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