Huelva 17 th and 18 th November 2008 – II Workshop on Entrepreneurship Statistics 1 Evidence on the role of Ownership Structure on Firm’s Innovative Performance.

Huelva 17th and 18th November 2008 – II Workshop on Entrepreneurship Statistics 1

Evidence on the role of Ownership

Structure

on Firm’s Innovative Performance.

Raquel Ortega-Argilés

European Commission, JRC-IPTS

Rosina Moreno

University of Barcelona, AQR-IREA

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A company’s ability to innovate depends on a series of factors:

– The environment in which the firm is operating: determinants of the market and location where the firm operates.

– The resources that the company allocates to engineering, design, research and marketing.

– The company’s management and internal organisation.

– The desire to differentiate its products or processes from those of its competitors.

• The specialization of tasks has lead the firm into the introduction

of EXTERNAL MANAGERS

in the decision-making process

• The dispersion of the ownership structure has leaded the firm a

DIFUSED CONTROL and ATOMISTIC OWNERSHIP

These are the most suitable situations for encountering

information asymmetries in the management decisions

Companies have become aware of their need to encourage their

capacity for innovation

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Motivation

Firm-specific characteristics play a role in explaining corporate innovation behaviour

• Aspects related to firm ownership and capital structure have been avoided in the innovative literature so far.

• There exist a number of papers that analyse the influence of some corporate governance variables on the R&D investments, there are only few attempts in the literature of innovative performance measured by innovative results (counts).

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Short review of the literature:

Large shareholders have strong incentives in profit maximization and enough control over the assets of the firm to put pressure on managers to

have their interest respected and risky projects maintained (Shleifer and Vishny, 1997).

In the same line, some studies explain that the concentration of capital in a small number of owners helps to align the management team with the shareholders’ interests, leading to reducing high risk investment policies such as the ones of innovation, and to a loss of some of the benefits of

specialisation (Hill and Snell, 1988; Burkart et al., 1997).

Has the concentration of the ownership in a few hands a direct impact in innovative performance?

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The entrepreneurship literature put emphasis in the role played by the entrepreneur, which most people recognize as meaning someone who

organizes and assumes the risk of a business in return of the profits. In many cases, owners delegate decisions to salaried managers,

and the question is: are independent directors better suited as decision-makers for innovative

strategies than insiders?.

The owners have appropriate information about firm’s activities and this is fruitful to enhance innovation, but in most cases, the innovative

activities carry out “new combinations” by such things as introducing new products or processes, identifying new exports markets or sources of supply, or creating new types of organization and the owner needs

judgment to deal with the novel situations connected with innovation (Casson, 1991; Crespi, 2004).

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Traditional literature (other aspects to control for):

“…There is no more pleasant fiction than that technological change is the product of the matchless ingenuity of the small man forced by competition to employ his wits to better his neighbor. Unhappily, it is a fiction.” (Schumpeter, 1942; Galbraith, 1956; Acs and Audretsch, 2005)

The technological opportunities have a firm-specific nature, the different sectoral innovative behaviors cannot be simply treated as a random phenomenon. (Dosi, 1988; Cohen and Klepper, 1992; Malerba and Orsenigo, 1994)

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Traditional literature (the role of space):

Firm innovative attitude is influenced by the environment where the firm is operating.

High competitive markets push firms to secure innovation from the competitors to maintain their market shares.

The accessibility to labour force or the industrial dynamicity generate agglomeration economies that influence positively the innovative behaviour of the located firms (Autant-Bernard, 2003).

Being located in highly innovative regions generates knowledge spillovers creating a favourable environment for innovation (Audretsch and Feldman, 1999 and 2005).

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Y= F (X, K, R, )

Y= output variableX= standard input variables (human capital, physical capital, materials)K= R&D input variable o technological knowledge (R&D investment)R= Environmental characteristics (market and region)

PATi= F(internal structure –size,ownership and controlStructure-, environmental vars -market and region-,

innovation inputs)

Methodology

“Knowledge Production Function”Griliches (1979, 1986)

[Pakes and Griliches, 84; Hausman et al, 84; Acs and Audretsch, 88; among others]

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Data:

Databases: Survey of Entrepreneurial Strategies (ESEE)Industrial Business Survey (regional data)Spanish Patents and Trademarks Office (regional panel data)

Sample:

Unbalanced panel that contains data of 2643 Spanish manufacturing firms for the 1994-2001 period.

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Dependent variable: Innovative output or Innovative PerformancePATit= number of patents and/or utility models

Excess of zerosDiscrete non-negative nature of the variable

Small values

Count Data Models: POISSON & NEGATIVE BINOMIALCount Data Models: POISSON & NEGATIVE BINOMIAL

I. POISSONI. POISSON•Most common CDM•Num. of events, given a set of regressors x, has a Poisson distribution with parameters

•The application of the Poisson requires equality of means and variance•Excess of zeros: the st.error will be biased giving spurious high values to the t statistics (Cameron and Trivedi, 1990)

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Dependent variable: Innovative output or Innovative PerformancePATit= number of patents and/or utility models

Excess of zerosDiscrete non-negative nature of the variable

Small values

Count Data Models: POISSON & NEGATIVE BINOMIALCount Data Models: POISSON & NEGATIVE BINOMIAL

II. NEGATIVE BINOMIALII. NEGATIVE BINOMIAL•Does not impose equidispersion in the dependent variable.•Assume that the variance is a quadratic function of the mean.•We can control for the overdispersion in the variables, and we can reduce the unobserved heterogeneity by means of time-series application.

NOTE: We have estimated two more count data models (pooled sample): ZIP and ZINB, the results are practically the same.

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Variables:

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Descriptive Statistics:

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Results:

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Results:

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Poisson Negative Binomial VARIABLES

I II III IV V VI VII VIII RD SIZE AGE

SHARE SHARE*FOR SHARE*PUB MARKET

EMPLREG INNOVREG RDREG PATREG SECTOR YEAR Intercept

0.115*** (0.018) 0.509*** (0.049)

-0.937*** (0.069) -0.006 (0.001)

-0.008*** (0.001) 0.009** (0.004) 0.432*** (0.067) 0.060*** (0.012) ----

----

----

YES

NO -0.967* (0.570)

0.443*** (0.027) 0.117** (0.050)

-0.388*** (0.074)

-0.002*** (0.001)

-0.007*** (0.001) 0.006

(0.004) 0.473*** (0.072) ----

0.613*** (0.207) ----

----

YES

YES -3.078*** (0.554)

0.066*** (0.023) 0.995*** (0.081)

-0.841*** (0.135) -0.002* (0.000)

-0.012*** (0.001) 0.004 (0.012) 0.564*** (0.111) ----

----

2.167** (1.052) ----

YES

NO -2.230* (0.732)

0.258*** (0.031) 0.287*** (0.053)

-0.611*** (0.074) 0.002* (0.001)

-0.007*** (0.001) 0.0078* (0.004) 0.937*** (0.082) ----

----

----

0.005*** (0.002)

YES

NO -3.078*** (0.554)

0.128*** (0.033) 0.171*** (0.056) -0.086 (0.062) -0.001 (0.002)

-0.007*** (0.002) -0.005 (0.004) 0.058

(0.147) 0.022*** (0.007) ----

----

----

YES

NO -3.155*** (0.467)

0.304*** (0.045) -0.054 (0.063) -0.061 (0.066) -0.001 (0.002)

-0.007*** (0.002) -0.007 (0.004) -0.030 (0.149) ----

0.353*** (0.133) ----

----

YES

YES -3.296*** (0.487)

0.140*** (0.043) 0.168** (0.078) 0.062

(0.101) -0.001 (0.003)

-0.006*** (0.002) -0.009 (0.006) -0.185 (0.227) ----

----

-0.058 (0.717) ----

YES

NO -3.543*** (0.594)

0.246*** (0.047) 0.041

(0.068) -0.018*** (0.067) -0.001 (0.002)

-0.008*** (0.002) -0.007 (0.005) 0.316** (0.166) ----

----

----

0.005** (0.002)

YES NO

-2.956*** (0.520)

Wald test: Chi2(K-1) N. Obs N. Indivs Log Likel: LR test

483.68***

2949 1441

-4780.76 12000***

891.52***

2952 1443

-4517.91 11000***

322.25***

1798 1029

-2246.51 6484.95***

384.92***

2330 1349

-3611.13 11000***

118.08***

2949 1441

-2825.95 190.45***

179.88***

2952 1443

-2794.41 196.28***

79.95***

1798 1029

-1618.95 47.83***

122.64***

2330 1349

-2414.28 150.51***

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Empirical results

•The control of the decision of the management team by means of concentration of the ownership and introduction of owners in management positions tasks reduced the amount of innovation pursued by the firm due to the reduction of the specialization of the decision tasks and the adverse-risky innovative strategy.

•Foreign ownership seems to increase the negative effect of the concentration of the ownership and the presence of insiders in decision tasks in firm patenting activities (liability of foreignness effects).

•The scales economies in R&D, firm size, learning by doing effect, high technological sectoral opportunities and the internalization of the market increase the chances of patenting.

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Empirical results

•The results show that firm’s location is important for explaining the patent activity of Spanish manufacturing firms. There is a positive effect of being located in an environment with a high innovative activity.

•Regional high innovative activity implies the existence of knowledge spillovers across individuals of different firms which would result in a higher patenting activity.

•The presence of agglomeration economies coming from a dense labour market with a subsequent higher endowment of human capital would also imply higher levels of innovation output.