Lecture 1: Facts To Be Explained

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1 ECONG205 MRes Applied Job Search Models Lecture 1: Facts To Be Explained Fabien Postel-Vinay Department of Economics, University College London. 1 / 31

2 WAGE DISPERSION 2 / 31

3 The Sources of Wage Dispersion Figure 1 Similar workers are paid differently, i.e. observed worker/job characteristics fail to explain cross-section wage variance. Mincer-type equations applied to individual worker data only explain a fraction (typically, less than 50%) of total wage variance. The recent advent of matched employer-employee panel data (MEE data) has made it possible to estimate wage equations with two-sided unobserved heterogeneity: w it = x itβ + α i + ψ J(i,t) + ε it, Notation: w it is individual log wage, x it is a vector of time-varying individual characteristics, α i is a person fixed effect, ψ j is an employer fixed effect and (i, t) J (i, t) is a matching function defining worker i s employer at date t. 3 / 31

4 Matched Employer-Employee Data MEE datasets are matched panel datasets with the following basic structure: One panel of workers with individual index i {1,, I } and time index t {1,, T }, One panel of firms with individual index j {1,, J} and same time index t {1,, T }, A matching function J (i, t) {1,, J} defining worker i s employer at time t. They usually are administrative data (employer payroll reports collected for tax purposes) Matching wage register data with firm accounting data is feasible by availability of firm ID, not always done. Matching with other individual social security/health insurance data done in some countries (Denmark, Norway, Sweden). 4 / 31

5 Wage Equations for MEE Data Basic descriptive model: error-component model with firm fixed-effects Abowd, Kramarz and Margolis (Econometrica, 1999) w it = x itβ + ψ J(i,t) + α i + u it J = x itβ + ψ j d j it + α i + u it, j=1 where the d j it, j {1,, J}, are indicator variables: d j it = 1 if J (i, t) = j 0 otherwise AKM estimate β, person effects α = (α 1,, α N ) and firm effects ψ = (ψ 1,, ψ J ) by OLS. 5 / 31

6 Identification of Firm Effects Only mobile workers, for whom d j it d j i, contribute to identification, as the OLS estimator of ψ is the within estimator: w it w i = (x it x i ) β + J j=1 ( ) ψ j d j it d j i + u it u i, where z i = 1 T T t=1 z it for any variable z it One needs to observe movers in every firm j for the identification of ψ j. E.g., if only one worker with no job mobility is observed in firm j, then ψj is not identified. More generally, partition of the firm sample into disconnected clusters with no observed cross-cluster worker mobility creates multicollinearity and requires normalization of some ψ j s. 6 / 31

7 Consistency of OLS OLS estimator of ψ consistent for fixed J, large I, if firm-worker assignment is strictly exogenous: ( ) d j it (u it ) t {1,,T }, i {1,, N}. t {1,,T } j {1,,J} acceptable if u it is transitory OLS estimator of α: consistent for large T. α i = w i x i β J j=1 ψ j d j i, Conclusion: If too few time periods and/or too little mobility: Imprecise fixed effect estimates Spurious negative correlation between αi and ψ J(i,t) (across individuals) 7 / 31

8 Wage Dispersion: Descriptive Evidence From the Error Component Model Statistical model (reminder): w it = x it β + ψ J(i,t) + α i + u it. Table 1 Table 2 The percentage of log wage variance explained by the error component model equation is around 70-80%. No component really dominates in the explanation of log wage variance. There is evidence of unobserved heterogeneity among workers and employers. There is little evidence of assortative matching. 8 / 31

9 Conclusions about wage dispersion The law of one price seems violated in labor markets. Firm heterogeneity is quantitatively important: Low-paying employers survive along with high-paying employers. 9 / 31

10 WAGE MOBILITY 10 / 31

11 Basics Wages tend to go up with: labor market experience; firm and/or occupation tenure (this latter point is debated); patterns of wage growth differ across worker groups. Figure 2 Individual wages go up and down over time, even within a job spell. Figures 3 and 4 11 / 31

12 The Persistence of Wage Shocks Wage shocks are highly persistent: The following ARMA decomposition is often referred to as the canonical model of wage dynamics : State-of-the-art reference in the field: Meghir and Pistaferri (Econometrica, 2004) sit P = sp i,t 1 + u it, u it i.i.d. w it = α i + sit P + sit T, with: q sit T = θ l ε i,t l, ε it i.i.d. and q = 0 or 1. l=0 12 / 31

13 The Persistence of Wage Shocks w it = α i + s P it + s T it, with: sit P i,t 1 + u it, u it i.i.d. q sit T = θ l ε i,t l, ε it i.i.d. and q = 0 or 1. l=0 Cov ( w it, w i,t+s ) : s = 0 s = 1 s = 2 s = Innovation variances: RW comp: Var (u it ) MA(0) comp: Var (ε it ) (0.002) (0.002) Source: Postel-Vinay and Turon (IER, 2009) using UK data from the BHPS 12 / 31

14 JOB MOBILITY 13 / 31

15 Basics Individuals sometimes lose their jobs, find new jobs, spend some time out of the labor force or move from job to job... Patterns of job mobility differ across countries, dates, and individuals. There is evidence of heterogeneity here, too. Figures 5-8 Table 3 Suggests that there is some selection going on in the process of wage mobility. 14 / 31

16 A Job Ladder? Employer heterogeneity also matters for job mobility: When moving from job to job, workers tend to move towards higher-wage/higher-productivity/larger employers [Figures 10 and 11 ] Note: Employer size is easier and better measured than productivity or wages, and is correlated with both [Figure 9] Layoff rates also follow a pattern across employer size classes [Figure 12] All this adds up to an interesting business-cycle pattern [Figure 13] Labor costs (wages) comove with job mobility rates [Figures 14 and 15] 15 / 31

17 CONCLUSIONS 16 / 31

18 Research Questions What is the source of residual wage dispersion? What are the equilibrium links between firm heterogeneity, worker heterogeneity, and wages? What are the main determinants of wage growth over the life cycle? What causes wage persistence? What is the nature of wage shocks? How are job and wage mobility related, if at all? / 31

19 (a) BEL DNK ESP FRA UNCORRECTED PR Employed workers Job entrants GBR GER IRL ITA NLD PRT USA (b) Employed workers Job entrants Fig. 3. (a) Wage cumulative distribution functions (wages in local currency). (b) Wage densities (wages in local currency). Figure: Observed wage densities Source: Jolivet, Postel-Vinay and Robin (EER, 2006). Data are from the ECHP and the PSID. back

20 Correlations of Components of Real Annual Wage Rates (Abowd, Kramarz, Lengerman, Roux, WP, 2003) France StD. ln w x β α = z γ + θ z γ θ ψ u Log real annual wage rate 0,9772 1,0000 Experience 0,4087 0,5377 1,0000 Person effect 0,5217 0,4569 0,0698 1,0000 Schooling 0,1522 0,1510-0,0469 0,2917 1,0000 Unobservable 0,4990 0,4316 0,0872 0,9565 0,0000 1,0000 Firm effect 0,4665 0,4287 0,1670-0,2225 0,0293-0,2415 1,0000 Residual 0,5545 0,5675 0,0000 0,0000 0,0000 0,0000 0,0000 1,0000 US StD. ln w x β α = z γ + θ z γ θ ψ u Log real annual wage rate 0,8941 1,0000 Experience 0,6965 0,2305 1,0000 Person effect 0,8434 0,4871-0,6085 1,0000 Schooling 0,2317 0,1733-0,1527 0,2748 1,0000 Unobservable 0,8110 0,4571-0,5893 0,9615 0,0000 1,0000 Firm effect 0,3586 0,4926 0,0635 0,0445 0,0824 0,0228 1,0000 Residual 0,3614 0,4042 0,0000 0,0000 0,0000 0,0000 0,0000 1,0000 A,K,L,R s estimate Table: of spurious Cross-section correlation wage around variance 0.10 for decomposition France and 0.02 for the US. 8 back

21 Ed yrs. Ed. 12 yrs. Ed yrs. level % V (w) level % V (w) level % V (w) V (w) V (x β) V (α) V (ψ) Corr (x β, ψ) explained variance Table: Cross-section wage variance decomposition Source: Bagger, Fontaine, Postel-Vinay and Robin (AER, 2014), Danish IDA data Assumptions: Cov (α i, x i ) = Cov (α i, ψ i ) = 0 back

22 Ed Ed. 12 Ed experience profiles 26 Ed Ed. 12 Ed tenure profiles Figure: Experience and firm tenure effects in the error component model Source: Bagger, Fontaine, Postel-Vinay and Robin (AER, 2014), Danish IDA data back

23 stayers movers Source: UK LFS, Jan Mar stayers movers Source: UK LFS, Jan Mar log weekly pay change log hourly pay change Figure: Year-to-year weekly wage changes Figure: Year-to-year hourly wage changes back

24 q1 1995q1 1996q1 Shaded areas indicate NBER contractions. Source: CPS (Fallick and Fleischman, 2004) and author's calculations. 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1 Figure: Job-to-job transitions (US) m1 1993m1 1994m1 1995m1 Source: BHPS, UKLHS, and author's calculations Vertical lines indicate end of BHPS/beginning of UKLHS 1996m1 1997m1 1998m1 1999m1 2000m1 2001m1 2002m1 2003m1 2004m1 2005m1 2006m1 2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1 2015m1 Figure: Job-to-job transitions (UK) back

25 q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1 Shaded areas indicate NBER contractions. Source: CPS (Fallick and Fleischman, 2004), ECI, and author's calculations. All series normalized for comparability. EE rate UE rate EU rate U rate Figure: Labor market flow rates (US) m1 1993m1 1994m1 Source: BHPS, UKLHS, and author's calculations Vertical lines indicate end of BHPS/beginning of UKLHS All rates normalized for comparability 1995m1 1996m1 1997m1 1998m1 1999m1 2000m1 2001m1 2002m1 2003m1 2004m1 2005m1 2006m1 2007m1 2008m1 2009m1 2010m1 2011m1 2012m1 2013m1 2014m1 2015m1 EE rate EU rate UE rate U rate Figure: Labor market flow rates (UK) back

26 1 3 Table 1 Descriptive statistics ARTICLE IN PRESS G. Jolivet et al. / European Economic Review ] (]]]]) ]]] ]]] 5 Country BEL DNK ESP FRA GBR GER IRL ITA NLD PRT USA # employed workers ,737 2,339 1,602 2, ,923 1,811 1,756 1,838 % of censored spells % of job-tononemployment transitions % of job-to-job transitions % of instantaneous job re-accessions % of job-to-job transitions with a y ywage increase ywage decrease # job entrants turnover category which comprises Denmark and the U.K., a clearly low-turnover 19 group with Belgium, Table: France, Mobility Italy, of employed Portugal and workers Spain, over and three finally years a middle-range group, with Germany, the Netherlands, the U.S. and Ireland the latter two countries 21 being closest to the high-turnover category. While it is not exactly obvious where the Source: dividingjolivet, line between Postel-Vinay the high- and Robin and(eer, low-turnover 2006). Data groups are from should the ECHP be drawn, and the PSID. strikingback fact 23 is that there is a three- to four fold increase in our job-to-job turnover indicator from one end of the spectrum to the other. In other words, the intensity of job-to-job worker D PROOF

27 Source: SUSB Firm Size ,000 1,499 1,500+ Figure: Wage-size relationship Source: Moscarini and Postel-Vinay (JoLE, 2016) back

28 Under 99 employees 100+ employees Source: Moscarini and Postel-Vinay (JoLE, 2016) Figure: Poaching and establishment size back

29 k=1 k=2 k=3 k=4 sλ x in class k [ 1 F(x)] dn(x) Q* /n (Data) 2001m1 2003m1 2005m1 2007m1 2009m1 2011m1 2013m1 The Data series are corrected for misclassification. Shaded areas indicate NBER contractions. Source: JOLTS, CPS, and authors calculations. Figure: Quits and establishment productivity (based on size) Source: Moscarini and Postel-Vinay (JoLE, 2016) back

30 employees employees employees employees employees 2001m1 2003m1 2005m1 2007m1 2009m1 2011m1 2013m1 Average layoff rate by size class, MA smoothed. Shaded areas indicate NBER contractions. Source: JOLTS and authors calculations. Figure: Layoff rates and establishment size Source: Moscarini and Postel-Vinay (JoLE, 2016) back

31 Correlation = Solid = Differential net job creation rate, Dash = HP detrended unemployment rate. Categories defined each year as <50 and >1000. Shaded areas indicate NBER contractions. Source: BDS and authors calculations. Figure: Net job creation and firm size over the cycle Source: Moscarini and Postel-Vinay (AER, 2013) back

32 q1 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 2010q1 2011q1 2012q1 2013q1 2014q1 2015q1 Shaded areas indicate NBER contractions. Source: CPS (Fallick and Fleischman, 2004), ECI, and author's calculations. All series normalized for comparability. EE rate UE rate ECI growth Figure: Labor market flows and earnings growth (US) q1 1993q1 1994q1 Source: BHPS and author's calculations. All series normalized for comparability. 1995q1 1996q1 1997q1 1998q1 1999q1 2000q1 2001q1 2002q1 2003q1 2004q1 2005q1 2006q1 2007q1 2008q1 2009q1 EE rate UE rate earnings growth Figure: Labor market flows and earnings growth (UK) back

Structural Econometrics: Equilibrium search models. Jean-Marc Robin

Structural Econometrics: Equilibrium search models Jean-Marc Robin Plan 1. Facts on wage distributions 2. On-the-job search and wage posting { Burdett-Mortensen 3. Sequential auctions { Postel-Vinay Robin