ministry of Education of Brazil. 3 SAEB dataset (Sistema de Avaliação de Educação Básica) consists of
microdata on students proficiency test-scores on portuguese and mathematics for students of the 4th and 8th
grades of the primary education, and 3th grade of the secondary education. It also includes two
questionnaires that elicit information on the family background of the student and on the school
characteristics. It runs every two years since 1995 and its sample is stratified to be representative at
different levels (with some variations across the years), e.g., grade levels, state and national levels, public
and private schools, metropolitan and non-metropolitan areas. It is important to note that it is not a panel
data. Rather, it is a series of cross-section data where the students take only one of the two exams,
Portuguese or Mathematics. The most recent year available is 2005.
The Prova Brazil dataset is a set of data on proficiency test-scores collected also by the Anísio
Teixeira National Institute of Studies and Research (INEP). It is intended to cover most of students and the
4th and 8th grades of the primary education in 2005. More precisely, it includes all students in urban public
schools with at least 30 students in the respective grade. The exams are in Portuguese and Mathematics as
well. Differently from SAEB, it is representative at the municipality level and the student takes both
Portuguese and Math exams. It also includes information on the student family background.
1.2 The Methodologies
Three different methodologies will be used to decompose the test results into school characteristics,
student characteristics, and teacher characteristics. The first two methods will explore the correlations of
observable characteristics and the test scores. The third one will make a further effort to disentangle the
effect of quality of education from other critical unobservable inputs.
1.2.1 Regression-Based Approach Decomposition
The first method to be used is the Regression-Based Approach decomposition of inequality
developed by Morduch and Sicular (2002). It is the decomposition method by factor components
developed by Shorrocks (1982) extended to the linear regression analysis. Its advantages are that it is very
flexible and fits in our objective easily. First, it admits OLS, weighted least squares, quantile regressions,
3
http://www.inep.gov.br/.
and corrections for endogeneity. Second, it is an exact decomposition (the sum of the parts adds to one) and
yields an exact allocation of the contributions of each variables. Third, it can be applied to many different
inequality indices that satisfy the most common required properties of inequality indices plus the property
of uniform additions. This property states that measured inequality should fall if everyone in the population
receives a positive transfer of equal size. This property is implied by the transfer axiom and the scale
invariance axiom. The Gini, the Coeficient of Variation and the Theil-T indices satisfy this property.
Briefly, the authors start with a linear equation
yi = X i β + ε i
where yi can be the proficiency score of student i and X is a vector of M x 1 explanatory variables that
include student, school, and teacher characteristics, and ε i is the error term. Note that y i =
M +1
∑ yˆ
m =1
m
i
, for all i,
where yˆ im = β̂ m xim for m = 1,..,M, and yˆ im = εˆi for m = M+1. These estimated test-scores can then be used
to directly compute decomposition components for all regression variables. The share of variable m of total
⎛ ∑ ai ( y ) xim
⎜
inequality index I(y) take the general form of s m = βˆ m ⎜ i
I ( y)
⎜
⎝
⎞
⎟
⎟ , for m = 1,…,M. The term ai ( y ) will
⎟
⎠
depend on the particular index used that can be written as a weighted sum of factor components. Examples
for the Gini, CV and Theil-T can be found in Morduch and Sicular (2002). 4
1.2.2.
Hierarchical Models
The advantage of the first method is that is simple. It gives us the first approximation to the relative
importance of the observable characteristics of schools, students, and teachers on test results. However, the
drawbacks are that it does not take into account the fact the variables have different levels of aggregation
and that it does not control for possible unobservable characteristics correlated with observable
characteristics and test results. The first drawback can be circumvented by a second method that uses multilevel models to assess school performance. When analyzing the determinants of students’ performance, one
is normally interested in modeling a response variable such as a test score or any other measure related to
proficiency or well-being. This variable can be continuous, discrete or even categorical, but in all
circumstances, the interest lies on the relationship between this variable and a set of explanatory variables
representing known characteristics of students. The rationale for using multilevel models rests on the issue
of exploring the fundamental relationships between different forms of aggregating the data.
At one extreme we can, for example, work with school averages when investigating the
relationships between the response and explanatory variables, while at the other extreme we could analyze
the same relationships using data at the students’ level, ignoring the effects caused by the fact that these
students are grouped in different schools. Some studies, such as Aitkin et alli (1986) and Woodhouse and
Goldstein (1989), demonstrate that results are not robust when ignoring these issues of aggregation.
When the different levels of aggregation are explicitly considered, it is possible to analyze the
interaction between these levels in terms of their relation to the response variable. It is possible to
simultaneously model data containing information on individual students, the classrooms (and teachers)
they are part of, their families, their schools, and even some meaningful “group of schools” such as
determined for instance by geographic location.
A simple relationship between a response variable and a explanatory variable illustrates the point. If
we index students in a sample by i, and schools by j, the values observed for a response variable can be
represented by yij , and the values for an explanatory variable by xij. Assuming a linear form, the
relationship between these variables can be modeled as
y ij = β 0 + β1 xij + ε ij
If we estimate this model by traditional methods such as Ordinary Least Squares, the residual term
will necessarily capture all the non-modeled relationships between students and their schools, and these
will remain unexplained. One possibility is to include dummy variables for schools, which will then control
for differences in the intercept between the schools. One can even interact these dummy variables with the
explanatory variable, obtaining different values for the associated coefficient, one for each school in the
data sample. This approach faces statistical problems in terms of decreased efficiency (due to the large
4
For more details, see Morduch and Sicular (2002).
number of coefficients to be estimated), but mainly from a lack of a probabilistic structure to capture the
important relationships between the different levels contained in the information in the data.
Multilevel models will explicitly circumvent these shortcomings assuming that the coefficients are
allowed to vary between units in a probabilistic way. We can assume, for example:
Coefficients are now random around a common value, assuming particular values for each unit of
the analysis. The relationships between the effects among these units are captured by the matrix of
correlations of these coefficients. These models have been applied to a variety of problems, including
assessing education performance. A good survey of the techniques and applications can be found in
Goldstein (1987).
The technique is general in the sense that the number of levels is part of the specification of the
model, along with the included explanatory variables and their functional forms. Each of the explanatory
variables can have associated coefficients at any of the different levels, with the benefit of statistical
inference tools to decide when it is appropriate or not to allow the coefficients to be random (capturing the
differences between the levels) or not.
Therefore, with the available information on students’ backgrounds, one can tentatively specify
models to capture the impacts of students’ characteristics, such as gender, but also on their belonging to
specific households, classrooms, schools and geographic regions upon measures of their academic
performance. Algorithms implementing the statistical inference procedures involved in this class of models
are found in dedicated software, such as MlWin, and general-purpose statistical software, such as STATA.
1.2.3 - Structural Model Using Minimum Distance Estimators
The fist and second method have the potential problem of not ideally controlling for the potential
bias of unobservable characteristics correlated with school, student, and teacher characteristics. One way
to tackle this issue is to estimate a structural model that formally assumes the presence of unobservable
characteristics. A third method to be used is the estimation of a structural panel model using minimum
distance estimators. It tries to extract information about the unobservable characteristics of the student by
exploring the correlation of test-scores of Portuguese and Math of the same student of the Prova Brasil. Of
course, this part of the project will be carried out only if the data is available on time. The advantage of this
method is that, under specific assumptions, it controls for some unobservable variables that are correlated
with the observable inputs and outcomes as well.
This is a variance component model that establishes how much of the total variance of the testscores are due to (unobservable) student effect and other effects. The structure of the model can vary
depending on the degrees of freedom and can include observable variables as well.
For simplicity, suppose the test-scores depend on individual fixed effect plus a random shock.
Formally, let N isp be the grade of student i in school s in exam p (normalized to mean 0) assumes the
additive form:
N isp = μ i + ε isp ,
where:
μ i = student fixed effect with N (0, σ μ2 )
ε i = error term with N (0, σ ε2 )
Supose:
E[ μ i , ε isp ] = 0, ∀p
E[ ε isp , ε isp ` ] = ρ , for the same i.
The variance-covariance system is given by (when two exams are considered):
V ( N isp ) = σ μ2 + σ ε2
V ( N isp ` ) = σ μ2 + σ ε2
COV ( N isp , N isp ` ) = σ μ2 + ρ
There are three observed moments in this system: Two variances and one covariance. One can estimate the
parameters σ μ , σ ε eρ using the observed moments. The ratio
2
2
σ μ2
V ( N isp )
gives the relative importance of the
student effect on the total variance of scores.
In this example, this system is exactly identified. However, if one considers different states, regions,
school systems, etc. as different moments, an over-identified system can be estimated. Moreover, one can
start with a regression equation where the observable student characteristics are added. The computed
residual can then be used to decompose its variance into the two components. The comparisons with the
two results can give us an idea of the relative weight of observable variables to explain total student effect.
The same can be done for schools and teachers. Of course, different models can be estimated and the
refinement of them will depend on the degrees of freedom available. The estimation can be done and the
over-identification restrictions tested by using of minimum distance estimation as in Chamberlain (1982,
1984) and Abowd and Card (1989).
2)
Identifying Classroom and School Effects using Longitudinal data
Instituto Futuro Brasil
2.1 Research Questions
(a) To what extent students’ achievement varies between classrooms and between schools?
(b) Which classroom and schools features promotes students learning?
(c) Which classrooms and schools feature promotes a more equitable social distribution of learning
within classrooms and schools?
2.2 Method
2.2.1 Design
Almost all attempts to study school effect in Latin American countries have important limitation,
related to the cross sectional feature of most database available. The quotations below stress this
methodological point boldly:
"[Due to] the cross-sectional nature of data, problems of causal inference are daunting."
(Raudenbush, Fotiu e Cheong 1998).
"A common obstacle to carrying out appropriate adjustments when modelling examination
results is the lack of suitable prior achievement measures". (Goldstein 1995).
"If only cross sectional data are used, whether with aggregate or individual level data, it is not
possible to make inferences about the 'effectiveness' of schools". (Goldstein, Huiqi, Rath e Hill
1995).
"A major strength of these two studies is their longitudinal designs, which allow us to
investigate learning (which measures change over time in academic status) rather than
achievement (which is a status measure). The ability to demonstrate how schools influence the
students who attend them is strengthened by being able to take into account the status of these
students at the point that they enter the schools (specially their academic status)." (Lee 2001)
"To establish appropriate estimates of a school's improvement over time a number of
components must be brought together. This, in our view, includes measures of outcomes and
prior attainment on individual pupils; data from 3 or (preferably) more years; a multilevel
statistical analysis; an orientation towards examining the data for systematic changes in school
performance over time." (Gray, Jesson, Goldstein, Hedger e Rasbash 1999).
"The fact that very few studies, if any, satisfy the minimum conditions for satisfactory
inference, suggest that few positive conclusions can be derived from existing evidence. The
minimum conditions can be summarized as: that a study is longitudinal so that pre-existing
student differences and subsequent contingent events among institutions can be taken into
account; that a proper multilevel analysis is undertaken so that statistical inferences are valid
and in particular that 'differential effectiveness' is explored; that some replication over time and
space is undertaken to support reliability; that some plausible explanation of the process
whereby schools become effective is available. (Goldstein 1997).
2.2.2 Data and measures
We are going to use panel data, gathered by the GERES project. The GERES project is financed by
the Ford Foundation, by the Brazilian National Research Council and the Ministry of Education. It follows
around 20 thousand students who attend 303 schools, in five major Brazilian cities. Up to now, the study
collected three waves of data: baseline data, at the beginning of 1st grade (March 2005); end of 1st grade
data (November 2005); and end of 2nd year data (November 2006). Although the project is going to collect
additional data on November 2007 and on November 2008, we are going to use the data that have already
been collected in waves 1 to 3. The GERES project is a joint project of six Brazilian Universities and one
member of our team is the coordinator of the Geres project.
The cities were chosen as a consequence of the location of the Universities that are collaborating in
the GERES project. Considering the whole population of schools offering 1st grade education in these
cities, a probabilistic sample, stratified by city, sector and level of resources available, were taken. Once a
school was selected all 1st grade classrooms and students were selected. These students were followed in
subsequent waves, even if they repeated grade. Although the project did not followed students that moved
for schools outside the school sample, every effort was taken to gather information, including school
academic records, on leaving students. This, along with the decision of measuring students that repeated a
grade, create a good context to differentiating value added from selection.
In each wave students took a reading and mathematics test and teachers were asked to fill a
questionnaire on their background, classroom features and school features. In wave 1 and wave 3 principals
filled a questionnaire. Around wave 2, parents filled a small questionnaire with basic family background
information (place of residence, father and mother education, father and mother occupation, family access
to services and to consumption goods, as a proxy of income). Variables available on classroom and schools
are classified in the following categories: (a) classroom social composition; (b) school social composition;
(c) classroom resources and size; (d) school resources and size; (e) classroom climate and practice; (f)
school academic climate.
2.2.3 Analytical Approach
Analytical approach is based on the hierarchical structure of data. It includes a preliminary step of
partition of variance in its three components, between-students-within-classrooms, between-classroomswithin-schools and between schools. This is going to be estimate for both achievement in each wave and
achievement in each wave controlled by previous achievement.
As the concept of “classroom” does not hold for more than one academic year, classroom effect is
going to be investigated based on two subsequent waves (wave 1 and 2; wave 2 and 3). Two and three
levels hierarchical linear models will be estimated. For school effect, it will also be useful to use
achievement of wave 3 as the dependent variable and to use achievement in wave 1 as the prior
achievement control.
3)
Benchmarking of Brazil’s Education Performance
Instituto Futuro Brasil
The objectives of this part of the research are the following:
1. To assess more precisely how low and unequally distributed the quality of education is in Brazil, both
in a regional perspective (Latin America), and in a more global perspective.
2. To compare Brazil with other countries (and complementarily to compare Brazilian intra-national
units), in terms of patterns of educational inequality and of educational inequity.
After finishing this research, we would like to be able to identify more clearly the characteristics of quality
of education in Brazil which are similar to those found in other countries, as well as those characteristics
which are unique to the Brazilian case. In order to accomplish the two main objectives stated above, we
will use PISA 2003 datasets.
3.1 - Research steps
Firstly, we will summarize, interpret, and contextualize all the available findings concerning the
performance – in an international perspective – of Brazilian students in PISA 2003, such as those contained
in the studies made by: Ravela (2004), OECD (2004a and 2004b), and Sprietsma (2007). Whenever
relevant and feasible, we will compare those findings with those related to PISA 2000, such as: OECD
(2001), Willms (2006), Brazil’s national report for PISA 2000 (INEP, 2001), our own previous study
(Waltenberg, 2005), and Fuchs and Wössman (2007). We would like to provide a precise description of the
education quality in the country, and an idea of its evolution in three years (from PISA 2000 to PISA
2003).
This initial descriptive exercise is all the more important if we take into account the fact that the
Brazilian educational authorities have not published a national report analyzing PISA 2003. For the
international comparisons, we will compare Brazil’s results with those of other Latin American countries.
But we also plan to compare the performance of students from Southern European countries’, which share
some cultural characteristics with Latin American countries (Portugal and Spain in particular) and which
could be taken as a more useful benchmark than much more developed and/or culturally-distant countries
like Northern and Eastern European, Asian, and Anglo-Saxon countries.
Secondly, due to Brazil’s vast territorial extension, its large and diverse population, and also due to
the heterogeneity verified in terms of quantitative educational indicators (mentioned above), it is important
to make further analysis at the intra-national level (according to the type of school students attend, to its
location, etc.), along the same lines followed by the studies mentioned in the previous paragraphs (detailed
descriptive statistics, analysis of variance, multilevel/cluster analysis etc.). We will come up with new
results, mapping the situation in terms of quality of education inside the country.
Thirdly, given the importance of quality of education along the whole distribution (cf. IDB, 2007), given
the unenviable record of Brazil in this respect, and finally given the fact that we dispose of a unique dataset
which allows us, both to compare Brazil to a great number of countries, and to compare Brazilian intra-
national units to some extent, we will devote some efforts in order to investigate patterns of educational
inequality and educational inequity.
Finally, we will interpret, discuss, and put into perspective our results, trying to distinguish those
which are specific to the Brazilian case from those which are relevant for other countries – emphasizing
how our results can be relevant to policy-making.
3-2 Data source: PISA 2003
We plan to work with PISA 2003, a choice which provides a series of advantages, and is restricted by a few
limitations. In what follows, the advantages and limitations are described, both generally (regarding all
countries) and specifically (regarding Brazil).
PISA 2003 not only is the most recent, but also it is to-date the most complete student achievement dataset
in terms of the number of participating countries and of the number of students represented by the samples.
PISA datasets also provide detailed information on students’ background and schools’ functioning
conditions, which paves the way for describing a great number of relationships between students and
schools characteristics with students’ performance. In particular, it is possible to decompose test results
into students, schools, and teacher characteristics, with potential policy implications.
Moreover, the focus of PISA is not restricted to assessing students’ knowledge, but also to evaluate
“their ability to reflect, and to apply their knowledge and experience to real-world issues” (OECD, 2003:
9), which is of paramount importance in today’s world. An additional virtue of those datasets is
comparability, since it is possible to compare results drawn from PISA 2003 with those of the two sets of
assessments which compose the first cycle (PISA 2000 and PISA Plus in 2002), and it will be possible to
compare them with future cycles, planned to take place every three years. Finally, PISA data and scales
occupy a privileged status – a kind of “currency of school quality“ – in the protocols that are being develop
by Hanushek & Wössman (2007) for pooling and analyzing different test scores data.
It is also relevant to compare Brazilian education quality to that of countries which are more similar
to Brazil in different respects. We would like to include in this subset of countries, not only LatinAmerican countries (which are the closest ones), but also the two Iberian countries (given their cultural
similarities with Latin American countries 5 ), and finally other Southern European countries (which have
relatively similar culture patterns, and while being more developed than Latin American countries, they are
not as far in terms of culture or development as Northern and Eastern European, Asian, or Anglo-Saxon
countries are). By proceeding this way, we will be able to more carefully compare Brazil to a group of
seven countries (ten if we include countries evaluated in the previous cycle of PISA) 6 , in a similar vein of
the one adopted in the Uruguay PISA 2003 national report (Ravela, 2004) to compare that country with
other small-scale countries.
An additional reason for analyzing education quality in Brazil by means of the PISA datasets relies
on the shortness of studies of such data made by Brazilian researchers. To the best of our knowledge, there
is little published, or on-going, research undertaken in the country using these datasets. One reason for that
scarcity is certainly the fact that many researchers have chosen to focus on the national equivalent of PISA
(the so-called SAEB), which is quite useful for intra-country analysis, as discussed the first topic of this
proposal.
The main limitation of PISA is the difficulty in inferring causality about the relationships unveiled
by the data, as acknowledged by Willms (2006: pp. 55 and 71-72). That is due to the following features: (i)
the cross-sectional design nature of the data and the absence of random assignment of students to treatment
and control groups; (ii) test scores at the age 15 reflect the cumulative effect of a series of factors over a
long period of time, and not only the recent impact of schools; (iii) there is no information on the classroom
level, but only at individual and school level. This latter point is also highlighted in the PISA Data Analysis
Manual (OECD, 2005: chapter 13). Yet, it is useful to employ PISA data as a powerful descriptive device,
as pointed out by Willms (2006), with presumable policy implications.
One limitation which is specific to Brazil is the very low coverage of 15-year-old Brazilians both in
PISA 2003 (around 54% of the cohort) and in PISA 2000 (around 2/3 of the cohort) which is due to the
5
The recently-created research network “Ibero-American PISA Group” (or GIP) is an evidence of the increasing interest of such
kind of comparison.
6
Greece, Italy, Mexico, Portugal, Spain, Turkey, Uruguay took part in PISA 2003. Argentina, Peru, Chile took part in PISA Plus
in 2002. Colombia joined this group of countries in PISA 2006.
high dropout rates of very young pupils in the country. 7 This is a problem which would affect any data
collected in Brazil, and whose procedure was focused in schools. It is thus a problem which is common to
other education quality datasets regarding Brazil. In any case, all the results should be interpreted taking
into account this limitation. Additionally, some variables (manually coded items) are not available for
Brazil in PISA 2003 because Brazil did not submit them on time (OECD, 2005: p. 240), but that should not
constitute a major problem for our analysis.
Finally, in PISA 2003, while information is available for relevant strata (regions, private/publicstate/public-municipal, school size, urban/rural, school infrastructure index), there is no information on
some important disaggregated levels (state, metropolitan/not, suburban/central), which is an important
limitation for a more detailed description of education quality in Brazil. This limitation is partially
compensated in another part of this research project, which uses the SAEB data.
3.3 - Methodology
Due to the nested nature of the data, HLM (or multilevel or clusters) regressions 8 will also be estimated in
this part of the proposal, in order to distinguish inter-individual variance from inter-school variance, as
discussed in topic 1 above. Various studies, including Hanushek & Wössman (2007), maintain that
teachers’ quality is of paramount importance for learning. Moreover, according to OECD data, teachers’
wages account for around ¾ of total expenditures on primary and secondary education in Brazil in the
beginning of the 2000s. So clearly, while teachers’ characteristics are a component of the more general
category of “school inputs”, both due to its impact on learning and due to its cost, it is advisable to include
specific variables describing them in the estimations.
We will take the models proposed and estimated by Willms (2006: p.57), Dumay & Dupriez (2004),
Sprietsma (2007) and Fuchs and Wössman (2007) as a departure point for the international comparisons
using all the countries’ data. Then we will check alternative specifications, taking into account relevant
7
This problem is also mentioned in Hanushek & Wössman (2007: Section 5), when they try to calculate the overall literacy in
different developing countries by combining household surveys and test scores data.
8
Bryk & Raudenbush (1992), Wooldridge (2002).
specificities of Brazil and of the subset of countries described above (Latin-American plus Southern
European countries).
To assess educational inequality, both across countries and across Brazilian intra-national units, we
will apply to the PISA data of usual distributional analysis tools, such as dominance analysis and
decomposable inequality indices. To assess educational inequity, both across countries and across Brazilian
intra-national units, we will calculate “SES gradients” and “school profiles” (e.g., Willms, 2006;
Vandenberghe & Zachary, 2000). But we will also employ recently developed opportunity-dominance
analysis tools (Pistolesi et al., 2005, Lefranc et al., 2006) and indices of equality of educational opportunity
(Checchi & Peragine, 2005), which are based, or try to go beyond, the normative framework stated by
Roemer (1998), and which we have recently explored using SAEB data (Waltenberg, 2007).
4)
Effects of the Quality of Education on Earnings
Instituto Futuro Brasil
In this topic we intend to investigate and quantify the impact of the quality of education on future wages in
Brazil, using a pseudo-panel formed of groups of individuals born in 1977/78 in various States and
followed until 2000.
4.1 - Methodology
Many international studies show that educational quality positively influences individuals’ future wages
[Murnane et. al. (1995), Murphy and Peltzman (2004)], their probability of continuing on to higher
education [Rivkin (1995)] and countries’ economic growth [Bishop (1989), Hanushek and Kimko (2000)].
The econometric analysis we propose to carry out in this topic will use a pseudo-panel and correct
for selection bias. The literature on pseudo-panels was pioneered by Browning et al. (1985), and the
technique is used by researchers who do not have panel data, but instead various mutually independent
cross sections in which different individuals are interviewed in each period. The objective of this technique
is to overcome the limits of the cross sections, while using their advantages in relation to panel data. The
limitations result from the fact that the researcher does not have lagged values for the variables, which in
principle would make it impossible to control for the specific effects and identify dynamic models. The
advantages involve the greater temporal coverage of these types of statistics, the fact they are not
susceptible to attrition problems and the reduction in measurement errors due to working with more
aggregated data.
In this work, the pseudo-panel will be formed by following a group of individuals over time. We
will analyze people born in 1977 and 1978 at three moments in their lives: in 1982 (at 4 and 5 years old), in
1995 (in the last year of high school, at 17 and 18), and in 2000 (at 22 and 23, when they are in the labor
market). We also intend to use gender, race and state of birth to form the groups. Hence, instead of
following the same individuals, we will follow the generations over time, through these cohorts.
The selection bias problem appears twice. The first is because the people who make up the SAEB
sample, that is, who took the achievement test in their senior year of high school, have completed ten years
of schooling and are still in school. In other words, the sample will be made up exclusively of students with
a high educational level. The second bias is that of migration. The samples from 2000 will exclude those
who migrated after reaching 17 or 18, in order to ensure that they took the achievement test in the same
state where they live and work 5 and 6 years later. Thus, the sample to be used in this work will not be
random, but is instead a selected sample. We intend to correct for the two selection biases by applying the
model proposed by Roy (1951) and further developed in Dahl (2002).
We propose to estimate the pseudo-panel model by weighted least squares at the cell level, with the
dependent variable being the logarithm of the real hourly wage (lw), and the explanatory variables are:
proficiency in the 11th grade (lprof), various observed cell characteristics (Xc ) the bias-correction variables
(MB and EB), state, gender and race dummies and a random error:
(2)
lwc , 2000 = α + β 1lprof c ,95 + β 2 X c ,t + β 3 MBc , 2000 + β 4 EBc ,95 + δ s + γ g + λ r + u c , 2000
where c represents the cells (c =1,..,108).
We then intend to instrument proficiency with the school input variables observed in 1995. The
identification assumptions are that the teachers, principals and schools are correlated with average
proficiency and uncorrelated with the average cohort-specific error term of the wage equation.
4.2 - Data
The data we intend to use come from three sources. The first is the National Household Survey (PNAD)
conducted by the Brazilian Institute of Geography and Statistics (IBGE) in 1982 and 1995. The second is
the proficiency test given in 1995 by the Anísio Teixeira National Institute of Studies and Research
(INEP), as part of the National System to Evaluate Basic Education (SAEB). The third is the 2000 Census
conducted by the IBGE 9 .
To analyze the relationship between school performance and earnings in Brazil we intend to form pseudopanels that accompany the generation born in 1977 and 1978 at various moments of their lives. To form the
database through the 2000 Census, we will obtain information on the individuals of the 1977 and 1978
cohorts who have income, studied at least 10 years and did not migrate after 1995 onward. To this database
we will add the average score obtained by this generation on the SAEB test in 1995. We also intend to
gather some characteristic variables of this generation that are in the 1982 and 1995 household surveys
(PNAD).
All the variables will be grouped by cells according to three characteristic vectors: sex, color and residence
state. This will generate 108 observations (cells) in each of the databases. In this way we will define the
variables: SEX (man or woman), RACE (white or others) and STATE to form these cells representing the
108 types of individuals whose cohort we follow at three stages of their lifetimes. We chose these three
characteristic vectors due to the results widely disclosed for Brazil and other countries on the wage
differences between men and women, between races [O’Neill (1990)], and between states, since the return
to education is not equal among the states [Dahl (2002)]. Figure 1 details the sample’s composition,
indicating the data to be used to form the pseudo-panel and its characteristics.
Figure 1 – Formation of the Pseudo-Panel
9
Sites of the IBGE – www.ibge.gov.br – and INEP – www.inep.gov.br .
Panel
Child
4 or 5 years
1982 PNAD
Student
17 or 18 years
1995 PNAD
Worker
22 or 23 years
1995 SAEB
2000 Census
High school
students
Wage (employee)
Took matemathics
test
At least
10 years of school
Non-migrant
(after 1995)
The sample from the 2000 Census will represent the individuals in the working phase of their lives. This
covers young adults 22 and 23 years old, with 10 or more years of school and who did not migrate after
1995, the year when the youths of this generation took the SAEB proficiency test. In other words, the
sample will consist of those who responded that they were born in the state where they were living and
working in 2000, and those who were not born in that state, but lived there without interruption since 1995.
Regarding wages, we intend to use the logarithm of the real hourly wage (LRHW) in the regressions
deflated by the National Consumer Price Index (INPC) for the Census.
5)
The Effects of Quality of Education on Health – FGV
As an extension of the previous analysis, we intend to measure possible channels that education quality
of the mother may impact health indicators associated with herself and her children. More specifically, we
will measure the impact of education quality evaluated by test scores in SAEB-1997 and SAEB-1999 on
the likelihood of teenage pregnancy and child nutrition observed in the National Household Budget Survey
(POF - 2002).
5.1 Methodology
Cognitive skills may affect the cited outcomes through a better understanding of the importance of
contraceptive methods, prenatal care, breastfeeding, nutrient in taking, among other procedures that
directly influence the probability of undesired pregnancy and child nutrition. The literature has a vast
number of studies trying to identify the casual relations between education and health (see Grossman and
Kaestner, 1997). Focusing on women’s health, (Behrman and Wolfe, 1989) also find that women’s
schooling positive affects their health and nutrient in taking..
Our identification strategy will follow a similar approach used in the previous section, i.e., we will
apply a pseudo-panel model and correct it for selection bias. As before, the pseudo-panel method is
necessary since we do not observe the same individual in the two different moments analyzed: when the
students take the test in 1997 or in 1999 and when they are interviewed by POF in 2002. Therefore, cells of
groups of women will be created based on state of birth, year of birth and race. More specifically, we will
form cohorts based on year of birth (1980 and 1982) with student who were in the eleventh grade and took
the SAEB test in 1997 and 1999, on race (white and non-whites) and on the state of birth, adding up 108
cells.
The average math and literature scores are computed in each cell using SAEB. The proportion of
teenage pregnancy and, as a proxy of child nutrition, the average weight and height ratio z-score of children
whose mothers belong to the respective cell are computed using POF. We plan to exclude from the POF
sample individuals who moved out of the state after turning fourteen or eighteen years old, in order to
guarantee that they live in the same state where they took the SAEB test.
We will estimate the pseudo-panel regression by generalized least squares at the cell level having
the proportion of teenage pregnancy as the dependent variables and the test scores as the regressors. We
will also include control variables such as the average in each cell of father’s education, teacher’s education
and wage, school principal’s education and wage, dummy variables indicating if the school has a library,
computer and science labs and if it is a private or a public school. As before, we will correct for the
selection biases by applying the model developed by Dahl (2002). Therefore, we will also include the biascorrection terms (MB and EB), state and race dummies and random error.
For child nutrient, we will estimate the impact of test scores on the average weight and height ratio
z-score of children whose mothers belong to the respective cell. Once more, beside all the explanatory
variables we will include the correction variables.
Again, we intend to instrument test scores with the school input variables. The same assumptions
are necessary: teachers, principals and schools are correlated with average proficiency and uncorrelated
with the average cohort-specific error term of the main equations.
6)
Design of Educational Policies - The Education Quality Index: Proficiency,
Measurement, and Incentives - Fundação Getulio Vargas
6.1 - The IDEB
On march 2007 the Brazilian federal government announced an Education Development Plan
(PDE), a set of proposals aiming to improve the quality of education in the country. The plan’s main
innovation was the creation of a synthetic indicator of education quality, the Basic Education Development
Index (Ideb), calculated on the basis of the passing rate and the results of Prova Brasil (and Saeb) for each
municipality of the country. The federal government will determine targets for the evolution of the Ideb
and then condition its educational transfers to the accomplishment of these targets. The thousand
municipalities with lowest Ideb will receive extra resources and the others only technical support.
The creation of a target system in education is a historical reference in Brazil, not only in the field
of education but in the national social policy scenario as a whole, and provides a unique opportunity for the
country to recover its educational delay. Although, despite all its virtues, there is still scope for
improvement concerning methodological issues and the design of incentive mechanism related to the Ideb.
This part of the research will focus then on measurement and policy issues concerning the
implementation of the synthetic index in the framework of a target system.
By improving the indicator of quality of education, which will base at the same time the education
debate and public transfers, we hope to improve the quality of education it self. By guaranteeing resources
to the areas that improve faster, we go towards fulfilling the promise of high quality education for all.
The Ideb is analytically expressed by the following formula
Ideb = Q
F
in which Q is a proficiency measure, that can be the students’ performance in the Prova Brasil or in the
Saeb,
and F is a measure of schooling flow, corresponding to the passing rate.
Weightening
One of the virtues of the Ideb as an indicator is its simplicity and its advantage is combining in a
synthetic index two central dimensions to the question of educational quality. Nevertheless, the equal
weight on its two components is an arbitrary choice. Why should they have the same weight?
In order to address this question, we will discuss the incentive it seeks to provide. On one hand, the
larger the weight of the flux component vis-à-vis the weight of the proficiency component, the larger the
incentive for the local administrator to accelerate artificially the promotion of the students, what can lead to
a large cost in terms of quality of education. On the other hand, the larger the weight of the proficiency
component vis-à-vis the flow component, the larger the incentive for the local managers to increase
retention, in order to improve proficiency. We should therefore seek to avoid these kind of unbalanced
behavior.
Formally we suggest an index in the form
α
Ideb = Q
F
β
One of the proposals of this section is, then, to analyze which would be a optimal weighting for the
index, that is, which should be the coefficients α̂ e β̂ .
Mathematically speaking, we have to avoid that the local government choose corner solutions when
trying to increase the index.
The use of a index in a Cobb Douglas fashion has some advantages. One of them is that its
exponents somehow express the degree of substitutability or complementarity between the index
components, an issue that will be analyzed as well. How do proficiency and flux affect each other? A
possible extension will be then to try measuring it.
Another advantage is that it allows us to decompose the growth rate of the education index into the
growth rates of each of these components in an additive fashion, as follows.
Ideb = (Q)α .( F ) β → ln Ideb = α ln(Q) + β ln( F ) → γ (Ideb) = αγ (Q ) + βγ (F )
Incorporating children out of school
Another relevant issue concerns taking into account children outside school, which the double aim
of making the local managers responsible for non-enrolled school age children and not ignoring the process
of schooling expansion in the evolution of the index.
We observe that in the range between 7 and 14 years old only 2,7% of children are not enrolled.
Nevertheless, when we examine the range from 15 to 17 we find 18,3% of children are not at school. For
this we can infer that the majority those out of school were students that evaded, not children that have
never been to school. Besides, the data shows as well that the expected rate of conclusion of basic
education was only 31,2% in 2004, even smaller than the 40,3% of 2004. This is a problem that has to be
attacked. Nevertheless, the Ideb, as it is built, do not provide incentives for the children to be brought back
to school, actually, it provides incentive for preventing children for evading, but giving up on them as soon
as they abandon school.
We will then try to discuss different ways of incorporating this dimension in the Ideb, and as a
possible extension to measure how would the index change if we do it. For this we would use the National
Sample Household Survey (PNAD) as a database and a methodology to be carry out based on Franco et all
(2003), Neri e Carvalho (2002) and Fernandes e Natenzon (2003).
6.2 Utilization of the indicator in a target based system
The central objective of this section is to discuss and build a model to analyze the use of the Ideb in a
framework of a target based system. We propose that the evaluation of the quality evolution of each municipally and
each school through the Ideb should be carry out using a standard methodology of evaluating social programs, the
differences-in-differences. The idea is to compare the municipalities - and the schools inside them - by the difference in
the value added by schooling to the students.
This methodology has several advantages. For instance, if the system is based on added value, the
private and public resources will tend to migrate to where the return is larger. Besides, as based on relative
measures of performance, this system is robust to aggregate shocks and to the arrival of new information. It
provides as well an incentive for schools to mix student with low and high backgrounds, what will not take
place if the target is based on level instead of on added value and can play a very important role an unequal
and diverse country like Brazil.
Another point concerning policy issues is that, for the success of this kind of target system it is
crucial to have a uniform and integrated system of evaluation. We already have a Provinha Brasil, that
evaluate the proficiency of 6 to 8 years old children, the Prova Brasil, for the end of primary school (4th
grade) and one for the end of fundamental school (9th grade), the ENEM or the Saeb for the secondary
school (3rd year of high school) and the ENADE in the end of the first and in the final year of university.
Nevertheless, these different proficiency exams are not uniform, some corresponding to the universe of
children, some to a sample and some are even voluntary. We discuss then ways of integrating them and
building a whole system of proficiency evaluation that will allow us to follow every student over all her
schooling trajectory, and the value added to them in each school level.
We can model the municipality problem as one involving two stages, concerning the two dimensions we want
to analyze.
The first one concerns the role of Ideb on influencing the public expenditures on education. In this stage the
local manager maximizes a political function involving the allocation of its resources between its different secretaries,
from where we derive its optimal investment in education.
Formally, we will have, for example:
Max V (Ge , Go (T ))
s.a Ge + Go ≤ Y + T
Ge ≥ 0,25Y
T = λIdeb
where Ge corresponds to the municipality’s expenditures on education,
Go to its expenditures with others secretaries, Y to its revenue,
the second restriction to the fact that by the Constitution is mandatory that the municipality spend at least 25% of
the budget with education, and the third one to the fact that the transfer of federal resources depends on the Ideb.
The solution for this problem is G * , the municipality optimal investment in education.
The second stage involves the local government’s decision over how to allocate its educational expenditures
towards seeking the different objectives contemplated in the index. The local government maximizes a function which
represents the net benefit it derives from education, which depends mainly on the Ideb – at least that is the idea
behind this target-based system.
Max z ( Ideb)
s.a : p q Q + p a F ≤ G * = G
Solving it, we find the optimal allocation of the local resources between the two components of the
index, Q * and F * .
We will analyze and compare different functional forms for the Ideb, such as perfect substitutes, quasi-linear and
Cobb Douglas, and also the cost of improving proficiency and the approval rate, as well as the interaction between
these costs.
7)
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•
Raudenbush , S.; Fotiu, R.; Cheong, Y. (1998). “Inequality of Access to Educational Resources: A
National Report Card for Eight-Grade Math”. Educational Evaluation and Policy Analysis, 4, v. 20,
pp. 253-267.
•
Roemer, J. (1998). Equality of Opportunity. Cambridge, MA: Harvard University Press
•
Rivkin, S. G. (1995) “Black/white differences in schooling and employment”. Journal of Human
Resources, vol.30 (4) (Fall), pp. 826-52.
•
Roy, A. D. (1951) Some Thought on the Distribution of Earnings. Oxford Economic Paper, 3, pp.
135-146.
•
Shorrocks, A. F. (1982). “Inequality Decomposition bu Factor Components”. Econometrica, vol.
50, n. 1, pp. 193-211.
•
Soares, J.F. & A.C.M. Collares (2006), “Recursos Familiares e o Desempenho Cognitivo dos
Alunos do Ensino Básico Brasileiro”, DADOS – Revista Brasileira de Ciências Sociais, Rio de
Janeiro,V. 49, n. 3, pp. 615-681.
•
Soares, J.F. & M.T.G. Alves (2003), “Desigualdades Raciais no Sistema Brasileiro de Educação
Básica”, Educação e Pesquisa, São Paulo, v. 29, n.1, pp. 147-165, Jan./Jul.
•
Soares, J.F. (2007), “Melhoria do Desempenho Cognitivo dos Alunos do Ensino Fundamental”,
Cadernos de Pesquisa v.37, n.130, pp. 135-160, Jan./Apr.
•
Soares, T.M. (2005), “Modelo de Três Níveis Hierárquicos para a Proficiência dos Alunos de 4ª
Série Avaliados no Teste de Língua Portuguesa doSIMAVE/PROEB-2002”, Revista Brasileira de
Educação, v.29 (2)
•
Sprietsma, M. (2007), Improving student performance : three micro-econometric studies, PhD
Dissertation Thesis, Economics Department, UCLouvain, Louvain-la-Neuve, Belgium: Presses
universitaires de Louvain
•
Sprietsma, M. & Waltenberg, F.D. (forthcoming) "The effect of teachers’ wages on student
achievement. Evidence from Brazil." In: Jürgen Backhaus, Raul Eamets, Jüri Sepp (eds.), Advances
in the economics of education: on markets and post-transformation issues
•
Vandenberghe, V. & S. Robin (2004) Evaluating the effectiveness of private education across
countries: a comparison of methods, Labour Economics, 11, pp. 487-506
•
Vandenberghe, V. & M.D. Zachary (2000) “Efficacité-Equité dans l’Enseignement Secondaire de
la Communauté Wallonie-Bruxelles : Essai d’Evaluation dans le Cadre d’Une Comparaison
Internationale”, Cahier de Recherche du GIRSEF, UCLouvain, Belgium, n. 7
•
Waltenberg, F.D. (2005), "Iniqüidade educacional no Brasil. Uma avaliação com dados do PISA
2000." Revista Economia (ANPEC, Brasília, Brazil), vol. 6 (1), pp. 67-118, Jan-Jul
•
Waltenberg, F.D. (2007), Normative and quantitative analysis of educational inequalities, with
reference to Brazil. PhD Dissertation Thesis Economics Department, UCLouvain n.527/2007,
Louvain-la-Neuve, Belgium: Presses universitaires de Louvain
•
Waltenberg, F.D. & Vandenberghe (forthcoming), "What does it take to achieve equality of
opportunity in education? An empirical investigation based on Brazilian data", Economics of
Education Review
•
Willms, J.D. (2006), Learning Divides: Ten Policy Questions about the Performance and Equity of
Schools and School Systems, Montreal, UNESCO Institute for Statistics
•
Wooldrige, J. M. 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press:
Cambridge, Massachussetts.
Annex 2 – Short Corricula Vitae of Researchers
NAÉRCIO AQUINO MENEZES FILHO
ACADEMIC BACKGROUND
- Ph.D in Economics , University College London, 1997
- M.A. in Economics, University of São Paulo, 1992
- B.A. in Economics, University of São Paulo, 1986
CURRENT POSITIONS:
-
Professor of Economics, IBMEC São Paulo
-
Associate Professor of Economics, University of São Paulo (Part-time)
-
Research Director – Instituto Futuro Brasil
PUBLISHED PAPERS
•
Menezes-Filho, N., Muendler, M. and Ramey, G. (forthcoming) “The Structure of Workers
Compensation in Brazil, with a Comparison to France and the United States”, Review of Economics
and Statistics.
•
Menezes-Filho, N. and Pazello (forthcoming) “Does Money in Schools Matter? Evaluating the
Effects of a Funding Reform on Wages and Test Scores in Brazil'”, Economics of Education
Review.
•
Gonzaga, G., Menezes-Filho, N. and Terra, C. (2006) “Trade Liberalization and the Evolution of
Skill Earnings Differentials in Brazil”, Journal of International Economics, vol. 68, pp. 345-67.
•
Menezes-Filho, N. (2005) “Is the Consumer Sector Competitive in the UK? A Test Using Household-Level
Demand Elasticities and Firm-Level Price Equations”, Journal of Business and Economics Statistics, vol.23, 3,
pp. 295-304.
•
Menezes Filho, N. And Andrade, A (2005). “O Papel da Oferta de Trabalho no Comportamento dos Retornos
à Educação no Brasil”. Pesquisa e Planejamento Econômico, v. 35, n. 2, 2005.
•
Menezes Filho, N. And Scorzafave, L. (2005) “Impacto da Participação das Mulheres na Evolução da
Distribuição de Renda do Trabalho no Brasil”. Pesquisa e Planejamento Econômico, v. 35, n. 2.
•
Menezes Filho, N. And Giuberti, A. (2005) “Discriminação de Rendimentos por Gênero: Uma Comparação
entre o Brasil e os Estados Unidos”. Economia Aplicada, 9, n. 3, p. 369-383, 2005.
•
Menezes Filho, N., Mendes, Marcos, and Almeida, Eduardo Simões (2004) “O Diferencial de Salários FormalInformal no Brasil: Segmentação ou Viés de Seleção?” Revista Brasileira de Economia, v. 58, n. 2, p. 235248, 2004.
•
Leon, F. e Menezes-Filho, N. (2003) “The Determinants of Failure, Evasion and Progress in the Brazilian
Educational System”, Pesquisa e Planejamento Econômico, vol. 32, número 3.
•
Camargo, J. M., Gonzaga, G. e Menezes-Filho, N. (2003) “The Unemployment Effects of the 1988 Working
Week Reduction in Brazil” Revista Brasileira de Economia Vol. 57 nº 3.
•
Menezes-Filho , N. e Rodrigues Jr. , M. (2003) “Technology and Demand for Skills in Brazilian
Manufacturing" Revista Brasileira de Economia Vol. 57 nº 2.
•
Menezes-Filho , N. e Picchetti , P. (2000) “The Determinants of the Duration of Unemployment in Brazil”,
Pesquisa e Planejamento Econômico, vol. 30, no.1 pp.23-48.
•
Menezes-Filho , N. e Fernandes , R. (2000) “The Evolution of Inequality in Metropolitan Brazil”, Estudos
Econômicos, vol. 30 (4), pp. 549-569 .
•
Menezes-Filho, N. (1997) “Unions and Profitability over the 80s: Some Evidence on Union- Firm Bargaining
in the UK” , The Economic Journal, vol .107, 442, pp. 651-670.
•
Menezes-Filho, N., Ulph, D. and Van Reenen, J. (1998) “The Determinants of R&D: The Role of Unions”,
European Economic Review, vol. 42, no. 3-5.
•
Menezes-Filho, N., Ulph, D. and Van Reenen, J. (1998) "R&D and Union Bargaining: Evidence from British
Firms and Establishments", Industrial and Labor Relations Review, vol. 52, no.1.
BOOKS
•
Lisboa, M. e Menezes-Filho, N. (2001) Microeconomics and Society: Surveys in
Microeconometric Studies in Brasil. Rio de Janeiro: Fundação Getúlio Vargas.
•
Chahad, J.. e Menezes-Filho, N. (2002) The Brazilian Labor Market.
São Paulo: LTR.
CHAPTERS IN BOOKS
•
Human Capital, Inequality and Pro-Poor Growth in Brazil; with Ligia Vasconcellos in: Delivering
on the Promise of Pro-Poor Growth, Besley, T. and Cord, L. (eds), World Bank, 2006.
•
“Unions and innovation: a survey of the theory and empirical evidence” with John van Reenen, in:
International Handbook of Trade Unions, Addison, John T., Schnabel, Claus (eds), USA, 2003.
•
“Adolescents in Latin American and the Caribbean: How Do They Decide to Allocate their Time?”
in: Critical Decisions at a Critical Age: Adolescents and Young Adults in Latin America, Duryea,
Suzanne, Edwards, Alejandra Cox, Ureta, Manuelita (eds), Washington, 2003.
•
“Opening the Convergence Black Box: Measurement Problems and Demographic Aspects” in
Spatial Inequality and Development, Ravi Kanbur and Anthony Venables (eds), 2003.
•
“Educação e Desigualdade” in Lisboa and Menezes-Filho (eds) Microeconomia e Sociedade. Rio de
Janeiro: Fundação Getúlio Vargas.2001
INTERNATIONAL CONSULTANCIES
Global Development Network (GDN) and
Partnership for Education Revitalization in the Americas – PREAL
•
Evaluating Two Education Programs in Brazil using Test Scores (2003).
Coordinator: Santiago Cueto - Preal
London Business School
•
Technical Change in Emerging Markets: Factors Driving the Diffusion of ICT and the Implications for
Productivity in Brazil and India (2004)
Coordinator: Simon Commander
The World Bank
•
Understanding Pro-Poor Growth (2003)
Coordinator: Louise Cord – Poverty Assessment Unit
•
Household Income Growth in Brazil and its Distribution (2001).
Coordinator: Mark Thomas – Brazil Unit
•
The Effects of Minimum Wages on Inequality (2003).
Coordinator: Wendy Cunningham – Policy Unit
Inter-American Development Bank - Research Network
•
Labor Market Dynamics and Reallocation in Latin America (2002).
Coordinators: Carmen Pages and Alexandro Micco
Consultant: John Haltinwanger
•
Unions and Economic Performance in Brazil (2001).
Coordinator: Gustavo Marques, Consultant: Peter Kuhn
•
Adolescents and Young Adults in LAC: Critical Decisions at a Critical Age (2000).
Coordinator: Suzanne Dureya, Consultants: Alexandra Cox-Edwards and Manuelita Uretra
•
Geography and Development (1999).
Coordinators: Alejandro Gaviria and Eduardo Lora , Consultant: John Gallup
PRIZES
•
1998: Haralambos Simeonidis – Best PhD Thesis in Economics
•
2006: Haralambos Simeonidis – Best Paper in Economics
CRESO FRANCO
1. PERSONAL DETAILS
Name
Creso Franco
Birth
31st August, 1958 - Rio de Janeiro/RJ - Brazil
Email:
[email protected]
2. EDUCATION
1990 - 1993
Ph D, Education.
University of Reading, UK
1984 - 1988
Master of Education
Pontifícia Universidade Católica do Rio de Janeiro, PUC-Rio, Brazil
1977 - 1984
B.Sc . (Physics)
Pontifícia Universidade Católica do Rio de Janeiro, PU-/Rio, Brazil
3. POSITIONS
1
Assistant Professor, 1994-1998
Pontifícia Universidade Católica do Rio de Janeiro, PUC-Rio, Brazil
2
Associate Professor, 1988Pontifícia Universidade Católica do Rio de Janeiro, PUC-Rio, Brazil
4. Published Papers (2002-2007)
4.1 Submitted
LEE, V.E., FRANCO, C. and ALBERNAZ, A. (submitted) Quality and equality in Brazilian
secondary school: a multilevel cross-sectional school effects study.
FRANCO, C., LEE, V.E and SATYRO, N. (submitted) National educational policies and their
consequences for quality and equality.
4.2 Journal papers
FRANCO, C.; ALVES, F.; and BONAMINO, A. (in press). Quality of Education in Brazil:
Policies, their strengths and their Limits. Educação e Sociedade.
FRANCO, C.; SZTAJN, P. and ORTIGÃO, I. (2007) Mathematics Teachers, Reform, and Equity:
Results from the Brazilian National Assessment. Journal of Research in Mathematics Education, 38 (4),
393 – 419.
ALVES, F; ORTIGÃO, I. and FRANCO, C. (2007). Social background and risk of repeating a
grade: a study on the interaction between race and economic status. Cadernos de Pesquisa 37, p. 161-180.
CAZELLI, S. e FRANCO, C., 2006. Schools that promote access of students to museums: a profile.
Musas, 2(2), p. 69-81.
FRANCO, C. 2004. Cycles and reading in primary education. Revista Brasileira de Educação, 25,
30-38.
ANDRADE, M., FRANCO, C. and CARVALHO, J.B.P. 2003. Gender and mathematics
achievement in high school. Estudos em Avaliação Educacional, 27, 77-95
FRANCO, C. et al.. 2003. The theoretical framework in the contextual questionnaires of the
national assessment. Estudos em Avaliação Educacional. 26, 39-71.
QUEIRÓZ, G. and FRANCO, C.. 2003. Reflective teachers in complex educational systems.
Enseñanza de las Ciencias, 21, 89-101.
COSCARELLI, C., BONAMINO, A. and FRANCO, C. 2002. Assessment and reading:
conceptions of literacy assumed in the Brazilian national assessment and in PISA. Educação e Sociedade,
v.23, 91-114.
FRANCO, C.. 2002. Teacher education and teacher experience as capture in surveys of educational
assessment. Cadernos de Pesquisa, 11-39
FRANCO, C., MANDARINO, M. and, ORTIGÃO, I.. 2002. The impact of school pedagogic
planning on students’ achievement. Pesquisa e Planejamento Econômico, v.32, 477-497.
ALBERNAZ, A., FERREIRA, F. and FRANCO, C. 2002. Quality and equality in Brazilian middle
schools Pesquisa e Planejamento Econômico (Rio de Janeiro), v.32, 453-476.
4.3 Book Chapters
FRANCO, C.. 2006. Large scale assessment of Brazilian primary and secondary education: from
relevance to new challenges. In Avaliação: desafio dos novos tempos, edited by Maria Marcia Sigrist
Malavazi; Regiane Helena Bertagna; Luiz Carlos de Freitas, 43-65. Campinas: Komedi
FRANCO, C., ORTIGAO, M. I., ALBERNAZ, Angela, BONAMINO, A. C., AGUIAR, G. S.,
ALVES, Fátima, SATYRO, N.. 2006. School effectiveness in Brazil: investigating policies and practives
associated to quality and equity.In Educación y brechas de equidad en América Latina, edited by Santiago
Cueto. Santiago: Preal
FRANCO, C.. 2005. The ENEM assessment: from reforming high school education to the Prouni
Program. In Cursos Pré-Vestibulares Comunitários: espaços de mediação pedagógica, edited by Carvalho,
José Carmelo; Alvim Filho, Hélcio; Costa, Renato Ponte, 258-262. Rio de Janeiro: Editora da PUC-Rio
FRANCO, C.. 2004. What are the contributions of assessment to educational policies? In Avaliação
da Educação Básica - Pesquisa e Gestão, edited by Alicia Maria Catalano Bonamino; Nícia Bessa; Creso
Franco, 65-78. Rio de Janeiro: Loyola
FRANCO, C.. 2003. The research on school effectiveness in Brazil In La investigación sobre
eficacia escolar en Iberoamérica, edited by Murillo Torrecilla, Javier, 191-208. Bogotá: Convenio Andrés
Bello
FRANCO, C.. 2003. Assessment of the education: new challenges in the context of the increasing
participation of municipalities in the offer of public education. In Desafios da Educação Municipal, edited
by Donaldo Bello de Souza; Lia Ciomar Macedo de Faria, 11-480. Rio de Janeiro: DP&A
QUEIRÓS, G., GOUVÊA, G., FRANCO, C.. 2003. Teachers´ education and science museums. In
Educação e museu: a construção do caráter educativo dos museus de ciências, edited by Gouvêa, G.;
Marandino, M.; Leal, M.C., 207-220. Rio de Janeiro, Faperj.
FÁBIO DOMINGUES WALTENBERG
PROFESSIONAL INFORMATION
Researcher at Instituto de Estudos do Trabalho e Sociedade [Institute for Studies on Labor and Society],
IETS
LATTES CV: http://lattes.cnpq.br/4329074392127607
WEBPAGE: http://fwaltenberg.webhop.net
EMAIL:
[email protected]
EDUCATION
2007
Ph.D. in Economics, Université Catholique de Louvain (UCLouvain), Belgium
2003
M.A. in Economics, Universidade de São Paulo (USP), Brazil
1998
B.A. in Economics, USP, Brazil
PROFESSIONAL EXPERIENCE
2007--
Researcher / Consultant at IETS, Rio de Janeiro, Brazil
2007--
Invited Lecturer, UCLouvain, Belgium
2001-2002
Research assistant, GIRSEF, UCLouvain, Belgium
1999-2000
Translator (English- Portuguese) for Saraiva Publishers, São Paulo, Brazil
1998-1999
Publishing assistant, Saraiva Publishers, São Paulo, Brazil
RESEARCH FIELDS
Economics of education / Economics of human capital
Applied micro-econometrics
Social and economic ethics / Theories of distributive justice / Normative economics
Inequality, poverty, and opportunity measurement
DISSERTATION THESIS
2007
Normative and quantitative analysis of educational inequalities, with reference to Brazil.
Economics Department, UCLouvain n.527/2007, Louvain-la-Neuve, Belgium: Presses
universitaires de Louvain
Jury: Vincent Vandenberghe (supervisor), Philippe De Villé, Axel Gosseries, Francisco Ferreira,
Erik Schokkaert
PUBLICATIONS – ARTICLES
Forthcoming (with Vandenberghe, V.), "What does it take to achieve equality of opportunity in
education? An empirical investigation based on Brazilian data", Economics of Education
Review
Forthcoming "Cotas na universidades brasileiras: contribuição das teorias de justiça ao debate". Revista
Sinais Sociais (SESC, Rio de Janeiro, Brazil)
2006
(with Vandenberghe, V.), "Mobilidade de alunos em um quase-mercado escolar: análise
econômica da situação na Comunidade Francesa da Bélgica.", Revista Pesquisa & Debate
(Departamento de Economia, Pontifícia Universidade Católica de São Paulo, Brazil) vol. 17
n.2(30), pp. 173-201, Dec.
2006
"Teorias econômicas de oferta de educação. Evolução histórica, estado atual e perspectivas."
Educação e Pesquisa (Faculdade de Educação, USP, Brazil) vol. 32 (1), pp. 117-136, Jan./
Apr.
2005
"Iniqüidade educacional no Brasil. Uma avaliação com dados do PISA 2000." Revista
Economia (ANPEC, Brasília, Brazil), vol. 6 (1), pp. 67-118, Jan-Jul.
PUBLICATIONS – BOOK CHAPTERS
Forthcoming (with Sprietsma, M.), "The effect of teachers’ wages on student achievement. Evidence from
Brazil." In: Jürgen Backhaus, Raul Eamets, Jüri Sepp (eds.), Advances in the economics of
education: on markets and post-transformation issues
Forthcoming "Quatre grandes écoles pour penser la justice dans le champ de l'éducation". In: Dupriez, V.,
J.-F. Orianne, and M. Verhoeven (eds.), Théories de la justice et inégalités dans les champs
de l'éducation et de la formation
2004
"Comments on Seekings and Tamir". In: Van Parijs, P. (ed.) Cultural diversity versus
economic solidarity, Bruxelles: De Boeck Université (Bibliothèque universitaire Francqui)
2004
(with Dauphin, N. & M. Verhoeven), "Mobilité scolaire". In: Frenay, M. & C. Maroy (eds.),
L'école, six ans après le decret ‘missions’. Regards interdisciplinaires sur les politiques
scolaires en Communauté française de Belgique. Louvain-la-Neuve, Belgium: Presses
universitaires de Louvain
2004
(with Vandenberghe, V.), "Polarisation et formulation des politiques actives" In: Orianne,
J.F., T. Moulaert, C. Maroy, V. Vandenberghe & F.D. Waltenberg, Mises en œuvre locales
des formules d'activation des politiques d'emploi. Brussels, Belgium: Academia Press
ON-GOING RESEARCH, DISCUSSION PAPERS, AND UNPUBLISHED WORK
On-going
Measuring educational inequality: methodological issues and application to Brazil
On-going
Measuring inequality of educational opportunity: methodological issues and application to
Brazil
2006
"Educational justice as equality of opportunity for achieving essential educational
outcomes", Documents de travail de la Chaire Hoover, n. 157, UCLouvain, Oct.
2004
"What is justice in education? Sketch of answer based on therories of justice and
economics.", Les cahiers de recherche en éducation et formation, CREF, n. 32,
UCLouvain, Oct.
2004
"Polarisation et appariements sélectifs des individus. Etat de la question.", Les cahiers de
recherche du GIRSEF, n. 14, UCLouvain, Belgium
2003
"Análise econômica de sistemas educativos. Uma resenha crítica da literatura e uma
avaliação da iniqüidade do sistema educativo brasileiro.", M.A. dissertation, Instituto de
Pesquisas Econômicas (IPE), USP, Brazil. (Supervisor: Prof. Antônio Carlos Coelho
Campino)
1998
"Transportes urbanos na Região Metropolitana de São Paulo. Metrô como alternativa". B.A.
dissertation, Faculdade de Economia, USP, Brazil. (Supervisor: Prof. Flávio A. M. Saes)
BOOK REVIEW
2005 Arnsperger, C. & Ph. Van Parijs, "Ética econômica e social". São Paulo: Loyola, 2004.
Published in Revista de Economia Política, São Paulo, Brazil, Vol. 25, n.2, Apr./Jun.
CONFERENCES, SEMINARS AND SUMMER SCHOOLS
(Information available at: http://fwaltenberg.webhop.net)
CURRICULUM VITAE
ALOÍSIO PESSOA DE ARAÚJO
1. PERSONAL DATA
Birth: 13/01/1946, Rio de Janeiro/ RJ - Brasil.
Professional Adress:
Associação Instituto Nacional de Matemática Pura e Aplicada.
ESTRADA DONA CASTORINA, 110.
J. BOTANICO
22460-320 RIO DE JANEIRO, RJ - Brasil.
Telefone: (21) 2529-5140
Fax: 2529-5129
E-mail: [email protected]
2. EDUCATION
1969 – 1974
Ph.D Statistics
University of California, U.C., Estados Unidos.
Thesis: O Teorema do Limite Central em Espaços de Banach. (The Central Limit theorem in Banach
Spaces)
Advisor: Lucien LeCam.
1967 - 1969
M.A Mathematics.
Associação Instituto Nacional de Matemática Pura e Aplicada, IMPA, Rio de Janeiro, Brasil.
Thesis: Some Aspects of Approximation Theory
Advisor: Djairo Figueiredo.
1964 - 1968
B.A Economics.
Universidade Federal do Rio de Janeiro, UFRJ, Rio de Janeiro, Brasil.
1964 - 1968
B.A Statistics
Escola Nacional de Ciências Estatísticas, ENCE, Rio de Janeiro, Brasil.
3. PROFESSIONAL POSITIONS
Instituto Nacional de Matemática Pura e Aplicada - IMPA
1981 – Senior Researcher III.
Activities
/1981 – Current Teaching, Mathematics, Level: Post-Graduation
Courses
1. Mathematics
Fundação Getúlio Vargas - RJ - FGV-RJ
1983 – Senior Professor.
Activities
/1983 – Teaching, Economics, Level: Post-Graduation
Courses
Advanced Economic Theory.
Université de La Sorbonne - SORBONNE
1991 – 1996
Visiting Scholar
University of Pennsylvania - U.P.
1990 – 1991
Visiting Scholar
University of Chicago - U.C.
1978 - 1979
Assistant Scholar
University of California - U.C.
1976 - 1977
Visiting Scholar, Lecturer
1975 - 1976
Visiting Scholar, Full Tuition Fellowship for Post-Doctorate
4. FIELDS OF RESEARCH
Mathematics, Applied Mathematics and Economics
5. LANGUAGES
Speak:
Read:
Write:
Spanish (Well), French (Intermediary), English (Well).
Spanish (Well), French (Well), English (Well).
English (Well).
6. AWARDS
2006
Foreign Associate, The National Academy of Sciences - USA.
2003
Foreign Honorary Member of the American Academy of Arts & Science, American Academy of Arts &
Science.
2003
Fellow of the Third World Academy of Sciences, TWAS - Third World Academy of Sciences.
1999
Scientific Merit of Honor, Presidency of the Republic
1999
Economista Homenageado do Ano, ANPEC.
1988
Fellow, Guggenheim Foundation.
1987
Fellow, Econometric Society.
1985
Member of the Brazilian Academy of Sciences
7. SCIENTIFIC PRODUCTION
Selected Papers:
ARAUJO, Aloisio Pessoa de; BARBACHAN, José Santiago Fajardo; PÁSCOA, Mario. Endogenous Collateral.
Journal of Mathematical Economics, Estados Unidos, v. 41, p. 439-462, 2005.
ARAUJO, Aloisio Pessoa de; ROCHA, V F Martins da; MONTEIRO, Paulo Klinger. Equilibria in Reflexive Banach
Lattices with a Continuum of Agents. Economic Theory, Estados Unidos, v. 24, n. 3, p. 469-492, 2004.
ARAUJO, Aloisio Pessoa de; GOTTLIEB, Daniel; MOREIRA, Humberto Luiz Ataide. A Model of Mixed Signals
with Applications to Countersignaling an the GED. Rio de Janeiro: Fundação Getulio Vargas, 2004. (Ensaios
Econômicos, 553).
ARAUJO, Aloisio Pessoa de; MOREIRA, Humberto Luiz Ataide; TSUCHIDA, Marcos Hiroyuki. The Trade-Off
Between Incentives and Endogenous Risk. Rio de Janeiro: Fundação Getulio Vargas, 2004. (Ensaios Econômicos,
523).
ARAUJO, Aloisio Pessoa de; PÁSCOA, Mario. Bankruptcy in a Model of Unsecured Claims. Economic Theory,
Estados Unidos, v. 20, n. 3, p. 455-481, 2002.
ARAUJO, Aloisio Pessoa de; PÁSCOA, Mario; MARTINEZ, Juan Pablo Torres. Collateral avoids Ponzi schemes in
Incomplete markets. Econometrica, Estados Unidos, v. 70, n. 4, p. 1613-1638, 2002.
ARAUJO, Aloisio Pessoa de; MOREIRA, Humberto Luiz Ataide. A General Lagrangian Approach for NonConcave Moral Hazard Problems. Rio de Janeiro: Fundação Getulio Vargas, 2001. (Ensaios Econômicos, 426).
ARAUJO, Aloisio Pessoa de; MOREIRA, Humberto Luiz Ataide. A general Lagrangian approach for non-concave
moral hazard problems. Journal of Mathematical Economics, Estados Unidos, v. 35, n. 1, p. 17-39, 2001.
ARAUJO, Aloisio Pessoa de; PÁSCOA, Mario; ORRILLO, Jaime. Equilibrium With Default and Endogenous
Collateral. Mathematical Finance, Estados Unidos, v. 10, n. 01, p. 1-21, 2000.
ARAUJO, Aloisio Pessoa de; MALDONADO, Wilfredo Leiva. Ergodic Chaos, Learning and Sunspot Equilibrium.
Economic Theory, Estados Unidos, v. 15, n. 1, p. 163-184, 2000.
ARAUJO, Aloisio Pessoa de; SANDRONI, Alvaro. On the Convergence to Homogeneous Expectations when
Markets are Complete. Econometrica, Estados Unidos, v. 67, n. 3, p. 663-672, 1999.
ARAUJO, Aloisio Pessoa de; MONTEIRO, Paulo Klinger; PÁSCOA, Mario. Incomplete Markets,
Continuum of States and Default. Economic Theory, Estados Unidos, v. 11, n. 1, p. 205-213, 1998.
ARAUJO, Aloisio Pessoa de; MONTEIRO, Paulo Klinger; PÁSCOA, Mario. Infinite Horizon, Incomplete Markets
with a Continuum of States. Mathematical Finance, v. 6, p. 119-132, 1996.
ARAUJO, Aloisio Pessoa de; MONTEIRO, Paulo Klinger. The General Existence Of Extended Price. Equilibria
With Infinitely Many Commodities. Journal of Economic Theory, Estados Unidos, v. 63, n. 2, p. 408-416, 1994.
ARAUJO, Aloisio Pessoa de; MONTEIRO, Paulo Klinger. General Equilibrium with Infinitely Many Goods: the
Case Separable Utilities. Journal of Mathematical Economics, p. 17-28, 1992.
ARAUJO, Aloisio Pessoa de; MONTEIRO, Paulo Klinger. Generic Non Existence Of Equilibria In Finance Models.
Journal of Mathematical Economics, Estados Unidos, v. 20, n. 5, p. 489-499, 1991.
ARAUJO, Aloisio Pessoa de. The once but not twice different ability of the policy function. Econometrica, Estados
Unidos, v. 59, n. 5, p. 1383-1393, 1991.
ARAUJO, Aloisio Pessoa de; MONTEIRO, Paulo Klinger. Notes On Programing When The Positive Cone. Has An
Empty Interior. Journal of Optimization Theory, 1990.
ARAUJO, Aloisio Pessoa de; MONTEIRO, Paulo Klinger. Existence Without Uniform Conditions. Journal of
Economic Theory, 1989.
ARAUJO, Aloisio Pessoa de. The Non-Existence Of Smooth Demand In General Banach Spaces. Journal of
Mathematical Economics, Estados Unidos, v. 17, n. 4, p. 309-319, 1988.
ARAUJO, Aloisio Pessoa de. A Note On The Existence Of Pareto Optima In Topological Vector Spaces. Economics
Letters, Estados Unidos, v. 23, n. 1, p. 5-7, 1987.
ARAUJO, Aloisio Pessoa de. Lack Of Pareto Optimility With Infinitely Many Comodities: The Need For
Impatience. Econometrica, Estados Unidos, v. 53, n. 2, p. 455-461, 1985.
ARAUJO, Aloisio Pessoa de. Monetary-Theory and Walrasian General Equilibrium with an Infinite Number of
Goods. Revista Brasileira de Economia, Brasil, v. 39, n. 2, p. 175-184, 1985.
ARAUJO, Aloisio Pessoa de. Regular Economics And Sets Of Measures Zero In Banach Spaces. Journal of
Mathematical Economics, Estados Unidos, v. 14, n. 1, p. 61-66, 1985.
ARAUJO, Aloisio Pessoa de; SCHEINKMAN, José Alexandre. Maximum Principle and Transversality Conditions
For Concave Infinite Horizont Economic Models. Journal of Economic Theory, Estados Unidos, v. 30, n. 1, p. 1-16,
1983.
ARAUJO, Aloisio Pessoa de; MANDREKAR, M Gine E; ZINN, J. On Row Sums Of Triangular Arrays And Their
Accompanying Poissonlaw In Banach Spaces. Annals of Probability, v. 9, n. 2, p. 202-210, 1981.
ARAUJO, Aloisio Pessoa de; SCHEINKMAN, José Alexandre. Notes On Comparative Dynamics. General
Equilibrium Growth and Trade, 1979.
ARAUJO, Aloisio Pessoa de; GINE, E. On Tails Domains Of Attractions Of Stable Laws In Banach Spaces.
Transactions of the American Mathematical Society, n. 248, p. 105-119, 1979.
ARAUJO, Aloisio Pessoa de; MARCUS, M.b. Stable Process With Continuous Sample Paths.
Springer Verlag Notes in Mathematics, v. 709, p. 9-32, 1979.
ARAUJO, Aloisio Pessoa de; COLELL, Andreu Mas. Notes On Continuity And Differentiability Of The Aggregate
Demand. Journal of Mathematical Economics, v. 5, n. ., p. 113-127, 1978.
ARAUJO, Aloisio Pessoa de; GINE, E; COSTA, A A. On Poissom Measures, Gaussian Measures And The Central
Limit Theorem In Banach Spaces. Advances in Probability, v. 4, p. 1-68, 1978.
ARAUJO, Aloisio Pessoa de. On The Central Limit Theorem In Banach Spaces. Journal of Multivariate Analysis, v.
8, n. 4, p. 598-613, 1978.
35 ARAUJO, Aloisio Pessoa de; GINE, E. Type, Cotype And Levy Measures In Banach Spaces. Annals of
Probability, p. 632-643, 1978.
ARAUJO, Aloisio Pessoa de; SCHEINKMAN, José Alexandre. Smootness, Compartive Dynamics And The
Turnpike Theorem. Econometrica, Estados Unidos, v. 45, n. 50, p. 601-620, 1977.
ARAUJO, Aloisio Pessoa de. On Infinitely Divible Laws In C(0,1). Process American Mathematical Society, v. 51,
n. 1, p. 179-185, 1975.
ARAUJO, Aloisio Pessoa de. On Levy Measures And Integrability Of The Norm In (0,1). Journal of Multivariate
Analysis, v. 7, n. 1, p. 220-222, 1975.
Published Books:
ARAUJO, Aloisio Pessoa de. Economia Dinâmica e Mercados Incompletos. Rio de Janeiro: Instituto de Matemática
Pura e Aplicada - IMPA, 1993.
ARAUJO, Aloisio Pessoa de. Introdução à Economia Matemática. RIO DE JANEIRO: Insituto de Matemática Pura
e Aplicada - IMPA, 1983.
ARAUJO, Aloisio Pessoa de. Tópicos em Teoria das Probabilidades. RIO DE JANEIRO: Instituto de Matemática
Pura e Aplicada - IMPA, 1981.
ARAUJO, Aloisio Pessoa de; GINE, E. The Central Limit For Real and Banach Valued Random Variable. RIO DE
JANEIRO: WILEY SERIES ON PROBALITITY AND STATISTICS, 1980.
Chapters in Published Books:
ARAUJO, Aloisio Pessoa de; FUNCHAL, Bruno. Reflexões a partir da Lei nº 11.101/05 - O Impacto Econômico da
nova Lei de Falência e Recuperação de Empresas. In: OLIVEIRA, Fátima Bayma de. (Org.). Recuperação de
Empresas - Uma Múltipla Visão da Nova Lei. 2006, v. 1, p. 32-42.
ARAUJO, Aloisio Pessoa de; LUNDBERG, Eduardo. A Nova Legislação de Falências - Uma Avaliação Econômica.
In: PAIVA, Luiz Fernando Valente de. (Org.). Direito Falimentar e a Nova Lei de Falências e Recuperação de
Empresas. São Paulo, 2005, v. 1, p. 325-351.
ARAUJO, Aloisio Pessoa de; PÁSCOA, Mario. New Applications of General Equilibrium to Finance: Default and
Colateral. In: KEHOE, Tomothy J.; SRINIVASAN, T.; WHALLEY, John. (Org.). Frontiers in Applied General
Equilibrium Modeling. 2005, v. 1, p. 151-172.
ARAUJO, Aloisio Pessoa de; MALDONADO, Wilfredo Leiva. Learning in intemporal equilibrium models and
sunspot case. In: GROUP, F. Petri And F. Hahn Routledge Taylor & Francis. (Org.). Learning in Intemporal
Equilibrium Models and Sunspot Case. New York, 2003, v. 1, p. 58-73.
ARAUJO, Aloisio Pessoa de; LEON, Marcia Saraiva. Monetary Regimes and Mercosur. In: HAEGEN, Pierre Van
Der; VIÑALS, José. (Org.). Regional Integration in Europe and Latin America - Monetary and Financial Aspects.
Hampshire, 2003, v. 1, p. 145-168.
ARAUJO, Aloisio Pessoa de; MONTEIRO, Paulo Klinger. General Equilibirum with Infinitely Many Goods: The
Case of Separable Utilities. In: UNIV. (Org.). Equilibrium and Dynamics: Essays in Honor of David Gale. Londres,
1992, v. 1, p. 17-28.
CURRICULUM VITAE
GABRIEL BUCHMANN
PERSONAL DATA
Date of Birth: 07/01/81 - Age: 26 years old
Nacionality: Brazilian and French
Place of Birth: Rio de Janeiro - Brazil
Marital Status: single
Id: 10804834-9
Address: Rua Nascimento Silva, 7 / 1402 – Ipanema
Home Telephone: +55 21 2239-8619
Work Telephone: +55 21 2559-5628
Mobile Telephone: +55 21 9206-0518
Skype Name: gabriel_buchmann
E-mail: [email protected]
CURRENT POSITION
Since march 2007- Researcher at Center for Social Policies
(CPS), Brazilian Institute of Economics (IBRE), Fundação
Getúlio Vargas, Rio de Janeiro (FGV-RJ), Brazil
Research on Education, Microfinance, Poverty, Inequality and
Welfare supervised by Marcelo Neri, PhD from Princeton
Since march 2007- Teaching Assistant - Social Policies - in the
undergraduate course at EPGE - Fundação Getúlio Vargas
Since march 2007- Teaching Assistant - Welfare Economics –
in the master course at EPGE - Fundação Getúlio Vargas2007
MASTER
2005/2006 Pontificia Universidade Católica do Rio de Janeiro
(PUC-Rio)
Specialization in Development and Labor Economics
Thesis “Interaction between Fertility, Education and Political
Economy and its consequences for Income Distribution” under
orientation by Rodrigo Soares, PhD, Chicago
UNIVERSITY COURSE
2000 – 2004 - Undergraduate course in Economics at
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
Full Tuition Fellowship for academic performance
2002 and 2004 - Sequential Course in International Affairs
at PUC-RIO
2000/2001 Undergraduated course in History at UFF-Rio –
4 semesters
2003.1 - Universidad Autónoma de Madrid
International Undergraduate Students Exchange Program
- Courses in Economics of European Union and Economics of
Natural Resources
2003.2 - Institute de Sciences Politiques de Paris
(SciencePo)
– Courses in Economics and International Affairs
Teacher´s Assistant
2002 – Macroeconomics II
2003/2004 – International Economics
Graduation Paper
“Determinants of Sharecropping Market Malfunctioning in
Brazil”,
under orientation by Juliano Assunçao, PhD, PUC-Rio and
Chicago
RESEARCH
2007 “The Brazilian Northeast Microentrepreneurs, their
relation to Microfinance and the Microcredit Program
CrediAmigo”, Banco do Nordeste
2007 “Microfinance in Nicaragua”, International American
Foundation
2007 “Monitoring the Dakar Goals – Education for All – Case
Study for Brazil”, Global Monitoring Report, UNESCO
2006 Post Graduation Thesis
2005 “Do Investments on Education Generate Political
Capital?”, working paper
2004 Graduation Paper
2001/2002 – “Causes of Income Inequality in Brazil” under
orientation by professor Francisco Ferreira, PhD, LSE - Special
Training Program (PET) – Scholarship from CAPES
FOREIGN EXPERIENCE
Dec 1996-Mar1997 - United States - Fort Pierce Westwood High
School
January 1998- Jan 1999 - Denmark – Exchange Program by
Rotary Club
January - May 1999 - Israel – working at Kibbutz Gaaton em
Naharya
January – June 2003 - Spain – University Interchange at UAM Madrid
July – September 2003 – Working as a waiter and
cooker in Ibiza
September 2003 – March 2004 - France – University
Interchange at SciencesPo - Paris
Trips to 40 countries in Europe, Latin America and the Middels
East.
FOREIGN LANGUAGES – ENGLISH, FRENCH, SPANISH AND
DANISH
Portuguese:
Native fluence
English:
8 years of course and 3 months studying in
United States
Danish:
1 year studying in Denmark
French:
5 years of course and 6 months studying in
France
Spanish:
8 months studying and working in Spain
COMPUTER SKILLS
Microsoft Office;
Stata;
MatLab and Mapple;
Latex and Scientific Workplace
CURRICULUM VITAE
Marcelo Côrtes Neri
Address :
Centro de Políticas Sociais
Praia de Botafogo, 190, 13º andar, Sala 1310.2
Fundação Getulio Vargas
Botafogo, Rio de Janeiro, RJ, Brazil, 22253-900
Phone :
E-mail:
55-21-2559-5628 or 55-21-2559-5675
[email protected]
Date of Birth: 03/27/1963
ID: 05935632-9 IFP (Brazil)
CPF: 893283617-53 (Brazil)
Education:
Ph.D., Economics, Princeton University, April 1996.
Ph.D. Thesis: Labor Markets Adaptation to Inflation and Household Financial Behavior: Lessons from
Brazil – Advisor: David Card
M.A., Economics, Princeton University, 1993.
M.A., Economics, Pontifícia Universidade Católica do Rio de Janeiro, 1989.
M.A. Thesis: Inflacão e Consumo:Modelos Teóricos Aplicados ao Imediato Pós-Cruzado. (Inflation
and Theoretical Consumption Models Applied to the Post-Cruzado Period) – Advisor: Gustavo Franco
B.A., Economics, Pontifícia Universidade Católica do Rio de Janeiro, 1984.
Professional Experience:
•
2000-present – Director, Center for Social Policies, Brazilian Institute of Economics (IBRE),
Fundação Getúlio Vargas, Rio de Janeiro, Brazil
•
2000-present – Assistant Professor, Graduate School of Economics (EPGE), Fundação Getúlio
Vargas, Rio de Janeiro. Teaches: Economics of Social Policies, Economics of Social Welfare.
•
1998-99 – Economist, Institute of Applied Economic Research (IPEA), Rio de Janeiro, Brazil.
•
1995-98 – Associated Researcher, Institute of Applied Economic Research (IPEA), Rio de Janeiro,
Brazil.
•
1990-2006 – Assistant Professor, Economics, Universidade Federal Fluminense, Rio de Janeiro.
Taught: Macroeconomics, Econometrics, Money and Banking, Economics of Social Welfare.
•
1987-90 – Visiting Professor, Economics, Universidade Federal Fluminense.
•
1986-89 – Assistant Professor, Economics, Pontifícia Universidade Católica do Rio de Janeiro.
Taught: Macroeconomics and Microeconomics.
•
1986-87 – Manager, Market Analysis, Banco da Bahia (BBM).
•
1986 – Manager, Technical Department, Tecnanpar Consultoria.
Prizes:
•
First Place in the Medal Competition for Outstanding Research on Development 2005, on Sixth
Annual Global Development Conference, Title: “Think Global, Act Local: Social Credit Based
on Millenium Development Goals,” Dakar, Senegal, January 2005.
•
First Place in the National Contest for IPEA, 1998 (340 candidates).
•
First Place in the First National Prize of Economics of Multiplic Bank, 1994.
•
Second Place in the 14th National Prize BNDES for Master Thesis in Economics, 1990.
Books:
•
Retratos da Deficiência no Brasil, (Portrait of the Disabled in Brazil). FGV, 200p., Rio de Janeiro,
2003. (10,000 books)
•
Cobertura Previdenciária: Diagnóstico e Propostas, (Social Security Coverage: Diagnosis and
Proposals). Ministério da Previdência e Assistência Social, Coleção Previdência Social, Série Estudos,
Vol. 18, Brasília, 2003. (5,000 books)
•
Ensaios Sociais, (Social Essays). FGV, 158p, Rio de Janeiro, 2003. (1,000 books)
•
Inflação e Consumo: Modelos Teóricos Aplicados ao Imediato Pós-Cruzado (Inflation and
Consumption: Applied Theoretical Models in the Post-Cruzado Time Period), BNDES, Rio de Janeiro,
1990. (10,000 books).
Selected Papers:
•
Equidade e Eficiência na Educação: Motivações e Metas. (Equality and Efficiency in Education:
Motivations and Targets). FGV, 2007.
•
A dinâmica da redistribuição trabalhista. (The Labor Redistributive Dynamic). In: Desigualdade de
Renda no Brasil: Uma Análise da Queda Recente. IPEA, p.207-235, 2007.
•
Desigualdade e Crescimento: Ingredientes Trabalhistas (Inequality and Growth: Labor Ingredients).
In: Desigualdade de Renda no Brasil: Uma Análise da Queda Recente. IPEA, p.397-423, 2007.
•
Desigualdade, Estabilidade e Bem Estar Social (Inequality, Stability and Social Welfare). In:
Desigualdade de Renda no Brasil: Uma Análise da Queda Recente. IPEA, p.129-161, 2007.
•
IAF Grant Evaluations: Microcredit in Nicaragua, Mexico and Peru. Inter-American Foundation,
2007.
•
Designing a National System of Social Targets based on the International Millennium Development
Goals. In: Developing and a Developed Worlds: Mutual Impact Global Development Center, 2005.
•
Think Global, Act Local: Social Credit Based on Millennium Development Goals. In: The Many
Dimensions of Poverty, International Poverty Center, UNDP, 2005.
•
Think Global, Act Local: Social Credit based on Millennium Development Goals. In: VII Meetings of
the LACEA / IADB / WB Research Network on Inequality and Poverty (NIP), 2005.
•
Educação na Primeira Infância (Early Childhood Education). FGV, 2005.
•
Retornos da Educação no Mercado de Trabalho. (Returns to Education in the Labor Market). FGV,
2005.
•
Negócios Nanicos, Garantias e Acesso à Crédito. (Small Business, Guarantees and Access to Credit).
Revista de Economia Contemporanea, v.9, p.643-669, 2005.
•
Desigualdade e Desenvolvimento (Inequality and Development). In: Brasil em Desenvolvimento. v.2,
p.319-333, 2005.
•
Aspectos Dinâmicos de um Sistema de Metas Sociais. (Dynamic Aspects of a Social Targets System).
Ensaios Econômicos, EPGE/FGV, 2004.
•
Idade, Incapacidade e o Número de Pessoas com Deficiência. (Age, Incapacity and the Number of
People with Disabilities). Revista Brasileira de Estudos da População, v.21, p.303-321, 2004.
•
De Volta as Metas Sociais (Returning to Social Targets). In: Saúde, Previdência e Assistência Social.
M.Books, p.211-216, 2004.
•
Inclusão Digital e Educação (Digital Inclusion and Education). In: Educação Corporativa,
Desenvolvendo e Gerenciando Competências. Pearson Education do Brasil, p. 220-225, 2004.
•
A Robust Poverty Profile for Brazil Using Multiple Data Sources. Revista Brasileira de Economia,
v.57, p.59-92, 2003.
•
Desenho de Metas Sociais (Social Target Designs). Ensaios Econômicos, EPGE/FGV, 2003.
•
Análise da Evolução da Qualidade do Ensino e seus Determinantes (Analysis of the Evolution of the
Quality of Education and its Determinants). FGV, 2002.
•
Seletividade e Medidas de Qualidade da Educação Brasileira 1995-2001 (Selectivity and Measures of
Quality for the Brazilian Education System 1995-2001). FGV, 2002.
•
Assets, Markets and Poverty in Brazil. In: Portrait of the Poor – An Assets Based Approach. IDB, p.85112, 2001.
•
O Tempo das Crianças (The Children’s Time). In: Caderno Adenauer – As Caras da Juventude. Ed.
Loyola, p.66-86, 2001.
•
A New Poverty Profile for Brazil Using PPV, PNAD, and Census Data. In: XXIX Encontro Nacional
de Economia Proceedings, 2001.
•
Microeconomic Instability and Children’s Human Capital Accumulation: The Effects of Idiosyncratic
Shocks on Father’s Income on Child Labor, School Drop-Outs and Repetition Rates in Brazil. Ensaios
Econômicos, FGV, 2000.
•
A Evolução da Pobreza e da Desigualdade Brasileiras ao Longo da Década de 90. (The Evolution of
Poverty and Inequality in Brazil During the Nineties). Revista Economia Aplicada, v.3, p.384-406,
1999.
•
Acumulação de Capital Humano e Distribuição de Renda (Accumulation of Human Capital and
Income Distribution). In: XXVI Encontro Nacional de Economia, 1998.
Conferences and Seminars:
•
Social and Economic Policy Seminar. EPGE / IBRE, FGV, 2007.
Series Organizer and Lecturer: The Education Debate in Brazil.
•
•
•
Linkages between Pro-Poor Growth, Social Programmes and Labour Market: The Recent Brazilian
Experience. LACEA-LAMES 2006.
Designing a national system of social targets based on international Millenium developmente goals.
The Impact of Globalization on the Poor in Latin America, UNU-WIDER Project Conference. 2006.
Quality of Education Seminar. EPGE / IBRE, FGV, 2005.
Organizer and Lecturer.
CURRICULUM VITAE
PAULO PICCHETTI
June/2007
Personal Information
Name Paulo Picchetti
Birth 12/22/1961 - Sao Paulo/SP - Brasil
Professional Address
Escola de Economia de São Paulo da Fundação Getulio Vargas
Rua Itapeva, 474 - 13° andar- CEP 01332-000 - São Paulo - SP - Brasil
Fone: 55 (011) 3281-3350 - Fax: 55 (011) 3281-3357
e-mail : [email protected]
Titulation
1991 - 1995
PhD in Economics
University of Illinois, U.I., Urbana, Estados Unidos
Dissertation Title: Towards an Economic Theory of Strikes: Further Evidence from
Brazilian data
Advisor: Wallace Walter Hendricks
1987 - 1991
Master in Economics
Universidade de São Paulo, USP, Sao Paulo, Brasil
Dissertation Title: A Teoria das Decisoes Interdependentes aplicada ao estudo dos objetivos
da firma
Advisor: Juan Herztajn Moldau
PROFISSIONAL Background
1
Universidade de São Paulo - USP
1995 - 2007 Assistant Professor at the Department of Economics
Fundação Getúlio Vargas – FGV
2007 –
Associate Professor at the Department of Economics (Escola de Economia de Sao
Paulo), coordinator of the Consumer Price Index for the city of Sao Paulo (IPC-S/SP), and researcher at the
Instituto Brasileiro de Economia (IBRE).
Areas of research
1
2
3
4
Applied Econometrics
Labor Economics
Index-Number Theory
Economic Regulation
Languages
Portuguese (fluent in speaking, reading and writing)
English (fluent in speaking, reading and writing)
French (fluent in speaking, reading and writing)
Awards
1996 Haralambos Simeonides - Mencao Honrosa na categoria Teses, Associacao Nacional dos Cursos de
Pos-Graduacao em Economia
Published Articules
1. POSTALI, F. A. S., PICCHETTI, P.
Geometric Brownian Motion and Structural Breaks in Oil Prices: A Quantitative Analysis. Energy
Economics. , v.28, p.506 - 522, 2006.
2. PICCHETTI, P., ORELLANO, Veronica
An Analysis of Quit and Dismissal Determinants between 1988 and 1999 using the bivariate probit
modelo. Revista de Econometria. , v.25, p.57 - 73, 2005.
3. MAGALHAES, M. A., PICCHETTI, P.
Regress and Progress! An Econometric Characterization of the the Short-Run Relationshipe Between
Productivity and Labor Input in Brazil. Revista de Econometria. , v.25, p.253 - 275, 2005.
4. PICCHETTI, P., ROCHA, Fabiana Fontes
Fiscal Adjustment in Brazil. Revista Brasileira de Economia. , v.57, p.239 - 252, 2003.
5. PICCHETTI, P.
An Econometric Analysis of Strike Activity in the Brazilian Industrial Sector. Review Of Labour
Economics And Industrial Relations. , v.16, p.177 - 200, 2002.
6. PICCHETTI, P., TOLEDO, C.
Estimating and Interpreting a Common Stochastic Component for the Brazilian Industrial Production
Index. Revista Brasileira de Economia. , v.56, p.107 - 120, 2002.
7. PICCHETTI, P., TOLEDO, C.
How Much to trim? A Methodology for calculation Core Inflation, with an application for Brazil. Revista
de Economia Aplicada. , v.4, 2000.
8. PICCHETTI, P., MENEZES FILHO, N.
Os determinantes da duração do desemprego em São Paulo. Pesquisa e Planejamento Econômico. , v.30,
2000.
9. PICCHETTI, P., FERNANDES, R.
Uma Análise da Estrutura do Desemprego e da Inatividade no Brasil Metropolitano. Pesquisa e
Planejamento Econômico. , v.29, 1999.
10. PICCHETTI, P., OLIVEIRA, A. R.
The Applied Perspective for Seasonal Cointegration Testing. Revista de Economia Aplicada. , v.1, 1997.
Chapters in books
1. PICCHETTI, P., CHAHAD, Jose Paulo Zeetano
A evolução da taxa de desemprego estrutural no
Brasil: uma análise entre regiões e características dos
trabalhadores In: Mercado de Trabalho no Brasil: Padrões de Comportamento e Transformações
Institucionais.1, 2003, v.1, p. 27-54.
2. PICCHETTI, P., ZYLBERSTAJN, Helio
Um estudo sobre as fontes de recursos para os desempregados na Rgião Metropolitana de São Paulo - 1986
a 2001 In: Mercado de Trabalho no Brasil: Padrões de Comportamento e Transformações Institucionais.1,
2003, v.1, p. 57-84.
3. PICCHETTI, P., MENEZES FILHO, Naercio Aquino
Uma análise da duração das relações de emprego em São Paulo - 1988 a 1999 In: Mercado de Trabalho no
Brasil: Padrões de Comportamento e Transformações Institucionais.1, 2003, p. 145-166.
4. PICCHETTI, P., MENEZES FILHO, N. A.
Os determinantes da duração do desemprego em São Paulo In: O mercado de trabalho no Brasil: políticas,
resultados e desafios ed.São Paulo : FEA/USP e Ministério do Trabalho e Emprego, 2002, p. 93-116.
5. PICCHETTI, P., CHAHAD, J. P. Z., ORELLANO, V.
Um modelo de decisões relacionadas à rotatividade de mão-de-obra no Brasil In: Mercado de Trabalho no
Brasil ed.São Paulo : LTR Editora LTDA, 2002, p. 247-276.
6. PICCHETTI, P., MENEZES FILHO, Naercio Aquino
Desemprego In: Microeconomia e Sociedade no Brasil ed.Rio de Janeiro : Contra Capa, 2001, p. 227-250.
7. PICCHETTI, P., FERNANDES, R., MENEZES FILHO, N.
3.
A Evolução da Distribuição de Salários no Brasil: fatos estilizados para as décadas de 80 e 90 In:
Desigualdade de Probreza no Brasil ed.Rio de Janeiro : IPEA, 2000
8. PICCHETTI, P.
1.
Extensões ao Modelo Básico de Regressão Linear In: Manual de Econometria dos Professores da
USP ed.Sao Paulo : Atlas, 1999
9. PICCHETTI, P.
2.
Econometria das Variáveis de Resposta Qualitativas e Limitadas In: Manual de Econometria dos
Professores da USP ed.Sao Paulo : Atlas, 1999
Congress participation
1. PICCHETTI, P., CHAHAD, Jose Paulo Zeetano
A evolução da taxa de desemprego estrutural no Brasil: uma análise entre regiões e características dos
trabalhadores In: Encontro da Associação Nacional dos Cursos de Pós-Graduação em Economia, 2003,
Porto Seguro.
Anais do XXXIV Encontro da Associação Nacional dos Cursos de Pós-Graduação em Economia. ,
2003.
2. PICCHETTI, P., ORELLANO, Veronica
A Bi-Variate Probit Analysis of Job Turnover in Brazil In: XXIII Congresso da Sociedade Brasileira de
Econometria, 2001, Salvador.
. , 2001.
3. PICCHETTI, P., KANCKZUK, F.
An Application of Quah and Vahey's Methodology for Estimating Core Inflation in Brazil In: XXIXI
Congresso da Associação Nacional de Pós-Graduação em Economia (ANPEC), 2001, Salvador.
. , 2001.
4. PICCHETTI, P., KANCZUK, F.
An Application of Quah and Vahey's SVAR Methodology for Calculating Core Inflation in Brasil In: Nona
Escola de Series Temporais e Econometria, 2001, Belo Horizonte.
. , 2001.
5. PICCHETTI, P., ROCHA, F.
Fiscal Adjustment in Brazil In: XXVIII Congresso da Associação Nacional de Pós-Graduação em
Economia (ANPEC), 2000, Campinas.
Anais do Congresso da Associação Nacional de Pós-Graduação em Economia (ANPEC). , 2000.
6. PICCHETTI, P., TOLEDO, C.
How Much to Trim ? A Methodology for Calculating Core Inflation, with an application for Brazil In: III
Meeting of the Latin American and Caribbean Economic Association (LACEA), 2000, Rio de Janeiro.
. , 2000.
7. PICCHETTI, P., MENEZES FILHO, N.
Os determinantes da duração do desemprego em São Paulo In: XXII Congresso da Sociedade Brasileira de
Econometria, 2000, Campinas.
Anais do XXII Congresso da Sociedade Brasileira de Econometria. , 2000.
8. PICCHETTI, P.
Demand Estimation in a Non-Linear Tariff Context In: XXI Congresso da Sociedade Brasileira de
Econometria, 1999, Belem do Para.
Anais XXI do Congresso da Sociedade Brasileira de Econometria. , 1999.
9. PICCHETTI, P.
O Uso da Informática na Educação em Economia In: Seminário promovido pelo NUCA/UFRJ em
conjunto com o CORECON/RJ, 1997, Rio de Janeiro.
. , 1997.
10. PICCHETTI, P., FERNANDES, R.
Uma Análise Econométrica das Condicionantes do Desemprego no Brasil In: Desemprego no Brasil:
Evidências e Perspectivas - Seminário promovido pelo Instituto de Economia Aplicada IPEA, 1997, Rio de
Janeiro.
. , 1997.
11. PICCHETTI, P.
An Econometric Analysis of Strike Activity in the Brazilian Industrial Sector. In: XIV Latin American
Meeting of the Econometric Society, 1996, Rio de Janeiro.
. , 1996.
12. PICCHETTI, P., ALVES, D. C.
The Determinants of Real Estate Prices in the City of São Paulo: A Hedonic Regression Approach In:
International Real Estate Conference - American Real Estate and Urban Economics Association, 1996,
Orlando.
Vladimir Pinheiro Ponczek
Rua Itapeva, 474 12 andar.
EESP-FGV
Sao Paulo - SP Brazil 01331-010
[email protected]
(+55-11) 3281-3570
(+55-11)9250-4993 (cell phone)
EDUCATION
Ph.D. in Economics
Princeton University, December 2006.
Major fields of interest: Development, Education, Health, Econometrics and Public Finance.
Master of Arts in Economics
Princeton University, January 2005.
Master of Arts in Economics
University of Sao Paulo (USP), May 2002.
Bachelor of Arts in Economics
University of Sao Paulo (USP), March 1999.
PROFESSIONAL EXPERIENCE
•
Assistant Professor, Economics, Fundação Getúlio Vargas, São Paulo School of Economics.
(January 2007 – current)
• The World Bank (June 2005 – September 2005)
• Fundação Instituto de Pesquisas Econômicas FIPE/USP (November 1999 – August 2002)
• Instituto Brasileiro de Economia IBRE/FGV (March 2001 – July 2001)
WORKING PAPERS AND CURRENT RESEARCH
• Income and Bargaining Effects on Education and Health, Princeton University mimeo, June 2007.
• Segmentation in the Brazilian Labor Market (joint with Fernando Botelho). Princeton University, May
2007, .
• Correcting the Fixed-effect Estimator for Endogenous Switching (joint with Fernando Botelho),
EESP_FGV mimeo, April 2007.
• Casual Effects of Family Size on Child Labor and Education (joint with Andre Portela). EESP-FGV,
mimeo, March 2007.
•
Aid Volatility (joint with Soonhwa Yi and Anil Markandya), World Bank, mimeo, September 2005
• Democracy, Risk of Expropriation and Economic Volatility. Second-year paper, Princeton University,
mimeo, March 2003
• Credit Market Imperfection and Absolute Convergence in a Technological Diffusion Model (in
Portuguese). Master Thesis defended at University of Sao Paulo, mimeo, May 2002
• Impacts of Adult Alphabetization Programs in Brazil (joint with Carlos Bozzoli). Princeton University
(working in progress)
FELLOWSHIPS, HONORS AND AWARDS
• Princeton University Fellowship (September 2002 – December 2006).
• Graduate School Summer Fellowship, Princeton University, (2002-2006)
• Capes Scholarship for MA Program in Economics (Brazilian government agency scholarship) (March
1999 – March 2001).
• 2nd place in the National Exam for Admission to Graduate Programs in Economics in Brazil - ANPEC
(1999)
• FAPESP "Special Training Program" Scholarship (Brazilian government agency scholarship)
(September/96 – September 1997).
CURRICULUM VITAE
André Portela Souza
Correspondence:
Escola de Economia de São Paulo
Fundação Getúlio Vargas
EESP/FGV
Rua Itapeva, 474, 12 andar, Bela Vista
São Paulo, SP Brazil 01332-000
Phone: (55 11) 3281-3358
Fax: (55 11) 3281-3357
E-mail: [email protected]
EDUCATION:
Ph.D. in Economics, Cornell University, 2001
M.A. in Economics, Cornell University, 1998
M.A. in Economics, University of São Paulo, 1995
B.A. in Economics, Federal University of Bahia, 1995
ACADEMIC POSITION:
August 2005 – present: Associate Professor, São Paulo School of Economics, Getúlio Vargas Foundation,
Brazil
August 2004-present: Associate Researcher, Scientific and Technological Development Council (CNPq),
Science and Technology Ministry, Federal Government of Brazil, Level 1D
PREVIOUS ACADEMIC POSITIONS:
August 2005 – July 2006: Visiting Assistant Professor, Department of Labor Economics, School of
Industrial and Labor Relations, Cornell University
August 2002 – July 2005 Assistant Professor, Department of Economics, University of São Paulo
August 2002 – July 2005: Adjoint Assistant Professor, Economics Department, Vanderbilt University
August 2001– July 2002: Visiting Assistant Professor, Economics Department, Vanderbilt University
August 1993 – July 1995: Assistant Professor, Department of Economics, Pontifícia Universidade Católica,
São Paulo
OTHER ACADEMIC AND PROFESSIONAL EXPERIENCES:
Consultant, Tendências Consulting Associates, São Paulo, 2004 - 2005
Researcher, Economic Research Institute Foundation (FIPE), University of São Paulo, 2002-2005
Editor, Estudos Econômicos, August 2004 – July 2005.
FIELDS OF SPECIALIZATION:
Labor Economics; Development Economics; Applied Econometrics
AWARDS, GRANTS, AND SCHOLARSHIPS:
IPEA/CAIXA Award, 2006 (IPEA research institute prize for the best paper on poverty and inequality)
Haralambos Simeonides Award, ANPEC, 2003 (Brazilian Economic Association Prize for the best
published paper)
Scientific and Technological Development Council (CNPq), Science and Technology Ministry, Research
Project: Avaliação de Programas de Transferências de Renda”, Oct 2004 – Sept 2005
National Academy of Science (NAS). Project: The Effect of Early Work on Adult Earnings (with Patrick
Emerson), August 2001- May 2002.
Scientific and Technological Development Council (CNPq), Science and Technology Ministry, PhD
Scholarship, Aug 1995 - July 1999
Scientific and Technological Development Council (CNPq), Science and Technology Ministry, Master of
Arts Scholarship, March 1990 - Dec 1992
Fundo de Amparo à Pesquisa do estado de São Paulo (FAPESP), Master of Arts Scholarship, January 1993
- Dec 1994
REFEREED PUBLICATIONS:
1. “Child Labor, School Attendance, and Intrahousehold Gender Bias in Brazil” (forthcoming in World
Bank Economic Review) (with Patrick M. Emerson)
2. “(Un)Sustainability and Reform of the Social Security System in Brazil: A Generational Accounting
Approach”, (forthcoming in Brazilian Economic Review) (with Fábio M. Sanches)
3. “Resultados Fiscais da Reforma de 2003 no Sistema de Previdência Social Brasileiro”, Política e
Planejamento Econômico, Volume 36, 2006. (with Hélio Zylberstaijn, Luis Afonso, and Priscila Flori)
4. “Reforma da Previdência Social e Custo de Transição: Simulando um Sistema Universal Para o Brasil”,
Revista Contabilidade & Financas, Volume 5, 2006. (with Hélio Zylberstaijn and Luis Afonso)
5. “Is There A Child Labor Trap? Intergenerational Persistence of Child Labor in Brazil”, Economic
Development and Cultural Change, Volume 51, Number 2, 2003. (with Patrick M. Emerson)
6. “The Role of The Family in Immigrant´s Labor Market Activity:An Evaluation of Alternative
Explanations: Comment” American Economic Review, Volume 93, Number 2, 2003. (with Francine D.
Blau, Lawrence Kahn, and Joan Moriarty)
7. “Aspectos do Trabalho Infantil no Brasil,” Revista de Economia e Relações Internacionais, Volume 3,
2003.
8. “Crescimento Econômico e Geração de Empregos:Considerações sobre Políticas Públicas”
Planejamento e Politicas Publicas, Volume 12, 1995. (with Maria Cristina Cacciamali, Júlio Pires,
Guilherme Lacerda, and Edson Luciano Pires)
9. “O papel do Salário Mínimo nos Anos 80: Novas Observações Para o Caso Brasileiro” Brazilian
Economic Review, Volume 48, Number 1, 1994.
BOOK CHAPTERS
1. “Intergenerational Persistence of Child Labor”, in: Peter Orazem; Guilherme Sedlacek; Zafiris
Tzannatos. (Org.). Child Labor in Latin America. Washington DC: IADB and World Bank. (with
Patrick M. Emerson) (forthcoming)
2. “The Impact of Cash Transfer Programs on Child labor and School Attendance in Brazil”, in: Peter
Orazem; Guilherme Sedlacek; Zafiris Tzannatos. (Org.). Child Labor in Latin America.
Washington DC: IADB and World Bank. (with Eliana Cardoso) (forthcoming)
3. “Reducción del Trabajo Infantil y Aumento de la Asistencia a la Escuela. Análisis de
Descomposición para Brasil en los Años Noventa”, in: Luis Felipe López Calva. (Org.). Trabajo
Infantil. Teoría y Lecciones de la América Latina. Ciudad de México: Fondo de Cultura
Económica, 2006 (with Reynaldo Fernandes)
4. “Os direitos fundamentais dos trabalhadores, a cláusula social e o comércio internacional: o caso do
trabalho infantil no Brasil”, in: Paulo Zeetano Chahad; Maria Cristina Cacciamali. (Org.). Mercado
de Trabalho no Brasil. 1 ed. São Paulo: LTR Editora Ltda, 2003 (with José Paulo Chahad)
SUBMITTED ARTICLES:
1.
2.
3.
4.
5.
6.
“The Causal Effect of Family Size on Child Labor and Education”, (submitted to Economic
Journal) (with Vladimir Ponczek)
“Is Child Labor Harmful? The Impact of Working Earlier in Life on Adult Earnings”, (submitted to
Journal of Development Economics) (with Patrick M. Emerson)
“Birth Order, Child Labor, and School Attendance in Brazil”, (submitted to World Development)
(with Patrick E. Emerson)
“Desigualdade Salarial no Brasil: Permanente ou Transitória? Um Estudo de Componentes de
Variância para o Setor Formal de São Paulo”, (submitted to Brazilian Review of Econometrics) (with
Antônio Loureiro Santos)
“Medidas e Determinantes da Mobilidade de Renda no Brasil”, (submitted to Política e
Planejamento Econômico), (with Marcos Nascimento)
“Fighting Long Run Poverty in Brazil: Are Conditional Cash Transfer Program Making a
Difference?” (submitted to Brazilian Review of Econometrics)
WORKING PAPERS:
1. ”Does the Gradient Matter? Futher Understanding the Intergenerational Transmission of Human
Capital” (with George Jakubson)
2. ” The Dispersion of Intra-Household Human Capital Across Children: A Measurement Strategy and
Evidence” (with Andrew Horowitz)
3. ” Inequality in Child Academic Achievement in Single Parent Households: Evidence from Brazil”
(with Andrew Horowitz)
4. “The Impact of The Conditional Cash Transfer Program on the Human Capital Formation in Brazil”
5. ”Reforma da Previdência: Uma Avaliação de Mudanças Paramétricas no RGPS” (with Hélio
Zylberstaijn and Luis Afonso)
6. ”Um Novo Modelo de Previdência para o Brasil” (with Hélio Zylberstaijn and Luis Afonso)
7. “A Linha Híbrida de Pobreza para o Brasil” (with Henrique Vinhais)
8. “Earnings Dynamics and Inequality in Venezuela” (with Samuel Freije)
9. “Wage Inequality in Brazil: Market Forces, Macroeconomic Instability and Labor Market Institutions”
ARTICLES IN NEWSPAPERS AND MAGAZINES
1.
“Uma Proposta de Reforma da Previdência”, Diário do Comércio, 01/04/2007 (with H.
Zylberstaijn and Luis Afonso)
2.
3.
4.
5.
6.
“Para Onde Vai o Bolsa Família?”, Conjuntura Econômica, Dec. 2006
“Tiro Certeiro no Alvo Errado”, FOCO, Economia e Negócios, Year 2, Number 26, 15/07/2005
“Aposentadoria e equidade”, Folha de São Paulo, 08/06/2003 (with H. Zylberstaijn, M. Millan,
and E. Stancioli)
“Impactos fiscais do novo texto”, O Estado de São Paulo, 07/18/2003 (with H. Zylberstaijn, M.
Millan, and E. Stancioli)
“Devo, não nego. Mas como pagar?”, O Estado de São Paulo, 07/18/2003 (with H. Zylberstaijn,
M. Millan, and E. Stancioli)
OTHER PUBLICATIONS:
1. Book Review for: Cigno, Alesandro and Camillo Rosati, The Economics of Child Labor, Oxford:Oxford
University Press, 2005 (forthcoming in Industrial and Labor Relations Review)
2. Coments on “Early Childhood Development in Latin America and the Caribbean” by Norbert Shaddy.
Washington, DC: Brookings Institution Press, 2006 (Economía. Journal of the Latin American and
Caribbean Economic Association).
3. “Por Uma Política de Metas de Redução da Pobreza”, São Paulo em Perspectiva, Fundação SEADE,
Volume 18, Number 4, Oct-Dec 2004.
4. Book Review for: Rocha, Sônia. A Pobreza no Brasil. Rio de Janeiro: FGV, 2003. Brazilian Political
Economy Review, 2003.
5. “Por uma política de metas de redução da pobreza”, Boletim FIPE, 2004.
6. “Imposto sobre a folha salarial ou imposto sobre o faturamento das Empresas”, Boletim FIPE, 2003.
7. “O Trabalho infantil e a freqüência escolar no Brasil dos anos noventa”, Boletim FIPE, 2003.
8. “Impactos dos programas sociais de transferências de renda sobre a freqüência à escola e trabalho
infantil no Brasil”, Boletim FIPE, 2003.
9. “As novas medidas de geração do desemprego”, Boletim FIPE 2003.
10. “Metodologia de menasuração do desemprego”, Boletim FIPE, 2003.
11. “A persistência Intergeracional do trabalho infantil no Brail”, Boletim FIPE, 2002.
INTERNATIONAL CONSULTANCIES AND RESEARCH PROJECTS
Comissão Econômica para a América Latina (CEPAL) – Research Project: Evolução do
Emprego e da Hetogeneidade Estrutural do Mercado de Trabalho (with Nadya Araújo Guimarães),
November 2006 – May 2007
U.S. Agency for International Development (USAID – BASIS/CRSP), Research Project: Conditional Cash
Transfer Programs in Rural Brazil: Coverage and Impacts on Poverty Reduction and Human Capital
Formation, August 2006 – May 2007
Fondo de Investigaciones Educativas del Programa de Promoción de la Reforma Educativa en América
Latina y el Caribe (PREAL), Research Project: Educating the Poor in Brazil: Evaluating Bolsa-Escola and
FUNDEF (with naércio Menezes Filho and Elaine Pazello), May 2004 – April 2005
Japonese Bank for International Cooperation – JBIC. Research Project: Education in Brazil
Coordinator: Paulo Renato Souza (2005)
CONFERENCE PRESENTATIONS:
Latin America and Caribbean Economic Association Meeting (LACEA 2006)
Latin American Econometric Society Meeting (LAMES 2006)
Northeast University Economic Development Consortium (NEUDC 2006)
Brazilian Econometric Society Meeting (SBE 2006)
Latin America and Caribbean Economic Association Meeting (LACEA 2005)
Latin America and Caribbean Economic Association Meeting (LACEA 2005)
Brazilian Econometric Society Meeting (SBE 2005)
GDN/PREAL Conference (2005)
Brazilian Economic Association Meeting (ANPEC 2004)
Northeast University Economic Development Consortium (NEUDC 2004)
75 Years of Development Research (Cornell University, 2004)
Latin America and Caribbean Economic Association Meeting (LACEA 2003)
Brazilian Economic Association Meeting (ANPEC 2003)
Latin America and Caribbean Economic Association Meeting (LACEA 2002)
Latin American Econometric Society Meeting (LAMES 2002)
Northeast University Economic Development Consortium (NEUDC 2002)
Brazilian Economic Association Meeting (ANPEC 2002)
Latin America and Caribbean Economic Association Meeting (LACEA 2001)
Northeast University Economic Development Consortium (NEUDC 2001)
Northeast University Economic Development Consortium (NEUDC 2000)
ADVISING:
Doctorate Dissertation:
Euclides Pedrozo junior (in progress)
Izabel Cristina do Nascimento (in progress)
Máster of Arts Thesis:
Kátia Tiemi Saito (in progress)
Fernanda Cabral Santos (2007)
Henrique Vinhais (2006)
Antônio Tiago Loureiro Araújo dos Santos (2005)
Fábio Miessi Sanches (2005)
Marcos Aurélio do Nascimento (2005)
LANGUAGE PROFICIENCY:
Portuguese (Speak, Read, and Write)
English (Speak, Read, and Write)
Spanish (Speak, and Read)
French (Read)