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The Swedish validation study part II; using ESS data from round 2
Class Schemas and Employment Relations
Comparisons between the ESeC and the EGP class schemas using European data
2006-06-27
By
Erik Bihagen, Magnus Nermo, and Robert Erikson
Swedish Institute for Social Research
Stockholm University, SE-106 91 Stockholm,
Sweden
[email protected], [email protected]
Validation report prepared for the ESeC conference in Bled, Slovenia 29-30 June 2006
NB. Preliminary results
Introduction
A common feature of most European social stratification and mobility research is the practice of allocating individuals to a social class based on their labour market position. One of the most influential contemporary social class schemas is the EGP (Erikson, Goldthorpe, Portocarero) class schema. The new ESeC class schema could basically be viewed as an update of the EGP schema. One obvious disadvantage with the EGP schema is that there has not existed a common agreed way of operationalizing it, since in the days of its construction quite different standards of occupational classifications were used across countries (cf. Erikson & Goldthorpe 1992). The most widely used algorithm for making EGP is developed within the ISMF project by Ganzeboom and Treiman, which takes the International Occupational Classification ISCO-88(com) as a starting point (Ganzeboom & Treiman 1996: 204-205). Hence, ESeC and EGP have the same theoretical base and, as far as the operationalization of Ganzeboom and Treiman is concerned, both are derived with the ISCO code and similar additional information. Thus, we are led to believe that the schemas are quite similar, and moreover, if ESeC is viewed as an update of EGP it should be at least somewhat better in predicting class relevant outcomes.
The overall purpose of this paper is to contribute to the validation of the new ESeC class schema by comparing it to EGP. The analyses in the paper are based on the European Social Survey (ESS) round 2. Scholars in this field are more familiar with EGP than ESeC. It is therefore useful to first compare the two class schemas in terms of distributions and also to what extent respondents are classified in equivalent class categories with the two schemas. Second, we compare the link between class and relevant empirical outcomes when measuring class with ESeC and EGP.
The focus of this study is employment relationships, or more precisely the preconditions of employment relationships; the degree to which jobs of employees require attainment of specific human capital and the degree to which jobs can be easily monitored on the behalf of the employer (cf Goldthorpe 2000). These aspects are only relevant for employees and, thus, employers and self-employed are excluded from our analyses. Besides this we also study the relation between class and wage, where the employment relationship theory suggests a ‘dynamic relation’; classes with a service relationship have better wage mobility than other classes.
The validation begins with a brief description of the theoretical base of the ESeC and the EGP class schemas, and a description of the variables used here to study aspects of employment relationships. Followed by the empirical part, and a concluding discussion.
Class and employment relationships
Both the EGP and the ESeC class schema aims at grouping occupations by similarities in requirements of specific human capital, and levels of monitoring problems. Specific human capital is associated with the degree to which on-the-job training and experience in the job is necessary in order to perform the work tasks at hand. Monitoring problems ultimately concerns the employer’s possibilities to measure the quality and the quantity of what is ‘produced’. Employees in occupations with high requirements of specific human capital are offered long-term benefits by the employer in order to keep replacements costs low, i.e. in order to diminish the risk that employees with expensive on-the-job training leave the firm. Long-term advantages include e.g. job security, career opportunities, and fringe benefits. Such advantages are likewise offered to employees in occupations characterized by high levels of monitoring problems as a way to keep work incentives high.
The employment relationships are divided into i) the service relationship or the salariat, which is characterized by high requirements of specific human capital and high levels of monitoring problems, ii) the labour contract, characterized by low requirements of specific human capital and low degree of monitoring problems, and iii) the mixed relationship with the combinations of low requirements of specific human capital and high degree of monitoring problems or vice versa.
Due to their common theoretical base the two class schemas are very similar. The rows in Table 1 represent equivalent class categories for employees. The last column displays the typical employment relationships, in accordance with the ideas of Goldthorpe 2000 (and the ESeC consortium).
Table 1 about here
In Figure 1 we draw on Goldthorpe (2000: 223) and locate ESeC classes in a two-dimensional space with specific human capital requirements (SHC) and monitoring problems (MP) on the axes. As is clear from the diagram two basic clusters should be formed by the two classes in the salariat and the three with labour contracts, respectively, while the classes with a mixed contract combine low MP with high SHC and vice versa. Since Goldthorpe (2000) describes the EGP equivalents to ESeC 1 and ESeC 9 as the most typical classes within each employment relationship we are led to believe that the ESeC 1 has somewhat higher levels than ESeC 2 and that ESeC 9 has lower levels than 7 and 8. When it comes to wage mobility we expect that the salariat, and especially ESeC 1, is characterized by the most advantageous wage mobility, while classes with a labour contract have the least (and especially ESeC 9). It is less clear what to expect when it comes to classes with a mixed contract. If we make, an admittedly, strong assumption, that wage mobility within classes is determined equally by SHC and MP, we are led to believe that classes with a mixed contract are ranked in between the salariat and classes characterized by a labour contract.
Figure 1 about here
Data and variables
The data used in this validation study is the European Social Survey (ESS); round 2 (edition 2). This data set includes variables required for measuring class, variables related to employment relations, and variables to construct a measure of hourly wage. We use ESS both as a sample of Europeans, with weighted data (‘pweight’) where the sample size of each country is proportional to the size of the population in that country, and as separate samples for 22 countries without such a weight. Moreover, we also aggregate countries in order to construct five regions of countries weighted by pweight; I) Central Europe (Austria, Belgium, Switzerland, Germany, Luxembourg, Netherlands), II) Northern Europe (Denmark, Finland, Iceland, Norway, Sweden), III) Eastern Europe (Czech Republic, Estonia, Poland, Slovenia, Slovakia, Ukraine), IV) UK & Ireland, and V) Southern Europe (Spain, Greece, Portugal). Obviously the aggregation is not solely geographical, but is guided partly by what appears to be common distinctions in comparative research (in the Esping-Andersen tradition so to say). The total N for ESS in the analyses is about 16.000 respondents (see Appendix A for further information). For both data sets all analyses are restricted to employees between 20 and 60 years of age. The selection based on age is made in order to reduce the impact of retired people who may often have a kind of ‘leisure job’ and to reduce the impact of young people being only temporarily employed.
The ESeC schema
We base ESeC (version 4) on the three digit code of ISCO-88. Since we do not want to base the derivation of ESeC and EGP on ISCO codes at different levels of specificity in different countries we choose the three-digit version of ISCO (see Appendix A for more information). Hence, all four-digit codes are truncated to three digits and respondents with less specified codes than the three-digit level are excluded.
In a second step we construct a variable that separates between supervisors and other employees. Respondents who answer that they have any supervisory tasks are classified as supervisors. This means that we may overestimate the number of ‘true’ supervisors, i.e. employees with supervising as a main task in their job. However, the presented results do not change much if the additional criterion, i.e. number of subordinates is used to distinguish supervisors. In the final step we match the ESeC matrix version 4 to our data set, and allocate the respondents to the corresponding class dependent on if they are an employee or supervisor.
The EGP schema
The measure of EGP relies on the algorithms made by Ganzeboom and Treiman in order to transform ISCO codes, together with additional information, to EGP classes (August 21 2005 www. home.fsw.vu.nl/~ganzeboom/ISKO88/INDEX.HTM). This operationalization is also based on the three digit version in ESS in order to make the encoding of the two class schemas comparable. The operationalization of ‘employment status’ in EGP is the same as for ESeC. In all, we separate seven comparable classes of employees in ESeC and EGP.
Other variables in the analyses
We aim to measure SHC, MP and wage in order to estimate the levels on these dimensions for EGP and ESeC classes. The operationalizations of these concepts are not optimal, but in our opinion good enough to compare the EGP and the ESeC class schemas. The only control variable included in the regression analyses is age, and country variables in the case of hourly wage.
Requirements of specific human capital (SHC)
The variable used here for measuring SHC is rather straightforward (see Table 2). It is based on a question about the time required to do your job reasonably well apart from necessary education. Hence, as far as we see it the variable is a good indicator of both firm and industry specific human capital that comes close to Goldthorpes concept of ‘human asset specificity’. The variables are transformed to Z-scores (mean: 0.0, sd: 1.00).
Table 2 about here
Monitoring problems (MP)
MP is measured using an index based on two variables. The variables used are not optimal for measuring the level of MP, but certainly good enough to compare the results of the ESeC and EGP class schemas, since they at least capture some aspects of MP. These questions are actually more about the degree of autonomy in relation to management, and we believe that this autonomy is highly correlated with MP, i.e. jobs that are difficult to monitor from the viewpoint of the employer would in general be characterized by a high level of autonomy. The variables of the index are transformed to Z-scores before adding them together in order to let each variable have the same impact on the index. The index is also transformed to Z-scores since we want to standardize the distributions. The Z-scores for both SHC and MP are based on country level in the analyses on country level, and on ‘regional’ level when we compare regions, and finally on European level when we pool all data sets.
Wage per hour
The ESS data include a variable of gross wage and a variable of work hours, which can be used to construct wage per hour. In the analyses we use the natural logarithm of hourly wage in order to minimise the effect of outliers. Wage per hour is of course not an indicator of wage mobility. However, we attempt to indicate the dynamics by comparing age slopes of estimated hourly wages. Hence, we use age, age2, and interaction terms between those and dummies of class in order to let the wage slopes to be curvilinear and different in shape between classes. We expect the salariat to have a steeper positive wage slope than other classes. There are some problems with the gross wage variable in ESS. Most important the non-response rate is very high in several countries and we have chosen to exclude countries where the internal non-response is 50 % or greater, i.e. Austria, the Czech Republic, Greece, Portugal, and Slovenia. Moreover, Iceland is excluded due to unrealistic values. In order to avoid small sample sizes we do not present these regressions on country level. Instead we pool data based on the above mentioned five regions. We use country dummies in order to control for differences in levels of wages between countries within the regions (the reference category in respective region is Sweden, Germany, Poland, Spain and Ireland).
RESULTS
The empirical part of this validation is divided in three sections. In the first we compare the relative distributions of ESeC and EGP classes, but also show to what extent respondents are allocated differently in the two class schemas. In the second part we compare empirical outcomes with regard to MP and SHC of the two class schemas. In the third part, we examine class differentials in wage age gradients.
Descriptives of ESeC and EGP
From Figure 2 it is obvious that there are great similarities in the relative class distributions with the ESeC and the EGP schema (using ESS data) for employees. Two differences are however noticeable; comparing correspondent classes, ESeC 1 is smaller than EGP I and ESeC 6 is considerably larger than EGP V. As seen in Table 3 this holds true in the results for all countries reported, although to more extreme levels in some countries (as for the higher salariat in Austria and ESeC 6/EGP V in the United Kingdom). Generally speaking we believe that the smaller higher salariat class is a desirable feature of the ESeC schema (Cf. Evans & Mills 2000). It has for instance been indicated with Swedish data that the higher salariat in EGP is a rather heterogeneous group (Bihagen 2005). However, it is only desirable to the extent that this class has a distinct position in terms of e.g. levels of MP and SHC, which is something that we will shed some light on in the second part of this validation.
Figure 2 about here
Table 3 about here
Since the class distributions are rather similar we also expect that most respondents are allocated in the ‘equivalent’ classes with the two schemas. The cross tabulations presented in Table 4 show how employees in EGP classes are distributed across ESeC classes using ESS data. The cells in the diagonal represent the respondents who are allocated in the corresponding classes of the two class schemas, and those in the same grey area are allocated to the same contract within the two schemas. Even though some are allocated to different contracts according to the two schemas a vast majority is found on the diagonal. As could be expected from the distributions above a substantial proportion of those allocated in the higher salariat by EGP are allocated to the lower salariat according to ESeC. Moreover, a substantial proportion of the respondents with a mixed contract according to EGP are allocated in other employment contracts by ESeC.
Table 4 about here
In order to report similarities and dissimilarities in the allocation of respondents according to ESeC and EGP for all included 22 countries we choose to report the findings in a condensed way by summarizing cells in cross-tabulations. In Table 5 the way of summarizing the cells is described. First, of all we have the respondents who are allocated to exactly the same class by the two ways of operationalizing class (denoted as 1 in the figure). Second, we have the respondents who are allocated in the same broader class category, separating the employees into a service, a mixed and a labor contract, however not in exactly the same class (denoted as 2 in the table). Third, we have the respondents who are found in different classes and we have actually divided those into ‘different’ and ‘very different’ classes (denoted as 3 and 4 in the table).
Table 5 about here
As seen in Table 6 about 69 percent of the respondents are allocated in exactly the same class with the two class schemas (62 to 75 percent), and about 84 percent in the same contract (73 to 88 percent). The rest is mainly classified as being in ‘different’ classes and extremely few as ‘very different’.
Table 6 about here
A comparison between ESeC and EGP in regard to indicators of MP and SHC
The purpose of the following analyses is to test to what extent ESeC and EGP are related to Specific human capital (SHC) and Monitoring problems (MP) as in Figure 1. We noted above that ESeC could be understood as an updated version of EGP. We therefore expect ESeC to perform better in terms of these empirical patterns. The presented diagrams below are based on OLS regressions of specific human capital and monitoring problems (see Figure 3 to 8). Besides social class the regressions include control variables for age (for all coefficients from the regressions see appendix B). The estimates for each class on the two dimensions of employment relationships are put together in a diagram, and refer to a person 40 years of age.
Figure 3 reveals a rather clear hierarchy where ESeC 1 is followed by ESeC 2, ESeC 6, ESeC 3, and finally ESeC 7, 8 and 9, where the last mentioned classes have the lowest levels of SHC and MP. ESeC 9 has considerably lower levels of SHC than any other class. This picture is basically also true for EGP. The largest difference is found between EGP category V and ESeC 6. The results presented are in general in line with our theoretical expectations (see Figure 1). One would however, expect ESeC class 3 to be in the lower right square and ESeC class 6 in the upper left, while ESeC 8 would be situated on the diagonal from low-low to high-high. More important, here, is that both EGP and ESeC produce similar patterns on these two dimensions. One could argue that ESeC is more distinct, i.e. the distance between category 1 and 9 is larger, than the corresponding distance between EGP I and VII. Also, ESeC follows the theory more closely.
Figure 3 about here
In the second step, here, we have divided the countries into five regions; I) Central Europe, II) Northern Europe, III) Eastern Europe, IV) United Kingdom and Ireland, and V) Southern Europe. The pattern is roughly the same for all regions, i.e. ESeC 1 in the upper Right Square and ESeC 9 in the lower Left square. The strongest difference between the two schemas is once again found between ESeC 6 and EGP V. Overall ESeC seems to perform better than EGP. Also, ESeC is more distinct than EGP in separating between the classes when we divide the data by regions.
Figure 4-8 about here
We will not discuss the differences between these regions in detail, since such a discussion is a research question in its own. However, it is worth noting that the relative difference between the classes is somewhat smaller in Northern and Eastern Europe, than in Central and Southern Europe. Even so, a general conclusion is that the ESeC schema is applicable in all regions.
A comparison between ESeC and EGP in regard to age gradients in wage
In a final set of analyses, we compare ESeC and EGP in relation to age gradients in gross wage per hour. We expect the wage differences between classes to be smaller at young ages, and that especially EGP I/ESeC 1 but also EGP II/ESeC 2 has a steep positive age slope. It is also reasonable to assume that the labour contract classes have the flattest age wage curves, especially EGP VII/ESeC 9. It is worth emphasizing that these analyses are based on cross-sectional data and since the rationale for our expectations are based on the idea that some classes have better wage mobility than others it would be more appropriate to use longitudinal data, i.e. to follow individuals over time. Still we believe that age gradients can be used as a proxy for wage mobility.
In Figure 9 to 13 the estimated hourly wage levels for employees in different classes in the ages 25 to 55 are reported in different regions. Since country dummies are used in order to control for country differences in wages, the level of the estimations are valid for the reference country (this country is mentioned in brackets), while the pattern so to say is based on all respondents in the region. In the two first diagrams for Central Europe (Germany) all our expectations are confirmed; the increase in wage differentials between classes with age is striking. The results are very similar for EGP and ESeC, although with ESeC, the ‘lowest’ class has an even more negative wage development. Going over to Northern Europe (Sweden), the expectations are in large confirmed again, although more so with ESeC than EGP. With ESeC the lower service class, ESeC 2, has a steeper wage gradient than ESeC 6, while this is not true for the corresponding classes with EGP. Moreover, the most advantaged class, EGP I/ESeC 1, is even more advantaged with the ESeC schema. Compared to Central Europe the wage differentials between classes are smaller in the Northern countries.
Figure 9 and 10 about here
The diagrams for the Eastern countries (Poland) presented in Figure 11 does not at all confirm our expectations. It even seems as if the salariat has a negative age slope. With the risk of being very ad hoc we believe that this result may be linked to drastic societal changes in those countries. More research is however needed in order to study this further. Nonetheless, from a pragmatic point of view the ESeC schema does a better job than the EGP schema even in Eastern countries; the salariat is most advantaged and the classes with a labor contract are least advantaged when relying on the ESeC schema.
When it comes to UK and Ireland (Ireland) our expectations are once again broadly confirmed, with two exceptions; EGP V show a relative steep positive curve, and ESeC 1 a very steep positive slope, but unexpectedly; a quite steep negative slope after the age of 45. However, we believe that ESeC is preferred even here, since we doubt that wages are actually lowered for individuals in ESeC 1 after 45 at least for them staying at the same firm over time. We believe that this result must be due to some kind of cohort effects.
Figure 11 and 12 about here
Finally, in Southern Europe (Spain) the age-wage patterns are largely in line with the expectations, although classes with mixed contracts have steeper positive age slopes than expected; especially after the age of 40. However, we would prefer ESeC to EGP even for the Southern countries since the wage differentials at high ages indicate that the Salariat is best off, followed by intermediate classes and finally those characterised by a labor contract.
If we try to draw more general conclusions from the ‘age-wage’ diagrams, we find ESeC preferable to EGP, since ESeC performs better in distinguishing classes in a hierarchical way with the Salariat as generally having the steepest positive age gradient followed by the intermediate classes. Furthermore, ESeC appears to create the most stable hierarchies at each age level as well (including the otherwise problematic results for Eastern countries). The differences between the results based on EGP and ESeC should not be exaggerated. Nevertheless, one striking difference is the position of EGP V/ESeC 6, where the results based on EGP indicate that this class is far too advantaged in relation to theory.
Figure 12 and 13 about here
Concluding remarks
Our main conclusion is that the ESeC schema and the EGP schema are similar to each other in the sense that (a) a vast majority of cases are allocated to the same basic contract with the two schemas, (b) the patterns of specific human capital and monitoring problems are very similarly associated with both schemas. Moreover, (c) it is indicated that both class schemas have a similar relation to ‘wage dynamics’. We had expected ESeC to discriminate better in relation to empirical outcomes than EGP, and the analyses confirm this. The empirical patterns of specific human capital requirements and monitoring problems are more in line with our expectations about class differentials when it comes to ESeC than EGP. Moreover, all in all, the R2s in the regression models indicate that the discriminatory power is somewhat higher with ESeC than EGP. However, the differences in results between the schemas should not be overemphasized; they are quite similar after all.
In sum, our analyses of ESeC and EGP support the idea that classes for employees are characterized by typical levels of specific human capital requirements (SHC) and by typical levels of monitoring problems (MP). However, our measures of SHC and MP do not indicate any strong support for the idea that classes with a mixed contract combine low levels of SHC with high levels of MP and vice versa, as expected from the writings of Goldthorpe (2000). Nevertheless, in general the relative positions of the classes, especially when measured by ESeC, are in line with the theory even though not all of them end up in the correct “square” in our diagrams. Overall the category EGP V appears to be the most troublesome in the EGP schema and the corresponding class in the ESeC schema is more in line with our expectations throughout most of our analyses. Another important difference found between the class schemas, when it comes to empirical outcomes, is that the most advantaged class, the higher salariat, is both smaller and also generally more distinct when measured with ESeC,. We believe that this is a desirable characteristic of the new schema.
References
Bihagen, E. (2005) ‘Labour Market Rewards and the Neo-Classical Soup’, Acta Sociologica, 48(1): 63-84.
Erikson, R. & Goldthorpe, J. H. (1992) The Constant Flux. Oxford: Clarendon Press.
Evans (1993) ‘Class, Prospects and the Life-cycle’, Acta Sociologica, 36(3): 263-276.
Evans, G, & Mills, C. (2000) ‘In search of the wage-labour/service contract: new evidence on the validity of the Goldthorpe class schema’. British Journal of Sociology, 51(4): 641-661.
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: 201-239.
Goldthorpe, J. H. (1987) Social Mobility and Class Structure in Modern Britain (2nd ed.). Oxford: Clarendon Press.
Goldthorpe, J. H. (2000) On Sociology. Numbers, Narratives, and the Integration of Research and Theory. Oxford: University Press.
Table 1. Seven classes of employees defined in EGP and ESeC
ESeC
EGP
Employment relationship
1
Higher salariat occupations
I
Higher grade Professional etc.
Service relationship
2
Lower salariat occupations
II
Lower grade Professional etc.
3
Intermediate occupations
IIIa
Higher grade routine non-manual
Mixed Contract
6
Lower supervisory and lower technician occupations
V
Lower technical, and manual supervisory
7
Lower services, sales and clerical occupations
IIIb
Lower grade routine non-manual
Labour contract
8
Lower technical occupations
VI
Skilled manual
9
Routine occupations
VII
Non-skilled manual
Note: IIIb is here characterized by a labour contract in accordance with Goldthorpe (2000:223).
Figure 1. ESeC classes in a two dimensional diagram in accordance to specific human capital requirements and levels of monitoring problems
ESeC9
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
-1
-0,5
0
0,5
1
-1-0,500,51
monitoring problems
specific human capital
Table 2. The dependent variables of the study
Question
Number of valid categories
Specific human capital requirements (SHC);
(JBLRN/ G63) If somebody with the right education and qualifications replaced you in your job, how long would it take for them to learn to do the job reasonably well?
8
Monitoring problems (MP);
(WKDCORGA/ F18); … how much the management at your work allows you to decide how your own daily work is organised?
11
(WRKSPV/ G78)… About the organisation you work for…My work is closely supervised
5
Wage;
ln(gross wage per hour); derived variable (using GRSPAY G91, WKHTOT F21, PAYPRD G93)
-
Figure 2. The relative class distributions for Europe (for the 22 countries included in the ESS data, see Table 3)
0
5
10
15
20
25
30
ESEC1/EGP I
ESEC2/EGP
II
ESeC3/EGP
IIIa
ESeC6/EGP
V
ESeC7/EGP
IIIb
ESeC8/EGP
VI
ESeC9/EGP
VII
%
ESeC
EGP
EseC labels
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
ESeC9
EGP labels
I
II
IIIa
V
IIIb
VI
VII
AT Austria
ESeC
5.7
32.6
18.0
13.2
13.4
6.0
11.1
100.0
EGP
12.9
29.0
14.7
4.2
19.8
6.5
13.0
100.0
BE Belgium
ESeC
14.6
29.4
7.4
12.6
10.3
9.2
16.3
100.0
EGP
18.5
31.1
11.2
4.8
7.1
10.0
17.3
100.0
CH Switzerland
ESeC
13.9
31.9
13.2
16.6
8.2
5.8
10.3
100.0
EGP
20.2
32.9
12.9
6.7
10.4
6.3
10.6
100.0
CZ Czech Republic
ESeC
5.5
22.1
13.0
6.4
13.3
13.8
26.0
100.0
EGP
7.7
19.7
19.8
2.7
10.5
16.0
23.6
100.0
DE Germany
ESeC
11.9
29.9
11.5
12.4
10.2
10.9
13.1
100.0
EGP
19.7
25.8
15.0
5.9
10.3
11.3
12.0
100.0
DK Denmark
ESeC
16.0
29.7
11.8
13.5
9.6
5.2
14.1
100.0
EGP
21.7
32.0
10.0
3.9
10.4
5.3
16.7
100.0
EE Estonia
ESeC
10.5
20.1
6.7
8.6
10.8
16.7
26.6
100.0
EGP
13.8
20.9
8.7
1.7
10.6
17.3
27.1
100.0
ES Spain
ESeC
14.7
16.5
14.6
9.7
11.7
11.1
21.7
100.0
EGP
16.1
23.0
10.9
4.0
13.6
10.2
22.2
100.0
FI Finland
ESeC
17.0
24.1
8.8
5.7
15.5
12.3
16.6
100.0
EGP
21.9
20.4
11.2
1.3
15.9
11.8
17.4
100.0
GB United Kingdom
ESeC
16.8
24.8
11.9
13.3
13.7
4.7
14.7
100.0
EGP
21.7
27.8
11.2
2.8
16.3
5.8
14.5
100.0
GR Greece
ESeC
8.3
16.0
14.7
11.5
12.5
11.5
25.5
100.0
EGP
13.6
14.2
11.5
4.4
20.2
11.5
24.6
100.0
IE Ireland
ESeC
10.5
28.3
9.6
13.0
14.3
9.5
14.8
100.0
EGP
16.1
30.5
9.3
3.3
14.4
9.8
16.5
100.0
IS Iceland
ESeC
11.7
25.8
9.7
26.2
11.3
4.4
10.9
100.0
EGP
20.6
28.8
9.5
7.4
14.4
4.1
15.2
100.0
LU Luxembourg
ESeC
10.9
27.0
11.7
14.9
7.5
8.7
19.3
100.0
EGP
14.2
31.8
12.7
4.5
6.5
8.6
21.7
100.0
NL Netherlands
ESeC
18.7
34.7
7.7
15.0
9.5
5.9
8.5
100.0
EGP
25.7
33.2
12.9
5.9
6.5
5.3
10.5
100.0
NO Norway
ESeC
10.7
32.1
10.3
17.3
13.7
6.5
9.5
100.0
EGP
20.8
29.4
13.0
5.9
11.8
6.1
13.0
100.0
PL Poland
ESeC
5.7
22.6
10.0
14.7
10.9
16.3
19.7
100.0
EGP
10.8
22.9
11.9
6.9
8.6
16.9
22.1
100.0
PT Portugal
ESeC
4.6
15.9
9.9
7.5
18.9
14.4
28.8
100.0
EGP
7.3
16.5
10.9
2.9
22.6
16.1
23.7
100.0
SE Sweden
ESeC
14.5
23.1
11.7
10.7
16.1
8.7
15.3
100.0
EGP
17.7
27.2
9.5
3.1
16.3
9.6
16.6
100.0
SI Slovenia
ESeC
12.2
25.3
8.8
17.5
5.5
14.7
16.0
100.0
EGP
15.7
26.1
11.7
7.4
5.5
15.9
17.8
100.0
SK Slovakia
ESeC
9.2
27.0
7.1
12.6
7.1
15.2
21.8
100.0
EGP
14.6
22.3
11.0
4.3
9.5
16.6
21.7
100.0
UA Ukraine
ESeC
10.2
26.2
4.9
10.2
10.3
13.8
24.3
100.0
EGP
15.8
19.9
9.4
4.0
10.4
14.4
26.1
100.0
ALL (pweight used)
ESeC
12.3
25.5
10.8
12.1
11.6
10.4
17.4
100.0
EGP
17.5
25.0
12.1
4.5
12.3
10.8
17.8
100.0
Table 3 The relative class distribution among employees (ESS data for two rows of Table Table 4. Cross-tabulation between EGP and ESeC for the 22 countries included in the ESS data
EGP
EseC
I
II
IIIa
V
IIIb
VI
VII
ESeC1
57.5
9.0
0.0
0.0
0.0
0.0
0.0
ESeC2
37.3
67.4
15.9
0.0
0.4
0.0
0.0
ESeC3
0.0
8.3
60.9
0.0
11.2
0.0
0.0
ESeC6
5.1
15.3
0.1
100.0
0.4
1.7
13.7
ESeC7
0.0
0.0
17.1
0.0
65.0
4.6
6.7
ESeC8
0.0
0.0
0.0
0.0
0.0
86.2
5.7
ESeC9
0.0
0.0
6.0
0.0
22.9
7.5
74.0
Sum
100
100
100
100
100
100
100
EGP
EseC
I
II
IIIa
V
IIIb
VI
VII
Sum
ESeC1
81.7
18.3
0.0
0.0
0.0
0.0
0.0
100
ESeC2
25.8
66.4
7.6
0.0
0.2
0.0
0.0
100
ESeC3
0.0
19.1
68.2
0.0
12.7
0.0
0.0
100
ESeC6
7.6
32.0
0.1
37.9
0.4
1.6
20.4
100
ESeC7
0.0
0.0
17.7
0.0
68.0
4.2
10.2
100
ESeC8
0.0
0.0
0.0
0.0
0.0
90.2
9.8
100
ESeC9
0.0
0.0
4.2
0.0
16.0
4.6
75.2
100
Table 5. Categorizing degree of similarity between ESeC and EGP
ESeC;
1. Higher professional managerial etc.
2. Lower professional managerial etc.
3. Mixed relationship, clerical sales etc.
6. Mixed relationship, supervisor etc.
7. Labour contract, services
8. Labour contract, lower technical
9. Labour contract, routine
EGP;
I
1
2
3
3
4
4
4
II
2
1
3
3
4
4
4
IIIa
3
3
1
2
3
3
3
V
3
3
2
1
3
3
3
IIIb
4
4
3
3
1
2
2
VI
4
4
3
3
2
1
2
VII
4
4
3
3
2
2
1
Where ‘1’ is “identical” classes, ‘2’ within the same basic contracts but not identical, ‘3’ different classes and ‘4’ very different classes
Table 6. Degree of similarity between EGP and ESeC (in percent)
Country
1. ‘Identical’
2. Same contract but not identical
3. Different
4. Very different
5. Sum of 1 and 2
AT Austria
68.1
9.1
22.6
0.2
77.2
BE Belgium
73.7
11.2
15.1
0.0
84.9
CH Switzerland
67.4
14.9
17.7
0.0
82.3
CZ Czech Republic
75.1
13.2
11.8
0.0
88.3
DE Germany
69.0
15.9
15.1
0.0
84.9
DK Denmark
67.1
13.4
19.5
0.0
80.5
EE Estonia
75.1
14.1
10.7
0.0
89.2
ES Spain
71.4
15.3
13.3
0.0
86.7
FI Finland
74.3
12.9
12.8
0.0
87.2
GB United Kingdom
68.8
13.4
17.7
0.0
82.2
GR Greece
68.7
11.1
19.9
0.3
79.8
IE Ireland
69.3
13.1
17.6
0.0
82.4
IS Iceland
62.1
11.1
26.3
0.4
73.2
LU Luxembourg
69.4
12.0
18.6
0.0
81.4
NL Netherlands
64.1
18.0
17.9
0.0
82.1
NO Norway
65.9
16.4
17.7
0.0
82.3
PL Poland
72.1
12.2
15.7
0.0
84.3
PT Portugal
73.6
13.6
12.8
0.0
87.2
SE Sweden
69.2
13.7
17.1
0.0
82.9
SI Slovenia
69.9
14.2
15.9
0.0
84.1
SK Slovakia
67.9
15.5
16.6
0.0
83.4
UA Ukraine
65.6
21.4
12.7
0.3
87.0
All (pweight used)
69.3
15.1
15.5
0.1
84.4
Figure 3. Location of ESeC and EGP classes, in a two dimensional space with the dimensions of specific human capital and ‘monitoring problems’ (based on estimations from OLS regressions for a person in the age of 40) for all countries in ESS
ESeC8
ESeC7
ESeC6
ESeC3
ESeC2
ESeC1
ESeC9
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
EGP VII
-1
-0,5
0
0,5
1
-1
-0,5
0
0,5
1
monitoring problems
specific human capital
Figure 4. Location of ESeC and EGP classes, in a two dimensional space with the dimensions of specific human capital and ‘monitoring problems’ (based on estimations from OLS regressions for a person in the age of 40) for Central Europe.
ESeC8
ESeC7
ESeC6
ESeC3
ESeC2
ESeC1
ESeC9
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
EGP VII
-1
-0,5
0
0,5
1
-1
-0,5
0
0,5
1
monitoring problems
specific human capital
Figure 5. Location of ESeC and EGP classes, in a two dimensional space with the dimensions of specific human capital and ‘monitoring problems’ (based on estimations from OLS regressions for a person in the age of 40) for Northern Europe.
ESeC8
ESeC7
ESeC6
ESeC3
ESeC2
ESeC1
ESeC9
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
EGP VII
-1
-0,5
0
0,5
1
-1
-0,5
0
0,5
1
monitoring problems
specific human capital
Figure 6. Location of ESeC and EGP classes, in a two dimensional space with the dimensions of specific human capital and ‘monitoring problems’ (based on estimations from OLS regressions for a person in the age of 40) for Eastern Europe.
EGP VII
EGP VI
EGP IIIb
EGP V
EGP IIIa
EGP II
EGP I
ESeC9
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
-1
-0,5
0
0,5
1
-1
-0,5
0
0,5
1
monitoring problems
specific human capital
Figure 7. Location of ESeC and EGP classes, in a two dimensional space with the dimensions of specific human capital and ‘monitoring problems’ (based on estimations from OLS regressions for a person in the age of 40) for United Kingdom and Ireland.
EGP VII
EGP VI
EGP IIIb
EGP V
EGP IIIa
EGP II
EGP I
ESeC9
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
-1
-0,5
0
0,5
1
-1
-0,5
0
0,5
1
monitoring problems
specific human capital
Figure 8. Location of ESeC and EGP classes, in a two dimensional space with the dimensions of specific human capital and ‘monitoring problems’ (based on estimations from OLS regressions for a person in the age of 40) for Southern Europe.
EGP VII
EGP VI
EGP IIIb
EGP V
EGP IIIa
EGP II
EGP I
ESeC9
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
-1
-0,5
0
0,5
1
-1
-0,5
0
0,5
1
monitoring problems
specific human capital
Figure 9. Estimated age gradients in hourly wages, Central Europe
a. With EGP
Central Europe
0
5
10
15
20
25
25303540455055
age
Euro
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
EGP VII
b. with ESeC
Central Europe
0
5
10
15
20
25
25303540455055
age
Euro
ESeC 1
ESeC 2
ESeC 3
ESeC 6
ESeC 7
ESeC 8
ESeC 9
Figure 10. Estimated age gradients in hourly wages, Northern Europe
a. With EGP
Northern Europe
0
5
10
15
20
25
25303540455055
age
Euro
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
EGP VII
b. With ESeC
Northern Europe
0
5
10
15
20
25
25303540455055
age
Euro
ESeC 1
ESeC 2
ESeC 3
ESeC 6
ESeC 7
ESeC 8
ESeC 9
Diagram 11. Estimated age gradients in hourly wages, Eastern Europe
a. With EGP
Eastern Europe
0
0,5
1
1,5
2
2,5
3
3,5
4
253035404550
age
Euro
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
EGP VII
b. With ESeC
Eastern Europe
0
0,5
1
1,5
2
2,5
3
3,5
4
253035404550
age
Euro
ESeC 1
ESeC 2
ESeC 3
ESeC 6
ESeC 7
ESeC 8
ESeC 9
Figure 12. Estimated age gradients in hourly wages, UK and Ireland
a. With EGP
UK and Ireland
0
5
10
15
20
25
30
35
25303540455055
age
Euro
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
EGP VII
b. With ESeC.
UK and Ireland
0
5
10
15
20
25
30
35
25303540455055
age
Euro
ESeC 1
ESeC 2
ESeC 3
ESeC 6
ESeC 7
ESeC 8
ESeC 9
Figure 13. Estimated age gradients in hourly wages, Southern Europe
a. With EGP
Southern Europe
0
5
10
15
20
25303540455055
age
Euro
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
EGP VII
b. With ESeC
Southern Europe
0
5
10
15
20
25303540455055
age
Euro
ESeC 1
ESeC 2
ESeC 3
ESeC 6
ESeC 7
ESeC 8
ESeC 9
APPENDIX A; ESS data, reductions of the sample size
In the table below the reductions of the sample size for our analyses are reported. In order to base the class schemas in ESS on an ISCO code with the same level of specificity across countries a variable was constructed which indicates false three digit codes (two and one digit codes with zeros imputed). At the three digit level there are quite few false codes. Thus, we choose the three digit level as our point of departure, and we will ignore respondents with false three digits.
Table A1; Reductions of the sample size of ESS: round 2, where column D represents the data sets of the analyses in this study
Country
A. All in ESS
B. Restricted to
employees
20-60 years
C. As B and
reported ISCO
code
D. As C and
false three digit codes
excluded
AT Austria
2256
880
848
806
BE Belgium
1778
712
709
649
CH Switzerland
2141
858
850
811
CZ Czech Republic
3026
1193
1082
1079
DE Germany
2870
1025
1016
923
DK Denmark
1487
677
672
667
EE Estonia
1989
833
829
826
ES Spain
1663
671
599
599
FI Finland
2022
852
849
845
GB United Kingdom
1897
752
743
717
GR Greece
2406
585
583
574
IE Ireland
2286
842
833
828
IS Iceland
579
268
267
248
LU Luxembourg
1635
835
834
830
NL Netherlands
1881
757
756
729
NO Norway
1760
861
859
834
PL Poland
1716
578
576
568
PT Portugal
2052
665
644
630
SE Sweden
1948
899
897
889
SI Slovenia
1442
543
478
475
SK Slovakia
1512
568
550
533
UA Ukraine
2031
722
717
712
All
42377
16576
16191
15772
Appendix B; OLS regressions with specific human capital requirements (SHC) and monitoring problems (MP) as dependent variables
Table B1; OLS regressions (coefficients) with SHC as dependent variable and EGP classes as independent (ESeC9 as ref cat) with ESS data
Country
Constant
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
Age
R2
AT Austria
-0.499
1.242**
1.022**
0.796**
1.106**
0.610**
0.887**
0.005
0.106
BE Belgium
-0.473
0.953**
0.686**
0.663**
0.787**
0.043
0.364*
0.013
0.131
CH Switzerland
-0.980
1.320**
1.032**
0.754**
0.886**
0.466**
0.500**
0.007*
0.132
CZ Czech Republic
-0.062
1.152**
1.045**
0.609**
0.714**
0.078
0.561**
0.004
0.179
DE Germany
-0.946
1.387**
1.222**
0.859**
1.373**
0.571**
0.857**
-0.002
0.194
DK Denmark
-0.817
1.014**
0.901**
0.776**
0.547**
0.239
0.440*
0.015**
0.140
EE Estonia
-0.293
0.996**
0.879**
0.654**
0.555**
0.040
0.401**
-0.007*
0.143
ES Spain
-0.506
1.009**
1.105**
0.816**
1.125**
0.527**
0.913**
0.005
0.170
FI Finland
-0.615
1.011**
0.647**
0.572**
0.694**
-0.037
0.468**
0.013
0.151
GB United Kingdom
-0.697
1.423**
1.470**
0.861**
0.883**
0.515**
1.131**
0.009**
0.261
GR Greece
-0.718
1.357**
1.007**
0.798**
1.159**
0.335*
0.955**
0.003
0.218
IE Ireland
-0.971
1.040**
0.688**
0.235
0.746**
0.112
0.475**
0.000
0.106
IS Iceland
-1.051
1.500**
1.196**
0.786**
1.057**
0.517*
0.607
0.014**
0.188
LU Luxembourg
-0.574
1.309**
1.033**
0.917**
1.142**
0.626**
0.850**
0.002
0.181
NL Netherlands
-0.733
0.894**
0.639**
0.468**
0.965**
0.008
0.903**
0.012**
0.109
NO Norway
-0.939
1.152**
0.836**
0.450**
0.803**
-0.128
0.525**
0.009**
0.177
PL Poland
-0.300
1.550**
1.046**
0.765**
0.864**
0.247
0.585**
0.010**
0.194
PT Portugal
-0.733
1.100**
0.941**
0.489**
0.852**
0.115
0.330**
-0.001
0.136
SE Sweden
-0.771
0.750**
0.757**
0.441**
0.734**
-0.198
0.729**
0.012**
0.162
SI Slovenia
-0.426
1.436**
1.213**
0.981**
1.057**
0.595**
0.828**
0.007
0.197
SK Slovakia
0.089
1.088**
0.810**
0.664**
1.081**
-0.124
0.525**
0.006
0.179
UA Ukraine
-0.255
0.865**
0.711**
0.377*
0.719**
-0.011
0.800**
0.010**
0.141
ALL
-0.685
1.199**
1.054**
0.741**
0.986**
0.340**
0.746**
0.007**
0.179
Table B2; OLS regressions (coefficients) with SHC as dependent variable and EGP classes as independent (EGP VII as ref cat) with ESS data
Country
Constant
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
Age
R2
AT Austria
-0.499
1.125**
0.768**
0.459**
0.760**
0.454**
0.598**
0.005
0.092
BE Belgium
-0.473
0.786**
0.509**
0.254
0.790**
-0.396*
0.158
0.013**
0.134
CH Switzerland
-0.980
1.046**
0.783**
0.309*
0.963**
0.077
0.516**
0.008*
0.136
CZ Czech Republic
-0.062
1.24**
0.972**
0.580**
0.891**
-0.003
0.495**
0.004
0.175
DE Germany
-0.946
1.223**
0.987**
0.573**
1.522**
0.248
0.803**
0.00
0.182
DK Denmark
-0.817
1.001**
0.676**
0.586**
0.287
0.131
0.350
0.015**
0.140
EE Estonia
-0.293
1.058**
0.688**
0.608**
0.796**
-0.044
0.423**
-0.007*
0.145
ES Spain
-0.506
0.979**
0.961**
0.659**
1.253**
0.320*
0.890**
0.005
0.163
FI Finland
-0.615
1.002**
0.538**
0.468**
1.293**
-0.125
0.610**
0.012**
0.181
GB United Kingdom
-0.697
1.168**
1.017**
0.469**
1.094**
0.167
0.824**
0.007*
0.197
GR Greece
-0.718
1.287**
0.753**
0.782**
1.382**
0.303**
0.913**
0.006
0.212
IE Ireland
-0.971
0.765**
0.455**
0.127
0.975**
-0.307*
0.390**
0.001
0.121
IS Iceland
-1.051
1.195**
0.818**
0.298
0.629*
0.267
0.342
0.012*
0.175
LU Luxembourg
-0.574
1.222**
0.825**
0.620**
1.334**
0.106
0.673**
-0.002
0.182
NL Netherlands
-0.733
0.565**
0.249*
-0.189
1.032**
-0.585**
0.554**
0.010**
0.144
NO Norway
-0.939
0.754**
0.722**
0.245*
1.191**
-0.361**
0.521**
0.010**
0.193
PL Poland
-0.300
1.406**
0.800**
0.477**
1.002**
0.025
0.535**
0.009*
0.203
PT Portugal
-0.733
1.054**
0.870**
0.403**
1.483**
0.112
0.512**
-0.002
0.152
SE Sweden
-0.771
0.684**
0.617**
0.083
0.899**
-0.311**
0.640**
0.014**
0.167
SI Slovenia
-0.426
1.222**
0.929**
0.513**
1.130**
0.417
0.521**
0.009*
0.164
SK Slovakia
0.089
0.838**
0.638**
0.267
1.225**
-0.193
0.236
0.004
0.141
UA Ukraine
-0.255
0.942**
0.600**
0.268
0.955**
0.165
0.765**
0.008*
0.141
ALL
-0.685
1.106**
0.872**
0.488**
1.166**
0.174**
0.670**
0.006**
0.173
Table B3; OLS regressions (coefficients) with MP as dependent variable and ESeC classes as independent (ESeC 9 as ref cat) with ESS data
Country
Constant
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
Age
R2
AT Austria
-0.499
0.398*
0.501**
0.392**
0.284*
-0.010
-0.054
0.005
0.040
BE Belgium
-0.473
1.127**
0.819**
0.624**
0.607**
0.300*
0.067
-0.002
0.138
CH Switzerland
-0.980
1.017**
0.904**
0.877**
0.463**
0.313*
0.090
0.008*
0.125
CZ Czech Republic
-0.062
1.252**
0.906**
0.556**
0.514**
0.270**
0.111
-0.009**
0.156
DE Germany
-0.946
1.072**
0.886**
0.647**
0.874**
0.242
-0.054
0.008**
0.177
DK Denmark
-0.817
0.540**
0.481**
0.433**
0.353*
0.169
0.185
0.011**
0.037
EE Estonia
-0.293
1.045**
0.948**
0.638**
0.381**
0.136
0.151
-0.003
0.163
ES Spain
-0.506
0.721**
0.540**
0.402**
0.715**
0.002
-0.215
0.005
0.108
FI Finland
-0.615
0.889**
0.480**
0.572**
0.220
0.261*
-0.050
0.006
0.099
GB United Kingdom
-0.697
0.750**
0.495**
0.248
0.157
-0.036
0.182
0.010**
0.073
GR Greece
-0.718
0.909**
1.049**
0.203
0.597**
0.045
-0.125
0.010**
0.200
IE Ireland
-0.971
1.141**
0.855**
0.921**
0.860**
0.226
0.107
0.009**
0.167
IS Iceland
-1.051
0.773**
0.302
0.529
0.320
0.482
0.707*
0.016**
0.056
LU Luxembourg
-0.574
1.000**
0.628**
0.572**
0.494**
0.322*
-0.164
0.004
0.112
NL Netherlands
-0.733
0.949**
0.779**
0.742**
0.795**
0.294
0.152
0.002
0.078
NO Norway
-0.939
0.580**
0.564**
0.320*
0.368**
-0.017
-0.176
0.014**
0.097
PL Poland
-0.300
1.144**
0.712**
0.705**
0.531**
0.245
-0.007
-0.003
0.116
PT Portugal
-0.733
1.205**
0.982**
0.668**
0.653**
0.193
-0.002
0.009**
0.184
SE Sweden
-0.771
0.898**
0.726**
0.658**
0.500**
0.119
0.215
0.007*
0.108
SI Slovenia
-0.426
0.140
0.313*
0.063
0.065
-0.189
0.407*
0.007
0.014
SK Slovakia
0.089
0.614**
0.619**
0.412*
0.365*
0.046
-0.290*
-0.009**
0.104
UA Ukraine
-0.255
0.407**
0.274**
0.119
-0.055
-0.291*
-0.059
0.004
0.035
ALL
-0.685
0.923**
0.683**
0.545**
0.555**
0.178**
-0.073*
0.006**
0.122
Table B4; OLS regressions (coefficients) with MP as dependent variable and EGP classes as independent (EGP VII as ref cat) with ESS data
Country
Constant
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
Age
R2
AT Austria
-0.499
0.587**
0.613**
0.445**
0.283
0.288*
0.222
0.006
0.039
BE Belgium
-0.473
1.051**
0.776**
0.427**
0.337
0.238
0.030
-0.002
0.142
CH Switzerland
-0.980
0.850**
0.865**
0.670**
0.533**
0.302*
0.033
0.009**
0.107
CZ Czech Republic
-0.062
1.269**
0.887**
0.498**
0.650**
0.138
0.082
-0.009**
0.168
DE Germany
-0.946
0.916**
0.893**
0.371**
0.893**
0.226
-0.134
0.010**
0.179
DK Denmark
-0.817
0.479**
0.390**
0.436*
0.337
0.136
0.102
0.011**
0.036
EE Estonia
-0.293
1.057**
0.807**
0.488**
0.957**
0.255*
0.157
-0.003
0.151
ES Spain
-0.506
0.749**
0.561**
0.342*
0.570**
0.123
-0.287
0.005
0.104
FI Finland
-0.615
0.919**
0.601**
0.440**
-0.009
0.291*
0.170
0.006*
0.105
GB United Kingdom
-0.697
0.706**
0.444**
0.155
0.798**
0.020
0.239
0.009**
0.073
GR Greece
-0.718
1.017**
1.159**
0.388**
0.533**
0.336**
0.011
0.014**
0.212
IE Ireland
-0.971
0.875**
0.693**
0.422**
0.721**
0.045
0.003
0.009**
0.127
IS Iceland
-1.051
0.726**
0.402*
0.655*
0.321
0.550*
0.750*
0.014*
0.067
LU Luxembourg
-0.574
0.869**
0.629**
0.593**
0.652**
0.146
-0.180
0.004
0.118
NL Netherlands
-0.733
0.841**
0.498**
0.210
0.639**
0.390*
0.090
0.002
0.066
NO Norway
-0.939
0.633**
0.495**
0.103
0.364*
0.145
-0.153
0.015**
0.097
PL Poland
-0.300
1.061**
0.609**
0.567**
0.502**
0.343*
0.000
-0.003
0.113
PT Portugal
-0.733
1.158**
0.968**
0.417**
0.283
0.163
-0.003
0.009**
0.179
SE Sweden
-0.771
0.728**
0.576**
0.323*
0.440*
0.054
0.102
0.008**
0.082
SI Slovenia
-0.426
0.173
0.370*
0.024
0.451
0.067
0.274
0.008
0.011
SK Slovakia
0.089
0.734**
0.546**
0.262
0.423*
0.109
-0.353**
-0.008*
0.122
UA Ukraine
-0.255
0.504**
0.372**
0.326*
-0.167
-0.037
0.030
0.005
0.043
ALL
-0.685
0.865**
0.697**
0.378**
0.557**
0.251**
-0.075*
0.006**
0.123
Table B5; OLS regressions (coefficients) with MP and SHC as dependent variable and EGP classes as independent (EGP VII as ref cat) with ESS data by regions
MP
Constant
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
Age
R2
Central Europe
-0.809
0.895**
0.789**
0.372**
0.747**
0.253**
-0.090
0.007**
0.138
Northern Europe
-0.773
0.696**
0.524**
0.325*
0.368
0.137
0.080
0.010**
0.072
Eastern Europe
-0.255
0.754**
0.519**
0.411**
0.243**
0.113
0.000
-0.001
0.075
UK and Ireland
-0.704
0.716**
0.460**
0.169
0.793**
0.023
0.214*
0.009**
0.080
Southern Europe
-0.603
0.816**
0.669**
0.356**
0.533**
0.154*
-0.195*
0.007**
0.125
SHC
Constant
EGP I
EGP II
EGP IIIa
EGP V
EGP IIIb
EGP VI
Age
R2
Central Europe
-0.790
1.057**
0.804**
0.414**
1.309**
0.122
0.664**
0.003*
0.151
Northern Europe
-0.984
0.838**
0.637**
0.302*
0.861**
-0.190
0.587**
0.013**
0.153
Eastern Europe
-0.784
1.101**
0.716**
0.400**
0.978**
0.083
0.606**
0.007**
0.160
UK and Ireland
-0.932
1.142**
0.982**
0.446**
1.092**
0.138
0.797**
0.007**
0.196
Southern Europe
-0.768
1.007**
0.920**
0.639**
1.287**
0.288**
0.823**
0.004*
0.168
Table B6; OLS regressions (coefficients) with MP and SHC as dependent variable and ESeC classes as independent (EseC 9 as ref cat) with ESS data by regions
MP
Constant
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
Age
R2
Central Europe
-0.845
1.015**
0.835**
0.658**
0.761**
0.244**
-0.025
0.006**
0.143
Northern Europe
-0.787
0.769**
0.591**
0.530**
0.404**
0.132
0.074
0.009**
0.079
Eastern Europe
-0.222
0.697**
0.510**
0.416**
0.249**
-0.026
-0.040
0.000
0.067
UK and Ireland
-0.714
0.771**
0.517**
0.284**
0.199*
-0.020
0.160
0.010**
0.081
Southern Europe
-0.586
0.775**
0.671**
0.409**
0.696**
0.045
-0.165
0.007**
0.127
SHC
Constant
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
Age
R2
Central Europe
-0.980
1.246**
1.061**
0.77**
1.205**
0.445**
0.787**
0.003
0.157
Northern Europe
-1.037
0.929**
0.770**
0.532**
0.673**
-0.073
0.585**
0.012**
0.146
Eastern Europe
-0.899
1.090**
0.863**
0.592**
0.786**
0.088
0.658**
0.009**
0.158
UK and Ireland
-1.268
1.398**
1.422**
0.824**
0.875**
0.491**
1.084**
0.009**
0.252
Southern Europe
-0.782
0.996**
1.054**
0.754**
1.077**
0.426**
0.812**
0.003
0.169
� In an earlier version of this paper we also used the Swedish Level of Living Survey from 1991 and 2000 where almost identical variables of interest to us are found. All the results were similar in the case of Sweden when using those data-sets.
� The deletion of self-employed and employers should result in no restriction on the results, since these two groups are treated in an identical way in EGP and ESEC.
� This is the way the class schema is described in Goldthorpe 2000, and the description is similar in Erikson and Goldthorpe 1992. Earlier the class schema was said to even cluster occupations on similarities in income levels and autonomy in the work place (Goldthorpe 1987. Cf. Evans 1993).
� Goldthorpe 2000 labels specific human capital for ‘specificity of human assets’ but we believe that specific human capital is a more general term in the literature.
� � HYPERLINK "http://www.europeansocialsurvey.org/" ��http://www.europeansocialsurvey.org/�. Besides the data (edition 2) we update the occupational classification of Slovenia with data available at the website.
� 24 countries are included in edition 2. However, France is dropped since the occupational classification is not on the three digit level and Hungary is dropped (in this version of the paper) due to problems with one variable (emplrel). The data are weighted with Dweight in order to correct for differences in sample techniques between countries.
� We remade a selection of the analyses of the paper, with alternatives ways of dealing with the distinction of supervisors. First, we used the so-called simplified class (SC) version where a variable of employment relations is not used at all, than the more elaborated version by letting those with supervisory tasks and at least 2 subordinates be supervisors and then by rising this criteria to 3, 4 and 5 subordinates. Hence, all in all we compared results with 5 different ways of dealing with supervisors. The results are most different concerning ESeC 6. First, of all it is worth mentioning that only 1 percent is allocated to this class with SC. This rises to 12 percent in the version used in this paper and falls down to 7 percent if adding the strictest criteria of 5 subordinates and more. Analyses where classes are operationalised differently indicate that the results are very similar even for the ESeC 6 category both in terms of estimated values of SHC and MP and R2s. However, the SC version diminishes the R2s with about .02 and does also alter the estimated values a bit for ESeC 6. Hence, our conclusion is that it appears to be enough to use the variable about any supervisory tasks and that some caution is required concerning ESeC 6 if relying on the SC version.
� On the web pages of the ESS project there is a syntax available for making EGP classes in round 1 of ESS, which is based on the syntax of Ganzeboom and Treiman, adapted to ESS by Håkon Leiulfsrud, Ivano Bison, & Heidi Jensberg . Their version of the EGP algorithm is intended to reduce the size of the service class. However, the original syntax is more well-known and, hence, we have chosen to use that one.
� The organizers of ESS are investigating this (June 2006).
� The R2s of the models suggest that the explanatory powers of EGP and ESeC are rather similar. The R2s, in the same order as in the diagram, are; .26/.26; .19/.20; .65/.64; .08/.12; .28/.26. The large R2s of Eastern countries are mainly due to large country differences in income levels, which is captured by the ‘country-dummies’. For UK and Ireland the ESeC model is the better one.
PAGE
6
Chart2
0.478
0.238
0.1
0.11
-0.267
-0.518
-0.445
0.428
0.26
-0.059
0.12
-0.186
-0.512
-0.437
0.68
PT
Olle Perssons lösning på problemet att enkelt koppla lablar till punkter i punktdiagram
Tillägnas alla lata doktorander och andra tvivlare
Klistra in dina nya värden här nedan och ändra indataområdet som det passar genom att klicka på diagramknappen ovan.
diagrammet måste vara aktiverat (Tips: kör med klistra in special/värden)
Skalorna i diagrammet kan behöva ändras. Jag har vidden 7,5-12 för att pass med de MDS-koordinater Inforsk brukar jobba med.
Vill du ändra skalorna så klicka bara på dem i diagrammet, de syns här bara som streck.
LablarX-värdenY-värden
ESeC10.8320.714
ESeC20.6090.555
ESeC30.2950.103
ESeC60.280.466
ESeC7-0.18-0.271
ESeC8-0.375-0.056
ESeC9-0.373-0.386
0.680.48-0.010.29-0.23-0.06-0.71
PT
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PL
Olle Perssons lösning på problemet att enkelt koppla lablar till punkter i punktdiagram
Tillägnas alla lata doktorander och andra tvivlare
Klistra in dina nya värden här nedan och ändra indataområdet som det passar genom att klicka på diagramknappen ovan.
diagrammet måste vara aktiverat (Tips: kör med klistra in special/värden)
Skalorna i diagrammet kan behöva ändras. Jag har vidden 7,5-12 för att pass med de MDS-koordinater Inforsk brukar jobba med.
Vill du ändra skalorna så klicka bara på dem i diagrammet, de syns här bara som streck.
LablarX-värdenY-värden
ESeC10.7240.904
ESeC20.2920.4
ESeC30.2850.119
ESeC60.1110.218
ESeC7-0.175-0.399
ESeC8-0.427-0.061
ESeC9-0.42-0.646
0.680.48-0.010.29-0.23-0.06-0.71
PL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
NL
Olle Perssons lösning på problemet att enkelt koppla lablar till punkter i punktdiagram
Tillägnas alla lata doktorander och andra tvivlare
Klistra in dina nya värden här nedan och ändra indataområdet som det passar genom att klicka på diagramknappen ovan.
diagrammet måste vara aktiverat (Tips: kör med klistra in special/värden)
Skalorna i diagrammet kan behöva ändras. Jag har vidden 7,5-12 för att pass med de MDS-koordinater Inforsk brukar jobba med.
Vill du ändra skalorna så klicka bara på dem i diagrammet, de syns här bara som streck.
LablarX-värdenY-värden
ESeC10.2960.25
ESeC20.126-0.005
ESeC30.089-0.176
ESeC60.1420.321
ESeC7-0.359-0.636
ESeC8-0.5010.259
ESeC9-0.653-0.644
0.680.48-0.010.29-0.23-0.06-0.71
NL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
IE
Olle Perssons lösning på problemet att enkelt koppla lablar till punkter i punktdiagram
Tillägnas alla lata doktorander och andra tvivlare
Klistra in dina nya värden här nedan och ändra indataområdet som det passar genom att klicka på diagramknappen ovan.
diagrammet måste vara aktiverat (Tips: kör med klistra in special/värden)
Skalorna i diagrammet kan behöva ändras. Jag har vidden 7,5-12 för att pass med de MDS-koordinater Inforsk brukar jobba med.
Vill du ändra skalorna så klicka bara på dem i diagrammet, de syns här bara som streck.
LablarX-värdenY-värden
ESeC10.530.544
ESeC20.2440.192
ESeC30.31-0.261
ESeC60.2490.25
ESeC7-0.385-0.384
ESeC8-0.504-0.021
ESeC9-0.611-0.496
0.680.48-0.010.29-0.23-0.06-0.71
IE
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
UK
Olle Perssons lösning på problemet att enkelt koppla lablar till punkter i punktdiagram
Tillägnas alla lata doktorander och andra tvivlare
Klistra in dina nya värden här nedan och ändra indataområdet som det passar genom att klicka på diagramknappen ovan.
diagrammet måste vara aktiverat (Tips: kör med klistra in special/värden)
Skalorna i diagrammet kan behöva ändras. Jag har vidden 7,5-12 för att pass med de MDS-koordinater Inforsk brukar jobba med.
Vill du ändra skalorna så klicka bara på dem i diagrammet, de syns här bara som streck.
LablarX-värdenY-värden
ESeC10.4530.46
ESeC20.1980.507
ESeC3-0.049-0.102
ESeC6-0.14-0.08
ESeC7-0.333-0.448
ESeC8-0.1150.168
ESeC9-0.297-0.963
0.680.48-0.010.29-0.23-0.06-0.71
UK
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
DE
Olle Perssons lösning på problemet att enkelt koppla lablar till punkter i punktdiagram
Tillägnas alla lata doktorander och andra tvivlare
Klistra in dina nya värden här nedan och ändra indataområdet som det passar genom att klicka på diagramknappen ovan.
diagrammet måste vara aktiverat (Tips: kör med klistra in special/värden)
Skalorna i diagrammet kan behöva ändras. Jag har vidden 7,5-12 för att pass med de MDS-koordinater Inforsk brukar jobba med.
Vill du ändra skalorna så klicka bara på dem i diagrammet, de syns här bara som streck.
LablarX-värdenY-värden
ESeC10.4460.418
ESeC20.260.253
ESeC30.021-0.11
ESeC60.2480.404
ESeC7-0.384-0.398
ESeC8-0.68-0.112
ESeC9-0.626-0.969
0.680.48-0.010.29-0.23-0.06-0.71
DE
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
SWE
Olle Perssons lösning på problemet att enkelt koppla lablar till punkter i punktdiagram
Tillägnas alla lata doktorander och andra tvivlare
Klistra in dina nya värden här nedan och ändra indataområdet som det passar genom att klicka på diagramknappen ovan.
diagrammet måste vara aktiverat (Tips: kör med klistra in special/värden)
Skalorna i diagrammet kan behöva ändras. Jag har vidden 7,5-12 för att pass med de MDS-koordinater Inforsk brukar jobba med.
Vill du ändra skalorna så klicka bara på dem i diagrammet, de syns här bara som streck.
LablarX-värdenY-värden
ESeC10.4070.307
ESeC20.2350.314
ESeC30.167-0.002
ESeC60.0090.291
ESeC7-0.372-0.641
ESeC8-0.2760.286
ESeC9-0.491-0.443
0.680.48-0.010.29-0.23-0.06-0.71
SWE
0
0
0
0
0
0
0
0
0
0
0
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CE
Olle Perssons lösning på problemet att enkelt koppla lablar till punkter i punktdiagram
Tillägnas alla lata doktorander och andra tvivlare
Klistra in dina nya värden här nedan och ändra indataområdet som det passar genom att klicka på diagramknappen ovan.
diagrammet måste vara aktiverat (Tips: kör med klistra in special/värden)
Skalorna i diagrammet kan behöva ändras. Jag har vidden 7,5-12 för att pass med de MDS-koordinater Inforsk brukar jobba med.
Vill du ändra skalorna så klicka bara på dem i diagrammet, de syns här bara som streck.
LablarX-värdenY-värden
ESeC10.410.386
ESeC20.230.201
ESeC30.053-0.09
ESeC60.1560.345
ESeC7-0.361-0.415
ESeC8-0.63-0.073
ESeC9-0.605-0.86
EGP I0.3660.387
EGP II0.260.134
EGP IIIa-0.157-0.256
EGP V0.2180.639
EGP IIIb-0.276-0.548
EGP VI-0.619-0.006
EGP VII-0.529-0.67
ESeC1
ESeC2
ESeC3
ESeC6
ESeC7
ESeC8
ESeC9
0.680.48-0.010.29-0.23-0.06-0.71
CE
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