Before downloading the tables, users are strongly recommended to read:
| DOWNLOAD CAMSIS FILES | |
| version 1.2; author: Paul Lambert; released on: 1 April 2003 |
Data Russian Longitudinal Monitoring Survey, sweeps from 1992 and 1995 as used by the LIS project. Cases 4,800 Male-female within household working couples Occupational classification ISCO 1988, unit groups Status in employment No differences retained, reflecting reduced sample size Other comments 1) Sparsity of sample representation of occupational units
2) Russian exceptionalism in patterns of occupational gender segregation and subsequent impact on CAMSIS scores.
Notes: In a recent analysis, Alexey Bessudnov has generated a relational SID scale for contemporary Russian data using a similar, but not identical, approach as that of other CAMSIS scales. His analyses are described in:
- Bessudnov, A. (2009). An Occupational Status Scale for Russia. Oxford: Working paper 2009-02 of the Department of Sociology, University of Oxford.
- Bessudnov, A. (2012). A relational occupational scale for Russia. In P. S. Lambert, R. Connelly, R. M. Blackburn & V. Gayle (Eds.), Social Stratification: Trends and Processes (pp. 53-65). Aldershot: Ashgate.
Occupational Sparsity and Concentration
The original Russian data came from a very small sample by normal CAMSIS standards - the approximately 10,000 members of the Russian Longitudinal Monitoring Survey. The scale construction was based, in this case, not on only the both-working husband-wife couples found in that sample, but instead on all cross-gender both-working couples found in the same households at two of the panel waves (1992 and 1995), of which we obtained some 4,800 combinations. We do not believe that this different sampling frame makes any appreciable difference to the derivation of CAMSIS scores, and this method follows a strategy of boosting sample sizes from other smaller resources used for instance in the construction of a number of interim CAMSIS versions for the CHER datasets. The scale derivation proceeded at the level of analysis of ISCO88 4-digit units (there were at least some examples of ISCO88 minor groups containing many cases from more than one unit group). However very many ISCO88 unit groups were analysed when combined with other sparsely represented unit groups, and so the level of accurate occupational differentiation in score values achieved by this CAMSIS version is likely to be lower than in other examples.
The Russian data on male and female occupations exhibited marked patterns of occupational gender segregation. However the nature of this segregation differs from most other examples seen in the CAMSIS project. Whilst there is, as is common elsewhere, a basic male-manual v's female-non-manual trend, the strength of this pattern is greater in Russia than most other countries examined. More interestingly, this trend also overrides membership of even the most priviledged non-manual jobs, where in Russia the RLMS data suggest that women predominate in most professionalised white collar occupations (in most other countries, the manual/non-manual divide is offset by the propensity for the most advantaged non-manual jobs, in terms of renumeration and entry requirements, to be taken by men). The strength of this gender division has implications for the CAMSIS scale scores for Russia. The most significant pattern is the higher occurrence of male-female combinations which would be distant in most other examples (for instance, examples of female professionals married to a male manual labourers are widespread). Such examples have implications for the scoring of occupations under the CAMSIS methods, some of which can be seen from the table below which compares Russian with German ISCO88 CAMSIS scores.
| Comparison of Russia 1992/5 and Germany 1995 by ISCO88 Major groups (original CAMSIS samples of both-working couples) : Mean CAMSIS; correlation CAMSIS to ISEI; and population distribution | ||||
Males |
Females |
|||
Russia |
Germany |
Russia |
Germany |
|
| ISCO88 major group averages: | ||||
| 0. Armed Forces | 62.5 - 1.8 |
49.0 - 0.6 |
63.5 - 0.4 |
63.9 - 0.6 |
| 1. Legislators, senior officials and managers | 72.2 0.20 4.8 |
57.9 0.26 9.0 |
55.9 -0.64 2.1 |
56.7 0.71 3.8 |
| 2. Professionals | 78.1 0.39 11.8 |
74.2 0.40 9.0 |
70.0 0.42 22.6 |
72.4 0.17 10.8 |
| 3. Technicians and associate professionals | 59.6 -0.11 5.8 |
54.9 0.11 16.1 |
51.0 -0.06 22.2 |
52.7 0.26 26.9 |
| 4. Clerks | 59.3 -0.62 1.3 |
50.1 0.60 7.0 |
46.6 0.60 12.5 |
57.7 0.56 22.1 |
| 5. Services, shop and market trades workers | 55.7 -0.80 3.7 |
46.8 0.01 4.6 |
44.2 0.49 8.7 |
44.3 -0.41 19.5 |
| 6. Skilled agriculture and fisheries workers | 55.0 - 0.5 |
44.1 0.03 3.1 |
36.9 - 0.1 |
31.1 0.05 1.9 |
| 7. Craft and related trades workers | 43.6 0.55 30.7 |
40.4 0.41 28.0 |
38.6 0.40 8.0 |
34.7 0.49 3.7 |
| 8. Plant and machine operators and assemblers | 40.5 0.25 31.0 |
32.5 0.41 10.9 |
37.9 -0.36 7.5 |
27.2 0.32 2.6 |
| 9. Elementary occupations | 41.3 -0.64 0.85 |
33.5 -0.10 4.6 |
36.3 0.09 16.1 |
21.3 0.75 8.7 |
| Total Sample N | 4811 |
52952 |
4811 |
52952 |
| CAMSIS Eta-2 by major group | 0.78 |
0.80 |
0.67 |
0.80 |
| ISEI Eta-2 by major group | 0.90 |
0.90 |
0.88 |
0.80 |
The column percents illustrate Russia's greater gender segregation than Germany (remember that Germany itself is often characterised as a state with greater gender segregation than most in Western Europe). The mean CAMSIS scores by major groups can be read for two trends : amongst men and women the traditionally manual ISCO major groups 7-9 have higher average scores in Russia, and, amongst women, non-manual major groups 2-5 have slightly lower scores than Germany. These averages reflect the net lesser role of manual/non-manual gender segregation in structuring the derived Russian scores (that is, because segregation is so strong, the differentiial social association patterns modelled by the Russian scale derivations are prone to cross-cut the manual/non-manual division more often than in Germany). The Eta-squared statistics are consistent with a similar pattern - for women in particular, the Russian scores are less strongly associated with the ISCO88 major group structure than in Germany, whilst the Russian CAMSIS scores for both genders connect more weakly with the ISCO88 major group structure than do the cross-nationally derived ISEI scores. Lastly, many of the consequences of the unusual patterns of Russian gender segregation are most visible in the finer details of differences between particular occupational unit group scores. The correlations between CAMSIS and ISEI scores within ISCO88 major groups illustrate the considerable variation present in different situations, although low sample numbers my adversely effect the iterpretation of some values. Overall, both the Russian and German populations, for both men and women, exhibit correlations with ISEI of the order of 0.8; however in comparison with Germany, internal Russian differences from ISEI scores are more erratic, suggesting differential influences in defining the two sets of scores.
Back to National Versions.
This document is maintained by Paul Lambert.