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Are labour markets polarising?

Are labour markets polarising?. Craig Holmes. VML workshop on Skills Department of Business, Innovation and Skills, Sheffield July 23 rd 2010. Introduction. A person’s skills derive from a number of sources: Education (general skills)

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Are labour markets polarising?

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  1. Are labour markets polarising? Craig Holmes VML workshop on Skills Department of Business, Innovation and Skills, Sheffield July 23rd 2010

  2. Introduction • A person’s skills derive from a number of sources: • Education (general skills) • Experience (soft skills, informal learning, transferable skills) • Occupations (specific skills) • There are important interactions – education or experience grants access to certain occupations. • Different skills attract different returns in the labour market • These returns may vary along the wage distribution

  3. Introduction • Routinisation hypothesis (Autor, Levy and Murnane, 2003): • Price of computer capital has fallen since late 1970s • Computer capital replaces labour engaged in routine tasks • Non-routine tasks may be complementary to computer capital (e.g. management, skilled professionals) • Result: growth in non-routine occupations due to changes in demand (complementarities) and supply (displaced routine workers) • Polarisation hypothesis (Goos and Manning, 2007) • Routine occupations found in middle of income distribution • Non-routine occupations found at top and bottom of distribution • Managers, skilled professionals at the top • Non-routine ‘service’ occupations at the bottom e.g. hairdressers, cleaners

  4. Introduction • Change in employment share of wage deciles: Source: Holmes, (2010), SKOPE research paper no. 90

  5. Introduction • Similar evidence found for UK using NES (Goos and Manning), US (Autor, Katz and Kearney, 2006) and Germany (Spitz-Oener, 2006) • However, existing evidence relies on a strong assumption that average wages have remained constant. • If they have not, it is not clear what this evidence means for final wage distributions. • Also masks a lot of changing variation within occupations. • This paper looks to explain changes in wage distributions resulting from a range of explanatory factors.

  6. Wage distributions • Three changes: • Composition effect • Between-group effect – relative mean wages of different groups change. • Within-group effect – wages spread out due to new characteristics of groups. • In terms of the routinisation/polarisation hypothesis: • Workers move to high wage and low wage non-routine occupations (Goos and Manning) • Increased productivity of non-routine occupations raises relative wage (Autor, Katz and Kearney) • Some of new employees in non-routine occupations have different abilities to incumbents (Holmes and Mayhew)

  7. Wage distributions

  8. Wage distributions • Biggest issue with analysing changing distributions is separating out all effects: • Wage determination process: • yt = gt(x) • Composition effects come through changes to x • Level of education, occupational structure, union membership, demographics • Wage effects come through changes to g • Returns to education, occupational premia, union premia, returns to experience, discrimination • These may be different at different points in the wage distribution

  9. Data • Family Expenditure Survey • Household expenditure and income 1957-2001 • Two surveys for sample: 1987 and 2001 • Covers period of routinisation • Has wages and education attainment (unlike LFS and NES respectively) • Variables: • Age finished full-time education – convert this into dummies for degree, post-compulsory education and high school education • Experience, sex, union membership • No variables on racial background. • Not used at present: marital status, industry

  10. Data • The 1987 survey first to include data on occupation through socio-economic groups. • Broad groups; captures some of the pattern of routinisation

  11. Data • Creates larger occupational groups: • Seven groups • Corresponds to high skill non-routine, routine and low-skill non-routine occupations

  12. Data • Descriptive statistics (mean values):

  13. A quantile regression approach • Number of approaches to measuring changing distributions, usually involving some form of quantile regression: • Typical OLS regression computes mean values conditional on explanatory variables • Conditional quantile regressions compute quantiles of a distribution conditional on explanatory variables • However, we need to look at unconditional distributions • Firpo, Fortin and Lemieux (2007) – henceforth FFL – supply an appropriate methodology • Individual contribution of covariates to wage and composition effects • Similar to Blinder-Oaxaxa decomposition of the mean, but for all statistics

  14. A quantile regression approach • Data: • N observations, N0 from initial distribution, N1 from final distribution • Ti = 1 if from final distribution, i = 1,...,N. Pr(Ti) = p • Yi and Xi observed • Yi = Yi0 (1 – Ti) + Yi1 Ti where Yit = gt(Xi, ei), t = 0,1 • Data can be reweighted to find the (unobserved) counterfactual distribution. • Counterfactual is wage distribution that would have arisen given initial wage determination process but final explanatory variables

  15. A quantile regression approach • Reweighting: • where p(x) = Pr (T=1 | X = x) • Calculate p(x) using logistical regression • This counterfactual can be used to decompose wage and composition effects of a distributional statistic: • Give statistic represented by functional v(F) – e.g. percentile • Δv(F) = ΔvW + ΔvC

  16. Results: reweighting

  17. Results: reweighting

  18. Results: reweighting

  19. A quantile regression approach • FFL’s second contribution is to break wage and composition effects into individual components e.g. occupation, education etc. • Method found in final paper, omitted here for time. • Idea is to find a linear approximation of each statistic in each distribution using explanatory variables: • Composition effects are sum of change in composition of each explanatory variable, multiplied by wage return in initial distribution • Wage is sum of change in wage returns between counterfactual and final distribution, multiplied by final composition of each explanatory variable.

  20. Results: individual contributions • Decomposition by wage and composition

  21. Results: individual contributions • Large impact of declining unionisation at bottom and middle. • Increased female participation has a negative effect (through initial gender pay gap) and a positive effect (through a declining gender pay gap at bottom and middle. • Pay gap widening at the top. • Expansion of higher education has impact even on low wage jobs • Relative graduate premium increased at top and bottom, but not in middle

  22. Results: individual contributions • Small composition effect of occupational change at bottom • Correct signs, but tiny relative to unions and education variables • Larger effect of occupation composition at top esp. managers • Degrees important at top • Managerial jobs have become important in middle of final distribution – they were not in initial distribution. • Expansion of managerial class to middle and lower management • Relative wage of manual routine occupations increasing, rather than decreasing • Occupations in decline should have falling relative wages • Ability is important – those that stay are best at job

  23. Results: individual contributions • All percentiles had positive effects from increasing returns to experience – proxy for informal training and soft skills • These effects were largest at the top. • Hard to interpret changing constant term – contains wage effects for reference group as well as general ‘shift’ e.g minimum wage policies should push up wages at bottom. • The problem of selecting a reference group in decompositions is well known.

  24. Results: individual contributions

  25. Conclusion • There are effects on wage distribution from Goos and Manning’s polarisation effect • However, at the bottom end these are very small. • Occupational composition is just one factor explaining wage distribution changes • Education, experience and institutional factors play a role • In many cases, this role appears to be larger. • Little support for Autor, Katz and Kearney’s “polarisation of wages” effect • Antonczyk, DeLeire and Fitzenberger (2010): a US-only phenomena. • Evidence to suggest our within-group effect dominates

  26. Implications and further work • Understanding how the labour market is changing is important for a number of policy areas and goals • Inequality • Polarisation suggests a different explanation to rising inequality than SBTC or institutional explanations. • Social mobility • Declining routine occupations may alter traditional career paths • Do routine workers get trapped in declining occupations? • Skills policy • Are current skills sufficient for changing occupational structure? • Are skills already produced being utilised fully?

  27. Contact Details Craig Holmes ESRC Centre on Skills, Knowledge and Organisational Performance (SKOPE), Department of Education, Norham Gardens, Oxford Email: craig.holmes@education.ox.ac.uk

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