Brazil’s case study

7/28/2019 Brazils case study

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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


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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


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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


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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:


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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.


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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,


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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.


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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


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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.


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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


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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).


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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


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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


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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


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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


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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


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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.


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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


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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)


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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


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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.


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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


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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:


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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.


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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


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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.


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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

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