Recent changes in British wage inequality: Evidence from firms and occupations

Transcription

1 1 / 17 Recent changes in British wage inequality: Evidence from firms and occupations Daniel Schaefer & Carl Singleton The University of Edinburgh International Workshop on Establishment Panel Analyses IAB Nuremberg, 5th October 2017

2 2 / 17 What drives British wage inequality trends? - Within-firm or between-firm? - Job polarisation? Why we care: - Productivity - Policy recommendation - Inform economic theory

3 3 / 17 Two main contributions: - Robust measurement of between-firm and within-firm inequality among large British firms - Link to occupational wage premia and concentration Two novel findings: - The within-firm component is the main driver of inequality - The between-firm component is almost entirely explained by the rising wage premia and concentration of occupations

4 4 / 17 Motivation Percentiles of real log earnings, full-time employees only

5 5 / 17 Motivation - Many explanations for changes in the variance of earnings: The rise of the supermanager (Piketty, 2013) Skill-biased tech. change (Machin & van Reenen, 1998) Institutions (Card et al., 2004; Machin, 2011) - Firm-specific component of wages recent empirical focus: Alvarez et al. (forthcoming AEJMacro), Barth et al. (2016 JoLE), Card et al. (2013 QJE), Card et al. (forthcoming JoLE), Song et al. (2016) More related literature

6 6 / 17 New Earnings Survey Panel Dataset / ASHE - One per cent of all PAYE employees in Britain: up to 180,000 employees per year since Annual questionnaire (legal req.) to employers every April - Collects gross weekly earnings, its components, hours and annual earnings (1999-) - For we can exactly identify enterprises - Enterprise level information from IDBR since This is how we identify firms/enterprises - 4-digit records of occupations

7 7 / 17 Baseline sample - Ages 16-64, and exclude jobs where pay has been affected by absence or leave - Exclude overtime from hourly and weekly wages - Drop jobs with over 100 hours, or hourly rate less than 80% of the minimum wage - Annual wages: only keep jobs which are at least a year old - Use only jobs at enterprises with 250+ employees - Baseline sample: Full-time (30+ hours), and 10+ observations in the firm both varied for robustness - Mostly represents very large British firms - 1,640 enterprises, representing 10,804,000 employees ( 40%) 2013 Wages in large firms vs All in the NESPD

8 8 / 17 Wage inequality trends: the role of between-firm variance Decomposition of variance in log employee wages: V e = V wf + V bf, (1) The role of unobservable wage inequality - we estimate in each year: w ij = µ + β x ij + α j + ε ij }{{} unobs. - ψ ij, (2) var( ˆψ ij ) = var( ˆα j ) + var(ˆε ij ) (3) w ij - x ij - log wage of employee i in firm j controls for sex, age, age 2, region, (occupation) ψ ij - unobservable: firm-specific component α j and remaining heterogeneity ε ij Detailed expressions

9 9 / 17 Within and between-firm components of the variance in log ANNUAL wages, Weekly wages and hours

10 Variance of total log Annual wages and estimated unobservable log Annual wages, var( ˆψ ij ), / 17

11 Share of within-firm component, var(ˆε ij ), in the variance of estimated unobservable log ANNUAL wages, var( ˆψ ij ), / 17

12 Change in unobservable real log WEEKLY wages: controls for sex, age, age 2, region, firm w ij = µ + β x ij + α j + ε ij ˆψ ij = ˆα j + ˆε ij 12 / 17

13 Change in unobservable log WEEKLY wages: controls for sex, age, age 2, region, firm, 3-dig occupations w ij = µ + β x ij + α j + ε ij ˆψ ij = ˆα j + ˆε ij 13 / 17

14 14 / 17 Average real (UN)OBSERVABLE log WEEKLY wage of employees in selected ventiles, relative to 1996, and contributions from firms No controls

15 15 / 17 Robustness - Firm size / number of employee observations: 1+, 5+ and 20+ (all with 3-dig. occ. ctrls) - Private sector only - Hourly wages - Annual wages - All employees

16 16 / 17 Reconciling results with the literature In US and Brazil between-firm inequality changes dominate. So why are our results different? - Firm size matters - Occupational polarisation trends, which account for a large part of British inequality trends, even without the firm-specific dimension (Goos & Manning, 2007). - Studies of wages in Sweden, Portugal and Germany have all found a more substantial role for within-firm variance and the role of occupations - Extra results: Changing occupation shares vs wages between firms

17 17 / 17 Conclusion - GB wage inequality changes (for employees of mostly very large firms) have not been driven by firm average wage differences - This is especially clear for unobservable wages, with controls for the occupational content of wages - Changes in between-occupation inequality or the occupational concentration of firms are significant

18 Appendix 18 / 17

19 19 / 17 Related Literature - US: Davis & Haltiwanger (1992), Barth el al. (2016), Song et al. (2016) Song et al. (I) Song et al. (II) Song et al. (III) Song et al. (IV) - Brazil: Benguria (2015), Alvarez et al. (forthcoming), Helpman et al. (2017) Benguria (I) Benguria (II) - West Germany: Card et al. (2013) - Sweden: Nordström Skans et al. (2009), Akerman et al (2013). - Portugal: Cardoso (1997,1999) - Britain: Faggio et al. (2010), Mueller et al. (2017), Lee (2016)

20 Motivation / Literature 20 / 17 Song et al. (2016), Figure 4: Change in inequality of annual earnings across percentiles from 1981 to 2013

21 21 / 17 Song et al. (2016), Figure 2a: Decomposition of Annual Earnings Variance, Within and Between Firms: All employees

22 22 / 17 Song et al. (2016), Figure 2c: Decomposition of Annual Earnings Variance Within and Between Firms: more than 10,000 employees

23 23 / 17 Song et al. (2016), Figure 3: Change in percentiles of annual earnings between firms relative to 1981

24 24 / 17 Benguria, 2015: Change in inequality of earnings in Brazil,

25 25 / 17 Benguria, 2015: Change in inequality of RESIDUAL earnings in Brazil,

26 26 / 17 Comparison of baseline sample firm size distribution, and represented employees, with UK population of enterprises, 2013 Number of obs. Total employees in enterprises (000s) Enterprise size Sample firms UK enterprises Sample firms UK enterprises , ,927 1,000-1, , ,455 2,000-4, ,098 2,612 5, ,204 8,805 Total 1,640 8,915 10,804 15,799 Values for sample firms use the IDBR record of the number of employees in the enterprise which includes the firm - this is not the number of observations of employee jobs in the sample. All firms in the baseline sample of those with a minimum of ten full-time employee observations in the NESPD in 2013, subject to the other sampling criteria described in the text. Notes.- author calculations using NESPD. UK enterprises population figures from UK Business: Activity, Size and Location (IDBR, March 2015).

27 27 / 17 Percentiles of real log wages in large firms, full-time employees only, and comparison with the whole NESPD sample, Notes.- Shaded areas represent official UK recessions. Solid lines are the series for a large firm sub-sample of the NESPD.

28 Within and between-firm, hourly rate and usual hours components of the variance in log WEEKLY employee wages, : FULL-TIME only V bf = V w bf + Vh bf + 2Cov bf (w,h), (4) V wf = V w wf + Vh wf + 2Cov wf (w,h). (5) Full & part-time 28 / 17

29 29 / 17 Components of the variance in log weekly employee wages, : FULL & PART-time

30 30 / 17 Share of within-firm component, var(ε ij ), in the variance of estimated unobservable log employee wages, var(ψ ij ), Notes.- Total gives the within share of actual total wage variance. All unobservable log wages are estimated using regressions with controls for sex, age, age squared and major regions. (2.)-(4.) include estimates of firm fixed effects, and respectively (3.) and (4.) add controls for ISCO 2 and 3 digit groups. (5.) is the variance of residuals from an estimation of the wage regression without firm fixed effects.

31 Change in the average real log weekly wage by percentile of employees and the contribution from firms: NESPD large firms sample vs ASHE large enterprises 31 / 17

32 Change in unobservable real log weekly wages: controls for sex, age, region, occ-2 digit, firm 32 / 17

33 Change in unobservable real log weekly wages: controls for sex, age, region, occ. 3-dig., w/out firm effs 33 / 17

34 34 / 17 Change in the average real log weekly wage by percentile of employees and the contribution from firms: all large firms in the NESPD with 1+ employee observations

35 35 / 17 Average real log WEEKLY wage of employees in selected ventiles, relative to 1996, and contributions from firms

36 36 / 17 Change in the average real log weekly wage by percentile of employees and the contribution from firms: all large firms in the NESPD with 5+ employee observations

37 37 / 17 Change in the average real log weekly wage by percentile of employees and the contribution from firms: all large firms in the NESPD with 20+ employee observations

38 38 / 17 Change in the average real unobservable log weekly wage by percentile of employees and the contribution from firms: PRIVATE SECTOR ONLY

39 Change in the average real log HOURLY wage by percentile of employees and the contribution from firms: comparison with unobservable wages 39 / 17

40 Change in the average real log ANNUAL wage by percentile of employees and the contribution from firms: comparison with unobservable wages 40 / 17

41 Change in the average real log weekly wage by percentile of employees and the contribution from firms, FULL & PART-TIME workers: comparison with unobservable wages 41 / 17

42 42 / 17 Decomposing the firm component of employee wage inequality patterns, : the role of changing occupation shares vs wages Equation

43 43 / 17 Decomposition of variance in log employee wages: N j N j J 1 N (w ij w) 2 = 1 J j=1 i=1 N (w ij w j ) 2 J N j + j=1 i=1 j=1 N (w j w) 2, (6) }{{}}{{}}{{} Overall - V e Within-firm - V wf Between-firm - V bf where i and j denote employee and firm respectively, N the number of employees, and w j the average wage of employees in firm j, etc. Or for hours and wage rates: N j N j V wf = 1 J [ ] 2 N wij w j + 1 J [ ] 2 j i N hij h j + 2 J [( )( )] j i N wij 2 w j hij h j j i }{{}}{{}}{{} Vwf w Vwf h 2cov wf (w,h) (7) and J N j [ V bf = wj w ] J 2 N j [ + j N hj h ] J 2 N j [( +2 j N wj j N 2 w )( h j h )]. (8) }{{}}{{}}{{} Vbf w Vbf h 2cov bf (w,h) N j

44 Let each decile be denoted by d, where N d is all employees observed in a period in that decile of the unobservable wage distribution. Let k denote an employment type, with K types in total. The share of all employees, irrespective of decile, in type k in the firm of an employee i is given by λ k,i. The mean log wage of type k in the firm of employee i is given by w k,i. We let this value be zero where a firm does not employ anybody of type k. We can write the mean of firm-specific log wages for employees in a decile as 44 / 17 N 1 d N d {α j } i = 1 K N d i d N d λ k,i w k,i k i d ( )( ) K N 1 d N 1 d = k N d λ k,i i d N d w k,i + 1 N d ( i d N λk,i d λ )( ) k wk,i w k. (9) i d }{{}}{{}}{{} λ w k k cov(λ k,w k ) Using (9), and denoting historical values by, representing the difference operator by, we can write the change over time (between two years) in the mean of firm-specific log wages for employees in some decile as K λ k w k + w k k }{{} λ k + λ k w k + cov(λ k,w k ). (10) }{{}}{{}}{{} Wages effect Shares effect Interaction effect Covar. effect