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LESSONS LEARNED FROM CROSS-NATIONAL RESEARCH ON MARITAL HOMOGAMY Albert Esteve and Luis López Integrated European Census Microdata Project Centre dEstudis.

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Presentation on theme: "LESSONS LEARNED FROM CROSS-NATIONAL RESEARCH ON MARITAL HOMOGAMY Albert Esteve and Luis López Integrated European Census Microdata Project Centre dEstudis."— Presentation transcript:

1 LESSONS LEARNED FROM CROSS-NATIONAL RESEARCH ON MARITAL HOMOGAMY Albert Esteve and Luis López Integrated European Census Microdata Project Centre dEstudis Demogràfics Census Microdata: findings and futures University of Manchester September 1-3, 2008 Centre dEstudis Demogràfics Universitat Autònoma de Barcelona

2 Comparative work on marital homogamy on an international scale is rather scarce Assortative mating patterns have been little investigated in the developing world Most research on the subject have been done in the developed world Availability of international census microdata may contribute to bridge the gap Background

3 In the area of educational homogamy, the most comprehensive cross-national studies are those of Ultee and Luijkx (1990) and Smits et al.(1998, 2000, 2003): Use of large representative data from 55-65 countries (Sample Censuses and World Fertility Surveys). Use of loglinear analysis to control for differences in the educational distributions of men and women. Global measures of educational homogamy were used. Educational structures of the countries differ very much. Cross comparative studies: Methodology

4 Discuss the advantages and limitations of carrying out cross-national research on marital homogamy based on census microdata (IPUMS). Lessons learned from my own research on educational and race-ethnic homogamy Structure of the presentation Concepts, definitions and scientific relevance Advantages of using census microdata Limitations of using census microdata Concluding remarks Main purpose and structure of the presentation

5 Homogamy: concepts and definitions Homogamy: Spousal unions between two people being similar with respect to some socially significant trait linked to the system of social hierarchy (education, religion, ethnicity, race, tribe, caste occupation…) Heterogamy: Unions between people with different characteristics Hypergamy: Women marry up Hypogamy: Women marry down

6 An indicator of the openness of the social stratification system An indicator of the characteristics of the unions (gendered distribution of roles) Marriage patterns arise from the interplay between three social forces: the preferences of individuals for certain characteristics in a spouse, the influence of the social group of which they are members and the constraint of the marriage market in which they are searching for a spouse. (Kalmijn, 1998, p.398) Homogamy: scientific relevance

7 Advantage no. 1 : IPUMS international dark green = already integrated (35 countries, 111 censuses, 263 millon person records) green = to be integrated (39 countries, 103 censuses, 150 mill.) Integrated Integrated census microdata open to the research community!!!

8 Advantage no. 1 : IPUMS international

9 Advantage no. 2 : sample densities and coverage CountrySample % PersonsCountrySample % PersonsCountrySample % Persons Argentina 19702466,892Colombia 19642349,652México 19601.5502,800 Argentina 1980102,667,714Colombia 1973101,988,831México 19701483,405 Argentina 1991104,143,727Colombia 1985102,643,125México 1990108,118,242 Argentina 2001103,626,103Colombia 1993103,213,657México 200010.610,099,182 Brasil 196053,001,439 Colombia 2005104,117,607Panamá 1960553,553 Brasil 197054,953,759Costa Rica 1963 582,345Panamá 197010150,473 Brasil 198055,870,467Costa Rica 1973 10186,762Panamá 198010195,577 Brasil 19915.88,522,740Costa Rica 1984 10241,220Panamá 199010232,737 Brasil 2000610,136,022Costa Rica 2000 10381,500Panamá 200010284,081 Chile 1960188,184 Ecuador 19623136,443Venezuela 1971101,158,527 Chile 197010890,481Ecuador 197410648,678Venezuela 1981101,441,266 Chile 1982101,133,062Ecuador 198210806,834Venezuela 1990101,803,953 Chile 1992101,335,055Ecuador 199010966,234Venezuela 2001102,306,489 Chile 2002101,513,914Ecuador 2001101,213,725

10 Advantage no. 2 : sample densities and coverage Unions of men and women aged 30 – 39 that belong to a minority group that represents less than 5% of the total population

11 Advantage no. 3 : persons in households Using SPOUSE LOCATION, we can easily match all characteristics of the spouses reported in the census!!! And the IPUMS extraction system does it systematically!!!!!!!

12 Advantage no. 4 : multiple dimensions of homogamy

13 Homogamous marriages are favored and are increasing at higher levels of educational attainment over time. Log odds for homogamous pairings by level of schooling and census year, Brazil

14 In Union Not in Union Spouse absent Spouse present Consensual Union Marriage Consensual Union Marriage IN UNION SPOUSE PRESENCETYPE OF UNIONPLACE TIME Country Abroad Country Abroad After migration Before migration After migration Country Abroad Country Abroad CENSUS Advantage no. 5 : complete universe of unions

15 Advantage no. 6 : hierarchical analysis Most often, datasets are organized in hierarchichal way: –Individuals –Households –Region –Country This makes multilevel modeling possible. Independent variables can be incorporated at any level Surprisingly, samples for some developed countries do not use this sample design.

16 Advantage no. 6 : hierarchical analysis Dependent Variable : Educational Homogamy Independent Variables Contextual Effects SEXRATIO: Log transformation of the number of educational group members of the opposite sex divided by the number of group members of the same sex by region of residence GROUPSIZE: Log transformation of the relative educational group size at the regional level ETHNIC / RACE / BIRTHPLACE SIMILARITY: Percentage of the educational group that have the same race-ethnicity/birthplace country Individual Effects SEX EDUCATION: Based on IPUMSI Educational Attainment RACE / ETHNICITY / BIRTHPLACE

17 ArgentinaBrazil EcuadorMexico Advantage no. 6 : hierarchical analysis Effect of Race-ethnic similarity in educational homogamy by level of educational attainment

18 Censuses only capture current unions and not all unions prevail Separation Divorce Widowhood Remarriage Is the likelihood of union dissolution equal for all types of unions? Limitation no. 1 : only prevailing unions

19 Brazil, women born 1945 – 49 (log odds ratio) Do homogamy and hypergamy levels change for the same cohort at different ages?

20 Limitation no. 2 : only co-residing partners CountryPercentage Argentina 20012.4% Brazil 20002.2% Chile 20022.4% Costa Rica 20002.6% Ecuador 20011.8% México 20001.7% Proportion of married individuals with spouse absent

21 AfricaLatin AmericaWestern EuropeEastern Europe Asia Distribution (%) of migrants by union status and type of union, Spain Census 2001, males Limitation no. 2 : only co-residing partners

22 When did they get married? When was the union formed? What were the individuals characteristics at the time of marriage? Is this the first time they marry? Where did they get married? What was the parents educational attainment? Limitation no. 3 : cross-sectional data with little biographical information

23 In Union Not in Union Spouse absent Spouse present Consensual Union Marriage Consensual Union Marriage IN UNION SPOUSE PRESENCETYPE OF UNIONPLACE TIME Country Abroad Country Abroad After migration Before migration After migration Country Abroad Country Abroad CENSUS MARRIAGE RECORDS Limitation no. 3 : cross-sectional data with little biographical information ? ? ? ?

24 Limitation no. 4 : international comparability

25 Proportion of Active Women by Age and Country

26 Concluding remarks Availability of international census microdata offers an unprecedented opportunity to carry out cross-national research on marital homogamy and other topics Most of developing countries have census microdata samples There are obvious limitations (prevalence, co-residing partners, cross-sectional data), that can be overcome by restricting the analysis to certain types of unions The most important challenge is to obtain meaningful results for the countries individually and in comparative perspective

27 Thanks!!! Albert Esteve aesteve@ced.uab.es Census Microdata: findings and futures University of Manchester September 1-3, 2008 Centre dEstudis Demogràfics Universitat Autònoma de Barcelona


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