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Homogamy and social structure over time in Finland Jani Erola and Paul Lambert Social Stratification Research Seminar, Utrecht, 8-10 September 2010.

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Presentation on theme: "Homogamy and social structure over time in Finland Jani Erola and Paul Lambert Social Stratification Research Seminar, Utrecht, 8-10 September 2010."— Presentation transcript:

1 Homogamy and social structure over time in Finland Jani Erola and Paul Lambert Social Stratification Research Seminar, Utrecht, 8-10 September 2010

2 Aims Construct CAMSIS scales for Finland Test the effects of homogamy on partnership matching with the scaling approarch

3 CAMSIS scale for Finland Model the social distance between occupational units using average patterns of social interaction/ cohabitation between incumbents of occupations Correspondence analysis for large numbers of units using Stata macro; RC2 association models in R (gnm) for smaller numbers of units for standard errors  Interpret the social distance dimension as indicating the structure of social stratification (the reproduction of social inequality)

4 Census data inputs Cohabiting couples, current or previous occupation – Job coded into 4 digit ISCO – Plus 2 category employment status 5 yearly and pooled data

5 Employment status (self-employed/ employee)

6 Could ignore this data (noise/inconvenience)  We choose to use it for groups 6-9 only

7 Occupational units Occupations were recoded to finest level of detail available (in ISCO-by-employment status location) according to a rule of thumb that at least 30 cases must represent the occupation Pre-defined recoding rules (use ISCO sub-groups) all Number of occs M F

8 Scale derivations using algorithms Algorithms proceed by: i.Define any cohabiting combinations to be excluded from models (ISCO diagonals and farming related pseudo-diagonals – 1311; 6***; 9211) ii.Recode occupations to avoid sparsity for the remaining cases iii.Perform CA and extract dimension scores iv.Standardise scores to mean 50, sd 15 v.Distribute dimension scores from analytical categories to all ISCO categories according to recoding algorithm

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17 Some further illustrative examples The role of detailed occupational differences Illustrative male and female scale values The problem of farmers

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20 Males: Combined scale ( ) Male and female CAMSIS scale scores for those ISCO units with over 4000 males in each ISCO

21 Females: Combined scale ( ) Male and female CAMSIS scale scores for those ISCO units with over 4000 females in each ISCO Note how men in female jobs often have higher averages: e.g is a ‘average’ job in the male distribution, but is more than 1 SD below average in the female distribution.

22 i.e. The placement of farmers is influential to the correlation with other things, and is likely to change between scales/time

23 CAMSIS/ISEI micro comparisons Education Income quintiles Erikson-Goldthorpe classes Separately for men and women – Using male scales

24 Associations with education, men R2ISEIFCS

25 Associations with education, women R2ISEIFCS

26 Associations with income, men R2ISEIFCS

27 Associations with income, women R2ISEIFCS

28 Associations with EGP, men R2ISEIFCS

29 Associations with EG, women R2ISEIFCS

30 Changes in homogamy Argument: homogamy increasing in most societies – Kalmijn, 1998; Sweeney & Cancian, 2004; Schwarz & Mare, 2005; Blossfeld, 2009 ”Natural” test with couple-data based stratification data Change in scales = change in the way how couple relationships are stratified Does changing homogamy play a role in this?

31 Loglinear RC-models 1.RC-coefficients for men and women, changing marginals, diagonal cells controlled 2.RC-estimates for men and women, allowing change in diagonals RC-coefficients assuming no yearly change should be the same as the scale values from CA The impact of the changes in homogamy should be observed as a change in RC coefficients between the models

32 Models for ISCO88 Major groups L-squareddfDIBICAIC Mod 1. year:men + year:women + Diagonals(men:women)48087,64960, ,347081,6 Mod RC-association3713,94810, ,12737,9 Mod Linear change in diagonals3015,74730, ,12057,7 Mod Non-lin. change in diagonals2816,64250, ,31966,6

33 The effects of the change in homogamy on RC coefficients No change Linear changeNon-lin change CoeffSECoeffSECoeffSE MenManag/legis/offic Profess-0,030,01-0,010,01-0,010,01 Tech/Assoc profs0,420,010,420,010,420,01 Clerks0,620,010,610,010,610,01 Service/Sales0,590,010,590,010,590,01 Agriculture0,780,010,780,010,780,01 Crafts/Trades1,030,011,020,011,020,01 Mach operators1,090,011,080,011,080,01 Elementary WomenManag/legis/offic Profess-0,050,01-0,050,01-0,050,01 Tech/Assoc profs0,240,010,240,010,240,01 Clerks0,410,010,400,010,400,01 Service/Sales0,640,010,640,010,640,01 Agriculture0,850,010,850,010,850,01 Crafts/Trades0,830,010,830,010,830,01 Mach operators1,050,011,050,011,050,01 Elementary101010

34 Homogamy estimates change to wrong direction! Homogamy change for… Coef.SE Manag/legis/offic0,000,01 Profess-0,120,01 Tech/Assoc profs0,000,01 Clerks0,020,01 Service/Sales0,000,01 Crafts/Trades-0,040,01 Mach operators0,01 Elementary-0,080,01

35 Caveats Previous results suggest increasing homogamy according to education => occupations different story? 1-digit level simply too coarse? Now all cohabs, restricting analyses to marriages?

36 Conclusions Social interaction distance scales appear to measure stratification quite well on Finnish data – Differences between men and women – Main difference between years involves farmers – Homogamy trends don’t seem to matter Work very much in beginning…


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