ifo Institute for Economic Research at the University of Munich

Employment Effects of Innovation at the Firm Level

Stefan Lachenmaier*, Horst Rottmann♦

* Ifo Institute for Economic Research at the University of Munich♦ University of Applied Sciences Amberg-Weiden and Ifo Institute

3. Konferenz für Sozial- und Wirtschaftsdaten, Mai 2006, Wiesbaden

ifo Institute for Economic Research at the University of Munich

The Research Question

• Do innovations have a significant effect on employment?

• Concentrate on the analysis of long-term effects

ifo Institute for Economic Research at the University of Munich

Motivation

• Theoretical contributions show different results– Product innovations: increase demand

increase employment level decrease competition increase market power reduce output decrease employment

– Process innovations: increase labour productivity decrease employment level lower costs lower prices higher demand stimulate emplyoment

Overall effect is depending on elasticity of demand

• Empirical evidence is necessary• Panel studies are rare due to the lack of appropriate data

ifo Institute for Economic Research at the University of Munich

Main Idea

• Exploit long innovation panel data set

• Distinguish between product and process innovations

• Introduce different innovation categories

ifo Institute for Economic Research at the University of Munich

Related Literature

• Theoretical Contributions:– Petit (1995)– Stoneman (1984), Hamermesh (1993)

• Empirical Contributions:– Chennels / Van Reenen (1999)– Cross-Sectional Analyses: Zimmermann (1991), König et al.

(1995)– Employment Growth Analyses: Brouwer et al. (1993),

Blanchflower/ Burgess (1999), Blechinger et al. (1998)– Panel Analyses: Smolny (1998), van Reenen (1997),

Rottmann/Ruschinski (1998)

ifo Institute for Economic Research at the University of Munich

Database

• Ifo Innovation Survey• Panel Structure: 1982-2003 (unbalanced)• German Manufacturing Sector• ~1300 observations per year• Contains information on:

– Innovation: Product and Process Innovation, Innovations introduced, Innovation expenditure

– Firm characteristics: firm size, NACE, German states, turnover

• Control variables added on sector level (2digit NACE)

ifo Institute for Economic Research at the University of Munich

Empirical Model

• Modelling employment adjustment process is complex, esp. for small firms (e.g. Hamermesh / Pfann 1996)

• Labour demand reacts slowly to changes in innovation behaviour

• Estimating long-term effects: Following Blanchard / Wolfers (2000), Nickell (1997, 2003)

– Calculating averages for 4-(and 5-year periods)

– Use period averages for panel analysis (time index t indicates period)

ifo Institute for Economic Research at the University of Munich

Estimation

),,( XQTfL

L: Labour demand T: TechnologyQ: Product quality X: Controls

Level Equation:

Linear Equation in differenced log values:

3210 xqtl

- transformed into growth rates

- allows to introduce innovation variables

- Eliminates unobservable firm effect

ifo Institute for Economic Research at the University of Munich

itititititPd

itPc

it uegwIIl 543210

w: Growth of Real Hourly Wage Rate (sectoral)g: Growth of Real Gross Value Added (sectoral)eit: log of employment start level

Estimation Equation:

Pc: Process Innovation (proxy for t) Pd: Product Innovation (proxy for q)

Remember:

-Variables are expressed in averages over periods

ifo Institute for Economic Research at the University of Munich

The problem of endogeneity

• Potential contemporaneous correlation of innovation and error term resulting from a shock simultaneously affecting employment and innovation

• IV Strategy– So far we tested Innovation Impulses, Innovation Obstacles,

lagged values– No robust results: Either instruments are not good (low

significance in first stage) or not valid (Sargan test)

ifo Institute for Economic Research at the University of Munich

Descriptive Statistics

Until 1990: Former West Germany, since 1991: GermanyUnbalanced panel: 9142 „observations“, 4567 different firms, 5 time categories

Descriptive Statistics Mean Std. Dev. Min MaxEmpoloyment Growth (log) -0.016 0.261 -2.708 2.996Innovation 0.497 0 1 Product Innovation 0.406 0 1 Process Innovation 0.317 0 1Employment Start Level (log) 4.682 1.506 0 11.513Sectoral GVA Growth 0.005 0.046 -0.265 0.283Sectoral Real Wage Growth 0.018 0.026 -0.231 0.428

n=9142, N=4567

ifo Institute for Economic Research at the University of Munich

Regressions I

Dependent Variable: Average Yearly Employment Growth(1) (2) (3)

Estimated OLS standard Heteroskedasticity Covariance robustCoefficients errors robust s.e. standard errors

Employment Start Level -0.034 (0.002)*** (0.003)*** (0.003)***Real Wage Growth -0.437 (0.132)*** (0.162)*** (0.161)***Real GVA Growth 0.257 (0.081)*** (0.102)** (0.102)**Product Innovation 0.033 (0.008)*** (0.009)*** (0.009)***Process Innovation 0.057 (0.009)*** (0.009)*** (0.009)***Year incl.Sector incl.States incl.Constant 0.112 (0.026)*** (0.024)*** (0.026)***Observations 9142Adj. R-squared 0.039Standard errors in parentheses* significant at 10%; ** significant at 5%; *** significant at 1%

ifo Institute for Economic Research at the University of Munich

Regressions II

Dependent Variable: Average Yearly Employment Growth

Employment Start Level -0.039*** (0.004) -0.034*** (0.004) -0.060*** (0.008)Real Wage Growth -0.500** (0.220) -0.572** (0.255) -0.471 (0.433)Real GVA Growth 0.282** (0.124) 0.341** (0.137) 0.134 (0.244)Product Innovation 0.053*** (0.012) 0.049*** (0.014) 0.066*** (0.024)Process Innovation 0.052*** (0.013) 0.062*** (0.014) 0.036 (0.028)Year incl. incl. incl.Sector incl. incl. incl.States incl. incl. incl.Constant 0.099** (0.039) 0.076* (0.039) 0.200*** (0.048)Observations 5485 4136 1349Adj. R-squared 0.038 0.031 0.087Robust standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

1991-2003 West 1991-2003 East 1991-2003(4) (5) (6)

ifo Institute for Economic Research at the University of Munich

Regressions III

Dependent Variable: Average Yearly Employment Growth

Employment Start Level -0.044*** (0.005) -0.025*** (0.006)Real Wage Growth -0.498** (0.215) -0.399 (0.250)Real GVA Growth 0.157 (0.145) 0.405*** (0.140)Product Innovation 0.044*** (0.012) 0.018 (0.013)Process Innovation 0.064*** (0.012) 0.044*** (0.013)Year incl. incl.Sector incl. incl.States incl. incl.Constant 0.142*** (0.030) 0.067 (0.060)Observations 6062 3080Adj. R-squared 0.035 0.031Robust standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

less than 200 employees

equal or more than 200 employees

(7) (8)

ifo Institute for Economic Research at the University of Munich

Regressions IVDependent Variable: Average Yearly Employment Growth

Employment Start Level -0.034*** (0.003) -0.035*** (0.003)Real Wage Growth -0.439*** (0.161) -0.444*** (0.162)Real GVA Growth 0.260** (0.102) 0.256** (0.102)Innovation 0.063*** (0.014) ---Innovation (R&D) -0.007 (0.014) ---Innovation (Patents) 0.026** (0.011) ---Product Innovation --- 0.044*** (0.017)Process Innovation --- 0.050*** (0.013)Product Innovation (R&D) --- -0.027* (0.016)Process Innovation (R&D) --- 0.006 (0.014)Product Innovation (Patents) --- 0.026** (0.012)Process Innovation (Patents) --- 0.031 (0.025)Year incl. incl.Sector incl. incl.States incl. incl.Constant 0.107*** (0.026) 0.119*** (0.026)Observations 9140 9096Adj. R-squared 0.038 0.039Robust standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

(9) (10)

ifo Institute for Economic Research at the University of Munich

Summary

• Innovations show positive effects on employment growth• True for product as well as process innovations. Process

innovations show even higher effect• No additional effect for R&D based innovations• Additional effect for product innovations which involved

patent applications• Further Research: Dynamics of adjustment process