Occupational marriage networks in the USA, 1970-2010 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 http://www.camsis.stir.ac.uk/sonocs/
20
Embed
Generating networks sociograms of the occupational structure
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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
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.
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.
BibliographyBlackwell, D.L. (1998) ‘Marital Homogamy in the United States: The Influence of Individual and Parental Education’, Social Science Research, 27, 159-188.Blau, P.M. & Duncan, O.D. (1967) The American Occupational Structure. New York: Wiley.Chan, T.W. (ed) (2010) Social Status and Cultural Consumption. Cambridge: Cambridge University Press.Ganzeboom, H. B. G., & Treiman, D. J. (1996). Internationally Comparable Measures of Occupational Status for the 1988 International Standard Classification
of Occupations. Social Science Research, 25(3), 201-235.Griffiths, D., and Lambert, P.S. (Forthcoming) Dimensions and Boundaries: Comparative Analysis of Occupational Structures Using Social Network and Social
Interaction Distance Analysis Grusky, D. B., & Weeden, K. A. (2001). Decomposition Without Death: A Research Agenda for a New Class Analysis. Acta Sociologica, 44(3), 203-218.Grusky, D. B., & Weeden, K. A. (2006). Does the sociological approach to studying social mobility have a future? in S. L. Morgan, D. B. Grusky & G. S. Fields
(Eds.), Mobility and Inequality. Stanford: Stanford University Press.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.Kalmijn, M. (1998). Intermarriage and homogamy: Causes, patterns, trends. Annual Review of Sociology, 24, 395-421.King, M., Ruggles, S., Alexander, J.T., Flood, S., Genadek, K., Schroeder, M.B., Trampe, B., and Vick, R. (2010) Integrated Public Use Microdata Series, Current
Population Survey: Version 3.0. [Machine-readable database]. Minneapolis: University of Minnesota.Laumann, E.O., & Guttman, L. (1966) ‘The Relative Associational Contiguity of Occupations in an Urban Setting’, American Sociological Review, 31, 169-178.Lambert, P. S., Tan, K. L. L., Gayle, V., Prandy, K., & Bergman, M. M. (2008). The importance of specificity in occupation-based social classifications.
International Journal of Sociology and Social Policy, 28(5/6), 179-192.Lin, N., & Erickson, B.H. (eds) (2008) Social Capital: An International Research Program. Oxford: Oxford University Press.McDonald, K.I. (1972) MDSCAL and distances between socio-economic groups’ in K. Hope (ed) The Analysis of Social Mobility: Methods and Approaches.
London: Clarendon.Oesch, D. (2006). Redrawing the Class Map: Stratification and Institutions in Britain, German, Sweden and Switzerland. Basingstoke: Palgrave.Putnam, R. (2000) Bowling Alone: The collapse and revival of American Community. New York: Simon & Schuster.Schartz, C.R., and Mare, R.D. (2005) “Trends in Educational Assortative Marriage from 1940 to 2003”, Demography, 42(4), 621-646.Stewart, A., Prandy, K., & Blackburn, B. (1973) Measuring the Class Structure, Nature, 415-417.Treiman, D. J. (1977). Occupational Prestige in Comparative Perspective. New York: Academic Press.van der Gaag, M., Snjiders, T., and Flap, H. (2008) “Position Generator Measures and their Relationships to Other Social Capital Measures”, in N. Lin & B.H.
Erikson (eds). Social Capital: An International Research Program. Oxford: Oxford University Press.Weeden, K. A., (2004). "Profiles of Change: Sex Segregation in the United States, 1910-2000" in M. Charles, & D. Grusky, (eds) Occupational Ghettos.
Stanford: Stanford University Press, 131-178.Weeden, K. A., & Grusky, D. B. (2005). The Case for a New Class Map. American Journal of Sociology, 111(1), 141-212.