brazil’s case study
TRANSCRIPT
-
7/28/2019 Brazils case study
1/27
Brazils case study:Do rates of return to education
differ between different economic
sectors?
Students:
Lok Kei Ka 10491309
Raica Andreea 10377972Rolea Ilinca 10377980
Monti Lorenzo 10378421
April 2013
-
7/28/2019 Brazils case study
2/27
2
Table of contents
Introduction 3
1. Theory and review of literature 4
1.1. The human capital model 4
1.2. The signaling model 6
2. Brazils Economic Structure 8
2.1. Economic sectors 8
2.2. Demand and supply of labour 10
3. Brazils economic development and education system 11
4. Analysis of the return to schooling in Brazil 16
4.1. Wage-Inequality 16
4.2. Differences in wages by sector 18
4.2.1. Primary, secondary and tertiary sectors 18
4.2.2. Formal and informal sector 20
Conclusion 24
References 25
-
7/28/2019 Brazils case study
3/27
3
Introduction
According to World Banks database, in 2008 the total public spending on education in
developing countries from Latin America was 4.4% of GDP, while in Brazil this figure reached
the level of 5.4%. Motivation for this investment lies in the belief that the education of children is
key to future economic growth and lasting democracy, leading to greater stability and improvedstandards of living. (George W.Bush), view also shared by the President of the World Bank,
James D. Wolfensohn - All agree that the single most important key to development and to
poverty alleviation is education.
Education affects the life of individuals, their participation in economic activities, and overall
economic development in various ways. Education clearly possesses economic value: the
acquisition of education and skills by an individual tends to increase his productivity and hence
his earnings. However, the rates of return to education vary among countries on the one hand
and also across groups of individuals and economic sectors within countries on the other.
Moreover, the reasons for the variation in the rates of return can fall into different categories,
like the educational path followed, size of the firm individuals work in, level of technology and so
on.
In this paper, the emphasis will be put on analyzing the way rates of return to education vary
between different economic sectors (primary, secondary and tertiary, but also formal and
informal, including self-employment) only with respect to the specific educational path followed
by individuals. Although one might argue that the value of a certain qualification is consistent
from sector to sector, the reality is that apparently similar qualifications still differ enormously in
terms of content and applicability to the job, and therefore can vary in terms of labour market
value across different sectors (Powdthavee and Vignoles 2006).
In order to see how these rates of return vary in practice, we will observe the situation of the
Brazilian education and economic structure. This Latin America developing country presents
some characteristics that make it interesting to study in relation to our topic. Brazil is a high-
sized nation, marked by profound contrasts and diversity, having high and persistent levels of
income inequalities over time (IBRD, 2004). One way of trying to solve this is that in the last
decade Brazil has made a significant effort to increase investment in education, having the
steepest increase in expenditure per student from primary to secondary education among 30
countries with available data (OECD,2011). Also the earnings premium for tertiary graduates is
the highest among all countries surveyed (156%) and there has been a continuous rise in the
demand for high-educated and skilled workers. Thus, there exist solid incentives for completing
higher levels of education, supposedly leading to higher rates of return substantially differing
among the economic sectors.
The remainder of this paper will be structured as follows. Firstly, a theoretical approach
regarding education and earnings will be presented, more specifically the model of human
capital and the signalling model. Secondly, some general background information about the
economic and educational structure of Brazil will be provided. Thirdly, the returns to education in
-
7/28/2019 Brazils case study
4/27
4
Brazil are analyzed from two perspectives: primary, secondary and tertiary sectors and also
formal and informal sectors, including self-employment . Finally, conclusions will be drawn as to
whether rates of return to education differ or not among economic sectors in Brazil..
1. Theory and review of literature
Returns to education can be generally thought of as private returns, social returns and labour
productivity returns; each of these measures having, as key component, the impact of education
on earnings (Blundell, Dearden, Sianesi - 2001). This paper will focus on the first element, the
concept of private rate of return to investment in education, meaning the differences in earnings
of individuals that have different levels of education.
To be more specific, we will further discuss about the two main theories which attempt to
explain this causal relationship between education and earnings and the specific mechanism
through which education affects earnings. The first one, human capital theory, argues intuitively
that education endows an individual with productivity-enhancing human capital, and that this
increased productivity results in increased earnings. Competitive market theory does, after all,
require that workers receive a wage equal to their marginal product. The second one, signaling
theory, proffers an oppositional argument, which holds that education only reflects inherent
human capital. This inherent human capital, not education itself, is what increases productivity
and leads to higher wages (Kjelland).
1.1. The human capital model
The human capital model, pioneered by Gary Becker, Jacob Mincer and Theodore Schultz,explains the earnings differences solely through differences in the level of cognitive skills of
people which ultimately stem from their level of schooling. As education increases human
capital (knowledge and skills that can be rented out in the labour market to produce economic
value), it can be said that it increases the productive capabilities of employees. The increased
productivity translates into higher earnings (the marginal product is assumed to be the same as
the wage level) which is another way of saying that the return to human capital in the labour
market is actually the wage level.
The basic principle behind this model is the one of the present value of expected future benefits,
which in the utility maximizing view of people, needs to exceed near-term investments costs.
People decide to invest differently in education due to their comparison results of the present
value of the costs and benefits of education: each individual compares the different present
value streams of different levels of schooling with each other. The present value of a stream of
yearly benefits (B1, B2,...) over time (T) can be computed as follows:
-
7/28/2019 Brazils case study
5/27
5
where the interest rate (or discount rate) is r (Ehrenberg & Smith, 2006).
The costs incurred in adding to ones human capital can be split into 3 main groups: direct
expenses (tuition costs, books and other supplies), forgone earnings (generally during the
investment period it is not possible for individuals to work) and physic losses (learning is often
difficult and tedious). Taking into account these costs, individuals find their optimal level ofschooling: the present value of the benefits of those years of schooling equals the costs of
schooling. Investing in additional schooling is attractive if the present value of future benefits
exceeds costs (Ehrenberg & Smith, 2006):
To measure whether this criterion is met, a financed-based investment theory can be used -
consisting of 2 main methods. The discount rate can be estimated either using the internal rate
of return method (How large could the discount rate be and still render the investment
profitable?) by raising it to the level at which financial benefits equal costs, which is then theinternal rate of return, or through the present value method by setting the discount rate at a rate
that takes into consideration the risk involved in the investment, which is then a net present
value calculation, with the gains expressed in monetary units (OECD, 2012).
Besides the investment approach, another theory that can be used to estimate the financial
returns to education is the labour economics-based econometric specification, which will be
discussed further on.
From theory to practice: from the human capital model to the Mincer equation
Using as a starting point the fact that the human capital model approaches the difference in
educational costs and benefits between individuals, Jacob Mincer came with a method to
estimate the private rate of return to education, starting from these costs and benefits.
Jacob Mincer's model of earnings (1974) is a cornerstone of empirical economics. Being mostly
used as a framework to estimate returns to schooling, it represents the basis for economic
studies of education in developing countries and has been estimated using data from a variety
of countries and time periods.
The Mincer (1958, 1974) model specifies
where w(s, x) is wage at schooling level s and work experience x, !is the rate of return to
schooling (assumed to be the same for all schooling levels) and is a mean zero residual with
E(|s,x) = 0.
-
7/28/2019 Brazils case study
6/27
6
According to Heckman, Lochner and Todd (2003), Mincer provided two theoretical motivations
for his specification, one based on a compensating differentials principle (1958) and a second
based on an accounting identity model of human capital formation (1974).
The first Mincer model (1958) uses the principle of compensating differences to explain why
persons with different levels of schooling receive different earnings over their lifetimes. Thismodel assumes that individuals have identical abilities and opportunities, that there is perfect
certainty, that credit markets are perfect, that the environment is perfectly certain, but that
occupations differ in the amount of training required. Because individuals are assumed to be ex
ante identical, they require a compensating differential to work in occupations that require a
longer training period. The size of the compensating differential is determined by equating the
present value of earnings streams net of costs associated with different levels of investment.
Being motivated by entirely different assumptions than his earlier model, Mincer's second model
(1974) yields an earnings specification similar to that of the first. The second model builds on an
accounting identity model, which focuses on the life-cycle dynamics of earnings and on the
relationship between observed earnings, potential earnings and human capital investment, both
in terms of formal schooling and on-the-job investment. At the same time, no explicit
assumptions are made about the background economic environment.
According to Polachek, the earnings function approached by Mincer in his latter model from
1974 yields at least three important empirical implications:
earnings levels are related to human capital investments. This means that all years of
education generate an equal rate of return to the student and that the cost of an
additional year of education equals the lost wages one might earn in that time, or better
said the more human capital investments an individual makes the higher his or her
earnings. earnings functions are concave. Earnings rise more quickly for the young, then earnings
growth tapers off mid-career.
the model has implications regarding the distribution of earnings, meaning individuals
choose how many years of schooling to pursue with the goal of maximizing the present
value of lifetime earnings. (Polachek, 2007)
1.2. The signaling model
The signalling model, also known as ability or screening model, takes a different view on
education, stating that schooling doesnt add to an individuals knowledge and human capital.
Thus the educational system serves purely as a signal of ability and as a filter through which the
most able students pass (Dahlin).
The idea that individuals possess traits which employers value but do not fully observe at the
time of hiring (ability, determination, and so on) forms the starting point of signalling models. If
there is a systematic association between these traits and the costs and benefits of education,
-
7/28/2019 Brazils case study
7/27
7
this may lead to an equilibrium in which high-ability individuals stay in school for longer because
this decision signals their ability to employers (Temple, 2001).
Signals/screens are helpful to solve the informational asymmetries existing in these principal-
agents relationships by pinpointing to inherent human capital, mainly for individuals who have
productivity-enhancing skills or aptitudes that are not easily determined by employers.Therefore, from inferring information from the school decisions, employers are prevented from
incurring additional recruitment and training costs and the turnover rates are reduced (Jim
Kjelland).
In the early 1970's, economist Michael Spence examined the role of signalling in labor markets
by considering the problem faced by employers: starting from the assumption that all employees
vary in quality, the employer finds difficult to ascertain quality prior to hiring. Thus, in the Spence
model, hiring an employee is viewed as an investment decision with uncertainty concerning the
employees value. In this situation, the relevant information is available to only one side of the
market (the potential employees), while the other side of the market (the employer) has to try to
infer this information from some observable characteristics, which are known as signals when
they are under the control of the applicant (alterable characteristics, particularly education level),
whereas those characteristics which cannot be modified by the applicant (sex, race, etc.) are
called indexes. (Bonanno)
He constructs a simple model in which the population is divided in two groups: those with high
productivity and those with low productivity, where the former group can acquire education at a
lower cost (in terms of time, money, annoyance, etc) than the latter. Spence shows that there
will be a signalling equilibrium in which high productivity workers will seek more education than
low quality workers, and employers will offer higher wages to those with higher levels of
education. Under these circumstances, in Spences model, education - rather than increasingworker productivity - merely serves as a reliable signal of it. (Bergstrom, 2002)
Evidence of the value of education as a signal could be obtained by studying the difference in
earnings between self-employed people and employees, the latter having higher returns to
education according to a study conducted by Brown and Sessions (1999). The argument for this
would be that returns to self-employment can only represent returns to human capital; after all,
there are no information asymmetries when a person employs himself and, as a result, there is
no need for signals (Kjelland). However, opponents of the signalling models argue that self-
employment isnt random and thus its inconclusive if the differences are due to a signalling
component or to unobservable traits that lead individuals to choose self-employment.
-
7/28/2019 Brazils case study
8/27
8
2. Background: Brazils Economic Structure
Brazil - an upper-middle-income country - is the largest and most important country in South
America in terms of size, population and economic power. It is the largest economy in Latin
America and it also occupies the worlds seventh largest economy in terms of nominal GDP in
2012, according to the estimates of International Monetary Fund. Its GDP is estimated to beover 2.4 trillion US dollars (IMF 2012). Furthermore, it is one of the fastest growing countries in
the world in the last decade and was given the title BRIC, together with Russia, India and
China. The GDP growth of the country exceeded 4% in the past five years, except in 2009,
when the financial tsunami hit all over the world.
2.1. Economic sectors
According to statistics published by the Brazilian government, the main component of GDP is
the service sector, accounting for 67% of the GDP, followed by the industrial sector (27.5%) and
agricultural one (5.5%). Moreover, the evolution of labour force by occupation is depicted in
Figure 1. In the last 30 years, services have gained weight in the Brazilian labour market,
reaching more than 60% of the labour force in 2007. On the other hand, agriculture has
continuously lost jobs, although the agriculture production in Brazil has increased in the period.
In this sector its clear the effect of productivity gains that arise after trade openness process
together with credit policy and investments in research in agricultural products (Menezes Filho
and Scorzafave). As far as the industry sector is concerned, a relatively steady trend can be
observed with no major ups and downs, slightly exceeding 20% in 2007.
Figure 1 - Labour force composition by activity sector in percentages, 1985-2007
Source: Pesquisa Nacional por Amostra de Domiclios (PNAD), 1985-2007
In the last decades, the population has more than tripled, which lead to a huge demand in the
country for agricultural products necessary to supply food to everybody. In order to meet this
demand, it was necessary to take a step further to develop in cattle and crop raising. In addition,
regional income inequality became serious in Brazil as the economy grew (Azzoni 2000). The
Northern region is much poorer than the Southern one, so in the North people may not have
-
7/28/2019 Brazils case study
9/27
9
access to technology and capital for industrial development. These are the possible reasons for
which Brazil still has sizeable amounts of labour working in the agricultural sector even though
the country has undergone the process of industrialization.
Most of the industry in Brazil is concentrated in the Southern part of the country, which is
traditionally the richest. There is a great diversity of industry sectors in the country, includingautomobiles, steel and petrochemicals, computers, aircraft and consumer durables. Its
increasing economic stability attracted new investments in equipment and technology,
investments mostly coming from the United States. Companies in Brazil are becoming more and
more international: 36 Brazilian companies were listed in the Forbes Global 2000 list in 2011.
Among the top are Petrobras and Vale, which ranked the fourth and the twentieth in the world,
coming from the oil industry and mining industry respectively. This shows that industry in Brazil
is not just important within the country, but also plays a crucial role in the world.
The services sector in Brazil is also well-developed, being one of the main forces driving the
economy of the whole country to grow consistently. The major components include
telecommunications, banking, energy and commerce. Brazil has a strong banking system and
also framework of regulation and supervision which has strengthened the financial stability of
the country (IMF 2012). In the past decades, the banking sector in Brazil has gone through a lot
of changes. More exactly, through mergers and acquisitions, it has become more concentrated
in the present: the six largest banks in Brazil have more than 75% of assets of the whole sector
(Bank Track 2012).
Apart from this classification in primary, secondary and tertiary sectors, we can also look at the
economy from the point of view of formality, by dividing it into formal and informal sectors.
Individuals can choose to be wage earners in the formal or informal sectors or be self-
employed. Statistics show that self-employment accounts for 34% of total employment in Brazil,while informal salary work accounts for 12% (Bargain & Kwenda, 2011).
In Brazil, self-employment is a self reported working status. Eighty two percent of those self-
employed run businesses on their own, with no partners or employees. Eighty five percent do
not contribute to social security. Seventy five percent work full time. Sixty four percent are
males. Sixty three percent have less than nine years of schooling. Thus, it can be said that most
of the self employed evade payroll taxes, have low education and are found in small scale
businesses which require low skills, thus are unlikely to expand and employ other workers
(Narita, 2010). In Figure 2, we see how the composition of workforce is divided along age and
there appears a strong dependence of self-employment on individuals age which might be due
to cohort effects, whereby older individuals experienced different education and labour market
opportunities in their past than youngers do now.
-
7/28/2019 Brazils case study
10/27
10
Figure 2. Composition of workforce by age (Source: PME survey, 2002-2007)
2.2. Demand and supply of labour
In what the demand for labour is concerned, if a sector asks for low skilled workers, the Human
Capital theory will predict a lower wage than in a sector whose demand is oriented towards high
skilled workers. The wage of a worker is determined by the labour market and employers will
have to compensate high skilled workers with higher wages to attract them. Within a sector, a
higher demand for a specific skill will increase the wage of workers with that skill, but a higher
offer of that skill will decrease the wages of the same workers. For instance, if employers need
more skilled workers, the demand for these workers will shift out (see figure 3). Regarding the
supply, lets say that suddenly, every worker in the labor market completes tertiary education:
this shifts out the supply curve (see figure 4). As a result, the wage falls from w0 to w1, and total
employment increases from E0 to E1.
Figure 3 Figure 4
-
7/28/2019 Brazils case study
11/27
11
In 1994, Psacharopoulos estimated the return to schooling for 23 countries and Brazil was part
of it. At this time, it turns out that the private return to investment in education by level
(percentage) full method was 28.2 if the student completed tertiary education. Comparing the
previous result with the return of 5.1 if the student dropped out after the completion of
secondary education, it can be highlighted the fact that high skilled workers benefit from a large
compensation relative to the years they spent in school.
The rate of return on education in general has continued to be quite high, which may be
interpreted as an indication that Brazil still has a relative lack of highly educated workers,
especially workers with higher education, area which presents the highest marginal return.
Marginal return on higher education has risen in a relatively marked way during the last decade
of the 20th century, which shows there was also a relative increase in the demand for workers
with higher educational levels. (Paulo Renato Souza Consultores,2005).
In 2011, The Guardian reported that the Brazilian economy was hampered by lack of qualified
labour: two-thirds of Brazilian employers have difficulty recruiting suitable staff in 2010, which
made the Brazils President Dilma Rousself acknowledged that the lack of skilled labor is one of
the Brazilian economys biggest shortcomings. Therefore, the fact that supply falls short of
demand increases wages for skilled workers, hence the return to schooling. An ambitious youth
training scheme has been launched to remedy this massive problem that damages economy by
increasing inequality. Here, the signaling model predicts that workers with a specific number of
years will be seen as adequate to satisfy or not the demand for skills. Indeed, more years will
signal a better qualified worker to the employer.
3. Background: Brazils economic development and education system
In February 2013, Brazils president, Dilma Roussef, said extreme poverty will soon disappear in
Brazil (The Economist, 2013). In the last 20 years, Brazil has focused on reducing poverty.
Indeed, since 1993, Brazil has struggled against poverty to enhance its economic situation. In
fact, the government of President Fernando Henrique Cardoso and the Brazilian Ministry of
Education began to pursue three areas of national education policy, at that time - namely
education finance equalization, conditional cash transfers and education results measurement.
As a result, a sharp and uninterrupted decline in inequalities has been observed. All of this has
clearly pinpointed the good performance of the labor market and the effectiveness of the various
policies that have been undertaken. We can illustrate this evolution with two poverty indicators
constructed by using data from IPEADATA (see figure 5 & 6). The first one - poverty headcount
ratio - measures the number of individuals below the poverty line as a fraction of population
(Ray, 2011). The second one gives the GINI coefficient, that is a measure of inequality of
income whose value is between 0 (perfect equality) and 1 (maximal inequality).
-
7/28/2019 Brazils case study
12/27
12
Figure 5
Figure 6
From figure 5, we can notice that the poverty rate was reduced by approximately 50% in thespan of 20 years. New measures have been settled by the Brazilian government to hinder
poverty and inequality from increasing again. More precisely, social programs such as the Bolsa
Familia orBrasil sem Miseria (Brazil without destitution) aim at reducing poverty at a relatively
low budgetary cost. The Bolsa Familia - part of the Fome zero program (Hunger Zero) - is a
conditional program which requires the beneficiaries to fulfill some conditions concerning
education. In 2010, 12.7 million households benefited from this program and it cost the
equivalent of 0.4% of GDP, while 11% of GDP was dedicated to social security allowances - the
middle class is the main beneficiary of these allowances (OECD, 2011; Abrahao de Castro &
Modesto, 2010). So far, one of the key achievements of this program has been the setting up of
an almost exhaustive register of poor households in the country. And this could be used toimprove the management and targeting of social policies - needless to say the more recent
program Brazil Sem Miseria has the same objective. Since its launch in June 2011, 2.8 million
extremely poor people have benefitted from this new federal program or have been put on the
list of potential welfare recipients. The poverty line - i.e. the critical threshold of income,
consumption, or, more generally, access to goods and services below which individuals are
declared to be poor (Ray, 2011. p.250) - in Brazil, is currently set at 70 reais (35$) per
-
7/28/2019 Brazils case study
13/27
13
household member, per month. So far, millions of people have been given additional cash
benefits to bring them above the absolute poverty line (The Economist, 2013).
Major improvements have been achieved concerning the access to education and its quality.
Indeed, besides conditional cash transfers, a more equal access to education has proved to be
efficient to fight against poverty and inequality. The reduction of wage premiums compensatingyears of schooling and a homogenous distribution of academic achievement have proved to be
the main drivers in reducing inequality, thus have been more effective than the social transfers
in the observed changes (Barros and al., 2010).
From the Instituto Brasileiro de Geografica e Estatisticas data, we can point out that enrollment
ratio by age groups has increased over the 20 years of poverty fighting (see figure 7), depicting
the results of policies making it easier to access education.
Figure 7: Enrollment ratio (per cent), by age groups
Consequently, the illiteracy rate has considerably dropped (see figure 8): the data from
seriesestatisticas.ibge.gov.brconfirm it .
Figure 8: Illiteracy rate (per cent), by age group
-
7/28/2019 Brazils case study
14/27
14
Nevertheless, theres a real need to improve the quality of education and to reduce the dropout
rates in secondary education. The main problem with Brazilian schools was repetition rate. The
SAEB (National Assessment of Basic Education) - developed in the 1980s and applied in 1990 -
initiated the trend of assessing the quality of education. With further measurements and
evaluations, shortcomings at all levels of education have been detected and treated step by
step.
In 2007, as part of the Development Plan of Education (PDE), the Index of Basic Education
Development (IDEB) - created by Inep (National Institute for Educational Studies and Research)
- was implemented by the Ministry of Education. This tool - established on a scale of zero to ten
was thought to introduce the most complete ranking of public schools ever made in Brazil. The
indicators computation is based on the Prova Brasil (Test of Brazil) and the SAEB that are
basically two exams evaluating the system of basic education in Brazil. In short the index is
based on students performance, i.e. graduation and rates of repetition, but also on student
enrollments. Hence if the grade of the institution is high - i.e. a high index - it will mean low
dropout rates, low repetition rates and high students performance.
By showing the conditions of education in Brazil and mapping it, the Ministry of Education is
able to know which municipalities need financial or technical support and offer it. Since 2008, all
cities and states of Brazil have committed to achieve goals such as literacy for all children up to
a maximum of eight years old. On the whole, the IDEB has been increasing since 2005, but it
remains far below the results obtained by the countries of the Organization for Economic
Cooperation and Development (see figure 9). Nevertheless, Brazil has a competitive edge in
education compared with other developing countries: problems can be tracked and immediately
addressed.
Figure 9: Observed IDEB, by level of education
-
7/28/2019 Brazils case study
15/27
15
The 2012 comparison between Brazil and the OECD countries in terms of education
Even after 20 years of substantial investment in education, a gap persists between Brazil and
the OECD countries. For instance, from the data presented in the OECD report, we can see that
the percentage of population that has attained at least upper secondary education is two times
higher in the OECD countries and concerning the population that has attained tertiaryeducation, three times higher (see figure 10).
Figure 10:
(1) Percentage of population that has attained at
least upper secondary education
(2) Percentage of population that has attained
tertiary education
The annual expenditure per student reported in Education at a glance 2012: OECD Indicators
depicts that Brazil is still below OECD average (see figure 11). Yet, the proportion of GDP
invested in education has considerably grown since the 1990s: it is relatively high, compared
with OECD (see figure 12). Above all, in an article published by The Economistin October 2012,
we learn that a promising target has been set concerning education. Indeed a National
Educational Plan has been approved by the Special Committee of the National Plan for
Education (PNE). The Brazils National Congress set to spend 10 percent of GDP on public
education by 2020. In this case, it would be the highest share, in the world, spent on education.
Figure 11. (2009) annual expenditure per student Figure 12. Percentage of GDP
(in ecquivalent USD, using PPP) devoted to education
-
7/28/2019 Brazils case study
16/27
16
4. Analysis of the return to schooling in Brazil
4.1. Wage-Inequality
One the one hand, the improvement of the Brazilian educational system has lead to a more
educated workforce. In fact, educational attainment rate have been rising over the past decadedue to the increasing percentage of GDP invested in education (Education at a Glance, 2012).
On the other hand, the education appears to play an important role in the wage-inequality,
which is an well-known subject among economists and which we will further analyze.
Firstly, it is said that education increases the likelihood of employment. Indeed, Education at a
glance 2012reports Brazilian employment rates by level of education pointing out that schooling
is positively correlated with the chance of getting a job (see the figure below).
Moreover, according to the Human Capital model, one extra year of schooling yields more
earnings in order to compensate the year spent in school. In the Work Bank Country Study on
Inequality and Economic Development in Brazil(p.256), the authors have estimated returns to
schooling from Mincerian regressions proving that wages increase with the level of education(see figure 13).
Figure 13: Returns to Schooling by Education level
-
7/28/2019 Brazils case study
17/27
17
To go further, the same authors have analysed the impact of return to schooling on wage-
inequality (see figure 14). They do it by studying how wage inequality would have developed
from 1976 to 1999 if only return to schooling changed, i.e. ceteris paribus (p.257).
Figure 14: Wage-Inequality if only returns to schooling changed
They conclude that the overall decline in returns to schooling, ceteris paribus, explains at least a
third of the reduction in wage-inequality from 1989 to 1996. Moreover, Education at a glance
2012compare the average earnings premium obtained by workers - aged 25 to 64 years old -
who completed tertiary education with upper secondary educated people. Setting upper
secondary at 100, having a tertiary education yields to an average earnings premium of 256.
Furthermore, relative to people with secondary education, not attaining upper secondary
education - still setting upper secondary at 100 - results in an average earnings penalty of 53.
From the analysis of the return to schooling, the authors of Inequality and Economic
Development in Brazildraw 4 main conclusions (p.269):
Graduates from Brazilian colleges are highly rewarded for their education;
The rising wage of workers with tertiary education exacerbates wage-inequality;
An asymmetric expansion in the Brazilian education system and a constant skill-biased
change in labour demand fully explain the rise in the skill-premium;
A major expansion in the tertiary education system could reverse the rising relative wagethat deteriorates wage-inequality.
-
7/28/2019 Brazils case study
18/27
18
4.2. Differences in wages by sector
4.2.1. Primary, secondary and tertiary sectors
Several studies have been conducted over time analyzing the issue of differences in salaries in
Brazil with the view of enlarging the knowledge of the Brazilian labour market mostly for policymaking purposes. One of the most general approaches, belonging to Ferreiro da Silva (1987),
studies 4 groups of characteristic variables: individual, business, industrial sector and region
and concludes that the most important category to explain salary differences is the individual
one, more specifically the level of human capital. Furthermore the main findings of more specific
studies: Langoni (1973), Reis and Barros (1990), Ramos and Trinidade (1991) indicate that
differences in education are by far the most relevant cause of earnings inequality in Brazil. This
variables gross contribution accounts for 30 to 50% of overall inequality, depending on the
period and sample of the study.
Now, if we look closer at the Brazilian sectors, we will notice that each sector is composed
differently regarding the level of education. The data from Inequality and Economic
Development in Brazil(p.275) reveals that the primary sector contains low skilled workers, i.e.
few workers have completed secondary education - e.g. only 1.8 percent in Agriculture and
Mining). Reversely, in the secondary and tertiary sectors, a much bigger percentage of workers
working in the respective sectors have finished the secondary education. Though, the tertiary
sector is doing better than the two others. First, more tertiary-workers in proportion have
completed secondary schooling - e.g. 9 percent in services against 3.8 percent in construction
(see figure 15). Secondly, the share of tertiary-workers who have completed tertiary education is
higher than workers in the primary and secondary sectors. In the end, the theory of Human
Capital predicts that the average wage by sector will differ because of the specific share of
skilled workers. Therefore, due to the higher concentration of skilled workers in the tertiarysector, the average wage will be higher there than in the secondary and primary sectors.
Figure 15: Sector Labor Intensity, Average 1976-1999
-
7/28/2019 Brazils case study
19/27
19
To further support the arguments previously mentioned, we continue by analyzing the levels of
completed education among sectors by taking practical examples of job positions from each
sector, using data from Meusalario.org.br website. In the primary sector for rural workers we
observe the following: 82% studied 11 years or less, 18% 12 years or more and 0% followed
postgraduate studies. Comparatively in the secondary sector for pharmaceutical process
operators and in the tertiary sector for telecommunications engineers, the percentages are49%, 46%, 5% and 8%, 73%, 19% respectively. Their average salary (computed only based on
education with no previous work experience) is 900 R$ in the primary sector occupation, 1400 in
the secondary one and 3100 in the tertiary one, emphasizing the positive relationship between
education level and earnings which will also be discussed next in the context of skills-job-
earnings.
After looking at the education levels across sectors between 1976 and 1999 (Figure 15), we turn
to Castellar Pinheiro and Ramos (1994) work on a smaller timeframe 1981-1990 on inter-
industry wage differentials in order to see whether the human capital prediction mentioned
above stands or not.
Based on data from 3 PNADs - household surveys conducted by IBGE, the Brazilian Bureau of
Statistics - on males aged 18-65 working in 45 different industries, the authors run several
regressions to measure total wage differentials, wage premiums and inter-industry dispersion.
They observe that sectors that concentrate less skilled workers (agriculture and
livestock,vegetal and animal extraction, wood, leather) tend to show negative wage premiums.
Conversely, segments more intensive in high skilled labour display positive premiums. For
example, financial institutions and air transportation are among sectors with the largest mean
wages, paying 2.5 the market mean and also with the largest wage premiums.
The authors came to a noteworthy conclusion, also supported by other studies, concerning thesimilar structure of wage differentials and premiums. The evidence suggests that in Brazil wage
premiums are an important part of wage differentials, tending to accrue to workers that are best
qualified.
The idea that the workers who receive work premiums are the most skilled ones can also be
observed in Ribeiros paper (high differences in wages between skilled workers and those semi-
skilled or unskilled) which also exemplifies the interaction between supply and demand in the
manufacturing sector - discussed in section 4.2. - between 1997 and 2003. It shows that
because the number of skilled and semi-skilled workers rose from 9 to 12.5% on the one hand
and that the number of unskilled individuals dropped 13 percentage points on the other, there
was a sharp decrease in skilled workers wages. (see figure 16)
-
7/28/2019 Brazils case study
20/27
20
Figure 16. Manufacturing average wages by skill levels (skilled, semi-skilled, unskilled)
4.2.2. Formal and informal sector
We will continue our research by looking at the differences in the rates of return to education
within the two main sectors of the economy: the formal and the informal sector. To be more
specific, we will try to analyze the effect of schooling on workers wage in different sectors of theeconomy depending on their employment choices. In order to prove our hypothesis, we will
combine the analysis of the empirical results of Bargain and Kwenda (2011) and Narita (2010).
Both of them make use of the Monthly Employment Survey (Pesquisa Mensal de Emprego,
PME) conducted by the Instituto Brasileiro de Geografiae Estatistica (IBGE), to follow
individuals in the six largest metropolitan areas of Brazil (i.e.Belo Horizonte, Porto Alegre,
Recife, Rio de Janeiro, Salvador, and Sao Paulo). These surveys provide information about
individuals job characteristics, incomes, work duration, demographics and education.The
sample period used starts on January 2002 and runs until December 2007. Also, its worth
mentioning the fact that they focused more on men because in Brazil a large proportion of
women are not active or are engaged in unpaid work.
In Earnings structures, informal employment and self-employment, Bargain and Kwenda
(2011) estimated the conditional earnings gap between formal and informal sectors, carefully
distinguishing between salary and self-employed workers. They exploited large panel data sets
of individual Brazilian workers to define informality in a comparable way and to account for
-
7/28/2019 Brazils case study
21/27
21
workers unobserved heterogeneity by estimating fixed effects regressions at the mean, but also
at different points of the conditional earnings distribution.
In what the econometric approach is concerned, they estimated standard Mincer earnings
equations at the mean and at various quantiles on pooled years data with clustered standard
errors at the individual level, the model used being simply written as:
yit= xit + Iit+ Sit+ ui,
where yit denotes the hourly earnings of worker iat time t, explanatory variables xit comprise
standard human capital information (age, age squared, education), dummy Iit (respectively Sit)
takes value one if person i observed at time t is an informal salary worker, (respectively a self-
employed). The estimated coefficients and are interpreted as a measure of the conditional
earnings premium/penalty experienced by informal salary workers and self-employed workers,
respectively, compared to formal wage earners.
Their main findings can be summarized as follows:
concerning the conditional earnings gap between informal and formal salary workers,
informal salary workers face an average earnings penalty of around 9% (magnitude
close to the penalties estimated at the median of the conditional earnings distribution),
which confirms that wages in the informal salary sector are systematically lower than in
the formal sector at all conditional quantiles, while self-employment earnings dominate
formal sector wages for a large part of the unconditional distribution.
considering the conditional earnings gap between informal self-employed workers and
formal wage earners, results reveal that on average self-employment confers an
earnings premium of 11% (magnitude again close to QR estimates at the median of the
conditional earnings distribution), which indicates that self-employment contributes to a
more dispersed earnings distribution possibly due to the heterogeneous nature ofactivities in this sector that range from small traders to self-employed professionals,
Brazil being an intermediary case with moderate premiums (penalties) at the top
(bottom).
considering the role of education, the authors examined the heterogeneity of the
conditional premiums/penalties by simply interacting the informal sector dummies Iand
S with workers age and education groups. Thus, they found that higher education leads
to higher earnings premiums (or lower penalties), while the opposite is true for low
education levels. This result is in line with one possible interpretation also suggested by
Rees and Shah (1986), where was shown that education serves as a filter such that
the more educated tend to be better informed and more efficient at assessing
opportunities and risks and hence are able to run their businesses well relative to low
educated entrepreneurs, which is especially true for Brazil, where being low-educated
and self-employed would systematically lead to penalties.
-
7/28/2019 Brazils case study
22/27
22
In her paper, Narita (2010) developed and estimated a life cycle search model of the labour
market to understand individuals employment choices. In her model, the workers could be
unemployed, self-employed, wage earners in the formal sector or wage earners in the informal
sector, whose potential work experience was consider to be computed as age minus
completed schooling minus 7 (which is the statutory age of entry at school in Brazil).
In order to address the issue of workers heterogeneity, she assumed markets can besegmented by sex and education, separating the sample by sex and three groups of education
(up to and including 9 years of schooling, 10 and 11 years of schooling, and 12 or more years of
schooling) and estimating the model for each of those six markets (see figure 17). Within each
subgroup of workers by sex and education, work experience varies, being categorized as
follows: 0-5, 6-10, 11-15, 16-20, 21-29, 30-39, and 40 and above years.
Figure 17
Summarizing Naritas results, it can be concluded that:
When analyzing labour incomes (net of taxes) by work sector and age, self-employment
incomes are around 30 to 40% lower than formal sector wages and are higher (lower)
than wages of younger (older) informal employees. Remarkably, the gap between formal
and self-employment incomes increases as workers get older, which is consistent with
-
7/28/2019 Brazils case study
23/27
23
the fact that the majority of those self-employed have low education, are older and
operate businesses on their own.
Estimates by sex and education groups of the model transition probabilities across
sectors of employment and unemployment tend to be higher for low education than for
high education workers.
Transitions within the formal sector decrease with work experience for low education males, but increases for medium and high education ones.
Mobility within the informal sector exhibits a U-inverted shape with experience for low
and high education groups and is flat or increasing with experience for medium
education ones.
The mobility rates from being an informal wage earner or self employed to a job in the
formal sector are very low in general, except for workers with medium and high
education, who seem to get more easily a job with a formal firm.
Formal employment increases with experience for younger workers then decrease for
older workers. Informal employment decreases with work experience for workers with 0-
9 years of schooling and that it is somehow U-shaped for workers in the 10-11 and 12+
education groups. Self-employment increases with experience suggesting the need to
learn about good projects to undertake as self-employed, the self-employment rate being
superior for high education males than for low education males, thus suggesting that
education plays a larger role in determining self-employment among males.
By combining the empirical results of the two papers previously analyzed, we can conclude that
indeed education appears to play an important role especially in what the gap between the
salary (formal and informal) and the self-employed workers is concerned. Using as a starting
point the fact that the formal sector attracts only the medium and high educated workers, it
becomes obvious why the wage is higher in this sector than in the informal one, which brings usto the assumption that a higher level of education results in high premiums and consequently
low penalties. The rest of the population represented mainly by low and medium educated
workers are spread among the informal salary and self-employed workers, the wage of the self-
employed being caught somewhere in between the two main sectors, being higher than the
wage of younger informal workers and lower than the one of older informal workers.
Conclusion
In conclusion, education - improving in Brazil - has been a priority for the Brazilian federalgovernment in the last two decades. As a result, the average age of schooling has almostdoubled over this period. The same has happened in the case of adults completingsecondary school. All in all, there is what Bruns, Evans and Luque call the ManagedRevolution of Brazilian education:
-
7/28/2019 Brazils case study
24/27
24
A six-year-old Brazilian child born today into the bottom quintile of the incomedistribution will complete more than twice as many years of schooling as her parents did.The rise in the average educational attainment of Brazils labor force since 1995 has beenone of the fastest on recordfaster than Chinas. Major gaps in performance with middle-income countries in Latin America and the Caribbean and elsewhere are closing, such as inprimary school completion and preschool coverage. In key areas such as assessing student
learning and education performance monitoring, Brazil has become a globalleader. (Bruns, Evans & Luque, 2011. p.xviii)
Even though reforms and resources have been allocated to education, Brazilsfederal government still has to work on it to get to the same level as wealthier nations. Infact, many students in Brazil dont go beyond secondary school. Thats one of the mainreasons why inequality persists in the Brazilian economy. The different sectors ask workerswith different skills and the supply of skilled workers is not enough to satisfy the demand.Ultimately, the rate of return to education will be higher for those who have completedtertiary education. As a corollary, sectors whose demand for skilled workers is higher will
offer a higher wage, on average. One of the key challenges for the next decade is tomaximize the impact of federal policy on basic education and to incentivize students tocomplete tertiary education: a more educated population will lead to a better economy,hence less inequality. Yet, its important to keep in mind that wages are determined by thelabor market. Therefore, if a certain type of worker is not valued in the labour market, wecant expect inequality between sectors to completely die out. After all, schooling has amassive impact on future earnings and the labour market values the skills acquired duringthese years differently, by level of schooling and by sector.
-
7/28/2019 Brazils case study
25/27
25
References
Azzoni, C. (2000), Economic Growth and Regional Income Inequality in Brazil. The Annals ofRegional Science. Springer.
Baer, W. and Kerstenetzky, I. (1964), Import Substitution and Industrialization in Brazil, TheAmerican Economic Review, Vol 54, No. 3. pp 411 - 425.
Bank Track (2012), An Introduction to the Brazilian Banking Sector and Its SustainabilityPractices.
Bargain, O., Kwenda, P. (2011): Earnings structures, informal employment and self-employment: New evidence from Brazil, Mexico and South Africa, Review of Income andWealth, Series 57, Special Issue,
Barros, B., M. de Carvalho, S. Franco and R. Mendona (2010), Markets, the State and theDynamics of Inequality: Brazils case study.
Becker, B. (1992), Brazil: A New Regional Power in the World Economy, Cambridge UniversityPress.
Becker G. S. (1993), Human Capital: A Theoretical and Empirical Analysis, with SpecialReference to education
Bergstrom, C. T. (2002), Honest signals in economics: Spence's theory of educationhttp://octavia.zoology.washington.edu/handicap_old/spence.html
Blundell, R., Dearden, L. and Sianesi, B. (2001), Estimating the Returns to Education: Models,Methods and Results
Bonanno, G., SIGNALING IN THE JOB MARKET : EDUCATION AS A SIGNAL, Departmentof Economics, University of California, Davis.
Borjas, G., & van Ours, J. C. (2006). Labour economics. McGraw-Hill Education.
Brown, S. and Sessions, J. Education and Employment Status: A Test of the ScreeningHypothesis for Italy. Economics of Education Review, 1999, 18, pp. 397-404.
Dahlin, B.G., The Impact of Education on Economic Growth: Theory, Findings, and PolicyImplications.
Ehrenberg, R. G. and Smith, R. S. (2006), Modern labour economics: Theory and publicpolicy, Pearson Education, Inc, 8th edition.
Ferreira da Silva, J. C. (1987), "Difcreneiago Salarial na Industria Brasileira", Serie Teses,nm. 14, Editora da Fundayo Gelulio Vargas, Ro de Janeiro.
Gatica, J., Mizala, A., and Romaguera, P. (1991): Estrutura salarial y diferencias de salario enla industria brasilena. El Trimestre Economico, Vol. LVIII, 769-792
-
7/28/2019 Brazils case study
26/27
26
Heckman, J. J., Lochner, L. J. and Todd, P. E. (2003), Fifty Years of Mincer EarningsRegressions
IBRD - The International Bank for Reconstruction and Development / The World Bank (2004):Inequality and Economic Development in Brazil
International Monetary Fund (IMF) (2012a), World Economic Outlook Database.
International Monetary Fund (IMF) (2012b), Brazil: 2012 Article IV ConsultationStaff Report;Public Information Notice on the Executive Board Discussion and Statement by the ExecutiveDirector for Brazil.
Kjelland, J., Economic Returns to Higher Education: Signaling v. Human Capital Theory AnAnalysis of Competing Theories, The Park Place Economist, Volume XVI
Langoni, C.G., (1973): Distribuo de Renda e Crescimiento do Brasil, Editora Expresso eCultura
Lpez-Calva, L. and Lustig, N. (dir. publ.), Declining Inequality in Latin America: a Decade ofProgress?, Brookings Institution, Washington, D.C.
Menezes Filho, N., Scorzafave, L. (2009): Employment and inequality outcomes in Brazil
Narita, R. (2010): Self Employment in Developing Countries: a Search-Equilibrium Approach
OECD (2011) .org, Etudes conomiques de lOCDE Brsil.
OECD (2011), Education at a Glance 2011: OECD Indicators, Country note Brazil, OECDPublishing.
OECD (2012), Education at a Glance 2012: OECD Indicators, OECD Publishing.
Paulo Renato Souza Consultores (2005): Sector Study for Education in Brazil - Summary,JBIC Sector Study Series
Polachek, S. (2007): Earnings Over the Lifecycle: The Mincer Earnings Function and ItsApplications, State University of New York, Discussion Paper No. 3181
do Prado Monteiro, V. and de Lima Amaral, E. F.(2005), An Evaluation of the Impact of theEducational Conditions of Brazils Bolsa Famlia Program.
Powdthavee, N., Vignoles, A. (2006): Using Rate of Return Analyses to Understand Sector
Skill Needs, Center for the Economics of Education
Ramos, L., Pinheiro Castelar, A. (1994): Inter-industry wage differentials and earningsinequality in Brazil
Ramos, L. and Trinidade, C. (1991): Educao e Desigualdade de Salrios no Brasil,Perspectivas da Economia Brasileira, IPEA
Ray, D. (2011), Development economics, Princeton University Press.
-
7/28/2019 Brazils case study
27/27
Reis, J.G.A. and Barros, R.P. (1990): Desigualdade Salarial e Distibuio de Educao: aEvoluo das Difenas Regionais no Brasil, Pesquisa e Planejamento Economico, Vol. 20,415-478
Ribeiro, J. (2011): A closer look at the recent skilled labor demand increase in Brazil
http://www.fep.up.pt/investigacao/cef.up/workshopleed2011/papers_leed/RIBEIRO_JACINTO.Pdf
Temple, J. (2001), Growth effects of education and social capital in the OECS coutries, OECDEconomic Studies No. 33, 2001/II
Bruns, B., Evans, D., & Luque, J. (2011).Achieving world-class education in Brazil: Thenext agenda. World Bank Publications.Trading Economics (2013), Brazil Export Statistics.http://www.tradingeconomics.com/brazil/exports
World Trade Organization (2013), Brazil Statistics Database.
http://stat.wto.org/CountryProfile/WSDBCountryPFView.aspx?Country=BR&Language=S
Websites
http://www.ipeadata.gov.br/
http://seriesestatisticas.ibge.gov.br/
http://sistemasideb.inep.gov.br/resultado/
http://en.wikipedia.org/wiki/Education_policy_in_Brazil
http://www.oecd.org/edu/EAG2012%20-%20Country%20note%20-%20Brazil.pdf
http://www.economist.com/blogs/americasview/2012/10/education-spending-brazil
http://www.oecd.org/edu/EAG%202012_e-book_EN_200912.pdfhttp://meusalario.uol.com.br/main/salario-e-renda/Salario-Check
http://www.economist.com/news/international/21573613-growth-national-income-poor-predictor-welfare-not-bread-alone
http://www.worldsalaries.org/brazil.shtml
http://www.cfgnh.org/Portals/0/Uploads/Documents/Public/giveANDlearn-reports/01_Dropout_Rates_0.pdf
http://www.economist.com/blogs/americasview/2013/02/social-spending-brazil