Comparing network and association models in the analysis of historical patterns of occupational interactions and stratification Paul Lambert 1 , David Griffiths 1 , Richard Zijdeman 2 , Ineke Maas 2 , Marco van Leeuwen 2 Paper presented to the European Social Science History Conference, 11-14 April 2012, University of Glasgow, UK 1) University of Stirling, UK, contact email: [email protected]2) University of Utrecht, Netherlands 1
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Paul Lambert 1 , David Griffiths 1 , Richard Zijdeman 2 , Ineke Maas 2 , Marco van Leeuwen 2
Comparing network and association models in the analysis of historical patterns of occupational interactions and stratification. Paul Lambert 1 , David Griffiths 1 , Richard Zijdeman 2 , Ineke Maas 2 , Marco van Leeuwen 2 - PowerPoint PPT Presentation
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Comparing network and association models in the analysis of historical
patterns of occupational interactions and stratification
Paul Lambert1, David Griffiths1, Richard Zijdeman2, Ineke Maas2, Marco van Leeuwen2
Paper presented to the European Social Science History Conference, 11-14 April 2012, University of Glasgow, UK
1) University of Stirling, UK, contact email: [email protected]) University of Utrecht, Netherlands
1
Motivation• Studying social interactions and social connections can
help us to understand social trends and transformations • Social mobility; homogamy; industrialisation; etc
• Taking full advantage of historical occupational codes, new data, and new analytical opportunities
• HISCO/NAPPHISCO/Microclass standardised codes…• …capture fine-grained details, but potentially aggregate
some occupations by sector rather than level
– GB 1831 census “..occupational returns as ‘crude, undigested, and essentially unscientific’, a document ‘whose insufficiency is a national disgrace to us, for there the trading and working classes are all jumbled together in the most perplexing confusion, and the occupations classified in a manner that would shame the merest tyro’” [Thompson 1963: 25, citing Mayhew 1862] 2
What’s new?1) Data resources• Census returns with household sharers’
N refers to number of adults in dataset with valid occupational records. The number of unique within household connections between these adults is usually between 1 and 2 times the number of adults.
4
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 proprietors1304. Elementary and secondary school teachers
1305. Librarians1306. Creative artists
1307. Ship officers1308. Professional, technical, and related workers, n.e.c.
1309. Social and welfare workers1310. Workers in religion
1311. Nonmedical technicians1312. Health semiprofessionals
2001. Proprietors3101. Real estate agents
3102. Other agents3105. 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
4104. Printers and related workers4105. Locomotive operators
4106. Electricians4107. Tailors and related workers
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
4202. Chemical processors4203. Miners and related workers
4204. Longshoremen and freight handlers4205. 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 conductors4304. Food service workers
4305. Mass transportation operators4306. Service workers, n.e.c.
4307. Hairdressers4309. 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
0 2000 4000 6000 8000
Men
Women
Canada 1891, Males and females by microclass units. (5201/5201 downweighted by factor of 5).
Preliminary versions – contemporary microclasses a convenient way to measure and analysis fine-grained historical detail?
5
Sample Model CAM/USC Microclass HISCO NAPPHISCO (OCCGB)
CA 1871 R2 in predicting 0.155 0.247 0.270 0.303
CA 1881 alter’s HISCAM 0.194 0.279 0.309 0.310
CA 1891 0.299 0.404 0.433 0.437
CA 1901 0.143 0.252 0.280 0.283
IC 1801 R2 in predicting 0.060 0.137 0.166 0.167
IC 1901 alter’s HISCAM 0.009 0.032 0.043 0.043
SE 1900 ` ` 0.000 0.167 0.192 0.192
GB 1851 R2 in predicting 0.300 0.319 n/a 0.344
GB 1881 (EW) alter’s CAMSIS 0.236 0.258 n/a 0.282
GB 1881 (S) 0.189 0.228 n/a 0.245
US 1850 R2 in predicting 0.027 0.053 0.057 0.058
US 1860 alter’s literacy 0.026 0.059 0.065 0.066
US 1870 (plus father’s hiscam 0.067 0.145 0.151 0.151
US 1880 If literacy missing) 0.040 0.099 0.103 0.104
US 1900 0.032 0.069 0.075 0.076
NO 1801 R2 in predicting 0.067 0.115 0.156 0.157
NO 1865 alter’s HISCAM 0.028 0.064 0.081 0.081
NO 1875 0.057 0.099 0.116 0.117
NO 1900 0.084 0.162 0.180 0.1816
What’s new?3) Methods for analysing {within-household} social
connections on large-scale and fine-grained data
…Focus on the individual outcome.. Model with occupation-based indicators
(plus random or fixed effects)
…Focus on the social connection.. Association models
• HISCAM (Lambert et al. 2012)• Chan (2010) on ‘status’ scales
What can we do with such data?a) Statistical models of occupation-based outcomes b) Statistical models of the association processc) Network depictions of prevalence of connections
Intergenerational HISCAM (all m-m) R
Canada 1871=0.57; 1881=0.47; 1891=0.46; 1901=0.43
Iceland 1801=0.41, 1901=0.07
Sweden 1900=0.37
Britain 1851=0.21; 1881ew=0.36; 1881s=0.30
USA 1850=0.30; 1860=0.33; 1870=0.33; 1880=0.31; 1900=0.33
Norway 1801=0.23; 1865=0.23; 1875=0.29; 1900=0.27
4060
8010
0S
on
40 60 80 100Father
Values Perfect fit Regression fit
Father-Son Social Mobility [HISCAM, microclass, N=45k]Canada 1891
13
(a) Model individual outcomes: Linear/random/fixed effects
(1) (2) (3) (4) (5) (6)
OLS (1)+fath HISCAM
(2) + f.e. HISCO
(2) + f.e. microclass
(2) + r.e. HISCO
(2) + r.e. microclass
Age (linear) 29.5 32.1 35.7 34.6 35.7 34.5
Female -120.9 -127.2 -128.6 -130.1 -128.8 -130.1
Jewish 7.9 7.5 7.1 7.0 7.1 7.0
Sami 1.6 1.8 2.2 2.1 2.2 2.1
Finnish -2.0 -1.7 -1.7 -1.9 -1.7 -1.9
Urban 36.6 32.3 18.7 19.6 19.0 19.8
Cohabits -19.6 -18.5 -16.5 -17.0 -16.5 -17.0
Father’s HISCAM
37.5 5.4 3.6 6.5
Rho 0.197 0.038 0.086 0.026
r2 0.109 0.119
Data: Sweden 1900, N=124238, Child HISCAM predicted by father’s HISCAM. T-statistics.
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(b) Association models‘Cambridge Social Interaction and Stratification Scales’ See www.camsis.stir.ac.uk/hiscam & Lambert et al. (2012) for historical data e.g.s
• Social Interaction Distance (‘SID’) analysis
• RC(II) model / Correspondence analysis
• First dimension of association can usually be labelled as ‘stratification’
Automated scales, selected scores onlyNorway, 1900 and 1801
Male score, 1900 Male score, 1801
• Main contribution of association models are to tell us about average social positions of the incumbents of occupations (and change over societies) 17
Canada Norway Scotland USA
Cases 123,749 54,067 261,187 22,349
Links 101 136 111 208
Microclasses (older cohort) 45 50 41 45
Microclasses (younger cohort) 35 38 39 41
Strongest bond (* times expectation) 239 146 19 55
Network: Degree centrality .10 .14 .12 .18
Network: Closeness centrality .23 .23 .27 .26
Network: Components 2 1 2 1
Network: Distance 10 12 7 5
Network: average distance 3.8 3.7 3.2 2.6
Note, for Canada and Scotland closeness centrality refers to largest component only.
c) Network analysis
Still looking at number of connections {within household} but change in emphasis on features of connections
Canada 1881
USA 1880
Scotland 1881
Norway 1876
Microclasses with ties *2 expected + non-sparse; male-male links if >16yrs age gap
Scotland 1881
Librarians (1305) and creative artists (1306) with links to printers (4104) and craftsmen
Housekeepers (4310)
Farming community (5201, 5202), forestry workers (4210) and gardeners (4312)
Managers (1202) and ships’ officers (1307) link to their subordinates (4306)
Clerks (3203) and agents (3102) interact with various professionals
Lawyers (1101), medics (1102), teachers (1304) and the clergy (1310) form a clique at centre of the network
Canada 1881
Ties not as obvious; sparse connections within mesoclasses, but stratification effects most observable
Farmers (5201) and farm labourers (5202) do not have mutual ties to forestry workers
Teachers (1304), clergy (1310), lawyers (1101) and medics (1102) have sparse ties
Clerical and sales workers (3***) strongly interact, but few ties to professionals (1***)
Librarians (1305) and creative artists (1306) don’t form any strong ties and aren’t represented
Food service workers (4304) are the ‘sons’ of many other routine workers
Housekeepers (4310)
Canada 1881 (left) with microclasses split by religion (red=catholic; white=non-catholic).
Clear division on religious grounds in 1881.
Canada 1891 (right) with microclasses split by religion (red=catholic; white=non-catholic).
Religious divide continues, but much more common for cross-religion and microclass households.
Canada (by religion) 1881 1891
Cases 92,048 22,084
% Roman Catholic 33.1% 28.6%
% Catholics with Catholic alter 84.1% 60.6%
% non-Catholics with Catholic alter 8.2% 17.4%
Mean HISCAM (All cases)(Standard deviation)
58.0 (10.9)
57.7 (11.4)
Mean difference in HISCAM (all cases)(Standard deviation)
9.2 (11.5)
10.1 (11.6)
% HISCAM difference<1/2 s.d.
…. (Catholic – Catholic) 52.0% 51.7%
… (non-Catholic to non-Catholic) 51.5% 49.3%
… (Catholic to non-Catholic) 45.5% 44.4%
% HISCAM difference>2 s.d.
… (Catholic to Catholic) 11.4% 16.6%
… (non-Catholic to non-Catholic) 12.8% 11.9%
… (Catholic to non-Catholic) 12.4% 11.8%
Summary: Social connections between occupations
• Connections are central to social organisation of the stratification system [e.g. Bottero 2005]
• Problems of data preparation and scale• Occupational coding – NAPP; HISCO; Microclass • Identify social connections (within hhld NAPP)• Select/discard some types of connections (e.g. farming)
• Analytical approachesModel with proxy indicators, random or fixed effects…Focus on the social connection..Association models Network analysis
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References cited• Bottero, W. (2005). Stratification: Social Division and Inequality. London: Routledge.• Griffiths, D., & Lambert, P. S. (2011). Dimensions and Boundaries: Comparative analysis of occupational
structures using social network and social interaction distance analysis Paper presented at the ISA RC28 Spring meeting, University of Essex, 13-16 April 2011.
• 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.
• Lambert, P. S., Zijdeman, R. L., Maas, I., van Leeuwen, M. H. D., & Prandy, K. (2012). The construction of HISCAM: A stratification scale based on social interactions for historical research. Historical Methods, forthcoming.
• Mayhew, H. (1862) London Labour and the London Poor. • Thompson, E. P. (1980[1963]). The Making of25 the English Working Class. London: Penguin.• Weeden, K. A., & Grusky, D. B. (2005). The Case for a New Class Map. American Journal of Sociology,
111(1), 141-212.Data from: • Minnesota Population Center. (2011). Integrated Public Use Microdata Series, International: Version 6.1
[Machine readable database]. Minneapolis: University of Minnesota, and https://international.ipums.org/ (accessed 1 July 2011).
• North Atlantic Population Project and Minnesota Population Center. (2008). NAPP: Complete Count Microdata. NAPP Version 2.0 [computer files]. Minneapolis, MN: Minnesota Population Center [distributor] [http://www.nappdata.org]