Are labour markets polarising?

1. Are labour markets polarising? Craig Holmes and Ken Mayhew Department of Education, University of Oxford May 10th 2010

2. Polarisation, segmentation and mobility • 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

3. Polarisation, segmentation and mobility • Labour market segmentation theory developed as a departure from traditional models of labour supply and demand in the 1960s and 1970s • LMS suggests it is possible to identify parts of the labour market between which mobility is severely or entirely restricted • These restrictions are related to factors other than individual skills or abilities • Dual market: primary and secondary sector distinguished by wages, security, prospects for promotion and training investment • Initial employment matters  workers becoming ‘trapped’

4. Polarisation, segmentation and mobility • Obvious overlap between the primary and secondary segments in LMS and growth occupations in polarisation hypothesis • Individuals tend to move short distances within the labour market in terms of job quality. Declining middling occupations reduces options for transitory upward steps to better occupations. • Hence, a “hollowed-out” labour market could create two segments with limited mobility between them.

5. Job polarisation in the UK: an assessment • Holmes, (2010), SKOPE research paper no. 90 • Need to understand the ways the polarisation phenomena has or has not manifested within a dataset that can be used for analysing working life mobility • Looks at single cohort from National Child Development Study between 1981 (aged 23) and 2004 (aged 46). • Replicates the Goos and Manning methodology for our NCDS dataset • Finds growth in high wage and low wage occupations, decline in mid-range occupations, proxied by 1981 wage • Evidence of routinisation driven employment changes

6. Job polarisation in the UK: an assessment • Change in employment share of wage deciles.

7. Job polarisation in the UK: an assessment • Resulting wage distributions are important • Absent of other effects, a polarising labour force should be observed as in the diagram below:

8. Job polarisation in the UK: an assessment • Wage distributions exhibit little evidence of polarisation • Most jobs still fall in the middle of wage distribution

9. Job polarisation in the UK: an assessment • How can these two observations be reconciled? • Existing evidence relies on a strong assumption that wage structures have remained constant • Changing wage structures may have led to a new type of middling occupation. • Assumption used throughout literature (Autor, Katz and Kearney, 2006, for US; Spitz-Oener, 2006, for Germany) • Need a new methodology to support polarisation hypothesis • Continue to look at wage distributions • Resulting wages, rather than distribution of jobs, matter • Proxy for job quality?

10. Wage distributions • Large literature on wage distributions, especially focusing on inequality • Prasad (2002), UK 1975-1999: • faster growing upper wage inequality than lower wage inequality • U-shaped wage growth – lowest earning occupations had highest growth rate between 1975-80 • Machin and Van Reenen (2007), UK & USA 1979-2004: • 1980s – U-shaped wage growth in UK, monotonic wage growth in USA • 1990s – U-shaped wage growth in USA • Lower wage inequality constant or reducing over time period • Autor, Katz and Kearney, (2006), USA, 1973-2004: • Patterns of inequality can be explained by “polarisation” – specifically, the wage effects of change in demand.

11. Wage distributions • Prasad (2002) also finds within-group inequality explains 75% of all changes in inequality

12. Wage distributions • Biggest issue with analysing changing distributions is separating out all effects: • Wage determination process: • yt = gt(x) • Wage effects come through changes to changes to g • Returns to education, occupational premia, union premia, returns to experience, discrimination • Composition effects come through changes to x • Level of education, occupational structure, union membership, demographics • Polarisation is a composition effect (Goos and Manning), which leads to a wage effect (Autor, Katz and Kearney)

13. Wage distributions • Polarisation vs. SBTC wage growth ln wage ln wage

14. 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 (i.e. conditional distributions integrated over all explanatory variables) • Firpo, Fortin and Lemieux (2007) – henceforth FFL – supply an appropriate methodology • Individual contribution of covariates to wage and composition effects

15. 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 give initial, final and (unobserved) counterfactual distributions. • Counterfactual is wage distribution that would have arisen given initial wage determination process but final explanatory variables • FC (y) = Pr (Y0 <y | T = 1)

16. A quantile regression approach • Reweighting: • where p(x) = Pr (T=1 | X = x)

17. A quantile regression approach • FFL show that this requires that: • Errors must be independent of T • There must be overlap of covariates – 0 < p(x) < 1 • 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, Gini co-efficient etc. • Δv(F)= (v(F1) - v(Fc)) + v(Fc) - v(F1) Δv(F)=ΔvW + ΔvC

18. A quantile regression approach • FFL’s second contribution is to find a linear approximation of each distributional functional, conditional on the explanatory variables • An influence function, IF, of v(F) is a measure of sensitivity to outliers, where E(IF) = 0 • A recentered influence function, RIF = v(F) + IF, so E(RIF) = v(F) • RIF’s can be conditional on X • Assume a linear projection of RIF onto X: • where j = {0, C, 1}

19. A quantile regression approach • FFL show that: • ΔvW = E(X|T=1) (γ1 – γC) • ΔvC = E(X|T=1) γC - E(X|T=0) γ0 • Moreover, if expectation of RIF is linear, γC = γ0. • This is a more general case of the Blinder-Oaxaca decomposition, where v(F) is the mean. • Our approach looks at 10th, 50th and 90th percentile • v(F) = qτ = inf(y|F(y)<τ), τ = {.1,.5..9} RIF= v(F) + (τ - I(y< q) j = {0,C,1) fj(qτ) • Estimate fi(qτ) using kernel density methods

20. Data • Family Expenditure Survey • Household expenditure and income 1957-2001 • Two surveys for sample: 1987 and 2001 • Covers period of routinisation • Cross-sectional data, rather than longitudinal • 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

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

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

23. Data • Descriptive statistics:

24. Results: reweighting

25. Results: reweighting

26. Results: reweighting

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

28. Results: individual contributions • Low wage jobs: • Large impact of declining unionisation • Small impact of occupational change • No evidence of increasing wage returns for low skill non-routine occupations • Expansion of higher education has impact even on low wage jobs • Majority of change explained by increasing returns to education, especially high school • Declining gender pay gap • Large negative impact from constant term – policy environment? Would expect a positive effect from national minimum wage

29. Results: individual contributions • Middling jobs: • Again, large impact of declining unionisation • Occupational composition effects through growth of managerial and intermediate occupations and decline of manual routine occupations • Education (levels, rather than returns) has important effect • Largest effect of declining gender pay gap

30. Results: individual contributions • High wage jobs: • Insignificant effect of unions, service and manual routine occupations • Polarisation of employment in managerial occupations, as expected • Wage growth effects by occupations, rather than education • Compare wage growth effects for high skill occupations to median group – increased demand for high skill non-routine leads to higher wages only for “best” workers. • Effect of education through composition rather than wage effects. Education composition effects larger than occupational composition effects • All percentiles had large positive effects from increasing returns to experience – proxy for informal training, learning by doing etc.

31. Comparison with other studies • FFL similarly find institutional factors, such as union membership, and education has a much stronger impact of distributions than occupations and industries • Important role for union membership and gender pay gap in our results • Composition effects from occupations fit in with Goos and Manning predictions (although much less so for low wage occupations). However, these effects often outweighed by other composition and wage effects. • Antonczyk, DeLeire and Fitzenberger (2010) – polarisation of wages is a US phenomena – our results support this.

32. Conclusion • Polarisation of employment has played some role in changing wage distributions, however: • Our results suggest this is mostly for high skill non-routine occupations • The growth of these occupations also impacts the middle of the distribution • Other compositional effects (e.g. education and union membership) and other wage effects (e.g. gender pay, returns to education and experience) often dwarf polarisation effects. • “Polarisation“ of wages not found at low end when controlling for education • Evidence of new type of middling occupation

33. Extensions and further work • FES is relatively small sample, with limited data on occupations and omits period 1975-1986 • Repeat using alternative, larger dataset • Labour Force Survey has no wage data until 1994 • New Earnings Survey has no data on education. • Need to test for misspecification of RIF regressions. • Implications for segmentation: • More complicated picture of labour market than initial hypothesis • Middle of distribution is not disappearing or “hollowing out” in the way suggested by literature • Routinisation may still have implications for mobility – role of education, informal learning and possible career paths