WOMEN S MEN S & WAGES Detail analyses Significance of other assumptions... side 58 Wage differences within main occupations... side 63 Wage differences within major sectors... side 67 Labour market experience and wage differences... side 70
3. Detail analyses The analyses in Chapter 2 focused on the blue-collar workers and white-collar workers employed in the areas covered by DA/LO collective agreements. This chapter explores the effect of changing the assumptions behind the analyses, both with respect to the specification of the model and the data foundation, and to shed light on the wage differences for various subgroups of employees in the DA-covered areas. Significance of other assumptions The analyses include many factors at a detailed level All information utilised The analyses in Chapter 2 included detailed data on the employees' occupation, sector affiliation and education. Other studies often apply only the generally categorised groups within the three above-mentioned categories; cf. Ch. 4. For example, whereas other studies differentiate between about 8-9 occupations, the analyses here distinguish between some 300 different occupations; this degree of detail also holds true for sector and education. The analyses here thus utilise all available information. However, it is of independent value to elucidate what it means for the results of the analyses that the factors are applied at such a detailed level. The analyses are therefore also conducted with other levels of detail of the three factors. It has been chosen to examine the effects of incorporating occupation and sector at two other levels of detail and education at one other level of detail. Effect of second level of detail The effect of changing the degree of detail of one of the three factors is generally speaking about the same. The unexplained part is increased by about 0.5-1 percentage points in many of the cases, while the explained part is correspondingly reduced. This is the case for both blue-collar workers and white-collar workers; see tables 3.1. and 3.2. 58
Table 3.1 Effect of degree of detail, blue-collar workers 2000 Wage difference Model at fully detailed level Occupation, middle level Occupation, low level Sector, middle level Sector, low level Educ., low level Model fully aggregated Explained Unexplained Gross Per cent 10.9 3.4 14.3 11.1 4.3 15.4 10.1 4.3 14.3 10.3 5.1 15.4 9.8 4.5 14.3 10.1 5.3 15.4 10.9 3.4 14.3 11.1 4.3 15.4 10.7 3.5 14.3 11.0 4.4 15.4 10.8 3.5 14.3 10.7 4.7 15.4 7.3 7.0 14.3 7.8 7.5 15.4 NOTE: The model at a fully detailed level means that the factors of occupation, sector and education are disaggregated at the most detailed level possible. 'Occupation middle level' means that the occupations are aggregated up to a higher level with fewer categories, and 'lower level' means that they enter at the highest aggregate level. For occupations, the middle level = 50 groups, low level = 9 groups. For sector, the middle level = 17 groups and lower level = 3 groups. For education, the low level = 10 groups. 'Fully aggregated' means that all three factors are included in the analysis at a low level of detail. The factors included are the same as in the analyses in Ch. 2. SOURCE: Statistics Denmark (2000), IDA-database and Coordinated Social Statistics, DA (2000) Wage Statistics and own calculations. 59
There are differences in how the changes in the level of detail affect the individual contributing explanatory factors. For both blue-collar workers and white-collar workers, the changes in the contribution of the explanatory factors indicate signs of a certain covariation between occupations and sector, such that one explanatory factor to a certain degree can replace the other. Conversely, there seems to be no interaction between education and the other explanatory factors. Large unexplained part when less detailed model used If the entire model is thus specified such that all factors are included at a very aggregated level, the unexplained part for blue-collar workers increases to about double. For whitecollar workers, the level of aggregation is apparently less important. 60
Table 3.2 Effect of degree of detail, white-collar workers 2000 Wage difference Model at fully detailed level Occupy., middle level Occup., low level Sector, middle level Sector, low level Educ., low level Model fully aggregated rerun. Explained Unexplained Gross Per cent 12.4 6.6 19.0 12.3 7.4 19.7 11.8 7.2 19.0 11.6 8.1 19.7 11.8 7.1 19.0 11.6 8.1 19.7 12.5 6.4 19.0 12.5 7.2 19.7 12.4 6.5 19.0 12.4 7.3 19.7 11.9 7.0 19.0 11.9 7.8 19.7 11.0 7.9 19.0 10.9 8.8 19.7 NOTE: See note to table 3.1. SOURCE: Statistics Denmark (2000), IDA-database and Coordinated Social Statistics, DA (2000) Wage Statistics and own calculations. 61
Aggregated and detailed model give nearly identical results for white-collar workers The explanatory contributions for the fully aggregated model are shown in table 3.3. For white-collar workers, the model in aggregated form is very similar to the fully disaggregated model as shown in Chapter 2. The greatest difference is that sector does not appear to be significant when only three sectors are involved, whereas they contribute 1 percentage point to explaining the wage difference when they are analysed at the disaggregated level. For blue-collar workers, the contribution from occupation changes For the group of blue-collar workers as a whole, the degree of explanation, as mentioned, is far less when sector, education and occupation are included at the general level. It is the degree of explanation from occupations, which is strongly reduced by the more aggregated form when compared to the model at the disaggregated level, as indicated in Chapter 2. The explanatory contributions from the remaining factors are nearly unchanged; cf. table 3.3. 62
Table 3.3 Wage difference - aggregated model 2000 DA/LO-bluecollar workers Difference in average hourly wage Of which explained by: Per cent DA/LO-whitecollar workers 14.3 15.4 19.0 19.7 Occupation (9) 2.6 2.5 5.4 5.4 Education (10) 1.4 1.5 2.2 2.3 Sector (3) 1.7 2.0 0.0 0.0 Experience 0.8 1.0 0.9 0.9 Overtime - - 1.0 1.0 Region (3) 0.4 0.4 0.0 0.0 Leave (2) 0.4 0.4 0.8 0.7 Job change 0.0 0.0 0.7 0.7 Children (3) 0.0 0.0-0.1-0.1 explained 7.3 7.8 11.0 10.9 Unexplained 7.0 7.5 7.9 8.8 1,000 persons Women 43.2 44.5 Men 146.2 39.1 NOTE: The figures in parenthesis indicate the number of categories within each factor. SOURCE: Statistics Denmark (2000), IDA-database and Coordinated Social Statistics, DA (2000) Wage Statistics and own calculations. Wage differences within main occupations Differences in occupations most important in explaining wage differences For both blue-collar workers and white-collar workers, different occupations are the most important individual cause of gender wage differences; cf. also Ch.2. In the detailed model, 179 different occupations are included for bluecollar workers and 106 for white-collar workers. It is therefore difficult to obtain an overview of where the difference 63
in salaries is greatest and which occupations contain most women and men. Analyses of wage differences within the general occupation levels can help create a better overview. Wage differences narrowed as groups become more homogenous Smallest wage difference among workers in sales and services Generally speaking, gender wage differences are reduced when the analyses are undertaken among more homogenous groups, of which the division into main occupations is an expression. This picture is confirmed by the difference in occupations being an important parameter for explaining gender wage differences. Regardless of the wage concept used, wage differences between women and men are smallest for those employed in sales and service jobs (group 5). For this group, education, sector and experience contribute roughly equally to the explanation of the wage differences. This means that men dominate the educations and sectors where wages are relatively high, while women dominate in the areas for which wages are relatively low; see table 3.4. 64
Table 3.4 Main occupations 2000 per hour worked Main occupation s 2 3 4 5 7 8 9 Per cent Wage difference 9.0 17.4 8.1 6.3 10.6 7.6 14.8 Of which, explained by: Occup., detailed 1.2 2.6 0.2-1.6 2.8 1.2 7.6 Education 1.6 1.5 1.1 0.7 2.6 1.0 0.6 Sector -1.6 1.2 0.2 0.6 0.9 1.9 1.3 Experience 2.1 1.8 0.5 0.6-0.3 0.5 1.0 Overtime 0.2 0.8 0.4 1.3-0.2 0.1 0.1 Job changes 0.0 0.4 0.4 0.3 0.2-0.1 0.0 Other included factors b 0.7 0.7 0.5 0.0 1.2 0.4 0.4 explained 4.2 9.0 3.3 1.9 7.2 4.9 11.0 Unexplained 4.7 8.4 4.9 4.4 3.6 2.7 3.8 per hour worked Per cent Wage difference 11.0 17.2 9.1 7.2 12.2 9.2 14.8 Of which, explained by: Occupation detailed 1.2 2.0 0.4-1.7 3.2 1.3 6.8 Education 1.6 1.5 1.1 0.7 2.9 1.1 0.6 Sector -1.5 1.2 0.1 0.5 1.0 1.9 1.3 Experience 2.1 1.9 0.6 0.7-0.2 0.7 1.1 Overtime 0.2 0.7 0.4 1.2-0.2 0.1 0.1 Job changes 0.0 0.4 0.4 0.3 0.3-0.1 0.0 Other included factors b 0.8 0.7 0.2 0.2 1.2 0.3 0.5 explained 4.4 8.4 3.2 1.9 8.1 5.3 10.4 Unexplained 6.5 8.9 5.9 5.3 4.1 3.9 4.4 1,000 Women 3.9 18.6 19.0 13.3 3.8 21.4 15.3 1,000 Men 14.7 31.3 5.2 8.5 63.7 52.8 37.2 a: Occupations are defined as follows: 2: High qualification level, 3: Medium-high qualification level, 4: Office work, 5: Sales and service, 7: Skilled craftwork, 8: Processing work and 9: Other work. b: The model is as in Ch. 2, i.e., other factors contain region, children and leave. NOTE: In this analysis, full time employees are in the entire DA-covered area. SOURCE: Statistics Denmark (2000), IDA-database and Coordinated Social Statistics, DA (2000) Wage Statistics and own calculations. 65
Conversely, the more detailed description of the occupation contributes negatively to the explanation of the wage difference for this occupation. This means that women in this area predominate in the relatively highly paid occupations and that men predominate in the relatively low-paid functions. For example, the wages for service work in the transport sector are relatively high - i.e., occupations such as stewardess and the like - at the same time as there are relatively many women employed in such occupations. Largest wage difference at mediumhigh level of qualification The groups with the greatest gender wage difference are found among the employees employed, respectively, in the medium-high qualification level (group 3), with a 17 per cent wage differential, and those in the group other work (group 9), where men earn 15 per cent more than women. The two groups are very heterogeneous. The group of employees at the medium-high qualification level includes both pilots and laboratory technicians, where the former group earns over double the amount as the latter. The group 'other work' ranges from relatively lower paid cleaning jobs to relatively higher paid assembly work. For both groups, the detailed occupations are also relatively more important in explaining the wage differences. This is especially true for the category other work, where this factor explains over half the total wage difference. This confirms the picture that appears in most of the OECD countries, whereby the labour market for people with shorter educations is more gender-segregated than the remaining labour market; see Chapter 4. Experience plays large role for employees at high level For the group with a medium-high qualification level, job experience also plays a relatively large role. This accords with the general picture that women on average have less job experience than do men; see Chapter 1. At the same time, it is especially the case for employees at high and medium-high levels of qualification that job experience has a significant effect on wages, while for other occupations the wage curve remains relatively flat over the life cycle; see Chapter 2. Among employees at a high level of qualification, the detailed sector divisions contribute negatively to the explanation of wage differences. This means that within this particular main occupation, women are typically employed in the sector that pays relatively best. 66
Wage differences within major sectors Occupational sector differences in are also an important parameter for the explanation of wage differences between women and men. Employees differ, also within a single sector An analysis of the main sector level can therefore also be used to increase the level of comprehension in the analyses, even though a distribution by sector does not necessarily contribute to making the employee groups analysed here any more homogenous. Within the different main sectors, there are thus employees organised within the many different trade unions and with different employer organisations as counterparts. This means that there will also be employees with different types of labour agreements, just as the occupations they carry out will also differ. The analyses also show small variations in wage differences between the various sectors. For blue-collar workers, however, the wage differences are less at the major sector level than when all the blue-collar workers working under collective agreements are considered as a whole. Very few women within building and construction sector Gross wage difference lower in manufacturing sector Among blue-collar workers, the most employees are found within manufacturing, but large groups also work in both building and construction and in the service sector. Nevertheless, it is difficult to draw any fixed conclusions regarding wage differences within building and construction, because there are only about 800 female workers in the analysis compared to 38,000 male. We have therefore chosen not to elaborate an analysis of wage differences for this sector. Among blue-collar workers in the manufacturing sector, the wage difference is about 3 percentage points lower than among all blue-collar workers, but the unexplained part of the difference is also about 4 percentage points. Just as for all blue-collar workers, it is also in the manufacturing sector that the detailed occupations constitute the most important explanatory contribution to the wage differences. The general pattern among blue-collar workers of a relatively flat 67
wage profile over the life cycle also means that in the manufacturing sector, despite great differences in the number of years of job experience between women and men, the job experience factor contributes relatively little to the explanation of gender wage differences; see table 3.5. Table 3.5 Main sectors blue-collar workers in the DA/LO-area Wage difference in per cent Of which, explained by: Manufacturing Services All sectors Per cent 10.0 11.5 14.4 13.5 14.3 15.4 Occupation 3.4 3.5 9.8 8.8 6.1 5.6 Education. 1.8 2.1 0.7 1.5 1.9 2.1 Sector 1.2 1.2-2.5-2.9 1.7 2.1 Experience 0.2 0.2 1.4 0.7 0.5 0.6 Leave 0.3 0.3 0.2 0.2 0.3 0.3 Other factors 0.3 0.4 0.0 0.1 0.4 0.4 explained 7.2 7.6 9.6 8.4 10.9 11.1 Unexplained 2.8 3.8 4.8 5.1 3.4 4.3 1,000 Women 28.0 28.0 14.1 14.1 43.2 43.2 1,000 Men 78.7 78.7 23.4 23.4 146.2 146.2 NOTE: Data delimitation to the sector analyses is identical with the data delimitation in Ch. 2. 'Other factors' include region, job mobility and children. SOURCE: Statistics Denmark (2000), IDA-database and Coordinated Social Statistics, DA (2000) Wage Statistics and own calculations. For blue-collar workers employed in the service sector, the occupations are relatively important. About two-thirds of the wage difference can be explained by women and men being employed in different occupations, together with the fact that those occupations dominated by men are relatively better paid. This is the case, for example, with automobile 68
mechanic work, which is male-dominated, whereas women predominate within cleaning, which is a relatively low-wage sector. Wage difference for white-collar workers not changed by division into sector The pattern is somewhat the same when the division into sectors is undertaken for the white-collar workers. They are found primarily within the two sectors of manufacturing and services. Wage differences in these two sectors are nearly the same as for all white-collar workers taken together as an aggregate. At the same time, the explanatory significance in accounting for wage differences is also nearly the same. Within the manufacturing sector, however, a further division into sub sector contributes nearly nothing to the explanation of the wage differences. This is just the opposite in the service sector, where the 'sector' factor accounts for over two percentage points of the difference; see table 3.6. Table 3.6 Main sectors white-collar workers in the DA/LO-area Manufacturing Service All Per cent Wage difference 18.7 19.7 18.7 19.4 19.0 19.7 Of which, explained by: Occupation 4.9 5.0 5.2 5.3 5.2 4.3 Education 3.6 3.7 1.5 1.6 2.5 2.5 Sector -0.2-0.1 2.2 2.1 1.3 1.2 Experience 1.6 1.7 1.3 1.3 1.2 1.2 Leave 0.8 0.9 0.9 0.9 0.8 0.8 Other factors 1.0 0.5 1.9 1.6 1.4 1.4 explained 11.7 11.8 13.0 12.8 12.4 12.3 Unexplained 7.0 7.8 5.8 6.6 6.6 7.4 1,000 Women 16.3 16.3 25.7 25.7 44.5 44.5 1,000 Men 14.1 14.1 23.2 23.2 39.1 39.1 NOTE: Data delimitation for the sector analysis is identical with the data delimitation in Ch. 2. 'Other factors' include region, job mobility and children. SOURCE: Statistics Denmark (2000), IDA-database and Coordinated Social Statistics, DA (2000) Wage Statistics and own calculations. 69
Labour market experience and wage differences Experience in the labour market, measured in number of years employed, is an important parameter in wage formation. As men have longer average job experience than women, it also results in a relatively large contribution to the determination of wage differences, especially for white-collar workers. The more experience, the more different the employees Wage difference lowest among employees without job experience The more experience employees have, the more they will differ, as their experiences will be composed of different jobs in different firms, etc. This means that it becomes more difficult to explain the individual wage differences between women and men with the help of the parameters for which statistical information can be found. For blue-collar workers with 0-5 years experience in the labour market, the average wage difference is less than among all blue-collar workers taken together, and a large part of the difference can be explained by factors in the analysis; see table 3.7. 70
Table 3.7 Experience groups blue-collar workers in the DA/LO-area Wage difference 0-5 yrs. experience Of which, explained by 5-10 yrs. experience 10-15 yrs. experience Per cent. > 15 yrs. Experience 8.6 9.9 11.8 13.4 13.5 14.3 14.4 15.0 Occupation 4.2 3.9 5.6 5.4 7.2 7.2 6.2 6.2 Education 0.8 0.9 1.2 1.4 1.3 1.4 1.1 1.2 Sector 1.5 1.7 1.3 1.4 0.4 0.3 0.9 1.0 Leave 0.3 0.3 0.5 0.3 0.5 0.5 0.2 0.2 Other factors 1-0.4-0.2 0.6 0.7 0.3 0.4 0.9 0.4 explained 6.4 6.6 9.2 9.3 9.7 9.8 9.3 9.0 Unexplained 2.2 3.2 2.5 4.1 3.8 4.5 5.0 6.0 1,000 women 10.4 10.4 8.0 8.0 7.2 7.2 16.5 16.5 1,000 men 17.7 17.7 23.8 23.8 18.8 18.8 72.2 72.2 NOTE: Data delimitation for the experience analysis is identical with the data delimitation in Ch. 2. 'Other factors' include region, job changes and children. SOURCE: Statistics Denmark (2000), IDA-database and Coordinated Social Statistics, DA (2000) Wage Statistics and own calculations. The smaller wage differences among the younger groups reflects the tendency in Denmark and other OECD countries that the youth's choice of employment tends to be less gender-determined than the older generations; see Chapter 4. For the group of wholly new entrants to the labour market, the larger part of the wage difference can be described within the framework of the model. The general differences in wages between women and men of 9-10 per cent is due primarily to the fact that they are employed in different occupations and different sectors, each of which results in different levels of. 71
Greater wage differences among employees with long work experiences The picture is entirely the same for white-collar workers, where wage differences for the groups with 0-10 years' experiences are lower than for the total group and for whitecollar workers with longer job experience. At the same time, the wage differences for the groups with short job experience can be explained to a much higher degree by the factors included in the analysis, just as for blue-collar workers; see table 3.8. Table 3.8 Experience groups white-collar workers in the DA/LO-area Wage difference Of which: explained by: 0-5 yrs. experience 5-10 yrs. experience Per cent 10-15 yrs. experience > 15 yrs. experience earnngs 14.0 15.2 15.0 16.0 20.6 21.2 19.7 20.1 Occupation 5.4 5.6 5.2 5.2 6.2 6.3 5.3 5.2 Education 1.9 2.0 1.8 1.9 3.7 3.6 2.3 2.3 Sector 2.4 2.2 0.6 0.5 0.6 0.6 0.9 0.9 Leave 0.5 0.5 2.6 2.4 2.3 2.2 0.5 0.5 Other factors 0.9 0.8 1.2 1.2 1.8 1.6 1.5 1.4 explained 11.1 11.1 11.4 11.2 14.6 14.3 10.5 10.3 Unexplained 2.9 4.2 3.6 4.8 6.0 6.8 9.2 9.8 1,000 women 7.4 7.4 8.6 8.6 6.9 6.9 21.4 21.4 1,000 men 5.3 5.3 6.9 6.9 5.0 5.0 22.9 22.9 ANM.: Data delimitation to the job experience analyses is identical with the data delimitation in Chapter 2. 'Other factors' includes region, job mobility and children. SOURCE: Statistics Denmark (2000), IDA-database and Coordinated Social Statistics, DA (2000) Wage Statistics and own calculations. Wage differences can evolve over time For the groups with over 15 years of job experience, the total wage difference between male and female white-collar workers is about 20 per cent, only half of which can be explained by the included factors. The great differences in wage differences between women and men dependent on 72
the number of years of job experience can be due to the fact that wage differentials develop over time along with the employees becoming more and more different. 73