Multilevel modelling of social networks and occupational

structureDave Griffiths¹, Paul S. Lambert¹ & Mark Tranmer¹ ²

¹ School of Applied Social Science, University of Stirling² 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 http://www.camsis.stir.ac.uk/sonocs/

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)

Lawyers 11.6% 0.6% 81.5Primary school teachers 7.2% 4.5% 66.2Registered nurses 4.4% 4.5% 56.8Secretaries 3.8% 5.3% 55.5Preschool and kindergarten teachers 2.8% 1.2% 62.7Accountants and auditors 2.4% 1.8% 65.2Counsellors 2.4% 0.8% 65.0Paralegals and legal assistants 2.4% 0.5% 64.2Postsecondary teachers 2.4% 1.0% 79.8Managers 2.1% 1.8% 62.2Bookkeepers 2.1% 2.5% 53.1

% of Labourers married to…

% of all working husbands married to..

CAMSIS score (US 2000)

Registered nurses 3.9% 4.5% 56.8Nursing, psychiatric and home healing assistants

3.9% 1.9% 42.6

Secretaries 3.9% 5.3% 55.5Customer service representatives 3.6% 1.7% 51.8Receptionists 3.2% 1.6% 53.2Cashiers 3.2% 1.8% 41.3Labourers 2.9% 0.4% 32.0Janitors and building cleaners 2.5% 1.7% 32.5Maids and housekeeping cleaners 2.2% 0.3% 27.4Retail salespersons 2.2% 1.9% 51.9Tellers 2.2% 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 respondents 26,090

People cases 90,784 Largest Mean

People-job cases 347,542 22 3.8

Occupations (SOC) 374 5,176 235

Networks identified (NID) 9,846 36 2.7

Families identified (FID) 12,096 19 2.2

Data extracted from the British Household Panel Survey, 1991-2008 waves

20 40 60 80 100CAMSIS of SOC

0 10 20 30 40subjective wellbeing (ghq) 1: likert

1 2 3 4 5how often: walk/swim/play sport

1 2 3 4 5financial situation

1 2 3 4 5strong trade unions protect employees

1 2 3 4 5family suffers if woman works full time

CAMSIS score of occupational advantageSelf-rated healthParticipation in exerciseFeeling financial secureAttitudes towards trade unionismAttitudes towards motherhood and employment

• 7 models to measure outcome:Controls: age, gender and CAMSIS scoresLevels:

1. None2. Family3. Network4. Occupation5. Family and network6. Family, network and occupation7. Family, network, occupation and occupation-by-gender

Outcome 1: CAMSIS score (scale from 1 to 99, modelled as linear scale)

(1) (2) (3) (5)Intercept 50.1* 50.2* 49.8* 41.2*Female 2.6* 2.5* 2.5* 2.5*(Age – 40)/10 3.8* 3.6* 4.0* 3.9*(Age2 -1600)/1000 -4.2* -4.1* -4.4* -4.3*Deviance 208281 206576 206268 206228AIC 208291 206588 206281 206243ID variance ICC 100% 70.2% 72.6% 70.6%FID variance ICC 29.8% 7.9%NID variance ICC 27.4% 21.5%N 25971 25971 25971 25971

Intercept 49.3* 49.0* 48.4* 48.4*Deviance 740014 726250 725592 725352AIC 740019 726256 725598 725359ID variance ICC 100% 70.7% 72.4% 71.3%FID variance ICC 29.3% 7.9%NID variance ICC 27.6% 20.8%N 90784 90784 90784 90784

n of FID groups 12096 12096n of NID groups 9846 9846Analysis 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)

Intercept 10.9* 10.9* 10.9* 10.9* 10.9* 10.9* 10.9*

Female 1.3* 1.3* 1.3* 1.2* 1.3* 1.2* 1.2*(Age – 40)/10 0.77* 0.84* 0.83* 0.79* 0.84* 0.86* 0.86*(Age2 -1600)/1000 -0.57* -0.63* -0.62* -0.59* -0.63* -0.65* -0.65*

(CAMSIS -50)/10 -0.19* -0.17* -0.17* -0.19* -0.17* -0.17* -0.17*

(Female*CAMSIS)/100 -0.94 -0.93 -0.89 -0.67 -0.92* -0.69 -0.68

Deviance 791908 157917 157960 158161 157915 157912 157909

AIC 158181 157933 157976 158177 157933 157932 157933ID variance ICC 100% 89.3% 91.2% 99.8% 89.3% 89.1% 89.3%FID variance ICC 10.7% 9.4% 9.3% 9.4%NID variance ICC 8.8% 1.3% 1.3% 1.3%SOC variance ICC 0.2% 0.2% 0.1%Fem | soc variance 0.03%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)Intercept 13.5* 12.8* 13.1* 13.4* 12.9* 12.9* 12.8*Female 0.86* 0.82* 0.80* 1.12* 0.81* 1.02* 1.14*(Age – 40)/10 0.42* 0.63* 0.59* 0.42* 0.61* 0.61* 0.63*(CAMSIS -50)/10 -0.08 0.05 0.13 -0.11 0.08 0.06 -0.02(Age*CAMSIS)/10 -0.24* -0.25* -0.27* -0.23* -0.26* -0.25* -0.25*Deviance 154306 154393 155459 154356 154255 154310AIC 155522 154320 154407 155473 154415 154273 154419ID variance ICC 100% 71.6% 74.2% 99.1% 71.4% 71.2% 70.9%FID variance ICC 28.4% 19.8% 19.3% 19.9%NID variance ICC 25.8% 8.7% 8.9% 8.3%SOC variance ICC 0.9% 0.6% 1.0%Fem | soc variance 0.3%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)Intercept 2.26* 2.27* 2.25* 2.24* 2.26* 2.25* 2.25*(Age – 40)/100 -0.74* -0.67* -0.67* -0.73* -0.66* -0.66* -0.67*(CAMSIS -50)/10 -0.13* -0.11* -0.12* -0.13* -0.11* -0.11* -0.11*(Age*CAMSIS)/1000 -0.08* -0.10* -0.10* -0.08* -0.10* -0.09* -0.10*Deviance 70424 70606 71407 70406 70351 70324AIC 71511 70436 70618 71419 70420 70367 70344ID variance ICC 100% 75.1% 80.3% 98.9% 75.0% 74.8% 74.5%FID variance ICC 24.9% 20.2% 19.7% 19.8%NID variance ICC 19.7% 4.8% 4.7% 4.6%SOC variance ICC 1.2% 0.8% 1.0%Fem | soc variance 0.1%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)

Intercept 3.04* 3.04* 3.04* 3.04* 3.04* 3.04* 3.04*

Female 0.18* 0.18* 0.18* 0.18* 0.18* 0.17* 0.18*

(Age – 40)/10 -0.23* -0.23* -.023* -0.23* -0.23* -0.23* -0.23*

(Age2 -1600)/1000 48.9* 50.9* 52.2* 49.9* 51.7* 52.5* 52.8*

(CAMSIS -50)/10 0.004* 0.004* 0.004* 0.004* 0.005 0.005 0.005*

(Female*CAMSIS)/100 0.003* 0.001 -0.001 -0.004 -0.001 -0.001 -0.0008

Deviance 66122 66158 66619 66111 66088 66084

AIC 66667 66138 66174 66635 66129 66108 66108

ID variance ICC 100% 83.5% 85.2% 99.3% 83.4% 83.2%FID variance ICC 16.5% 11.8% 11.6% 11.6%

NID variance ICC 14.8% 4.8% 4.7% 4.4%

SOC variance ICC 0.7% 0.5% 0.3%

Fem | soc variance 0.2%

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)Intercept 2.55* 2.54* 2.54* 2.51* 2.54* 2.51* 2.51*Female -0.10* -0.10* -0.10* -0.06* -0.09* -0.06* -0.05*(Age – 40)/10 .02* 0.02* 0.02* 0.02* 0.02* 0.02* 0.02*(Age2 -1600)/1000 -0.17* -0.17* -0.17* -0.16* -0.17* -0.16* -0.16*(CAMSIS -50)/10 0.01* 0.01* 0.01* 0.01* 0.01* 0.01* 0.01*(Female*CAMSIS)/100 -0.01* -0.01* 0.01* -0.01* -0.01* -0.01* -0.01*Deviance 61327 61307 61450 61289 60917 60908AIC 61914 61343 61323 61466 61307 60937 60932ID variance ICC 100% 80.3% 81.2% 96.0% 80.0% 78.3% 77.3%FID variance ICC 19.7% 8.5% 7.3% 7.0%NID variance ICC 18.8% 11.5% 10.9% 10.9%SOC variance ICC 4.0% 3.5% 4.3%Fem | soc variance 0.5%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).

CAMSIS Health Sports Financial security

Working mothers

Trade unions

ID variance ICC 71.3% 89.1% 71.2% 74.5% 83.2% 77.3%

FID variance ICC 7.9% 9.3% 19.3% 19.8% 11.6% 7.0%

NID variance ICC 20.8% 1.3% 8.9% 4.6% 4.7% 10.9%

SOC variance ICC 0.2% 0.6% 1.0% 0.5% 4.3%

Fem | soc variance 0.1% 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