Occupational marriage networks in the USA, Dave Griffiths & Paul Lambert School 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

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) 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.

% 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.

**create frequency dataset capture drop freq gen freq = 1 collapse (count) freq, by(hocc wocc) list in 1/20 *****Find total for each category capture drop tot egen tot=sum(freq) summarize tot *******Find totals for men and women capture drop nhocc capture drop nwocc egen nhocc=sum(freq), by(hocc) egen nwocc=sum(freq), by(wocc) ****Find percentage for each category for men and women capture drop phocc capture drop pwocc gen phocc=nhocc/tot gen pwocc=nwocc/tot Summarize *******Calculate expected numbers of women capture drop ewocc gen ewocc=pwocc*nhocc Summarize **************create expectation surplus capture drop value gen value=freq/ewocc ************Create standard error predictions capture drop prop gen prop = freq/tot capture drop staner gen staner = sqrt((prop)*(1 - prop) / tot) list freq tot phocc pwocc ewocc value prop staner in 1/20 capture drop pro_obs gen pro_obs = freq/tot capture drop pro_exp gen pro_exp = ewocc/tot capture drop pro_min gen pro_min = pro_obs – staner capture drop pro_max gen pro_max = pro_obs + staner capture drop value gen value = pro_obs / pro_exp capture drop val_min gen val_min = pro_min / pro_exp capture drop val_max gen val_max = pro_max / pro_exp ***label variables label 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"

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 2010 Source: CPS 2010 Green: Professional; Yellow: routine non-manual; Red: Manual; Black: Farming

Occupational Structure, USA 2010 Source: CPS 2010 Green: Professional; Yellow: routine non-manual; Red: Manual; Black: Farming Food 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

Position of Receptionists

GraduateNon-graduate 1970Mechanical engineerNurses Buyers and department heads Clerical and kindred workers PharmacistsSalespersons Managers n.e.c Personal and labour relations Primary school teachers Real estate agents AccountantsHucksters and peddlers Artists and art teachersFarmers Social workersAuctioneers 1975Managers n.e.cDental assistants AccountantsHucksters and peddlers Health advisorsSecretaries Public administrators Industrial engineers Craftsmen Secondary school teachersCafeteria workers FarmersFarm Labourers Electrical engineers Teacher aides Misc. electrical workers 1980Physicians and surgeonsNurses Public administrators School administrators Teacher aides Secondary school teachersPrimary school teachers Managers n.e.c.Health advisors Kindergarten teachers Sales representatives (retail, n.e.c) Sales representatives (Manufacturing) Managers n.e.c. Cafeteria workersWaitresses GraduateNon-graduate 1985 Sales representatives Secondary school teachers Physicians and surgeons Public administrators Other financial workers Nurses Sales representatives Dentists Receptionists DentistsManagers n.e.c VeterinariesBookkeepers Purchasing agentsSecretaries 1990 Health diagnosing professionals Managers n.e.c 1995Accountants and auditorsPublic administrators Secondary school teachersElectrical power installers 2000ClergyManagers n.e.c. Social workersHairdressers LawyersDesigners Legal assistants Data processing repairersSecretaries 2005MaidsJanitors 2010BookkeepersConstruction managers DentistsOffice supervisors Over-represented graduate non-graduate marriage ties in USA Source: Current Population Survey Note: Italics indicate the female occupation.

Managers in… CAMSIS Public Administration/education Personnel and labour relations Chief Executives Business and promotions Medicine and health Marketing Financial managers Purchasing Properties Construction Food service Gaming n.e.c Source: Current Population Survey: ( Percentage of graduates in managerial roles, USA

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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