STRATEGIES OUT OF POVERTY Microeconomic Data Management

DECEMBER 14, 2015 COMPILED BY: BRIAN LYNCH AND MICHAEL THOMOPOULOS

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Table of Contents Introduction… (2) Background Information … (3-10) Changes in Distribution of Schooling… (11-32) Migration… (33-41) Child Labor… (42-59) Resource Pooling… (60-65)

Conclusion… (66-67) Appendix… (69-85) Works Cited… (86)

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1.1: Introduction

The purpose of this study is to understand strategies adopted by Brazilians in order to

escape poverty. To fully understand these strategies, we begin by looking at the historical development of Brazil’s political-economy and how it has promoted the current economic state. We then look at the changing education system in Brazil and its impact on income inequality. From here, we look at strategies Brazilians use to escape poverty.

We break down these strategies into two decision points. The first being short term poverty alleviation, which is done by parents entering their children in to the labor force in order to see current income gains. We also see families moving in together in order to minimize living costs. We classify these as short term income alleviation strategies, however they come at a long term cost of lower income for future generations. The second decision would be long term poverty alleviation through parents enrolling their children in school. This tradeoff is the decision parents have to make between short term poverty alleviation and future alleviation for their children.

First we look at migration and the returns migrants receive from making the decision to migrate. We then look at the role children play in poverty alleviation and the decisions families make in order to escape poverty. 1.2: Data

The reason for the choice of Brazil as the focus of this study is due to its large population

of 200 million people. Brazil also demonstrates a high level of income inequality with a GINI coefficient of 52.67 (World Bank). There is also an ample amount of data available for use through the Pesquisa Nacional por Amostra de Domicílios or PNAD. The PNAD is a survey, which ascertains the general household characteristics of the population in respect to educational attainment, labor, income, and housing. The survey also procures data concerning migration, fertility, marriage, and health and food safety to help influence government policy. The dataset includes 362,451 observations, which allowed us to parse the data while maintaining large, significant sample sizes. We cleaned the data, removing survey respondents with miscoded data points or entries with missing responses.

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Section 2: Background Information

Changes in Brazil since 1960 12/7/2015

Ali Bryant

Tyler Linsky Alex Montiel

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2.1: Urbanization and Population Growth in Brazil, 1960-2012 Brazil has experienced significant population growth and urbanization in the last 50 years. Table 2.1 shows a decrease in rural population (as a %age of total population) from 54% to 15% and a corresponding increase in urban population from 46% to 85% in 1960 and 2012 respectively. Brazil also saw rapid population growth from 72.8 million in 1960 to 198.7 million in 2012. In the graph below, Brazil has seen a steady decline in population growth rate since 1960. There is a steady decrease from 3% in 1960 to just under 1% in 2012.

Table 2.1

Rural/Urban Population Comparison 1960, 2012

^^ 1960 2012

Rural Population (% of total) 53.86 15.1

Urban^ Population (% of total) 46.14 84.9

Urban >1M (% of total) 21.06 39.66

Total Population 72.7759M 198.656M ^Urban defined by Brazilian government and reported to World Bank (exact definition unknown). YAP(1975) reported 30% in areas >20,000 in 1960 ^^All statistics compiled from the World Bank (http://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS/countries, http://data.worldbank.org/indicator/EN.URB.MCTY.TL.ZS/countries)

Data from World Bank (http://data.worldbank.org/indicator/SP.POP.GROW/countries/1W?display=default), graph generated by Google on 10/5/15

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Brazil’s economy has grown rapidly since 1960, especially in the 1960s and 1970s. The average annual growth rate was 6.2% in the 1960s and 8.5% in the 1970s (Devlin, 1995). However, Brazil’s GDP growth has consistently outpaced Brazil’s GDP per capita growth illustrating Brazil’s struggle to keep up with its growing population. The 4.73% decline in GDP per capita for the 1980s can be explained by significant contractions in the Brazilian economy in the years 1981-3 as well as contractions in 1988, and 1991-3. This period is known as the “Lost Decade” due to the Latin American debt crisis during this time. The majority of Latin American governments (most notably Mexico, Argentina, and Brazil) acquired large loans from foreign banks in order to finance their unprecedented growth. The economic recessions faced by several major economies in the 1970s and 1980s coupled with rising oil prices slowed economic growth in Brazil. In addition, the US and Europe both increased interest rates making debt repayment for Brazil increasingly difficult. Eventually, some banks called for immediate loan repayments forcing Brazil (as well as other governments) to enact widespread austerity measures further deepening the recession. (Devlin, 1995)

Table 2.2

Brazil’s GDP and GDP per capita Growth since 1960^

Year Range %Change in GDP %Change in GDP/capita

1961-70 61.92 32.87

1971-80 85.09 59.69

1981-90 20.89 -4.73

1991-00 26.26 10.63

2001-2012 42.42 29.09

Total (1960-2012) 236.58 127.55 ^Data compiled from the World Bank (http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG/countries/1W?display=default, http://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG?display=default)

Brazil has the 8th largest GNP, but one of the highest levels of income inequality in the world. However, from 2003-2013 inequality was greatly reduced. Since 2013, 26 million people have been lifted out of poverty, causing the Gini coefficient to fall by 6% (The World Bank, 2004). Income of the bottom 40% grew nearly twice as much as the income of the total population. However gender inequality is still prevalent, where average female income is 29% less than average male income. When controlling for age, education, and hours worked, the difference rises to 34%. (The World Bank, 2004)

To further understand the regional, gender, and racial inequalities discussed in this paper, it is important to understand the history of slavery in Brazil and the significant role it has played in Brazil’s development. 2.2: Slavery in Brazil Slaves were first brought to Brazil in the 1500s, and Brazil quickly became the largest importer of African slaves in the world (Setti, 2015). When slavery was finally abolished in

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1888, almost 5 million slaves had been imported from Africa (Setti, 2015). Slaves provided an essential labor force for the growing sugar industry of colonial Brazil, Brazil’s chief export at the time. The Northeast was home to some of the largest sugar plantations in the world, and subsequently a large share of Brazilian slaves were sent there. In the years leading up to 1888, many slaves escaped the sugar plantations and formed their own colonies in Northeast Brazil. These colonies grew substantially after 1888 as freed slaves migrated to these already established colonies. To this day the Northeast is still largely dominated by those of African descent. Unfortunately, the economies of these colonies never grew substantially as the Northeast still lags behind the rest of the country in economic growth. 2.3: Migration/Urbanization Policies Migration and urbanization has long played a significant role in the development of Brazil and has been a main topic of political debate and reform in the last 50 years. This started in 1964 when a military regime took control of the country and attempted to stimulate rural economies in order to decrease migration to larger cities (Martine and McGranahan, 2010). These attempts failed and after the fall of the military regime in 1986, a different approach was needed. In 1986 a democratic regime took power and voters signaled a desire for better urban planning. Overall, the most successful urbanization policy was the Statue of the City (Estatuto da Cidade) passed in 1988, but not implemented until 2001. The Statue of the City regulates the master plans of all municipalities with more than 20,000 urban inhabitants. In addition, the Statue now allows squatting under certain conditions and the local government can now tax uninhabited property or property that is not being used in a socially beneficial manner (Martine and McGranaham, 2010). These policies have created additional opportunity for migration to urban areas while “organizing” it in a socially efficient way. This signaled a change in Brazilian politics as urban policy became a significant tool in fighting social inequality. The Statue of the City has generated much debate over social inequality and pushed the political process of urbanization in that direction. It is important to understand the political pressures of migration and urbanization in Brazil when assessing the analysis of later sections. 2.4: New Brazilian States

Migration and Urbanization policies have played a large part in the development of Brazil. Brazil has experienced significant growth, particularly in the Northern region. The population and economy of Brazil has been drifting from the crowded coast inwards since 1900. This shift was fueled in part by the relocation of the capital from Rio de Janeiro to Brasília in 1960 (Wade, 2011). In addition, six new states were created since 1960 (Acre, Rondônia, Mato Grosso do Sul, Amapá, Roraima, and Tocantins) and one has been dissolved (Guanabara). Five out of the six new states are located in the North, with Mato Grosso do Sul in the center.

The territory of Acre became important due to its rubber trees in the early 1900s but its economic importance was short-lived. During the Second World War the rubber industry was revived, attracting migrants from the crowded East coast and poor Northeast regions. In 1962 Acre won its statehood after complaining of official neglect (Minahan, 2007). After Rondônia became a state in 1981, the Brazilian government launched “Polonordeste,” an initiative to distribute land in Rondônia to poor settlers (Wade, 2011). As a result, the population of

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Rondônia exploded from 116,620 in 1970 to 1,130,874 in 1991 (Wade, 2011). The last restructuring act of Brazil was the 1988 Constitution which granted the Northern territories of Amapá and Roraima statehood and split the state of Tocantins from Goiás.

Table 2.3

New Brazilian States

Year New States

1962 Acre gains statehood

1975 Guanabara merges with Rio de Janeiro

1977 Mato Grosso do Sul splits from Mato Grosso

1981 Rondônia gains statehood

1988 Amapá and Roraima gain statehood and Tocantins splits from Goiás

Figure 2.1 Map of the New Brazilian States

http://www.brazil-help.com/brazilian_states.htm

The reclassification of territories and changes to state lines can be attributed in part to

population growth. While the population of Brazil has nearly tripled in the last 60 years, the populations of all new states have grown much faster. As shown in Table 5, populations of the youngest states have at least quadrupled in size. Most notably, Roraima and Rondônia have experienced a staggering 1528% and 2207% growth, respectively.

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Table 2.4 Population by Major region from 1960-2010 from the Demographic Census^

Federative Units and Household Situation

Population from the Demographic Census

9/1/1960 (1)

9/1/1970 (1)

9/1/1980 (1)

9/1/1991 (2)

9/1/2000 (2)

8/1/2010 (2)

Brazil 70,992,343 94,508,583 121,150,573 146,917,459 169,590,693 190,755,799

Rondônia 70,783 116,620 503,125 1,130,874 1,377,792 1,562,409

Acre 160,208 218,006 306,893 417,165 557,226 733,559

Roraima 29,489 41,638 82,018 215,950 324,152 450,478

Amapá 68,889 116,480 180,078 288,690 475,843 669,526

Tocantins 328,486 537,563 738,688 920,116 1,155,913 1,138,445

Rio de Janeiro 6,709,891 9,110,324 11,489,797 12,783,761 14,367,083 15,989,929

Mato Grosso do Sul 579,652 1,010,731 1,401,151 1,778,741 2,074,877 2,449,024

Mato Grosso 330,610 612,887 1,169,812 2,022,524 2,502,260 3,035,122 ^Data from the Instituto Brasileiro de Geografia e Estatística 2010 census

Table 2.5

Population Growth 1990-2010^

State % Population Growth

Brazil 269%

Rondônia 2,207%

Acre 458%

Roraima 1,528%

Amapá 972%

Tocantins 421%

Mato Grosso do Sul 422% ^Data calculated from the Instituto Brasileiro de Geografia e Estatística 2010 census

2.5: Education Policies In addition to infrastructure developments, Brazil’s education systems and policies have improved drastically since the 1960s. Even during the military regime two major educational improvements took place. The first was the MOBRAL, Movimento Brasileiro de Alfabetizacao.

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This was a large movement to eradicate adult literacy from the country. It was mainly experimental, however its success led to similar future illiteracy-combatting programs. The second major event was Law 5,692 passed in 1971. This law completely changed the structure of the entire education system. It led to a sharp increase in average education. Progression to secondary school increased 40% to 85% in two years. (Schwartzman, 2003)

Data from the World Bank: (http://data.worldbank.org/indicator/SE.SEC.ENRR/countries/1W?display=graph) In the 1990s after the military regime, Brazil created the National Program of Literacy and Citizenship to further combat illiteracy. It reduced illiteracy in Brazil by 70%. A new model of elementary school was also put into place in the mid-90s called CIACs, centro integrado de Educacao Popular (Integrated Center for General Education). CIACs supported children from poor families with education and food. In 1997, mandatory exams were implemented to assess primary education quality, called Povaos (Schwartzman, 2003). The program was then extended to high schools and called ENEN (Exame Nacional do Ensino Medio). Finally the program was expanded to elementary schools and called SAEB (Sistema de Avaliacao da Educacao Basica). Since the successful policies in the 90s, Brazil has given educational policy much greater attention.

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A three-pronged approach has been adopted:

1. Education of finance equalization 2. Conditional cash transfers 3. Education results measurement Taking what we know about the current and historical political and economic situation ln

Brazil, we now look at the income effect of educational expansion and attainment. We find that the returns to education have changed over time with respect to race, gender, and geography. We also find that there is a gap in educational quality based on geographic location'

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Section 3

Changes in Distribution of Schooling and its Impact on Income Inequality and Disparity in Brazil

Joel Ferguson Porter Reim

Frederic Chasin

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3.1: Introduction

It is well established that education is one of the most important determinants of income and that the distribution of education has some impact on income inequality. The question of how much of an impact the distribution of schooling has on income distribution is an important question that has been studied before. Lam and Levinson (1992) found that changes in the distribution of education were working to equalize incomes in Brazil despite the increase in income inequality that had occurred over the time period studied. This conclusion was reached by analyzing the changes in mean education, variance of schooling, and returns to education among cohorts.

We expand upon the work done by Lam and Levinson by looking at how the schooling inequality between different groups of people has affected income inequality, attempting to quantify the impact of education on income inequality, and predicting future trends of income inequality in Brazil. In section 3.2 we replicate Lam and Levinson’s study using more recent survey data to see if it is still true that changes in the distribution of education have continued to be an equalizing force for income. We find that current trends are in line with those found in 1992. In fact, we find that there has been significantly more convergence in the distribution of schooling and steep declines in returns to education for working age males since the original study was published. Lam and Levinson’s original only looked at the distribution of education and income for working-aged men. In section 3.3 we extend the methodology used by Lam and Levinson to get a sense of how the distribution of schooling between groups of people has changed over time. In particular, we look at how differences in educational attainment and returns to education for Afro-Brazilians and rural Brazilians as well as labor force participation of women have changed over time and affected income inequality. We find a general fall in disparity between these groups. In section 3.4 we try to understand how the distribution of schooling affects income inequality by simulating different distributions of education while keeping all other characteristics the same. Despite significantly lower returns to education than in the past, inequality in schooling still has a very large effect on the distribution of income. In section 3.5 we forecast how education inequality will continue to change in Brazil and provide some concluding remarks. 3.2: Lam and Levinson replication

Lam and Levinson (1992) split income-earning men born between 1925 and 1963 into three-year birth cohorts in order to approximate secular changes in mean education and variance of education as well as returns to schooling. By using the same survey we can easily compare our results with those found by Lam and Levinson. Using the same age group as Lam and Levinson, 22-60, there are four cohorts that perfectly overlap with the 1985 data. This allows us to directly compare those four cohorts between 1985 and 2012. For the purpose of this section, we use the exact same subsample of the survey, 22-60 year old men with positive earnings. By splitting up the population into three-year cohorts it is possible to estimate change over time with a single cross-section of data. We utilize the power of this novel methodology later on in our analysis of changes in income between different groups. Thanks to the large size

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of the PNAD data sets, the cohort samples still have adequately large sizes with the smallest having more than 4000 observations. Educational attainment and distribution of schooling

Table 3.1 Educational attainment for three-year birth cohorts, Brazilian males, 2012 and 1985

PNAD

Age Group (1)

Birth cohort, 1985 (2)

Birth cohort, 2012 (3)

Sample size, 1985 (4)

Sample size, 2012 (5)

Mean, 1985 (6)

Mean, 2012 (7)

Variance, 1985 (8)

Variance, 2012 (9)

Coeff. of variation, 1985 (10)

Coeff of variation, 2012 (11)

22-24 1961-1963

1988-1990 13,937 8,730 5.98 9.52 16.08 11.36 0.67 0.32

25-27 1958-1960

1985-1987 13,024 8,482 5.93 9.58 17.80 13.25 0.71 0.34

28-30 1955-1957

1982-1984 11,734 8,662 5.89 9.26 19.33 15.52 0.75 0.38

31-33 1952-1954

1979-1981 10,622 8,378 5.77 8.80 20.66 17.47 0.79 0.42

34-36 1949-1951

1976-1978 9,643 7,674 5.24 8.31 21.00 19.10 0.87 0.47

37-39 1946-1948

1973-1975 8,386 7,283 4.95 7.87 20.84 19.27 0.92 0.49

40-42 1943-1945

1970-1972 7,634 7,160 4.43 7.61 19.12 19.54 0.99 0.51

43-45 1940-1942

1967-1969 7,123 6,590 4.08 7.93 17.79 20.98 1.03 0.51

46-48 1937-1939

1964-1966 6,109 6,511 3.92 7.38 16.75 22.37 1.04 0.56

49-51 1934-1936

1961-1963 5,588 6,096 3.78 7.03 16.52 21.72 1.08 0.57

52-54 1931-1933

1958-1960 4,942 5,526 3.58 6.83 15.70 21.44 1.11 0.69

55-57 1928-1930

1955-1957 4,590 4,820 3.32 6.46 14.84 21.90 1.16 0.62

58-60 1925-1927

1952-1954 4,099 4,246 3.05 6.07 14.03 22.94 1.23 0.67

22-60 1925-1961

1952-1990 107,431 90,158 4.98 8.28 19.11 19.18 0.88 0.53

Table 3.1 shows descriptive statistics on the educational attainment of males by age

group for both the PNAD data sets. Columns (6) and (7) show mean educational attainment in 1985 and 2012 respectively. Columns (8) and (9) show the variance in schooling which can be considered a measure of schooling inequality. Columns (10) and (11) present the coefficient of variation, a measure of inequality independent of mean.

In line with what Lam and Levinson found, mean educational attainment has continued to rise since 1985 with the average 22-24 year old male getting 3.5 more years of education in 2012 than in 1985. From the1925-1927 cohort to the 1988-1990 cohort mean education attainment has more than tripled. Notably, the increase in mean educational attainment has been relatively steady over the entire observed period as seen in fig. 3.1. One notable exception to this steady increase can be seen in the 1970-1972 cohort. This may have to do with the fact that they grew

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up during Brazil’s “lost decade,” when economic growth stagnated and pessimism was high, perhaps leading parents to pull their children out of school.

Variance in level of schooling and the coefficient of variation have also, for the most part, been decreasing. The continued fall in the coefficient of variation is particularly important, showing that schooling inequality has continued decreasing between 1985 and 2012. This and the rise in mean educational attainment suggest that there has been significant convergence in educational attainment since 1985.

Interestingly, the four cohorts that overlap between the 1985 and 2012 data sets (1952-54, 1955-57, 1958-60, and 1961-63) have all experienced increases in schooling variance. However, as shown in columns (10) and (11) these four cohorts have also all experienced decreases in the coefficient of variation of schooling, suggesting that the distribution of education among them has become more equal since 1985.

Figure 3.1 Mean educational attainment by birth cohort, Brazilian Males: PNAD 1985 and PNAD

2012

Another comparison of note is that the mean level of schooling has increased for all of the overlapping cohorts. The inverse relationship between education and mortality suggests that the changes we find by using cross-sectioned data to estimate time trends have a downward bias. However, as can be seen in fig. 3.1, the greatest increase in educational attainment came to the cohort that was the youngest in 1985, rather than the oldest. Therefore, it is safe to say that this increase is due at least in part to continued educational attainment between 1985 and 2012. This

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makes intuitive sense as Lam and Levinson noted that the youngest cohorts might not have completely finished schooling (202).

Overall, these trends show a convergence in educational attainment. With a rising mean schooling level and coefficient of variation, it appears that people with lower levels of education have been catching up since 1985. To get a better sense of this phenomenon, we look at frequency of completion by year of schooling for three different cohorts, the oldest, middle, and youngest, shown in Fig. 3.2. It is easy to see that educational attainment has been converging to eleven years, or the equivalent of secondary school completion in the Brazilian schooling system. Other smaller but still notable peaks occur at the end of the first half of primary schooling and the end of middle school. Unfortunately, we did not have access to Lam and Levinson’s complete PNAD 1985 data set, but fig. 3.2 can also be compared to fig. 2 in their paper (205). The most striking difference between the figures is how the largest peak in educational attainment has shifted over time from no education to completion of high school. The other peaks are now much lower in relative size than they were in 1985, showing a more equal distribution of schooling.

In order to visualize the decline in inequality in schooling we present the education Lorenz curves for the youngest cohort, the cohort with the highest variance in educational attainment, and the oldest cohort in fig. 3.3. What we see is that distribution of schooling has unambiguously and substantially improved over the observed period. The Gini coefficients for the 1952-1954, 1964-1966, and 1988-1990 cohorts are .45, .35, and .18 respectively, showing just how great this improvement has been.

Figure 3.2

Frequency distribution, years completed schooling: 1952-54, 1964-66, 1988-90 birth cohorts, Brazilian males, PNAD 2012

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Figure 3.3 Lorenz curves, years of completed schooling: 1952-54, 1964-66, 1988-90 birth cohorts,

Brazilian males, 2012

Returns to education and income inequality

While the convergence of educational attainment is important for ensuring that education acts as an equalizing force on income inequality, we must also look at returns to education. We follow Lam and Levinson (1992) and estimate these returns using the same simple Mincer OLS regression

ln 𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝛽𝛽𝑖𝑖𝑆𝑆𝑖𝑖𝑖𝑖 (Equation 3.1) Where Y is income, S is an educational attainment, and subscripts i and c index individual ‘i’ in cohort ‘c’. Because the cohorts have so little variation in age, the coefficient found for schooling can be seen as indicative of mean returns to schooling. As shown in table 3.2, returns to education have steadily decreased over time. This, coupled with the fact that the distribution of schooling has become more equal suggests that education has continued to equalize incomes in Brazil. We calculate the impact of variance in education on variance of the log of income in column (10) of table 3.2 and find a nearly unambiguous downward trend. Additionally, column (5) shows that the variance in log income has been generally declining over the observed period,

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so not only has education’s contribution to income inequality been decreasing, but income inequality within cohorts has also been decreasing. These trends are also shown in fig. 3.4. This is in contrast to what was found by Lam and Levinson using the 1985 data, which was that variance in income peaked for the 37-39 year old age group and declined for younger groups (211). It is important to note that due to currency reforms, this table cannot be directly compared to the results found by Lam and Levinson.

Figure 3.4. Earnings inequality, three-year birth cohorts: Total variance of log earnings, explained

variance, and residual variance, males with positive earnings, PNAD 2012

In general, we find that trends have continued since 1985. The distribution of schooling has continued to become more equal with educational attainment converging to completion of high school. In contrast with the 1985 results, variance in the log of income has steadily declined between cohorts. This along with the decline in schooling inequality and reduction of returns to education shows that changes in the distribution of education for the observed cohorts have been equalizing. As shown in column (5), mean returns to education have also fallen. This is noteworthy because holding all else equal, declines in returns to education lead to a more equal distribution of income. Not only is this true, but as shown by Lam and Levinson the effect a change in the mean return to education has on variance of income is the square of the magnitude of the change

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(1992, 209). This means that returns to education have an especially significant role in determining income inequality. The effect of the decline in returns to education as well as the convergence in mean education found in table 3.1 can be seen in column (10), which presents the portion of variance of income explained by education, as well as in Fig. 3.4. The clear downward trend in the explained portion of income inequality shows that changes in the distribution of education and returns to education have been equalizing.

Table 3.2 Monthly income by birth cohort: Descriptive statistics and cohort-specific earnings

equations, Brazilian males with positive earnings, 2012 PNAD Age group (1)

Birth cohort (2)

Sample size (3)

Mean log earnings (4)

Variance log earnings (5)

β (6)

Std. err. (7)

R2 (8)

V(u) (9)

β2V(S) (10)

22-24 1988-1990 6,881 6.77 0.375 0.066 0.0021 0.126 0.325 0.049 25-27 1985-1987 7,229 6.91 0.482 0.091 0.0020 0.217 0.372 0.110 28-30 1982-1984 7,703 6.99 0.573 0.100 0.0019 0.267 0.418 0.155 31-33 1979-1981 7,610 7.07 0.634 0.104 0.0019 0.288 0.455 0.189 34-36 1976-1978 6,976 7.09 0.681 0.105 0.0019 0.303 0.470 0.211 37-39 1973-1975 6,717 7.08 0.724 0.107 0.0020 0.294 0.504 0.221 40-42 1970-1972 6,607 7.11 0.721 0.110 0.0020 0.321 0.484 0.236 43-45 1967-1969 6,123 7.13 0.797 0.100 0.0022 0.260 0.588 0.210 46-48 1964-1966 6,004 7.14 0.828 0.106 0.0021 0.298 0.577 0.251 49-51 1961-1963 5,624 7.16 0.872 0.120 0.0022 0.351 0.560 0.313 52-54 1958-1960 5,065 7.15 0.908 0.121 0.0024 0.339 0.594 0.314 55-57 1955-1957 4,387 7.14 0.968 0.130 0.0025 0.379 0.598 0.370 58-60 1952-1954 3,869 7.16 0.978 0.120 0.0027 0.334 0.648 0.330 22-60 1952-1990 80,795 7.07 0.719 0.096 0.0005 0.246 0.542 .177

Additionally, the unexplained portion of income inequality, shown in column (9) has also been decreasing, leading the decreases in income variance seen in column (5). However, as seen in Fig. 3.4, the unexplained portion of inequality has grown in terms of its share of total income variance. So while education still plays an important role in determining income inequality, that role is diminishing. While the analysis in this section shows how changes in income affect overall income inequality, it does not show anything about how income disparity between groups has changed. Lam and Levinson focused on the picture of Brazil as a nation, ignoring differences between groups of people in order to gain a broad understanding of how income inequality was changing. We now extend the Lam and Levinson (1992) methodology to analyze the differences in education and income between different groups. 3.3: Changes of differences in educational attainment and income inequality between groups

One interesting way we can use Lam and Levinson’s methodology to learn more about how changes in schooling inequality have affected inequality in general is to look at how the distribution of education between groups of people has changed over time and how that has affected the incomes of these groups. We look at Afro-Brazilians, women, and people living in rural areas.

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Afro-Brazilians

Figure 3.5 compares how mean educational attainment has changed for Afro-Brazilians and White Brazilians. While Afro-Brazilians have lower mean schooling for every observed cohort, we can see that the gap between them has been slowly closing. The difference in average years completed of schooling between Afro and White Brazilians has decreased from 2.36 for the 1952-54 cohort to 1.47 for the 1988-90 cohort. While this graph shows great advancement in mean educational attainment for both groups, it also shows the extent to which Afro-Brazilians continue to lag behind whites. The mean schooling of Afro-Brazilians in the youngest cohort is only slightly above that of whites in the 1964-66 cohort, suggesting that Afro-Brazilians get the same education as White Brazilians did about 24 years ago.

Figure 3.5 Mean educational attainment by birth cohort: Afro-Brazilians and white Brazilians, males,

PNAD 2012

This disparity in educational attainment may be partially due to the fact that Afro-

Brazilians experience lower returns to education than whites for every cohort observed. Lower returns to education make the investment in education less attractive for Afro-Brazilians, which can lead to lower mean educational attainment. Fig. 3.6 compares returns to education for White

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and Afro-Brazilians using the same simple Mincer regression as in the previous section. As the variable being modeled is log of income, returns to schooling are defined as the %age increase in income that is the result of one additional year of schooling. It is clear from Fig. 3.6 that Afro-Brazilians experience systematically lower returns to education. Take the example of a 28-31 year old male. The expected incomes for an Afro-Brazilian and a white Brazilian with no education in this age group are the same. However, if they have completed high school, the white Brazilian can expect to earn around 300 more reais a month than his Afro-Brazilian counterpart. This is equivalent to nearly a third of a standard deviation difference in log of income. This is problematic for ensuring that distribution of education is changing in a way that is conducive to decreasing income inequality. As it stands, Afro-Brazilians not only earn significantly less per year of education than white counterparts, but also get less education, possibly due to these lower returns. Both effects contribute to income disparity between the two groups. Also problematic is the fact the difference in returns to education appears to be increasing. As the youngest cohorts are generally still in school, considering their returns to education is problematic. However, the difference in log of income between White and Afro-Brazilians has decreased1, suggesting that reduction of the educational attainment gap has overpowered changes in returns to education.

Figure 3.6 Returns to education by birth cohort: Afro-Brazilians and white Brazilians, males, PNAD

2012

1 Appendix A3.1

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Women Women were left out of Lam and Levinson’s original study due to differences in their labor markets that would have obscured results. While these differences still exist, it is important to understand how these differences affect income disparity between men and women. For this reason we analyze differences educational attainment and remuneration for men and women. As seen in figure 3.7, women tend to have completed more schooling than men and the difference has been growing over the years. Women also generally have higher returns to education than men2, so it makes sense that women get more education than men as they can expect more for their investment. In general both these effects should be equalizing or even lead to women having higher mean incomes, but this is not the case.

Figure 3.7 Mean educational attainment by birth cohort: Men and women, income earners, PNAD

2012

There are two difficulties in comparing women with men. Firstly, women have not

experienced decreases in returns to education. On the contrary, younger women tend to have higher returns to education than older women. The average %age increase in income for one additional year of education has risen from 9.9% to 13.8%. This may be due to the fact that young women must be significantly better remunerated to entice them to enter the labor market. 2 Appendix A3.2

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Secondly, women have a much lower predicted income if they are uneducated. Taking the example of a 28-31 year old again, an uneducated man can expect to earn almost 300 reais more per month than an uneducated woman. This is again about a third of a standard deviation in log of income for males of that age group. Not only that, but a woman in this age group would need on average about seven years of schooling to earn the same income as man with no education.

To better understand how income disparity between men and women has changed between the observed cohorts we graph the expected log of income for a man and woman with the mean education for each cohort in fig. 3.8. Except for some progress in the youngest cohorts, the gap in earnings has remained relatively constant with an average difference of .61. So while women continue to get more education than men and experience higher returns to education the income disparity between men and women has not changed much.

Figure 3.8 Predicted log of income using mean educational attainment by birth cohort: Men and

women, income earners, PNAD 2012

Rural

Rural Brazilians also face disparities in educational attainment and income as illustrated by Fig. 3.9. There has been slow but steady progress with the difference in mean educational attainment shrinking from 3.73 years for the oldest cohort to 2.60 years for the

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youngest. This could be due to a number of things, among them the educational reform that took place in 1971, which raised the mandatory amount of schooling. Since mean educational attainment in rural areas was very low before the reform, if the new schooling requirements were well enforce they most likely had a significant impact on mean education in rural areas. Individuals living in rural areas also experience lower returns to education and similar to Afro-Brazilians, the difference in returns to education between rural and urban Brazilians has generally been increasing, peaking for 25-27 and 37-39 year olds3.

Figure 3.9 Mean educational attainment and log income by birth cohort: Rural and urban, Brazilian

males, 2012

This makes the rural case particularly interesting. For the two oldest cohorts, returns to

education in rural areas are higher than in urban areas despite the mean educational attainment in rural areas for these cohorts being less than half that of in urban areas. This may be due to an extra large premium put on relatively scarce education in rural areas for these cohorts. Additionally, despite the fact that the gap in returns to education has grown, with returns to education in rural areas now substantially lower in rural than in urban areas, the gap in mean educational attainment has steadily shrunk. This may be due to differences in education quality. As will be shown in Section 4, rural areas, particularly those in the North East of Brazil, have lower than average education quality, which could lead to lower returns to education. 3 See Appendix A3.3

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It is important to note that the difference in mean log earnings between urban and rural individuals has been generally decreasing. This suggests that despite the changes in returns to education, the ground being made up in rural areas in terms of educational attainment has been working to lower disparity between urban and rural Brazil. Summary

In general, we have seen that differences in education and earnings profiles of Afro-Brazilians, women, and rural Brazilians have led to disparities in income for between groups. For Afro-Brazilians, lower returns to education and lower mean educational attainment contribute to income disparity between them and whites, but there has been some progress for the observed cohorts. The gap between White and Afro-Brazilian mean educational attainment has decreased from 2.36 years to 1.47 and the log earnings gap has decreased from 0.24 to 0.11 over all observed cohorts. Women’s earnings profiles have kept mean income below that of men despite having higher rates of return to education and on .6 more years of schooling on average. For those living in rural areas, the education gap has been closing while the difference in returns to education has been generally increasing, in the end reducing disparity between rural and urban individuals. In the next section, we look at how much these differences in education and earnings profiles affect overall income inequality. 3.4: Income inequality simulations To determine the impact of education on income disparities, we estimated inequality models incorporating three distinct educational adjustments, and then compared each to the existing system. We tested the effects on income of the rural-urban, afro-white, and the female-male education gaps. Additionally, we simulated income inequality if every male had the mean years of education and compared it to income inequality under the existing education distribution. Method We predicted current income inequality by first calculating determinants of monthly income with:

𝑙𝑙𝑙𝑙𝑌𝑌𝑖𝑖 =∝ +𝛽𝛽1𝐸𝐸𝑖𝑖 + 𝛽𝛽2𝐴𝐴𝑖𝑖 + 𝛿𝛿𝑽𝑽�𝒊𝒊 (Equation 3.2)

where for individual i, E represents years of education, A represents age, and V represents gender, race, rural status, and migration status. We held V fixed, applied educational corrections, and predicted new income distributions with:

𝑌𝑌𝑖𝑖 = 𝑒𝑒^(∝ +𝛽𝛽1𝐸𝐸𝑖𝑖 + 𝛽𝛽2𝐴𝐴𝑖𝑖 + 𝛿𝛿𝑽𝑽�𝒊𝒊) (Equation 3.3)

Lorenz curves were drawn to compare hypothetical inequalities with current inequality, and Gini coefficients were calculated to quantify disparities. The actual GINI coefficient associated with existing inequality was calculated to be .33. Rural

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We found that people living in rural communities had an average of 3.24 fewer years of education than urban Brazilians. We amended education by 3.24 years for all rural-located individuals. This adjustment had a low impact on earnings, as shown in figure 3.9, improving the GINI coefficient from .33 to .31. Of the tests done, this modification produced the least impact per additional years of education. This small change for such an improvement in education indicates that rural communities experience significantly lower returns to education than urban counterparts, and benefit less from additional education than Afro-Brazilians (discussed below).

Figure 3.9 Lorenz curve of real earnings vs earnings without rural-urban educational gap

Race To determine economic inequality not due to the education race gap, we applied an additional 1.93 years of schooling to each Afro-Brazilian. This amount represents the difference in years between the mean Afro-Brazilian education and the mean White-Brazilian education. This was done to more closely align average Afro-Brazilian cohort education levels with those of the same age White-Brazilian cohorts. Due to this reassignment, income inequality improved slightly, and GINI improved from .33 to .31 (figure 3.10). While the Gini improvement is equal to that of the rural education simulation improvement, the lesser educational curve indicates that Afro-Brazilians respond more positively to educational corrections than rural Brazilians. When

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compared to White-Brazilians, Afro-Brazilian earnings are impacted by education and educational inequality more than rural Brazilians are when compared to urban Brazilians.

Figure 3.10 Lorenz curve of real earnings vs earnings without education-race gap

Gender Investigating our findings that women have an average of one year more of education than men, are experiencing rising returns to education, and still earn significantly less; we predicted the real income distribution for men and women and compared it to a simulated income distribution if women were subjected to the same pay scheme as men. To calculate the real income distribution, we first regressed income against years of education and gender using the equation:

𝑙𝑙𝑙𝑙𝑌𝑌𝑖𝑖 =∝ +𝛽𝛽1𝐸𝐸𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐺𝐺𝑖𝑖𝑖𝑖 (Equation 3.4)

where, for individual i in cohort c, E represents years of education and G is a dummy variable that represents gender. The outputted coefficients 𝛽𝛽1 and 𝛽𝛽2 were then used to estimate average income per years of education and gender with the equation:

𝑌𝑌𝚤𝚤� = 𝑒𝑒^(∝ +𝛽𝛽1𝐸𝐸𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐺𝐺𝑖𝑖𝑖𝑖) (Equation 3.5)

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Both of these equations were used in conjunction with the entire data set; working men and working women, ages 22-60. We then calculated corrected earnings using similar equations, with the following modification:

𝑙𝑙𝑙𝑙𝑌𝑌𝑖𝑖 =∝ +𝛽𝛽1𝐸𝐸𝑖𝑖𝑖𝑖 (Equation 3.6) In this equation (3.6), gender was removed as an independent variable, and only data for

men was processed in the regression. The outputted 𝛽𝛽1 was then used as a coefficient for income prediction in equation:

𝑌𝑌𝚤𝚤� = 𝑒𝑒^(∝ +𝛽𝛽1𝐸𝐸𝑖𝑖𝑖𝑖) (Equation 3.7)

into which we inputted data for both men and women, incorporating the true number of years of education for each individual. This was done to discern impact on earnings of non-educational gender-linked factors. As above, Lorenz curves were draw for both curves (Figure 3.11).

Following is method, the actual Gini was calculated to be .30 and the Gini coefficient after equalization was .27. A larger improvement in inequality than that of the race or rural simulations, this rise indicates that women are subjected to greater inequality due to non-educational factors than Afro-Brazilians are due to educational-factors. Figures 3.7 and 3.8, respectively indicating the greater education levels of women and significantly lower wages support these findings.

Figure 3.11 Lorenz Curve of overall real earnings vs if men and women were paid at same rate

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Overall Equality Finally, to test income disparity due to educational distribution, all working men were assigned the mean level of education, 8.28 years. As opposed to the previous trials which only raised educational levels, this modification both lowered and raised educational attainment depending on where the individual fell. Understandably, this had a large effect on improving income inequality within the country (figure 3.11). Improving the GINI coefficient from .33 to .23, this test shows that a significant portion of income disparities within Brazil is due to overall educational inequality and not strictly due to racial, geographical, or biological inequality. While there is no single categorically defined cause of income inequality within Brazil, there is still great economic inequality due to overall schooling inequality.

Figure 3.12 Lorenz curve of real earnings vs earnings if all had mean years of education

3.5: Inequality predictions and conclusions Predictions To get an idea of how the distribution of education will continue to affect income inequality we predict mean educational attainment for the birth cohorts that form the 22-60 age group in 2021 and use these figures to compare future inequality to current inequality.

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To estimate mean education in 2021 for each cohort in the observed age group, we look at the percentage of each age group still in school. In order to predict mean education in three years, we use the formula:

𝑃𝑃𝑎𝑎 = 𝑛𝑛𝑎𝑎𝐸𝐸𝑎𝑎+3𝑛𝑛𝑎𝑎𝑅𝑅𝑎𝑎+1+𝑛𝑛𝑎𝑎(𝑅𝑅𝑎𝑎−𝑅𝑅𝑎𝑎+1)𝑛𝑛𝑎𝑎

(Equation 3.8) or equivalently:

𝑃𝑃𝑎𝑎 = 𝐸𝐸𝑎𝑎 + 𝑅𝑅𝑎𝑎 + 2𝑅𝑅𝑎𝑎+1 (Equation 3.9) where: P is predicted mean educational attainment in three years E is mean educational attainment R is the %age of individuals in school n is the number of observations a is an age group index Equations (3.8) estimates the mean schooling for a cohort in three years by first finding the total years of schooling for an age group. Then the number of students who will still be in school in three years is estimated using the % of the population of the next oldest cohort still in school. This number is multiplied by three and added to the total years of schooling. Assuming that all other students get one year of education, this number is also added to the total years of schooling to generate an estimated number of total years of schooling in three years. Finally, this new total number of years of schooling is divided by the number of observations in the cohort to generate a predicted mean level of education in three years. This prediction method provides a lower bound because as Fig 3.2 shows, more Brazilians are staying in school longer, suggesting the %age of students in each age group is rising rather than remaining constant.

We use this estimation method three times to predict mean educational attainment for the observed age group in 2015, 2018, and 2021, presented in table 3.4. As expected, we see mean educational attainment increase for each age group in each three-year period. One interesting prediction is that the best-educated age group becomes 28-30 in 2021 rather than 25-27 as seen in 2012. This is feasible because as people continue to attain higher and higher levels of education, they take longer to do so. Thus, it will most likely take longer to reach a terminal level of mean education for a cohort. We saw evidence of this in the increase in mean schooling for the four cohorts born in 1952-1963 between 1985 and 2012.

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Table 3.4 Educational attainment, Brazilian males, PNAD 2012

Age group (1)

Mean, 2012 (2)

Predicted mean, 2015 (3)

Predicted mean, 2018 (4)

Predicted mean, 2021 (5)

Increase in mean from 2012 to 2012 (6)

22-24 9.52 9.87 9.86 9.98 0.46 25-27 9.58 9.93 10.27 10.27 0.69 28-30 9.26 9.85 10.20 10.54 1.28 31-33 8.80 9.44 10.03 10.38 1.58 34-36 8.31 8.94 9.58 10.17 1.86 37-39 7.87 8.42 9.05 9.70 1.83 40-42 7.61 7.96 8.51 9.14 1.53 43-45 7.93 7.69 8.04 8.59 0.66 46-48 7.38 7.99 7.75 8.10 0.72 49-51 7.03 7.43 8.04 7.80 0.77 52-54 6.83 7.07 7.47 8.08 1.25 55-57 6.46 6.86 7.11 7.51 1.05 58-60 6.07 6.49 6.89 7.13 1.06 22-60 8.28 8.30 8.68 9.19 0.91

In order to estimate how these changes will affect income inequality, we graph predicted Lorenz curves for the 22-60 age group in 2012, 2015, 2018, and 2021 using the methodology used in the previous section. However, for these predictions individuals are assigned their cohort’s (predicted) mean educational attainment. In order to account for decreasing returns to education, we reduce the return to education by .002, the average change between cohorts in 2012, every three-year period. Because the Lorenz curves for the three future cohorts are so close, only those for 2012 and 2021 are shown in fig. 3.10.

Figure 3.13

Estimated Income Lorenz Curves, 2012 and 2015

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We find that the estimated Gini coefficient using this prediction method went from .34 in 2012 to .22 in 2021. While it is not likely that actual income inequality in Brazil will experience so extreme a decline by 2021, this result is still valuable. We see from this prediction that even if the %age of the population in school for each age group does not change, higher mean education of younger cohorts and expected decreases in returns to education will continue to equalize both the distribution of schooling and income. Conclusions

By replicating the methodology used in Lam and Levinson (1992) and expanding on their work, we found that education and schooling inequality continue to be extremely important factors of income and income inequality. Mean educational attainment of Brazilian males has continued to rise since 1985. The youngest adult cohort in 2012 has on average more than 3.5 more years of education than the youngest cohort in 1985. Returns to education and the share of income inequality attributable to schooling inequality have also continued to fall, showing that changes in the distribution of income have been equalizing. This is shown by the fact that the proportion of income variance explained by education has fallen continuously. However, inequality not due to changes in education or returns to education persists and has grown significantly as a proportion of total income inequality.

Although mean educational attainment is increasing, we observe significant differences in educational attainment and earnings profiles between groups. We find that although the difference in mean education between white and Afro-Brazilians has largely remained stable, it is beginning to decrease amongst the youngest cohorts. This coupled with a shrinking gap in returns to income suggests that disparity between white and Afro-Brazilians is decreasing. For women, we find that different earnings profiles have kept disparity between men and women relatively stable despite women continuing to receive more education and experience higher returns to education than men. For rural areas, we find that the gap in educational attainment has been steadily decreasing despite little obvious progress in terms of returns to education. However, the effect of increased education has overpowered any changes in returns to education and urban-rural income disparity has decreased. In order to estimate how much of an impact schooling inequality has on income inequality we run simulations using different hypothetical distributions of education. We find that distributing education equally across races explains about one third of income inequality in Brazil. Although this is a very large proportion of existing income inequality, there still remains a very significant portion of inequality that is unexplained by the distribution of education. The effects of eliminating the gaps in educational attainment between white and Afro-Brazilians and between rural and urban Brazilians also have significant impacts on the predicted Gini coefficient, showing that disparity between groups as a result of unequal distribution of education has a significant effect on overall income inequality. Eliminating differences in the earnings profiles of men and women also has a large effect on income inequality, showing how different returns to education between groups also have a significant effect on overall income inequality

Looking to the future of inequality in Brazil, we estimate a lower bound for the education of the observed age group in the year 2021 and find that mean educational attainment continues to increase and converge among all cohorts. This coupled with decreasing returns to education suggest that education will continue to have an equalizing effect on income inequality in Brazil.

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Section 4: Migration as a Strategy Out of Poverty

Authors:

Frederic S. Chasin

Tyler Linsky Mary Penn

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4.1: Introduction This section examines the effectiveness of migration within Brazil as a long-term strategy out of poverty. We present Yap’s findings from 1960 and offer our analysis to determine income returns to migration in 1960. We then replicate Yap using data from 2012 to see whether migration remains an effective means of escaping poverty half a century later. We conclude by identifying similar trends in income returns to rural-urban migration and migrant assimilation into the urban workforce between the two periods, suggesting similar workforce dynamics in urban Brazil. 4.2: Yap (1976) Methodology In the middle of the last century, policy makers considered rural-urban migration a catalyst in the expansion of urban poverty and the dilution of the traditional, low-productivity labor market in metropolitan areas. However, Yap’s empirical analysis of the economic returns to migration told a different story. Yap (1976) used the 1960 population census in Brazil to examine the income gains associated with migration as well as the assimilation of migrants into the urban labor market. She found both significant income benefits and rapid assimilation of migrants. The author estimated OLS earnings equations to examine the returns to migration and migrant assimilation into urban areas. Her sample included 56,000 people currently living in three major regions in Brazil – Northeast, East, and South. Appendix 4.2 offers an explanation as to why the North was excluded from Yap’s analysis and our results for returns to migration in the region. Other variables that may affect income, sex, age, race and completed education, are accounted for in her analysis. These variables are held constant when interpreting the effect of migrant status on income. Yap also compared incomes of migrants living in urban areas to their foregone earnings, estimated as the current mean income of non-migrants in rural areas. Differences in age, educational attainment, sex and race are taken into consideration in order to isolate income differences attributable to migration status from differences in human capital and other characteristics that may affect pay. The functions are of the following form:

𝐿𝐿𝑙𝑙𝑌𝑌𝑖𝑖 = 𝛼𝛼𝑜𝑜 + ∑𝑏𝑏𝑖𝑖𝐸𝐸𝑖𝑖𝑖𝑖 + ∑𝑐𝑐𝑖𝑖𝐴𝐴𝑖𝑖𝑖𝑖 + 𝑑𝑑1𝑆𝑆𝑖𝑖 + 𝑑𝑑2𝑅𝑅𝑖𝑖 + ∑𝑒𝑒𝑖𝑖𝑀𝑀𝑖𝑖𝑖𝑖 (Equation 4.1) Where: Yj = average monthly income of individual j, Eij = education level i of individual j [no formal education (omitted), some primary education, primary education completed, more than a primary education], Aij = age group i of individual j [10-19, 20-29 (omitted), 30-39, 40-49, 50 and over], Sj = sex of individual j [male (omitted), female] Rj = race of individual j [white (omitted), nonwhite] Mij = migration status I of individual j [rural non-migrant (omitted), rural-rural migrant, recent rural-urban migrant (0-4 years), less recent rural-urban migrant (5+ years)]

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Two additional variables were also used in subsequent regressions: employment status [wage earners (omitted), self-employed, employer] and sector of employment [traditional (omitted), modern]. 4.3: Description of Yap’s Results Yap found significant income gains associated with migration in all three regions. As shown in Table A 4.1, rural-urban migrants in the South earned about 38% more than non-migrants, on average, holding all else constant, which is similar to 40% in the Northeast, but significantly higher than Eastern states, whose recent immigrants only experienced an average of 20% improvement in income, holding all other variables constant. Less recent rural-urban migrants gained even higher income returns across all region, ranging from 43% in the South, 45% in the Northeast and almost 60% in the East, on average, holding all else constant.

The returns for rural-rural migrants, however, were not as substantial. In fact, rural-rural migrants in the South and East experienced negative returns to migration compared to non-migrants of, on average, holding all other variables constant, 13% and 1% respectively. While the Northeast did not have negative income returns, the effect of being a rural-rural migrant was only a 4% increase in income compared to non-migrants. Overall, Yap found that, in 1960, it was financially beneficial to migrate from a rural area to a non-rural area. This is likely due to more job opportunities educated or non-educated workers in cities. In rural areas, job opportunities are most likely limited to agriculture. 4.4: Replication Methodology We replicate Yap’s article using data from the 2012 PNAD. The dependent variable is the natural log of income, so that the results may be interpreted in ages. Migrant status is divided into the same groupings as Yap’s article: recent rural-urban migrants (0 to four years since migration), less recent rural-urban migrant (greater than five years since migration) and rural-rural migrant. The PNAD survey does not specify from where individuals migrated, so we assume that migrants originated from a rural area. The group “non-migrant” is excluded so that we can compare the incomes of various migrant types to non-migrants living in the same region. The replication also includes the same control variables: sex, age, race and completed education (one to three year, primary completed or more than primary completed). 4.5: Replication Results: Income Returns to Migration in Brazil, 2012 The income returns to migration have significantly changed since 1960 across all regions. In the South, recent rural-urban migrants experience a 15% increase in income, the East and Northeast have a 30% increase compared to non-migrants, on average, holding all other variables constant. Table A 4.2.9 in the Appendix shows that recent rural-urban migrants in Northern states have about a 13% greater income than non-migrants, holding all else constant. Income returns for less-recent migrants is highest in the Northeast, 28% then drops to 21% in the Northeast and finally only 8% in the South, on average, holding all else constant. The North actually experiences negative income returns of 20%, on average, holding all other variables constant. However, income returns for rural-rural migrants are negative for the South, Northeast and East compared to non-migrants. The North is the only region where it is beneficial to be a

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rural-rural migrant, who experiences an average of 8% increase in income, holding all else constant. 4.6: The Control Group: Returns to White Males, 20-29, with No Formal Education Tables 4.2a and 4.2b show the log of predicted monthly income for white males, 20-29 years old, with no formal education, separated by migration status. No significant conclusions come from comparing the regional returns to migration due to many changes in Brazil’s currency since 1960. The results are separated into two tables, table 4.2a showing Yap’s results and table 4.2b showing the results from 2012.

The 2012 differ from those found in Yap in several ways depending on the region observed. In the South in 1960, migrating correlated with an increase in monthly income of about 1.719 R$, and correlated with an increase of about 1.337 R$ per month. While average monthly predicted income was relatively similar for both recent and non-recent migrants in these regions, they differed significantly in the East, where recent migrants experienced an increase in monthly income of about .764 R$, and non-recent migrants experienced an increase in monthly income of 1.216 R$. These correspond to about a 45-50% increase in the monthly-predicted income in the South, around 35% increase in the Northeast, and 20% increase for recent migrants and 32% increase for non-recent migrants in the East.

However, the percent change in monthly-predicted income based on being a migrant in 2012 was significantly smaller. In the South, recent migrants experienced a 15% increase and an 11% increase for non-recent migrants versus rural non-migrants, 21% for recent and 19% increase for non-recent migrants in the Northeast, and a 9% increase for non-recent migrants and, interestingly, a 19.2% decrease for recent migrants in the East. It is unclear why there is suddenly a decrease in expected monthly income in the East.

These numbers were found by using the same equations used by Yap as well as 𝛼𝛼𝑜𝑜 by region from Table 4.1. To obtain our regression estimates of returns to migration for rural non-migrants, recent rural-urban migrants, and less recent rural urban migrants, we use:

a: = 𝑒𝑒α0

10

b: = 𝑒𝑒

(α0+Rr)

10 c: = 𝑒𝑒

(α0+Rl)

10 (Equation 4.2)

Where: Rr is returns to recent migrants (1-5 years) and Rl is returns to less recent migrants (5+ years)

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4.7: Urban Labor Force Assimilation Table 4.3 shows the effects of the assimilation of rural-urban migrants into the labor force. For this regression, we again make the assumption that all migrants came from rural areas. This table shows the log of the total monthly income per capita regressed against when they migrated (non-migrants), sex (male), age (20-29), race (white), educational attainment (no education), which sector they worked in (Traditional Sector), and their employment status (employed). We include the sector in which they work post-migration to see if they are assimilated into the formal or informal workforce. To do this, we looked to see if migrants paid income taxes, indicating work in the modern sector.

Interestingly, and perhaps expectedly, there is larger return to working in the modern sector compared to the traditional sector. The magnitudes of the increase in returns are 24.5%, 42%, and a 46% increase in income from working in the modern sector in 2012 compared to those in the traditional sector in the South Northeast and East respectively, versus a 27.3% increase in income in 1960 in the South (the only region for which the results for Yap were statistically significant.) Also, returns for employers have increased dramatically in the south by about 60%, decreased very slightly but still retain a huge magnitude in the Northeast from 123 %

Table 4.1 Changes in Returns to Migration in Brazil

Independent Variable

Yap South (1960) (1)

PNAD 2012 South (2)

Change in South (3)

Yap Northeast (1960) (4)

PNAD 2012 Northeast (5)

Change in Northeast (6)

Yap East (1960) (7)

PNAD 2012 East (8)

Change in East (9)

Recent (0-4) rural-urban migrant (Compared to Rural non-migrants)

.379** (2.59)

0.115** (7.05) -0.264 0.412

(2.11) 0.303** (11.46) -0.109 0.179

(0.99) 0.299** (15.34) 0.120

Recent (0-4) rural-urban migrant (Compared to Urban non-migrants)

0.011 (0.09)

-0.043** (-2.00) -0.054 0.245

(1.3) 0.184** (7.87) -0.061 0.100

(0.81) 0.111** (5.47) -0.011

Less recent (5+) rural-urban migrant (Compared to Rural non-migrants)

.428** (3.77)

0.083** (9.54) -0.345 0.454

(2.11) 0.279** (20.96) -0.175 0.585**

(4.95) 0.212** (20.37)

-0.373

Less recent (5+) rural-urban migrant (Compared to Urban non-migrants)

0.08 (-1.13)

-0.029** (-3.55) -0.109 0.289**

(3.16) -0.195

(-18.18) -0.484 0.296** (4.18)

0.121** (13.87) -0.175

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to 117% increased returns, and have become statistically significant in the East, at 88.1% increased returns for being an employer. These results imply that many of the changes in Brazil between 1960 and 2012 have significantly benefitted the bottom line of employers. 4.8: YAP Comparison The coefficients associated with both recent and less recent rural-urban migrants when compared to rural non-migrants have generally decreased compared to Yap’s figures, suggesting that there are now lower income returns to migration. There is 26.4% decrease in returns to migrating in the South between 1960 and 2012, an 11% decrease to migrating in the Northeast between 1960 and 2012 and a 12 % decrease in migrating in the East between 1960 and 2012 for recent rural-urban migrants when compared to rural non-migrants, on average, holding all else constant. When compared to urban non-migrants the coefficients for both recent and less-recent migrants have decreased in all regions, but the data from 1960 is not statistically significant. Recent rural-urban migrants earn 4 % less than urban non-migrants in the South. However, rural-urban migrants earn 18.5 % more in the Northeast, and 11 % more in the East when compared to urban non-migrants.

It is not certain why urban non-migrants earn more than migrants in the South but less than migrants in the Northeast and East, but one possible explanation is that migrants have characteristics that cause them to migrate. These characteristics may be positively correlated with higher earnings, like grit, hard-work or determination. These characteristics are difficult to capture, but may express themselves through higher levels of returns. Income returns to age have been relatively stable except for a notable increase for ages 50 and above in almost every case. For example, in the South (where the effect appears to be weakest) those 50 or older have expected incomes on average 38.2 % to 56.5 % higher than 20 to 29 year olds (holding all else equal). This could be interpreted as an increase in returns to experience, as shown in Table 4.5.

Table 4.2 Gender and Race Effects in Brazil

Independent Variable PNAD 2012 South (1)

Yap South (1960) (2)

PNAD 2012 Northeast (3)

Yap Northeast (1960) (4)

PNAD 2012 East (5)

Yap East (1960) (6)

Rural-born Female (compared to Male)

-0.444** (-55.38)

-.572** (-6.17)

-0.450** (-38.96)

-0.337 (-4.33)

-0.494** (52.55)

-0.551** (6.91)

Urban Female (compared to Male)

-0.366** (-51.85)

-0.549** (-11.82)

-0.416** (-44.47)

-0.588** (-10.46)

-0.464** (-61.81)

-0.503** (-11.07)

Rural-born Nonwhite (compared to white)

-0.186** (-21.61)

-.214** (-2.16)

-0.175** (-13.82)

-0.135 (-2.69)

-0.291** (29.58)

-0.076 (-1.32)

Urban Nonwhite (compared to white)

-0.275** (-36.37)

-0.175** (-3.25)

-0.008 (-0.82)

-0.118** (-2.21)

-0.324** (42.29)

-0.286** (6.81)

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The effects of being female and non-white have both remained negative. Being rural-born female compared to a male results in 44 % less income in the south, 45 % less income in the northeast, and 49.4 % less income in the East in 2012. However, these magnitudes have slightly decreased since 1960; although the Northeast is statistically insignificant, being a rural-born woman in the South results in a 13 % smaller decrease in income in 2012 than it did in 1960, and a 6 % smaller decrease in 2012 versus 1960 for a woman in the East. Females living in urban areas experience an even greater decline in gender-related pay differences. Compared to 1960, a woman in 2012 earns 18 % less in the South, 17 % less in the Northeast, and 4 % less in the East.

Table 4.3 Returns to Age in Brazil

Independent Variable

PNAD 2012 South (1)

Yap South (1960) (2)

PNAD 2012 Northeast (3)

Yap Northeast (1960) (4)

PNAD 2012 East (5)

Yap East (1960) (6)

50 and Over, Rural-born (compared to 20-29 year old)

0.382** (28.92)

.444** (4.06)

0.794** (43.55)

0.114 (1.49)

0.696** (44.92)

0.392** (4.32)

50 and Over, Urban (compared to 20-29 year old)

0.568** (53.48)

0.193** (-3.25)

0.778** (58.08)

0.656** (7.75)

0.662** (60.91)

0.369** (5.80)

Another important change is the decrease of income returns as a result of being non-

white. Where they are statistically significant in the South and East, returns have increased by 10 % and 4 % respectively from 1960 to 2012 among the urban non-white. For the urban non-white, there are also significant negative returns associated with race: people receive almost 19 % less income in the South, 17.5 % less income in the Northeast, and 29 % less income in the East if they are non-white. We cannot observe the net effect of these changes, since 1960 values are statistically insignificant in the East and Northeast, but we can be certain of a 3 % decrease in income returns in the South.

Income returns to age have remained constant except for a notable increase for ages 50 and above across all regions. For example, in the South (where the effect appears to be weakest) those 50 or older have expected incomes on average 38.2 % to 56.5 % higher than 20 to 29 year olds (holding all else equal). In the Northeast, where effects appear strongest, those 50 years or older have 77.8 to 79.4 % higher incomes when compared to 20-29 year olds. This could be interpreted as an increase in returns to experience over time, or possibly an increase in the return to experience based on education. An interesting finding is that returns to education above the primary level have decreased substantially, as illustrated by Table 4.4. The urban Northeast is an example of where the returns to having more than primary education have dropped from over 100 % to 54.8 % from 1960 to 2012. This decline is most likely due to the education reforms mandating that children stay in school longer. Since most people now have higher than a primary education, the income returns to that education have diminished. However, education is still clearly a strong, positive force in increasing income. By attaining more education, people are unequivocally better off compared to those with less education. When compared to those with no education, having more than primary education increased a rural-born migrant’s earnings by almost 60 %. Those increases also hold

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true in the East, where earnings increase by almost 73 %, and in the Northeast, where earnings increase by 58 %. It is important to note that this data captures all people with more than primary education, so there is some significant effect from those with very high levels of education, but overall it is clear that education has very positive effects on income.

Table 4.4 Changes in Returns to Education in Brazil

Independent Variable

Yap South (1960) (1)

PNAD 2012 South (2)

Change in South since 1960 (3)

Yap Northeast (1960) (4)

PNAD 2012 Northeast (5)

Change in Northeast since 1960 (6)

Yap East (1960) (7)

PNAD 2012 East (8)

Change in East since 1960 (9)

More than Primary, Rural-born (Compared to no education)

.931** (3.78)

0.592** (28.92) -0.339 1.148

(3.83) 0.580** (36.06) -0.568 1.043**

(4.54) 0.727** (48.93) -0.316

More than Primary, Urban (Compared to no education)

0.754** (9.86)

0.577** (32.25) -0.177 1.285**

(14.66) 0.548** (34.02) -0.737 1.077**

(16.20) 0.588** (41.28) -0.489

The returns to working in a modern sector have remained about the same in the South, decreasing by about 3 % (from around 27 % to 24 % increase in wages.) However, income effects of working in the modern sector have become statistically significant Northeast and East and relatively large, at 42 % and 46 % respectively (Table 4.5). These changes are important for two reasons. First, they show evidence that the modern sector has moved into both the Northeast and the East, and is having significantly positive effects on employee income. Secondly, for the South, they show that gains to income based on working in the modern sector have persisted, and may imply some ways to increase income through moving those in the traditional sector in to the private sector. The returns to being an employer have decreased in all regions but still remain relatively high with a 77 % increase in income in the South where this estimate is lowest. Finally, self-employment still has a wide difference in effects between regions, between a 9 % decrease and a 5 % increase, but the effect has declined in all regions compared to 1960 (Table 4.5).

Table 4.5 Returns to Self-Employed and Employers in Brazil

Employment Status

PNAD 2012 South (1)

Yap South (1960) (2)

PNAD 2012 Northeast (3)

Yap Northeast (1960) (4)

PNAD 2012 East (5)

Yap East (1960) (6)

Self-employed 0.052** (4.58)

0.137** (2.57)

-0.085** (-6.71)

-0.015 (-0.26)

0.024** (2.11)

-0.130** (2.56)

Employer 0.765** (39.02)

0.112** (8.02)

1.169** (37.70)

1.226** (5.41)

0.881** (34.24)

0.232 (1.76)

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4.9: Conclusions Migration still remains an effective strategy out of poverty in some regions, but it appears that migration is not as lucrative as it was in 1960. Brazil may have even attained the maximum benefit of migration, as shown by decreasing returns to migration in every region: around 26 % less for recent migrants and 35 % less for non-recent migrants in the 2012 South compared to the 1960 South, around 11 % and 18 % for recent and non-recent migrants respectively in the 2012 Northeast compared to those in the 1960 Northeast, and 37 % less returns to migration for those in the 2012 East than those in the 1960 East, on average, holding all else constant (changes in the incomes of recent migrants in the 1960 Census were not statistically significant). Furthermore, a 20% income decrease for non-recent migrants in the North compared to non-migrants is observed in our new analysis using 2012 data. Due to the preceding effects of both decreasing returns to migration as time goes on (and even negative returns) it will be interesting to observe if Brazil’s population continues to move away from the countryside or if the tide of migration will stop or perhaps even reverse itself. It is also important to note that differential educational quality by region may also play some part in returns to migrating. A migrant from the Northeast may be paid less than a migrant from the South simply because they suffer poorer quality education. This phenomenon will be explored further in this report.

However, if the returns to work in modern sectors continue be significantly higher than the return to work in traditional sectors, at 24.5 %, 42 %, and 46 % higher for the South, Northeast, and East respectively using 2012 data, it may not make sense for large groups of people to move back to rural areas, and instead they may try to enter modern labor markets. It will be interesting to see how much Brazil’s migration patterns shift over the next forty years.

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Section 5: Analyzing the Trade Off Between Labor and School

By Alexandra Bryant, Rachel Butler, Natalie Melville, Alexander Montiel, and Thaddeus Pinakiewicz

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5.1: Introduction

In section 4, the difference in school quality due to geographical differences is apparent. This leads to a new choice parents may take, choosing to put their students in school and where to put them in school. To look at this analysis this next section will look at whether parents are choosing to put their children in school or in work. As discussed in section three, education does have returns. Even though these returns are decreasing, they are still positive. Therefore, the choice between school and labor is a choice between a long-term investment in your child and a short term gains from the child working. To start off this analysis, section 5.2 will describe the characteristics of the students in school and in the labor workforce. This will show which people are in school, in work, or a mix of the two by age, race, and geographical factors. To further understand these relations, section 5.3 will analyze how these various characteristics affect the decision between entering the labor force and attending school. This will show how various factors influence these decisions including personal characteristics, geographical characteristics, home characteristics, and income factors.

Once the choices have been analyzed, the next step is to look at the outcome of these choices. This will be done by analyzing how far behind students are in school based on the typical grade level for a child’s grade. The effect of work on causing students to be behind in school will be further addressed to find the opportunity cost of choosing to put a child in the labor force.

The following analysis will be based upon 4 possible decisions of child labor and schooling.

Figure 5.1 Child Schooling and Labor Choice Set

Figure 5.1 shows the four possible outcomes.

1. In school and working 2. In school and not working 3. Not in school and working 4. Not in school and not working

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Table 5.1 Proportion of Children Ages 10 to 16 in

School and Working

Attends School (%)

Yes No

Works (%)

Yes 8.1 1.1

No 88.0 2.8

Figure 5.1 shows the %age of children in each choice set. The vast majority of children attend school (96.1 %). Of those who attend school, 8.2 % also work. Since our research is primarily concerned with the trade-off between schooling and work, we exclude the children who neither work nor attend school from our analysis. 5.2: Descriptive Statistics

The data used in this paper is a subset of the data from the 2012 PNAD. We looked at

children ages 5 to 16 and the choices their families made about sending their children to school and having them work. The survey includes data for 68,439 children in this age range.

There was a slight majority of male respondents, 51.48% to 48.52% female. The racial breakdown of respondents was 37.68% White, 0.28% Asian, 61.59% Afro-Brazilian (Black or “brown”), and 0.44% Indigenous. The majority of respondents lived in urban environments with only 17.69% living in rural environments. The children lived mostly in the more densely populated Northeast (30.87%) and Southeast (25.76%), followed by the Northwest (19.06%), South (13.87%) and finally, Central Brazil (10.45%).

Families ranged in size from 2 to 16 members with an average of 4.47 members. These families had from 1 to 11 children age 16 or below with an average of 2.25 children age 16 and below. They had an average monthly household income of $138.87 per capita. 15.65% of children live below the poverty line and 8.01% of children live below the destitute line, which we defined as making $38.74 per capita or less and $26.25 per capita or less, respectively. 24.89% of households only had one parent, while 87.35% of children live with their mother.

The majority of children age 5 to 16, 95.89%, attend school and this rises to 96.11% when looking at children age 10 to 16. Enrollment peaks at age 11 with 99.26% of children attending.

For the purpose of this section, we limit the scope of our analysis to children between 10 and 16 years of age. First, approximately 96% of the entire child labor force (2.2 million) falls within this age range. Figure 5.2 shows that a very small proportion children below the age of 10 are in the labor force. At any given age between 5 and 9, less than 1% of children have a job. This proportion begins to grow around the age of 10 and then sharply increases beyond the age of 14, when children are no longer required to attend school by law. At age 16, children become independent economic agents and can legally enter the workforce full-time, without restriction.

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Therefore, we exclude observations beyond the age of 16 because parents are no longer the primary decision maker in the schooling-labor tradeoff.

Figure 5.1 Child Labor Force Participation by Age

This age sample is consistent with historical trends, which show a changing child labor demographic. Figure 5.2 shows the mean age of entry into the labor force over time. The average age of entry into the labor force for the 1947-1949 birth cohort was approximately 13. This age has increased steadily over time until the most recent cohort, born between 1992 and 1994, with a mean age of 16.

Figure 5.2 Mean Age of Labor Force Entry Over Time

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9.19% of children 10 to 16 have worked in the past week, and of them 88.12% also attended school. Children who reported working in the past week worked from 1 to 98 hours per week, with an average of 24.45 hours.

Upon looking at demographics of those who attended school and those who worked, a common trend appeared. Those who attend school at higher rates are less likely to work, although by varying degrees.

Girls were only slightly more likely attend school, but boys reported working at a rate of 12.03% versus 6.17% of girls. Indigenous respondents reported the lowest enrollment %age and Asian respondents reported the highest, although both groups have less than 175 respondents. Indigenous children also reported working at much higher rates than other children. 30.43% of indigenous children work while every other race reported from 7.03 to 9.68% of children working. While rural children reported only slightly lower enrollment, they reported working at over three times higher rates than their urban counterparts. Region had little impact on enrollment %age, but it did affect the %age of children working. 11.04% of children in the Northwest reported working, the highest of any region, while only 6.94% of children in the Southeast reported working, the lowest of any region. There was a positive a positive correlation between the number of children under age 16 and below and employment. The employment rate increased from 8.97% with only one child 16 and under to 70% with 11 children age 16 and under. Schooling level of the head of household and attendance also appeared to have a positive correlation, and schooling level of the head of household and rate of employment of the children appeared to have a negative correlation.

5.3: Factors that Influence Decisions

Having discussed the characteristics of the Brazilian family, we now consider how these characteristics affect the parent’s decision to send their child to school or to work. Under the human capital model of education, education increases future income while working only increases current income. Therefore, the decision to work or go to school is a trade-off between current and future income.

To show the effects of the school-work decision we utilize a logistic regression. A logistic regression analyzes the dependent binary variable in the equation where: p() is the probability of a variable being equal to 1, x is a vector composed of our regression inputs, α is the intercept, and β is the regression coefficient, and ɛ is the error term. A logistic regression can be represented as follows:

𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙�𝑝𝑝(𝑥𝑥)� = log � 𝑝𝑝(𝑥𝑥)

1−𝑝𝑝(𝑥𝑥)� = 𝛼𝛼 + 𝛽𝛽𝑥𝑥𝑋𝑋𝑥𝑥 + 𝜀𝜀 (Equation 5.1)

Where 𝑋𝑋𝑥𝑥 is a vector composed of variables for: Personal characteristics Geographical characteristics Home characteristics Income factors

We can interpret the βx coefficients of the regression via their odds ratio point estimates. There is an estimate for every independent variable in the regression and the value ranges from

*See appendix A5.2 for list of all variables used in regression

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zero to infinity. An odds ratio point estimate with a value greater than one indicates that an event is more likely to occur, whereas a value less than one suggests that it is less likely to occur. The % concordance is determined by taking every data point from the dependent variable, and calculating the % of the cases where the model correctly estimates the variable.

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4 Values in parenthesis are the p-value results associated with each result. 5 This predicted wage was found by analyzing the working students and finding how different factors such as race, geographic locations, education, and age affect their wage. The results of this analysis can be found in appendix table A.5.2. This creates an equation that can be used on each child who works or does not work. This information will then be used to see how it affects the choice to go into the labor force. By analyzing this information, it will be clearer if a higher wage causes a student to be more likely to work due to the increased benefit the child can have from working

Table 5.2 Logistic Model Assessing Likelihood of School-Work Choices

Dependent Variable4 Attending School Attending School and Working

Not Attending School and Working

Age 2.6** 0.95** 13**

(<.0001) (<.0001) (<.0001)

Age2 0.95** 1.0** 0.93**

(<.0001) (<.0001) (<.0001)

Female 1.1** 0.43** 0.27**

(<.0001) (<.0001) (<.0001)

Rural 1.34** 2.9** 2.1**

(<.0001) (<.0001) (<.0001)

Birth Order 0.93** 1.1** 1.3**

(0.508) (<.0001) (<.0001)

Number of Children in Family 1.0** 1.1** 1.0**

(<.0001) (<.0001) (0.0002)

Head of Household's Years of Schooling 1.1** 0.96** 0.96** (<.0001) (<.0001) (<.0001)

Head of Household's Spouse's Years of Schooling

1.1** 0.98** 1.0** (<.0001) (<.0001) (<.0001)

Afro-Brazilian 0.81** 0.95** 1.1**

(<.0001) (<.0001) (<.0001)

Asian 0.55** 0.52** <0.001

(<.0001) (<.0001) (0.592)

Indigenous 0.90** 2.1** 0.35**

(<.0001) (<.0001) (<.0001)

Migrant 0.89** 1.0** 0.96**

(<.0001) (<.0001) (<.0001)

North Region 0.69** 1.2** 1.0**

(<.0001) (<.0001) (0.0009)

North-East Region 0.72** 0.83** 0.82**

(<.0001) (<.0001) (<.0001)

South Region 0.76** 1.7** 1.2**

(<.0001) (<.0001) (<.0001)

Central Region 0.77** 1.6** 1.0**

(<.0001) (<.0001) (0.0043)

Federal District 1.3** 4.4** 0.77**

(<.0001) (<.0001) (<.0001)

Predicted Income5 1.0** 1.0** 1.0**

(<.0001) (<.0001) (<.0001)

Poverty Status 0.76** 1.2** 0.95**

(<.0001) (<.0001) (<.0001)

% Concordance 81% 81% 75% # of Observations 22,457,290 21,586,850 870,440

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An interesting result is the insignificant effect of one’s predicted income on determining child labor. Traditional labor theory would suggest an upward sloping labor supply curve. As wages increase, the incentive to work increases and labor supply increases. However, traditional theory does not apply to the market for child labor. In all three logistic models, the point estimate for a child’s predicted wage was approximately 1.0, indicating no effect. This means that a child’s wage does not function as an incentive to work, as theory would suggest. A number of trends in child labor can help to explain this result. First, a large proportion of children work unpaid jobs, which include jobs in the informal sector, family businesses, and domestic work at home. Secondly, the insignificant effect of predicted wage on childhood labor can be explained by framing child labor as something undesirable-an inferior good. The strong, positive relationship between poverty incidence and child labor suggests that child labor is a necessity for low-income families to alleviate short term poverty.

One of the most significant determinants of child labor and child schooling is family income. This is unsurprising, due to the opportunity cost associated with sending a child to school. The family must take into account the child’s foregone earnings or the value of domestic labor done at home. This opportunity cost is relatively greater for low income families. To analyze the impact of on a family’s decision to enroll a child in school or put him in the labor force, we compare children belonging to poor and non-poor households. Utilizing data on Brazil’s poverty lines from the World Bank, we define poor to be all individuals living in households with a monthly per capita income of less than 150 reals. This equates to approximately $40 per month. Our analysis shows a significant relationship between family income and both child labor and schooling. First, children from poor households are significantly less likely to attend school and more likely to be employed. These results support the trade-off between labor and schooling. Table 5.3 shows that children from poor households are 1.31 times less likely to attend school than children who are not poor and 1.15 times more likely to work while in school. Although, poor children are less likely to not attend school and work than the children who are not poor. This has significant implications for school performance, which we will further discuss later in our analysis.

Gender also functions as an important determinant of child labor and schooling. Table 5.3 shows that female children are less than half as likely to work as their male counterparts. This can be attributed to two factors: 1) Wage differentials between male and female children and 2) societal attitudes surrounding gender roles. Gender norms are a socially constructed set of “rules” that define appropriate behavior and roles of men and women within a society. For example, in Western culture, the traditional role of women surrounds motherhood and domestic responsibilities within the home. These unequal attitudes surrounding gender originated thousands of years ago but persist to this day, manifested in relatively low female labor force participation rates (Alesina et. al). Societal gender roles clearly have an impact on child labor force participation. Furthermore, because girls are less likely to have a job, they do not experience the same trade-off between schooling and work as boys experience. Consequently, female children are more likely to be enrolled and school and on average have higher educational attainment relative to male children.

Living in a rural area, rather than an urbanized one, has significant effects of the school-work choice. Those children that are living in rural areas are more than twice as likely to work than those living in urban areas. This is mostly due to the nature of work in a rural area compared to an urban area. Those children in urban areas are restricted in the extent that they can work, they must get a job at a business or hawk their own wares. Those children in rural areas on the

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other hand are not as restricted in their ability to work, as many more of them are able to and do work on family farms and for their own consumption. This is reflected in the distribution of the job types between rural children and urban children. Those children from rural areas make up 82% of children who are producing goods for their own consumption and 68% of children who are unpaid (presumably working on a family farm), while only 18% of children are classified as living in rural areas. Furthermore, children from rural areas are far more active, children from rural areas are more like to work, more likely to go to school and more likely to attend school while working.

5.4: Student Efficiency

In regards to the schooling system of Brazil an important factor to consider is the “school for age” of Brazilian schools. School-for-age refers to the level of education that children have attained in relation to the level of education that they ought to have attained at their age. In other words, we examine how far ahead or behind Brazilian students are in their schooling. In our study we created a simple definition for school-for-age, as shown in equation 5.5. This equation will yield a number (normally between 1 and -4) that tells us how many years of schooling each student has completed in relation to how many years of schooling he or she should have completed. A positive number means that the student is ahead of where they should be, a negative number means he or she is behind, and if school-for-age is zero the student has completed exactly the amount of schooling he or she should have at that age.

𝜀𝜀 = 𝛼𝛼𝐹𝐹 − 𝛼𝛼𝐸𝐸 (Equation 5.2)

𝜀𝜀: School for Age

𝛼𝛼𝐹𝐹 : Years of schooling student has finished

𝛼𝛼𝐸𝐸 : Age of student – 6 We first calculated overall school-for-age for all children in Brazil, as shown by Figure 5.2. We chose to start at age six because schooling is mandatory at age six. While it ceases to be mandatory after age fourteen, we did not stop until age sixteen because that is when secondary schooling ends, and students choose whether to work or continue on to higher education. As the graph shows, average efficiency is initially positive for children of age six, then immediately becomes increasingly negative. By age sixteen, average school-for-age is down to almost negative two and a half.

Afterwards, we calculated school-for-age for various sub populations, comparing the averages by age for gender, race, geographic region, and employment status. Figure 5.3 shows the average school-for-age of boys and girls. School-for-age for girls was more efficient on average than for boys. The two genders begin to split at age 8, and the gap becomes increasingly larger at every additional year old. The largest gap is at the end at age 16, with girls being ahead of boys by almost a full point.

The results of the race calculations were what we had expected, with white leading black and pardo by about half a point. Black and pardo were close together for all ages. Indigenous,

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however, trails by quite a large margin. The furthest Indigenous fell behind was at age 15, trailing behind black and pardo by almost two full points. These results are shown in Figure 5.4.

Figure 5.5 shows the average efficiency by geographic region. The regions are arranged from highest returns to education to lowest returns to education, with South being the highest and Northeast being the lowest. South, Central West, and Southeast are all roughly the same. However the Northeast, unsurprisingly, fell behind the rest, ending about half a point lower than the other three by age sixteen.

Finally we compared working children to non-working children, shown by Figure 5.6. We only looked at ages thirteen to sixteen because the %age of working children was extremely low before age thirteen. As expected working children are behind those who are solely attending school, although not by as much as we had expected (ending at less than half a point at age sixteen).

Figure 5.3 Overall School-For-Age

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Figure 5.5

Average School-for-Age by Race

Figure 5.4 Average School-for-Age by Gender

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Figure 5.6 Average School-for-Age by Region

Figure 5.7

Average School-for-Age by Employment Status

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While comparing school-for-age gives a good picture of where students are compared to where they should be for different characteristics, the relation is clearly a multivariate one. To capture these effects, we ran two OLS regressions, the first being for general characteristics and the second including household environment characteristics. The restricted regression is shown in equation 5.6. The left side of Table 5.6 shows the results of this regression. All coefficients were statistically significant to 1%. Employment had a negative coefficient, meaning that a student was likely to have a lower school-for-age if they were working. All other coefficients were positive, meaning they were positively correlated with school-for-age. The strongest effect was that of working, while the weakest was the region variable.

𝑌𝑌� = 𝛼𝛼 + 𝛽𝛽1𝐹𝐹𝐸𝐸𝑀𝑀𝐴𝐴𝐿𝐿𝐸𝐸 + 𝛽𝛽2𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝐸𝐸 + 𝛽𝛽3𝑅𝑅𝐸𝐸𝐺𝐺𝑊𝑊𝑅𝑅𝑅𝑅 + 𝛽𝛽4𝑊𝑊𝑅𝑅𝑅𝑅𝑊𝑊 (Equation 5.3) Where: 𝛼𝛼: Intercept

FEMALE: 0 if Male, 1 if Female WHITE: 0 if Nonwhite, 1 if White

REGION: Variable indicating returns to education of student’s region, higher values for higher returns to education

WORK: 0 if Not Working, 1 if Working In our expanded OLS regression we included several household environment variables, as detailed in equation 5.7. The results from this regression are in the right half of Table 5.6. The addition of the new variables did not dramatically change the estimates on any of the other coefficients, with the exception of the effect of being white being reduced by about 50%. The employment coefficient is again negative, along with the coefficients on age, age squared, number of kids in family younger than 16, and sex of head household. These variables are negatively correlated with school-for-age, while all other variables are positively correlated with school-for-age. The strongest effect was that of age, while the weakest effect was that of geographic region.

𝑌𝑌�� = 𝛼𝛼 + 𝛽𝛽𝐴𝐴𝐺𝐺𝐸𝐸 + �̂�𝛽2𝐴𝐴𝐺𝐺𝐸𝐸2 + 𝛽𝛽3𝐴𝐴𝐺𝐺𝐸𝐸3 + 𝛽𝛽4𝑅𝑅𝑁𝑁𝑁𝑁𝑊𝑊𝑙𝑙𝑑𝑑𝑁𝑁𝐿𝐿𝐸𝐸16 + 𝛽𝛽5𝐵𝐵𝑙𝑙𝐵𝐵𝑙𝑙ℎ𝑅𝑅𝐵𝐵𝑑𝑑𝑒𝑒𝐵𝐵 + 𝛽𝛽6𝑊𝑊𝑊𝑊𝑊𝑊𝑒𝑒𝐻𝐻𝑑𝑑𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + 𝛽𝛽7𝑊𝑊𝑊𝑊𝑆𝑆𝑝𝑝𝑙𝑙𝑁𝑁𝑁𝑁𝑒𝑒𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + 𝛽𝛽8𝑊𝑊𝑊𝑊𝑊𝑊𝑒𝑒𝐻𝐻𝑑𝑑𝑆𝑆𝑒𝑒𝑥𝑥 + 𝛽𝛽9𝑆𝑆𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑒𝑒𝑃𝑃𝐻𝐻𝐵𝐵𝑒𝑒𝑙𝑙𝑙𝑙 𝛼𝛼: Intercept (Equation 5.4) AGE: Basic age variable

AGE2: AGE * AGE AGE3: AGE * AGE * AGE

NumKidsLE_16: Number of children in family younger than 16 BirthOrder: Where child is in family’s birth order

HHHead_Skul: Schooling level of household head HHSpouse_Skul: Schooling level of spouse of head of household

HHHead_Sex: 1 if female head of household, 0 if male SingleParent: 0 if single parent, 1 if not

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5.5: Opportunity Cost of Working Now it is known that some parents are

choosing to put their children in work rather than in school. How much of an effect does this choice have on school achievement? In the data set being used the data represents a snapshot of the situation in Brazil in 2012. Therefore, we will analyze people who are currently in school and working. To analyze this, the variable used will calculate the number of years the student is behind in school by finding the difference between the year of school the student should be in and their current year in school. To analyze this, OLS estimation is used using the following equation to find a line of best fit.

(Equation 5.5)

The S stands for how many years behind the

student is in school, the H represents the number hours the child is working, W is a dummy variable that accounts for the race of the child, F stands from female, R stands for whether the student lives in a rural or urban area. The results of the line of best fit show the effect of each hour of work per week on being behind in school. The coefficient attached to the white, female, and rural variables change the intercept showing the difference of working versus non-working on educational attainment. The hours variable shows the slope of the line of best fit which represents how one hour of work affects how far behind the child will be in school.

As discussed above, the descriptive statistics showed that the characteristic that affected parents choosing to put their children in the labor force was

living in a rural area. Therefore, the first comparison that will be analyzed is the difference between a white, male, living in an urban area versus a white, male living in a rural area. The different lines of best fit are shown below.

Table 5.3

School for Age

Variable Restricted Model Estimate

Expanded Model Estimate

Intercept -1.87 (-171.36**)

7.73 (31.97**)

FEMALE 0.25 (22.49**)

0.28 (29.58**)

WHITE 0.33 (28.1**)

0.15 (14.18**)

REGION 0.01 (9.55**)

0.01 (10.07**)

WORK -0.82 (-35.92**)

-0.08 (-4.02**)

AGE 0 -2.37 (-33.13**)

AGE2 0 0.19 (27.97**)

AGE3 0 -0.01 (-25.39**)

NumKidsLE_16 0 -0.14 (-27.14**)

BirthOrder 0 0.1 (11.36**)

HHHead_Skul 0 0.04 (35.25**)

HHSpouse_Skul 0 0.04 (24.18**)

HHHead_Sex 0 -0.03 (-2.7**)

SingleParent 0 0.11 (6.37**)

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Figure 5.8 Hours worked compared to years behind in school urban versus rural

The figure above shows how extreme the change is from simply working versus not working. In an urban area, this change is higher by about two years. This is likely due to the smaller number of people working in the urban area. Since this is much less common in the urban areas, the situation must be extreme. The slope of the line is higher for the rural students than the urban students. For the urban students, working a 40 hour week is correlated with the students lagging behind 3 more years in school. For the rural students, working a 40 hours week is correlated with the students lagging behind by 3.5 more years. This means that working one more hour in a rural setting has a larger decrease on a student’s educational attainment. The next analysis compares females to males. To separate out this difference the two groups being compared both will be white living in urban areas.

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Figure 5.9

Hours worked compared to years behind in school male versus female

This graph shows that working as a female on average has a larger decrease in educational attainment, or increase in number of year behind in school. This difference shows that females working will be behind by 1.3 more years than males. However, each hour of work per week has less of a decrease in educational attainment for females. For example, working 40 hours a week as a female causes a student to be behind by 2.4 more years.

The third difference being analyzed in between white and non-white. The two groups will stay the same on the other factors by comparing males that live in urban areas.

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Figure 5.10

Hours worked compared to years behind in school white versus non-white

These two OLS estimations are the most similar, which shows race doesn’t effect average educational attainment by hours worked relative to differences between rural, urban and male, female. The difference in the intercept, or amount behind in years simply by choosing to work, is 0.3 years with white students having a higher decrease in educational attainment. The slopes are also very similar. For a non-white male living in an urban area, a 40 hour work week is correlated with the students lagging behind by 2.8 more years in school. Similarly, the white student lags behind by 3 years due to a 40 year work week.

This analysis verifies that not only are the biggest differences in putting students in school between urban and rural families but there are also the largest consequences for each hour of work for rural areas being an increase of being behind by 3.5 more years to schools solely due to work. This shows that each hour of work in a rural area has the largest opportunity cost on future educational attainment which as has been analyzed, leads to an opportunity cost on future earnings. It is important to also note, that white females living in urban areas have the largest opportunity cost when choosing simply to work versus not work. For these students the initial choice to put their child in school causes a decrease in educational attainment. This shows that this choice as well has a large opportunity cost on future earnings.

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5.6: Conclusion In this section, we have analyzed the determinants of child labor and schooling and conclude that there is a trade-off between labor and schooling. For Brazilian families, it represents a choice between short-run gains and long-run gains. Though a small proportion (1.1 %) of children within our sample are exclusively working, we find that a much larger proportion (8.1) % simultaneously works and attends school. We found that gender, poverty-status, and urban-rural residency affect a family’s decision in sending their child to school or work. This analysis showed that being female or living in a rural area increased the likelihood of a student going to school. However, living in a poor family decreased the likelihood of going to school, and increased the likelihood of working if in school. Our analysis shows that male children from poor, rural families are at the greatest risk for prematurely entering the labor force. We also find that predicted income did not affect the decision to enter into the labor force. This implies that child labor may function as an unfortunate necessity and a way for poor families to alleviate short-term poverty.

Then, we analyze how the choice of child labor affects school performance. This is done by finding how far behind a student is based on the typical grade a student should be in at their age. This analysis shows that students who work more hours do tend to fall behind in school more often. To further understand this choice, we looked into the effect of work on a student falling behind in school. As the number of hours a child works increases, the further the child falls behind in school ceteris paribus; the effect is especially pronounced for males living in rural areas. Our analysis shows that the choice to work reduces educational attainment, which will adversely affect future income.

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Section 6: Sharing Resources

as a Potential Strategy Out of Poverty

An Econometric Analysis: Pooling Resources through Cohabitation in Brazil

By: Richard (Duke) Butterfield, Marisa Lucchesi, and Lindsey Ward

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6.1: Introduction to Sharing Resources Poverty is the single greatest obstacle to a safe and healthy life—and, for many, the freedom from poverty is considered a basic human right. Yet in Brazil, a country that has seen exponential growth in GDP and rapid entry into “first world” status, economic security continues to elude vast populations of active citizens. Where governmental and societal institutions fail to provide opportunities for these active citizens to overcome their economic predicament—through, for example, educational systems—families are forced to utilize communal institutions to improve their situation in the short term. For this reason, it is imperative that a microeconomic analysis of Brazilian poverty includes an examination of the dynamics of family decisions within Brazilian communities. One example of a family’s reliance on communal institutions is the decision to move into a household with another family (relative or neighbor, for example), referred to as “cohabitation” in this paper. Through an original OLS regression we analyze the decision of poor, working families to move in to households with other families as a strategy to alleviate current poverty. Our samples are restricted to family heads age 16-65 years who are currently economically active. Additionally, we eliminate observations with missing or unreliable data. Our frequency outputs are weighted to represent the entire Brazilian population that shares these characteristics. Note that the term “family heads” describe individuals who make the family’s economic decisions. We first set out to determine the characteristics of Brazilian families that are cohabitating. We found a correlation between monthly income per capita poverty and cohabitation—as well as an expressed desire (intention) to move out of cohabitation. We found that single mothers and women living away from their husbands are more likely to utilize cohabitation as a strategy relative to couples, and this strategy is adopted irrespective of educational background. Furthermore, we found that cohabitating families are often larger families with more dependent members. This led us to an investigation into the income specific characteristics of families, particularly of family heads. We found that family heads with high personal monthly income are more likely to cohabitate than are family heads with low personal monthly income. In conclusion, we relate our findings back to the broader investigation of potential strategies for Brazilian families to alleviate poverty, connecting cohabitation to the decision to send a child into the labor force. 6.2: Hypotheses on Household Crowding and Income We hypothesize the following statements given household cohabitation as a potential strategy to alleviate poverty in the short term. First, more family heads in a household earn income, and therefore more families living under one roof will be correlated with higher monthly incomes per capita. Second, lower income per capita—or, financial insecurity—influences families to share resources by moving into the same household. This may achieve the benefits of economies of scale in the production of household goods, such as living space. However this efficiency is not without its costs; with cohabitation comes crowding and potential loss of control over the household.

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6.3: Prevalence of Multi-Family Households in Brazil First, we wanted to find the proportion of multiple-family households relative to single family households in Brazil. We found that 89.31% of all Brazilian family heads report that they are both their family’s head and their household’s head, and that 10.69% of all Brazilian family heads report they are their family’s head but not their household’s head (Table 6.1). Thus, we interpret that approximately 21% of Brazilian families share a dwelling. Based on these findings, we see that families in Brazil are, in fact, moving in together. Now we want to look at the dynamics of these families and see if they are making this decision to move into multi-family households for economic reasons as a way to alleviate the burden of housing costs, which contributes to poverty.

6.4: Poor Households in Brazil We consider “poor” families as families with monthly income per capita less than the national poverty line provided by the World Bank. Table 6.2 shows a positive linear relationship between the number of families per household and the %age of those families who are “poor.” Therefore, poverty and cohabitation are correlated. 6.5: Reasons to Live with Others We then set out to examine the behavior characteristics of cohabitating families. Specifically, we were interested in the reasons families are cohabitating and whether or not these cohabitating families has the desire (intention) to move out of cohabitation. As shown in Table 6.3, almost half of respondents answered that their reason to live with others is for financial reasons. More specifically, 14.5

million Brazilians live with others is for financial reasons (49.13% of the population). This may indicate that people choose to live with other working individuals to lower the cost of living. Out of these people who answered that they were living with others for financial reasons, 71.96% (approximately 10.5 million people) indicated a desire (intention) to move, as shown in Table 6.4. This possibly indicates dissatisfaction with living situations due to financial constraints.

Table 6.1 Number of Single-Family

Households and Multi-Family Households in Brazil

Families per Household

Frequency of Households

Percent of Households

Single-Family Household 20,110,000 89.31%

Multi-Family Household 2,406,191 10.69%

Table 6.2 % age of poor and non-poor

families within a given household number

Number of families in a household

Non-poor Poor

1 53.52% 46.48%

2 43.53% 56.47%

3 31.91% 68.09%

4 18.61% 81.39%

5 24.88% 75.12%

^Note: Our definition of “poor” is based on the national poverty line, in PPP terms.

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6.6: Demographics and Omitted Categories Analysis

Knowing that cohabitating families are more likely to be poor, be living together for financial reasons, and have the desire (intention) to move out of cohabitation, we wanted to examine more specifically the income characteristics of family heads, the monetary decision makers in the family. We OLS regress both the log of total family head monthly income (Appendix A6.1) and the log of monthly income per capita (Appendix A6.2) against a variety of family head characteristics that may contribute to income, such as race, gender, age, years of study, living environment, migration status, working status, and familial status (i.e. couple). Employing omitted categories allow us to statistically compare relative characteristics of one group to another so we can see how they vary against different groups of individuals. We consider four categories of race different than the omitted category (white family heads). We include a female dummy variable, thereby comparing the income of female-headed families to the omitted category (male-headed families). We omit family heads living in urban environments, family heads that have not migrated, family heads that work in the “informal sector.” All these omitted categories have a single comparison category (living in rural environment, have migrated, and work in the “formal sector,” respectively). We then include age and study cohorts, omitting family heads age 16-25 and family heads with some primary education (but that have not completed primary education). Finally, we include a dummy variable for cohabitation. This variable considers families who have expressed they are family heads but not household heads and therefore only captures those family heads who are not the primary family head in a household. The omitted category are family heads who have expressed they are both the family head and the household head. Therefore, our research question is how does the decision to cohabitate affect non-household head family heads’ income (and income per capita)? Thus, we model the regression as follows:

Table 6.3 Reasons to live with others, of those

families living with others

Reasons to live with others Percent

Financial reasons 49.13%

Sentimental reasons 3.27%

Own Will 41.91%

Other (unspecified) 5.69%

Table 6.4 Out of the respondents living with

others for financial reasons, the %age of those who intend to move

Intention to move? Percent

Yes 71.96%

No 28.04%

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(Equation 6.1)

Where: : the regression’s intercept : the parameters to be estimated in the regression

W: the woman dummy variable R: racial dummy variables, including black, Asian, and mixed A: age dummy variable cohorts S: educational attainment dummy variable cohorts R: rural dwelling dummy variable Mig: migrant dummy variable WF: working in the formal sector dummy variable OM: single (only) mother dummy variable MulFam: dummy variable for cohabitating families

: the error term We find that while non-household head family heads have total income 62% higher than household heads, they have 11% lower income per capita (multi-fam variable outputs in Appendices A6.1 and A6.2). This discontinuity led us to re-consider the characteristics of family heads in multi family households with specific attention to familial status (the best indicator of dependents in the family). We regress non-household head family head status (multi-fam) against the same characteristics in the previous regression, utilizing the same omitted categories (Appendix A6.3). The model is as follows:

(Equation 6.2) Where:

MulFam: dummy, dependent variable for cohabitating families : the regression’s intercept : the parameters to be estimated in the regression

W: the woman dummy variable R: racial dummy variables, including black, Asian, and mixed A: age dummy variable cohorts S: educational attainment dummy variable cohorts R: rural dwelling dummy variable Mig: migrant dummy variable WF: working in the formal sector dummy variable OM: single (only) mother dummy variable

: the error term We find that non-household head family heads are 19% more likely to be single mothers than married couples and 7.5% more likely to be married women who are not living with their spouse than married couples (OnlyMother and MarriedOnlyMother variables in A6.3). This gives a

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good indication that parents with dependent family members, and particularly mothers without other parental support (i.e. present husband), are more likely to cohabitate. 6.7: Conclusions on Pooling Resources and Cohabitation From 2012 data, it appears that cohabitation is a frequently used method of alleviating short-term poverty conditions among Brazilian families. Frequency analysis suggest that families do move in with others and pool resources. Relative to single-family households, multi-family households tend to be poorer. Moreover, the most common reasons for cohabitation are financially-based, and the majority of people who indicate financially-based cohabitation have the intention to move, which may imply pooled resources to reduce the burden of housing costs. Further, these families appear to be dissatisfied with their current living situation, indicating that financial factors are forcing them to live with others—at least in the short-term. OLS regression techniques offer equally compelling results. First, increases in family size are correlated with higher head of family income; cohabiting heads of household are significant breadwinners, pulling in financial resources for the family. However, increases in family size are correlated with lower per capita incomes. This is unsurprising; poor families are more likely to cohabitate relative to the non-poor, and we would expect lower per capita incomes associates with this group. Through further regression analyses, we have found a small but significant negative correlation between cohabitation and child labor. When looking at children under the age of 18, the data suggests a decrease of 3.68% in the rate of child labor in households that have more than one family. This suggests that families are potentially moving in together as a means of offsetting the living expenses that force families to take their children out of school. There seems to be a tradeoff between short-term family choices: child labor and cohabitation.

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Section 7: Summary and Conclusion 7.1: Summary

It is evident that Brazil is currently undergoing significant economic changes. With a majority of its population migrating from the urban to rural sector and rising educational attainment, Brazil is undergoing a political, social and economic transformation. In education, we see a decline in the returns to additional years of schooling, but a convergence in educational attainment. We know that race, gender, and geography are still a factor in the returns to education as well. We also examine the micro strategies families use to escape poverty ranging from migration to cohabitation, and we find that the returns to migration are still positive and result in a increase in income. Child education is also considered a long term strategy out of poverty with children achieving a higher level of education earning a higher future income. This long term decision is countered by the decision to enter a child into the labor force, which has negative future individual income gains, but positive current family income gains. Through OLS analysis we can also see the reasons and outcomes of families making the decision to move in together.

7.2: Conclusion

In conclusion, we have looked at the economic environment that has lead to the large inequality in wealth and education in Brazil. Brazil is the largest country in terms of GDP, Land Area, and population in Latin America, and has seen many political regimes ranging from a military dictatorship to a democracy. There have been currency, educational, political, and economic reforms. The one constant that has remained is poverty.

We first looked at the role of educational attainment and its impact on potential income since the 1960’s. Our findings have shown that increased educational attainment has a positive impact on income, however, this return to education has decreased over the past forty years. We have also found that there has been a convergence in educational attainment throughout Brazil in terms of inequality.

From our analysis of migration, we have found that there are still positive returns to income, but these returns have decreased over time. There is also still a general movement from the rural sector to the urban base. We have also found that race, gender, age and education are still important facets when quantifying the returns to migration. There has been an across-the-board decrease in returns to migration since the 1960s, but migration remains one of the most effective ways to help alleviate poverty. Women, and non-white people receive lower returns to both education and migration, and they have a harder time assimilating and entering the labor force post-migration. Employers still make significantly more that employed people, whether waged employees or self employed people. Finally, people who do assimilate into the labor force see much larger returns to migration if they work in the modern sector. This provides a viable reason for people to leave the traditional sector. We then examined the decisions that families make in regards to putting children into school. Parents make a decision between short term financial alleviation, but a long term decrease in earning potential for their children. As was established in section three of this report, education plays an important role in predicting the wages of adults in Brazil, and children leaving school to work has a negative impact on income.

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We have also found that another short-term solution is cohabitation. To reduce cost of we see many low income families moving in together. This allows the minimization of rent costs. However, there tends to be a feeling of discontent in these multi-family households, as is evidenced by the almost 72% of people who have the intention to move. These short-term solutions are attempts to alleviate a large portion of the population struggle with poverty. The economic environment that Brazil is currently in is extremely unequal, as is seen by the GINI index score of 52.67 (World Bank). This paper explored some of the techniques that people have used since the 1960’s, and seen how the returns to these methods have changed over time.

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APPENDIX A

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Appendix A.3

Table A3.1 Monthly income and cohort specific earnings equations, Afro and White Brazilian males

with positive earnings, PNAD 2012

Age group (1)

Birth cohort (2)

Sample size, White (3)

Sample size, Afro (4)

Mean log earnings, White (5)

Mean log earnings, Afro (6)

β, White (7)

β, Afro (8)

Std. err., White (9)

Std. err., Afro (10)

R2, White (11)

R2, Afro (12)

22-24 1988-1990 2,850 3,985 6.91 6.66 0.063 0.060 0.0033 0.0028 0.110 0.105

25-27 1985-1987 3,041 4,138 7.11 6.77 0.098 0.075 0.0033 0.0027 0.227 .0160

28-30 1982-1984 3,162 4,489 7.22 6.83 0.108 0.085 0.0032 0.0024 0.268 0.215

31-33 1979-1981 3,135 4,414 7.32 6.90 0.110 0.088 0.0030 0.0024 0.296 0.235

34-36 1976-1978 2,899 4,024 7.34 6.90 0.114 0.088 0.0031 0.0024 0.316 0.246

37-39 1973-1975 2,740 3,920 7.33 6.91 0.116 0.090 0.0033 0.0026 0.306 0.243

40-42 1970-1972 2,715 3,845 7.37 6.93 0.113 0.099 0.0032 0.0026 0.314 0.281

43-45 1967-1969 2,572 3,505 7.38 6.94 0.110 0.082 0.0035 0.0027 0.273 0.208

46-48 1964-1966 2,668 3,296 7.36 6.95 0.109 0.095 0.0034 0.0028 0.283 0.261

49-51 1961-1963 2,539 3.042 7.40 6.95 0.122 0.108 0.0033 0.0030 0.351 0.302

52-54 1958-1960 2,271 2,737 7.40 6.93 0.125 0.106 0.0036 0.0033 0.349 0.275

55-57 1955-1957 2,005 2,351 7.41 6.90 0.133 0.116 0.0038 0.0035 0.387 0.316

58-60 1952-1954 1,835 2,004 7.41 6.92 0.118 0.108 0.0040 0.0039 0.327 0.274

22-60 1952-1990 34,432 45,750 7.29 6.88 0.101 0.082 0.0010 0.0008 0.246 0.201

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Table A3.2 Monthly income and cohort specific earnings equations, Brazilian males and females with

positive earnings, PNAD 2012

Age group (1)

Birth cohort (2)

Sample size, Men (3)

Sample size, Women (4)

Mean log earnings, Men (5)

Mean log earnings, Women (6)

β, Men (7)

β, Women (8)

Std. err., Men (9)

Std. err., Women (10)

R2, Men (11)

R2, Women (12)

22-24 1988-1990 6,881 5,787 6.77 6.45 0.066 0.137 0.0021 0.0033 0.126 0.228

25-27 1985-1987 7,229 6,525 6.91 6.45 0.091 0.147 0.0020 0.0029 0.217 0.287

28-30 1982-1984 7,703 7,166 6.99 6.53 0.100 0.145 0.0019 0.0027 0.267 0.294

31-33 1979-1981 7,610 7,218 7.07 6.55 0.104 0.135 0.0019 0.0025 0.288 0.291

34-36 1976-1978 6,976 6,649 7.09 6.54 0.105 0.131 0.0019 0.0024 0.303 0.306

37-39 1973-1975 6,717 6,170 7.08 6.55 0.107 0.130 0.0020 0.0025 0.294 0.307

40-42 1970-1972 6,607 6,189 7.11 6.58 0.110 0.129 0.0020 0.0025 0.321 0.306

43-45 1967-1969 6,123 5,542 7.13 6.60 0.100 0.126 0.0022 0.0027 0.260 0.292

46-48 1964-1966 6,004 5,554 7.13 6.62 0.106 0.131 0.0021 0.0025 0.298 0.330

49-51 1961-1963 5,624 4,998 7.15 6.63 0.120 0.134 0.0022 0.0026 0.351 0.353

52-54 1958-1960 5,065 4,494 7.15 6.65 0.121 0.137 0.0024 0.0027 0.339 0.359

55-57 1955-1957 4,387 4,139 7.14 6.77 0.130 0.109 0.0025 0.0025 0.279 0.313

58-60 1952-1954 3,869 3,925 7.16 6.76 0.120 0.99 0.0027 0.0025 0.334 0.288

22-60 1952-1990 80,795 74,356 7.06 6.57 0.096 0.115 0.0006 0.0007 0.246 0.255

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Table A3.3 Monthly income and cohort specific earnings equations, Afro and White Brazilian males

with positive earnings, PNAD 2012

Age group

(1)

Birth cohort

(2)

Sample size,

Urban (3)

Sample size, Rural

(4)

Mean log

earnings, Urban

(5)

Mean log

earnings, Rural

(6)

β, Urban

(7)

β, Rural

(8)

Std. err.,

Urban (9)

Std. err.,

Rural (10)

R2, Urban (11)

R2, Rural (12)

22-24 1988-1990 6,038 843 6.82 6.38 0.059 0.057 0.0022 0.0072 0.104 0.068

25-27 1985-1987 6,393 836 6.98 6.41 .0087 0.061 0.0022 0.0065 0.193 0.097

28-30 1982-1984 6,693 1,010 7.07 6.45 0.096 0.075 0.0021 0.0060 0.240 0.132

31-33 1979-1981 6,625 985 7.16 6.49 0.098 0.083 0.0020 0.0061 0.257 0.157

34-36 1976-1978 6,068 908 7.18 6.48 0.100 0.085 0.0021 0.0066 0.276 0.154

37-39 1973-1975 5,757 960 7.18 6.49 0.102 0.076 0.0022 0.0078 0.279 0.091

40-42 1970-1972 5,733 874 7.21 6.50 0.103 0.101 0.0021 0.0074 0.293 0.178

43-45 1967-1969 5,239 884 7.23 6.52 0.094 0.077 0.0023 0.0068 0.236 0.126

46-48 1964-1966 5,115 889 7.24 6.52 0.098 0.102 0.0023 0.0074 0.271 0.174

49-51 1961-1963 4,817 807 7.27 6.49 0.113 0.101 0.0023 0.0083 0.332 0.156

52-54 1958-1960 4,365 709 7.27 6.42 0.113 0.102 0.0025 0.0096 0.315 0.139

55-57 1955-1957 3,685 702 7.27 6.43 0.120 0.140 0.0028 0.0092 0.341 0.247

58-60 1952-1954 3,239 630 7.26 6.60 0.114 0.122 0.0029 0.0104 0.324 0.178

22-60 1952-1990 69,758 11,037 7.15 6.47 0.090 0.076 .0006 0.0020 .219 0.117

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Appendix A.4.1.0 Regional Division: 1. South: São Paula, Paraná, Santa Catarina, and Rio Grande do Sul 2. Northeast: Maranhão, Piauí, Ceará, Rio Grande do Norte, Paraiba, Pernamhuco, and Algoas 3. East: Sergipe, Bahia, Minas Gerais, Espirito Santo, Rio de Janeiro, and Guanabara

Table A 4.1.1 Income Gaines Associated with Rural- Urban Migration (Dependent Variable

Ln(average per capita monthly income from all sources)

Migrant Status

PNAD 2012 South (1)

Yap South (1960) (2)

PNAD 2012 Northeast

(3)

Yap Northeast (1960)

(4)

PNAD 2012 East (5)

Yap East (1960)

(6)

Recent (0-4) rural-urban migrant

0.115** (7.05)

.379** (2.59)

0.303** (11.46)

0.413** (2.11)

0.299** (15.34)

0.179 (0.99)

Less recent (5+) rural-urban migrant

0.083** (9.54)

.428** (-3.77)

0.279** (20.96)

0.454** (-3.78)

0.212** (20.37)

0.585** (4.95)

Rural-rural migrant

-0.187** (-10.81)

-0.133 (-1.77)

-0.103** (-2.11)

0.004 (-0.06)

-0.135** (7.53)

-0.013 (0.15)

Sex: Female

-0.444** (-55.38)

-.572** (-6.17)

-0.450** (-38.96)

-0.337** (-4.33)

-0.494** (52.55)

-0.551** (6.91)

Age 10-19 -0.693**

(-29.31) -.595** (-6.88)

-0.679** (-22.82)

-0.773** (-11.37)

-0.777** (28.93)

-0.519** (6.51)

30-39 0.240** (17.31)

.202** (-2.21)

0.241** (12.72)

0.122 (-1.68)

0.279** (17.10)

0.370** (4.46)

40-49 0.290** (20.88)

.292** (-2.84)

0.318** (16.11)

0.127 (-1.60)

0.406** (24.33)

.0247** 2.73

50 and Over

0.382** (28.92)

.444** (-4.06)

0.794** (43.55)

0.114 (-1.49)

0.696** (44.92)

0.392** (4.32)

Race: Nonwhite

-0.186** (-21.61)

-.214** (-2.16)

-0.175** (-13.82)

-0.135** (-2.69)

-0.291** (29.58)

-0.076 (1.32)

Education 1-3 years primary

-0.006 (-0.29)

-.327** (-4.48)

-0.018 (-0.96)

-0.011 (-0.18)

0.003** (0.19)

0.316** (5.11)

Primary Completed

0.154** (8.65)

.443** (-4.91)

0.048** (2.16)

0.874** (-6.56)

0.159** (8.83)

0.448** (3.49)

More than Primary

0.592** (28.92)

.931** (-3.78)

0.580** (36.06)

1.148** (-3.83)

0.727** (48.93)

1.043** (4.54)

Constant 6.605** 3.546 5.866 3.222 6.165 3.252

R2 0.211 0.4 0.249 0.4 0.296 .40

Sample Size 34981 417 22411 613 30550 558

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Table A 4.1.2a and 4.1.2b Predicted Monthly Income for White Males, 20-29 Years of Age, with No Formal

Education, by Migration Status

Yap Results^: Migration Status YAP South Yap Northeast YAP East Recent (0-4) rural-urban migrant

1.355 .995 .881

Less recent (5+) rural-urban migrant

1.386 1.020 1.200

Rural Non-migrant .916 .654 .680

^Base (Average)= 3.8222222

PNAD 2012 Results^^:

Migration Status PNAD 2012 South PNAD 2012 Northeast PNAD 2012 East Recent (0-4) rural-urban migrant

1.388 .800 .797

Less recent (5+) rural-urban migrant

1.345 .780 1.075

Rural Non-migrant 1.237 .591 .985

^^Base (Average)= 59.7111111

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Table A 4.1.3

Income Differences in the Urban Labor Market: Dependent Variable: Ln(average per capita monthly income from all sources)

Migrant Status

PNAD 2012 South

(1)

Yap South

(2)

PNAD 2012 Northeast

(3)

Yap Northeast (4)

PNAD 2012 East (5)

Yap East (6)

Recent (0-4) rural-urban migrant

-0.043** (-2.00)

0.011 (-0.09)

0.184** (7.87)

0.245 (-1.3)

0.111** (5.47)

0.100 (0.81)

Less recent (5+) rural-urban migrant

-0.029** (-3.55)

0.08 (-1.13)

-0.195** (-18.18)

0.289** (-3.16)

0.121** (13.87)

0.296** (4.18)

Urban-urban migrant

.

. 0.19

(-4.6) . .

0.418** (7.55)

.

. 0.237** (5.98)

Sex: Female -0.366** (-51.85)

-0.549** (-11.82)

-0.416** (-44.47)

-0.588** (-10.46)

-0.464** (61.81)

-0.503** (11.07)

Age

10-19 -0.597** (-44.06)

-0.622** (-9.49)

-0.695** (-37.19)

-0.176** (-2.05)

-0.691** (41.95)

-0.422** (6.78)

30-39 0.386** (-31.9)

0.189** (-3.88)

0.173 (12.37)

0.188** (-2.84)

0.267** (21.64)

0.424** (8.38)

40-49 0.401** (-34.29)

0.322** (-5.3)

0.378** (24.58)

0.128 (-1.74)

0.342** (27.53)

0.565** (9.69)

50 and Over 0.568** (-53.48)

0.193** (-2.83)

0.778** (58.08)

0.656** (-7.75)

0.662** (60.91)

0.369** (5.80)

Race: Nonwhite

-0.275** (-36.37)

-0.175** (-3.25)

-0.008 (-0.82)

-0.118** (-2.21)

-0.324** (42.29)

-0.286** (6.81)

Education

1-3 Years Primary

-0.022 (-0.23)

0.174** (-2.52)

-0.401** (-18.81)

0.257** (-4.14)

0.032 (1.74)

0.107 (1.86)

Primary Completed

0.072** (-3.59)

0.462** (-6.92)

0.132** (6.36)

0.55** (-7.59)

0.114** (6.77)

0.396 (6.85)

More than primary

0.577** (-32.25)

0.754** (-9.86)

0.548** (34.02)

1.285** (-14.66)

0.588** (41.28)

1.077** (16.20)

Sector: Modern

0.245** (-31.18)

0.273** (-6.61)

0.422** (40.27)

.065 (1.12)

0.460** (53.60)

0.178 (4.36)

Employment Status

Self-employed 0.052** (-4.58)

0.137** (-2.57)

-0.085** (-6.71)

-0.015 (-0.26)

0.024** (2.11)

-0.130** (2.56)

Employer 0.765** (39.02)

0.112** (8.02)

1.169** (37.70)

1.226** (5.41)

0.881** (34.24)

0.232 (1.76)

Constants 6.47 3.8 5.960** 3.196 6.269** 3.582

R2 0.27 0.45 0.34 0.56 0.33 0.57

Sample Size 54113 1044 32727 618 51623 1198

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4.2.1: Introduction

Yap originally excluded the North from her regression model because the area was largely uninhabited when she wrote her paper in the 1960’s. This region consists of Amapa, Amazona, Tocantins, Acre, Rodonia and Para, which is the largest region in Brazil, but with lowest population. Since the 1960’s, the North has undergone substantial changes in terms of industrialization, agriculture, tourism and population growth due to migration. Our study will analyze how the North differs from other regions, how the individual states in the North compare to each other and finally if the North should be included in a reproduction of Yap’s regression model. We will also present Yap’s regression model applied to the North with and without Para, the largest state in the North, and discover that the results change drastically when Para is included or excluded. 4.2.2: Arguments to Include the North in Yap Replication

We argue that the North region ought to be included in further migration analyses since it has grown drastically since Yap’s original study and is not significantly different from other regions in terms of demographic makeup. We first created frequency tables to compare demographic characteristics of the North to that of other regions. Table A 4.2.1 illustrates Northern Brazil’s growth over time. Approximately 75% of Northern Brazil’s total population of 15 million resides in urban areas. Furthermore, Table A 4.2.2 shows that migrants comprise a majority of the population in the Northeast. Tables A 4.2.1 and A 4.2.3 included below show that the North closely resembles the Northeast in terms of population distribution and racial composition, with a majority of people identifying as Parda (mixed) in all regions except the South, where white is the overwhelming majority. Since the North is very similar to other regions in Brazil, it is logical to include this region in the replication of Yap’s article.

TABLE A 4.2.1 Distribution of Population by Region in Brazil

North Northeast East/Southeast South

Urban 73.91 75.06 90.0 92.11

Rural 26.09 24.94 10.0 7.89

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Table A 4.2.3

Distribution of Race by Region in Brazil

North

Northeast

East/Southeast

South

Indígena 0.97 0.28 0.18 0.15

Branca 22.03 27.87 45.44 68.81

Preda 7.75 10.59 12.30 5.75

Amarela 0.33 0.19 0.25 1.02

Parda 68.92 61.07 41.82 24.28

Table 4.2.4 Poverty Incidence by Region

North Northeast East/Southeast South Frequency 1772275 1801155 786379 1013719 % 13.57 4.04 2.39 1.74

Table A 4.2.2 Migrant Status by Region

North

Northeast

East/Southeast

South

Migrant 52.91 39.33 42.77 54.15

Rural Migrant 11.69 7.43 3.23 3.63

Urban Migrant (Recent) 3.85 3.09 3.76 4.31

Urban Migrant (Non-Recent) 18.23 19.48 21.87 26.91

Non-Migrant 47.09 60.67 57.23 45.85

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4.2.3: Arguments to Exclude the North from Yap Replication

We further compared demographics within individual states in order to analyze regional variation in Northern Brazil. We generated frequency distributions of poverty incidence, race, rural/urban population and migrant population in Tables A 4.2.5 through 4.2.8 included below. As shown in Table A 4.2.6, Pará has the largest Parda population. Pará also stands out in Tables A 4.2.7 and A 4.2.8 as having the largest urban population (33%) and the largest migrant population (19%). It is also important to note that over half of the Northern population lives in Para. Therefore, would Pará be over representing the North?

For this reason alone, one might be tempted to leave out the North from the regression, since the results would not be reflective of the entire region. However, we completed similar frequencies in the Northeast and found that the region also has significant regional variation, so if we exclude the North for this reason we would also have to exclude the Northeast.

Table A 4.2.5

Poverty Incidence in Northern Brazil by State

Rondônia Acre Amazonas Roraima Pará Amapá Tocantins Total

Frequency 110818 94245 410306 41628 921727 68929 124622 1772275

% 0.85 0.72 3.14 0.32 7.06 0.53 0.95 13.57

Row % 6.25 5.32 23.15 2.35 52.01 3.89 7.03

Column % 8.52 15.72 14.70 10.89 14.80 11.87 10.59

Table A 4.2.6

Distribution of Race in North Region (% of total North population)

Rondônia Acre Amazonas Roraima Pará Amapá Tocantins Total

Indígena 0.01 0.16 0.52 0.10 0.21 0.01 0.01 1.03

Branca 3.47 1.04 4.38 0.67 9.10 0.80 2.05 21.50

Preta 0.72 0.32 0.91 0.27 3.53 0.38 0.77 6.90

Amarela 0.04 0.06 0.09 0.00 0.06 0.01 0.03 0.29

Parda 5.71 3.01 15.48 1.90 34.79 3.25 6.16 70.29

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Table A 4.2.7 Distribution of Population Currently Living in Rural/Urban Area (% of total North

population)

Rondônia Acre Amazonas Roraima Pará Amapá Tocantins Total

Rural 0.54 0.83 2.92 0.07 11.77 0.32 1.06 24.26

Urban 7.36 3.44 18.06 2.49 33.44 3.97 6.99 75.74

Table A 4.2.8

Distribution of Total Migrants in North Region (% of total North population)

Rondônia Acre Amazons Roraima Pará Amapá Tocantins Total

Non-Migrant 3.15 2.82 13.82 1.10 28.00 2.51 3.34 54.73

Migrant 6.80 1.77 7.56 1.83 19.69 1.94 5.68 45.27

4.2.4: Brief History of Pará

The differences between Pará and the rest of the region are largely due to the history of the state. Pará transformed into a rubber production state in late 1800’s, attracting large waves of migration from the rest of Brazil into a booming labor-intensive industry. Its economy mirrored the decline of rubber after WWI, and people grew poorer. The Amazon also experienced similar migration, but because of diversification into other industries, has since recovered more completely. For example, the capital of Amazonas, Manaus, is a free trade zone that attracts much business and jobs for the city. A revitalization effort began in 1966 (SUDAM) to transform the state of Pará, but it ultimately failed, and finally shut down in 2002 amidst numerous accusations of corruption. Currently, the main industries of Pará are the mining and service sectors. These mining and service sector might explain part of the regression results, which we will discuss later on in the paper.

4.2.5: Regression results including Pará vs. excluding Pará

We suspected that Pará is so drastically different from the other regions that it would misrepresent the North in a regression analysis. If this is true, then it may be favorable to exclude the North from the dataset. As expected, the results for migration are very different. Tables A 4.2.9 and A 4.2.10 show that coefficients for sex, age, race and education are similar to other regions and that income returns for “more than primary” are drastically higher than “primary completed.” Migration coefficients, however, require a closer look.

When Pará is excluded, recent immigrants earn about 4 % more than non-migrants, on average, holding all else constant. While this is a small return, it is no longer negative; included Pará, less recent urban migrants earn about 20% less than non-migrants, holding all else constant. The increase in income for recent migrants to urban areas decreased from 13 % (including Pará) to 10 % (excluding Pará). The sign for rural-rural migrants also changed from positive to negative, with a 25% lower income than non-migrants, on average, holding all else

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constant. All of the results are statistically significant at the 95% level (except for the less than 1-3 years of primary school in Table A 4.2. 9), and many are statistically significant at the 99% level. These changes indicate that Pará may be overrepresented in the regression. This may not be an issue, as discussed in previous sections, since the Northeast is a similar region. However, it is important to address these concerns so that the results may be interpreted more accurately. We suggest conducting a separate analysis of Pará, given its economic history and the econometric problem it may present.

4.2.6: Conclusion

Overall, we argue that the North should be included in a new replication of Yap’s original paper. This is based on two main arguments. First, the North resembles other states in terms of demographics and labor characteristics. One of the original concerns about demographics in the North hinged on very high expected levels of migration, and how damaging those levels could be when compared to other states. In fact, while the levels of migration in the North are relatively high, they are significantly lower than both the East/Southeast and the South. Similarly, the North shared other demographic characteristics between the Northeast, with a similar percentage of people who identify as Parda (67% in the North, 62% in the Northeast) Preda (8.3% in the North to 9.7% in the Northeast) and Branca (23% in the North to 27.8% in the Northeast.)

Second, the regression reveals some unexpected information that may deserve further analysis. When Para is included in the North regression, we find that the returns to migration for older migrants turn negative, and significant. This could mean a couple of things: that there are more opportunities for other migrants in other states and cities in the North, or that there are fewer costs to migrating in Para, which means that people who may not have chosen to migrate given higher migration costs in other states may have chosen to migrate in Para.

Finally, including the North as part of a Yap replication takes nothing away from the rest of the data. Because all regressions are just estimating returns to migration within states, even if there are econometric questions associated with the North, they would not take away from any analysis of the rest of the paper. For further research, we suggest including Para in the Northeast and analyzing the effects that this inclusion has on the regression results. This further analysis was beyond the scope of the research question explored by these authors, but would yield valuable analysis and further influence the discussion of the effect of migration on log income for migrants.

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Appendix A.5.0

Table A.5.1 Variables Analyzed in Logistic Regression in Section 5.3

Description Range

Age (10-16)

Age2 (100-256)

Female (0,1)

Rural (0,1)

Birth Order (1-9)

Number of Children in Family (1-11)

Head of Household's Years of Schooling (1-16)

Head of Household's Spouse's Years of Schooling (1-16)

Afro-Brazilian (0,1)

Asian (0,1)

Native (0,1)

Migrant (0,1)

North Region (0,1)

North-East Region (0,1)

South Region (0,1)

Central Region (0,1)

Federal District (0,1)

Predicted Income (0-729)

Poverty Status (0,1)

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Table A.5.2

Predicted Wage used in Section 5.3

Variable Coefficient

Female -61** -(6.05)

Age -1200 -(1.59)

Age2 92 (1.62)

Age3 -2.2 -(1.58)

Years of Education 42 (17.9)

(Years of Education)2 -2.5 (-0.46)

(Years of Education)3 -0.38 -(0.87)

Rural -47** -(3.53)

North-East -180** -(6.0)

North -110** -(5.96)

South 43** (3.16)

Constant 5300** (1.55)

R2 0.2881

Sample Size 2272

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Appendix A.6.0

Table A.6.1 Total Monthly Income

Variable Parameter Estimate t Value (*significance)

Intercept 6.612 452.31*

Female -0.065 -8.28*

Black -0.250 -23.97*

Asian 0.124 2.73*

Mixed -0.253 -0.52

Indigenous -0.206 -4.21*

Age 26-35 0.221 20.10*

Age 36-45 0.396 34.87*

Age 46-55 0.565 48.48*

Age 56-65 0.726 58.58*

Study (Primary Complete) 0.167 13.44*

Study (Some Secondary) 0.221 19.67*

Study (Secondary Complete) 0.332 27.66*

Study (Some Higher) 0.701 74.25*

Rural -0.256 -24.30*

Migrant 0.041 6.71*

WorkingFormal 0.270 39.47*

OnlyMother -0.208 -19.92*

MarriedOnlyMother -0.334 -15.03*

Multi-Fam 0.616 59.33*

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Table A6.2

Monthly Income per Capita Variable Parameter Estimate t Value (* significance)

Intercept 5.630 369.00*

Female 0.040 5.24*

Black -0.290 -27.01*

Asian 0.150 3.20*

Mixed -0.310 -45.12*

Indigenous -0.320 -6.28*

Age 26-35 0.080 6.84*

Age 36-45 0.190 16.13*

Age 46-55 0.470 38.78*

Age 56-65 0.800 61.51*

Study (Primary Complete) 0.160 11.94*

Study (Some Secondary) 0.200 17.45*

Study (Secondary Complete) 0.330 26.49*

Study (Some Higher) 0.790 79.81*

Rural -0.260 -23.95*

Migrant 0.060 9.17*

WorkingFormal 0.300 42.36*

OnlyMother -0.290 -26.97*

MarriedOnlyMother -0.680 -29.17*

Multi-Fam -0.110 -10.52*

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Table A6.3

Multi-Fam (Non-household Head Family Head) Variable Parameter Estimate t Value (* significance)

Intercept 0.250 43.24*

Female -0.008 -2.53*

Black 0.005 1.10

Asian -0.025 -1.39

Mixed 0.017 6.40*

Indigenous 0.021 1.10

Age 26-35 -0.160 -36.15*

Age 36-45 -0.200 -45.18*

Age 46-55 -0.190 -41.43*

Age 56-65 -0.200 -40.90*

Study (Primary Complete) 0.010 2.00*

Study (Some Secondary) 0.006 1.33

Study (Secondary Complete) 0.006 1.33

Study (Some Higher) 0.002 0.52

Rural -0.006 -1.47

Migrant -0.024 -9.82*

WorkingFormal -0.014 -5.31*

OnlyMother 0.190 46.96*

MarriedOnlyMother 0.075 8.51*

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Works Cited

Alesina, Alberto, Paola Giuliano, and Nathan Nunn (2013) “On the Origins of Gender Roles: Women and the Plough” The Quarterly Journal of Economics, 128:2, 469-530. Araujo, Ana Lucia. African Heritage and Memories of Slavery in Brazil and the South Atlantic World. n.p.: Cambria Press, 2015. "Brazil - History & Background." StateUniversity.com. N.p., n.d. Web. 19 Oct. 2015. Brazil. Instituto Brasileiro De Geografia E Estatística. Tabela 1.8 - População Nos Censos Demográficos, Segundo as Grandes Regiões, as Unidades Da Federação E a Situação Do Domicílio - 1960/2010. Devlin, Robert, and Ricardo French-Davis. "The Great Latin America Debt Crisis: A Decade of Asymmetric Adjustment." Revista De Economia Politica 15.3 (1995): 117-42. Web. Lam, David, Levison, Deborah (1991/11)."Declining inequality in schooling in Brazil and its effects on inequality in earnings." Journal of Development Economics 37(1-2): 199-225. http://hdl.handle.net/2027.42/29052 Law, Gwillim. "Brazil States." Statoids. N.p., n.d. Web. <http://www.statoids.com/ubr.html>. Minahan, James. "Acreaños." Encyclopedia of the Stateless Nations. Westport, CT: Greenwood, 2002. 34. Schwartzman, Simon "The Challenges of Education in Brazil". 26 Feb. 2003. Version 3. Print. Setti, Ricardo. “VERGONHA AINDA MAIOR: Novas informações disponíveis em um enorme banco de dados mostram que a escravidão no Brasil foi muito pior do que se sabia antes.” Para. (2015). Encyclopædia Britannica. Retrieved from http://www.britannica.com/place/Para-state-Brazil “Pesquisa Nacional por Amostra de Domicílios” (PNAD) Martine, George and McGranahan, Gordon. Brazil’s Early Urban Transition: What can it Teach Urbanizing Countries? London: International Institute for Environment and Development (IIED), 2010 Wade, Robert H. Boulevard of Broken Dreams: The inside Story of the World Bank’s Polonoroeste Road Project in Brazil’s Amazon. Grantham Research Institute on Climate Change and the Environment, Aug. 2011. The World Bank (2004). Inequality and Economic Development in Brazil, A World Bank Country Study.

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Veja.com. n.p., July 3, 2015. Web. December 7, 2015. Yap, L. (1976) “Rural-urban Migration and Urban Underemployment in Brazil,” Journal of Development Economics, 3, 227-243.