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Total Family Impact on Status Attainment - Sources of Sibling (Dis)similarity
Social Stratification Research Conference, UtrechtSeptember 10, 2010Antonie Knigge, Ineke Maas, Marco van Leeuwen
Background
• Towards Open Societies?
• Occupational status father as indicator for family impact• Underestimates the true influence of the family
• Genes, socialization, financial resources, social capital, etc.
• Problem: how to measure total family impact?• Impossible to measure all aspects
• Traditional measures explain about 60% of total family impact
• Solution: sibling models• the more similar siblings in status compared to unrelated persons,
the larger total impact of the family background
• Intra-class correlation (ICC) as measure
Background (2)
• Aim: Examine whether sibling models are a valid tool for assessing (trends in) the total family impact on status attainment
• Implicit Assumption: siblings benefit equally from the resources of their parents
• Hunch: not always realistic
• Example: equal vs unequal inheritance
Inheritance practices in 19th/early 20th century the Netherlands
Partible & Equal
Impartible & Equal
Impartible & Unequal
Research Question
• Does the extent to which siblings benefit differently from the resources of their parents form part of the explanation of trends in siblings’ status similarity for different regions in the Netherlands from 1842 to 1922?
Theory
• We formulate conditions under which we expect siblings to benefit systematically different from the resources of their parents
• Example Hypothesis:• H5. Siblings with parents who are land-owning farmers will be less
similar in their attained status in communities with an unequal
inheritance system than in communities with an equal inheritance
system
Data: Genlias
• Information from around 600.000 Dutch marriage acts• covering 5 of 11 provinces during the 1842-1922 period
• Only look at grooms
• Information on Son + Father• occupation, place & year of birth, place & year of marriage
• Marriage act groom linked to marriage act parents• We know the married siblings of a groom
• Complemented with other historical sources • Dutch Bur. Statistics, archives, etc.
Strategy: Multilevel sibling models
• Standard multilevel sibling model: siblings nested in families
• Grooms also share a context • Add cross-classified levels for time and place
• Auto-correlative structure for time and place
FamiliesF1 F2 F3
Siblings
FamiliesF1 F2 F3
P1 P2 T2 T3P3 T1 Plaats & Tijd
Siblings
Standard model
Dependent variable: Occupational Status Groom
Constant 47.11
Independent Variables
Occ status father .48
Sibsize -.10
Etc.
Variance Components
Sibling level 45
Family level 65
Standard Model + Extension 1
Dependent variable: Occupational Status Groom
Constant 47.11
Independent Variables
Occ status father .48
Sibsize -.10
Etc.
Variance Components
Sibling level 45
Family level 40
Place level 20
Time Level 5
Strategy: Extension 2
• Standard multilevel sibling models: variance components of each level are homogeneous
• According to hypotheses, we expect them to be heterogeneous
• Siblings less similar in unequal than siblings in equal inheritance
farming families
• Explicitly model the variance components at sibling and family level
Standard model
Dependent variable Occupational Status Groom
Constant …
Independent Variables …
Variance Components
Sibling level 45
Family level 65
Standard model + Extension 2
Dependent variable Occupational Status Groom
Constant …
Independent Variables …
Variance Components
Sibling level 40
Equal inheritance 40 0
Unequal inheritance 50 10
Family level 63
Equal inheritance 63 0
Unequal inheritance 67 4
Results
• Not succeeded in both extensions at the same time
• First: Extension 1
• Second: Extension 2
• Compare results both • Results not so important
• More important: what to compare?
Cross-classified Multilevel Models for farming and non-farming background and different inheritance practices
Dependent: Occ. status groom
Father Non-farmer Father Farmer
Partible,Equal
Impart.,Equal
Impart.,Unequal
Partible,Equal
Impart.,Equal
Impart.,Unequal
Constant 53.54 50.01 46.61 53.64 51.03 47.68
Var(sibling) 71.97 67.38 69.61 66.48 53.57 55.33
Var(family) 93.37 74.52 65.88 19.98 16.42 10.74
Var(cohort) 5.93 2.57 2.89 .80 .76 1.02
Var(region) 84.06 57.7 10.78 68.96 42.62 2.50
“ICC” .56 .53 .49 .23 .23 .16
N(siblings) 31174 107101 59657 9067 35608 42132
N(families) 18358 62501 35528 5172 19643 24307
N(cohorts) 18 18 18 18 18 18
N(regions) 271 155 72 184 146 71
2-level Multilevel Model: heteroskedastic variances for farming background and inheritance practice
Dependent: Occ. Status groom
Constant 47.48
Variance
Sibling Family
Constant 71.33 88.18
Partible equal (non-farming) 15.68 44.15
Impart. equal (non-farming) 8.24 26.07
Impart. Unequal (non-farming) 0 0
Farming, parible equal 13.75 -84.09
Farming, impartible equal -15.39 -89.86
Farming, impartible unequal -15.00 -74.52
N 564686 324382
2 approaches compared
Model(s) Father Non-farmer Father Farmer
Partible,Equal
Impart.,Equal
Impart.,Unequal
Partible,Equal
Impart.,Equal
Impart.,Unequal
Hetero-skedastic, all provinces
Var(sib) 87.01 79.57 71.33 100.76 64.18 56.33
Var(fam) 132.33 114.25 88.18 48.24 24.38 13.65
ICC .60 .59 .55 .32 .28 .20
Separate, cross-classified, 2 provinces
Var(sib) 71.97 67.38 69.61 66.48 53.57 55.33
Var(fam) 93.37 74.52 65.88 19.98 16.42 10.74
ICC .56 .53 .49 .23 .23 .16
Conclusion & Discussion
• It seems promising to model variance components to be heterogeneous
• Sources of heteroskedasticity not always clear
• Results sometimes not in line with hypotheses
• Issues to explore: • Deepen out historical context
• Disentangle openness from other sources of (dis)similarity
• What is the right measure: ICC or something else?
• Non-random missings