in food industry only through the channel of food industry market concentration. Turning
to capital asset intensity, one might argue that capital intensity can affect NPI as a more
capital intensive industry might facilitate incremental product introductions or a more
capital intensive industry might be indicative of high cost of introducing new products.
In either case, the premise that capital intensity can potentially influence NPI is addressed
in our model by controlling for an annual measure of capital expenditure intensity
(annual capital expenditure to sales ratio). This measure of capital intensity represents the
annual (variable) expenditure to maintain the capital as opposed to the capital asset
intensity that represents investments in capital (fixed) assets that acts as barrier to entry
and hence influences market concentration. Hence controlling for the capital expenditure
intensity controls for the channel through which capital asset intensity can have any
influence on NPI and thus capital asset intensity can be used as a component of the
instrument for market concentration.
Results: We assess the strength of the instrument(s) using the F test in the first
stage regression of concentration index on all exogenous variables in our model. As
reported in table 4a, the magnitudes of the test statistic are greater than 1011, which imply
that instrument(s) perform very well in explaining market concentration.
The test for first order auto-correlation (Table 4b) revealed the existence of serial
correlation. Table 4c presents the fixed effect estimates for models with exogenous as
well as endogenous treatment of concentrations where the estimation procedures account
11A good instrument is expected to have F statistic of 10 or higher in the first stage as the weak instrument
bias (1/1-F) is an inverse function of the F statistic and an acceptable benchmark of this weak instrument
bias is approximately10% or less.
11
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