Question #2

In a 2004 study, Clarkson, Li, and Richardson examine the market valuation of environmental capital expenditure (ECE) investment related to pollution abatement in the pulp and paper industry. They predict that the ECEs of low polluting firms (those that over-comply with existing environmental regulations) will be viewed as an asset by the capital markets (i.e., NPV > 0) while the ECEs of high polluting firms (those that just meet minimal environmental requirements) will be viewed by the capital markets as an expense with no future benefit potential (i.e., NPV = 0). In addition, they predict, that high polluting firms will have unbooked environmental liabilities but that low polluting firms will not (i.e., that the market value of the high polluting firms will be lower, all else held equal).

The valuation model they use to test this prediction has the following form:

V = β0 + β1 ABV + β2 AE + β3 NECE + β4 NECE * POLLUTE

+ β5 ECE + β6 ECE*POLLUTE + β7 POLLUTE + υ

where V = market value of common equity in million dollars, measured three months after the firm’s fiscal year end;

ABV =  adjusted book value of common equity equal to book value of common equity 

(BV) minus current period capital expenditure (ECE + NECE), in million dollars;

AE = abnormal earnings to common equity, defined as earnings to common equity less an assumed cost of capital based on the CAPM times beginning-of-period book value of common equity, in million dollars

NECE =  current period non-environmental capital expenditure, in million dollars;

ECE =  current period environmental capital expenditure, in million dollars; and

POLLUTE = an indicator variable set equal to 1 for high polluting firms, and zero otherwise.

A modified set of the data used in the study can be found in the file ‘6923.Q2(20-2).xls’. The data in this file are for 28 “pure play” pulp and paper companies that disclosed ECE data over the 12-year period 1989 – 2000. There are a total of 248 firm-year observations in the provided data set.

Based on the regression model presented above (equation (Q2)), do the data support the predictions that Clarkson, Li, and Richardson make? In your answer, you should identify the relevant coefficient (or combination of coefficients) to test each of the predictions, state their statistical significance, and finally, explain your conclusion in terms of the significance of the coefficients.