The class-origin wage gap: heterogeneity in education and variations across market segments.

The British Journal of Sociology 2013 Volume 64 Issue 4

The class-origin wage gap: heterogeneity in education and variations across market segments1 Martin Hällsten

Abstract This paper uses unique population-level matched employer–employee data on monthly wages to analyse class-origin wage gaps in the Swedish labour market. Education is the primary mediator of class origin advantages in the labour market, but mobility research often only considers the vertical dimension of education. When one uses an unusually detailed measure of education in a horizontal dimension, the wage gap between individuals of advantaged and disadvantaged class origin is found to be substantial (4–5 per cent), yet considerably smaller than when measures are used which only control for level of education and field of study. This is also the case for models with class or occupation as outcome. The class-origin wage gap varies considerably across labour market segments, such as those defined by educational levels, fields of education, industries and occupations in both seemingly unsystematic and conspicuous ways. The gap is small in the public sector, suggesting that bureaucracy may act as a leveller. Keywords: Wage inequality; social mobility; class origin; register data; field of study; qualification fixed effects; OED

Introduction Studies of social mobility generally find that much of the inequality in labour market outcomes in one generation (parents) is transferred to the next generation (sons and daughters). By using a stylized model, this tendency for social origin to impact on labour market outcomes can be explained by the indirect effect of class differences in educational investments and by the direct class-origin effect of resources linked to parents’ class position, such as contact networks, personality or class bound skills. Studies of social mobility generally find that education explains a large share of the intergenerational correlation, Hällsten (Department of Sociology, Stockholm University) (Corresponding author email: [email protected]) © London School of Economics and Political Science 2013 ISSN 0007-1315 print/1468-4446 online. Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA on behalf of the LSE. DOI: 10.1111/1468-4446.12040

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even though parents’ class position still demonstrates a non-negligible direct effect on their children’ outcomes (see Blau and Duncan 1967 for the classical treatise; and Breen 2004 for recent results). Previous studies have, however, used very crude measures of education, which may overstate the extent of direct class-origin effects and underestimate class-origin differences in educational attainment. This paper advances the literature by using an extremely detailed measure of education which distinguishes between different levels and fields of study (down to specific majors and degrees). It analyses the extent to which the direct effects of class origin differ across labour market segments such as those defined by different skill levels, educational fields of study, institutional sectors, industries and types of occupation.The analysis uses monthly wage as outcome to measure inequality with high specificity. This is important since social reproduction does not end with occupational attainment. Any advantages of the class of origin are also likely to operate within the destination occupation or class, e.g. differences in career progression and rewards. Using the detailed measure of education reduces the extent of direct classorigin effects by an average of 30 per cent compared to results obtained when using standard measures of education. When one controls for detailed education, the wage difference between individuals of advantaged and disadvantaged class origin is, on average, four to five per cent, but this varies across labour market segments, and is concentrated to the private competitive parts of the economy, indicating that bureaucracy may act as a social leveller.

Class origin and heterogeneity in educational attainment A stylized model of social mobility is the three factor origin-educationdestination model,where the factors form an equilateral triangle (OED).The total rate of social reproduction across generations is found in the total effect (the association between O and D). This overall association can then be broken down into an indirect path of origin on education and of education on destination (OE × ED) and the direct path of class origin on destination when differences in education are accounted for (OD|E). In an economic sense, inequalities in the labour market arise because of differences in the supply of individuals with skills and characteristics which employers and other agents in the market demand, define as meritorious, and reward accordingly. Educational attainment is a key factor in both human capital theories and theories of social closure, and it influences labour market outcomes (ED). Educational investment differs across individuals from different class origins (OE) for many reasons (see Erikson and Jonsson 1996 for an overview). In short, one can differentiate between primary British Journal of Sociology 64(4)

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achievement differences and secondary choice effects (Boudon 1974), both of which depress educational attainment in disadvantaged classes. Returns on educational investments (ED) differ not only by years of education as in the parsimonious human capital model (Mincer 1974), but also by fields of study or type of degree (Daymont and Andrisani 1984). Recent research shows that individuals with different class origins not only end up with different levels of schooling, but also in different fields within each level (Ayalon and Yogev 2005; Lucas 2001). This kind of heterogeneity should be taken into account when analysing the role of education in social mobility processes. However, the social mobility literature often only looks at the vertical dimension of education, which may indicate erroneously large direct effects of class origin (inflating OD|E), and consequently downplay the role of education (suppressing the OE effect). The attempts in social mobility studies to incorporate the horizontal dimension in mobility analyses give ambiguous results. In a cross-national analysis of class mobility tables, Jackson et al. (2008) found that direct class-origin effect remained largely intact when educational field of study had been controlled for. In a regression framework, Erikson and Jonsson (1998) analysed differences in entering the service class, occupational prestige and income by class origin. They used educational level, field of study and expected earnings for a combination of 56 levels and fields as their measure of education. Even though the detailed controls for education did reduce the remaining direct class effect (OD|E), a small yet non-negligible advantage for the most privileged class origin nevertheless remained. Mastekaasa (2011) advanced this issue by including 300 educational ‘fixed effects’ in a regression of earnings on parental education and earnings. Compared to specifications that used only level of education as the control, this approach proved to have only limited impact on the direct class-origin effects (OD|E). It therefore appears to be important to compare the observable effect of different levels of detail in the education measure to see what, if any, their relevance is for direct class-origin effects.

Mechanisms of direct class-origin effects Over and above education, a number of mechanisms have been put forward to explain direct class-origin effects (OD|E) in the labour market. Erikson and Jonsson (1998) summarise these as differences in productivity, social capital, career aspirations and favouritism, i.e., employers’ preferences for advantaged classes. Productivity is ultimately defined by the employer and dependent on corporate needs. One aspect of this mechanism could be occupation-specific human capital: individuals learn and gain knowledge from their parents that increases their labour market returns in occupations which are the same or similar to those held by their parents. Social capital can operate both via © London School of Economics and Political Science 2013

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information flows (e.g. about vacancies) and via influence on hiring and promotion decisions (Lin 1999). Having an advantaged class origin may provide better information as well as influence. Career aspirations may be more pronounced among those from advantaged backgrounds, which may be explained by differences in parents’ job values and shaped by living conditions (Halaby 2003). For example, in the disadvantaged class, it may be more important to trade-off the potentially greater rewards to be gained from career-oriented occupations for job security, because job loss is perceived as a much greater threat by the disadvantaged. Finally, favouritism towards those perceived to be from higher classes can be explained either by stereotypes or prejudices rooted in societal discourse: the employer or the person responsible for recruitment may themselves, for example, come from an advantaged background. Apart from these general mechanisms, there are others that are specific to particular segments of the labour market.A re-emergent literature suggests that individual traits such as self-efficacy, work ethic, orientation toward the present vs. the future, and deviant behaviour may influence earnings and labour market rewards (Farkas 2003). Many (but not all) of these traits are productive; some can be termed non-cognitive skills and may be more important influences than education on labour market outcomes (Bowles and Gintis 2002).The skills and personal characteristics needed in the productive industries are also influenced by the broader social context, such as customer preferences. A view developed by Erikson and Jonsson (1998), Goldthorpe (2000a) and Jackson, Goldthorpe and Mills (2005) is that individuals from advantaged origins possess advantages such as self-presentation, lifestyle, appearance, accent and manners that may enhance their productivity in settings where the work requires smooth face-toface interaction (this can be understood as a form of cultural capital, as defined by Bourdieu 1984; DiMaggio 1982). Jackson, Goldthorpe and Mills’ prime example focuses on the sale of products and personal service trades, typically found in in lower skilled jobs (i.e., jobs that do not require a university education). They found that recruitment advertisements for these occupations focus on high-level social skills and that formal qualifications matter little for recruitment into these occupations. On the assumption that an upper middleclass origin nurtures superior social skills, the ‘sales and services’ hypothesis they have put forward suggests that individuals from advantaged origins are more productive in lower skilled sales and service occupations (cf. Mastekaasa 2011). As a consequence, the class-origin wage gap should be especially high in industries and occupations dominated by face-to-face interactions and economic transactions. Several studies show that the direct influence of class origin (OD|E) is lower at higher levels of education (e.g., Breen and Jonsson 2007). This is often interpreted as meaning that labour markets for the better-educated are more meritocratic. Goldthorpe and Jackson (2008) offer an alternative interpretation of this finding, namely that people with advantaged class origins, who for British Journal of Sociology 64(4)


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some reason have failed in education, can draw on resources from their class of origin to obtain higher labour market rewards than their achieved education would otherwise allow. Although Goldthorpe (2000a) mentions that one such resource can be social connections, he argues that social skills will be the most important resource.The consequence of this, he argues, is that lower skilled but socially intense service occupations will act as refuge occupations for children of the service class with little education. However, in an analysis of the variation in the effects of social origin on earnings across industries, Mastekaasa (2011) did not find any evidence of a stronger effect in the sales and services industries as expected according to the sales and services hypothesis. Hansen (2001) found that the direct class-origin effects on annual earnings vary by field of study at the tertiary level. In ‘soft’ fields such as the social sciences, economics and law, the earnings gap between the most and the least advantaged classes was around 25 per cent. Typical ‘hard’ fields of study such as the natural sciences and engineering had earnings gaps below 10 per cent, net of educational controls. Hansen suggested that these differences reflect the ambiguities of evaluating performance among practitioners in the labour market. In mono-paradigmatic (‘hard’) fields of study such as the natural sciences, performance is readily observable, whereas in multi-paradigmatic (‘soft’) fields such as the humanities and social sciences, evaluation criteria are often viewed as fuzzy and contestable. In such circumstances, the evaluation of performance is open to class-biased processes and resources such as cultural and social capital. Hence, the wage gap is expected to be larger among those who have studied soft subjects than hard subjects. Favouritism towards certain classes is in essence discrimination, but is not prohibited by Swedish law.2 The scope for favouritism and discrimination should, however, be limited if the employer relies on formal rules and a formalized process for hiring and making promotion decisions. In this sense, bureaucracy is viewed by many scholars as ‘the great leveller’ (Baron, et al. 2007).A large body of research suggests that disadvantaged groups (e.g. women and non-whites) benefit from bureaucratic regulations (Reskin and McBrier 2000).According to the classic Weberian ideal type, the public sector is assumed to be the bureaucracy par excellence. Some use of formal rules is of course required by discrimination laws which apply to the whole economy, but private organizations are less subject to other formal regulations and can be assumed to use rules and regulations less often. Studies have shown public and non-profit organizations to be more likely to install affirmative action procedures (Dobbin, et al. 1988). It has also been shown that formalized employment procedures (e.g. due process arrangements, job descriptions, performance evaluations) are more common in large companies and in the public sector (Bridges and Villemez 1991; Marsden, Cook and Kalleberg 1996). The bureaucracy hypothesis proposes that the scope for class-origin wage effects decreases with the degree of bureaucracy in the organization.3 The present © London School of Economics and Political Science 2013



study is unable to apply direct measures of bureaucracy to explore this hypothesis, but uses company size and sector as rough proxies. Mastekaasa (2004) analysed the impact of class origin on elite membership of various sectors of the economy in Norway, and concluded that class origin is important for elite status in the private sector but not in the public sector. Mastekaasa (2011) analysed the effects of social origin on earnings more generally, and again found that the effects of origin were smaller in the public sector and large private companies than in smaller private firms.

Summary of research questions and outline of hypotheses My first task in this study is to improve the estimate of the direct class-origin effect (OD|E), i.e. the effect of social-class origin on outcomes net of people’s own educational investment. I hypothesize that the direct classorigin effect shrinks considerably when education is specified comprehensively by a fine-grained measure that simultaneously identifies level of education, type of degree and specific field of study, compared with the results obtained from standard specifications which only include level or aggregate field of study. I then examine how the specificity of education impacts differently across educational levels in order to shed more light on previous results which indicate that the direct class-origin effect is lower at higher levels of education. In order to search for further explanations for the direct class-origin effect, I will examine whether the direct class-origin effect is general or unique to specific labour market segments, such as those defined by the educational field, industry, occupation, sector and firm size. Here, the literature has generated three hypotheses. The first is based on the proposal, mentioned earlier, that classes are differently endowed with social skills, the expectation arising from this being that the direct class-origin effect should be especially high in sales and services industries and occupations. The second proposes that if bureaucracy acts as a levelling mechanism, we should find markedly smaller direct class-origin effects in large as opposed to small private companies and the public sector. Finally, if consensus in evaluating performance varies across educational fields, the expectation is that the direct class-origin effect should be higher for ‘soft’ than in ‘hard’ fields of education. Analytical strategy When analysing inequality by ascribed dimensions such as class origin, a number of assumptions are necessary since most potential controls will be caused in part by class origin.4 I adopt the pre-market approach (cf. Neal and Johnson 1996) according to which controls are justifiable as long as they are British Journal of Sociology 64(4)


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determined before the individual enters the labour market. Apart from education and class origin, the equations contain controls for year, age, age square, county of birth, immigration status, civil status and children in household (see below). The impact of class origin on wages is analysed using linear log wage OLS regressions where different class origins are represented by dummies. I gauge direct class-origin effects (OD|E) as (1) the difference between individuals from the upper service class and individuals from the unskilled manual class (see below), and (2) the weighted adjusted standard deviation (WASD) of all class origin differences (coefficients and their reference category). The former represents inequality between the two endpoints of the class scheme, which I refer to as the class origin wage gap or,in short,the wage gap (cf.the gender wage gap). The latter captures an overall gradient of class origin differences, which may be important since definitions of endpoints can differ across class-schemes and countries.The WASD is interpreted as the average inequality across classes, adjusted for differences in size of the classes of origin.5 In order to analyse the wage gaps across labour market segments, the baseline model with the most comprehensive measure of education is estimated within each segment (see definitions below). A standard critique of this approach would be that any pattern of effects would reflect differential selection into the categories by ability. If people of high (low) ability from the service class and low (high) ability from the unskilled manual class select into the same occupation or industry, for example, this would generate an upward (downward) bias of the wage gap. However, by examining general ability selection, differential selection appears unlikely to produce the results given below (not shown). 6

Data The data consist of population records from the STAR data collection (Sweden over Time – Activities and Relations), organized by Statistics Sweden on behalf of the Swedish Institute for Social Research (SOFI). This data set contains a wide range of data from official registers, linked by a common personal identifier. Important features of this dataset include the combination of high quality population data and a match between employers/companies and their employees. The registers of primary interest in this paper are registers on work-time standardised monthly wages, annual earnings, education and parental occupation. The focus population includes employed individuals above age 30, born between 1945 and 1970, and excludes inequality at the verge of the labour market (i.e. being unemployed).7 The outcomes are measured in 2001, 2004 and 2007 (the selection of years is to reduce computational burden) when these individuals were aged 31 to 62. The limit for the older cohort was drawn © London School of Economics and Political Science 2013



with reference to the class-origin measure: those born in 1945 were 15 years old in the 1960 census, the earliest time point at which we could record parental occupation, and they were probably still living with their parents at that time. The lower age limit was set at 30 in order to focus on permanent wage inequality. Dependent variables Statistics Sweden’s earnings structure register consists of wage records reported by the employer once a year, work-hour-standardised to reflect a full time monthly wage. It covers all employees in the public sector and in large private companies (> 500 employees). For employees in private companies with fewer than 500 employees, information about wages was obtained through a stratified survey procedure. In total, there are a good two million observations per year. Retained stratum identifiers and sampling weights allow inferences about the underlying population.8 Wage is used in log form and transformed into fixed prices using the consumer price index. Even though wage is the preferred outcome measure, a complementary proxy for wages based on information on annual earnings from labour obtained from tax records is used to corroborate the findings.9 The wage measure is superior in its definition (excluding effects of employment and work hours) and is a better reflection of permanent inequality; the earnings wage proxy, on the other hand, is superior in representation since it covers the whole economy without sampling (this is especially important for small companies in the private sector). I also use percentile ranks of wages and earnings in order to cancel out the influence of wage dispersion, which is important since this can potentially blow up differences across labour market segments (Blau and Kahn 1996). If some segments have a higher wage dispersion this is likely to produce larger wage gaps despite a similar positioning in the wage distribution. Ranks of wages reflect hierarchical aspects of the labour market career, and one can thus to some extent compare whether inequality in real monetary terms and in hierarchical terms diverges or converges. I have also coded a proxy for service class membership based on the highest skill level in ISCO-88(com)10 and Treiman’s prestige score (Ganzeboom and Treiman 1996) as alternative outcomes, in order to scrutinize whether the findings are limited to wage returns or also representative for class and occupational attainment. Independent variables Year, age and its square, civil status and children in the household were obtained from population registers. Information about highest attained education was British Journal of Sociology 64(4)


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obtained from the Education Register. This register contains data mainly from school registers,but also uses other methods such as surveys to minimise missing values (in the period under study educational information is missing for around 2.5 per cent of the population aged 16–75). The key measure of education is a combination of type of degree, level of education and field of study and based on a concatenation of the international standard ISCED-97 level and field codes at the finest level (i.e., 3-digit level and field codes, see Statistics Sweden 2000).This code describes both length of study (in years),detailed field of study and subject. It also distinguishes between achieved degree and unfinished studies at each level, and between degrees of vocational, professional and general character at each level. In the estimation sample, the number of categories in this educational qualification fixed effect is slightly above 1,300. In addition, six levels of education and 23 fields of study are coded using the level and field codes separately, and are represented by dummy variables. Supplementary Table A shows the distribution of educational levels, while Appendix Table B shows the distribution of the constructed education field variables. Class origin is constructed by linking occupational information in the quintennial censuses 1960–1990 between parents and their offspring. As opposed to annual earnings, for example, class gives a very accurate reflection of permanent inequality. Class origin is coded to the EGP-scheme with seven classes according to the dominance principle (Erikson 1984). The scheme distinguishes between individuals in the manual, non-manual and service classes, with additional categories for farmers and entrepreneurs. The manual, non-manual and service categories differ in the type and levels of skills needed for the job (Tåhlin 2007), and in the degree of mutual dependence in the employer-employee relation (Goldthorpe 2000b). The upper service class typically comprises university graduates and represents the most privileged group. The least privileged group consists of unskilled manual workers, usually with basic or some vocational schooling. Class origin is difficult to measure for immigrants. Information about their parents’ labour market position is seldom available in the censuses (nor from their country of origin), and they have therefore been omitted unless they can be found in at least one census while aged 0–15. To reduce heterogeneity, I include a category for missing class origin together with a detailed measure of immigrant origin (distinguishing between first and second generation immigrants and mixed families). I also control for county of birth in order to control for some initial differences in local labour markets. Appendix Table A summarizes the control variables. Labour market segments I now turn to the labour market segments. The educational fields have been described above (see also Appendix Table B). Occupation was obtained from © London School of Economics and Political Science 2013



the earnings structure database (for the wage measure) and the occupation register (for the earnings wage proxy); both are employer-reported information. The constructed measure distinguishes between similar occupational groups (based on three digit ISCO-88 codes; the coding scheme can be obtained from the author on request) relevant to separate, among other things, out sales and services jobs. The term salesperson refers to workers in both high and low value sales. The category cooks, waiters and bartenders also includes housekeeping workers. Personal services here refer to travel attendants and hairdressers (and the like). Unfortunately, the category elementary sales and services is not very precise and contains a range of miscellaneous occupations. The underlying measure of occupation does not pick up positions of authority within the work group (apart from those positions that qualify as managerial positions, e.g. ISCO 121, 122, 123 and 131). Hence, wage differences can exist within each occupation related to authority position. Appendix Table C shows the full frequencies of the occupation variable. Sector is combined with company size and coded into four categories: public, small private companies (up to 49 employees), medium-sized private companies (50 to 499 employees) and large private companies (500 or more employees). Industry is coded into 23 categories, distinguishing between, for example, manufacturing, construction, transportation, public administration, education and health care, as well as more narrow categories for sales of valuables and durables (e.g. cars, furniture, travel agencies), hotels and restaurants, personal services (hairdressing, beauty treatment and the like), which should most closely resemble the context described by the sales and services hypothesis. I also include real estate, although this tends not to be a field for unskilled workers.Appendix Table D shows the full frequencies of the industry variable. The Appendix Tables B–D also include the interquartile range (IQR) measure of wage dispersion, which may aid our understanding of discrepancies between estimates based on log wage and wage rank.

Results Education and the class-origin wage gap Table I shows estimates of pooled OLS regressions of log wages on class origin for males in 2001, 2004 and 2007. The raw class-origin wage gap (unskilled manual vs. service class) is on average more than 0.28 log points in Model 1 and the corresponding average class origin inequality, WASD, is 0.09 log points (that is, 32 and 9 per cent respectively, using exponentiated coefficients). Controlling for level of education with six categories (typical for standard stratification studies) diminishes the gap by 70 per cent, to 0.09 log points (the WASD changes to 0.03). Introducing field of study with 23 categories in Model British Journal of Sociology 64(4)


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Table I: OLS regressions of log monthly wage and alternative outcomes on class origin and control variables in 2001, 2004 and 2007 for males (1)

(2)

(3)

(4)

(5)

(6)

(7)

Class origin: Unskilled manual (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) (ref.) Skilled manual 0.028** 0.011** 0.010** 0.008** 0.008** 0.003** 0.005** Routine non-manual 0.119** 0.046** 0.043** 0.034** 0.033** 0.020** 0.033** Lower service 0.175** 0.064** 0.055** 0.041** 0.040** 0.023** 0.038** Upper service 0.284** 0.095** 0.086** 0.054** 0.053** 0.034** 0.051** Self-employed 0.348** 0.134** 0.114** 0.068** 0.064** 0.055** 0.061** professionals Entrepreneurs 0.075** 0.028** 0.027** 0.021** 0.022** 0.009** 0.020** Farmers 0.014** −0.002 −0.004** −0.005** −0.004** −0.005** −0.007** Missing −0.001 −0.008** −0.007** −0.007** −0.005** −0.005** −0.004** Education level X X (6 categories) Field of study X (23 categories) Educational X X X X qualification fixed effects Endogenous variables: Sector and firm size X dummies (4 categories) Occupation dummies X (26 categories) Industry X (16 categories) Observations 1,789,011 1,789,011 1,789,011 1,789,011 1,789,011 1,736,982 1,760,927 Adjusted R-squared 0.141 0.312 0.375 0.437 0.476 0.582 0.487 Individuals 783,661 783,661 783,661 783,661 783,661 773,688 780,221 # Education FE – – – 1,320 1,320 1,318 1,320 Class gradient (WASD) 0.0883 0.0316 0.0282 0.0195 0.0191 0.0122 0.0189 Alternative outcomes (upper service coefficient) Wage rank 0.177** 0.045** 0.043** 0.029** (percentiles) Ln wage proxy 0.280** 0.087** 0.079** 0.052** Wage proxy rank 0.142** 0.033** 0.032** 0.020** (percentiles) Service class 0.385** 0.090** 0.082** 0.053** membership (linear prob. model) Ln prestige score 0.295** 0.063** 0.062** 0.042** (Treiman) Notes: ** p < 0.01; * p < 0.05 based on individual cluster-robust standard errors. Equations contain year dummies, age, age squared, birthcounty, immigration status, civil status and children in household dummies.

3 reduces the gap by less than one percentage point. Hence, in line with previous studies (e.g., Jackson, et al. 2008 in their analysis of mobility tables), educational field does not contribute much to the class-origin gap. However, replacing educational level and field dummies with fixed effects for detailed © London School of Economics and Political Science 2013



educational qualifications in Model 4 reduces the class-origin wage gap dramatically by a further 0.031 log points, or in relative terms more than 30 per cent (this also applies to the WASD).11 The class-origin wage gap is now 0.054 log points and the WASD 0.019, still a substantial inequality.12 Model 4 is considered the baseline model for further inquiry. It can be noted that the changes in the endpoint comparison of unskilled manual vs. service class and the average inequality WASD follow closely on each other, and class inequality is approximately a one-dimensional gradient. I consequently concentrate on the endpoint comparison further on. The results for females are presented in Table II.The pattern is closely related to that for males. The raw gap without controls is lower, around 0.22 log points. When the educational control variables are introduced, the similarity between the male wage models and the female wage models becomes striking, although the pay gap is slightly smaller for women in all models. In the baseline Model 4, the difference between individuals of unskilled manual and service class origin is 0.039 log points (and the average inequality WASD is 0.013 log points). A very similar pattern emerges when we analyse wage percentile ranks and the wage proxy based on truncated annual earnings (see the lower panel in Tables I and II). For females, the earnings based wage proxy deviates somewhat from wages and indicates lower inequality. However, earnings are a poorer measure here since women’s work time is more variable and may correlate with class origin. The level of the absolute percentile wage gap is somewhat lower than the relative log wage gap, but the changes in the classorigin effect across model specifications are remarkably similar. Hence, the displayed inequality exists both in relative and hierarchical terms, and the sample selection in the wage data (where small private firms are sampled) is unlikely to bias the results.13 The inclusion of educational qualifications as a fixed effect also reduces the class-origin effect in models with alternative outcomes: service class membership and occupational prestige. For service class membership, the reduction in entry opportunity when qualification fixed effects are introduced is more than 35 per cent for both men and women. When one uses Treiman’s prestige score as outcome there is a more than 30 per cent reduction in class-origin effects for both men and women. Hence, if one does not take the detailed nature of education into account, the direct effect (OD|E) of class origin is likely to be overestimated also in more traditional social mobility studies (and the OE relation is underestimated). Next, I analyse how the reduction in the gap interacts with educational level in order to find where the specificity of education most greatly confounds the direct class-origin effect. Table III shows the absolute reductions in both wages and wage ranks for the endpoint comparison across levels of education. The number of specific educational qualifications within each level is shown in the right-hand column. The widest variety of qualifications is found at the British Journal of Sociology 64(4)


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Table II: OLS regressions of log monthly wage and alternative outcomes on class origin and control variables in 2001, 2004 and 2007 for females

Class origin: Unskilled manual Skilled manual Routine non-manual Lower service Upper service Self-employed professionals Entrepreneurs Farmers Missing Education level (6 categories) Field of study (23 categories) Educational qualification fixed effects Endogenous variables: Sector and firm size dummies (4 categories) Occupation dummies (26 categories) Industry (16 categories) Observations Adjusted R-squared Individuals # Education FE Class gradient (WASD) Alternative outcomes (upper service coefficient) Wage rank (percentiles) Ln wage proxy Wage proxy rank (percentiles) Service class membership (linear prob. model) Ln prestige score (Treiman)

(1)

(2)

(3)

(4)

(5)

(6)

(ref.) 0.017** 0.082** 0.123** 0.220** 0.265**

(ref.) 0.005** 0.029** 0.043** 0.083** 0.106**

(ref.) 0.005** 0.028** 0.039** 0.074** 0.089**

(ref.) 0.004** 0.022** 0.026** 0.039** 0.048**

(ref.) 0.004** 0.018** 0.022** 0.035** 0.042**

(ref.) 0.001** 0.012** 0.015** 0.026** 0.035**

(7) (ref.) 0.004** 0.018** 0.021** 0.034** 0.043**

0.054** 0.016** 0.017** 0.013** 0.011** 0.006** 0.011** 0.031** 0.002** 0.002* −0.001 0.001 −0.000 −0.000 0.002 −0.002 −0.003** −0.003** −0.004** −0.002** −0.003** X X X X

X

X

X

X X X 2,315,660 2,315,660 2,315,660 2,315,660 2,315,660 2,298,319 2,250,138 0.127 0.351 0.397 0.487 0.527 0.622 0.545 960,880 960,880 960,880 960,880 960,880 957,787 953,746 – – – 1,361 1,361 1,359 1,360 0.0627 0.0237 0.0212 0.0127 0.0110 0.00780 0.0108

0.227**

0.065**

0.058**

0.037**

0.224** 0.176**

0.082** 0.057**

0.071** 0.048**

0.037** 0.027**

0.298**

0.096**

0.076**

0.045**

0.300**

0.068**

0.064**

0.040**

Notes: ** p < 0.01; * p < 0.05 based on individual cluster-robust standard errors. Equations contain year dummies, age, age squared, birth county, immigration status, civil status and children in household dummies.

post-secondary and the tertiary levels (with a great deal of variety also at the non-academic upper-secondary level). The reduction in the class origin gap is shown in the left-hand columns (obtained by comparing models 3 to 4 as above, within each level of education). Not surprisingly, it is at the tertiary level © London School of Economics and Political Science 2013


The class-origin wage gap 675 Table III: Reduction in class origin gap with detailed measure of education compared to standard specification across educational levels (log points/percentiles × 100) Reduction in Class origin gapa

Males Basic education Non-acad. upper sec. Acad. upper secondary Post-secondary Tertiary Females Basic education Non-acad. upper sec. Acad. upper secondary Post-secondary Tertiary

Remaining gapa Ln wage

# Educational qualifications

Ln wage

Rank wage

Rank wage

−0.53 (−0.68) −0.85 (−2.58)

−0.50 (−0.7) −0.81 (−2.49)

8.44 (15.23) 6.28 (12.5) 3.70 (16.05) 2.82 (12.26)

24 291

−1.94 (−3.78)

−1.35 (−3.73)

10.04 (27.88) 5.36 (20.98)

24

−1.32 (−3.82) −5.64 (−19.08)

−0.65 (−2.82) −2.62 (−18.04)

6.56 (27.21) 3.54 (22.1) 5.08 (25.34) 1.86 (18.95)

534 448

−0.87 (−1.19) −0.40 (−1.88)

−1.22 (−1.47) −0.46 (−1.78)

5.77 (11.12) 6.63 (11.34) 3.00 (20.34) 3.61 (20.04)

27 285

−1.59 (−4.44)

−1.80 (−4.96)

7.71 (30.64) 8.03 (31.36)

23

−0.80 (−3.8) −6.87 (−38.36)

−0.85 (−4.22) −3.95 (−29.85)

4.21 (28.92) 4.01 (28.86) 3.82 (33.16) 2.89 (32.58)

574 453

Notes: The numbers are derived by comparing models 3 and 4 in Tables I and II within levels of education. t-values in parenthesis. a This refer to the difference between unskilled manual and service class origins.

that the specificity of education matters the most: log wage inequality between unskilled manual and service class origin is reduced by around 6 percentage points when qualification fixed effects are introduced. In terms of wage ranks, the reductions lie between 3 percentiles for men and 4 percentiles for women. The class gradient follows the same pattern. The heterogeneity of qualifications at the non-academic upper-secondary level and the post-secondary levels is far less significant. Thus, it is mainly at the tertiary level that a more exact measure of education will influence the conclusions. Class differences at tertiary level are an important inequality-generating mechanism (as recent findings in the literature of educational choice have shown, Hällsten 2010). Table III also shows the size of the remaining gap in the middle columns. In line with previous studies, there is a gradient over educational levels; higher levels tend to have lower class-origin gaps, although the secondary level deviates from this. Vocational studies at upper-secondary school are associated with smaller gaps, while academic studies are associated with larger gaps. Since the latter prepare for tertiary education, and the gap per definition applies to those who for some reason have failed to complete the next level, this suggests that such individuals from advantaged origins are much better at finding rewarding jobs without further educational investments. I will now return to Tables I and II and assess the role of selection into labour market segments for the overall gap. In sequences, I introduce dummy variables for selection into sector/company size, occupation and industry to get a picture of how differential selection processes influence the class origin gap. British Journal of Sociology 64(4)


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Although these variables are part of the educational returns and mute the effect of education, they give some indication of how the origin gap is related to differential sorting across segments after the initial educational investment. The pattern of selection is very similar for men and women and can be described as follows. Model 5 controls for a combination of private sector and company size. The class-origin gap is almost unaffected. Even though the estimated wages are considerably lower in the public sector (coefficient not shown), it demonstrates that the origin gap does not arise because individuals of unskilled origins self-select into lower paying but more secure public sector jobs. In Model 6, dummies for occupational groups are added and the wage gap is reduced by more than 0.02 log points, or about half of the gap. This indicates that individuals of disadvantaged origins are less likely to be found in rewarding occupations than individuals with advantaged origins. However, an origin wage gap of around 0.03 log points, net of own occupation, does nevertheless exist. As shown by Model 7, segregation across industries and company size does not account for any of the wage gap. In summary, finely-grained class differences in education contribute to the wage gap, and the direct effect of class origin (OD|E) is rather sensitive to how education is specified, especially at the tertiary level. Using level of education alone, or level in combination with field of study, does not capture all of the differences that education generates. Educational data at the level of specific (especially tertiary/university) degrees can explain more of the wage gap than any other measure.14 Over and above this, segregation across occupations contributes to the wage gap, but segregation across sector, firm size and industry does not. Variation across labour market segments I will now turn to the variation in the origin gap across labour market segments. The estimations were calculated separately for males and females, and use the baseline specification (see Model 4 in Tables I and II). In order not to confound the gap with variation in the wage dispersion within segments, I use percentile ranked wages. To save space, only the contrast for wage ranks is shown. However, the level of inequality is lower when wage ranks instead of log wages are used. The pattern is very similar for wages and for the wage proxy, which suggests that the delimited sample analysed in order to use wage as the outcome measure does not impact on the results (see above and note 6). Any further differences between the methods are commented on in the following section. Figure I shows the class-origin wage gap for fields of study at the tertiary level, where we previously saw that the heterogeneity by specific educational qualification was most important. For most categories, the origin gap is larger for females in the point estimate (although there is often a great deal of © London School of Economics and Political Science 2013


The class-origin wage gap 677 Figure I: The class origin gap in wage ranks across different educational fields at the tertiary level Teacher training and educ. Arts Humanities Social and behavioural sci. Journalism and information Business and administration Law Life sciences Physical sciences Mathematics and statistics Computing Engineering and eng. trades Manufacturing and process. Architecture and building Agricult., forestry, fish. Veterinary Health Social services Personal services Transport services Environmental protection

15

10

5

0

-5

Advantage (percentiles) Female

Male

Note: Categories with less than 500 observations have been omitted. 95 per cent confidence intervals super-imposed in grey.

overlap in the confidence intervals). Fields such as arts and computing demonstrate higher gaps than average. At first glance, there is no immediately noticeable tendency for the variation of the gap. ‘Hard’ fields such as life sciences, health, physics and engineering do however demonstrate lower wage gaps than average, which becomes apparent if the categories are divided into three groups (soft, hard and other). In an equivalent to Figure I based on log wages (not shown), the differences are greater, with business and law especially demonstrating extreme origin gaps, precisely as in Hansen’s analysis. As is revealed by Appendix Table B, the dispersion (IQR) in these fields is also clearly above the grand mean. Hence, part of the inequality is generated by structural features of the labour market destinations of these graduates. British Journal of Sociology 64(4)


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Figure II: The class origin gap in wage ranks across different occupation groups Officials & public admin. prof. Corporate managers (incl. small) Physical, math., engin. prof. Physical and engin. technicians Life sci. and health prof. Life sci. and health associates Care workers, health & childcare Teaching and research prof. Teaching associates Business professionals Business associates Law professionals Creative prof. Creative associates Religious prof. and workers Office clerks Customer services clerks Salespersons Sales and services, elementary Cooks, waiters, bartenders Personal service workers Protective workers Skilled agricult./fishery workers Extract. and build. workers Metal, machinery, etc. workers Precision, handicraft workers Craft workers Stationary-plant operators Machine operators and assemblers Drivers and mobile-plant operators Labourers in mining, constr. etc.

15

10

5

0

-5


Male

Note: Categories with less than 500 observations have been omitted. 95 per cent confidence intervals super-imposed in grey.

Figure II shows estimates for occupation segments. Here, the correspondence of the gap across gender is higher. The average level of the gap is generally lower because the between-occupation selection of individuals accounts for a substantial part of the wage gap (compare models 6 and 4 in Tables I and II respectively). Firstly, all inequalities relate to service and nonmanual (EGP I, II and III) occupations. Manual occupations have low wage gaps, and in two cases, there is evidence of negative gaps: female construction workers and male drivers and mobile plant operators. Secondly, in service and non-manual occupations, the highest level of inequality is found among female personal service workers.15 Occupation groups such as creative associates, business professionals and associates, law associates and officials and public administration professionals also have generally large gaps, while life science and health professionals display strikingly low gaps. Engineering occupations lie somewhere in between. The large gap among female personal service © London School of Economics and Political Science 2013



workers lends support to the sales and services hypothesis, yet the wage gap is zero in the two categories for sales occupations and is not marked for cooks, waiters and bartenders. The class-origin wage gap is wider in more highlyskilled service occupations than in the lower skilled sales and personal services occupations. These findings could be interpreted according to the soft/hard distinction mentioned above, but it is equally striking that the gaps are large in business occupations – where ‘money making’ and financial decisions are central (this holds for both business professionals and associates, law professionals, and officials and public administration officials, even though the relatively small average effect among corporate managers is slightly at odds with such an interpretation). Figures III (a) and (b) display the origin gap in different industries separately for the public sector and the private sector. Again, the estimated gap is

Figure III (a): The class origin gap in wage ranks across sectors and industries in the public sector (a) Farming, forestry, fishing Extraction Manufacturing Core supplies (energy, water, radio, deposit) Construction Transport Consumer services and repairs Retail sales Wholesale Sales of valuables and durables Hotels and restaurants Personal services Real estate Legal and acccounting Financial services Firm services Other services Public administration Education Academia and research Health NGOs Media

15

10

5

0

-5


Male

Note: 95 per cent confidence intervals super-imposed in grey. British Journal of Sociology 64(4)


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Figure III (b): The class origin gap in wage ranks across sectors and industries in the private sector (b) Farming, forestry, fishing Extraction Manufacturing Core supplies (energy, water, radio, deposit) Construction Transport Consumer services and repairs Retail sales Wholesale Sales of valuables and durables Hotels and restaurants Personal services Real estate Legal and acccounting Financial services Firm services Other services Public administration Education Academia and research Health NGOs Media

15

10

5

0

-5


Male

Note: 95 per cent confidence intervals super-imposed in grey.

larger for females. The gap is conspicuously small for all industries in the public sector (the only noteworthy exception being among women in construction and core supplies, and in public administration, where there is a small gap for both genders of around 4 percentile points). The gap is small in education and tiny in health care. In contrast, the gap is wide in the private sector. The variation in origin gap by industry follows no immediately clear and logical pattern. Service industries have larger wage gaps than production. Sales, and especially sales of durable goods, do not display larger wage gaps than average. Unlike the “cooks, waiters and bartenders” occupation group, the gaps in the hotel and restaurant industries is large. Hotels, restaurants and personal services may be a refuge industry for women of high class origin but with little education (as Goldthorpe 2000a proposes). For women, personal © London School of Economics and Political Science 2013


The class-origin wage gap 681 Table IV: Variations in class origin advantages across sectors (log points/percentiles × 100) Class origin gapa

Males Public sector Private small firm Private middle firm Private large firm Females Public sector Private small firm Private middle firm Private large firm

Class gradient (WASD)

Ln wage

Rank wage

Ln wage

Rank wage

2.12 (18.5) 6.34 (9.41) 7.26 (26.28) 7.15 (40.86)

1.87 (19.32) 4.37 (8.45) 4.18 (23.37) 3.26 (32.19)

0.96 2.22 2.42 2.27

0.84 1.93 1.60 1.26

1.63 (27.27) 6.41 (9.98) 6.68 (21.91) 7.04 (38.55)

1.88 (29.83) 6.37 (9.96) 5.57 (21.18) 6.07 (41.58)

0.58 1.97 2.07 2.18

0.67 2.26 1.90 2.05

Notes: Estimated using the standard specification with education fixed effects, t-values in parenthesis. a This refer to the difference between unskilled manual and service class origins.

services again demonstrate a large gap in the point estimate, which corresponds to the results for occupation groups, but the estimate is very imprecisely estimated. The industry and occupation analyses give the sales and services hypothesis of social skills mixed support. The mixed support for the sales and services hypothesis can be interpreted in several ways. Firstly, social skills may be valuable in all industries and occupations, and could still be a general explanation of inequality. Secondly, sales and service occupations are typically low in prestige and pay, and one can question whether such occupations can function as a favourable refuge for persons from the service class with low education. Thirdly, ‘egalitarian’ Sweden could be a deviant case, where little importance is accorded to class-determined manners, and where social distinctions are comparatively blurred. Finally, Table IV shows the sector differences when the private sector has been divided into three groups based on firm size (large, medium-sized and small). As was apparent in figures III (a) and (b), the lion’s share of the class-origin wage gap is found in the private, competitive parts of the economy. Table IV shows results both for wage ranks and for log wages. In terms of wage ranks, the gradient for both men and women is consistent with the hypothesis that larger companies have lower gaps due to (presumed) higher rates of bureaucracy. However, this is not the case for log wages, where the greatest inequality is found in large private firms. The wage dispersion is higher in larger firms than small ones (not shown), and this may explain the differences between the outcome measures.The public sector clearly has the smallest wage gaps by all measures. These findings suggest that bureaucracy may act as a leveller and that government by formal rules may be an important mechanism for muting the influence of class origin.16 British Journal of Sociology 64(4)


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Discussion This paper has shown that class-origin differences in educational attainment are stronger when education is measured with a high level of detail (a combination of detailed information about type of degree and major subjects) than when standard specifications, with only broad educational level or level combined with field of study, are used.As a consequence, the direct class-origin effects on wages are much exaggerated when education is measured in the standard way. When fixed effects for detailed educational qualifications are controlled for, a further 30–40 per cent of the direct class-origin effect on labour market outcomes is accounted for. While a large part of the direct class-origin effects tends to be accounted for by unmeasured differences in education in disguise, it is striking that education does not explain away all direct class-origin effects, no matter the degree of detail. On average, individuals with advantaged class origins earn four to five per cent more than individuals with disadvantaged class origins. These results hold not only for wages, which distinguishes between finely-graded differences in the labour market, but also for outcomes such as service class membership or occupational prestige. One implication of this is that the potential importance of employers’ discrimination by class origin is considerably smaller than previous studies have found. These results have implications for the study of the role of education in social mobility processes more generally. It is likely that previous studies have overestimated direct class-origin effects and under-estimated the association between class origin and educational attainment. Moreover, adding broad fields of study to a specification with broad levels of education does not improve the estimation of direct class-origin effects; it is, rather, the high level of detail and the interaction of educational level and specific field of study that is needed to explain much of the direct class effect. This means that in order to estimate direct class-origin effects and class differences in education attainment without bias, one needs to take heterogeneity seriously and employ measures which combine levels and fields at a high level of detail. Unfortunately, this necessitates large samples sizes to estimate models with many education cells.Breaking down direct and indirect effects of class origin on subsequent outcomes using standard measures of education can produce misleading results. The direct class-origin effect (on average four to five per cent), was not constant across the whole labour market but tended to vary across labour market segments in seemingly both arbitrary and systematic ways. The explanatory power of the detailed education measure varies across levels of educational attainment. The reduction is largest at the tertiary level where the heterogeneity in returns on different kinds of degrees is very large. After extensive educational controls, direct class-origin effects are relatively small at this level, especially if one takes the large wage dispersion at the tertiary level © London School of Economics and Political Science 2013



into account and analyses ranks of wages. This is in line with suggestions that the graduate labour market is more meritocratic. Direct class-origin effects were unambiguously lower in the public sector than in the private sector, and slightly lower in large than in small private companies. These findings are in line with Mastekaasa (2004; 2011) and they imply that bureaucratic routines may act as a levelling mechanism. Another interesting finding was that direct class-origin effects were larger in ‘soft’ than in ‘hard’ fields of study, which echoes some previous findings (Hansen 2001) and may suggest that clarity of evaluation criteria may limit the scope for direct class-origin effects. The findings of a recent study (Jackson, Goldthorpe and Mills 2005), which suggest that individuals with advantaged class origins have superior social skills and that these are rewarded in lower skilled sales and services industries and occupations, receive mixed support from the present study. Direct classorigin effects were high in some of these segments, but it is difficult to distinguish the ‘sales and services’ effect from the private sector effect. Nonetheless, high and low skilled service and non-manual occupations seem to display wider class-origin gaps than other segments. The present exploration gives only tentative guidance to more careful examinations of the underlying mechanisms of direct class-origin effects. One disclaimer is that the present analysis lacks the important direct measures of personality, career aspirations, social network resources, and specialised human capital which are needed to understand the various explanations better. It is likely that some of the remaining wage differences are due to these supply factors.The markedly lower wage gap in bureaucratic settings, however, suggests that personality and social capital cannot explain the entire remaining wage gap. Demand factors such as employer behaviour and practices should therefore be examined more carefully (see e.g., Jackson 2009; Stainback, Tomaskovic-Devey and Skaggs 2010). (Date accepted: July 2013)



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Appendix Appendix Table A: Descriptive statistics Continuous variables

Mean

SD

Min

Max

N

Ln wage (2003 prices) Rank wage Age (wage sample) Ln wage proxy (2003 prices) Rank wage proxy Age (wage proxy sample)

10.06 0.55 46.73 5.61 0.54 46.35

0.31 0.28 7.78 0.40 0.28 7.92

9.00 0 31 4.79 0 31

14.20 1 62 10.38 1 62

4,104,671 6,471,419

Percent positive Categorical variables Female Married status Children 0–6 yrs in household Children 7–12 yrs in household Immigration status Native First generation Second generation Semi-native Semi-first Class origin (EGP classes) Unskilled manual (VII) Skilled manual (VI) Routine non-manual (III) Lower service (II) Upper service (I) Self-employed professionals (∼I) Entrepreneurs (IVab) Farmers (IVcd) Missing Level of education Basic education Non-academic upper secondary Academic upper secondary Post-secondary Tertiary Post-graduate


Wage proxy sample

Wage sample

46.7 54.6 16.3 33.9

56.4 55.3 14.7 33.9

87.8 2.3 2.7 6.7 0.1

88.0 2.3 2.7 6.6 0.1

22.4 22.3 9.9 17.6 6.9 0.3 8.5 7.3 4.8

23.0 22.8 9.7 17.8 6.8 0.3 7.7 7.0 4.9

14.5 36.8 13.5 16.7 17.4 1.1

11.5 35.2 12.4 18.6 20.8 1.5


The class-origin wage gap 685 Appendix Table B: Sample sizes for various fields of study overall and at the tertiary level 2001, 2004 and 2007 Tertiary education samplea Wage proxy sample

Wage sample

Wage proxy sample

Wage sample

Wage dispersion (IQR)

Basic programmes Teacher training and educ. Arts Humanities Social and behavioural sciences Journalism and information Business and administration Law Life sciences Physical sciences Mathematics and statistics Computing Engineering and eng. trades Manufacturing and process. Architecture and building Agricult., forestry, fish. Veterinary Health Social services Personal services Transport services Environmental protection Security services

1,404,582 489,301 55,009 74,321 107,546 31,925 1,024,403 57,204 13,401 18,795 22,393 74,560 1,236,121 101,807 286,291 106,608 4,439 710,409 241,382 215,256 94,757 4,790 96,119

788,742 429,613 30,052 51,949 75,081 22,760 608,858 34,908 10,977 14,337 15,330 44,905 637,306 57,407 129,294 47,426 2,263 606,392 206,575 147,823 56,702 3,897 82,074

290,939 24,614 45,881 86,266 17,257 171,811 52,328 13,206 17,373 18,422 23,026 144,038 7,106 37,201 10,538 4,241 220,490 71,414 1,133 4,992 3,395 139

252,767 12,562 31,455 58,134 13,373 103,585 30,825 10,102 12,881 12,373 13,872 91,302 5,627 21,586 7,312 2,096 191,471 64,706 5,270 2,555 2,705 950

0.189 0.287 0.354 0.387 0.222 0.562 0.527 0.404 0.438 0.467 0.411 0.470 0.597 0.430 0.439 0.476 0.570 0.212 0.235 0.569 0.263 0.352

Total

6,471,419

4,104,671

1,265,810

964,313

0.345

Note:

a

used in Figure I.



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Appendix Table C: Sample sizes and wage dispersion for various occupations 2001, 2004 and 2007

Officials & public admin. prof. Corporate managers (incl. small) Physical, math., engin. prof. Physical and engin. technicians Life sci. and health prof. Life sci. and health associates Care workers, health & childcare Teaching and research prof. Teaching associates Business professionals Business associates Law professionals Creative prof. Creative associates Religious prof. and workers Office clerks Customer services clerks Salespersons Sales and services, elementary Cooks, waiters, bartenders Personal service workers Protective workers Skilled agricult./fishery workers Extract. and build. workers Metal, machinery, etc. workers Precision, handicraft workers Craft workers Stationary-plant operators Machine operators and assemblers Drivers and mobile-plant operators Labourers in mining, constr. etc. Total


Wage proxy sample

Wage sample


171,667 477,844 263,121 347,473 209,243 236,424 702,026 338,348 148,588 169,208 476,957 28,372 64,013 16,697 9,628 490,914 77,667 184,435 179,346 56,859 23,354 73,264 71,796 339,492 240,900 24,622 18,985 97,937 319,707 225,029 73,416

127,377 230,461 152,592 222,128 182,053 200,924 673,565 311,212 131,594 94,731 241,440 16,038 33,238 5,093 7,161 311,863 46,592 66,386 151,015 35,204 13,219 68,670 20,388 143,216 105,135 9,136 5,172 74,632 174,107 85,409 49,956

0.395 0.505 0.371 0.334 0.503 0.191 0.150 0.194 0.130 0.467 0.385 0.529 0.296 0.278 0.255 0.193 0.178 0.227 0.129 0.130 0.306 0.274 0.174 0.267 0.189 0.239 0.228 0.214 0.204 0.189 0.212

6,157,332

3,989,707

0.269


The class-origin wage gap 687 Appendix Table D: Sample Sizes and Wage Dispersion for Various Industries 2001, 2004 and 2007 Wage proxy sample

Wage sample


Farming, forestry, fishing Extraction Manufacturing Core supplies (energy, water, radio, deposit) Construction Transport Consumer services and repairs Retail sales Wholesale Sales of valuables and durables Hotels and restaurants Personal services Real estate Legal and accounting Financial services Firm services Other services Public administration Education Academia and research Health NGOs Media

93,098 15,015 1,167,137 145,390 418,458 363,565 103,589 200,803 315,557 132,752 55,773 19,085 110,204 119,123 160,201 363,042 144,666 423,721 598,088 87,623 1,167,370 91,598 89,510

19,354 11,082 712,552 109,451 151,736 193,279 37,538 83,069 59,392 48,574 12,781 1,370 46,788 20,285 115,698 138,465 74,779 376,337 537,346 80,823 1,034,460 67,260 33,385

0.302 0.251 0.355 0.422 0.262 0.290 0.308 0.295 0.458 0.336 0.271 0.313 0.361 0.571 0.524 0.552 0.281 0.385 0.306 0.450 0.248 0.402 0.367

Total

6,385,368

3,965,804

0.361

Notes 1. I wish to thank Jan O. Jonsson for sharing his class origin algorithm. I also thank Magnus Bygren, Markus Jäntti, John Goldthorpe and Michael Tåhlin for fruitful suggestions on earlier drafts, and two anonymous BJS reviewers for critical commentaries and productive suggestions. 2. One should remember that class origin, unlike gender and immigration status, which are covered by anti-discrimination laws, is not readily observable. 3. In a non-experimental analysis it is intrinsically hard to test whether economic returns on class-specific personality, like other types of discrimination, are due to productivity or favouritism. It should be stressed that any association between bureaucracy and wage inequality does not necessarily mean that differences in, for example personality, are unrelated to productivity. Even if British Journal of Sociology 64(4)

the wage gap is zero under bureaucracy, alternative interpretations would be that either the work tasks usually performed in bureaucratic organizations do not require the types of personality associated with the upper classes, or that bureaucracy entails an ideology of only rewarding formal merits and disregarding informal productive characteristics. 4. In principle, including mediating controls (whether in a path model or in a single regression equation), requires that the errors from a regression of the mediating variable on the focal variable are independent of the error in the main outcome equation (Björklund and Jäntti 2009), an assumption which is difficult to test, but which many research designs – the present included – hinge upon. 5. The WASD was proposed by Krueger and Summers (1988) to measure © London School of Economics and Political Science 2013

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inter-industry wage differentials. The measure is a weighted standard deviation so that larger classes contribute more to the measure. Krueger and Summers note that the measure should be adjusted to account for sampling variations, but these adjustments give negligible differences given the large sample used in this paper. 6. These supplemental analyses can be obtained directly from the author: martin. [email protected]. For a sub-sample of males born between 1952 and 1970, the data contains a measure of cognitive ability and also non-cognitive abilities from the mandatory military enlistment evaluation conducted at age 18 (Lindqvist and Vestman 2011).These measures are indirectly affected by class-origin through differences in schooling choices, but will nevertheless provide an opportunity to test whether individuals of different origins are selected into the various labour market segments on similar grounds. Controlling for the cognitive and non-cognitive ability measures for enlisted males changes the wage gap pattern in occupations, industries, etc. by very little. 7. Limiting the sample to employees introduces selection bias if one is interested in the total wage gap including potential employees. As we place a condition on there being some minimum level of labour market success, these estimates are more valid to estimate the disadvantages of upwardly mobile individuals (as opposed to the advantages of downwardly mobile individuals, where unemployment may be a key inequality indicator). 8. Since many units are not sampled and therefore weighted with unity, and the sampling and response fraction is very low among some sample stratums (j), this makes the distribution of weights extremely skewed. I therefore weigh estimates by 1+ln(Nj/nj) in order reduce the extreme impact of some stratums. 9. By truncating annual earnings to include only individuals with earnings above SEK 120,000 in 2003 prices, this mimics a wage measure (Antelius and Björklund 2000). © London School of Economics and Political Science 2013

10. The codes are 111 to 249 except 131 (managers of small enterprises), and 233 to 235 (lower level teachers). 11. A risk when using a very detailed measure is that cells can be incomplete, i.e. that a simultaneous representation of individuals of both origins (unskilled manual, upper service) is missing for a unique value of education. In that case, identification would rest entirely on a linearity assumption. Twelve per cent of all education cells are incomplete, but this only applies to 0.014 per cent of all individuals in the sample (results not shown). 12. All differences in coefficients across models are significant at levels below p < 0.001. 13. Lindqvist and Vestman (2011) use more sophisticated techniques to evaluate the sample selection bias of the earnings structure data and conclude that the selected sample does not bias their results. 14. Using a similar approach, Mastekaasa (2011) found that his 300 educational dummies did not diminish the class origin earnings gap more than the parsimonious controls for educational level.This is surprising in the light of the present results. In my analysis, I use a higher resolution with >1300 categories that distinguish educational down to the level of single academic subjects (e.g. M.A. in sociology vs. B.Sc. in economics), which could explain the difference in results (given that Norway and Sweden share many institutional characteristics and pronounced country differences are less likely). This has implications for the specification of education in mobility studies. It should however be remembered that social origin was measured differently in the Norwegian study (as parents’ earnings and education), so the comparison is not entirely straightforward. 15. Paradoxically, the wage dispersion in this category is lower than average (see supplementary Table B), and in terms of log wages, this category does not stand out as the most unequal (not shown). 16. A further argument is that educational qualifications have less weight in the wage determining process in the private British Journal of Sociology 64(4)

The class-origin wage gap 689 sector and in smaller private firms. The explained variance of the qualification fixed effects is 0.55 in the public sector,

0.30 in large private firms, 0.27 in mediumsized private firms and 0.28 in small private firms.

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