Multilevel modelling of social networks and occupational structure Dave Griffiths¹, Paul S. Lambert¹ & Mark Tranmer¹ ² ¹ School of Applied Social Science, University of Stirling ² CCSR& Mitchell Centre for Social Network Analysis, University of Manchester Work for this paper is supported by the ESRC as part of the project Social Networks and Occupational Structure, see

Occupations as explanatory variables Occupations are important sociological concepts Usually operationalised in research based upon inherent characteristics Voluminous sociological studies show occupations are important explanatory variable Is it the occupations which improve outcomes, or are they capturing other effects?

% of Lawyers married to.. % of all working husbands married to.. CAMSIS score (US 2000) Lawyers11.6%0.6%81.5 Primary school teachers7.2%4.5%66.2 Registered nurses4.4%4.5%56.8 Secretaries3.8%5.3%55.5 Preschool and kindergarten teachers2.8%1.2%62.7 Accountants and auditors2.4%1.8%65.2 Counsellors2.4%0.8%65.0 Paralegals and legal assistants2.4%0.5%64.2 Postsecondary teachers2.4%1.0%79.8 Managers2.1%1.8%62.2 Bookkeepers2.1%2.5%53.1 % of Labourers married to… % of all working husbands married to.. CAMSIS score (US 2000) Registered nurses3.9%4.5%56.8 Nursing, psychiatric and home healing assistants 3.9%1.9%42.6 Secretaries3.9%5.3%55.5 Customer service representatives3.6%1.7%51.8 Receptionists3.2%1.6%53.2 Cashiers3.2%1.8%41.3 Labourers2.9%0.4%32.0 Janitors and building cleaners2.5%1.7%32.5 Maids and housekeeping cleaners2.2%0.3%27.4 Retail salespersons2.2%1.9%51.9 Tellers2.2%0.6%46.3 Most common occupations for the wives of lawyers and labourers in the USA Source: Current Population Survey 2010.

Contexts and identities Family – Are identities formed by family background? Social background? Wider social network – Are identities shaped by those we associate with? Social capital? Occupation – Are identities formed through our choice of occupation? Social status?

British Household Panel Survey Ran from 1991 to 2008 Selected 5,500 initial households (plus later booster households for regions/minorities) All initial sample members interviewed each year – Any they cohabit with also interviewed Around 30,000 different people interviewed Personal identifiers (PID) are for life; household identifiers (HID) alter each year This enables us to link together individuals into networks

Geller Household: Initial household

Geller households: (up to 1995 (ish) )

Grouped by cohabitation networks

Grouped by family ties

Grouped by occupation

BHPS respondents26,090 People cases90,784LargestMean People-job cases347, Occupations (SOC)3745, Networks identified (NID)9, Families identified (FID)12, Data extracted from the British Household Panel Survey, waves

CAMSIS score of occupational advantage Self-rated health Participation in exercise Feeling financial secure Attitudes towards trade unionism Attitudes towards motherhood and employment

7 models to measure outcome: Controls: age, gender and CAMSIS scores Levels: 1.None 2.Family 3.Network 4.Occupation 5.Family and network 6.Family, network and occupation 7.Family, network, occupation and occupation-by-gender

Outcome 1: CAMSIS score (scale from 1 to 99, modelled as linear scale) (1)(2)(3)(5) Intercept50.1*50.2*49.8*41.2* Female2.6*2.5* (Age – 40)/103.8*3.6*4.0*3.9* (Age )/ *-4.1*-4.4*-4.3* Deviance AIC ID variance ICC100%70.2%72.6%70.6% FID variance ICC29.8%7.9% NID variance ICC27.4%21.5% N25971 Intercept49.3*49.0*48.4* Deviance AIC ID variance ICC100%70.7%72.4%71.3% FID variance ICC29.3%7.9% NID variance ICC27.6%20.8% N90784 n of FID groups12096 n of NID groups9846 Analysis for BHPS respondents (panel 1) and for all strong and weak ties identified (panel 2) (scale from 1 to 99, modelled as linear scale)

Outcome 2: GHQ score (scale from 0 to 36, 36=healthiest, modelled as linear scale) (1)(2)(3)(4)(5)(6)(7) Intercept10.9* Female1.3* 1.2*1.3*1.2* (Age – 40)/100.77*0.84*0.83*0.79*0.84*0.86* (Age )/ *-0.63*-0.62*-0.59*-0.63*-0.65* (CAMSIS -50)/ *-0.17* -0.19*-0.17* (Female*CAMSIS)/ * Deviance AIC ID variance ICC100%89.3%91.2%99.8%89.3%89.1%89.3% FID variance ICC10.7%9.4%9.3%9.4% NID variance ICC8.8%1.3% SOC variance ICC0.2% 0.1% Fem | soc variance0.03% Notes: For model (7), the ICC estimates refer to variance proportions for males at the intercept (due to the random coefficients formulation of that model).

Outcome 3: Scale ranking for self-rated sports participation level (scale from 1 to 5, 1=very active, modelled as linear scale) (1)(2)(3)(4)(5)(6)(7) Intercept13.5*12.8*13.1*13.4*12.9* 12.8* Female0.86*0.82*0.80*1.12*0.81*1.02*1.14* (Age – 40)/100.42*0.63*0.59*0.42*0.61* 0.63* (CAMSIS -50)/ (Age*CAMSIS)/ *-0.25*-0.27*-0.23*-0.26*-0.25* Deviance AIC ID variance ICC100%71.6%74.2%99.1%71.4%71.2%70.9% FID variance ICC28.4%19.8%19.3%19.9% NID variance ICC25.8%8.7%8.9%8.3% SOC variance ICC0.9%0.6%1.0% Fem | soc variance0.3% Notes: For model (7), the ICC estimates refer to variance proportions for males at the intercept (due to the random coefficients formulation of that model).

Outcome 4: Scale ranking for self-rated level of financial security (scale from 1 to 5, 5=lowest security, modelled as linear scale) (1)(2)(3)(4)(5)(6)(7) Intercept2.26*2.27*2.25*2.24*2.26*2.25* (Age – 40)/ *-0.67* -0.73*-0.66* -0.67* (CAMSIS -50)/ *-0.11*-0.12*-0.13*-0.11* (Age*CAMSIS)/ *-0.10* -0.08*-0.10*-0.09*-0.10* Deviance AIC ID variance ICC100%75.1%80.3%98.9%75.0%74.8%74.5% FID variance ICC24.9%20.2%19.7%19.8% NID variance ICC19.7%4.8%4.7%4.6% SOC variance ICC1.2%0.8%1.0% Fem | soc variance0.1% Notes: For model (7), the ICC estimates refer to variance proportions for males at the intercept (due to the random coefficients formulation of that model).

Outcome 5: Scale ranking for attitudes towards families suffer if the mother works full time (scale from 1 to 5, 1=strongly agree, modelled as linear scale) (1)(2)(3)(4)(5)(6)(7) Intercept3.04* Female0.18* 0.17*0.18* (Age – 40)/ * -.023*-0.23* (Age )/ *50.9*52.2*49.9*51.7*52.5*52.8* (CAMSIS -50)/ * * (Female*CAMSIS)/ * Deviance AIC ID variance ICC100%83.5%85.2%99.3%83.4%83.2% FID variance ICC16.5%11.8%11.6% NID variance ICC14.8%4.8%4.7%4.4% SOC variance ICC0.7%0.5%0.3% Fem | soc variance0.2% Notes: For model (7), the ICC estimates refer to variance proportions for males at the intercept (due to the random coefficients formulation of that model).

Outcome 6: Scale ranking for attitudes towards strong trade unions protect employees rights (scale from 1 to 5, 1=m, modelled as linear scale) (1)(2)(3)(4)(5)(6)(7) Intercept2.55*2.54* 2.51*2.54*2.51* Female-0.10* -0.06*-0.09*-0.06*-0.05* (Age – 40)/10.02*0.02* (Age )/ * -0.16*-0.17*-0.16* (CAMSIS -50)/100.01* (Female*CAMSIS)/ * 0.01*-0.01* Deviance AIC ID variance ICC100%80.3%81.2%96.0%80.0%78.3%77.3% FID variance ICC19.7%8.5%7.3%7.0% NID variance ICC18.8%11.5%10.9% SOC variance ICC4.0%3.5%4.3% Fem | soc variance0.5% Notes: For model (7), the ICC estimates refer to variance proportions for males at the intercept (due to the random coefficients formulation of that model).

CAMSISHealthSports Financial security Working mothers Trade unions ID variance ICC71.3%89.1%71.2%74.5%83.2%77.3% FID variance ICC7.9%9.3%19.3%19.8%11.6%7.0% NID variance ICC20.8%1.3%8.9%4.6%4.7%10.9% SOC variance ICC0.2%0.6%1.0%0.5%4.3% Fem | soc variance0.1%0.5%

Weak ties associated with occupational position and attitudes towards trade unionism Strong ties associated with health, fitness and financial perspective outcomes and attitudes to family roles Occupational role not overly important in measuring outcomes – But, is this captured by controlling for CAMSIS position?

Next steps Distinguishing between family positions? Multiple-membership models – families? – households? – occupations? Controlling for types of initial households – are those consisting of one family different? Operationalising occupations differently – Microclasses?

Thank you