Does Demographic Aging Contribute to the Innovation Divide Across German Labor Markets?

Transcription

1 Does Demographic Aging Contribute to the Innovation Divide Across German Labor Markets? Melanie Arntz ZEW Mannheim and University of Heidelberg Terry Gregory ZEW Mannheim 8th European workshop on Labour Markets and Demographic Change, - Vienna 12/9/2013

2 Regional projections of average population age, Figure : 2010 Figure : 2030 Source: Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR)

3 Aging may affect innovative capabilities at the individual level declining cognitive abilities after some peak in mid-ages (Levin and Stephan 1991; Rouvinen 2002) a turnaround point between 50 and 55 for inventors in longitudinal studies (Mariani and Romanelli 2007; Schettino et al. 2008)... at the firm level a mostly hump-shaped link between age and productivity/innovations (e.g. Schneider 2008, Verworn and Hipp 2009) positive effect of age diversity (Grund and Westergard-Nielsen 2008) at least for creative tasks (Backes-Gellner and Veen 2012) firm performance may be more than the sum of individual performances

4 ... at the macro level performance may diverge from sum of individual and firm performances due to knowledge externalities younger workers: ability to generate and recombine new knowledge older workers: ability to use skills, knowledge and experience different skill endowments may complement each other and create positive spillovers interactions may take place inside and outside the workplace (e.g. social interaction) innovations are considered to be a regional outcome Total effect of aging should be analysed at the macro/regional level

5 Macro-level evidence on age-performance link Study and country Performance indicator Age indicators Age-performance pattern OLS FE IV Country-level studies: Lindh/ Malmberg 1999 Growth rate of Population age shares (OECD) real GDP per capita (15+ years) Prskawetz et al. 2007a Growth rate of Population age shares (EU-25) real GDP per capita (15+ years) Feyrer 2008 Total factor productivity Working-age population (worldwide) age shares (10-70 years) Regional-level studies: Brunow/ Hirte 2006 Growth rate of Population age shares (Germany) real GDP per capita (15-75 years) Bönte et al New firm formation rate Population age shares (West Germany) (20-64 years) Frosch 2008 Invention rates Workforce age shares (Europe) Tang/ MacLeod 2006 Real GDP per worker two age groups: younger than 55, (Canada) and older than 55 years = hump-shaped, = positive effect, = negative effect

6 Objective and contribution of this work 1. estimate knowledge production function and age-innovation profile for German regions 2. conduct a cross-sectional analysis and contrast this approach to a panel estimation that accounts for unobserved heterogeneity 3. address potential endogeneity by instrumenting the regional workforce age structure by lagged population age data 4. test for interaction effects between age groups 5. examine to what extent demographic trends contribute to East-West innovation divide

7 Data on regional in- and output measures ( ) Innovation output number of published patents from European Patent Office (EPO) focus on patent inventor (fractional accounting approach) calculate number of regional patents innovation count data provide a fairly reliable measure of innovative activity on the regional level (Acs et al. 2002, RP) R&D input measures private R&D expenditures (German Stifterverband) public R&D expenditures, i.e regular and external funding (DeStatis)

8 Data on regional workforce ( ) Use Sample of Integrated Labour Market Biographies (SIAB) to calculate age structure skill composition share of creative professionals (Florida 2002) workforce size at the level of 141 labour market regions (Kosfeld and Werner 2012)

9 Summary statistics for labour market regions, (1) (2) (3) (4) Variable All Lowest Highest Differential Data regions innovative innovative between Source* regions regions (3) and (2) number of patents per worker EPO private RaD expenditures (in 1000 Euro) GST public RaD expenditures (in 1000 Euro) DeStatis average workforce age SIAB workforce age dispersion SIAB share of workers aged < 36 (in %) SIAB share of workers aged (in %) SIAB share of workers aged 50+ (in %) SIAB share of creative professionals (in %) SIAB share of high-skilled workers (in %) SIAB share of medium-skilled workers (in %) SIAB share of low-skilled workers (in %) SIAB workforce size (in 1000) SIAB population density (population per 100 km2) DeStatis * EPO: European Patent Office, SIAB: Sample of Integrated Labour Market Biographies released by German Federal Employment Agency, DeStatis: Regional database released by Federal Statistical Office, GST: German Stifterverband (Innovation Agency for the German science system)

10 The demographic and the innovation divide Average workforce age Number of patents per worker Kiel Kiel Lübeck Bremershaven Hamburg Lübeck Bremershaven Mecklenburgi Schwerin Bremen Hamburg Hannover Wolfsburg Hannover Berlin Osnabrück Wolfsburg Berlin Osnabrück Potsdam-Mitt Potsdam-Mitt Magdeburg Münster Mecklenburgi Schwerin Bremen Magdeburg Münster Bielefeld Bielefeld Cottbus Dortmund Cottbus Dortmund Gà ttingen Düsseldorf Düsseldorf Kassel Kassel Dresden Erfurt Dresden Erfurt Jena Aachen Jena Aachen Chemnitz Kà ln Chemnitz Kà ln Fulda Bonn Gà ttingen Fulda Bonn Koblenz Koblenz Frankfurt am Frankfurt am Bayreuth Bayreuth Mainz Mainz Darmstadt Darmstadt Würzburg Heidelberg Saarbrücken Würzburg Heidelberg Saarbrücken Nürnberg Nürnberg Regensburg Regensburg Karlsruhe Karlsruhe Stuttgart Stuttgart Ingolstadt Ulm Augsburg Ingolstadt Augsburg Ulm Patents per Average age 100 worker München Freiburg Konstanz Kempten München (41.0,41.6] (40.5,41.0] (40.1,40.5] (39.7,40.1] [37.7,39.7] Freiburg Konstanz Kempten Source: Calculations based on Sample of Integrated Labour Market Biographies (SIAB) ρage,patents = 0.34 (53,218] (35,53] (18,35] (9,18] [1,9]

11 Related trends in aging and innovation intensities age between 1995 and 2008 patents between 1995 and 2008 Kiel Kiel Lübeck Bremershaven Hamburg Lübeck Bremershaven Mecklenburgi Schwerin Bremen Hamburg Emsland Emsland Hannover Wolfsburg Hannover Berlin Osnabrück Wolfsburg Berlin Osnabrück Potsdam-Mitt Potsdam-Mitt Magdeburg Münster Borken Mecklenburgi Schwerin Bremen Magdeburg Borken Bielefeld Münster Bielefeld Cottbus Dortmund Cottbus Dortmund Gà ttingen Düsseldorf Wuppertal Kassel Düsseldorf Wuppertal Dresden Oberhavel Kassel Dresden Oberhavel Erfurt Erfurt Jena Aachen Altenkirchen Chemnitz Kà ln Fulda Bonn Jena Aachen Chemnitz Kà ln Gà ttingen Altenkirchen Fulda Bonn Koblenz Koblenz Frankfurt am Frankfurt am Bayreuth Mainz Trier Bayreuth Aschaffenbur Darmstadt Würzburg Trier Mainz Aschaffenbur Darmstadt Würzburg Erlangen Erlangen Heidelberg Saarbrücken Heidelberg Saarbrücken Nürnberg Nürnberg Regensburg Regensburg Karlsruhe Karlsruhe Stuttgart Stuttgart Ingolstadt Ingolstadt Passau Ulm Augsburg Passau Change in Augsburg Ulm Change in Patents average age per worker München Freiburg Konstanz Kempten München (3.2,4.3] (2.9,3.2] (2.5,2.9] (2.2,2.5] [1.0,2.2] Freiburg Konstanz Kempten Source: Calculations based on Sample of Integrated Labour Market Biographies (SIAB) ρ age, patents = 0.32 (28.3,103.6] (15.3,28.3] (10.2,15.3] (5.5,10.2] [-2.4,5.5]

12 Scatterplot between workforce age and innovation (cross-section, ) number of patents per worker average workforce age 95% CI Fitted values East German regions West German regions

13 Estimating the regional knowledge production function ln P it = α + β ln RD it + γ 1 MAGE it + γ 2 MAGEit 2 3PROF it +δx it + c i + τ + ɛ it P it - innovation output in region i and (3yr-)period t RD it - public and private R&D investments MAGE it - mean workforce age PROF it - share of creative professionals X it - controls including dummies for industries (16 categories), population density (plus squared term), size of local workforce (log) c i - time-constant unobserved effect τ - time fixed effects

14 Choice of instruments age may be endogeneous due to age-selective migration and hiring use lagged population demographics as instruments should address migration and reverse causality if lags sufficiently long should address potentially age-selective hiring since instrumenting the supply of workers

15 Estimation results for Germany (cross-section, ) (1) (2) (3) (4) Dependent variable: lnpit OLS OLS OLS IV GMM R&D inputs private RaD exp. (log, in 100 tsd Euro) 0.57*** 0.48*** 0.41*** 0.35*** (11.51) (9.17) (8.74) (5.33) public RaD exp. (log, in 100 tsd Euro) 0.05** 0.04** 0.05** 0.08** (2.37) (2.07) (2.29) (2.42) Human capital inputs average workforce age 18.20*** 20.95*** 20.75*** 70.20*** (3.08) (3.75) (4.20) (2.68) average workforce age (squared) -0.23*** -0.26*** -0.26*** -0.88*** (-3.09) (-3.78) (-4.23) (-2.69) num. of creative professionals (in log) 0.16* (1.90) (1.43) (0.35) (0.28) Regional indicators dummy for East Germany -0.58*** (-3.59) (-1.14) (-0.34) (0.58) population density (log, in tsd) 0.40*** 0.34*** 0.46*** (4.46) (3.77) (4.12) workforce size (log, in tsd) (-1.13) (0.30) (0.28) constant *** *** *** *** (-3.05) (-3.71) (-4.27) (-2.67) With industry dummies? no no yes yes N R F hansen (j statistic) Hansen (p-value) Note: t statistics in parentheses * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered by region.

16 Regional workforce age-innovation profile (cross-section) number of patents patents West Germany 1.2 years East Germany average workforce age turnaround point at 39.5 years 37/144*100= 26 percent of East-West innovation divide can be explained by differences in the average workforce age (1.2 years)

17 Estimation results for Germany (panel, ) (1) (2) (3) (4) Dependent variable: lnpit POLS POLS FE FE IV GMM R&D inputs private RaD exp. (log, in 100 tsd Euro) 0.33*** 0.32*** 0.07*** 0.05** (9.48) (9.32) (3.34) (2.10) public RaD exp. (log, in 100 tsd Euro) 0.06** 0.06*** -0.07** -0.09*** (2.29) (2.66) (-2.60) (-3.43) Human capital inputs average workforce age *** (0.96) (1.35) (1.31) (5.29) average workforce age (squared) *** (-0.96) (-1.48) (-1.38) (-5.46) num. of creative professionals (in log) (0.87) (1.17) (0.89) (-0.63) Regional indicators population density (log, in tsd) 0.20** 0.28*** 2.04*** (2.30) (3.12) (2.95) (-0.12) workforce size (log, in tsd) * -2.34*** (0.04) (-0.13) (-1.80) (-4.03) time trend 0.15*** 0.15*** 0.37*** (3.43) (3.67) (4.44) constant * (-1.42) (-1.74) (-1.09) With industry dummies? no yes yes yes N R F hansen (j statistic) Hansen (p-value) Note: t statistics in parentheses * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered by region.

18 Regional workforce age-innovation profile (panel) number of patents patents West Germany 1.2 years East Germany average workforce age turnaround point at 38.6 years 29/144*100= 20 percent of East-West innovation divide can be explained by differences in the average workforce age (1.2 years)

19 Alternative age specifications (cross-section) R&D inputs (coef. for R&D exp. suppressed) age group shares age group size (1) (2) (3) (4) share of workers aged < 36 (log) ** (-1.49) (-2.48) share of workers aged 50+ (log) -6.42*** (-3.05) (-1.25) num. of workers aged < 36 (log) (-1.44) (-1.18) num. of workers aged (log) 5.74** (2.16) (-0.22) num. of workers aged 50+ (log) -5.55*** (-2.92) (0.31) num. of creative professionals (log) * 1.12 (1.40) (-0.06) (1.66) (0.95) Interaction terms young x old 35.29** (2.25) (-0.21) young x midage (0.37) (0.39) midage x old (-0.39) (-0.64) Regional indicators (coef. for pop. density and E-W dummy suppressed) workforce size (log, in tsd) (-1.14) (0.30) constant ** ** (1.52) (2.36) (-2.37) (0.23) N R F hansen (j statistic) hansen (p-value) Note: t statistics in parentheses * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered by region. All specifications are estimated with FE IV GMM and include industry dummies.

20 Conclusion evidence in favor of hump-shaped age-innovation profile aggregate impact of demographic aging may be negative, given the German age structure East-West age divide largely explains East-West innovation divide positive interaction effect between youngest and oldest workers hints at knowledge spillovers Future work use flexible production function to allow for age complementarities (translog) estimate substitution elasticities between age group shares simulate regional innovation outcomes based on demographic projections

21 Thank you for your attention!

22 Scatterplot II number of patents per worker age dispersion of workforce 95% CI Fitted values East German regions West German regions

23 The regional production of knowledge Regional knowledge production function by Griliches (1979) P i = αrd β i HK γ i P i - innovation output in region i RD i - public and private R&D investments (Jaffe 1989, AER) HK i - human capital base proxied by - share of high-skilled (Feldman 1999, EINT, Malecki 1997) α - share of creative professionals (Florida 2002) - workforce age structure - constant shift-factor

24 Definition of the creative sector Definition: group of technological employees characterized as improving technology in the line of business they pursue (Wedemeier, 2012, Murghy et al. 1991) Occupational title Mechanical and vehicle engineers. Electrical engineers. Architects and construction engineers. Surveyors, mining, metallurgists and related engineers. Miscellaneous engineers. Chemists, physicists, chemical/physical engineers. mathematicians, and civil engineering technicians. Mechanical engineering technicians. Electrical engineers technicians. Surveyors, chemical, physical, mining, metallurgists, and miscellaneous engineering technicians. Miscellaneous technicians. Biological/mathematical/physical-technical assistant, chemical and related laboratory technician workers. Draft persons. Computer related professions. Statisticians, humanists, natural scientists, and pastors.

25 Firm-level evidence on age-innovation profile Study and country Performance indicator Age indicators Age-performance pattern OLS FE IV Firm-level studies: Backes-Gellner & Veen 2012 Productivity Workforce mean age, Germany age diversity Crépon et al Productivity/ Output Workforce age shares France Daveri & Maliranta 2007 Productivity in electronics Workforce mean Finland tenure and tenure 2 Frosch, Göbel & Zwick 2011 New products or services Workforce age shares Germany Göbel & Zwick 2009 Productivity Workforce age shares Germany Grund & Westergard-Nielsen 2008 Value added per employee Workforce mean age, Denmark standard deviation Ilmakunnas & Maliranta 2007 Productivity growth in ICT Workforce age shares Finland Malmberg et al Value added per employee Workforce Age shares Schweden Meyer 2009 Adoption of new technologies Workforce age shares Germany Schneider 2008 New products or services Workforce mean age Germany and mean age 2 Verworn & Hipp 2009 New products or services Share of older Germany workers in workforce = hump-shaped, = positive effect, = negative effect

26 Instruments in first stages, (1) (2) (3) (4) cross-section panel Dependent variable: mwfage mwfagesq mwfage mwfagesq lagged pop. share, (in log) *** *** (2.72) (2.72) lagged pop. share, (in log) *** *** (-2.81) (-2.80) lagged pop. share, (in log) ** ** (-2.24) (-2.21) lagged pop. share, (in log) 43.25*** *** (3.85) (3.88) lagged pop. group size, (in log) -1.17*** *** (-3.33) (-3.08) lagged pop. group size, (in log) 0.31** 28.68*** (2.43) (2.86) lagged pop. group size, (in log) -1.14*** *** (-5.24) (-5.09) lagged pop. group size, (in log) 2.16*** *** (6.94) (6.62) constant 45.41*** *** (12.93) (7.32) N R F F-Test of excluded insturments

27 Estimation results for West-Ger (cross-section, ) (1) (2) (3) (4) Dependent variable: lnpit OLS OLS OLS IV GMM R&D inputs private RaD exp. (log, in 100 tsd Euro) 0.53*** 0.46*** 0.40*** 0.40*** (9.11) (7.67) (8.16) (10.02) public RaD exp. (log, in 100 tsd Euro) (1.47) (1.25) (1.21) (1.15) Human capital inputs average workforce age 16.00** 19.66*** 19.72*** 27.73** (2.40) (3.24) (3.27) (1.98) average workforce age (squared) -0.20** -0.25*** -0.25*** -0.35** (-2.40) (-3.26) (-3.28) (-2.00) num. of creative professionals (in log) 0.23** (2.48) (1.55) (-0.25) (-0.43) Regional indicators population density (log, in tsd) 0.41*** 0.33*** 0.38*** (4.29) (2.99) (3.83) workforce size (log, in tsd) (-1.12) (1.09) (1.41) constant ** *** *** ** (-2.39) (-3.21) (-3.31) (-1.98) With industry dummies? no no yes yes N R F hansen (j statistic) Hansen (p-value) Note: t statistics in parentheses * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered by region.

28 Estimation results for West Germany (panel, ) (1) (2) (3) (4) Dependent variable: lnpit POLS POLS FE FE IV GMM R&D inputs private RaD exp. (log, in 100 tsd Euro) 0.32*** 0.32*** 0.08*** 0.07*** (8.03) (7.87) (3.18) (2.74) public RaD exp. (log, in 100 tsd Euro) ** -0.09*** (1.16) (1.34) (-2.05) (-3.63) Human capital inputs average workforce age 2.50** 3.06*** 1.77* 7.70*** (2.26) (2.97) (1.86) (4.42) average workforce age (squared) -0.03** -0.04*** -0.02** -0.10*** (-2.29) (-3.10) (-1.99) (-4.69) num. of creative professionals (in log) (0.21) (0.48) (0.18) (-0.32) Regional indicators population density (log, in tsd) 0.19** 0.26** *** (2.01) (2.47) (-0.06) (-3.03) workforce size (log, in tsd) * (0.92) (0.70) (-0.46) (-1.81) time trend 0.09** 0.17*** 0.54*** (2.00) (3.49) (5.15) constant ** *** * (-2.47) (-3.17) (-1.97) With industry dummies? no yes yes yes N R F hansen (j statistic) Hansen (p-value) Note: t statistics in parentheses * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered by region.

29 Innovation gap and age shifts between most and least innovative regions differential in workforce age group shares differential in patents per worker year 35<age<50 age<=36 age>=50 patents per worker

30 Innovation gap and age shifts between most and least innovative regions, by age group differential in average workforce age *(-1) year differential in patents per worker average workforce age patents per worker

31 Kernel densities of average regional innovation density East West East West average regional number of patents

32 Kernel densities of average regional innovation density East West East West average regional workforce age