Structural analyses of occupational homogamy

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Structural analyses of occupational homogamy

• Paul Lambert (University of Stirling, UK)
• Dave Griffiths (University of Stirling, UK)
• Mark Tranmer (University of Manchester, UK)
• 14 November 2011, SOFI, Stockholm
• Social Networks and Occupational Structure
  www.camsis.stir.ac.uk/sonocs

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Three ways of analysing social connections
between occupations

1. Social Network Analysis
2. Social Interaction Distance analysis
3. Random Effects (Multilevel) Modelling

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1) Social Network Analysis
Network analysis to look for influential channels
of social connections between occs.
(camsis.stir.ac.uk/sonocs)
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Hypothetical network 469 US OUGs micro-classes
Medical and dental technicians
Medical professionals
(Four different isolated components with internal
links within microclass but no external links)
Pseudo-diagonal or situs
Green prof. Blue routine non-mnl Red manual
Yellow primary Green military
Dental hygienists
(further isolated components)
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lt-Hypothetical
Actual composition of occupational networks in
USA in 2000 links reflect stratification as much
as they do microclasses and psds
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Red to violet for low to high CAMSIS (grouped
into 7). Structures similar to CAMSIS scales.
Using Kamada-Kawai algorithm and no manual
adjustment (expect removing some occs with no
ties/relations)
Romania, 2002
Philippines, 2000
Venezuela, 2001
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2) Social Interaction Distance Analysis
(www.camsis.stir.ac.uk correspondence
analysis RC-II association models)
A large cross-tabulation of pairs of occupations
is modelled dimension scores help predict
frequency of occurrences in cells scaled
dimension scores are then presented as CAMSIS
scale scores.
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• Using CAMSIS approaches, www.camsis.stir.ac.uk
• First dimension of SID scales is usually social
  stratification
• Wed interpret it as the contour of social
  reproduction
• Gradational, but lumpy for operational reasons
  (occ.s)
• Specificity (many scales!)
• Dimensions
• 1 main one
• numerous subsidiary patterns
• Boundaries
• None(?)

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• Dimensions1 Boundaries none or maybe 1 in Ro?

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3) Multilevel modelling

• In general, can analyse Individual level data (i)
  with clustering in higher level units (j)
  (occupations person groups)

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MLMs on social network data?

• We could analyse some other process/outcome
• Own job f(x), i(spouses job)

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Or analyse the counts of occurrences of links
themselves?
Y of links Hocc at level 2, wocc at level 1.
(Philippines)
(ICCs around 1 10)

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• BHPS own, family friends jobs

(xtmixed, j370 occs, yspouse link)
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Appendices
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French occupational codes
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ICC examples analysing occurrences..
Level 2 ICC, by outcome Level 2 ICC, by outcome Level 2 ICC, by outcome Level 2 ICC, by outcome
Model (hocc at level 2, wocc at level 1) freq (freq gt0) Lnfreq (freq gt 0) freq2 (with zero) Level 1 camsis score (freq gt 0)
1a (Normal null regression) 0.0086 0.1290 0.0136 0.4185
1b (1a gwage) 0.0086 0.1295 0.0136 0.4231
1f (1a diag1) 0.0446 0.1487 0.0728 0.2370
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Linear outcomes regression
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Logit model