Home  BADM449   Handout #7 Joseph T. Mahoney

College of Business
Department of Business Administration
BADM449  Strategic Management/Business Policy

Empirical Testing of
Structure-Conduct-Performance Model

Empirical Testing of the "Structure-Conduct(Strategy)-Performance Paradigm" (and Porter's Structural Model):

ROE  =   6.3 + .050  CR4   +   .119  [CAP/S]    +  1.30  [A/S]    
  j             (2.08)   j     (1.98)        j     (7.20)      j

     +  1.40  [R&D/S]     +    0.26  [GROW]              R   =  .43
       (2.95)        j        (2.90)       j       t-statistics in 

where     CR4 = 4-firm concentration        
        R&D/S = R&D/Sales 
        CAP/S = capital expenditures/sales      
         GROW = Demand Growth
          A/S = advertising/sales
          ROE = Return on Equity in industry j.

Model specification: In practice, researchers estimate a statistical model of the following form where data are aggregated to the industry level:

Industry Profit Rates = f (Concentration, Barriers to Entry, Demand, ...)
Multiple regression analysis seeks to evaluate the degree to which deviations of the dependent variable (and in this course our focus has been on profit rates as the dependent variable) from its mean are "explained by" or associated with variations in each of a set of independent or explanatory variables (i.e., concentration, barriers to entry, demand, etc.).

The nature of this association is captured by regression coefficients or parameters relating the profit rates in the industry to each independent variable, allowing us to determine the effect, for example of a 10% increase in seller concentration on profit rates for the industry, holding all other explanatory variables constant (i.e., "ceteris paribus").

In the multiple regression at the top of the page, we see a particular model specification based on theory from our strategy model derived from Porter and "structure-strategy-performance."

Predicted Impact on Performance (ROE):

CR4 + The higher the concentration the higher the pricing power
CAP/S + Capital-cost barrier to entry
A/S + Advertising as a product differentiation barrier to entry
R&D/S + Technological know-how as a barrier to entry
GROW + Demand growth leads to less likelihood of price wars

Note that the multiple regression on the previous page is consistent with (but does not prove!) our Strategy theory. All the coefficients were positive as expected and all were statistically significant.

As you probably are aware from your statistics classes, there are many potential problems that can interfere with the reliable estimation of regression models, leading to incorrect inferences about the statistical significance and economic importance of explanatory variables.

Three problems:

  1. Mis-specification problems
  2. Measurement problems
  3. Identification problems

Mis-specification problems:

  1. Important variables omitted. In the regression above, the impact of substitute products and the power of suppliers and buyers have not been measured.

  2. Irrelevant variables included. If you believe fervently in "perfect capital markets" then you may question the idea of capital cost entry barriers and therefore you would question the inclusion of the independent variable [CAP/S] in the model.

  3. Model assumes a linear relationship. Since the regression above assumes a linear relationship, this may turn out to be a poor approximation if some of the explanatory variables (e.g., ADV/S) influence the dependent variable (i.e., ROE) in a non-linear way.

  4. Independent variable may not be truly independent. For example, not only can increased concentration affect profit rates but profit rates may affect industry concentration.

  5. Multicollinearity. If independent variables such as (ADV/S) and (R&D/S) are highly correlated, then the validity of the t-statistics comes into question.

Measurement Problems:

For example, CR4 may not be the best measure of industry concentration and the HHI (Hirschman-Herfindahl Index) may be a better measure. Perhaps some performance measure other than ROE would also be better for testing the theory.

(NOTE: If the evidence is not consistent with the theory, it is not necessarily the case that we abandon the theory. One of many possibilities is that we do not have good measures of the theoretical concepts).

Identification Problems: This problem is related to the idea that "correlation does NOT imply causality."

For example, you might argue that high advertising/sales is a barrier to entry (product differentiation) strategy that causes high profit rates. The regression is consistent with the Porter theory. However, you might argue that high profit rates allow more discretionary spending in marketing and thus, high profit rates cause high advertising/sales. The empirical evidence is also consistent with this theory. Thus, we have an "identification problem." The data are consistent with multiple theories and we must find more refined tests and better econometric methods in order to advance our scientific knowledge in strategic management.

Recall, once again that our major objective in this course is to explain why some firms succeed while other firms fail. Throughout this semester we can grow to appreciate that there are many factors that must be considered in order to begin to answer this (deceptively) simple question.

While the industry in which a firm competes is an important factor (and some economists regard the industry factor as the most important factor) there are other factors that also must be considered. We should note, for example, that the (adjusted) R-squared in the above regression was 0.43. Thus, 57 percent of the variation in ROE across industries remains unexplained.

To increase explanatory power strategic management holds that while industry analysis is important, we must go beyond industry analysis and consider "smaller units of analysis."

We need to consider:

  1. Industry
  2. Strategic Groups
  3. Firms
    • Resources and Capabilities
    • Core (Distinctive) Competencies
    • Organizational Structure
    • Incentive Systems
    • Implementation
  4. Managers
    • Impact of Management Teams
    • Impact of Individuals

Last Update: January 05, 2006