Copyright EM LYONPar accord du CFC

Cession et reproduction interdites

Research in Entrepreneurship- The problem of unobserved

heterogeneity

Frédéric DelmarProfessor, PhD

EMLYON Business School, FranceAnd

Research Institute of Industrial Economics, Sweden

I Agenda

1. Who am I?2. Unobserved heterogeneity and

entrepreneurship• Related subject and example

working paper

3. Delmar & Wiklund (2008) The effect of small business managers’ growth motivation on firm growth. ETP, Spring: 437-457

4. Delmar & Shane (2006) Does experience matter? Strategic Organization, vol 4(3): 215-247

• Easy to understand subject, lot of research already, however a lot of reminding problems in theory and method

II Who am I?

• Editorial board: JBV, ET&P, SO!• Occasional reviewer: SMJ, AMJ, MSc, SBE, and

others• In 16 years of research:

– Two A (SMJ and MSc)– Nine B (JBV, ET&P, ERD, SO!)– Five C and about 20 book chapters

• Subjects: behavior, growth, firm establishment• Center for Research in New Venture Creation

and Growth, EMLYON Business School

IV Unobserved heterogeneity and Management

• Heterogeneity=>– Unobserved heterogeneity=>

• omitted variable problem

• Observational data=>– Data is interpreted =>

• Theory describes what is the universe of relevant variables to be included to test hypotheses

• The role of theory: – Counterfactual causality reasoning in theory allows us to identify

relevant variables and how to test them

• Solutions both in cross-sectional and longitudinal data to handle the problem with omitted variables

Definition or why it is a problem

• Heterogeneity and causality:– The existence of an unobserved variable (u) that may

affect the interpretation of variable X on Y.– Hence the estimation (coefficient) of X on Y is biased

if u is not measured (coefficient and standard error). – Further more we cannot estimate the probability how

u will affect object i. That is u is not randomly distributed.

– For example, the value of the entrepreneurial opportunity for any entrepreneurial process, or motivation of individual in matched data

Here is a question for you

• You are asked to conduct an observational study to estimate the effect of business plan or experience on the risk of firm termination or firm performance. How would you design the study and what assumptions are you making?– Option 1: a large set of heterogeneous sample of firm (different

industries, at different locations, different opportunities …)– Option 2: a smaller more homogenous sample of firms (all on

the same opportunity)

• Would you use a cross-sectional or longitudinal data set?• Feel free to change to change business plan for helmet,

firm dissolution for death on a motorcycle

Here are some possible choices or assumptions you are making

• Theory:– What is really relevant to measure according to theory, what

other interpretations can be made, how can I exclude them?– Can I construct a counterfactual argument?– Am I primarily interested in between process or within process

variance

• Design:– I can control away a lot of the effects by including other variables– I can used randomization– I can assume that unobserved is too little to pose a problem

• Analysis:– Cross sectional– Longitudinal

Theoretical issues

1. The formal establishing of causality is a property of a model not of data or statistical analyses.

2. Many models can explain the same data.

3. Assumptions must be made to identify causal models. – Hence, a good theory allows us to derive a model which

represents a logically consistent system within which hypothetical “thoughts experiments can be conducted to examine the effects of changes in parameters and constraints on outcomes” (Heckman, 2000, p. 46).

• Entrepreneurship as heterogeneity and process

Counterfactual argument

• “if it had been the case that c (or not C), it would have been the case that E (or not E)”

• Important for observational data• The researcher need to be clear on what the specific

cause is, why it should affect the particular outcome and what would happen in the absence of the cause being present. The later is the counterfactual argument.

• The counterfactual argument is only as compelling as the logic and “evidence” offered by the researcher to verify the links between the hypothesized antecedent and its expected consequences

• Finding similar cases!

Design issues

• The use of randomization is not possible (unethical or infeasible)

• Control variables consume degrees of freedom, and u might be truly unobservable

• Are we more concerned with internal validity or generalizability?– Smaller, more homogenous sample reduces both

sampling variability and sensitivity to unobserved bias– Increasing the sample reduces sampling variability, but

not unobserved bias

• Longitudinal designs

Analysis issues

• Cross-sectional and longitudinal– Moderator and mediator– Instrument variables– Sensitivity analysis

• Longitudinal:– Random effects vs. fixed effects

• Unobservable is time invariant; between and within variance

– Matching or propensity Score methodology

Conclusions

• Unobserved heterogeneity, biased estimators and problems with inference of causality

• The role of theory in observational data• Design=>clever designs• Analysis

– Think carefully on the assumptions made– Sensitivity analysis

• How about wearing business plan or experience?

Having experience or a business plan

• Different outcomes due to different:– Different economic models, different periods, different

learning abilities, motivation, resources, network, opportunities, quality of information

• Ho to do it?– Firms started at the same time in the same context

using a matching procedure.

Wearing helmet

• Different crashes due to different:– motorcycles, speed, forces, roads, types of traffics,

obstacles.

• How to do it?– Two people riding the same motorcycle, one with

helmet and one without

• The effect?– 40% reduction in risk associated with helmet use

Conclusion

• The role of causality in research• The problem and specific challenges of

observational data• Theory and the counterfactual argument• Design – homogenous sample for greater

internal validity• Analysis- Many available techniques (fixed and

random effects, matching)