Germany : Camsis Report

CAMSIS Project Report : German occupational unit score constructions, ISCO-88 using 1995 data and KldB 75 using 1991 data

Last Update: 16.8.02

Contents (19pp): Page 1) Summary: available CAMSIS German files 2 2) Issues in scale construction process 5 3) Features of the constructed scales 11 4) Scale value correlates and associates 15 References 19

Citation: Please use the following to cite this document and any other material from within the contents of the associated CAMSIS webpages:

Prandy, K. and Lambert, P.S. (2002) CAMSIS Project Webpages http://www.cf.ac.uk/socsi/CAMSIS : Cardiff School of Social Sciences, Cardiff University (accurate at [insert date])

Information to contact the authors of this document can be found on the front page of the CAMSIS webpages, and details of the researchers involved in the production of the German CAMSIS report obtained by following the links from the ‘National Versions’ page of the website.

1 1) Summary: Available CAMSIS German files

The CAMSIS (Cambridge Social Interaction and Stratification Scales) project involves assigning scale values to occupational units which describe the location of an occupation within a general social order reflecting social stratification. Many different CAMSIS scales have been derived for different countries; this report discusses the results of scale derivations for contemporary Germany.

Since March 2002 it has been possible to download a zip archive, named ‘GermanyCAMSIS9195v1.zip’, from the CAMSIS project website,

www.cardiff.ac.uk/socsi/CAMSIS/

This archive (accessed by following links to the ‘national versions’ page, then the pointers to Germany), serves as the distribution file for the estimated CAMSIS scale scores appropriate to German data and occupational classifications. At time of writing, the latest version of this archive file, named version 1.2, had been released on 28.7.02.

A ‘readme.txt’ file in the archive serves as a general guide to the contents of the archive, which include SPSS data and syntax, plain text data, and Microsoft Excel files to link German occupational classification schema unit values with corresponding CAMSIS scores. Other pages in the CAMSIS website cover general introductions to the CAMSIS approach and generic information on the derivation and interpretation of CAMSIS scales. This report documents the construction and nature of the derived scale values in further detail.

In essence, the CAMSIS scale construction process works by analysing patterns of social interaction between the incumbents of occupational units, and using those patterns to assign values to the occupational units themselves. Those values reflect each unit’s position within the structure of social associations. Further discussion can be found in the CAMSIS project webpages and the references highlighted by them, but the end result is the generation of a score value for each occupation which is interpreted as an indicator of that occupation’s relative location within an order of social interaction and stratification.

Separate sets of CAMSIS scores have been derived for different countries but, in addition, varying data permutations have led to the derivation of different score structures within countries. Such different scales are referred to as different CAMSIS ‘versions’. Within countries we have typically distinguished three sets of different versions – different scores are assigned to occupations depending on the gender of their incumbents; the time period to which the source data applies; and the level of detail available on the occupational units. At this stage the available CAMSIS scores refer only to contemporary Germany, being based on datasets from 1991 and 1995. However the downloadable archive contains a total of 12 alternative scale score schema, illustrated in Table 1. These reflect differences in gender, the type of base occupational units to which individuals have been classified, and the level of information available on ‘employment status’, where the latter distinguishes up to 7

2 different categories for any given occupational unit incumbent, as listed in Table 1. Note that the 12 versions correspond in the zip archive with the 2 sets of SPSS data index files with 6 variables each; the 6 sets of Microsoft Excel summary files with 2 variables each; and the 12 SPSS syntax translation files covering 1 variable each.

Table 1: 12 CAMSIS versions for Germany, and variable names used to indicate score variables in the index files of GermanyCAMSIS9195v1.zip

Gender Occupational Level of Employment status detail unit scheme title-only title-by- title-by- status(4) status(6) t s r Male h KldB 75 k hktcs hkscs hkrcs ISCO-88 I hitcs hiscs hircs

Female w KldB 75 k wktcs wkscs wkrcs ISCO-88 I witcs wiscs wircs

Employment status categories: 9 only 1,2,3,5,9 1,2,3,4,5,6,9 (5=4+5+6) of r 1: Self-employed no employees 2: Self-employed with employees 3: Family assistant 4: Public service 5: Employee 6: Worker (non-contract) 9: Unknown status

It is generally expected that the most accurate CAMSIS measures are those with the greatest level of employment status detail, but equally, there is in all cases a strong correlation between scores across different employment status versions. Indeed, new users may find it simplest to begin with the ‘title-only’ version scores.

In order to use the CAMSIS scores on relevant occupational data, users should establish which version/s is/are relevant to their dataset, and match the appropriate scores from the appropriate occupational unit variables. Brief instructions on how to achieve this can be found in the ‘readme.txt’ files within the archive, and more general information, with discussions, can be found on the CAMSIS webpages, in particular a page dedicated to practical instructions on the use of CAMSIS scores,

http://www.cf.ac.uk/socsi/CAMSIS/useofscores.html

In particular, it is worth highlighting that different CAMSIS score versions have been assigned to male and female occupations. The implication is that the CAMSIS measures are most appropriately utilised within single gender subpopulations, since the scores reflect an occupational order within the gender group. However, because the scale scores within gender groups have been parameterised to equivalent distributions, the scores representing relative position within the gender order can

3 reasonably be combined for a mixed gender population – a derivative variable, where men have been assigned the appropriate male scores, and women the appropriate female scores, is thus defensible. (Further notes on this issue can be found on the above webpage).

We would also add an important qualification specific to the German data with regard to the use of the ‘title-by-status(6)’ version. This is to recommend that the tbs(6) scores should really only be used when sufficient information to distinguish the full range of employee categories (4,5,6) is available to end users. This qualification is significant because, if only partially complete employment status information is used (say, for example, identifying public sector workers ‘4’ but merging all other employees as the category ‘5’), we make a false assumption of equivalence between some of the employee categories, when in fact the CAMSIS scores of the tbs(6) versions differ markedly between the three employee categories.

Aside from encouraging users to match CAMSIS scores onto their occupational data, we have also taken some steps to match appropriate scores with pre-existing datasets. At time of writing (30.7.02) a file has been supplied to the LIS (‘Luxembourg Income Study’) project, which connects CAMSIS scores to information on the German studies included in LIS (www.lisproject.org). Some documentation on this is included on the CAMSIS webpage

http://www.cf.ac.uk/socsi/CAMSIS/lisles.html

Further linkages with other major datasets may be reported in the future.

4 2) Issues in scale construction process

The German CAMSIS scores were estimated from patterns of husband-wife associations between occupational incumbents, for both-working adult couples in Germany in 1991 and 1995. The data were obtained from the Micro-censuses, which are nationally representative samples, of those years. Approximately 87,000 both- working couple units were analysed from the 1991 dataset, and 50,000 from the 1995 dataset. The former had occupational information at the level of the ‘KldB 75’ occupational unit schema, which is a nationally specific classification. Further details of this classification are available from the CAMSIS webpages (including an English translation) and the German GESIS data archive, for instance http://www.gesis.org/Dauerbeobachtung/Mikrodaten/daten/abteilungsdaten/mikrozen sen/mz_1991/kldb75_91.htm

The national classification is little used by German researchers, so we also used the 1995 dataset which codes occupations, additionally, into ISCO-88(Com) units (ILO 1990). These datasets were used to derive German CAMSIS versions with scores for their respective occupational units (see Table 1).

Both datasets also included employment status information for each individual, and this was used to varying levels of detail, producing the three alternative versions described in Table 1. The reason for this multiplicity is that, although we expect the greatest degree of accuracy from using the fullest information, we also have to cater for users who may have less employment status information available to them.

The sample sizes available for the scale construction versions were relatively low by the standards of other countries for which CAMSIS scores have been derived. An important objective in the CAMSIS project is to produce scale scores for a very fine level of occupational detail, maximising the information from the dataset and, in principle, the strengths of the derived scales. CAMSIS scales are therefore estimated on patterns of social interaction between occupations classified at the finest possible level of occupational unit detail. In the German case, there were potentially 927 KldB occupational units represented in the sample, and 536 ISCO-88 units. When cross- classified by the up to 7 possible employment status positions, these generate 6489 and 3752 possible occupational units respectively. However, it is also a general principle of CAMSIS that any uniquely identified occupational unit is adequately represented in the sample: the general rule of thumb is that there should be 20 or more cases to represent any given unit. Clearly, the sample sizes available cannot allow this requirement to be met with either schema. Our solution, which is the one generally adopted for CAMSIS, is to merge sparsely represented occupational units with their near neighbours. Such ‘neighbours’ are identified by utilising the hierarchical coding element (industrial groupings or skill level) that are found in most occupational classifications and, additionally, using results from preliminary CAMSIS models for the unique categories; further methodological details are available on the CAMSIS project webpages. The end result for the German datasets were models for the patterns

5 of social interaction between the number of occupational units illustrated in Table 2 for the relevant versions.

Table 2: Number of unique occupational units modelled per CAMSIS version (male and female categories equivalent)

Data / Version # Occ Units # Couples in sample

1991 KldB Title-only 290 86,627 1991 KldB Title-by-status(4) 379 86,627 1991 KldB Title-by-status(6) 496 86,627

1995 ISCO88 Title-only 100 50,952 1995 ISCO88 Title-by-status(4) 186 50,952 1995 ISCO88 Title-by-status(6) 262 50,952

Scale scores were derived for these unique units. Then, to produce the final CAMSIS scales and index files, which show scores for every possible occupation/employment status combination, scores were additionally assigned to those units not uniquely represented in the initial model. This was done primarily by imputation on the basis of occupational unit subgroup mean scores (for further details on the general method, again see the CAMSIS project webpages).

The starting point in the modelling process for each version is the cross-tabulation of husband and wife occupational units across the whole sample. This leads to a very large, and very sparse, matrix, which is analysed using Goodman’s ‘RCII’ Association models (Goodman 1979). Using the package lEM (Vermunt 1997), we find that the large size of the tables is not a hindrance to the model estimation. The procedure is one in which row and column ‘scores’ are imputed, which serve to predict the frequency of cases in each of the cells of the husband-wife cross- tabulation. These imputed scores become the direct source of the CAMSIS occupational unit derived scores (illustrated in Figure 1).

Figure1: Illustration of the data matrix analysed to derive CAMSIS scores (the derived score values improve the prediction of the cell frequencies)

Husband’s Job Units Occ Units ↓ → 1 2 .. 496 Imputed scores ↓ → 75.0 70.0 .. 10.0 Wife’s 1 72.0 30 15 .. 0 Job 2 72.5 13 170 .. 1 Units ...... 496 11.0 0 2 .. 80 (cell frequencies for each combination)

6 Again, further methodological details are available on the CAMSIS project webpages, the most noteworthy point being that the scale scores available in the CAMSIS index files are a linear transformation of the ‘raw’ scores estimated by the RCII model. For every CAMSIS version, the scores made available have been standardised to have a mean of 50 and standard deviation of 15 in a nationally representative population of working couples; they have also been truncated to one decimal place, and ‘cropped’ to fit within the range 1.0 to 99.0. (The very few scores outside that range are adjusted to the limiting value).

The exact formulation of the RCII models used to estimate the CAMSIS scores involves a little more manipulation, however. As with CAMSIS estimations in other countries, the form of the model used incorporated an allowance for two supplementary features, referred to in our terminology as ‘pseudo-diagonal’ and ‘subsidiary dimensional’ effects. The key point is that the main (dimension of) scores estimated applies to the whole national population of husband-wife combinations. However, in Germany as elsewhere, there are additional factors that are seen as encouraging particular husband-wife combinations, but which are theorised as being specific and relatively trivial, rather than an aspect of a general influence of hierarchical position, on which the CAMSIS methods are premised.

The first factor involves those combinations, referred to diagonal and ‘pseudo- diagonal’ (‘PSD’s), where husbands and wives are particularly likely to hold exactly the same occupational unit titles, or else directly connected unit titles, as a result of joint business undertakings or institutional links. Examples of such combinations prominent in the German data include husband farmers married to wife agricultural workers, and husband medical doctors married to wife medical receptionists. Whilst the source of such combinations is felt to be relatively trivial, their empirical consistency is such that, unless specifically taken into account, they would dominate the nature of the RCII score estimates. To avoid this, we estimate specific parameters for each such identified combination through a design matrix specification, the net result being effectively to exclude those cases from influencing the score estimation process. In Table 3 below, our list of final RCII models used includes a figure showing the number of husband-wife combinations excluded from influence in this way in the final model. As we see, and as in many other CASMIS scale derivations, the proportion of cases treated as PSD’s is relatively high in each version, but we nevertheless believe that the CAMSIS method is robust to any such loss of data.

The second relevant influence, that of subsidiary dimension structures, is of substantive interest, whilst having only minor practical effect on the (primary dimensional) scale scores derived. In many cases we find examples where, aside from the first dimension of estimated CAMSIS scores, there are empirically smaller dimensions of association related to more specific features of the occupational units, such as employment status or occupational subgroup clusters. It proves possible to estimate separate dimension scores for these structures, typically constraining all scores within the cluster to be equal. Table 3 below, which shows details of the final RCII models used for the German versions, includes indicators of the influence of such subsidiary dimension structures. The ‘association statistics’ shown can be read as

7 an illustration of the relative magnitude of the strength of each dimension, within the version model, in predicting the frequency of husband-wife combinations.

Table 3: Final RCII models used for the 12 German CAMSIS versions

Version # total # PSD Dimension structures couples Dim 1 Dim 2 Dim 3 (dimension association statistic)

KldB Title-only 86,627 12,150 occ title majgp (198.5) (22.1) KldB Title-by- 86,627 13,084 occ*status status submaj. status(4) (259.7) (86.5) (58.3) KldB Title-by- 86,627 12,235 occ*status status submaj. status(6) (291.9) (107.2) (83.4)

KldB dimensions: occ title and occ*status: base units of analysis; majgp : Occupational title ‘major group’ (5 categories of occ unit classifications); status: employment status differences only (4 or 6 groups); submaj : occupational title ‘submajor group’ (32 categories of occ unit classifications).

ISCO88 Title-only 50,952 7,698 occ title majgp (81.8) (6.9) ISCO88 Title-by- 50,952 8,708 occ*status status majgp status(4) (129.8) (83.3) (16.7) ISCO88 Title-by- 50,952 7,829 occ*status status majgp status(6) (169.1) (51.9) (32.4)

ISCO88 dimensions: occ title and occ*status: base units of analysis; majgp : Occupational title ‘major group’ (10 categories of occ unit classifications); status: employment status differences only (4 or 6 groups);

As mentioned above, we see from Table 3 that a relatively high number of cases in each sample were treated as ‘pseudo-diagonals’ and effectively excluded from influencing the model of social association patterns. The cases excluded can be identified by their occupational titles in both the index data and Excel files, contained in the zip archive (the variable names {..}tot and {..}use indicate the number of cases which represented each occupational unit in the original sample (‘tot’), and the number of cases which were not excluded as pseudo-diagonals (‘use’)). For any given version, it is then simple to calculate the proportion of cases from an occupational unit which were treated as PSD’s. If they are checked, users should in general find inspection of these values to be reassuring, insofar as they show that the bulk of occupational units are represented in the final model by the large majority of their original cases. On the other hand, for a minority of occupational units this is not the case; Table 4 below highlights some of the most extreme examples, of occupational units in different versions where the majority of their incumbents are in occupational combinations which were treated as pseudo-diagonals. These circumstances are discussed in greater detail on the CAMSIS webpages (see the section ‘confounding

8 influences’ on the ‘overview’ page for the ‘construction’ section, and also the section on ‘statistical analyses’ linked from the detailed construction notes page). We argue that relatively consistent reasons for the large degree of exclusion can be given or inferred in each specific case when it occurs, and that the negative impact on the interpretation of the derived scoring values is usually small, especially if an analysis is being undertaken where information on a partner is known in combination with the individual. It is, nevertheless, worth reiterating the CAMSIS assumption used in these scale constructions, that the derived CAMSIS score of an occupational unit need not reflect the circumstances of every incumbent of that occupation.

Table 4: Selected occupations with unusually high proportion of cases treated as pseudo-diagonals, by version (title-by-status(4) and -(6) almost equivalent)

Male occupations Female occupations Versions: (proportion of cases used, ie not pseudo-diagonal) (average proportion used, by occ units1, m / f) KldB Title only Farmers (0.15) Farmers (0.21) (0.93 / 0.91) Restaurateurs (0.21) Farm workers (0.29) Doctors (0.31) Doctors (0.33) Retail trades (0.40) Vehicle drivers (0.33) KldB Title-by-status(6) Self-Emp farmers (0.09) Self-Emp farmers (0.08) (0.94 / 0.93) Self-Emp restaurateurs (0.11) Self-Emp restaurateurs (0.14) Self-Emp bakers (0.22) Employee doctors (0.28) Civil servant doctors (0.26) Family assist land worker (0.29)

ISCO Title only Farmers (0.18) Business professionals (0.17) (0.92 / 0.92) Business professionals (0.31) Farmers (0.20) General managers (0.46) Farm workers (0.25) Health professionals (0.47) Manual labourers (0.43) ISCO Title-by-status(6) Self-Emp farmers (0.07) Family assist cashier (0.05) (0.92 / 0.94) Self-Emp food processers (0.19) Worker farming (0.11) Worker textiles (0.21) Worker caretaker (0.14) Civil servant teachers (0.23) Employee farming (0.16) Semp General manager (0.26) Civil servant teacher (0.33)

Identifying each specific combination which was treated as pseudo-diagonal is slightly more complicated, since there are typically several hundred examples for each version model (although users may note that only a few, large, PSD combinations tend to be the most important). In producing the scale score models, we have created and saved files in SPSS syntax and plain text formats which can be used to identify each relevant combination. We anticipate that most users will not require these specific details and suggest that any who do should contact the authors for further details2. Table 4 does, however, indicate which PSD combination are the most influential – couples where both jobs are in farming occupations, joint enterprises, and 1 These figures are not the population proportions (which can be derived from the data in table 3), but the unweighted average proportion of each value for each occupational unit. 2 At 5.8.02 the relevant files are stored by Paul Lambert, [email protected]

9 institutionally linked combinations. An important point to note is that the patterns of PSD combinations used for Germany are very similar to those applied for every other CAMSIS version produced to date (for contemporary Britain, the US, Switzerland, Sweden, Turkey, Ireland, Mexico and Vietnam) : over the course of the CAMSIS project we have observed, overwhelmingly, cross-national similarity in pseudo- diagonal structures.

10 3) Features of the constructed scales

In the first instance, our own interpretations of the CAMSIS scales derive from inspection of the individual scores assigned to particular occupational units within versions. We would strongly encourage other researchers to undertake equivalent evaluations by downloading and examining the various index files, and we would anticipate that comparisons of interpretations would lead to more accurate descriptions. In brief, our interpretations, though a qualitative review that is not easily communicated, are that the CAMSIS scale scores, essentially similar across the 12 different German versions, all suggest an order of social interaction locations that follows a clear hierarchy of social disadvantage / advantage. Indeed, this interpretation is one justification for our equating the CAMSIS social interaction patterns with images of social stratification. The CAMSIS scores appear to be clearly aligned with educational credentialism and income returns (higher positive CAMSIS scores reflecting greater advantage) and we also see that the bulk of manual occupations have lower, and the bulk of non-manual occupations higher, CAMSIS scores.

Table 5 below shows some summary distribution statistics for the 12 German CAMSIS versions when weighted to a national population proportion (the microcensus samples used in the scale derivation). For illustration, Figure 2 also shows the general distribution of four of those versions.

Table 5 : Descriptive statistics on the 12 German CAMSIS versions (original census subsample of couples)

N Min Max Mean Std. Dev. Skewness Kurtosis KldB 75 Occupational Units HKTCS 86,627 10.4 99.0 49.9 15.0 0.72** 0.54* WKTCS 86,627 5.0 92.3 50.0 15.0 -0.18** -0.23* HKSCS 86,627 14.7 99.0 50.4 14.5 0.69** 0.21* WKSCS 86,627 7.8 89.3 50.1 14.9 -0.39** -0.34* HKRCS 86,627 1.0 99.0 49.9 14.9 0.90** 0.80 WKRCS 86,627 5.9 86.9 50.0 15.0 -0.41** -0.56* ISCO-88 Occupational Units HITCS 50,952 20.5 98.4 49.9 15.0 0.59* -0.13* WITCS 50,952 5.9 93.7 50.0 15.0 -0.19* -0.02* HISCS 50,952 20.0 99.0 50.3 14.7 0.65* 0.03* WISCS 50,952 4.3 87.7 50.1 14.9 -0.54* 0.27* HIRCS 50,952 21.1 96.1 50.0 15.0 0.57* -0.28* WIRCS 50,952 1.0 85.2 50.0 15.0 -0.72* 0.73*

** / * : Significant to 99% / 95% criteria

11 Figure 2: Histograms of the distribution of German CAMSIS scores for 4 example versions 'HKTCS' : Male KldB Title only, 'WKTCS' : Female KldB title-only

Germany 1991 CAMSIS scores Germany 1991 CAMSIS scores 20000 30000

10000 Std. Dev = 14.98 Std. Dev = 15.01 Mean = 49.9 Mean = 50.0 0 N = 86627.00 0 N = 86627.00 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 15.0 25.0 35.0 45.0 55.0 65.0 75.0 85.0 95.0

Distribution of scores through microcensus sample Distribution of scores through microcensus sample

'HKRCS' : Male KldB Title-by-status(6) 'WKRCS' : Female KldB Title-by-status(6)

Germany 1991 CAMSIS scores Germany 1991 CAMSIS scores 20000 20000

10000 10000

Std. Dev = 14.89 Std. Dev = 15.01 Mean = 49.9 Mean = 50.0 N = 86627.00 0 0 N = 86627.00

Distribution of scores through microcensus sample Distribution of scores through microcensus sample

Table 5 and Figure 2 highlight a number of features of the German CAMSIS scales. The most significant is that all versions have equivalent mean parameters (by design), with a wide spread across the range of values and, loosely, a normal distribution. We see, however, two distinctive trends in how the version scores begin to diverge from normality. First, for men, we see repeated examples of significant positive skew (which also manifests itself in the tendency for the range of values to start with a higher minimum). For women on the other hand, skew is more likely to be negative, and, more significantly, there is a high degree of ‘clumping’ to the score distributions, reflecting how women’s occupations in particular tend to be concentrated within relatively few occupational units3. A better purchase on the CAMSIS scores can be achieved through individual inspection of the score values assigned to each occupation, and we would encourage users to do this for the downloadable scales accessible from the German zip archive.

A contention of the CAMSIS research, across national versions, has been that the location of occupations within a social space fits a structure of even gradation, better

3 This is, of course, also an argument that base occupational units for Germany could be better designed to discriminate between types of work most common amongst women.

12 than one of (low-number) categorical class clustering. From many perspectives, the German labour market may be expected to be more sharply structured into categories than other examples, because German employment has often been characterised as rigidly defined around skill and qualification level boundaries (eg Esping-Anderson 1990, Ishida et al 1995, Li & Singelmann 1999). However, the distributions of the estimated scores reported here follow very similar patterns to those of CAMSIS score distributions for other countries, and, moreover, do not unambiguously coincide with conventional class categorisations (closer inspection of score values for different occupations reveals a mixture of locations). For such reasons we would suggest that the CAMSIS distributions can be regarded as metric locations, particularly so if less emphasis is placed on the extreme clustering of female occupational unit locations.

Table 6: Pearson’s correlations between CAMSIS versions by occupational units (Base units: title-only; title-by-status(4) (‘tbs(4)’); title-by-status(6) (‘tbs(6)’) ).

KldB 75 units, N = 86,627 Males Females tbs(4) tbs(6) tbs(4) tbs(6) title-only 0.95 0.87 title-only 0.96 0.92 tbs(4) 0.91 tbs(4) 0.93

ISCO-88 units, N=50,952 Males Females tbs(4) tbs(6) tbs(4) tbs(6) title-only 0.95 0.87 title-only 0.96 0.89 tbs(4) 0.94 tbs(4) 0.93

Table 6 illustrates the relatively small level of differences between the scores assigned to occupational units in the different CAMSIS versions defined by occupational unit information. Nevertheless, the correlations shown are actually slightly lower than those for other CAMSIS examples (Britain, the US, Sweden and Switzerland have been similarly assessed). This might suggest that employment status differences make a bigger difference to social stratification location in Germany than elsewhere. The relative size of the association statistics linked to a subsidiary employment status dimension in Germany (Table 3) was greater than in several other CAMSIS countries.

The first column of correlations in Table 7 shows the strength of relationship between estimated CAMSIS scores for men and women within couples. These values, which are broadly consistent between versions, also largely coincide with values found for those other Western societies for which CAMSIS scales have been derived (also reported in an overview paper given to the ISA 2002 Brisbane meeting, which is downloable from the CAMSIS website. These correlations, also illustrated by Figure 2, reflect the fundamental premise of the CAMSIS approach, that couple formation tends towards homogamy by social stratification position (cf Kalmijn 1998, Smits et al 1999).

13 The second column of correlations in Table 7 shows how closely the scores assigned to occupational units are related between the genders. Although the values are generally high, suggesting similar orders for men and women, these associations are relatively low in comparison with other CAMSIS versions, particularly those for the difference between the schema based on KldB occupational units (see for instance table 3 of ISA 2002 paper referred to above). These values suggest that the difference in social locations of occupational incumbents by gender are more marked in Germany than in other (Western CAMSIS) countries, a finding which is consistent with descriptions of Germany’s relatively pronounced, ‘conservative’ gender employment regimes (eg Lane 1993). The correlations also suggest that the KldB schema is better at representing gender differentiation in Germany than is the ISCO88 schema.

Table 7: Male-female correlations in German CAMSIS versions

Within couples Within occupational units (weighted by (weighted by average m + f Version microcensus sample) within unit) Pearson correlations 1991 KldB Title-only 0.465 0.796 1991 KldB Title-by-status(4) 0.424 0.672 1991 KldB Title-by-status(6) 0.448 0.719

1995 ISCO88 Title-only 0.447 0.879 1995 ISCO88 Title-by-status(4) 0.433 0.806 1995 ISCO88 Title-by-status(6) 0.440 0.734

Figure 2: Illustration of intra-couple correlations in German scores Male v's Female German CAMSIS scores

KldB title-only within couples 100

S I 80 S M A

y l n o - 40 e l t i t

e l 20 a m e 0 F 0 20 40 60 80 100

Male title-only CAMSIS

Cases from 1991 microcensus sample

'Sunflower' plot: circle / petal represents up to 25 cases

14 4) Scale value correlates and associates

A more comprehensive assessment of the German CAMSIS version scores, however, is likely to come through assessments of their patterns of association with other factors which are themselves expected to relate to social stratification differences. Tables 8, 9 and 10 below briefly collate some association, all based on ISCO88 occupational group definitions. (In the longer term we would hope to extend these investigations more completely).

Table 8: Summary associations for German (ISCO88) CAMSIS versions All versions mean 50, standard deviation 15 in a nationally representative population. See the overview paper “National Contexts..”, given to the ISA Brisbane 2002 congress and downloadable from the CAMSIS webpage, for these figures in international comparison. Data : ISSP 1992 (~570 M, 360 F)3

Survey sample associations with own CAMSIS measure correlation1 Eta1 Father’s ISEI2 Income2 Education2 CAMSIS (earned)

Title-only Male 0.35 0.87 0.39 0.63 Female 0.41 0.77 0.44 0.64

Title-by-status(4) Male 0.34 0.83 0.38 0.60 Female 0.43 0.79 0.43 0.63

Title-by-status(6) Male 0.29 0.66 0.40 0.50 Female 0.40 0.69 0.41 0.60

All summary statistics significant to 99% probability criterion Survey sample sources:. ISSP studies (eg Smith 1992) obtained from the Zentralarchiv, University of Cologne. 1) Pearson’s correlation value for continuous relations; Eta-statistic for relation from continuous to (3 category) qualitative schema. Weighted by number of individuals. 2) ISEI: Occupational unit’s ‘socio-economic status’ score (Ganzeboom et al 1992), translated from ISCO-88 via Ganzeboom and Treiman (1992). Income: log of personal earned income. Highest educational attainment: schematised into 3 categories approximating : “low school level”, “intermediate”, “college or university”. 3) CAMSIS scores were matched via a translation from the ISSP92 unit (ISCO-68) to that unit used in the CAMSIS project (ISCO-88) using Ganzeboom and Treiman (1992). There was very little information available on employment status, so most title-by-status scores are those for the ‘unknown’ employment status category

15 Table 9: Comparison of CAMSIS associations, working adults of Luxembourg Income Studies CAMSIS title-only versions. LIS studies contain random sample of individuals, plus information on their household sharers, including spouse and ‘Household Head’, see www.lisproject.org for documentation.

Germany US 1991 UK 1991 Switz. 1992 1989 m f m f m f m f Approx N: 2,200 1,300 10,000 8,850 3,400 3,000 1,700 1,000

Eta-squared statistic for category association Highest Education level1: -Self 0.35 0.18 0.34 0.26 0.23 0.20 0.27 0.18 -Household head 0.33 0.17 0.28 0.16 0.22 0.14 NA NA -Spouse 0.12 0.19 0.13 0.20 0.11 0.14 NA NA Ethnic group2 0.10 0.11 0.03 0.03 NA NA 0.03 0.03 Housing tenure3 0.03 0.02 0.02 0.01 0.08 0.06 0.01 0.00* (household) Correlation with higher CAMSIS scores Age in years 0.10 -0.12 0.18 0.06 0.04 -0.06 0.07 -0.10 Wages in national currency (LIS variables): -Own wage 0.36 0.29 0.49 0.42 0.40 0.54 0.26 0.23 -Household head 0.33 0.13 0.43 0.29 0.38 0.27 0.26 0.19 -Spouse 0.15 0.31 0.22 0.37 0.30 0.51 0.03* 0.19 Net household 0.21 0.14 0.35 0.29 0.29 0.23 0.13 0.16 disposable inc.

All estimates, using unweighted data, significant to 99% probability criteria unless indicated by ‘*’ 1. Highest educational level: 3-category schema, as for table 8. 2. Ethnic group categorisation: US: 5 categories, identity; Germany: 5 categories, nationality; Switzerland: 3 categories, nationality. (Schema derived as those of Lambert & Penn 2001) 3. Housing tenure: Dichotomy: Renting v’s Owner (US, Germany, Switz.); Social v’s Private (UK).

16 Table 10: Social Inequality and CAMSIS: Life background, situation and attitudes from selected ISSP surveys, Germany in comparative perspective Correlations and associations with ISSP variables, categorical variables recoded for parsimony. ISSP, representative random samples within countries : www.issp.org. Figures for mixed gender populations. Germany USA UK Switz. Sweden 1999 1999 1999 1999 1992 Approx n : 700 840 400 450 650

Continuous correlates Pearson’s correlation with CAMSIS, all working adults†

Father’s CAMSIS 0.39** 0.32** 0.17** 0.31** S: Position in society, near top 0.33** 0.25** 0.39** 0.30** 0.27** S: `` , 10 years ago 0.27** 0.20** 0.28** 0.16** S: [Position now] – {0.02} {0.00} 0.09 0.11* [Position 10 yrs ago]

Categorical associations Eta association statistics with CAMSIS, all working [labels]: Categorical options adults†, label indicates category associated with highest CAMSIS scores

Own highest education 0.62** 0.50** 0.57** 0.49** 0.61** [low / intermed. / coll., university] univsty. univsty. univsty. univsty. univsty. Father’s highest education 0.27** 0.21** 0.31** [low / intermed. / coll., university] univsty. univsty. univsty. No. books in house as child 0.30** 0.22** [below 10 / 20-50 / 100 or more] 100+ 100+ S: Subjective social class 0.45** 0.35** 0.19** [working / middle or upper] middle middle middle S: Family position in society 0.30** 0.27** 0.26** [near top / middle / near bottom] near top near top near top S: Own job level cf father’s 0.23** 0.09 0.24** {0.05} 0.18** [better / about same / worse] better better better better Union member 0.08* {0.07} {0.02} {0.07} [union member / not] not mem. S: Political leanings {0.07} 0.13** 0.23** 0.17** [Left / centre / right / none] no; right right left Greater pay should be for: S: ..responsibility 0.10* 0.11** 0.12* 0.08 0.11** [Yes / No] yes yes yes yes yes S: ..education / training {0.03} {0.02} {0.06} {0.00} {0.06} [Yes / No] S: ..doing the job well {0.03} 0.06 0.14** {0.04} [Yes / No] yes yes S: Is own pay just 0.17** 0.11 0.15* [too low / about right / too high] right/high too high right Eta statistic with [Own CAMSIS] – [Father’s CAMSIS]

S: Own job level cf father’s 0.45** 0.27** 0.25** 0.46** [better / about same / worse] better better better better

S: Subjective response to attitudinal question. * / ** / {} : association statistic estimated as significant to 95 / 99 / less than 90% probability criterion † : Statistics for men and women combined, assuming relative comparability of CAMSIS distributions.

17 Tables 8 to 10 suggest, primarily, that the German CAMSIS scale scores exhibit many of the expected patterns of a measure of social stratification and, moreover, follow approximately the same patterns as the CAMSIS versions of other societies. There are a few exceptions. For example, the lack of significant association with voting preferences and, for the LIS dataset only, the relatively low correlation with female educational levels, are unexpected. However, these aside, the data of the tables constitute a vindication of the CAMSIS measures for Germany.

The reason for constructing the German CAMSIS scores is to provide research users with an occupational index code which can inform analyses of social stratification. In this respect the success or otherwise of the versions described above will depend on the extent to which other users adopt and assess the versions. The early evidence would suggest that the CAMSIS scores have a number of properties and associations desirable for just such an analysis, but more detailed assessments in the light of particular features of the German labour market would provide a sterner assessment.

18 References

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