Strong and weak ties as predictors of occupational position

Dave Griffiths and Paul LambertUniversity of Stirling

18th March 2012Sunbelt Conference, Redondo Beach CA.

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/

Theories

• Social interaction: stratification effects can be demonstrated (Chan, 2010; Laumann & Guttman, 1966; Prandy, 1990; Stewart, Prandy & Blackburn, 1973; Stewart, Prandy, & Blackburn, 1980)

• Strong and weak ties: Strong ties provide support, weak ties provide substance (Grannovetter 1973, 1983)

• Social capital: access to beneficial resources is beneficial in itself (c.f. Lin & Erickson 2008)

• Occupational differentiation: more detail occupational distinctions provide more robust measures (Jonsson et al. 2009)

Aims

• Do more advantaged occupations have increased access to more beneficial resources?

• Do Position Generators accurately measure accesses to resources

• Does composition of strong and weak ties matter?

Methodology

• BHPS 1991-2008• Individuals linked to all they are related to,

named as a friend or lived with.• Individuals placed within networks of all the

alters of their alters, snowballed to include all possible friends of friends of friends of friends

• 30k individuals grouped into 9k networks

• Wave 1: Ego (1) lives with parents (2 & 3) and sibling (4)

• Wave 3: Ego lives with three friends (5, 6 & 7)

• Wave 5: Ego lives with partner (8)

• Wave 7: Ego and partner move in with partner’s parents (9 & 10)

• Wave 15: Ego shares house with three others (11, 12 & 13)

• Ego• Lives with parents in

1991• Lives with friends in 1993• Lives with partner in

1995 (away from hometown)

• They move into partner’s parents in 1997 (returning to hometown)

• They split up and ego lives in shared house in 2005

This produces a network of 13 individuals in the survey who have lived with the same ego. There would be 18 opportunities for people to name a best friend, possibly creating a network of 31 individuals.

If the sibling has a similar pattern, we could have 22 individuals linkable to the Wave 1 household, and 32 friends. With parent’s (10) friends, this is a network of 64 people.

020

4060

Per

cent

0 1 2 3 4 5Percentage of ties by type

1 'best friends'; 2 'household sharers'; 3 'parent-child'; 4 'spouses'; 5 'other'

05

1015

Per

cent

0 20 40 60 80 100Size of networks

Mean size: 8.7; largest: 102 (7 OSM, 33 sample members, 62 best friends)

Strong ties• Parent - child• Grandparent -

grandchild• Sibling - sibling• Spouse - spouse

Weak ties include ego to:

• Best friends and housemates

• Spouse’s friends and family

• Former housemates• Spouse’s former

housemates• Son’s spouses former

housemates• Friends of son’s spouses

former housemates

Jobs held

Most recent job

CAMSIS Guveli % male

University teaching professionals 1,821 1,076 82.3 2 52.3%Primary and middle school teachers 4,137 1,036 65.5 4 13.0%Other managers and administrators n.e.c. 3,865 1,560 63.5 1 71.3%Other secretaries, personal assistants 6,300 1,880 62.3 5 3.2%Managers and proprietors in service industries 7,615 2,633 62.3 3 56.3%Accounts and wages clerks, book-keepers 8,872 2,283 59.5 5 35.6%Farm owners and managers 2,266 1,094 58.3 8 77.6%Counter clerks and cashiers 4,183 1,190 55.4 5 30.7%Nurses 6,865 2,077 53.9 4 10.2%Clerks (n.e.c.) 12,197 3,937 52.4 5 30.4%Sales assistants 19,200 5,663 51.9 5 29.3%Other childcare and related occupations 3,882 1,123 51.5 5 2.0%Care assistants and attendants of older people 5,186 1,594 46.7 5 12.2%Chefs, cooks, hotel supervisors 3,794 1,215 43.5 6 44.5%Carpenters and joiners 3,135 1,098 42.3 6 99.1%Metal working production and maintenance 4,227 1,693 41.5 6 97.5%Storekeepers, warehousemen/women 4,543 858 37.5 5 82.5%Cleaners, domestics 12,468 3,784 36.4 7 19.2%Bar staff 3,681 1,161 36.0 5 41.0%Drivers of road goods vehicles 5,705 1,995 34.5 7 95.8%

20 most common occupationsSource: BHPS 1991-2007

% of all BHPS networks with at least one…

University teaching professionals 13.4%Primary and middle school teachers 12.4%Other managers and administrators n.e.c. 16.7%Other secretaries, personal assistants 21.9%Managers and proprietors in service industries 26.0%Accounts and wages clerks, book-keepers 22.6%Farm owners and managers 8.8%Counter clerks and cashiers 13.3%Nurses 21.3%Clerks (n.e.c.) 32.3%Sales assistants 44.8%Other childcare and related occupations 13.7%Care assistants and attendants of older people 17.3%Chefs, cooks, hotel supervisors 13.7%Carpenters and joiners 12.2%Metal working production and maintenance 16.7%Storekeepers, warehousemen/women 11.9%Cleaners, domestics 32.4%Bar staff 13.7%Drivers of road goods vehicles 19.4%

% of networks linking to

% of those with a link to occ. from all who have CAMSIS…..over 65 ..below 35 Diff.

University teaching professionals 13.4% 22.3% 7.1% 15.2%Primary and middle school teachers 12.4% 20.3% 6.4% 13.9%Other managers and administrators n.e.c. 16.7% 17.6% 9.8% 7.8%Other secretaries, personal assistants 21.9% 21.5% 14.2% 7.3%Managers and proprietors in service industries 26.0% 23.7% 18.4% 5.3%Accounts and wages clerks, book-keepers 22.6% 21.5% 14.7% 6.8%Farm owners and managers 8.8% 9.0% 7.0% 2.0%Counter clerks and cashiers 13.3% 11.9% 9.0% 2.9%Nurses 21.3% 20.0% 14.9% 5.1%Clerks (n.e.c.) 32.3% 28.2% 22.9% 5.3%Sales assistants 44.8% 36.5% 36.8% -0.3%Other childcare and related occupations 13.7% 10.5% 11.0% -0.5%Care assistants and attendants of older people 17.3% 11.4% 16.2% -4.8%Chefs, cooks, hotel supervisors 13.7% 9.9% 11.6% -1.7%Carpenters and joiners 12.2% 8.6% 10.0% -1.4%Metal working production and maintenance 16.7% 12.5% 13.5% -1.0%Storekeepers, warehousemen/women 11.9% 8.3% 10.5% -2.2%Cleaners, domestics 32.4% 22.8% 33.4% -10.6%Bar staff 13.7% 11.7% 10.3% 1.4%Drivers of road goods vehicles 19.4% 12.2% 23.5% -11.3%

6570

7580

85

1 2 3 4 5 6 7 8 9

CAMSIS

1314

1516

17

1 2 3 4 5 6 7 8 9

Position generator

1 Higher technocrats 2 High social cultural specialists 3 Low technocrats 4 Low social cultural specialists; 5 Routine nonmanuals 6 Skilled manual 7 Semi-unskilled manual 8 Farm labor 9 Selfempl farm

Highest CAMSIS and positions accesssed by Guveli categories

Public private divide

Secretaries

IT/software/ computer experts

educationalists

Laboratory worker

Healthcare workers

Managers

PR/ advertising

artists

Farm workers

No strong patterns – plenty of dyads with an obvious working relationship, but linking together unrelated areas (i.e., clothes makers and coal miners linking together)

All ties

Social workers

Weak ties (mostly friendship or distant hhld connections)

Strong ties (mostly close family/ household sharers)

mean CAMSIS

most recent job

Ever held job

% of people with any link to category

Education 71.6 4.2% 4.7% 31.0%

Healthcare 56.3 4.7% 5.6% 42.6%

Law 77.4 0.6% 0.7% 8.1%

Financial services 71.3 1.3% 1.9% 20.3%

Builders 42.2 5.7% 7.2% 52.9%

Car mechanics 43.3 0.9% 1.4% 17.0%

0.2

.4.6

.81

educ

atio

n

20 40 60 80 100

0.2

.4.6

.8fin

ance

20 40 60 80 100

.2.4

.6.8

1bu

ildin

g_tr

ade

20 40 60 80 100

0.1

.2.3

.4.5

car_

repa

irs

20 40 60 80 100

Average number of people in each job with a link to specific resource occupations

2040

6080

20 40 60 80 100

Strong ties

3040

5060

70

20 40 60 80 100

Weak ties

Mean CAMSIS score of incumbents of occupations

Weak tie Strong tieSame occupation 86.7% 13.3%Different occupation 84.0% 16.0%

CAMSIS Weak ties

Strong ties .38 .58

Weak ties .46

Correlation between most recent CAMSIS score and the mean of strong and weak tiesSource: BHPS 1991-2008

Percentage of within occupation connections attributable to strong and weak ties.Source: BHPS 1991-2008

Conclusions• Position Generators tend to lead to grouping together of

occupations with similar stratification positions, but:– Can elide nuanced differences between some occupations– Possibly due to the need to focus on selected common occupations– Other forms of network summary may better reflect social distances

than PG approach• Differences between strong and weak ties can be observed in

patterns of common connections between occupations, with weaker ties dispersed more widely and structurally less shaped by stratification position

• Little difference between strong and weak ties in strength of relation between own and alter occupation: both reflect the same overall trend for homophily

Bibliography• Chan, T. W. (2010). The social status scale: Its construction and properties. In T. W. Chan (Ed.),

Social Status and Cultural Consumption (pp. 28-56). Cambridge: Cambridge University Press.• Granovetter, M. (1973) The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360-

1380.• Granovetter, M. (1983) The Strength of Weak Ties: A Network Theory Revisited. Sociological

Theory, 1, 201-233.• Jonsson, J.O., Grusky, D.B., Di Carlo, M., Pollak, R., & Brinton, M.C. (2009) Microclass Mobility:

Social Reproduction in Four Countries. American Journal of Sociology, 114(4), 977-1036.• Laumann, E. O., & Guttman, L. (1966). The relative associational contiguity of occupations in

an urban setting. American Sociological Review, 31, 169-178.• Lin, N., & Erickson, B. (2008) Social Capital: An International Research Program. Oxford: Oxford

University Press.• Prandy, K. (1990). The Revised Cambridge Scale of Occupations. Sociology-the Journal of the

British Sociological Association, 24(4), 629-655.• Stewart, A., Prandy, K., & Blackburn, R. M. (1973). Measuring the Class Structure. Nature.• Stewart, A., Prandy, K., & Blackburn, R. M. (1980). Social Stratification and Occupations.

London: MacMillan.