Educational attainment, occupational outcomes and the distribution of earnings: an application of unconditional quantile regression techniques Craig Holmes Pembroke College, Oxford University and SKOPE QuantSIG seminar, April 28 th 2014

Outline Background on research project Methodology – Decompositions of distributions – Quantile regressions Data Decomposition results Discussion and future work

Background Wage inequality in the UK has risen since the 1980s

Background Rising upper- and lower-tail inequality until mid 1990s Small increases in upper-tail inequality since mid 1990s (except at very top), coupled with falling inequality at bottom end

Background A leading explanation of this has been the changing demands for skilled work Routinisation hypothesis (Autor, Levy and Murnane, 2003): – Technology related to tasks – Routine tasks substitutable for computer capital – Growth in non-routine jobs, decline in routine jobs Polarization hypothesis (Goos and Manning, 2007) – Routine occupations found in middle of income distribution – Non-routine occupations found at top and bottom of distribution

Background Goos and Manning (2007) – :

Background Similar results observed in: – US (Autor, Katz and Kearney, 2006; Caranci and Jones, 2011) – Germany (Spitz-Oener, 2006; Oesch and Rodríguez Menés, 2011) – Spain and Switzerland (Oesch and Rodríguez Menés, 2011) – Across Europe (Goos, Manning and Salomons, 2009) Other explanations have been put forward: – Offshoring – Growing wage inequality and demand for services

Background The shift away from routine work should increase the number of high-wage and low-wage jobs, everything else being equal

Background However, wage structure of occupations unlikely to remain constant Autor, Katz and Kearney (2006) – relative wage of routine occupations falls – “Wage polarisation” – a US phenomenon? Wage differences between different non-routine occupations (Williams, 2012) – Other compositional changes – more educated workforce, lower union membership, greater female participation – Non-uniform increase in demand for non-routine tasks? – Change in returns to other characteristics

Background Research questions: – To what extent has the shift towards non-routine employment increased wage inequality? – Has anything changed in the pay outcomes between and within jobs which explains recent trends in inequality? – Why, given that routine jobs have continued to decline, have earnings distribution polarisation halted since mid 1990s?

Methodology To be able to answer this question we need to be able to decompose the changes in distributional statistics Two main changes: compositional effects and wage effects......broken down into individual variable contributions of: —Occupational attachment —Educational attainment —Labour market experience —Union membership —Gender —Ethnicity —Type of contract (full-time/part-time)

Methodology Doing this for the mean is easy: – OLS regression estimates the mean conditional on the set of explanatory variables: – where t = {0, 1} – Unconditional estimates of the mean can be found from this regression: – The change in mean wage can then be broken down:

Methodology Estimated using Labour Force Survey Mean hourly wages Estimated Actual Decomposition of changes Compositional effectsWage effects Education4.9%-3.0% Occupation2.5%4.6% Unions-0.9%-1.7% Gender-0.2%2.8% Race0.0%-0.2% Contracts0.0%-0.5% Time0.0%9.7% TOTAL6.2%11.6%

Methodology My research questions require looking at changes in particular quantiles of the distribution, rather than the mean However, the same approach does not work for quantile regressions as it does for OLS regressions. The unconditional statistic can not be inferred from the as a weighted average of the conditional estimates: 75th percentile of hourly wage distribution Estimated from weighted average quantile regression Actual

Methodology Various solutions to this problem in the literature: 1.Juhn, Murphy and Pierce (1993) – Calculates aggregate composition and wage effects by imputation – Can not decompose composition effect – Strong assumptions 2.Machato and Mata (2005) – Calculates aggregate composition and wage effects from quantile regressions – Can not decompose composition effect 3.Firpo, Fortin and Lemieux (2009) – Calculates aggregate composition and wage effects from reweighting – Can estimate individual contributions to both using RIF-regressions

Methodology 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 Data can be reweighted Reweighting: – where p(X) = Pr (T=1|X)

Methodology This counterfactual can be used to decompose wage and composition effects of a distributional statistic,v(F) : A re-centered influence function measures the sensitivity of a distributional statistic to each observation. The RIF of a percentile τ, q τ, for an observation y is defined as: where E(RIF) = q τ

Methodology Then, assume a linear projection of RIF for each τ-th percentile onto the explanatory variables X: – where j = {0, C, 1} As E(RIF) = q τ, then it can be estimated: Hence, we get a more general Blinder-Oaxaca expression:

Data Family Expenditure Survey, – Around 10,000 observations each year – Usual gross pay and usual hours of work – Education – year left FT education  four levels – Union membership – subscription fees>0 Quarterly Labour Force Survey, – Around 150,000 observations each quarter (5 quarter membership) – Gross hourly pay directly reported – Educational qualifications – Union membership directly reported

Data Female47.3%50.3%50.7%52.1% Union membership29.0%15.3%36.9%31.5% Works part-time23.6%23.3%26.3%26.6% University graduates9.4%16.9%13.4%23.4% No qualifications36.1%18.6%17.2%8.3% Experience < 5 years11.9%9.4%7.7%8.2% Experience > 20 years49.1%53.5%57.8%53.8% Professional11.2%12.8% 11.9%14.4% Managerial7.3%11.7% 12.5%14.9% Intermediate10.1%13.7% 14.0%15.8% Manual Routine36.1%26.4% 26.9%19.9% 12.2% Admin Routine19.7%15.2% 14.7% Manual Non-routine1.8%0.8% 0.9%1.1% Service13.8%19.4% 17.9%21.7% N

Composition and wage effects FES, :

Composition and wage effects LFS :

Composition and wage effects Both periods find compositional changes decreasing the number of middle-wage jobs Wage structure changes reverse this – partially in the 1987 and 2001, and completely between 1994 and 2007 Year Jobs earning below 2/3 * median hourly wage Jobs earning above 1.5* median hourly wage Initial (1987)20.2%23.4% Composition effects only24.0%27.1% Final (2001)23.0%25.6% Initial (1994) 22.6%25.2% Composition effects only 25.2%27.3% Final (2007) 21.3%25.9%

Individual composition effects

Individual composition effects

The wage structure - aggregate

The wage structure - occupations

The wage structure - education

Discussion Changes in the composition of the workforce would have increased wage inequality since the 1980s Changes in the wage structure have offset this – almost entirely sine However, hard to interpret as educational or occupational opportunities pulling the middle up – despite the “room at the top” mindset of policymakers An alternative interpretation – downward sloping wage structure is a ‘correction’ of compositional changes – not as many people in high wage jobs as we’d predict

Discussion Increasingly heterogeneous occupational groups

Discussion Unrelated to educational attainment? Graduates only:

Discussion This could reflect a supply problem if it reflects quality of graduates Could also reflect suitability of university route into labour market vs. vocational education Can not ignore changes on the demand side – in particular, are technology and skilled labour always complements? Brown, Lauder and Ashton (2011): – “Knowledge work”  “Working knowledge” – “Digital Taylorism” – deskilling of high skill work – “War for Talent” – high premium paid for small pool of graduates at top universities

Contact Details Craig Holmes Pembroke College, Oxford, and ESRC Centre on Skills, Knowledge and Organisational Performance (SKOPE),