2010/ED/EFA/MRT/PI/05
Background paper prepared for the
Education for All Global Monitoring Report 2010
Reaching the marginalized
Educational marginalization across developed and
developing countries
Kenneth Harttgen and Stephan Klasen
2009
This paper was commissioned by the Education for All Global Monitoring Report as
background information to assist in drafting the 2010 report. It has not been edited by the
team. The views and opinions expressed in this paper are those of the author(s) and should
not be attributed to the EFA Global Monitoring Report or to UNESCO. The papers can be
cited with the following reference: “Paper commissioned for the EFA Global Monitoring
Report 2010, Reaching the marginalized” For further information, please contact
[email protected]
Educational marginalization across developed and developing countries Kenneth Harttgen and Stephan Klasen * September 3, 2009 First Draft: Please do not cite. Abstract Besides overall improvements in education in recent years, shortcomings in education persist in many countries, especially in developing regions. This paper focuses on educational marginalization and analyzes the access opportunities to the education system and the educational outcome for several pre‐defined population groups. Using census data for ten developing and developed countries for two points in time, we compare the levels in school attendance rates and educational attainment for each population sub‐group with the mean outcome of the country, aiming to identify the most educational marginalized population sub‐groups within countries. We find high within and across country inequality between pre‐defined population groups. The most educational marginalized groups are the very poor, women and girls, member of an indigenous group, the population that speaks only an indigenous language, the rural and the disabled population. Over time, we find an overall positive development both in access to education as well as in educational attainment. Although our results indicate that the educational marginalization partly persists over time, we also find an overall intergenerational upward mobility in education. However this upward mobility is concentrated at lower educational levels and bypasses higher educational levels. Key words: Education, inequality, marginalization. *University of Göttingen, Department of Economics, Platz der Göttinger Sieben 3, 37073 Göttingen, Germany, email: sklasen@uni‐goettingen.de; [email protected]‐goettingen.de. Any errors remain the responsibility of the author. 1 1. Introduction Spurred by international commitments and expanded funding at the national and international level, attendance in education and associated educational attainment have expanded substantially in developing countries in recent years. However, shortcomings in education persist in many regions of the developing world, hampering economic growth and human development. Moreover, low levels of access to the education system and of educational outcomes are often accompanied by high inequality between countries and within countries between population groups. The success in addressing the challenge of progress in education requires national and multinational response. Achieving progress in education is of fundamental importance for human development. A large body of literature shows that education accelerates economic growth, national productivity, political stability, and social cohesion (see, e.g. Chabott and Ramirez, 2000; Le Vine et al., 2004; Milligan et al., 2003). Education also has a direct impact on other dimensions of human well‐being (i.e. the other MDGs) such as child health and nutrition (see, e.g. Duflo and Breierova, 2000 and Schultz, 2002). In addition, there exists also a strong relationship between education, poverty and inequality. On the one hand, education reduces poverty and inequality. Sustained economic growth and poverty reduction result in higher levels of household resources allowing higher investments in their childrens’ education because parents are less dependent on their children’s labor. On the other hand, existing poverty and inequality may be worsened through poor education. Many researchers have shown that poverty significantly reduces the likelihood of school participation (see, e.g. Smith et al., 2007). In 1990, the World Conference on Education for All adopted the World Declaration on Education for All, which stated that everyone has a right to education. Because of insufficient progress in access to education and educational outcomes in the developing world, in Dakar in the year 2000, the World Education Forum adopted a new framework for Action containing six Education for All (EFA) goals to be reached until the year 2015 to overcome the persisting shortcomings in education. In addition, the explicit inclusion of 2 education among the MDGs reflects that these indicators are fundamental dimensions of human well‐being. Today, more than half of the time period to reach the EFA goals and the MDGs has passed. During the last decade, many regions, particular in East and South Asia, have experienced a significant economic and social progress towards the achievement of the goals by 2015 and many household and individuals have raised their levels of education. However, looking at average improvements hide that many groups within a country have already reached the EFA goals, while others are lagging far behind. Wide disparities in progress remain between population sub‐groups, e.g. between males and females and rich and poor (UNESCO 2008). Thus, overall progress in meeting the EFA goals will depend on reducing existing inequalities in educational access and educational outcome. The main focus of the 2010 Education for All Global Monitoring Report (GMR) is on the topic of “reaching and teaching the most marginalized”. Following the results of the 2009 Education for All Global Monitoring Report (UNESCO 2009), which has its main focus on inequality in access to education and educational outcomes, the 2010 report will look beyond inequality in education and analyzes what educational marginalization means by looking on empirical evidence both from developing and developed countries. Marginalisation is being defined in the GMR as situations of acute and persistent disadvantage in education (as distinct from the overall distribution of education opportunity). The aim of the 2010 Education for All Global Monitor Report is to identify the magnitude of educational marginalization, meaning to identify, which individuals are most likely to be educational marginalized and what socioeconomic, cultural, and political factors determine educational marginalization. The report will explore the factors that maintain educational marginalisation with the focus on access to schooling and learning opportunities and on teaching, learning and achievements. In particular, the report will look at four thematic categories of potential factors of marginalization, i.e. group based factors like language, race or religion; poverty related factors such as chronic and/or transient poverty, vulnerability, 3 and the socio economic status; location factors such as the rural/urban areas; and individual factors such as disability. This paper provides a background analysis for the 2010 Education for All Global Monitoring Report. The objective of the analysis is threefold. First, the analysis provides a detailed descriptive picture of which individuals are educational marginalized. In particular, the paper explores school attendance, educational attainment and literacy across several pre‐
defined marginalized groups (based on the thematic clusters outlined above) on a national and sub‐national scale. Second, the study also provides an analysis of intergenerational mobility in educational outcomes. Third, building on the descriptive part, this analysis will examine the heterogeneity among the groups. In particular, the paper aims to identify the most important factors that determine educational marginalization in order to explain why it is that certain individuals within the same pre‐defined groups are educationally disadvantaged or advantaged (e.g. out‐of‐school, with low attainment or low literacy skills) using geographic, demographic and socioeconomic indicators. For the empirical analysis to document the incidence and severity of educational marginalisation among these groups and across time, we use harmonised census micro‐data for 10 developed and developing countries for two periods: Canada, Colombia, Mexico, Philippines, Romania, Rwanda, South Africa, Uganda, United States, and Vietnam. The rest of the paper is organized as follows. The next section provides a short overview of the empirical approach to analyze marginalization in access to education and in educational outcome. Section three provides a detailed description of the data sources for the analysis. In section four, we present the descriptive results and the results of the heterogeneity analysis. Finally, section five concludes. 4 2. Empirical Approach 2.1 Educational indicators and pre­defined groups The focus of the first part of the analysis is to compare several indicators of access to education and of educational outcome of pre‐defined groups with the mean outcomes for each country, thereby providing an absolute measure of educational disadvantage for these groups as a whole. This approach assumes that these groups identified are among the educationally disadvantaged within a country using the available years. To analyze educational marginalization, we focus both on the access to the education system as well as on educational outcomes of pre‐defined population groups. In particular, we explore the following five educational indicators: a. School attendance rate b. Survival rate to grade 5 c. Educational attainment (primary and secondary schooling completion rate) d. Completed years of schooling e. Literacy rate Whereas the school attendance rate and the survival rate to grade five indicate the access opportunities to the education system, the completion rates of primary and/or secondary education, completed years of schooling and literacy rates indicate the educational outcome of pre‐defined population groups in the sample. As key indicator of educational access, we use the net attendance rates for primary and secondary education as well as the survival rate to grade five based on the respective country specific age‐bracket for primary and secondary education. The net attendance rate of primary and secondary education is calculated as the number of children in the relevant age bracket that are attending primary or secondary school divided by the size of the relevant age bracket. The survival rate to grade five is defined as the rate of children in the 5 respective age bracket that have completed at least five years of schooling. Children of other ages enrolled in primary or secondary education are not taken into account. As the net attendance rate covers only the children in the official age range that is associated with a given level of education, the net attendance rate is also an indicator of the functional capability of the educational system. A high net attendance rate is only possible if the education system has the capacity to educate entire cohorts and allow them to enter and progress through the school system according to their age. 1 To assess the educational attainment, we use two different indicators for two different age groups. First, we use average years of schooling completed and, second, the completion rates of primary, secondary or higher education. As the two age groups of adults, we use the age group aged between 17 and 22 and between 23 and 27. Age plays an important role when analyzing changes in non‐income indicators, especially for education. In particular, not much improvements in education can be expected among the adult population (for example the education of 30‐40 year olds in the first period should not be very different from the education of the 40‐50 year olds in the second period ten years later). To avoid misleading conclusions from potential low improvements, we, therefore, restrict the sample to these two cohorts of young adults as these age groups are likely to have experienced a change in their educational achievement. Only for the indicator years of education completed we additionally include the agegroup of 40 years and older. For the literacy rate, we only focus on adult literacy rate of women. Adults are defined to be persons above the age of 18. If the census data include a direct question on literacy, we use this variable. If the information is not available in the data, we use educational achievement as proxy for literacy. In particular, we consider adult women as literate if they achieved at least a grade, which corresponds to five years of schooling being aware that it is far from evident to determine after how many years of school a person is literate. This varies a lot from country to country or even within a country (for West‐Africa, see e.g. Michaelowa (2001)). 1
The gross attendance rate would also include children outside of the age range in the numerator (and use the same denominator). In countries where many children enter the school system late or progress slowly, the differences between the gross and net enrolment ratios can be large. 6 To identify who are the most educational marginalized population groups, we define several pre‐defined population groups based on four different categories: ƒ
Group‐based: ethnicity; language; race; religion; indigenous status ƒ
Poverty‐related: poverty and socio‐economic status ƒ
Location: urban and rural areas, migrational status ƒ
Individual: disability and special needs For each pre‐defined group, we analyze the respective five educational indicators for the total country as well as separately for males and females. Additionally, we also provide other absolute and relative measure of severe educational deprivation and low educational opportunities. In particular, as further absolute measures, we calculate: ƒ
the share of children (aged between 7‐16) who have never been to school and weren’t attending at the time of the survey as well as also for the agegroups 17‐22, 23‐27, and 40+ who have never been to school; ƒ
the share of population (for the agegroups 17‐22, 23‐27, and 40+) with less than 2 years of education and less than 4 years of education. As further relative measures, we calculate: ƒ
the share of population (for the agegroups 17‐22, 23‐27, and 40+) with less than 60% less than 40% and less than 20% of the mean number of years of education; ƒ
the quintiles for average years of education of population (for the agegroups 17‐22, 23‐27, and 40+). Finally, to have a closer look at the most marginalized population groups we also take into account two overlapping dimensions of pre‐defined groups and combine for each educational indicator several pre‐defined groups. 7 2.2 Calculating welfare quintiles To separate the population into welfare groups (i.e. percentiles and/or quintiles), in order to assess the access to and output of education in a country and over time, one typically uses information on income or expenditure. If available in the census data, we use household income per capita as segmentation variable to calculate welfare quintiles. For some countries, the information on income or expenditure is not available. In this case, we consider an alternative approach to define the socio‐economic status of a household, which we use as a proxy of income or expenditure. In particular, we use an asset‐based approach in defining well‐being proposed by Filmer and Pritchett (2001) and Sahn and Stifel (2001). 2 The main idea of this approach is to construct an aggregated uni‐dimensional index over the range of different dichotomous variables of household assets capturing housing durables and information on the housing quality that indicates the material status of the household. For the estimation of the weights and for the aggregation of the index, we use a principal component analysis proposed by Filmer and Pritchett (2001). In particular, as the components for the asset index, we include dichotomous variables whether the following assets in a household exist or not: radio, TV, refrigerator, bike, motorized transport, capturing household durables and type of floor material, type of wall material, type of toilet, and type of drinking water capturing the housing quality and we calculate the asset indices separately for each country and period. After having derived the aggregated index, one can than calculate based on the index values the population welfare subgroups p. For example, using quintiles as the segmentation dimension, quintile 1 would correspond to the poorest population subgroup and quintile 5 to richest, respectively. 3 2
Although income or expenditure data is preferable, Sahn and Stifel (2001), for example, show by comparing the asset index with income and consumption data that such an asset index is a useful and accurate indicator of long‐
term well‐being if neither income nor expenditure is available. 3
The use of the asset index approach to derive a welfare distribution faces some critical issues that should be mentioned when using this approach as a proxy for income. The asset index can be biased, because it might not reflect correctly differences in income between rural and urban areas, due to usually huge differences in prices and the supply of such assets as well as differences in preferences for assets between both areas. For example, urban 8 2.3 Intergenerational educational mobility In the second part of the analysis, we take a closer look at the intergenerational educational mobility, i.e. on the potential persistence of education marginalisation across generations by country. Unfortunately, in the census data there is no information that allows linking the children to their parents. Therefore, to show intergenerational educational mobility, we define two proxy variables for the education of parents. For each country and year, we provide household intergenerational mobility matrices including information on the educational attainment (incomplete primary education, primary education completed, secondary education completed, and higher education completed) of the household head and household member aged between 15 and 18 years on the respective household. These matrices then show, for example, the share of households where both the household head and the teenaged household member have no or only incomplete primary education. In addition, we calculate correlation coefficients for years of education and educational attainment between the household head and teenaged household member. 2.4 Heterogeneity analysis The third part of the analysis focuses on the factors, which account for within‐group variation in the selected educational indicators, helping to explain what factors determine educational marginalization. For this, we run several multivariate regressions using years of education completed and secondary education completion rates as dependent variables. Controlling for several characteristics including location characteristics (rural/urban, province, region), demographic characteristics (gender, age, household size), and socioeconomic characteristics (e.g. income, wealth, employment status, language, indigenous status, religion), we explore with this multivariate analysis what factors drive educational attainment and also which explanatory factors have differential impacts for households possess demand other assets than rural households. To deal with this issue, for the analysis of differences in access to education and in educational outcomes between rural and urban areas, therefore, we calculate the asset index separately for urban and rural areas. 9 marginalised groups (i.e. inclusion of interaction terms). The inclusion of interaction terms of a selection of pre‐defined groups with several socioeconomic characteristics allows us to demonstrate the most important combination of characteristics affecting education attainment in different countries. 3. Data For the analysis of the educational marginalized population groups, we use census micro‐
data for 10 developed and developing countries, which are provided and harmonized by the Integrated Public Use Microdata Series (IPUMS) International (Minnesota Population Center, 2008). The census data provided by IPUMS International include systematic samples of households and represent between two and 10 percent of the population of the respective country. Thus, the main advantage of using the census data for the analysis of educational marginalization is the large sample size of the data, which allows us to break down the analysis by small population sub‐groups. Besides information about household socio‐economic characteristics, the census data sets include also several indicators on education both for children and adults. Using the census data sets for two time periods allows us not only to capture changes in the access to the education system and in educational outcomes over time, but also to examine the question of who are the most educational marginalized population groups and analyze differences in the distribution of access and outcomes of education by welfare groups as well as by the other background characteristics such as urban and rural areas and/or by gender. The selection of countries in this analysis depends on the census data reliability and availability post‐2000. In particular, Table 1 shows the countries we use for the analysis and also, which pre‐defined groups can be analyzed and which educational indicators are available in the respective data set. The census data for Canada 1991 and Canada 2001 suffer from one main limitation, which has severe consequences for the analysis. First, the variables in both Canadian data sets are 10 not organized into households. This means that there exists no household identity variable that would allow linking the information available to a specific household. For example, with a missing household identity variable, one cannot calculate household income per capita. Typically young household member have no income but are betted into households. With no information on the household identity, these people would have zero income, which would lead to a misleading interpretation about their well‐being. Therefore, for Canada, we only can provide the descriptive analysis of literacy and school attendance. If available, we use the nativity information to separate the migrant population from the non‐migrant population. If the information on nativity is not available, we use the migration status 5 years ago to define the migration status of the household member. Hence, we define an individual as a being a migrant if the household head was born abroad or by the migrational status four years ago. For the disability status, we define an individual as being disabled it she or he has declared to be employment disabled. Since there is no information on disability for children and young adults, we assign to the children the disability status of the household head. In some countries (e.g. Vietnam) there exist many different regions for which there are only few observations (e.g. less than 100). Therefore, we think that it is more appropriate to drop these regions (religious group) from the sample or at least summarize the regions (religions), which have less than 1000 observations into “other region” (“other religion”). In the census data there is no information that allows linking the children to their parents. Therefore, to show intergenerational educational mobility, we define two proxy variables for the education of parents: •
years of education of the household head; •
the person with the maximal years of education in the household. 11 4. Results 4.1 Educational marginalization within and across countries
To provide an overview about the general relationship between education and economic development, Figure 1 shows the correlation between access to education (secondary school attendance) and educational outcome (incomplete primary education, secondary education completed and years of education completed) and GDP per capita for each country and period in our sample. Figure 1a shows that the countries with the lowest GDP per capita have also the highest share of individuals with incomplete primary education, whereas the richest countries in our sample, namely Canada and the United States show very low shares of incomplete primary education. A similar picture is found for the access to the education system. Figure 1b shows that richer countries have, on average, higher rates of secondary school attendance. Similar to Figure 1a, the countries from Sub‐Saharan Africa, namely Uganda and Rwanda show the lowest levels in GDP per capita as well as the lowest rates in secondary school attendance. Also Figure 1c and 1d confirm the general picture that higher levels in GDP per capita are associated with higher levels in access to education and with higher educational outcomes. We now turn to the results of the analysis of the access to the education system and of educational outcomes of the described pre‐defined group at a national and sub‐national scale to identify the educational marginalized population in each country and year in our sample. Table 2 shows the results for all educational indicators by the pre‐defined sub‐
groups as well as the mean values for each country and year in the sample. Starting with our rich countries, Canada and the United States, Table 2 confirms the picture of Figure 1. The overall educational level in both countries is high and has also been improved over time in Canada. In 1991 Canada showed an overall secondary school attendance rate of 0.81 and in 2001 of 0.90, whereas the United States showed a rate of 0.99 in the year 2000 and experienced a slight decline between 2000 and 2005 where the secondary school attendance rate was at 0.97. 12 However, besides high average levels of school attendance and educational outcome, both Canada as well as the United States shows considerable within country differences in education when looking at the pre‐defined sub‐groups. First, in both countries the urban population shows higher levels in education than the rural population. Furthermore, both countries show a clear gender bias in education. Boys and men show considerably higher access to the education system and also higher levels of educational achievements than girls and women. For example, in Canada 2001, the primary educational completion rate of the agegroup 17‐22 was 0.38 for males whereas females show a considerably smaller rate of 0.31. Second, there is also a clear bias in education over welfare quintiles. The poorer population has less access to the education system and lower educational achievements than the richer population. For example, in Canada 2001 the secondary school attendance rate was 0.91 for the richest quintile compared to 0.76 for the poorest quintile. Third, both countries also show differences in education by region and ethnicities. For example, it is interesting to note that in the United States 2005, although the black population shows a higher primary education completion rate than the white population (0.34 compared to 0.38 for the agegroup 17‐22), the white population shows higher achievements when it comes to higher education. The secondary education completion rate was 0.63 compared to 0.60. Forth, looking at the results of the two‐overlapping dimensions of pre‐defined groups, we also find interesting patterns. For example, in the United States in the year 2005, migrants in the poor population sub‐groups show higher educational achievement than non‐migrants whereas the opposite picture is found for the richer population sub‐groups. Here, non‐migrants show quite higher levels in educational outcomes than non‐migrants. Looking at the results for the poorer countries in the sample, two main findings emerge. First, the overall level of education is considerably lower than in the rich countries. Second, the within country differences in access to education and in educational outcomes by the pre‐defined population sub‐groups are considerably higher in the poorer countries. Looking at changes in access to education and in educational outcomes over time, improvements in access to education are made in Colombia, Mexico, Philippines, Romania, South Africa, Uganda, and Vietnam. Improvements in educational outcome are made in 13 Mexico, Philippines, Romania, South Africa, Uganda, and Vietnam. 4 Although the access to primary and secondary education has been improved between 1993 and 2005 in Colombia, the rates of primary and secondary education completion, the adult literacy rate and years of education completed have been slightly declined. Table 2 shows that especially the rural population, the member of an indigenous group, those who only speak an indigenous language and the disabled are the most educational marginalized population groups. For example, member of an indigenous group show lower levels in education than non‐member of an indigenous group. In Colombia in 2005, member of an indigenous group have completed 7.83 years of schooling, whereas non‐member have completed 9.31 years of schooling. The same result holds for the group of speaking an indigenous language. The population sub‐group that speaks an indigenous language has completed, on average, 4.31 years of schooling compared to 7.14 years of those who do not speak an indigenous language. The results are more mixed when looking at the migrational status of individuals. Overall, migrants seem to be better of in terms of access to education and terms of educational achievement. However, when further distinguishing between welfare groups, we again find that this stems from the fact that within the poorer population, migrants show higher educational levels than non‐migrants whereas a converse result if found for richer population sub‐groups. In South Africa, Uganda and Vietnam, migrants show lower rates of school attendance and of educational outcomes than non‐migrants. The employment disabled population sub‐group shows, on average, lower access to the education system and lower levels in educational outcomes than the non‐disabled population group. To analyze, which pre‐defined population group is the most educationally marginalized population group and which population group performance very good, Figure 2 shows the 4
No conclusion can be made about Rwanda, because for the year 1991, there is only information on literacy in the sample. 14 three highest and lowest pre‐defined groups by country and ears as well as the overall country mean for secondary school attendance and years of education completed. Looking at the best performing groups by country and year, Figure 2 confirms the findings of Table 2. Overall, we find that the best performing population subgroups are the 4th and 5th welfare quintiles and the urban population. Apart from that, the results are more mixed. No clear picture is found when it comes to the lowest performing groups in education. As already indicated in Table 2, among the most marginalized population are the poorest welfare groups, the rural population, the member of an indigenous group and those who only speak an indigenous language as well as the disabled population group. Again, mixed results are found for the migration status. For example, in Colombia, Mexico, and the Philippines, the migrant population belongs to the best performing population groups, whereas in Romania, Rwanda, South Africa, and the United States, the migrant population belongs to the most educational marginalized groups. Table 2 provides further absolute and relative measure for the access to education and for educational outcome to identify the most marginalized population groups. To analyze severe educational deprivation, Table 2 shows the share of individuals in several agegroups that have never been to school. For example, Table 2 shows that 34 percent of the rural population aged 40+ in Mexico 2005 has never been to school. Looking at the younger cohorts provides first insights into intergenerational mobility in education. The share of individuals that have never been attended is much lower for the younger cohorts. For example, In Mexico 2005, only 7 percent of the agegroup 17‐22 has never been attended to school, which shows an overall improvement of the access mobility to the education system. However, again, the groups that are identified as the most marginalized population groups show also relatively low levels in access to education and in educational outcome. Especially the rural population, member of an indigenous group or those who only speak an indigenous language as well as disabled individuals show the worse results. In Mexico 2005, 72 percent of the agegroup 40+ in rural areas have less than 4 years of schooling. In Vietnam 1999, this share was 38 percent. 15 Looking at the educational distribution of education across countries and pre‐defined groups, Table 2 also provides the education quintiles in years of education for each country and pre‐defined groups. Here, we find a strong inequality between the highest and lowest quintile across countries. 4.2 Intergenerational mobility in educational marginalization Table 3 and Table 4 show the results for the intergenerational mobility analysis of educational marginalization by country and years. Table 3 shows the intergenerational mobility matrix between the education of the household or the person with the maximal educational outcome per household and the educational attainment of young adolescents aged between 15 and 18. For example, looking at the educational attainment between the household head and the adolescents in the respective household in Mexico 2005, 19.08 percent of households, where the household head has no or only incomplete primary education transmit these low educational attainment to the next generation of household members. In the Philippines in the year 2000, the share is 31.04 percent, in Romania in the year 2002 41.07 percent and in Rwanda in the year 2002 even 81.86 percent. This finding indicates that educational marginalization strongly persists over time and is very often transmitted to the next generation. This finding holds especially when we look at the mobility of the maximal educated person in the household and the respective adolescents in the household. However, we also find improvements or upward mobility in educational attainment between generations. For example, in all countries, except for Rwanda and Uganda, looking again at the educational attainment of the household head and the respective adolescent household member, we find a higher share of adolescents that have complete primary education than the share of those who still have no or incomplete primary education completed. This indicates a clear trend towards an intergenerational upward mobility in education. But this promising upward mobility is limited to basic education. We find no clear trend of an upward mobility to higher education. This means that households, in which the head has no or incomplete primary education show a positive development 16 towards the completion of primary education of the adolescents, but these positive improvements bypass the attainment of higher education. In addition, households with higher education do not automatically transmit this educational level to the next generation. To have a denser zooming on the intergenerational relationship in education, Table 4 shows the correlation coefficients of educational attainment between the household head or the household member with the maximal education in the household and the educational attainment of the adolescents aged between 15 and 18 in the respective household for the country as a whole and also by sex and urban and rural areas. Table 4 confirms the findings of Table 3. A very close relationship exists between the educational level of the maximal educated household member and the educational level of young adults in the household. This relationship is higher than for the relationship between the level of education of the household head and the adolescent in the household. For example, in Rwanda in the 2002, the correlation coefficient between the years of education of the maximal educated household member and the respective adolescents is 0.556 compared to 0.291 for the correlation coefficient of the years of education of the household head and the adolescents. Overall, we found that this difference exist also between males and females as well as between urban and rural areas. In urban areas, the correlation coefficients are higher than in rural areas. The same holds for males compared to females. 4.3 Heterogeneity analysis The heterogeneity analysis addresses the question of what important factors drive educational marginalization. The results for the regression analysis are presented in Table 5, which shows for each country in the sample for the latest available census year the regression results. Where available, two dependent variables are analyzed. For school attendance the attendance rate for secondary education and for educational outcome the years of education completed are used. In addition, Table 6 shows for Mexico, Philippines, Uganda and South Africa the regression results for the probability of being in the bottom 17 quintile of the education distribution (measured in ears of education completed) for the agegroups 17‐22 and 23‐27. To summarize the findings from Table 5 and Table 6, the results confirm the findings of the previous sections. Determinants that strongly affect the marginalization in access to education and educational outcome are being a migrant, being disabled, living in rural areas, being a member of an indigenous group (or being the minority race), and being poor. On the other hand, living in urban areas and if the household head is employed significantly increas the access to education and to the educational outcome. Furthermore, the interaction terms show that these impacts are greater in urban than in rural areas. 5. Concluding remarks This paper provides a background analysis for the 2010 Education for All Global Monitoring Report. The focus of this study is on educational marginalization. First, this study provides a descriptive analysis of school attendance and educational attainment of pre‐defined population sub‐groups on a national and sub‐national scale to identify the educational marginalized population and to compare their education opportunities and their educational achievement with the overall country means. Second, we also provide an intergenerational mobility analysis to explore, if educational marginalization persists over time or whether there is an upward mobility of the less educated population. Third, we also provided a heterogeneity analyses to identify the most important factors that determine educational marginalization. For the empirical analysis of the incidence and severity of educational marginalization among the pre‐defined groups and over time, we use harmonised Census data for 10 developed and developing countries for two periods: Canada, Colombia, Mexico, Philippines, Romania, Rwanda, South Africa, Uganda, United States, and Vietnam. 18 We find large inequalities in access to education and in educational outcome across and within countries by pre‐defined group. Countries with an overall higher development show better access opportunities to the educational system and higher levels of educational attainment than countries with relatively lower human development. In addition, within country inequality in education by pre‐defined groups is higher for poorer countries than for richer countries. For all countries in our sample, we found a clear gender bias in education. Males show higher attendance rates and also higher educational outcomes than females. Overall, we identified the poorest population quintiles, the member of an indigenous group, the population that speaks only an indigenous language, the rural population, and the disabled population group as the marginalized educational groups. Over time, we found an overall positive development both in access to education as well as in educational attainment. Although our results indicate that the educational marginalization partly persists over time, we also found an overall intergenerational upward mobility in education. However, this upward mobility is concentrated at lower educational levels and bypasses higher educational levels. 19 References Chabbott, C. and F. O. Ramirez (2000). Development and education. In M. Hallinan (ed.), Handbook of the Sociology of Education, New York, Kluwer Academic. Duflo, E. and L. Breierova (2004). The Impact of Education on Fertility and Child Mortality: Do Fathers Really Matter less than Mothers? NBER Working Paper No. 10513. Filmer, D. and L.H. Pritchett (2001). Estimating Wealth Effects without Expenditure Data ‐ or Tears: An Application to Educational Enrollments in States of India. Demography, 38 (1), 115‐132. LeVine, R. A., S. E. LeVine, M. L. Rowe, and B. Schnell‐Anzola (2004). Maternal literacy and health behavior: a Nepalese case study. Social Science and Medicine, 58, 866‐877. Michaelowa K. (2001), Primary Education Quality in Francophone Sub‐Saharan Africa. World Development, 29 (10): 1699‐1716. Milligan, K., E. Moretti, and P. Oreopoulos (2003), Does education improve citizenship? Evidence from the US and the UK. Journal of Public Economics, 88 (9), 1667‐1695. Minnesota Population Center (2008), Integrated Public Use Microdata Series — International: Version 4.0, Minneapolis: University of Minnesota. Sahn, D.E. and D. Stifel (2003). Exploring Alternative Measures of Welfare in the Absence of Expenditure Data. Review of Income and Wealth, 49 (4), 463‐489. Schultz, P. T. (2002). Why governments should invest more to educate girls. World Development, 30 (2), 207‐225. Smits, J., J. Huisman, and E. Webbink (2007). Family Background, District and National Determinants of Primary School Enrollment in 62 Developing Countries. Paper presented at the XIII World Congress of Comparative Education Societies, Sarajevo, 3‐7 September. 20 UNESCO (2009). Overcoming Inequality: why governance matters EFA Global Monitoring Report 2009, Paris, UNESCO. UNESCO (2008). Education for All by 2015 ‐ Will we make it? EFA Global Monitoring Report 2008, Paris, UNESCO. 21 Tables and Figures Table 1: Data Sources: IPUMS International census data sets Country Years Pre‐defined group indicators
Education indicators Canada 1991 2001 Urban/Rural Wealth Region (Province) Ethnicity Migration Religion Language Indigenous status Primary and Secondary school attendance
Primary and Secondary education completion (no household identity variable; no information in individuals aged < 15) Colombia 1993 2005 Wealth Urban/Rural Region Indigenous status Disability Migration Language (only 2005) Primary and Secondary school attendance
Survival to grade 5 Primary and Secondary education completion Literacy Years of schooling Mexico 2000 2005 Wealth Urban/Rural Region Religion Indigenous status Migration Disability Primary and Secondary school attendance
Survival to grade 5 Primary and Secondary education completion Literacy Years of schooling Philippines 1995 2000 Wealth Urban/Rural (only 2000) Region Language Religion Disability Migration Primary and Secondary school attendance
Survival to grade 5 Primary and Secondary education completion Literacy Years of schooling Romania 1992 2002 Wealth Urban/Rural Region Religion Migration Ethnicity (only 2002) Primary and Secondary school attendance
Primary and Secondary education completion Rwanda 1991 2002 Wealth Urban/Rural Region Religion Language Disability (only 2002) Migration Primary and Secondary school attendance (only 2002) Survival to grade 5 (only 2002) Primary and Secondary education completion (only 2002) Literacy Years of schooling (only 2002) South Africa 1996 2001 Wealth Urban/Rural Region Religion Ethnicity/Race Language Migration Disability Primary and Secondary school attendance
Survival to grade 5 Primary and Secondary education completion Literacy Years of schooling 22 Country Years Pre‐defined group indicators
Education indicators Uganda 1991 2002 Wealth Urban/Rural Region Religion Ethnicity Migration Disability Primary and Secondary school attendance
Survival to grade 5 Primary and Secondary education completion Literacy Years of schooling United States 2000 2005 Wealth Urban/Rural Region Race Indigenous status Language Migration Disability Primary and Secondary school attendance
Survival to grade 5 Viet Nam 1989 1999 Wealth Urban/Rural Region Ethnicity Religion (only 1999) Disability Migration Primary and Secondary school attendance
Survival to grade 5 Primary and Secondary education completion Literacy Years of schooling 23 Figure 1: Correlation between education and GDP per capita (a) 45
50
Correlation of education and GDP per capita
GDP per capita PPP (in 1000)
10 15 20 25 30 35
40
United States 2005
United States 2000
Canada 2001
Canada 1991
0
5
Mexico 2005
Mexico 2000
Romania 2002
Romania 1992 Colombia 2005
Colombia 1993
Philippines
2000 1991
Philippines
VietnamVietnam
1999 1989
Uganda
2002
Rwanda
Uganda
19912002
0
.2
.4
.6
Percent with incomplete primary education
.8
1
(b) 45
50
Correlation of education and GDP per capita
GDP per capita PPP (in 1000)
10 15 20 25 30 35
40
United States 2005
United States 2000
Canada 2001
Canada 1991
Mexico 2005
Mexico 2000
0
5
Romania 2002
Colombia 2005
Romania 1992
Colombia 1993
Philippines1991
2000
Philippines
Vietnam
Vietnam
1989
Uganda
20021999
Rwanda
2002
Uganda
1991
0
.2
.4
.6
.8
Secondary school attendence rate
1
1.2
24 (c) 45
50
Correlation of education and GDP per capita
GDP per capita PPP (in 1000)
15
20
25
30
35
40
United States 2005
United States 2000
Canada 2001
Canada 1991
10
Mexico 2005
Mexico 2000
0
5
Romania 2002
Colombia 2005 Romania 1992
Colombia 1993
Philippines
Philippines
1991 2000
Vietnam
1999
Vietnam
Uganda
20021989
Rwanda
2002
Uganda
1991
0
.2
.4
.6
Percent with complete secondary education
.8
1
(d) GDP per capita PPP (in 1000)
5
10
15
20
Correlation of education and GDP per capita
Mexico 2005
Mexico 2000
Colombia 2005
Colombia 1993
0
Rwanda
20021991Uganda 2002
Uganda
2
4
Philippines 2000
Philippines 1991
VietnamVietnam
1989 1999
6
Years of education (adults)
8
10
Source: IPUMS International; own calculations. 25 Figure 2: High and low performing groups in education by country (Canada) Low performing groups - Canada
1
1
High performing groups - Canada
.9464
.8831
.8639
.937
.9278
.8977
.8977
.8946
.8859
.8604
.8123
.8283
.8073
.7917
.753
0
0
.2
.2
Secondary school attendence
.4
.6
.8
Secondary school attendence
.4
.6
.8
.8123
Hindu
Muslim
Buddhist
Total
Buddhist
Chinese
1991
Jewish
Total
Total
2001
White
No religionIndigenous
Total
White
1991
No religionIndigenous
2001
(Colombia) High performing groups - Colombia
12
12
14
14
Low performing groups - Colombia
8.398
9.329
8.302
8.118
6.976
8.118
6.976
6.083
4.26
3.863
0
0
2
2
6.051
5.205
4.697
4
Years of education (adults)
6
8
10
9.395
8.863
4
Years of education (adults)
6
8
10
10.48
Quintile 5
Migrant
Quintile 4
Total
Quintile 5 Quintile 4
1993
Migrant
Total
Total
Rural
2005
Quintile 1 Disabled
Total
Rural
1993
Disabled Quintile 1
2005
(Mexico) High performing groups - Mexico
12
10.01
9.953
9.445
9.739
9.46
8.154
8.158
8.154
8.158
5.867
5.862
5.49
5.392
1.656
1.635
0
0
2
2
4
Years of education (adults)
6
8
10
9.983
4
Years of education (adults)
6
8
10
12
14
14
Low performing groups - Mexico
Quintile 4 Quintile 5
Migrant
2000
Total
Quintile 5 Quintile 4
Migrant
2005
Total
Total
Indigenous Quintile 1Speaks indig.
2000
Total
Indigenous Quintile 1Speaks indig.
2005
26 (Philippines) Low performing groups - Philippines
12
12
14
14
High performing groups - Philippines
Years of education (adults)
6
8
10
10.28
9.551
9.531
8.811
9.193
9.193
8.522
7.274
6.656
6.204
3.745
0
0
2
2
4
6.596
5.793
4
8.522
Years of education (adults)
6
8
10
11.05
10.71
Quintile 5 Quintile 4
Migrant
Total
Quintile 5 Quintile 4
1991
Migrant
Total
Total
Muslim
Disabled No religion
Total
Muslim
1991
2000
Quintile 1 Disabled
2000
(Romania) Low performing groups - Romania
1
.9738
.9138
.9123
1
High performing groups - Romania
.9689
.9392
.8814
.8709
.8709
Secondary school attendence
.4
.6
.8
.7886
.7751
.6735
.6681
.7482
.6451
.587
0
0
.2
.2
Secondary school attendence
.4
.6
.8
.7886
Quintile 5 Quintile 4
Urban
Total
Quintile 5 Quintile 4
1992
Urban
Total
Total
Rural
Quintile 2 Quintile 1
Total
Quintile 2 Quintile 1
1992
2002
Muslim
2002
(Rwanda) Low performing groups - Rwanda
.6552
.6018
.576
.554
.5393
.5039
.4472
.34
Female literacy (adult)
.4
.6
Female literacy (adult)
.4
.6
.8
.8
1
1
High performing groups - Rwanda
.4472
.3671
.3649
.34
.2341
.2
.2
.1911
.0992
0
0
.0812
Quintile 5
Muslim
Quintile 4
1991
Total
Quintile 5
Migrant
Urban
2002
Total
Total
Quintile 1 Quintile 3 No religion
1991
Total
Quintile 2 Disabled No religion
2002
27 (South Africa) High performing groups - South Africa
11.25
11.07
11.14
11.11
12
11.1
9.124
9.124
8.86
8.278
7.928
7.778
8.202
7.756
7.657
0
0
2
2
4
4
8.86
Years of education (adults)
6
8
10
11.26
Years of education (adults)
6
8
10
12
14
14
Low performing groups - South Africa
White
Hindu
Asian
Total
Hindu
Asian
1996
White
Total
Total
Disabled No religion Migrant
2001
Total
Rural
1996
Migrant
Disabled
2001
(Uganda) High performing groups - Uganda
12
8.118
6.777
6.442
5.874
5.086
4.829
Years of education (adults)
6
8
10
8.207
7.135
5.874
4.829
4.266
4.234
4.263
3.626
4
4
Years of education (adults)
6
8
10
12
14
14
Low performing groups - Uganda
1.337
0
0
2
2
2.808
Urban
Quintile 5
Muslim
Total
Urban
Quintile 5
1991
Muslim
Total
Total
Quintile 4
2002
Migrant
Disabled
Total
Quintile 1 Quintile 2 No religion
1991
2002
(United States) Low performing groups - United States
.9848
.9689
.986
.9837
.9713
1
.9862
.9689
.9713
.9365
.9176
.9447
.9427
.8858
.8733
Secondary scool attendence
.4
.6
.8
.99
0
0
.2
.2
Secondary scool attendence
.4
.6
.8
1
High performing groups - United States
Quintile 5
Quintile 4
Chinese
2000
Total
Quintile 5
Chinese
2005
Total
Total non-disabled Migrant
2000
Disabled
Total
Indigenous Migrant
2005
Disabled
28 (Vietnam) Low performing groups - Vietnam
9.231
8.694
8.207
8.118
7.283
6.442
7.283
6.466
6.238
5.874
4.266
4.263
4
5.874
Years of education (adults)
6
8
10
9.271
4
Years of education (adults)
6
8
10
12
12
14
14
High performing groups - Vietnam
0
Migrant
Quintile 5 Quintile 4
Total
Urban
1989
1.337
0
2
2
2.964
Quintile 5
Muslim
2002
Total
Total
Rural
Quintile 1 Disabled
1989
Total
Quintile 1 Quintile 2 No religion
2002
29 Table 2: See Excel Spreadsheet “census_tables_1” 30 Table 3: Intergenerational mobility in education by country (Colombia) Intergenerational Mobility Matrices Colombia 1993
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 35.28 10.76 10.32 22.29 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 59.78 4.94 79.49 9.75 69.08 20.6 68.44 9.26 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 49.69 47.2 3.11 Complete Primary 16.93 76.73 6.34 Complete Secondary or higher 10.2 72.77 17.03 Total 22.23 68.51 9.26 Source: XVI National Population and V de Housing Census, IPUMS International Intergenerational Mobility Matrices Colombia 2005
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 24.29 7.09 4.17 14.7 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 65.58 10.14 73.99 18.93 56.98 38.86 66.75 18.55 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 34.06 59.29 6.65 Complete Primary 12.22 75.02 12.76 Complete Secondary or higher 5.03 64.31 30.66 Total 14.61 67.18 18.21 Source: XVI National Population and V de Housing Census, IPUMS International 31 (Mexico) Intergenerational Mobility Matrices Mexico 2000
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 19.12 5.5 7.91 12.45 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 75.93 4.95 86.91 7.6 76.93 15.16 79.98 7.57 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 26.32 69.33 4.35 Complete Primary 8.84 86.26 4.9 Complete Secondary or higher 7.89 78.77 13.34 Total 12.35 80.12 7.53 Source: XII General Population and Housing Census, 2000, IPUMS International. Intergenerational Mobility Matrices Mexico 2005
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 19.08 5.55 7.84 12.46 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 76.01 4.91 86.99 7.46 76.72 15.44 80 7.54 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 26.16 69.51 4.33 Complete Primary 8.87 86.26 4.87 Complete Secondary or higher 7.92 78.74 13.34 Total 12.35 80.14 7.51 Source: XII General Population and Housing Census, 2005, IPUMS International. 32 (Philippines) Intergenerational Mobility Matrices Philippines 1991
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 34.63 12 4.94 17.8 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 49.75 15.63 58.8 29.2 44.61 50.44 51.52 30.69 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 53.59 38.73 7.69 Complete Primary 20.81 62.08 17.11 Complete Secondary or higher 6.31 48.8 44.9 Total 17.65 51.53 30.81 Source: 1990 Census of Population and Housing, IPUMS International. Intergenerational Mobility Matrices Philippines 2000
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 31.04 10.31 4.14 13.5 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 53.79 15.17 63.76 25.92 50.48 45.39 56.41 30.1 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 50.36 42.7 6.94 Complete Primary 17.47 67.37 15.16 Complete Secondary or higher 5.41 53.28 41.31 Total 13.42 56.45 30.13 Source: 2000 Census of Population and Housing, IPUMS International. 33 (Romania) Intergenerational Mobility Matrices Romania 1992
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 16.58 7.16 4.83 8.46 Maximal Education per household Incomplete Primary Incomplete Primary 26.39 Complete Primary 10.1 Complete Secondary or higher 5.2 Total 8.45 Source: Population and Housing Census, IPUMS International. Intergenerational Mobility Matrices Romania 2002
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 41.07 20.53 10.94 17.92 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 73.5 9.92 80.44 12.4 81.81 13.36 79.32 12.21 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 64.78 8.83 79.98 9.93 81.17 13.63 79.39 12.16 Maximal Education per household Incomplete Primary Incomplete Primary 65.71 Complete Primary 28.56 Complete Secondary or higher 11.84 Total 17.96 Source: Population and Housing Census, IPUMS International. Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 53.43 5.5 72.27 7.21 82.36 6.71 75.43 6.64 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 31.48 2.81 66.02 5.42 80.98 7.19 75.43 6.61 34 (Rwanda) Intergenerational Mobility Matrices Rwanda 2002
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 81.86 59.97 49.33 76.45 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 18.09 0.05 39.75 0.28 49.42 1.25 23.42 0.13 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 86.31 13.65 0.03 Complete Primary 65 34.84 0.13 Complete Secondary or higher 44.6 54.21 1.17 Total 76.1 23.74 0.14 Source: IIIème Recensement Général de la Population et de l’Habitat, 16‐30 août 2002, IPUMS International. 35 (South Africa) Intergenerational Mobility Matrices South Africa 1996
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 25.47 10.31 3.89 16.49 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 72.57 1.97 85.66 4.03 83.52 12.59 79.31 4.19 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 34.52 63.96 1.52 Complete Primary 16.15 81.45 2.41 Complete Secondary or higher 5.99 85.19 8.82 Total 16.28 79.43 4.29 Source: South Africa Population Census 1996, IPUMS International; own calculations. Intergenerational Mobility Matrices South Africa 2001
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 19.93 7.91 3.36 12.59 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 77.41 2.66 87.13 4.96 83 13.63 82.07 5.34 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 25.89 71.75 2.36 Complete Primary 13.26 83.68 3.05 Complete Secondary or higher 5.18 85.7 9.12 Total 12.54 82.19 5.27 Source: South Africa Population Census 2001 IPUMS International; own calculations. 36 (Uganda) Intergenerational Mobility Matrices Uganda 1991
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 71.1 42.42 22.7 60.19 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 28.87 0.03 57.45 0.13 76.07 1.23 39.72 0.09 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 76.12 23.87 0.02 Complete Primary 49.17 50.76 0.08 Complete Secondary or higher 22.53 76.54 0.93 Total 60.13 39.78 0.09 Source: 1991 Population and Housing Census, IPUMS International. Intergenerational Mobility Matrices Uganda 2002
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 59.34 33.03 18.47 45.11 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 40.38 0.27 66.34 0.63 78.14 3.39 54.18 0.72 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 64.45 35.38 0.17 Complete Primary 38.47 61.07 0.46 Complete Secondary or higher 19.53 77.67 2.8 Total 45.13 54.16 0.71 Source: 2002 Population and Housing Census, IPUMS International. 37 (United States) Intergenerational Mobility Matrices United States 2000
Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 4.59 0.86 0.27 0.47 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 77.86 17.54 85.77 13.37 84.71 15.02 84.68 14.85 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 6.56 78.13 15.31 Complete Primary 1.56 85.77 12.68 Complete Secondary or higher 0.33 84.95 14.72 Total 0.47 84.95 14.58 Source: 2000 Census of Population and Housing, IPUMS International. Intergenerational Mobility Matrices United States 2005
Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 3.27 0.35 0.11 0.19 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 79.82 16.92 85.12 14.54 82.46 17.43 82.71 17.1 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 4.5 79.74 15.76 Complete Primary 0.55 85.77 13.68 Complete Secondary or higher 0.13 82.86 17.01 Total 0.18 83.01 16.81 Source: 2005 American Community Survey, IPUMS International. 38 (Vietnam) Intergenerational Mobility Matrices Vietnam 1989
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 40.16 14.62 5.5 29.57 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 56.42 3.42 76.79 8.59 77.51 16.99 64.31 6.12 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 57.06 41.09 1.84 Complete Primary 22.14 73.04 4.82 Complete Secondary or higher 6.51 75.36 18.14 Total 29.1 63.75 7.16 Source: 1989 Population and Housing Census, IPUMS International. Intergenerational Mobility Matrices Vietnam 1999
Educational Attainment (head) Incomplete Primary Complete Primary Complete Secondary or higher Total Incomplete Primary 48.76 13.94 4.04 25.31 Edcuational attainment (age 15‐18) Complete Primary Complete Secondary or higher 50.69 0.55 84.40 1.65 91.70 4.25 73.08 1.60 Edcuational attainment (age 15‐18) Maximal Education per household Incomplete Primary Complete Primary Complete Secondary or higher Incomplete Primary 66.45 33.31 0.23 Complete Primary 19.73 79.22 1.05 Complete Secondary or higher 4.21 91.92 3.87 Total 25.36 73.02 1.62 Source: 1999 Population and Housing Census, IPUMS International. 39 Table 4: Intergenerational mobility in education by country (Colombia) Correlation coeficients of intergenrational mobility in education
Colombia 1993 Years of education (head) 1 0.402 Years of education ( age 15‐18) 1 0.655 Maximal years of education Total Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.461 1 Maximal years of education 0.689 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.340 1 Maximal years of education 0.618 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.321 1 Maximal years of education 0.667 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.298 1 Maximal years of education 0.548 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: XVI National Population and V de Housing Census, IPUMS International Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 1 0.300 1 0.356 1 0.333 1 0.358 1 0.261 1 0.356 1 0.214 1 0.309 1 0.219 1 0.379 1 1 1 1 1 1 1 1 1 1 40 Correlation coeficients of intergenrational mobility in education
Colombia 2005 Years of Years of education education ( age Maximal years Total (head) 15‐18) of education Years of education (head) 1 Years of education ( age 15‐18) 0.406 1 Maximal years of education 0.672 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.436 1 Maximal years of education 0.675 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.370 1 Maximal years of education 0.667 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.329 1 Maximal years of education 0.702 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.347 1 Maximal years of education 0.585 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: XVI National Population and V de Housing Census, IPUMS International Educational attainment (head) 1 1 1 1 1 Educational attainment (age 15‐18) Maximal attainment 0.328 1 0.357 1 0.356 1 0.373 1 0.293 1 0.340 1 0.278 1 0.334 1 0.281 1 0.294 1 41 (Mexico) Correlation coeficients of intergenrational mobility in education
Mexico 2000 Years of education (head) 1 0.403 Years of education ( age 15‐18) 1 0.653 Maximal years of education Total Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.421 1 Maximal years of education 0.661 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.385 1 Maximal years of education 0.646 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.31 1 Maximal years of education 0.665 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.359 1 Maximal years of education 0.585 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: XII General Population and Housing Census, 2000, IPUMS International. Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 1 0.197 1 0.219 1 0.208 1 0.222 1 0.185 1 0.216 1 0.121 1 0.157 1 0.192 1 0.212 1 1 1 1 1 1 1 1 1 1 42 Correlation coeficients of intergenrational mobility in education
Mexico 2005 Years of Years of education education ( age Maximal years Total (head) 15‐18) of education Years of education (head) 1 Years of education ( age 15‐18) 0.404 1 Maximal years of education 0.652 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.430 1 Maximal years of education 0.657 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.387 1 Maximal years of education 0.646 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.307 1 Maximal years of education 0.661 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.364 1 Maximal years of education 0.585 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: XII General Population and Housing Census, 2005, IPUMS International. Educational attainment (head) 1 1 1 1 1 Educational attainment (age 15‐18) Maximal attainment 0.198 1 0.218 1 0.208 1 0.217 1 0.189 1 0.219 1 0.121 1 0.155 1 0.197 1 0.214 1 43 (Philippines) Correlation coeficients of intergenrational mobility in education
Philippines 1991 Years of education (head) 1 0.435 Total Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) 1 0.480 Years of education ( age 15‐18) 1 0.656 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 1 0.377 1 0.449 1 0.417 1 0.466 1 1 1 0.676 1 1 1 0.383 1 0.629 1 Maximal attainment Source: 1990 Census of Population and Housing, IPUMS International. 1 0.426 1 0.557 1 44 Correlation coeficients of intergenrational mobility in education
Philippines 2000 Years of Years of education education ( age Total (head) 15‐18) Years of education (head) 1 Years of education ( age 15‐18) 0.421 1 Maximal years of education 0.620 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.462 1 Maximal years of education 0.635 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.374 1 Maximal years of education 0.601 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.420 1 Maximal years of education 0.634 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.317 1 Maximal years of education 0.462 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: 2000 Census of Population and Housing, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 0.346 1 0.410 1 0.382 1 0.429 1 0.305 1 0.389 1 0.345 1 0.418 1 0.260 1 0.283 1 1 1 1 1 1 1 1 1 1 1 45 (Romania) Correlation coeficients of intergenrational mobility in education
Romania 1992 Years of education (head) 1 Years of education ( age 15‐18) 1 Total Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: Population and Housing Census, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 1 0.129 1 0.164 1 0.129 1 0.156 1 0.129 1 0.173 1 0.133 1 0.174 1 0.106 1 0.137 1 1 1 1 1 1 1 1 1 1 1 1 1 1 46 Correlation coeficients of intergenrational mobility in education
Romania 2002 Years of Years of education education ( age Total (head) 15‐18) Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: Population and Housing Census, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 0.221 1 0.256 1 0.229 1 0.288 1 0.231 1 0.284 1 0.218 1 0.302 1 0.191 1 0.241 1 1 1 1 1 1 1 1 1 1 1 47 (Rwanda) Correlation coeficients of intergenrational mobility in education
Rwanda 2002 Years of education (head) 1 0.291 Years of education ( age 15‐18) 1 0.556 Maximal years of education Educational attainment (head) Total Years of education (head) Years of education ( age 15‐18) Maximal years of education 1 Educational attainment (head) 1 Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.293 1 Maximal years of education 0.551 1 Educational attainment (head) 1 Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.291 1 Maximal years of education 0.561 1 Educational attainment (head) 1 Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.247 1 Maximal years of education 0.566 1 Educational attainment (head) 1 Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.294 1 Maximal years of education 0.463 1 Educational attainment (head) 1 Educational attainment (age 15‐18) Maximal attainment Source: IIIème Recensement Général de la Population et de l’Habitat, 16‐30 août 2002, IPUMS International. Educational attainment (age 15‐18) Maximal attainment 0.239 1 0.307 1 0.244 1 0.299 1 0.234 1 0.316 1 0.188 1 0.272 1 0.267 1 0.310 1 48 (South Africa) Correlation coeficients of intergenrational mobility in education
South Africa 1996 Years of education (head) 1 0.334 Years of education ( age 15‐18) 1 0.581 Maximal years of education Total Years of education (head) Years of education ( age 15‐18) Maximal years of education 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.341 1 Maximal years of education 0.572 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.340 1 Maximal years of education 0.592 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.240 1 Maximal years of education 0.571 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.323 1 Maximal years of education 0.510 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: South Africa Population Census 1996, IPUMS International; own calculations. Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 1 0.261 1 0.282 1 0.271 1 0.291 1 0.252 1 0.277 1 0.197 1 0.238 1 0.235 1 0.236 1 1 1 1 1 49 Correlation coeficients of intergenrational mobility in education
South Africa 2001 Years of Years of education education ( age Maximal years Total (head) 15‐18) of education Years of education (head) 1 Years of education ( age 15‐18) 0.319 1 Maximal years of education 0.540 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.325 1 Maximal years of education 0.532 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.314 1 Maximal years of education 0.550 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.213 1 Maximal years of education 0.525 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.312 1 Maximal years of education 0.496 1 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: South Africa Population Census 2001 IPUMS International; own calculations. Educational attainment (head) 1 1 1 1 1 Educational attainment (age 15‐18) Maximal attainment 0.243 1 0.244 1 0.247 1 0.247 1 0.240 1 0.243 1 0.167 1 0.134 1 0.221 1 0.193 1 50 (Uganda) Correlation coeficients of intergenrational mobility in education
Uganda 1991 Years of education (head) 1 0.323 Years of education ( age 15‐18) 1 0.617 Total Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.376 1 Maximal years of education 0.616 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.404 1 Maximal years of education 0.624 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.324 1 Maximal years of education 0.599 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.352 1 Maximal years of education 0.486 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: 1991 Population and Housing Census, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 1 0.300 1 0.322 1 0.291 1 0.300 1 0.313 1 0.345 1 0.251 1 0.287 1 0.269 1 0.236 1 1 1 1 1 1 1 1 1 1 51 Correlation coeficients of intergenrational mobility in education
Uganda 2002 Years of Years of education education ( age Total (head) 15‐18) Years of education (head) 1 Years of education ( age 15‐18) 0.416 1 Maximal years of education 0.621 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.407 1 Maximal years of education 0.615 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.437 1 Maximal years of education 0.628 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.372 1 Maximal years of education 0.611 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.352 1 Maximal years of education 0.489 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: 2002 Population and Housing Census, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 0.307 1 0.327 1 0.296 1 0.302 1 0.318 1 0.352 1 0.270 1 0.295 1 0.260 1 0.260 1 1 1 1 1 1 1 1 1 1 1 52 (United States) Correlation coeficients of intergenrational mobility in education
United States 2000 Years of education (head) 1 Years of education ( age 15‐18) 1 Total Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: 2000 Census of Population and Housing, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 1 0.008 1 0.039 1 0.023 1 0.034 1 0.018 1 0.020 1 0.020 1 0.026 1 0.044 1 0.047 1 1 1 1 1 1 1 1 1 1 53 Correlation coeficients of intergenrational mobility in education
United States 2005 Years of Years of education education ( age Total (head) 15‐18) Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 1 Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: 2005 American Community Survey, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 0.026 1 0.029 1 0.025 1 0.027 1 0.025 1 0.042 1 0.027 1 0.022 1 0.041 1 0.047 1 1 1 1 1 1 1 1 1 1 1 54 (Vietnam) Correlation coeficients of intergenrational mobility in education
Vietnam 1989 Years of education (head) 1 0.451 Years of education ( age 15‐18) 1 753 Total Years of education (head) Years of education ( age 15‐18) Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.443 1 Maximal years of education 0.739 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.458 1 Maximal years of education 0.766 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.428 1 Maximal years of education 0.751 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.405 1 Maximal years of education 0.691 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: 1989 Population and Housing Census, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 1 0.314 1 0.445 1 0.308 1 0.422 1 0.319 1 0.467 1 0.286 1 0.407 1 0.285 1 0.408 1 1 1 1 1 1 1 1 1 1 55 Correlation coeficients of intergenrational mobility in education
Vietnam 1999 Years of Years of education education ( age Total (head) 15‐18) Years of education (head) 1 Years of education ( age 15‐18) 0.510 1 Maximal years of education 0.713 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By sex Male Years of education (head) 1 Years of education ( age 15‐18) 0.503 1 Maximal years of education 0.706 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Female Years of education (head) 1 Years of education ( age 15‐18) 0.516 1 Maximal years of education 0.721 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment By Rural/Urban Urban Years of education (head) 1 Years of education ( age 15‐18) 0.502 1 Maximal years of education 0.736 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Rural Years of education (head) 1 Years of education ( age 15‐18) 0.445 1 Maximal years of education 0.623 Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment Source: 1999 Population and Housing Census, IPUMS International. Maximal years of education Educational attainment (head) Educational attainment (age 15‐18) Maximal attainment 0.403 1 0.474 1 0.399 1 0.466 1 0.407 1 0.482 1 0.391 1 0.469 1 0.347 1 0.400 1 1 1 1 1 1 1 1 1 1 1 56 Table 5: Regression results by country (Canada) Regression results Secondary school attendance Age Age^2 Sex (1=male) Urban Speaks English (=1) Migrant (=1) Member of an indigenous group Ln Income White British French Other_European South_Asian Other_Asia Black_Caribbean Aboriginal Canadian Other ethnicity Sex_age Sex_urban Sex_migration Sex_white Urban_age Urban_migration Urban_white Constant R^2 N Coef. ‐0.004 0.000 0.034 0.172 ‐0.077 0.016 0.011 ‐0.022 ‐0.036 ‐0.007 ‐0.003 0.037 ‐0.033 ‐0.031 ‐0.045 ‐0.156 ‐0.013 0.001 ‐0.003 0.007 ‐0.005 ‐0.005 0.009 0.008 ‐0.055 1.145 0.0226 Std. Err. 0.171 0.005 0.104 0.106 0.012 0.016 0.017 0.002 0.017 0.009 0.013 0.019 0.024 0.025 0.028 0.024 0.015 0.011 0.006 0.010 0.022 0.020 0.006 0.026 0.027 1.373 P>|t| 0.980 0.932 0.748 0.106 0.000 0.305 0.489 0.000 0.040 0.436 0.842 0.053 0.180 0.218 0.107 0.000 0.370 0.917 0.688 0.506 0.812 0.821 0.172 0.755 0.038 0.404 17091 Source: 2001 Census of Canada, IPUMS International. Note: Controlled for regions. 57 (Colombia) Regression results: Colombia 2005 Secondary school attendance Age Age^2 Sex (1=male) Urban Household Size Migrant (=1) Disabled (=1) Member of an indigenous group HH head employed (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 Central Bogotá Pacífico_N Eje_Cafetero Andina_Norte Andina_Sur Pacífico_Sur Caribe Orinoquia Coef. ‐0.022 ‐0.002 0.075 ‐0.150 ‐0.002 ‐0.081 ‐0.194 ‐0.166 0.008 ‐0.368 ‐0.241 ‐0.131 ‐0.056 0.068 0.048 0.010 ‐0.023 ‐0.001 0.001 0.006 0.131 0.042 Std. Err. 0.008 0.000 0.027 0.040 0.000 0.030 0.015 0.007 0.002 0.020 0.020 0.020 0.023 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.006 0.007 P>|t| 0.004 0.000 0.005 0.000 0.000 0.007 0.000 0.000 0.000 0.000 0.000 0.000 0.015 0.000 0.000 0.084 0.000 0.830 0.853 0.282 0.000 0.000 Sex_migration Sex_disability Sex_ethnicity Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_disability Urban_ethnicity Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 ‐0.007 ‐0.018 ‐0.021 ‐0.062 ‐0.068 ‐0.047 ‐0.023 0.070 ‐0.016 0.069 0.100 0.087 0.059 0.027 1.827 0.1859 0.020 0.014 0.009 0.004 0.004 0.004 0.004 0.030 0.015 0.009 0.020 0.020 0.020 0.023 0.068 0.733 0.192 0.015 0.000 0.000 0.000 0.000 0.020 0.294 0.000 0.000 0.000 0.004 0.235 0.000 N 394287 Source: XVI National Population and V de Housing Census, IPUMS International Note: Left out region: Amazonia. 58 Regression results: Colombia 2005 Maximal years of schooling in household Age Age^2 Sex (1=male) Urban Household Size Migrant (=1) Disabled (=1) Member of an indigenous group HH head employed (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 Central Bogotá Pacífico_N Eje_Cafetero Andina_Norte Andina_Sur Pacífico_Sur Caribe Amazonia Sex_migration Sex_disability Sex_ethnicity Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_disability Urban_ethnicity Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.059 ‐0.001 0.013 1.689 0.196 ‐1.032 ‐0.506 ‐0.083 0.057 ‐4.856 ‐3.051 ‐1.951 ‐1.200 0.336 0.442 ‐0.126 ‐0.190 ‐0.241 ‐0.126 0.024 0.798 ‐0.041 ‐0.143 0.037 0.027 0.059 0.017 0.011 ‐0.021 0.067 ‐0.349 ‐0.375 0.275 0.120 0.340 0.533 8.306 0.3546 Std. Err. 0.000 0.000 0.077 0.119 0.001 0.098 0.027 0.020 0.004 0.049 0.049 0.050 0.058 0.012 0.012 0.012 0.013 0.012 0.013 0.011 0.011 0.019 0.060 0.025 0.024 0.011 0.011 0.011 0.011 0.098 0.028 0.024 0.049 0.049 0.050 0.058 0.117 P>|t| 0.000 0.000 0.861 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.037 0.000 0.029 0.016 0.139 0.262 0.000 0.132 0.330 0.060 0.494 0.000 0.000 0.000 0.015 0.000 0.000 0.000 2647651 Source: XVI National Population and V de Housing Census, IPUMS International. Note: Left out region: Orinoquai. 59 (Mexico) Regression results: Mexico 2005 Secondary school attendance Age Age^2 Sex (1=male) Urban Household Size Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 No_Religion Christian Other religion Speaks inigenous language (=1) Sex_age Sex_urban Sex_hhs Sex_migration Sex_disability Sex_speaks indigenous language Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 urban_age | urban_hhs | Urban_migration Urban_disability Urban_speaks indigenous language Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.019 ‐0.005 0.025 ‐0.311 ‐0.006 0.000 ‐0.122 ‐0.145 ‐0.080 ‐0.010 0.057 0.085 0.144 0.162 ‐0.023 0.001 ‐0.018 ‐0.001 0.007 ‐0.006 0.077 0.026 0.029 0.008 0.003 0.041 ‐0.012 ‐0.051 0.000 ‐0.087 ‐0.135 ‐0.121 ‐0.092 ‐0.073 1.361 0.236 Std. Err. 0.007 0.000 0.026 0.027 0.000 0.023 0.008 0.005 0.005 0.005 0.007 0.012 0.012 0.021 0.004 0.001 0.003 0.001 0.023 0.009 0.004 0.005 0.004 0.004 0.004 0.001 0.001 0.024 0.009 0.005 0.006 0.005 0.005 0.007 0.060 P>|t| 0.012 0.000 0.333 0.000 0.000 0.992 0.000 0.000 0.000 0.052 0.000 0.000 0.000 0.000 0.000 0.393 0.000 0.016 0.765 0.531 0.000 0.000 0.000 0.069 0.414 0.000 0.000 0.037 0.989 0.000 0.000 0.000 0.000 0.000 0.000 470743 Source: XII General Population and Housing Census, 2005, IPUMS International. Note: Controlled for regions. 60 Regression results: Mexico 2005 Maximal years of schooling in household Age Age^2 Sex (1=male) Urban Household Size Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 No_Religion Christian Other religion Speaks inigenous language (=1) Sex_age Sex_urban Sex_hhs Sex_migration Sex_disability Sex_speaks indigenous language Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 urban_age | urban_hhs | Urban_migration Urban_disability Urban_speaks indigenous language Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.079 ‐0.001 0.126 4.455 0.218 ‐0.838 ‐0.169 ‐2.691 ‐1.369 0.000 0.987 ‐0.176 0.126 1.114 ‐0.905 0.002 Std. Err. 0.000 0.000 0.071 0.076 0.002 0.071 0.022 0.017 0.017 0.017 0.022 0.031 0.029 0.062 0.012 0.000 P>|t| 0.000 0.000 0.076 0.000 0.000 0.000 0.000 0.000 0.000 0.993 0.000 0.000 0.000 0.000 0.000 0.000 ‐0.029 ‐0.018 ‐0.063 ‐0.141 0.077 0.074 0.079 0.050 0.030 0.006 ‐0.111 ‐0.836 ‐0.321 ‐0.418 ‐2.413 ‐2.347 ‐2.113 ‐1.310 6.682 0.365 0.011 0.002 0.066 0.024 0.014 0.016 0.015 0.014 0.013 0.000 0.002 0.074 0.024 0.016 0.020 0.018 0.018 0.022 0.084 0.007 0.000 0.345 0.000 0.000 0.000 0.000 0.000 0.025 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 3039628 Source: XII General Population and Housing Census, 2005, IPUMS International. Note: Controlled for regions. 61 (Philippines) Regression results: Philippines 2001 Secondary school attendance Age Age^2 Sex (1=male) Household Size Urban (=1) Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 Christian Other religion Sex_migration Sex_disability Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_disability Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.161 ‐0.007 0.178 0.002 ‐0.305 0.085 ‐0.190 ‐0.121 ‐0.052 ‐0.021 0.011 0.012 ‐0.033 ‐0.050 0.014 ‐0.068 ‐0.068 ‐0.056 ‐0.039 0.050 0.087 0.014 ‐0.001 0.005 0.000 ‐0.012 0.411 2034396 Std. Err. 0.007 0.000 0.013 0.000 0.018 0.005 0.011 0.003 0.003 0.003 0.003 0.004 0.006 0.007 0.014 0.004 0.004 0.004 0.004 0.008 0.018 0.008 0.006 0.005 0.005 0.053 P>|t| 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.005 0.000 0.000 0.297 0.000 0.000 0.000 0.000 0.000 0.000 0.089 0.823 0.327 0.994 0.820 Source: 2001 Census of Population and Housing, IPUMS International. Note: Controlled for regions and languages. Left out religion: no religion. 62 Regression results: Philippines 2001 Maximal years of schooling in household Age Age^2 Sex (1=male) Household Size Urban (=1) Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 Christian Other religion Sex_migration Sex_disability Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_disability Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.039 0.000 ‐0.030 0.187 0.761 ‐0.317 ‐0.131 ‐4.467 ‐3.052 ‐2.045 ‐0.857 0.634 ‐0.017 0.008 0.043 0.129 0.097 0.067 0.040 0.010 0.175 ‐0.033 0.231 ‐0.139 ‐0.330 10.725 0.233 226065 Std. Err. 0.000 0.000 0.020 0.001 0.024 0.012 0.020 ‐0.009 ‐0.009 ‐0.008 ‐0.008 0.013 0.017 0.016 0.026 0.011 0.011 0.011 0.011 0.018 0.035 0.024 0.016 0.013 0.012 0.027 P>|t| 0.000 0.000 0.137 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.335 0.634 0.103 0.000 0.000 0.000 0.000 0.568 0.000 0.177 0.000 0.000 0.000 0.000 Source: 2001 Census of Population and Housing, IPUMS International. Note: Controlled for regions and languages. Left out religion: no religion. 63 (Romania) Regression results: Romania 2002 Secondary school attendance Age Age^2 Sex (1=male) Urban (=1) Household Size HH head is employed (=1) Migrant (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 Christian North_East South_East South South_West West North_West Center Romanian Hungarian Gypsy German Ukrainian Sex_migration Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.221 ‐0.010 0.014 0.030 ‐0.020 0.023 0.019 0.068 ‐0.157 ‐0.141 ‐0.067 ‐0.005 ‐0.004 ‐0.016 0.014 0.033 0.003 0.018 0.005 0.088 0.064 ‐0.241 0.102 0.078 ‐0.025 ‐0.011 ‐0.007 ‐0.003 ‐0.003 ‐0.004 0.022 0.028 0.021 0.005 ‐0.316 0.233 200689 Std. Err. 0.008 0.000 0.005 0.011 0.000 0.004 0.001 0.007 0.011 0.011 0.011 0.013 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.008 0.008 0.008 0.018 0.014 0.004 0.004 0.004 0.004 0.004 0.004 0.011 0.011 0.011 0.013 0.057 P>|t| 0.000 0.000 0.004 0.005 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.686 0.104 0.000 0.000 0.000 0.330 0.000 0.131 0.000 0.000 0.000 0.000 0.000 0.000 0.007 0.078 0.426 0.490 0.412 0.051 0.012 0.049 0.727 0.000 Source: Population and Housing Census, IPUMS International. Note: Left our region: Bukarest; left out ethnicity: other ethnicity. 64 (Rwanda) Regression results: Rwanda 2002 Secondary school attendance Age Age^2 Sex (1=male) Household Size Urban (=1) Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 Language = French Language = Swahili No religion Muslim Christian Sex_migration Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.060 ‐0.006 ‐0.174 0.018 ‐0.257 ‐0.189 ‐0.114 ‐0.109 ‐0.108 ‐0.062 ‐0.036 0.074 ‐0.207 ‐0.138 0.075 0.032 0.019 0.026 0.033 0.012 0.000 0.017 0.078 0.090 0.065 0.056 1.201 0.164 132205 Std. Err. 0.015 0.001 0.026 0.001 0.035 0.009 0.009 0.006 0.006 0.006 0.006 0.012 0.014 0.026 0.027 0.025 0.011 0.008 0.008 0.008 0.008 0.012 0.013 0.013 0.011 0.011 0.108 P>|t| 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.005 0.196 0.092 0.001 0.000 0.138 0.982 0.175 0.000 0.000 0.000 0.000 0.000 Source: IIIème Recensement Général de la Population et de l’Habitat, 16‐30 août 2002, IPUMS International. Note: Controlled for region.Left our language: other language; Left out religion: other religion. 65 Regression results: Rwanda 2002 Maximal years of schooling in household Age Age^2 Sex (1=male) Household Size Urban (=1) Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 No_religion Muslim Christian Language = French Language = Swahili Sex_migration Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.040 ‐0.001 0.244 0.328 1.725 ‐0.428 ‐0.102 ‐2.191 ‐2.196 ‐1.485 ‐1.113 ‐1.252 ‐0.479 ‐0.027 0.258 ‐2.167 ‐0.119 0.141 0.191 0.105 0.032 ‐0.490 ‐0.961 ‐0.949 ‐1.045 ‐1.204 6.912 0.343 620776 Std. Err. 0.001 0.000 0.041 0.002 0.048 0.026 0.017 0.017 0.017 0.017 0.018 0.078 0.080 0.076 0.031 0.032 0.032 0.023 0.023 0.023 0.023 0.034 0.035 0.035 0.032 0.031 0.085 P>|t| 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.724 0.000 0.000 0.000 0.000 0.000 0.000 0.158 0.000 0.000 0.000 0.000 0.000 0.000 Source: IIIème Recensement Général de la Population et de l’Habitat, 16‐30 août 2002, IPUMS International. Note: Controlled for region.Left our language: other language; Left out religion: other religion. 66 (South Africa) Regression results: South Africa 2001 Secondary school attendance Age Age^2 Sex (1=male) Urban(= 1) Household Size Ln income Migrant (=1) Disabled (=1) Member of an indigenous group (=1) HH head is employed (=1) Black Asian Speaks Afrikaans No_religion Hindu Muslim Christian Sex_urban Sex_migration Sex_disability Sex_white Sex_black Urban_migration Urban_disability Urban_employed Urban_white Urban_black Constant R^2 N Coef. 0.165 ‐0.007 ‐0.124 0.253 0.001 0.015 0.280 ‐0.117 ‐0.004 ‐0.034 ‐0.051 ‐0.038 ‐0.082 ‐0.018 0.052 0.023 0.037 ‐0.007 ‐0.006 ‐0.015 0.012 0.006 ‐0.184 0.028 0.034 ‐0.143 ‐0.113 ‐0.166 0.060 344265 Std. Err. 0.006 0.000 0.017 0.019 0.000 0.001 0.015 0.006 0.000 0.002 0.008 0.006 0.006 0.005 0.008 0.006 0.004 0.002 0.013 0.007 0.002 0.005 0.015 0.007 0.002 0.009 0.005 0.064 P>|t| 0.000 0.000 0.000 0.000 0.018 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.005 0.637 0.026 0.000 0.217 0.000 0.000 0.000 0.000 0.000 0.009 Source: South Africa Population Census 2001 IPUMS International; own calculations. Note: Controlled for region and language. Left out race: white. 67 Regression results: South Africa 2001 Maximal years of schooling in household Age Age^2 Sex (1=male) Urban(= 1) Household Size Ln income Migrant (=1) Disabled (=1) Member of an indigenous group (=1) HH head is employed (=1) Black Asian Speaks Afrikaans No_religion Hindu Muslim Christian Sex_urban Sex_migration Sex_employed Sex_disability Sex_white Sex_black Urban_migration Urban_disability Urban_employed Urban_white Urban_black Constant R^2 N Coef. 0.038 ‐0.001 ‐0.113 3.979 0.335 0.588 1.521 ‐0.363 ‐0.073 0.209 ‐1.753 ‐0.484 ‐0.229 ‐0.398 0.397 0.327 0.278 0.005 0.238 ‐0.121 ‐0.130 0.094 ‐0.056 ‐1.205 ‐0.021 ‐0.272 ‐2.341 ‐0.630 4.144 0.226 1725537 Std. Err. 0.001 0.000 0.034 0.042 0.001 0.002 0.035 0.018 0.001 0.009 0.027 0.022 0.019 0.018 0.028 0.024 0.017 0.010 0.027 0.009 0.021 0.017 0.012 0.033 0.021 0.010 0.031 0.020 0.143 P>|t| 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.626 0.000 0.000 0.000 0.000 0.000 0.000 0.308 0.000 0.000 0.000 0.000 Source: South Africa Population Census 2001 IPUMS International; own calculations. Note: Controlled for region. Left our language: other language; Left out religion: other religion. 68 (Uganda) Regression results: Uganda 2002 Secondary school attendance Age Age^2 Sex (1=male) Urban(= 1) Household Size Migrant (=1) Disability (=1) HH head is employed (=1) Muslim Christian Sex_migration Sex_disability Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_disability Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.301 ‐0.013 0.129 0.056 0.013 0.058 ‐0.102 0.001 0.245 0.255 ‐0.069 0.014 0.010 0.006 ‐0.023 ‐0.028 ‐0.038 ‐0.009 ‐0.051 ‐0.045 0.014 0.013 ‐1.381 0.194 278354 Std. Err. 0.008 0.000 0.012 0.014 0.000 0.020 0.019 0.002 0.009 0.009 0.012 0.009 0.004 0.004 0.004 0.004 0.015 0.016 0.003 0.003 0.003 0.003 0.060 P>|t| 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.427 0.000 0.000 0.000 0.112 0.028 0.203 0.000 0.000 0.008 0.562 0.000 0.000 0.000 0.000 0.000 Source: 2002 Population and Housing Census, IPUMS International. Note: Controlled for region and language. Left out religion: no religion. 69 Regression results: Uganda 2002 Maximal years of schooling in household Age Age^2 Sex (1=male) Urban(= 1) Household Size Migrant (=1) Disability (=1) HH head is employed (=1) Muslim Christian Sex_migration Sex_disability Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_disability Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.056 ‐0.001 0.506 2.639 0.292 0.578 ‐0.232 0.467 ‐0.073 0.209 ‐0.208 0.097 ‐0.095 ‐0.119 ‐0.174 ‐0.157 ‐0.604 0.002 ‐2.945 ‐2.895 ‐1.525 ‐1.195 5.025 0.312 1521432 Std. Err. 0.001 0.000 0.044 0.056 0.001 0.073 0.060 0.006 0.001 0.009 0.043 0.028 0.017 0.017 0.017 0.017 0.056 0.052 0.012 0.012 0.011 0.011 0.038 P>|t| 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.973 0.000 0.000 0.000 0.000 0.000 Source: 2002 Population and Housing Census, IPUMS International. Note: Controlled for region and language. Left out religion: no religion. 70 (United States) Regression results: Uganda 2002 Secondary school attendance Age Age^2 Sex (1=male) Urban(= 1) Migrant (=1) Member of an indigenous group (=1) White_race Black_race Chinese_race Korean_race Vietnamese_race Filipino_race Indian_race | Other_Asian race Two_races Other_races Polish | Arabic | Sex_migration Sex_indegenous Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_indegenous Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.087 ‐0.003 0.000 0.059 0.024 0.006 ‐0.002 ‐0.001 0.018 0.023 0.016 0.011 0.017 0.018 ‐0.001 ‐0.005 ‐0.031 ‐0.036 0.015 ‐0.007 ‐0.006 ‐0.003 0.000 ‐0.001 ‐0.004 ‐0.021 ‐0.023 ‐0.010 ‐0.005 ‐0.002 0.406 0.015 255303 Std. Err. 0.004 0.000 0.012 0.023 0.003 0.015 0.009 0.009 0.010 0.012 0.012 0.010 0.010 0.010 0.009 0.009 0.040 0.040 0.003 0.006 0.002 0.002 0.002 0.002 0.003 0.011 0.002 0.002 0.002 0.002 0.057 P>|t| 0.000 0.000 0.995 0.010 0.000 0.680 0.806 0.881 0.073 0.054 0.182 0.245 0.072 0.059 0.941 0.536 0.443 0.364 0.000 0.243 0.002 0.202 0.964 0.674 0.169 0.061 0.000 0.000 0.004 0.212 0.000 Source: 2005 American Community Survey, IPUMS International. Note: Controlled for region and language. Left out race: Japanese. 71 (Vietnam) Regression results: Vietnam 1999 Secondary school attendance Age Age^2 Sex (1=male) Urban(= 1) Migrant (=1) Buddhist Christian Kinh Tay Thai Hoa Kho_Me Muong Nung Hmong Dao Gia_Rai Sex_migration Sex_disability Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_disability Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. ‐0.010 ‐0.003 0.260 ‐0.227 0.176 ‐0.048 ‐0.039 0.105 0.125 ‐0.007 0.129 0.038 0.028 0.083 ‐0.244 ‐0.125 0.057 ‐0.067 ‐0.437 ‐0.144 ‐0.066 ‐0.006 0.008 0.043 ‐0.475 ‐0.271 ‐0.230 ‐0.121 ‐0.115 1.258 0.221 278453 Std. Err. 0.014 0.000 0.022 0.021 0.010 0.003 0.003 0.010 0.011 0.012 0.013 0.013 0.013 0.012 0.013 0.013 0.015 0.011 0.018 0.005 0.005 0.005 0.005 0.011 0.020 0.005 0.005 0.004 0.004 0.107 P>|t| 0.464 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.562 0.000 0.003 0.028 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.213 0.075 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Source: 1999 Population and Housing Census, IPUMS International. Note: Controlled for region. Left out religion: no religion; left out ethnicity: other ethnicity. 72 Regression results: Vietnam 1999 Maximal years of schooling in household Age Age^2 Sex (1=male) Urban(= 1) Migrant (=1) Buddhist Christian Kinh Tay Thai Hoa Kho_Me Muong Nung Hmong Dao Gia_Rai Sex_migration Sex_disability Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_migration Urban_disability Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant R^2 N Coef. 0.031 ‐0.001 1.288 2.571 ‐0.709 ‐0.400 ‐0.336 4.128 3.771 2.094 3.367 2.432 3.005 2.704 ‐2.058 ‐0.660 0.226 ‐0.367 ‐0.049 ‐1.614 ‐0.910 ‐0.257 ‐0.089 0.116 ‐0.211 ‐3.138 ‐2.713 ‐1.810 ‐1.531 4.653 0.349 1671960 Std. Err. 0.001 0.000 0.036 0.030 0.024 0.008 0.009 0.031 0.035 0.038 0.038 0.039 0.039 0.038 0.040 0.039 0.045 0.025 0.022 0.014 0.013 0.013 0.013 0.026 0.022 0.014 0.012 0.011 0.010 0.038 P>|t| 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.026 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Source: 1999 Population and Housing Census, IPUMS International. Note: Controlled for region. Left out religion: no religion; left out ethnicity: other ethnicity. 73 Table 5: Regression results for being in the bottom of the educational distribution (Mexico) Regression results: Mexico 2005 Being in the bottom education quintile (years od schooling‐
agegroup 17‐22) Age Age^2 Sex (1=male) Urban Household Size Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 No_Religion Christian Other religion Speaks inigenous language (=1) Sex_age Sex_urban Sex_hhs Sex_migration Sex_disability Sex_speaks indigenous language Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 urban_age | urban_hhs | Urban_migration Urban_disability Urban_speaks indigenous language Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant Pseudo R^2 Coef. 0.205 ‐0.004 0.071 ‐0.503 0.003 ‐0.014 0.645 0.541 0.134 ‐0.182 ‐0.400 ‐0.020 ‐0.207 ‐0.377 0.463 ‐0.002 ‐0.001 0.006 ‐0.072 0.046 ‐0.309 0.006 ‐0.009 ‐0.006 ‐0.003 ‐0.027 0.030 0.195 0.122 0.249 0.551 0.569 0.530 0.435 ‐3.136 0.158 Std. Err. 0.041 0.001 0.126 0.125 0.002 0.094 0.031 0.019 0.019 0.020 0.028 0.050 0.049 0.099 0.012 0.003 0.012 0.002 0.108 0.033 0.015 0.019 0.018 0.018 0.020 0.003 0.002 0.108 0.033 0.017 0.022 0.021 0.021 0.028 0.413 P>|t| 0.000 0.001 0.575 0.000 0.064 0.885 0.000 0.000 0.000 0.000 0.000 0.692 0.000 0.000 0.000 0.507 0.919 0.001 0.501 0.157 0.000 0.740 0.603 0.745 0.883 0.000 0.000 0.069 0.000 0.000 0.000 0.000 0.000 0.000 0.000 N 346072 Source: XII General Population and Housing Census, 2005, IPUMS International. Note: Controlled for regions. 74 Regression results: Mexico 2005 Being in the bottom education quintile (years od schooling‐
agegroup 23‐27) Age Age^2 Sex (1=male) Urban Household Size Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 No_Religion Christian Other religion Speaks inigenous language (=1) Sex_age Coef. ‐0.045 0.002 0.448 ‐0.528 0.029 ‐0.277 0.649 0.730 0.294 ‐0.073 ‐0.308 ‐0.030 ‐0.197 ‐0.341 0.525 ‐0.020 Std. Err. 0.093 0.002 0.175 0.174 0.002 0.122 0.037 0.023 0.023 0.024 0.033 0.061 0.060 0.115 0.015 0.004 P>|t| 0.629 0.413 0.010 0.002 0.000 0.023 0.000 0.000 0.000 0.002 0.000 0.623 0.001 0.003 0.000 0.000 Sex_urban Sex_hhs Sex_migration Sex_disability Sex_speaks indigenous language Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 urban_age | urban_hhs | Urban_migration Urban_disability Urban_speaks indigenous language Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant Pseudo R^2 ‐0.010 ‐0.013 0.144 ‐0.034 ‐0.308 ‐0.052 ‐0.051 ‐0.026 ‐0.034 ‐0.016 0.017 0.184 0.245 0.250 0.487 0.513 0.496 0.373 ‐0.230 0.189 0.014 0.002 0.132 0.039 0.018 0.022 0.021 0.022 0.023 0.004 0.002 0.133 0.039 0.020 0.026 0.025 0.025 0.034 1.164 0.477 0.000 0.276 0.379 0.000 0.020 0.018 0.221 0.141 0.000 0.000 0.168 0.000 0.000 0.000 0.000 0.000 0.000 0.843 N 257809 Source: XII General Population and Housing Census, 2005, IPUMS International. Note: Controlled for regions. 75 (Philippines) Regression results: Philippines 2001 Being in the bottom education quintile (years od schooling‐agegroup 17‐22) Age Age^2 Sex (1=male) Household Size Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 Christian Other religion Sex_age Sex_hhs Sex_migration Sex_disability Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_age Urban_hhs Urban_migration Urban_disability Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant Pseudo R^2 N Coef. ‐0.177 0.005 ‐0.066 ‐0.012 ‐0.068 0.681 0.976 0.558 0.245 0.008 ‐0.170 0.031 ‐0.005 0.025 0.024 ‐0.103 0.243 0.272 0.278 0.175 ‐0.008 0.013 ‐0.042 ‐0.006 0.072 ‐0.036 0.015 0.060 0.665 0.238 317350 Std. Err. 0.045 0.001 0.075 0.002 0.019 0.043 0.015 0.016 0.016 0.017 0.018 0.026 0.003 0.002 0.026 0.056 0.019 0.020 0.020 0.022 0.002 0.003 0.027 0.075 0.035 0.026 0.024 0.025 0.438 P>|t| 0.000 0.000 0.379 0.000 0.000 0.000 0.000 0.000 0.000 0.627 0.000 0.221 0.156 0.000 0.347 0.065 0.000 0.000 0.000 0.000 0.000 0.000 0.119 0.935 0.041 0.164 0.528 0.015 0.129 Source: 2001 Census of Population and Housing, IPUMS International. Note: Controlled for regions and languages. Left out religion: no religion. 76 Regression results: Philippines 2001 Being in the bottom education quintile (years od schooling‐agegroup 23‐27) Age Age^2 Sex (1=male) Household Size Migrant (=1) Disabled (=1) Quintile_1 Quintile_2 Quintile_3 Quintile_4 Christian Other religion Sex_age Sex_hhs Sex_migration Sex_disability Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_age Urban_hhs Urban_migration Urban_disability Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant Pseudo R^2 N Coef. 0.243 ‐0.004 0.359 0.001 0.141 0.680 1.182 0.778 0.509 0.158 ‐0.186 0.019 ‐0.012 ‐0.002 0.009 ‐0.162 0.133 0.142 0.087 0.103 ‐0.007 0.028 ‐0.114 0.030 0.093 ‐0.133 0.001 0.064 ‐4.849 0.251 221520 Std. Err. 0.103 0.002 0.129 0.002 0.022 0.051 0.020 0.020 0.021 0.023 0.022 0.031 0.005 0.003 0.029 0.066 0.025 0.026 0.026 0.028 0.002 0.004 0.032 0.084 0.043 0.032 0.030 0.031 1.289 P>|t| 0.018 0.045 0.006 0.563 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.537 0.018 0.591 0.755 0.014 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.723 0.029 0.000 0.972 0.042 0.000 Source: 2001 Census of Population and Housing, IPUMS International. Note: Controlled for regions and languages. Left out religion: no religion. 77 (South Africa) Regression results: South Africa 2001 Being in the bottom education quintile (years od schooling‐agegroup 17‐22) Age Age^2 Sex (1=male) Urban(= 1) Household Size Ln income Migrant (=1) Disabled (=1) Member of an indigenous group (=1) HH head is employed (=1) Black Asian Speaks Afrikaans No_religion Hindu Muslim Christian Sex_age Sex_urban Sex_hhs Sex_migration Sex_disability Sex_employed Sex_white Sex_black Urban_age Urban_hhs Urban_migration Urban_disability Urban_employed Urban_white Urban_black Constant Pseudo R^2 N Coef. ‐0.637 0.016 0.851 ‐0.812 ‐0.008 ‐0.161 ‐0.821 0.361 0.027 0.121 0.585 0.176 0.113 0.071 ‐0.375 ‐0.222 ‐0.211 ‐0.037 0.039 ‐0.004 ‐0.017 0.113 ‐0.047 ‐0.060 0.062 ‐0.015 0.023 0.183 0.163 ‐0.086 0.748 0.409 6.357 0.154 231100 Std. Err. 0.051 0.001 0.092 0.096 0.002 0.003 0.049 0.030 0.002 0.012 0.063 0.049 0.032 0.024 0.060 0.041 0.023 0.004 0.014 0.002 0.045 0.034 0.013 0.042 0.021 0.004 0.002 0.048 0.034 0.014 0.076 0.028 0.506 P>|t| 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.000 0.005 0.047 0.710 0.001 0.001 0.151 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Source: South Africa Population Census 2001 IPUMS International; own calculations. Note: Controlled for region and language. Left out race: white. 78 Regression results: South Africa 2001 Being in the bottom education quintile (years od schooling‐agegroup 23‐27) Age Age^2 Sex (1=male) Urban(= 1) Household Size Ln income Migrant (=1) Disabled (=1) Member of an indigenous group (=1) HH head is employed (=1) Black Asian Speaks Afrikaans No_religion Hindu Muslim Christian Sex_age Sex_urban Sex_hhs Sex_migration Sex_disability Sex_employed Sex_white Sex_black Urban_age Urban_hhs Urban_migration Urban_disability Urban_employed Urban_white Urban_black Constant Pseudo R^2 N Coef. 0.356 ‐0.006 0.235 ‐0.759 ‐0.015 ‐0.213 ‐0.706 0.423 0.027 0.112 0.616 0.203 0.161 0.086 ‐0.407 ‐0.244 ‐0.190 ‐0.011 0.044 ‐0.002 0.066 0.043 0.003 ‐0.060 0.032 ‐0.010 0.012 0.129 0.158 ‐0.068 0.586 0.358 ‐4.493 0.153 175774 Std. Err. 0.114 0.002 0.146 0.149 0.002 0.004 0.045 0.033 0.002 0.015 0.070 0.059 0.036 0.029 0.073 0.048 0.028 0.005 0.016 0.002 0.041 0.037 0.016 0.056 0.025 0.006 0.003 0.042 0.037 0.017 0.086 0.031 1.430 P>|t| 0.002 0.009 0.107 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.003 0.000 0.000 0.000 0.054 0.006 0.437 0.105 0.253 0.867 0.286 0.193 0.058 0.000 0.002 0.000 0.000 0.000 0.000 0.002 Source: South Africa Population Census 2001 IPUMS International; own calculations. Note: Controlled for region and language. Left out race: white. 79 (Uganda) Regression results: Uganda 2002 Being in the bottom education quintile (years od schooling‐agegroup 17‐
22) Age Age^2 Sex (1=male) Urban(= 1) Household Size Migrant (=1) Disability (=1) HH head is employed (=1) Muslim Christian Sex_age Sex_urban Sex_hhs Sex_migration Sex_disability Sex_employed Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_age Urban_hhs Urban_migration Urban_disability Urban_employed Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant Pseudo R^2 N Coef. 0.701 ‐0.014 0.576 0.837 ‐0.019 0.030 0.362 ‐0.040 ‐0.613 ‐0.692 ‐0.049 0.027 0.000 0.130 0.001 0.020 ‐0.283 ‐0.306 ‐0.182 ‐0.118 ‐0.060 ‐0.004 ‐0.039 0.091 ‐0.002 0.580 0.582 0.245 0.187 ‐7.056 0.181 220800 Std. Err. 0.053 0.001 0.102 0.144 0.004 0.076 0.080 0.029 0.029 0.027 0.004 0.026 0.002 0.044 0.038 0.015 0.024 0.024 0.025 0.024 0.006 0.003 0.061 0.069 0.025 0.014 0.014 0.014 0.013 0.513 P>|t| 0.000 0.000 0.000 0.000 0.000 0.692 0.000 0.173 0.000 0.000 0.000 0.292 0.859 0.003 0.975 0.201 0.000 0.000 0.000 0.000 0.000 0.118 0.526 0.186 0.925 0.000 0.000 0.000 0.000 0.000 Source: 2002 Population and Housing Census, IPUMS International. Note: Controlled for region and language. Left out religion: no religion. 80 Being in the bottom education quintile (years od schooling‐
agegroup 23‐27) Age Age^2 Sex (1=male) Urban(= 1) Household Size Migrant (=1) Disability (=1) HH head is employed (=1) Muslim Christian Sex_age Sex_urban Sex_hhs Sex_migration Sex_disability Sex_employed Sex_Quintile 1 Sex_Quintile 2 Sex_Quintile 3 Sex_Quintile 4 Urban_age Urban_hhs Urban_migration Urban_disability Urban_employed Urban_Quintile 1 Urban_Quintile 2 Urban_Quintile 3 Urban_Quintile 4 Constant Pseudo R^2 N Coef. 1.076 ‐0.021 ‐0.319 0.180 0.007 ‐0.022 0.492 ‐0.070 ‐0.450 ‐0.597 ‐0.008 0.035 ‐0.007 0.121 ‐0.005 0.045 ‐0.244 ‐0.211 ‐0.174 ‐0.100 ‐0.017 ‐0.010 ‐0.078 ‐0.172 ‐0.038 0.611 0.591 0.271 0.204 ‐13.456 0.173 142249 Std. Err. 0.123 0.002 0.173 0.255 0.005 0.100 0.106 0.038 0.037 0.034 0.006 0.032 0.003 0.061 0.047 0.020 0.029 0.029 0.030 0.030 0.010 0.004 0.076 0.095 0.032 0.017 0.017 0.017 0.016 1.531 P>|t| 0.000 0.000 0.065 0.480 0.153 0.825 0.000 0.063 0.000 0.000 0.171 0.268 0.017 0.047 0.913 0.022 0.000 0.000 0.000 0.001 0.075 0.011 0.307 0.069 0.241 0.000 0.000 0.000 0.000 0.000 Source: 2002 Population and Housing Census, IPUMS International. Note: Controlled for region and language. Left out religion: no religion. 81