Location Factors, Innovativeness and Firm Performance. Empirical Analysis of Firm Level

Data from East Germany and Poland.

Prof. Dr. Andreas StephanEuropean University Viadrina Frankfurt/Oder and German Institute for

Economic Research (DIW Berlin)

Anna LejprasEuropean University Viadrina Frankfurt/Oder and German Institute for

Economic Research (DIW Berlin)

16th January 2007

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

2

Background of the Research Project

• German Institute for Economic Research (DIW Berlin) has carried out large-scale surveys for the German Ministry of Economic Affairs on the situation and perspectives of East German firms over years 1995 to 2004.

• For example, in year 2000 about 8000 firms across all branches of the economy responded, and in 2004 about 6000 firms responded (response rates approximately 20%)

• Reports for the ministry were prepared and descriptive analyses of the data were performed, but no ambitious research on these unique firm panel data so far.

• Approval of an application to the German Science Foundation (DFG) on a research project based on this data and conducting a survey of Polish firms.

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

3

Some selected results on the importance and assessment of locational factors in East Germany from the survey 2004

• Qualified labour supply: – 19.8 % of the firms said it is of some importance,

– 3.3 % said it is very important,

– but 76.9 % responded it is not important:

– Assessment of conditions:• very good: 2.7 %, • very poor: 9.4%

• Closeness to universities– 75.3 % of the firms said it is of some importance,

– 2.9 % said it is very important,

– Assessment of conditions:• very good: 11.3 %, • very poor: 3.3%

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

4

Some selected results on the importance and assessment of locational factors in East Germany from the survey 2004

• Transportation infrastructure: – 37.8 % of the firms said it is of some importance,

– 3.8 % said it is very important,

– but 58.7 % responded it is not important:

– Assessment of conditions:• very good: 6.6 %, • very poor: 4.3%

• Further investigation: what is the difference in assessment of locational conditions between non-innovative vs. innovative firms?

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

5

Purpose of the seminar/meeting

• Presentation and discussion of research project• Presentation and discussion of the questionaire for the Polish firms• Agreement on conducting the survey in behalf of both European

University Viadrina and the Polish Academy of Sciences (PAN)• Further research cooperation

Location Factors, Innovativeness and Firm Performance. Empirical Analysis of Firm Level

Data from East Germany and Poland.

Prof. Dr. Andreas StephanEuropean University Viadrina Frankfurt/Oder and German Institute for

Economic Research (DIW Berlin)

Anna LejprasEuropean University Viadrina Frankfurt/Oder and German Institute for

Economic Research (DIW Berlin)

16th January 2007

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

7

Overview

Project aims

Cluster approach and Porter‘s Diamond model

Conceptual design of the model

Model estimation for East-German firms

Survey of Polish firms

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

8

Project Aims

• Analysis of the impact of location factors on the innovativeness and firm performance in East Germany and Poland

• To this end a structural equation model is developed that:

– derives from Porter‘s Cluster Approach

– is estimated with the Partial Least Squares method

– is based on the data that was collected in 2004 by DIW Berlin (survey „Situation and Perspectives of Firms in East Germany“)

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

9

Cluster - Definition

„Clusters are geographic concentrations of interconnected companies and institutions in a particular field.“

Examples for world-famous clusters are:

Hollywood, Wall Street, Ferragamo und Gucci or automotive manufacturers in Southern Germany

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

10

Porter‘s Diamond Model

Context for Firm Strategy and Rivalry

Demand Conditions

Factor (Input) Conditions

Related and Supporting Industries

Chance

Government

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

11

Structural Equation Model

• The determinants of the Diamond Model as the exogenous latent variables:

– Factor conditions and government impact (6 indicators)

– Demand conditions (3 indicators)

– Firm strategy and rivalry (4 indicators)

– Related and supporting industries (7 indicators)

• Two important aspects of the cluster approach as the endogenous latent variables:

– Innovativeness (5 indicators)

– Firm performance (5 indicators)

• All latent constructs are operationalized as formative measurement models

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

12

Structural Equation Model

Factor ConditionsRelated &

Supporting Industries

Demand Conditions

Strategy & Rivalry

Innovativeness Performance

++

+

+

+

+ +

+

Assigment of manistest variables to latent variables see attachement 1

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

13

PLS Estimation - Notes

• Division of firms into two groups – firms from highly innovative and low-innovative sectors of economy (see attachement 2)

We expect that the hypothesized relationships between the variables should be stronger in the model for the highly innovative firms than those for the low-innovative firms

• The preliminary analysis: the results for the innovative and non-innovative firms are presented in attachment 3

• Only the significant relationships are pictured

• The order of the listed indicators is with regards to their relationship strength with the appriopriate latent variable

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

14

PLS Estimation Results – Innovative Firms(Structural Model)

• Five of nine hypothesized paths turn out to be significant at the 5% level, additionally the path between „Rivals/Strategy” and „Innovativeness” is only significant at the 10% level

• The influence of „Demand Conditions” on „Innovativeness” is negative

• The impact of „Rivals/Strategy” on „Innovativeness” and „Firm Performance” is also negative

• The assumed positive impact of „Innovativeness” on „Firm Performance” is not confirmed

• R2 of „Innovativeness” is on average (0.353) and R2 of „Firm Performance” is low (0.189)

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

15

PLS Estimation Results – Non-innovative Firms(Structural Model)

• Four of nine hypothesized relationships are significant first at the 10% level

• The influence of „Demand Conditions” on „Innovativeness” is negative

• The impact of „Rivals/Strategy” on „Innovativeness” or „Firm Performance” is not confirmed

• R2 of „Innovativeness” is extremely low (0.083) and R2 of „Firm Performance” is on average (0.306)

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

16

Estimation Results – Innovative vs. Non-innovative Firms(Measurement Models)

• Higher intensity of cooperation activities and more significant cooperation fields in the case of the innovative firms

• For innovative firms the closeness to universities and research establishments are the most important factors, for non-innovative ones the closeness to research establishments and the supra-regional transportation system are most important

• All manifest variables of „Innovativeness” are significant for the innovative firms, for the non-innovative ones only the patent count

• „Firm Performance” is measured by the market volume in the case of the innovative firms and by the employment growth for their non-innovative counterparts

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

17

Model Comparision – Innovative vs. Non-innovative Firms

As expected the relationships between the variables in the model for the firms from highly innovative sectors of economy are stronger compared to those for the less-innovative firms

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

18

Survey of Polish firms

• The questionnaire for Polish firms is based on the survey that was sent in 2004 to East-German firms by the German Institute for Economic Research (DIW Berlin)

• The survey of 2000 Polish firms is planned in February/March 2007

• We took the firm adresses from the database on the webpage http://www.teleadreson.pl/ considering the following criteria:

– the economic sectors (NACE): 22.3 24.4 29.1 29.4 29.7 30 31 32 33 34 35.3 72.2 72.3 72.4 73 74.3

– the ownership form: private, mixed, foreign capital

– the legal form: corporations, individual enterprises, agencies of foreign enterprises

Thank you for your attention!

Location Factors, Innovativeness and Firm Performance. Empirical Analysis of Firm Level

Data from East Germany and Poland.

Backup

Location Factors, Innovativeness and Firm Performance. Empirical Analysis of Firm Level

Data from East Germany and Poland.

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

21

The Determinants of the Porter’s Diamond Model (I)

• Factor ConditionsThe location in clusters enables the access to specialized and cost-effective inputs

• Demand ConditionsDemanding local customers put pressure on the firms to innovate and to bring new products on the market as well as to establish new production processes

• Context for Firm Strategy and RivalryPowerful local competitors put visible pressure on each other resulting in new innovations, lower costs and quality improvements

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

22

The Determinands of the Porter’s Diamond Model (II)

• Related and Supporting Industriesefficient access to cost-effective inputs and machinery; lower transaction costs; in the close cooperation the suppliers help the firms to perceive new methods or technologies

• Governmentinfluence all determinands of the Diamond Model, e.g. by the subventions, the education policy, the regulations of the product norms or the tax policy

• Chance

providing the discontinuities, cause of the changes in the competition situation (e.g. the sudden changes of the input costs or wars)

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

23

Partial Least Squares Approach

• PLS and LISREL as path models with latent variables that are indirectly observed by manifest variables, called indicators

• PLS as „soft-modeling“, no assumptions concerning the distributional properties of the variables

• PLS is prediction-oriented, has an explanatory nature – no detailed knowledge about the relationships in the structural and measurement models is required

• The PLS algorithm proceeds in three stages:– stage 1 (iterative): the case values of the LV are estimated

– stage 2: estimation of loadings and weights

– stage 3: estimation „location parameters“

• PLS method generates explicit case values for LV• Reflective and formative measurement models can be

operationalized

Lo

catio

n F

acto

rs, Inn

ova

tiven

ess a

nd

Firm

Pe

rform

an

ce.

24

Reflektive vs. Formative Measurement Models

(b)

(a)

+/-

- Drunkenness

Blood alcohol level

Ability to respond

…

+

Fig.: Reflektive (a) vs. formative (b) measurement model

+/-

+ Drunkenness

Consumed beer

Consumed wine

…

+