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78 Seiten, Note: A-
Chapter 1. Introduction to Poverty in Brazil
1.2. Literature Review
Chapter 2. Brazilian Poverty Profile by Race
2.1. Data Issues
2.2. Income as an Indicator of Household Welfare
2.3. Poverty Lines
2.4. Aggregated Poverty Measures
2.4.2. FGT Class of Poverty Measures
2.4.3. Robustness Analysis
Chapter 3. Correlates of Brazilian Poverty
3.1. Regressions over the Whole Sample
3.2. Separate Regressions by Race
3.3. Interactive Regression Models
Chapter 4. Conclusions
List of Tables
Table 1. Monthly Per Capita Poverty Lines, Brazil, Sept. 1995 Reais
Table 2. Poverty Estimates, Brazil, 2002.
Table 3. Sample Size by Race
Table 4. Summary Statistics of Monthly Household Income per Capita by Race, Brazil, 2002.
Table 5. Poverty Estimates by Race, Brazil, 2002.
Table 6. Poverty Estimates for White and Asian vs. Black, Mixed Race and Indigenous Brazilians, 2002
Table 7. Mean and Median PCY of Poor and Non-Poor, White + Asian and Black, Mixed Race, Indigenous Households
Table 8. Correlates of Monthly Household Income and Poverty Status, Brazil, 2002.
Table 9: Demographic Structure of Poor and Non-Poor Households
Table 10. Mean and Median Years of Education of Poor and Non-Poor Households
Table 11. Correlates of Monthly Household Income by Race, Brazil, 2002.
Table 12. Correlates of Household Poverty Status by Race, Brazil, 2002
Table 13. Demographic Structure for White or Asian and Black, Mixed Race or Indigenous Households
Table 14. Correlates of Monthly Household Income, Brazil, 2002.
Table 15. Correlates of Household Poverty Status, Brazil, 2002
Table 16. Responsiveness of Poverty to Economic Growth
Table A1. Poverty Estimates by Region, Brazil, 2002
Table A2. Poverty Estimates for Urban and Rural Areas, Brazil, 2002
Table A3. Poverty Measures by Gender, Brazil, 2002
Table A4. Poverty Measures by Years of Education, Brazil, 2002.
Table A5. Poverty Measures by Employment Sector, Brazil, 2002
Table A6. Poverty Measures for Formal and Informal Employment, Brazil, 2002.
Table A7. Poverty Measures by Hours Worked, Brazil, 2002
List of Figures.
Figure 1. Extreme Poverty and Development Cross-Country.
Figure 2. Distribution of people among the 10% poorest and 1% richest, by race, Brazil, 2002
Figure 3. Population and Poverty Shares by Race, Brazil, 2002.
Figure 4. Mean Monthly Household Income per Capita by Race, Brazil, 2002
Figure 5. Population and Poverty Shares by Race and Region, Brazil, 2002
Figure 6. Distribution Functions for Real PCY by Race, Brazil, 2002
Figure 7. Distribution Functions for Real PCY by White and Asian vs. Black, Mixed Race and Indigenous Brazilians, 2002
“PERSISTENCE OF UNDERDEVELOPMENT IN THE MIDST OF ECONOMIC GROWTH” (Werner Baer)
Brazil is Latin America’s largest, as well as most populous, country with a population of 174 million people. Brazil is a resource rich country and has a solid industrial structure. GDP growth has been positive for most of the decade and per capita income levels are relatively high in comparison to other Latin American countries.
In 2002 expectations about presidential elections led to market uncertainty and a decline in investment. GDP growth was, however, positive for 2002 mainly due to an increase in exports. GDP growth equalled 1.5% in real terms and GDP came to
R$ 1,321.5 billion at market rates in 2002.
Despite positive economic growth Brazil still shows high rates of poverty. Figure 1 below shows that the incidence of poverty in Brazil is greater than for other countries with the same level of GDP per capita.
Figure 1: Extreme Poverty and Development Cross-Country
illustration not visible in this excerpt
Source: World Bank (2003)
Note: Based on World Development Indicators for 2002.
Moreover, Figure 1 illustrates that Brazil, despite its level of economic development, has extreme poverty rates comparable in magnitude to those for countries like Indonesia or Pakistan.
However, poverty does not affect the Brazilian population equally. Brazil has one of the world’s most inequitable income distributions. There are huge differences between rich and poor. In 2002 the poorest 10% of the population earned only about 1% of total income, whereas the richest 10% of the population earned almost 50%. Furthermore, incomes in Brazil differ across regions, between urban and rural areas, and across racial groups.
The racial composition of the Brazilian population is quite complex and diverse. In the second half of the 19th century the population consisted mainly of descendants of Portuguese, Africans, and Amerindians. At the end of the 19th century and the beginning of the 20th century large-scale immigration from different European and Middle Eastern countries took place, particularly to the South of Brazil, followed by large numbers of immigrants from Japan in the second decade of the 20th century. A large proportion of Brazil’s population today is of mixed ancestry.
Significantly the historic gap between whites and blacks, which originated in the legacy of slavery, has still not been offset. African Brazilians live in disproportionate numbers in urban shantytowns (“favelas”), are the majority of Brazil’s poor rural population and concentrated in the country’s poor Northeast and Northern regions.
Guimarães (2001) stressed that the racial hierarchy, which is still visible in Brazil today, is not just a leftover from the past era of slavery. After the abolition of slavery in 1888, the rights of black and mixed race Brazilians still were not guaranteed in social practice. Nonetheless, the existence of racial discrimination has been commonly denied in the recent history of Brazil. Guimarães emphasized that in the 1960s and 1970s the notion of Brazil as a “racial democracy”, a concept resulting from a national ideology that denied the existence of racial discrimination, became a dogma.
During the last two decades awareness of racial discrimination in Brazil has increased, but discrimination of non-whites in social relations remains practice today. The Brazilian Monitoring Report on the Millennium Development Goals (IPEA, 2004) mentions racial inequality as “one of the most serious social problems in Brazil”. Figure 2 below is taken from the report. It shows the distribution of Brazilian people among the 10% poorest and 1% richest by race in 2002.
Figure 2: Distribution of People among the 10% Poorest and 1% Richest, by Race, Brazil, 2002
illustration not visible in this excerpt
Source: IPEA (2004)
Note: Estimates are based on PNAD data for 2002
The figure shows that 86% of the 1% richest Brazilians were white, whereas 65% of the 10% poorest Brazilians were black or mixed race.
The aim of this analysis is to investigate the role of race in explaining poverty in Brazil as well as to analyse the correlates of poverty in Brazil in 2002.
It is hypothesised that poverty has a racial component that cannot be wholly explained by other factors such as differences in education.
There is a large body of empirical literature on poverty in Brazil beginning with Fishlow (1972) and Fields (1977) in the 1970s, followed by Fox and Morley (1991) as well as by Rocha (1993, as cited in Litchfield, 2001) in the early 1990s to mention just a few. More recent studies on poverty in Brazil include: Wodon (2000), Litchfield (2001) and Ferreira, Lanjouw and Neri (2003).
Fishlow (1972) and Fields (1977) both examined the distributional impact of economic growth in Brazil during the 1960s – a period in which the Brazilian economy had shown substantial growth rates. Both studies were based on Brazilian census data for 1960 and 1970, but used different methodologies and hence reached different conclusions.
Fishlow (1972) used a sample from the 1960 census as well as summary data from the IBGE for the 1970 census in order to investigate Brazilian income distribution. He chose a monetary definition of income (accounting for imputed rent as well as imputed rural home consumption for the 1960 census), and a poverty line based on the real minimum wage for 1960 in the poor Northeast region of Brazil. Fishlow’s study focused on income inequality in Brazil, and his main finding was that despite the “economic miracle” income inequality had increased over the period. However, apart from his focus on income inequality, he also presented one of the early poverty profiles for Brazil. Amongst household characteristics associated with a household being poor, he stressed the location in and non-migration from rural areas and residence in the North, as well as the limited number of workers per family, large family size and number of children. He also found that low levels of education as well as a concentration in agricultural activity of the household head were associates of household poverty in Brazil in 1960.
Fields (1977) used summary information from the 1960 and the 1970 census in order to examine income inequality and poverty in Brazil. He also chose a monetary definition of income, but his definition of the poverty line differed from the one adopted by Fishlow; Fields defined the poverty line as the bottom two income categories in 1970.
Fields criticised the focus of other studies on income inequality and stressed that he was primarily concerned with the bottom part of the income distribution, the very poorest. In contrast to Fishlow, Fields found only a small increase in income inequality during the 1960s. In addition his findings indicated a small reduction in the incidence of poverty over the period, from 0.37 in 1960 to 0.36 in 1970. According to his findings, even though most of the benefits associated with economic growth accrued to Brazilians above the poverty line, the incomes of the poor grew, as well. He therefore concluded that growth benefited all parts of the population, the poor as well as the non-poor.
At the time the papers of Fishlow and Fields provoked much controversy over whether the benefits from the high economic growth rates of the 1960s actually reached the poor. The debate on poverty in the 1970s was overall characterized by a lack of agreement on the behaviour of inequality and poverty measures.
Studies on Brazilian income distribution for later periods of Brazilian history were less controversial.
When micro-data household surveys became available for the 1980s new studies emerged. Fox and Morley (1991) used household survey data for their analysis of the impact of Brazilian policy in the 1980s on poverty alleviation during the period.
They chose household income per capita as the primary indicator of a household’s economic well-being, and a poverty line of ¼ of the 1980 minimum wage per capita. They found that while the 1970s showed significant improvements in poverty alleviation, the poverty record for 1981-87 was rather ambiguous. Fox and Morley split their analysis into three periods: recession (1981-83), recovery (1984-85) and “boom-bust” (1986-89). They found that the world recession and debt crisis of 1982 clearly hurt the poor, increasing the incidence of poverty by about 25% over the period 1980-83. However, by 1985 income growth brought the level and intensity of poverty back to their 1981 values. The Cruzado Plan in 1986 led to a further drop in poverty. The plan was, however, unsustainable and, as inflation resumed, poverty levels increased again. For the whole period under consideration they therefore estimated a roughly constant poverty share; they found only a very small increase in the poverty incidence of 5% from 1980-87.
Whereas the early studies on poverty in Brazil focused on the relationship between income distribution and macroeconomic performance, particularly growth rates, later studies emphasized microeconomic processes of household characteristics and income, and put greater emphasis on the structure of poverty.
Rocha (1993, as cited in Litchfield, 2001) provided a detailed profile of the poor population based on PNAD data for 1990. Since the use of poverty lines based on the national minimum wage had been criticized, she estimated a set of regionally specific poverty lines based on the cost of a food basket meeting recommended caloric requirements (see Chapter 2 for more details). Rocha’s analysis formed the basis of the World Bank poverty assessment of Brazil (World Bank, 1995). The World Bank report (World Bank, 1995) used low income, which included labour income as well as income received from other sources, as an indicator of economic welfare. Based on PNAD data for 1990, the report showed that more than half of poor Brazilians live in the Northeast. Moreover, according to the report, rural and urban areas contributed equally to national poverty. Poor rural households were characterized by an illiterate household head employed in agriculture; half of them working as smallholders or sharecroppers, the other half as employees or temporary workers. Furthermore, poverty affected disproportionately young people, particularly children in female-headed households, as well as large households.
Wodon (2000) provided a more recent poverty profile for Brazil and other Latin American countries. Based on household survey data he estimated extreme poverty and poverty headcounts for the period 1986 to 1996. He used per capita income-based poverty measures adjusted to per capita consumption in the National Accounts in order to correct for underreporting. Moreover he chose two poverty lines: an extreme poverty line based on the cost of country specific food baskets providing 2,200 kcal per day per person, and a moderate poverty line equal to twice the food poverty line. For the most recent year of his analysis, 1996, he found an extreme poverty headcount of 0.18 and a poverty headcount of 0.37 for Brazil in comparison to estimates of 0.12 for the former and 0.30 for the latter in 1986.
According to Wodons’ estimates the shares of the population living in poverty and extreme poverty in 1996 thus remained higher than the shares for 1986.
He concluded that the economic recovery during the 1990s had not been enough to compensate for the “lost decade” of the 1980s.
Wodon furthermore estimated a probit model as well as a log-linear model in order to investigate the determinants of poverty for 12 Latin American countries, including Brazil, in the period from 1995 to 1996. The explanatory variables used in his regressions included demographics as well as household head characteristics. Amongst other things he found that larger families tend to have lower incomes, whereas households with middle aged or older household heads tended to have higher incomes. He furthermore found lower incomes and a greater chance of being in poverty for households with their heads working in agriculture than for households with heads working in manufacturing or services. In addition, according to his estimates, an increase in the years of education of the household head decreased the probability of being poor and tended to increase per capita income in the period from 1995 to 1996. Wodon pointed out that education was also likely to indirectly influence poverty and per capita income through its impact on demographics and employment opportunities, and hence played an even stronger role as a determinant of poverty.
Litchfield (2001) analysed poverty and inequality in Brazil over the period 1981 to 1995. Her analysis was based on PNAD data; she chose household income per capita as an indicator of household well-being, and the poverty lines adopted by her were the regionally specific lines as defined by Rocha (1993).
She provided a detailed poverty profile for the Brazilian population, estimating several poverty measures as well as a probit model in order to investigate the determinants of poverty in Brazil over the mentioned period. For the most recent year of her analysis, 1995, she estimated a headcount ratio of 0.38.
The probit model was estimated separately for Brazil’s urban and rural areas. Explanatory variables included four regional dummies, some demographic characteristics, as well as several characteristics of the household head.
Litchfield found that the likelihood of a household being poor varied considerably across regions: relative to the Southeast, households located in the Northeast and Centre-West were more likely to be poor, whereas households located in the North and South were less likely to be in poverty.
Amongst the demographic characteristics she found that an increase in the number of workers reduced the chance of a household being in poverty, whereas an increase in the number of children increased the chance of a household being poor.
Furthermore, according to her estimates, the age of the household head had only a small negative impact on the probability of a household being poor. She also found that households with female heads are more likely to be poor than those with male heads. In addition she incorporated a racial dimension in her poverty assessment. She found that when aggregating the data into two racial groups (households with black, mixed race or indigenous heads vs. households with white or Asian heads) households with black, mixed race or indigenous heads were also more likely to be poor than households with white or Asian heads. Moreover, an increase in the years of education decreased the chance of a household being poor, whereas a household head working in the informal sector or in agriculture increased the chance of the household living in poverty.
Ferreira, Lanjouw and Neri (2003) presented a poverty profile for Brazil based on three different data sources: the Pesquisa sobre Padrões de Vida (PPV), “Contagem” data and PNAD data for 1996. They used a transformation of the total household income reported in the PNAD 1996 as the basic welfare indicator for constructing the poverty profile. Their welfare indicator incorporated a measure for imputed rent, as well as an equivalence scale and a spatial price deflator. Based on the adjusted PNAD data for 1996, Ferreira et al (2003) estimated several poverty measures and provided a detailed poverty profile for Brazil in 1996. Amongst other findings, they presented a headcount of 0.23 for a food-only poverty line, and a headcount of 0.45 for a poverty line which allows for expenditure on some non-food items.
Based on the PPV, an expenditure survey, they furthermore estimated a probit model by regressing the probability of being poor onto several household characteristics. In addition to regional dummies, demographic variables and characteristics of the household head, they also included some housing characteristics in their regression analysis. In congruence with Litchfield (2001) they found that the regional location of a household was a strong determinant of poverty in Brazil. They also found that the number of household members as well as the number of children in the household increased the chance of a household being poor, whereas the number of over 65-year olds reduced the probability.
Furthermore, education again turned out to be significantly negatively correlated with the probability of being poor. Amongst the housing characteristics, they found that the poor are significantly less likely to have access to piped water, electricity or a telephone line, and that they are less likely to have many bedrooms or floor covering.
In contrast to other studies Ferreira et al (2003) found that age, gender, occupational status as well as race (indicated by three dummies for a household head being black, “mulatto” or indigenous), all turned out to be insignificant correlates of poverty.
Much of this discrepancy in comparison to other studies surely results from the different methodologies chosen for their analysis. Concerning race, they did, however, find that poverty incidences differed considerably across households with heads of different races. They thus concluded that while households with black, mulatto or indigenous heads on average were more likely to be poor than white headed households, this was, amongst other things, likely to be the case because of differences in education or regional location.
There is thus a vast empirical literature on Brazilian poverty levels and changes over time, as well as for different periods in Brazilian history.
However, considering the recent efforts of the Brazilian government, initiated by President Luiz Ignácio Lula da Silva, to fight hunger and poverty, and the expansion of government resources in order to reach the really needy, an actual poverty profile for Brazil is required, which allows for appropriate targeting of resources.
Moreover, the fact that racial inequality in Brazil is very high suggests the usefulness of a detailed exploration into the role of race in explaining Brazilian poverty.
This thesis hence attempts not only to provide an actual poverty profile for Brazil by analysing the correlates of poverty in Brazil in 2002, but also aims to investigate the role of race in explaining poverty in Brazil.
The focus of this thesis is indeed on the racial dimension of poverty; it is hypothesised that poverty has a racial component that cannot be wholly explained by other factors such as differences in education.
Chapter 2 first introduces the data set and discusses methodological issues. The data set chosen was the Pesquisa Nacional por Amostra de Domicílios (PNAD) for 2002 collected by the Instituto Brasileiro deGeografia e Estatistica (IBGE). PNAD are nationally representative annual household surveys and are the largest data source on incomes for Brazil. As a consequence they have been widely used for poverty analysis in Brazil. The survey questionnaire consists of two parts: household-level information and individual level data. The core questionnaire contains information about the geographic location of the household, characteristics of the dwelling, demographic characteristics on relationships between individuals in the household, different sources of income and different labour characteristics such as: employment sector, age, sex, race, literacy and education. The size of the PNAD in 2002 was 385,431 individuals.
Chapter 2, moreover, considers the pros and cons of income as an indicator of economic welfare as well as introduces the adopted poverty lines; the set of regionally specific poverty lines defined by Rocha in 1993.
Furthermore, Chapter 2 presents the aggregate poverty measures chosen for this analysis, the headcount index, the poverty gap and the squared poverty gap.
Chapter 2 aims to investigate the racial dimension of poverty. It thus presents overall results for the chosen poverty measures as well as the results of a statistic decomposition of the poverty measures by the race of the household head.
The chosen racial groups are White (“Branca”), Black (“Preta”), Asian origin (“Amarela”), Mixed race (“Parda”), and Indigene (“Indigena”).
To test whether the poverty estimates for the racial groups are robust to the choice of the poverty line, the chapter furthermore contains a plot of cumulative distribution functions for the racial groups.
Chapter 3 examines the correlates of poverty by means of regression analyses and thus aims to provide a profile of the poor for Brazil in 2002.
The chapter contains both OLS and probit models, estimated over the whole sample as well as separately for each racial group.
In the regression analyses the data will be aggregated into two racial groups: “White and Asian” vs. “Non-White” (black, mixed race, as well as indigenous Brazilians) since the sample sizes for indigenous as well as Asian Brazilians have been too small to give significant results on their own.
The OLS regression model will be of the form log yi = bXi + eI, with the logarithm of monthly household income as the dependent variable, and several characteristics of the household, as well as of the head of the household, as explanatory variables, including race. The probit model will be of the form Prob[yi > 0] = Φ(bXi), where Φ denotes the cumulative distribution function of a standard normal distribution. The dependent variable is a binary variable, which equals one if the household is below the poverty line and zero otherwise.
Chapter 3 also presents the results of a re-estimation of the overall models, including several interaction terms, such as race and household size, as well as race and the dependency ratio, to account for differences in the effect of race across households of different sizes and with different numbers of dependent members.
Furthermore, Chapter 3 discusses the advantages and disadvantages associated with either type of model (OLS vs. probit).
Chapter 4 concludes by considering policy implications of the results as well as making suggestions for future research.
The analysis is based on the Pesquisa Nacional por Amostra de Domicílios (PNAD) for 2002, produced by the Instituto Brasileiro de Geografia e Estatística (IBGE).
PNAD are annual surveys collected from a representative national sample of households according to a three-stage sampling procedure: first, identifying municipalities, then selecting from a list of census sectors, choosing the households themselves at a third stage. The survey questionnaire consists of two parts: household-level information and individual level data.
The surveys cover every state in the Federation and are the largest data source on incomes for Brazil, with a sample size of 385,431 individuals in 2002.
PNAD have been fielded since the late 1960s and are conducted in the last quarter of each year using a reference period of one week for collecting data on income and work. In the 2002 survey, the reference week was the week from the 22nd to the 28th of September 2002.
So far most of the existing work on poverty in Brazil has been based on the PNAD.
However, the PNAD have some limitations. Household surveys automatically exclude homeless people as well as people living in various institutional settings. In the case of the PNAD these are armed forces, prisoners, interns in schools, orphanages, asylums and hospitals as well as residents in embassies and consulates.
In addition Ferreira, Lanjouw and Neri (2000) emphasized that the insufficiently detailed income questions for any income source other than wage employment in the PNAD are likely to lead to measurement errors. Household surveys generally tend to inaccurately measure the income of the self-employed, for those working in the informal sector as well as for subsistence households. Accruing measurement errors could principally go in either direction, in practice however income underreporting is more likely to occur because of recall bias. Hence, most survey estimates of income, including the PNAD, tend to be too low.
As a consequence poverty levels estimated from surveys on household income tend to be overestimated. Survey-based estimates of consumption are often substantially higher and a consumption measure is generally seen as a more reliable indicator of economic well-being than income since the difficulties of measuring consumption are less onerous than those of measuring income, particularly for rural households whose main income comes from self-employment.
The IBGE responded to the criticism of the PNAD data sets by radically redesigning the Pesquisa de Orcamentos Familiares (POF), a family expenditure survey planned to be nationally representative. It is therefore likely that future analysis of poverty in Brazil will be based on the new POF.
In the meantime, bearing its limitations in mind, PNAD remain the best available data source for poverty analysis for the whole of Brazil’s population.
A recent study by Elbers et al (2002) estimated a model that allows consumption estimates from a further smaller IBGE expenditure survey, the Pesquisa de Padrões de Vida (PPV) of 1996, to be imputed into the PNAD sample. Their study suggests that although one should be careful concerning absolute level estimates based on PNAD, income- based poverty profiles from PNAD seem to be robust in terms of poverty rankings.
PNAD permit the use of monthly household income as an indicator of economic welfare. In this analysis gross monthly household income per capita, measured in real terms in 1995 Brazilian Reais, has been chosen as the principal welfare indicator.
Income is expressed in per capita terms since the unit of analysis is the individual rather than the household. Moreover measuring income in per capita terms allows for comparisons with other studies.
Incomes were collected in 2002 Reais and have been converted to 1995 Reais by the use of an inflation index in order to allow for the adoption of the regional poverty lines as defined by Rocha (1993). The inflation index used was a single national, annual consumer price index published by ECLAC.
 Baer, W. (2001), p. 5
 United Nations (2003), Human Development Indicators
 See for instance World Bank (1995), p. 9.
 Banco Central do Brasil (2003), p. 15.
 See World Bank (2003), p. 7f.
 Author’s own calculations based on PNAD data for 2002
 See Baer, W. (2001), p. 8.
 See do Nascimento and Nascimento (2001), p. 108f.
 See Guimarães (2001), p. 18.
 See Gacitua-Mario and Woolcock (2005), p. 13.
 IPEA (2004), p. 11.
 See Fishlow (1972), p. 394.
 See Fields (1977), p. 577.
 See Litchfield (2001), p. 17f for the debate on poverty in the 1970s.
 See Fox and Morley (1991), p. 18.
 See Lichfield (2001), p. 163.
 See World Bank (1995), p. 11f for a description of the characteristics of the poor.
 See Wodon (2000), p. 15ff for poverty measures and p. 69ff for regression analyses.
 See Litchfield (2001), p. 162ff for her poverty profile for Brazil.
 See Ferreira, Lanjouw and Neri (2003), p. 21f for the results of the probit analysis and p. 25 for headcount indices.
 See Litchfield (2001), p. 34.
 The terms in brackets refer to the labels used for the racial categories in the PNAD.
 For the composition of the sample by race see Chapter 2.
 Except the rural population in some Northern States of the Amazon area, estimated to be 3% of the total national population in 1990. See Litchfield (2001), p. 39.
 See World Bank (2003), p. 17.
 See Litchfield (2001), p. 40.
 See Ferreira et al (2000), p. 11.
 See World Bank (2003), p. 17.
 See Elbers et al (2004), p. 20 for poverty rankings among Brazil’s regions and p. 26 for poverty rankings by occupation.
 The consumer price index chosen has 1995 as the base year (1995=100). The general level for 2002 is 166.1. See United Nations, Economic Commission for Latin America and the Caribbean (2005), p.300.
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Doktorarbeit / Dissertation, 265 Seiten
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Hausarbeit, 22 Seiten
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