Are UK labour markets polarising? Craig Holmes National Institute of Economic and Social Research, London, April 24 th 2012

Introduction Part of SKOPE’s ESRC funded research programme ( ) Main research question: what does the development of the “hourglass” labour market mean for: – Earnings and job quality – Mobility and mobility barriers – Skills policy This presentation draws on this research Main issues: – Why is occupational polarisation not clearly observed in wage distributions? – In what ways is the change in occupational structure actually important?

Structure of talk 1.Polarisation in occupations and wage distributions 2.Re-evaluation of theory 3.Decomposition of changes to wage distributions 4.Wage mobility

Polarisation of occupations 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

Polarisation and occupations Following Goos and Manning (2007), hourglass effect shown through changes in employment share of groups of occupations ranked by (initial) average wages – each of approx. 10% of labour supply. Data : – New Earnings Survey 1986 (ranking wage) – Labour Force Survey (employment shares) – Hours rather than headcount

Polarisation of occupations Growth in employment share, by ranked occupational group,

Polarisation of occupations Similar evidence found: – US (Autor, Katz and Kearney, 2006; Autor, 2011) – Germany (Spitz-Oener, 2006; Oesch and Rodríguez Menés, 2011) – Spain and Switzerland (Oesch and Rodríguez Menés, 2011) and across Europe (Goos, Manning and Salomons, 2009). What does this mean for wage distributions?

Polarisation and wage distributions Wage distributions: – Rising upper and lower inequality (Machin and Van Reenen, 2007) – Increasing proportion below low-paid threshold (Lloyd, Mason and Mayhew, 2008) Does this mean the middle of these distributions is disappearing? Density functions: – New Earnings Survey – Labour Force Survey

Polarisation and wage distributions New Earnings Survey:

Polarisation and wage distributions Labour Force Survey –

Polarisation and wage distributions Has employment in the middle declined? Using same datasets, look at changes in employment across the distribution: – Log gross hourly wage distribution standardised (0.5 th percentile up to 99.5 th percentile) – Wage range divided into ten groups – Look for changes in employment at different wage levels on this scale – Polarisation would be reflected by growth in low-paying and high paying jobs, and decline in middle-wage jobs

Polarisation and wage distributions New Earnings Survey – (1) and (2)

Polarisation and wage distributions Labour Force Survey –

Polarisation and wage distributions Family Expenditure Survey –

Discussion of results Why is there a difference between two approaches? Goos and Manning (2007) demonstrated a compositional effect – Leads to polarised wage distribution if wage structure of occupations remains constant There may be wage effects: – Between-groups effects (Autor, Katz and Kearney, 2006) – Within-groups effects Observable differences (e.g. educational composition) Unobservable differences (e.g. ability)

A model of polarisation Occupational group–area represents employment share wage Composition + increased within- group inequality Change: Initial wage structure =interquartilerange + increased between- group inequality

Discussion of results Wage distributions seem to exhibit an increase in spread in the upper half A small proportion have experienced very high wage growth. By comparison, many good jobs earnings become relatively closer to middle. A growing lower end in the “lovely” jobs? Polarisation of knowledge workers (Brown, Lauder and Ashton, 2011) Less increase in earnings variation at bottom end – minimum wage effect?

Decomposing wage distributions Given above patterns, would like to understand why wage distributions have changed as they have. Biggest issue with analysing changing distributions is separating out all effects: – Wage determination process: y t = g t (x) – Composition effects come through changes to x – Wage effects come through changes to g – These may be different at different points in the wage distribution

Decomposing wage distributions ChangeExample 1 (routinisation) Example 2 (expansion of HE) Example 3 (deunionisation) Composition effectGrowth in high wage and low wage non-routine occupations (Goos and Manning) Growth in number of graduates Decline of union membership Between-groups effect Increased productivity of non- routine occupations (Autor, Katz and Kearney) Changing graduate premium Decreasing union premium Within-group effect New employees in non-routine occupations have different unobserved characteristics (this paper) Graduates/non- graduates have different unobserved characteristics Union members/non- members have different unobserved characteristics

Decomposing wage distributions Number of approaches to measuring changing distributions, usually involving some form of quantile regression: – Usually conditional on explanatory variables, like OLS regressions – We need to look at unconditional distributions – Conditional regressions do not aggregate to unconditional quantile regressions, unlike OLS Firpo, Fortin and Lemieux (2007) – henceforth FFL: – Counterfactual distribution estimated by reweighting – Composition effects: initial  counterfactual – Wage effects: counterfactual  final – Estimates individual contribution of covariates to both – Similar to Blinder-Oaxaxa decomposition of the mean

Data Family Expenditure Survey ( ) – Two surveys for sample: 1987 and 2001 – Covers period of routinisation – Has wages and education attainment (unlike LFS and NES) Variables: – Hourly wage = gross weekly wage / (basic hours + usual overtime) – Age finished full-time education – convert this into dummies for degree (21+), post-compulsory education (18-20) and high school education (16-17) – Experience = age – age left FT education – Dummies for gender, union membership, type of work – No variables on racial background or industry.

Data The 1987 survey also has a narrower occupational coding. – 351 groups – KOS (pre SOC90) classification The 2001 survey uses SOC2000 classification – 353 groups at 4-digit level – 81 groups at 3-digit level Manual conversion using 1987 descriptions into SOC digit equivalent – Changed into 3 digit category – prevents losing 1987 occupations which fit into two closely matched SOC2000 categories Used in this presentation

Data Creates larger occupational groups: – Professional – Managerial – Intermediate – Admin – Manual routine – Manual non-routine – Service High skill non-routine Routine occupations Low skill non-routine

Results: reweighting

Results: reweighting Change in log real gross hourly wage,

Results: composition effects Estimated individual composition effects,

Results: composition effects Large impact of declining unionisation at bottom and middle. Increased female participation has a negative effect (through initial gender pay gap) Increase in part-time work has small negative composition effect Expansion of higher education has impact even on low wage jobs – Largest effect at top of distribution Occupational compositional effect negative at bottom and positive at top – Not as large as education or union at respective ends?

Results: wage effects Marked fluctuations in occupational premia (relative to administrative occupations):

Results: wage effects Stable graduate premium over majority of distribution:

Results: wage effects Other effects – Small effects through changing union premia and wage penalties to part-time work – Large positive wage effects through narrowing gender pay gap – (between 5% and 7% increase in wages across percentiles except at top decile of distribution) – General ‘shift’ very high at bottom end – possibly the result of minumum wage introduced in 2001.

Within-group effects Earnings within growing occupations should become more varied. May reflect differences in educational attainment. – If educational attainment has increased too, may reflect varying wage premia across the distribution May reflect unobservable differences: – General productive ability – Specific skills in certain occupations

Within-group effects NCDS earnings data on managerial workers, based on occupation five years before:

Within-group effects NCDS earnings data on intermediate workers, based on occupation five years before:

Within-group effects Is this the result of observable differences between the two? Ordered logit model: – Dependent variable, Y – earnings group – Y = 1,…,20 – Include qualifications and demographics – Observed wages in 1991, 1999 and Observed occupations five years before each date. ManagerialIntermediate Occupation of employment five years before PROFESSIONAL ** MANAGERIAL Ref.0.574*** INTERMEDIATE Ref. ROUTINE ***-0.982*** MANUAL ** SERVICE ***-1.339*** UNEMP ***-1.495*** NONEMP ***-1.098***

Conclusion Little previous work on evidence of polarisation in UK earnings distributions. We define middle relative to overall spectrum of wages In some cases, evidence that the middle has expanded – although with different occupational titles Occupational polarisation  wage polarisation if there are no wage effects – Other determinants of earnings have changed as well as occupational stucture – May be unobservable within-group effects

Contact Details Craig Holmes ESRC Centre on Skills, Knowledge and Organisational Performance (SKOPE), Department of Education, Norham Gardens, Oxford

Appendix Methodology slides for FFL Managerial wage distributions

Decomposing wage distributions Data: – N observations, N 0 from initial distribution, N 1 from final distribution – T i = 1 if from final distribution, i = 1,...,N. Pr(T i ) = p – Y i and X i observed – Y i = Y i0 (1 – T i ) + Y i1 T i where Y it = g t (X i, e i ), 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

Decomposing wage distributions 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: – Let this statistic be represented by functional v(F) – e.g. percentile – Δv(F) = Δv W + Δv C

Data Descriptive statistics (mean values):

Results: reweighting

Results: reweighting

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}

A quantile regression approach FFL show that: – Δv C = E(X|T=1) γ C - E(X|T=0) γ 0 – Δv W = E(X|T=1) (γ 1 – γ C ) Moreover, if expectation of RIF is linear, γ C = γ 0. – Composition effects are sum of change in composition of each explanatory variable, multiplied by wage return in initial distribution – Wage effect is sum of change in wage returns between counterfactual and final distribution, multiplied by final composition of each explanatory variable. This is a more general case of the Blinder-Oaxaca decomposition, where v(F) is the mean.

A quantile regression approach Our approach looks at quantiles across distribution – j = {0, C, 1} – τ = 0.05, 0.1, 0.15,...,0.95 Estimate f i (q τ ) using kernel density methods

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.

Results: individual contributions Decomposition by wage and composition

Wages at the top Gross weekly earnings data from UK Labour Force Survey 1990s: – Increased employment in higher wage jobs across all good, non- routine occupations – Long tail: some of growth occurred a long way from the median 2000s: – Some increase in low wage employment – despite increasing graduatisation – Some increase in very high wage employment  A hollowing out of the middle of the distribution – Differences by sector of employment (manufacturing, retail, financial intermediation and real estate/business activity)

Wages at the top Managerial occupations:

Wages at the top Professional occupations:

Wages at the top Example: managerial occupations in real esate, renting and business activities

Wages at the top Example: managerial occupations in manufacturing

Wages at the top Example: managerial occupations in retail and wholesale

Wages at the top Example: managerial occupations in financial intermediation