Project funded by the European Commission under the Seventh Framework Programme, Grant No 217512

www.coinvest.org

Do Intangibles Enhance Productivity Growth? Microeconometric Evidence from Germany

Dirk Crass and Bettina PetersZEW Centre for European Economic Research, Mannheim, Germany

Preliminary Results

Coinvest Conference “Intangible Investments at Macro and Micro Levels and Their Role in Innovation, Competitiveness and Growth”

Lisbon,18th and 19th March 2010

Outline

■ Motivation

■ Related literature

■ Data

■ Empirical approach

■ Econometric results

■ Conclusion

Motivation

■ Weak growth performance in European countries compared to US since the beginning of the nineties

■ Major challenge for Europe: reap benefits from knowledge economy (new strategy in Europe: Europe 2020)

■ Knowledge capital is more than R&D, e.g.: ● expenses for design and licenses, ● computerized information, ● brand equity , ● firm-specific human capital, ● organizational capital

■ Challenging task: Measuring such intangible assets and their impacts

Motivation

■ At the macro level: Investment in intangible assets

● amounts to 11.7% of GDP in US and is thus is even larger than

investment in physical capital (Corrado et al. 2005)

● much lower in European countries: 10.1% in UK (Marrano and

Haskel 2006), 7.02% in GER (Crass et al. 2009), 8.7 % in FR,

5.15% in IT, 5.2% in Spain (Hao et al. 2009)

● stimulated labour productivity growth in the US by 0.84.

● Contribution is smaller in many European countries:

UK - 0.58, GER - 0.53, IT - 0.34, Spain - 0.19.

● And contribution of intangible capital is decreasing in Germany from

0.65 (1995-2000) to 0.40 (2000-2004), see Crass et al. (2010)

Motivation

■ At the micro level:

● Much evidence on productivity enhancing effects of R&D

● But only scarce evidence on the effect of other types of intangibles

investments at the firm level

■ Research question: Do all types of investments in intangible

assets enhance productivity at the firm-level?

■ Analysis based on panel data for German companies from

2006-2008.

Outline

■ Motivation

■ Related literature

■ Data

■ Empirical approach

■ Econometric results

■ Conclusion

Impact of R&D on productivity

■ Starting with the seminal work by Griliches, many studies investigating the impact of R&D on productivity at the firm level

■ Most studies show a significant positive private return to R&D, ranging mostly between 20 to 30% (see Hall et al. 2009)

■ Corresponding output elasticity of R&D ranging from 0.01 to 0.25 in most studies (centered on 0.08)

Impact of IT on productivity

■ A number of previous studies have found a positive relationship between IT investment and firm productivity levels (Brynjolfsson & Hitt, 1995, 1996b; Lichtenberg,1995)

■ Brynjolfsson and Hitt (2003):● Over short horizons, estimated contributions of computers are

roughly equal to their costs. ● Over longer time horizons computerization is associated with an

output contribution between 2 and 5 times as much as the short-run impact.

● Relatively large and time-consuming investments in complementary inputs, such as organizational capital, are required.

Impact of human capital on productivity

■ Black and Lynch (1996): Human capital investments and productivity● Number of workers trained has no significant impact on

productivity.● For manufacturing, the greater the proportion of time spent in

formal off-the-job training, the higher the productivity.

■ Dearden, Reed and Reenen (2006): Impact of training on productivity and wages● Work-related training is associated with significantly higher

productivity. ● 1% increase in training is associated with an increase in value

added per hour of about 0.6 per cent.

Impact of human capital on productivity

■ Konings and Vanormelingen (2009): Effect of training on productivity and wages● Estimated productivity premium for a trained worker is 23%● Estimated wage premium of training is 12 %● Slightly higher impact of training in services compared to

manufacturing sectors.

■ Ballot et al. (2006): Effect of training and R&D on productivity ● Average output (VA/employee) elasticities of training and R&D:

17.3 and 5.4% in France and 7.3 and 6.1% in Sweden. ● Significant positive interaction between training and R&D.

Indicates that training (R&D) has a larger positive impact on productivity if the firm accumulates R&D capital (human capital).

The impact of intangibles on productivity

■ Bontempi and Mairesse (2008): ● Compare productivity of intangible relative to tangible capital● Differentiation between capitalized vs expensed and intellectual

(R&D, patents) vs customer intangible capital (adv., trademarks) ● Intangible capital is at least as productive as tangible capital.● Highest marginal productivity is found for intellectual capital,

lowest for capital based on expenses

■ Ramirez and Hachiya (2008): ● Measuring the Contribution of Intangibles to Productivity Growth● They distinguish R&D, human capital, advertising and firm-specific

organizational capital● Advertising is one of the most productive inputs

Outline

■ Motivation

■ Related literature

■ Data

■ Empirical approach

■ Econometric results

■ Conclusion

Data■ Mannheim Innovation Panel (MIP):

● Annual innovation survey in the German manufacturing, mining, energy and water supply industry and selected service industries.

● Conducted by ZEW on behalf of German ministry on education and research

● MIP is the German contribution to the European-wide harmonized Community Innovation Surveys (every two years).

■ Target population: firms with 5 and more employees

■ Stratified random sample (industry, size classes and region)

■ Designed as a panel.

Data■ Sample period: 2006-2008

■ Unbalanced panel: ● 7552 observations● 5117 firms ● average participation of 1.5● Balanced subsample consists of 656 firms

Data: Variables

■ Dependent variable:● Labour productivity: log (sales/employee)● Annual log rate of growth of labour productivity growth

■ Explanatory variables:● Traditional input factors:

• Labour : log (# employees)Note: to avoid double counting we correct the total number of employees for R&D employees

• Physical capital: log (tangible assets / employee)Note: tangible assets are direct information from the survey

• Material: log (material expenses / employees)

Data: Variables● Knowledge-related intangible input factors:

• R&D: log (R&D expenditure / employee)

• Non-R&D related innovation: log (expenses for external knowledge, design, market introduction, training and product preparation related to innovation / employee)

• Marketing: log (marketing expenditure / employee)Note: to avoid double accounting we correct for market expenditure for innovation projects (based on industry average in prior wave)

• Training: log (training / employee)Note: to avoid double accounting we correct for training expenditure for innovation projects (based on industry average in prior wave)

• Organisational innovation: 4 dummies for whether the firm has introduced new practices, workplace organisation, knowledge management and external relationships

Data: Variables

● Control variables:• East Germany: dummy whether firm is located in East

Germany• Export intensity: exports / sales• Group: company is part of a group (0/1)• Time dummies• Industry dummies

Descriptive statisticsMean SD Median Min Max

Productivity 0.268 1.293 0.138 0.002 89.938Productivity growth 0.016 0.375 0.015 -5.118 3.040Employees 851 6091 42 0 399796Physical capital 0.143 0.692 0.025 0 41.063Material costs 0.152 1.156 0.051 0 89.794R&D expenditures 0.008 0.114 0.000 0 5.941Non-R&D innovation 0.005 0.024 0.000 0 1.050Training 0.001 0.001 0.000 0 0.048Marketing 0.002 0.009 0.000 0 0.485Busin. Process 0.364 0.483 0 0 1Workplace orga 0.354 0.480 0 0 1External rel. 0.246 0.434 0 0 1Knowl. manag. 0.289 0.453 0 0 1East Germany 0.323 0.468 0 0 1Group 0.367 0.482 0 0 1Export intensity 0.167 0.252 0.011 0 1.118

Descriptive statisticsVariable Mean Std. Dev.

Overall Between WithinLabour 0.268 1.293 1.519 0.081Capital 0.143 0.692 0.531 0.409Material 0.152 1.156 1.363 0.130R&D 0.008 0.114 0.091 0.059Non-R&D Inno 0.005 0.024 0.027 0.007Training 0.001 0.001 0.001 0.001Marketing 0.002 0.009 0.010 0.002Business Process 0.364 0.483 0.471 0.141Workplace org. 0.354 0.480 0.467 0.149External rel. 0.246 0.434 0.421 0.138Knowledge man. 0.289 0.453 0.453 0.000East 0.323 0.468 0.466 0.012Group 0.367 0.482 0.472 0.097Export intensity 0.167 0.252 0.249 0.043

Outline

■ Motivation

■ Related literature

■ Data

■ Empirical approach

■ Econometric results

■ Conclusion

Estimation Approach

■ Cobb-Douglas production function

With Q: output, L: labour, K: physical capital, M: material, λ: exogenous technological change, u: error term

RD: R&D capital, NI: non-R&D related innovation capital, TR: human capital, MKT: marketing

■ As log specification:

31 2 4(1) t ituit it it it it it it itQ Ae L K M RD NI TR MKT e

1

2 3 4

(2) log( ) log( ) log( ) log( ) log( )

log( ) log( ) log( )it it it it it

it it it t it

Q a L K M RD

NI TR MKT u

Estimation Approach

■ Pooled OLS

■ Fixed effects estimator (assuming constant returns to scale)

■ First difference estimation, i.e productivity growth

Outline

■ Motivation

■ Related literature

■ Data

■ Estimation approach

■ Econometric results

■ Conclusion

Econometric results

FE(1) (2) (3) (4) (5)

Labor -0.023 *** -0.027 *** -0.036 *** -0.046 *** - -Capital 0.077 *** 0.073 *** 0.069 *** 0.080 *** 0.079 *** 0.085 ***Material 0.416 *** 0.414 *** 0.405 *** 0.44 *** 0.165 *** 0.151 ***R&D 0.013 *** 0.006 *** 0.016 *** 0.008 ** 0.007 *Non-R&D Innov. 0.004 *** 0.001 -0.001 0.000Training 0.00003 *** 0.00003 *** 0.00002 *** 0.00002 **Marketing 0.011 *** 0.007 * 0.012 *** 0.012 **Busin. Process 0.064 **Workplace orga -0.038Extern. rel. -0.005Knowl. manag. 0.013Controls yes yes yes yes yes yesTime D yes yes yes yes yes yesIndustry D yes yes yes yes no yesR2 0.695 0.698 0.705 0.746 0.17 0.137# Obs. 7552 7552 7552 2317 7552 2356

LevelPooled OLS

Econometric resultsProductivity

FE(5)

Labor - -Capital 0.079 *** 0.085 ***Material 0.165 *** 0.151 ***R&D 0.008 ** 0.007 *Non-R&D Innov. -0.001 0.000Training 0.00002 *** 0.00002 **Marketing 0.012 *** 0.012 **Busin. ProcessWorkplace orgaExtern. rel.Knowl. manag.Controls yes yesTime D yes yesIndustry D no yesR2 0.17 0.137# Obs. 7552 2356

Growth rateOLS(5)

Outline

■ Motivation

■ Related literature

■ Data

■ Empirical approach

■ Econometric results

■ Conclusion

Conclusion

■ We estimate the productivity effect of different kinds of intangible assets at the firm level.

■ We confirm strong positive productivity effects for R&D, marketing and training.

■ Effects of marketing are highest, even higher than those of R&D (in the short term).

■ Only weak evidence for positive productivity effects of non-R&D related innovation assets like design, licenses and product preparation.

■ Mixed productivity effects of firms increasing their organizational capital by introducing organisational innovations: positive effect for business practices but not for workplace organisation.

Further Research

■ Tackling potential endogeneity of inputs (though limited possibilities due to short time span)

■ Complementarities between tangible and intangible capital as well as different intangible assets.