The German Wage Curve with Spatial Effects

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1 The German Wage Curve with Spatial Effects Uwe Blien #+, Jan Mutl*, Katja Wolf # # IAB, Nürnberg + Otto-Friedrich University of Bamberg *EBS Business School, Frankfurt Work in Progress Kassel 2 Juni 2015

2 The Wage Curve Blanchflower & Oswald s (1994/ 2005) empirical law of economics : the Wage Curve hypothesis, described by: lnw = -0.1 lnu + other terms W: Regional wages U: Regional unemployment Observed initially for 12 (Blanchflower, Oswald 1994, coefficient: -0.10) and later for many other countries. A meta-analysis by Nijkamp & Poot (2005) of these and many further studies gave a publication-bias corrected estimate of about

3 The Wage Curve (II) regional wages Wage curve labor demand region 1 W 1 labor demand region 2 W 2 U 1 U 2 regional unemployment rate 3

4 The wage curve can be derived from: Efficiency Wages Approach: unemployment and wages are substitutes in motivating workers (Shapiro, Stiglitz 1984, Blanchflower, Oswald 1994, Schlicht 1978). Bargaining Models: unemployment weakens the position of unions and of individual workers (Blanchflower, Oswald, Sanfey 1996). 4

5 The wage curve can be derived from: Contract Theory: in regions with high productivity and high wages it is inefficient to have a high level of unemployment (Blanchflower, Oswald 1994). Labour-Market-Matching Theory: Reservation wages are lower in regions with high unemployment. The bargaining positions are also affected (Mortensen 2010, Pissarides 2000, Blien, Messmann, Trappmann 2012, Hummel, Elhorst 2012). New Regional Economics: Explanation of the stability of the wage curve by NEG and similar approaches (Südekum 2002, Zierahn 2011). 5

6 Derivation of the wage curve from Schlicht s efficiency wages approach (modified) A: effort function A i W Ai W Ai mit : W j j e 0,, U r, Ai U r 0, 2 A W i 2 j 0, 2 A U i 2 r 0, U 2 r Ai W j 0 Profit maximization: G jr PY ( A N ) W r jr i jr jr N jr With: W: Wages, U: Unemployment, Y: Product, G: Profit, N: Labor, i index for firms, j for workers, r: for regions 6

7 Derivation of the wage curve from an efficiency wages approach (II) Equilibrium condition: Ai e (Wjr / W ) Ai e W / W jr 1 aggregation: W r (U r ) = e W A A/ ( W / W r e ) negative slope: e d(w r / W ) du r e 2 e A / Ur W r / W ( A / (W r / W ) U e 2 e 2 W / W ( A / (W / W ) ) r r r ) 0 7

8 Empirical analyses on the wage curve Loglinear approximation of the wage curve Microdata OLS estimation of a wage function of the Mincer type Fixed effects for regions Aggregate level variables, especially the unemployment rate 8

9 Empirical analyses of the wage curve: Original model specifications Blanchflower/ Oswald: Individual data: ln W ijt lnu jt X ijt Blanchflower/ Oswald: Aggregate data: j t ijt ln W jt lnu jt X jt j t jt Extended approaches control for the endogeneity of variables by using IV estimations (either external instruments or lagged values of the unemployment rate, see Baltagi, Blien 1998, Blanchflower, Oswald 2005) 9

10 Kiel Rostock Hamburg Hannover Berlin Unemployment Rate June 2008 Dortmund Frankfurt Halle Leipzig Dresden von 1,6 bis 4,1 (78) von 4,1 bis 6,2 (86) von 6,2 bis 8,4 (84) von 8,4 bis 12,2 (84) von 12,2 bis 22,0 (81) Saarbrücken Nürnberg Min:1,6% (Eichstätt) Max: 22,0% (Demmin) Stuttgart Regensburg München

11 Kiel Rostock Hamburg Hannover Berlin Average Kalender Daily Wages of 2008 Saarbrücken Dortmund Frankfurt Halle Nürnberg Leipzig Dresden von 54,1 bis 71,9 (83) von 71,9 bis 79,3 (83) von 79,3 bis 85,2 (84) von 85,2 bis 91,3 (80) von 91,3 bis 114,6 (82) Min: 54,1 (Rügen) Max: 114,6 (München) Stuttgart Regensburg München

12 The Wage Curve in Germany Many studies: first round published in special issue of MittAb (1996): Blanchflower & Oswald, Büttner, Wagner, Bellmann & Blien, Möller and others. Methods: - Mostly similar to Blanchflower & Oswald (see also 2005) - Büttner (1999a, 1999b) first used a spatial model - variations of methods e.g. with data for establishments, e.g. to test various theoretical approaches (see Blien et al. 2011) Results: Wage curve exists with relatively small elasticity due to institutional effects. 12

13 Empirical analyses of the wage curve: Newer preferred specification: Two step approach First step: lnw eit e it K k X eptk ek Employee: e = l,...,n Region: i = 1,...,R Year: t = 1,...,T Second step (with an inclusion of dynamics): it ln u i t 1 it 1 2 eit it t i See Card (1995), Bell, Nickell, Quintini (2002), Baltagi, Blien, Wolf (2009, 2012), Shilov, Möller (2009), variation in Blanchflower, Oswald (2005) it 13

14 New approach: Second stage regression A dynamic spatial Durbin model Time lag to account for delayed adaption it i t N N 1 it 1 2 it 2 wij jt ln uit wij ln j 1 j 1 u jt t i it Second time lag to account for serial correlation Spatial lag of the dependent variable location specific spatial weight Spatial lag of an explanatory variable Stacking across different regions at a given time period α t μ λt 1α t 1 2αt 2 Wα t ut Wu t γt νt 14

15 Estimation procedure: LSDV-IV Estimation follows Kapoor, Kelejian, Prucha (2007) and Mutl (2011) Three endogeneity problems: (1) Dynamic panel model: LSDV-estimator biased, but consistent for T In our case: T = 25 ( ) (2) Spatially lagged dependent variable Instrumented with first and second order spatial lags of α t-1, α t-2 and u t (e.g. Wu t and WWu t ) (3) Unemployment rate Instrumented with lagged values of unemployment rate!!! Spatial explanatory variable: No problem!!! 15

16 Interpretation parameter estimates Short-run impact of unemployment on the wage level not equal β α Wα u Wu ν 1 I W u Wu ν Matrix of the first derivatives of α with respect to u is given as: J 1 I W I W The effect of a change in u j on α i is given by: i J u Following LeSage & Pace (2009): Direct effects (diagonal elements): J ii Indirect effects (off-diagonal elements): J ij (i j) j ij 16

17 Calculating spatial effects following LeSage & Pace (2009) Total effects = Direct effects + indirect effects 1 Starting point: J I W I W 1 Direct effect (average region): N tr( J) Impact on wage in region i from a change in unemployment in this region (including feedback effects) Total effect to an observation (average region): Impact on wage in region i from a change in unemployment across all N regions N 1 N J N 17

18 Discussion about Spatial Econometrics Criticism by Gibbons & Overman (2012) with reference to Angrist & Pischke (2009): Structure of W-Matrix given Identification of different models difficult Suggestion: Orientation towards experimentalist paradigm Answer: (W-Matrix given) Models identified Experimentalist paradigm no alternative for wage curve analyses Drop of spatially lagged endogenous variable with omitted variable problem 18

19 Data from the SIAB Employment Sample for Western Germany ) over 10 Mio. individual employment spells of people working full time (2% sample) gainfully employed. Variables: Age Sex Employment status (6 categories) Qualification level of an employee (4 categories) Industry classification (30 categories) Occupational group (14 categories) Establishment size (9 categories) Unemployment rates for 326 districts (Landkreise) and for 155 labour market regions (Agenturbezirke) 19

20 Analyses with three different regional weight matrixes Estimation with three different matrices: Contiguity (basic model) Distance in km (exponential distance weight matrices: w ij = exp(-λd ij ) with λ = 0.02 and < 100 km = 0) Travel time by car (time < 60 minutes) (only Kreise) 20

21 Results on the dynamic spatial wage curve (FE, IV), Kreise Non-spatial Contiguity Distance Time Log Wage (t-1) 0.546*** 0.550*** 0.540*** 0.540*** Log Wage (t-2) 0.098*** 0.085*** 0.091*** 0.091*** Spatial Log Wage Log Unemployment Spatial Log Unem. LONG-RUN Direct effect Indirect effect Total effect a : standard errors not yet calculated 21

22 Results on the dynamic spatial wage curve (FE, IV), Kreise Non-spatial Contiguity Distance Time Log Wage (t-1) 0.546*** 0.550*** 0.540*** 0.540*** Log Wage (t-2) 0.098*** 0.085*** 0.091*** 0.091*** Spatial Log Wage 0.136*** 0.225*** 0.207*** Log Unemployment Spatial Log Unem. LONG-RUN Direct effect Indirect effect Total effect a : standard errors not yet calculated 22

23 Results on the dynamic spatial wage curve (FE, IV), Kreise Non-spatial Contiguity Distance Time Log Wage (t-1) 0.546*** 0.550*** 0.540*** 0.540*** Log Wage (t-2) 0.098*** 0.085*** 0.091*** 0.091*** Spatial Log Wage 0.136*** 0.225*** 0.207*** Log Unemployment *** *** *** *** Spatial Log Unem. LONG-RUN Direct effect Indirect effect Total effect a : standard errors not yet calculated 23

24 Results on the dynamic spatial wage curve (FE, IV), Kreise Non-spatial Contiguity Distance Time Log Wage (t-1) 0.546*** 0.550*** 0.540*** 0.540*** Log Wage (t-2) 0.098*** 0.085*** 0.091*** 0.091*** Spatial Log Wage 0.136*** 0.225*** 0.207*** Log Unemployment *** *** *** *** Spatial Log Unem *** 0.017*** LONG-RUN Direct effect Indirect effect Total effect a : standard errors not yet calculated 24

25 Results on the dynamic spatial wage curve (FE, IV), Kreise Non-spatial Contiguity Distance Time Log Wage (t-1) 0.546*** 0.550*** 0.540*** 0.540*** Log Wage (t-2) 0.098*** 0.085*** 0.091*** 0.091*** Spatial Log Wage 0.136*** 0.225*** 0.207*** Log Unemployment *** *** *** *** Spatial Log Unem *** 0.017*** LONG-RUN Direct effect *** -0,035 a -0,067 a a Indirect effect Total effect a : standard errors not yet calculated 25

26 Results on the dynamic spatial wage curve (FE, IV), Kreise Non-spatial Contiguity Distance Time Log Wage (t-1) 0.546*** 0.550*** 0.540*** 0.540*** Log Wage (t-2) 0.098*** 0.085*** 0.091*** 0.091*** Spatial Log Wage 0.136*** 0.225*** 0.207*** Log Unemployment *** *** *** *** Spatial Log Unem *** 0.017*** LONG-RUN Direct effect *** -0,035 a -0,067 a a Indirect effect -0,015 a a a Total effect *** a a a a : standard errors not yet calculated 26

27 Results on the dynamic spatial wage curve (FE, IV), Agenturbezirke Non-spatial Contiguity Distance Log Wage (t-1) 0.570*** *** *** Log Wage (t-2) 0.065*** 0.067*** 0.101*** Spatial Log Wage 0.219*** 0.227*** Log Unemployment *** *** *** Spatial Log Unem * 0.008** LONG-RUN Direct effect *** -0,044 a -0,051 a Indirect effect -0,026 a a Total effect *** a a a : standard errors not yet calculated 27

28 Macroeconomic effects of unemployment on wages Unemployment t value Break Dummy 1994 No spatial -0,081 0,017 0,087 Contiguity -0,056 0,016 0,054 Distance Travel Time -0,037 0,028 0,031-0,041 0,023 0,036 The response variable is made up out of the period dummies of the second step analysis 28

29 A comparison: Estimates of the wage-setting function according to Layard et al. (1991/2005) Brücker & Jahn (2011) estimated the wage reaction on the national level of Germany using different qualification groups They recieved an elasticity larger that -0.1 (absolut) For the UK Bell, Nickell, Quintini (2002) found no effect at the national level after taking the regional level into account 29

30 Conclusions concerning the Wage Curve in Germany A wage curve exists in Western Germany. Two dimensions of regional disparities are linked. The reaction of regional wages is slower and not so strong as in the Anglo-Saxon countries. However, the wage reaction at the national level has to be taken into account. 30

Wage Inequality, Labor Market Participation and Unemployment

Wage Inequality, Labor Market Participation and Unemployment Testing the Implications of a Search-Theoretical Model with Regional Data Joachim Möller Alisher Aldashev Universität Regensburg www.wiwi.uni-regensburg.de/moeller/