Occupational marriage networks in the USA, 1970-2010
Dave Griffiths & Paul LambertSchool of Applied Social Science, University of Stirling
Paper presented to Social Stratification Research Seminar, 1 September 2011, University of Stirling
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/
• Generating networks sociograms of the occupational structure
• Testing reliability of obtaining data from large-scale social surveys
• Initial results of mapping occupational structure using SNA methods
• Examine occupational categorisation schemes
% 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.
% 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.
www.camsis.stir.ac.uk/sonocs/do/pajek.do**create frequency datasetcapture drop freqgen freq = 1collapse (count) freq, by(hocc wocc)list in 1/20*****Find total for each categorycapture drop totegen tot=sum(freq)summarize tot*******Find totals for men and womencapture drop nhocccapture drop nwoccegen nhocc=sum(freq), by(hocc)egen nwocc=sum(freq), by(wocc)****Find percentage for each category for men and womencapture drop phocccapture drop pwoccgen phocc=nhocc/totgen pwocc=nwocc/totSummarize*******Calculate expected numbers of womencapture drop ewoccgen ewocc=pwocc*nhoccSummarize**************create expectation surpluscapture drop valuegen value=freq/ewocc************Create standard error predictionscapture drop propgen prop = freq/totcapture drop staner gen staner = sqrt((prop)*(1 - prop) / tot)list freq tot phocc pwocc ewocc value prop staner in 1/20
capture drop pro_obsgen pro_obs = freq/totcapture drop pro_expgen pro_exp = ewocc/totcapture drop pro_mingen pro_min = pro_obs – stanercapture drop pro_maxgen pro_max = pro_obs + stanercapture drop valuegen value = pro_obs / pro_expcapture drop val_mingen val_min = pro_min / pro_expcapture drop val_maxgen val_max = pro_max / pro_exp***label variableslabel variable tot "total number in sample"label variable nhocc "total number of males in occupation"label variable nwocc "total number of females in occupation"label variable phocc "percentage of men in occupation"label variable pwocc "percentage of women in occupation"label variable ewocc "expected number of partnerships"label variable staner "Standard error for tie"label variable pro_obs "Observed proportion of all ties"label variable pro_exp "Expected proportion of all ties"label variable pro_min "Lower confidence interval of observed proportion"label variable pro_max "Higher confidence interval of observed proportion"label variable value "Observed value of representation"label variable val_min "Value of representation for lower confidence interval"label variable val_max "Value of representation for higher confidence interval"
1101. Jurists1102. Health professionals
1103. Professors and instructors1104. Natural scientists
1105. Statistical and social scientists1106. Architects
1107. Accountants1108. Journalists, authors, and related writers
1109. Engineers1201. Officials, government and non-profit organizations
1202. Managers1203. Commercial Managers
1204. Building managers and proprietors1301. Systems analysts and programmers
1302. Aircraft pilots and navigators1303. Personnel and labor relations workers
1304. Elementary and secondary school teachers1305. Librarians
1306. Creative artists1307. Ship officers
1308. Professional, technical, and related workers, n.e.c.1309. Social and welfare workers
1310. Workers in religion1311. Nonmedical technicians
1312. Health semiprofessionals1313. Hospital attendants
1314. Nursery school teachers and aides3101. Real estate agents
3102. Other agents3103. Insurance agents
3104. Cashiers3105. Sales workers and shop assistants
3201. Telephone operators3202. Bookkeepers and related workers
3203. Office and clerical workers3204. Postal and mail distribution clerks
4101. Craftsmen and kindred workers, n.e.c.4102. Foremen
4103. Electronics service and repair workers4104. Printers and related workers
4105. Locomotive operators4106. Electricians
4107. Tailors and related workers4108. Vehicle mechanics
4109. Blacksmiths and machinists4110. Jewelers, opticians, and precious metal workers
4111. Other mechanics4112. Plumbers and pipe-fitters
4113. Cabinetmakers4114. Bakers
4115. Welders and related metal workers4116. Painters
4117. Butchers4118. Stationary engine operators
Bricklayers, carpenters & related4120. Heavy machine operators
4201. Truck drivers4202. Chemical processors
4203. Miners and related workers4205. Food processors
4206. Textile workers4207. Sawyers and lumber inspectors
4208. Metal processors4209. Operatives and kindred workers, n.e.c.
4210. Forestry workers4301. Protective service workers
4302. Transport conductors4303. Guards and watchmen
4304. Food service workers4305. Mass transportation operators
4306. Service workers, n.e.c.4307. Hairdressers
4308. Newsboys and deliverymen4309. Launderers and dry-cleaners
4310. Housekeeping workers4311. Janitors and cleaners
4312. Gardeners5101. Fishermen
5201. Farmers and farm managers5202. Farm laborers
9990. Members of armed forces
USA
Romania
Phillipines
Venezuela
Male CAMSIS scale scores across four countries using 'microclass' units.
Hypothetical network: 469 US OUGs & micro-classes
Green: professional; Blue: routine non-manual; Red: manual; Yellow: primary; Black: military
Dental hygienists
Medical professionals
Medical and dental technicians
(Four different isolated components with internal links within microclass but no external links)
(further isolated components)
‘Pseudo-diagonal’ or ‘situs’
Occupational Structure, USA 2010Source: CPS 2010
Green: Professional; Yellow: routine non-manual; Red: Manual; Black: Farming
1970 19751980 1985
1990 19952000 2005
Occupational Structure, USA 2010Source: CPS 2010
Green: Professional; Yellow: routine non-manual; Red: Manual; Black: FarmingFood service
managers and food preparation managers and waitresses
Primary school teachers connecting to a range of occupations
Estate agents between stratified blocks
Farming communities with ties to all social levels
Teaching assistants crossing prestige levels
IT
teaching
Hotel workers
1990 19952000 2005Position of Receptionists
05,
000
10,0
0015
,000
20,0
0025
,000
Sam
ple s
ize
1970 1975 1980 1985 1990 1995 2000 2005 2010
Samplw sizes of both-working married couplesSource: CPS 1970-2010
02
46
8
% w
ithin
OU
G
1970 1975 1980 1985 1990 1995 2000 2005 2010
Percentage of married couples within same OUGSource: CPS 1970-2010
02
46
810
% w
ithin
mic
rocl
ass
1970 1975 1980 1985 1990 1995 2000 2005 2010
Percentage within same microclassSource: CPS 1970-2010
010
2030
4050
% w
ithin
mac
rocl
ass
1970 1975 1980 1985 1990 1995 2000 2005 2010
Percentage within same macroclassSource: CPS 1970-2010
020
4060
8010
0%
non
-gra
duat
es m
arry
ing
non-
grad
uate
s
1970 1975 1980 1985 1990 1995 2000 2005 2010
Percentage of non-graduates marrying non-graduatesSource: CPS 1970-2010
020
4060
80%
gra
duat
es m
arry
ing
grad
uate
s
1970 1975 1980 1985 1990 1995 2000 2005 2010
Percentage of graduates marrying graduatesSource: CPS 1970-2010
020
4060
80%
pro
fess
ions
with
deg
rees
Percentage of professionals with degreesSource: CPS 1970-2010
010
2030
4050
% m
anag
ers
with
deg
rees
Percentage of managers with degreesSource: CPS 1970-2010
020
4060
% a
ssoc
iate
pro
fess
ions
with
deg
rees
Percentage of associate professionals with degreesSource: CPS 1970-2010
010
2030
% r
outin
e no
n-m
anua
l with
deg
rees
Percentage of routine non-manual with degreesSource: CPS 1970-2010
05
10%
man
ual w
ith d
egre
es
Percentage of manual with degreesSource: CPS 1970-2010
05
1015
20%
farm
ers
with
deg
rees
Percentage of farmers with degreesSource: CPS 1970-2010
Graduate Non-graduate1970 Mechanical engineer Nurses
Buyers and department heads
Clerical and kindred workers
Pharmacists SalespersonsManagers n.e.c Personal and labour
relationsPrimary school teachersReal estate agents
Accountants Hucksters and peddlersArtists and art teachers FarmersSocial workers Auctioneers
1975 Managers n.e.c Dental assistantsAccountants Hucksters and peddlersHealth advisors SecretariesPublic administratorsIndustrial engineers
Craftsmen
Secondary school teachers Cafeteria workersFarmers Farm LabourersElectrical engineers Teacher aides
Misc. electrical workers1980 Physicians and surgeons Nurses
Public administratorsSchool administrators
Teacher aides
Secondary school teachers Primary school teachersManagers n.e.c. Health advisorsKindergarten teachers Sales representatives
(retail, n.e.c)Sales representatives (Manufacturing)
Managers n.e.c.
Cafeteria workers Waitresses
Graduate Non-graduate1985 Sales representatives
Secondary school teachersPhysicians and surgeonsPublic administratorsOther financial workers
Nurses
Sales representativesDentists
Receptionists
Dentists Managers n.e.cVeterinaries BookkeepersPurchasing agents Secretaries
1990 Health diagnosing professionals
Managers n.e.c
1995 Accountants and auditors Public administratorsSecondary school teachers Electrical power installers
2000 Clergy Managers n.e.c.Social workers HairdressersLawyers Designers
Legal assistantsData processing repairers Secretaries
2005 Maids Janitors2010 Bookkeepers Construction managers
Dentists Office supervisors
Over-represented graduate non-graduate marriage ties in USA 1970-2010Source: Current Population SurveyNote: Italics indicate the female occupation.
Managers in… 1985 1990 1995 2000 2005 2010 2010 CAMSIS
Public Administration/education
81.7 74.9 74.5 80.7 80.1 83.8 71.2
Personnel and labour relations
46.0 57.1 47.6 51.9 60.2 68.9 55.7
Chief Executives 37.5 14.3 40.0 37.5 67.5 67.0 70.7Business and promotions 52.0 58.8 63.6 66.7 70.6Medicine and health 62.5 66.2 47.5 50.2 62.9 66.8 66.6Marketing 45.8 52.3 62.8 65.3 56.4 65.8 65.3Financial managers 50.0 56.9 63.0 53.6 60.0 62.8 66.8Purchasing 65.4 43.2 61.7 46.2 55.6 54.6 60.5Properties 33.6 31.0 36.0 40.1 38.6 37.4 59.8Construction 23.5 29.1 57.8Food service 20.5 23.6 22.0 26.0 47.9Gaming 22.2 9.1 55.7n.e.c. 33.9 35.2 41.5 42.0 51.3 48.7 62.2
Source: Current Population Survey: 1985-2010 (http://www.cps.ipums.org/cps)
Percentage of graduates in managerial roles, USA 1985-2010
Conclusions
• Social Network Analysis can provide empirical evidence of occupational stratification
• Networks should be interpreted in terms of wide trends rather than specific occupations
• Little evolution of marriage networks in last 40 years of USA, but educational cohort effects are emerging
• Microclasses generally a sound way to group occupations, although social interaction patterns suggest not quite maximal.
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