The Linkage Between Environmental Management Systems Implementation and Firms’ Economic Performance: an Empirical Analysis

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The linkage between Environmental Management Systems implementation and Firms’ economic performance: an Empirical Analysis

The summary version

K. Nishitani
Dang Tien Loc

1. Paper’s objective the purpose of this paper is to answer the question whether EMS implementation can improves a firm’s performance or not by using panel data from Japanese manufacturing firms during 1996-2007. The theoretical model used is derived from the Cobb-Douglas production function and the inverse demand function, and predict that it increases firm value added after implementing EMS through the rise of demand and improvement in productivity. 2. The Approach

A simple theoretical economic model is developed takes into account the impact and simultaneously empirically estimates their parameters in the model. The value added is used to represent firm’s economic performance and it is revenue minus material cost. The value added is distributed through profits and wages. The regression model to estimate the effects of EMS implementation on value added is derived from a Cobb–Douglas production function and inverse demand function. The Cobb–Douglas production function with labor, capital and material for firm is:

Where, X is output, L is labor, K is capital, M is materials, A is total factor productivity (TFP), 0<α <1, 0<β <1and 0<α+β<1.Given total revenue Yi = piXi where p is the price of output, labor cost Wi= wLi where w is the wage rate, capital cost Ri=r Ki where r is the implicit rental rate of capital and materials cost Qi=q Mi where q is the price of materials, it follows that: The inverse demand function pi= a Xi-Y yields the price, and then the total revenue is expressed as follows:

Where, 1−γ>0. Accordingly, the value added is:

If is the ratio of the value added over the material cost, Eq.4 is transformed to:

Suppose that ai and Ai are the functions describing the implementation of EMSs, and they are described as ai=ew(0)+w1Si(a) and Ai= eδ(0)+δ1Si(A) (where as w1 > 0: the effect of EMS implementation through an increase in demand, δ1>0: the effect of EMS implementation through improvement in productivity and S(a) ≠ S(A) ), respectively, taking logarithms of both sides of Eq.5yields:

Eq.6 indicates that EMS implementation influences a firm’s value added through an increase in demand and improvement in productivity. Accordingly, Eq. 6 with an error term is the regression model to estimate the parameters of EMS implementation w1 and 1-γδ1, where

is the constant term. The parameter for the effect of EMS implementation through improvement in productivity δ1 is calculated from the estimated parameters because γ can be obtained by solving the following equations: (α−αγ) =B1, (β−βγ) =B2, and (−α−β−γ+αγ+βγ) =B3. The predicted signs of these parameters are positive for (α−αγ), (β−βγ), w1 and 1-γδ1 and negative for (−α−β−γ+αγ+βγ). 3. The Data

The data used in the study are panel data on 871 manufacturing firms. It’s from the industries of food, textiles, chemicals, pharmaceuticals, petroleum, rubber, glass, steel, general machinery, electrical appliances, transportation machinery, and other manufacturing and listed in September 2008 on the stock exchanges of Tokyo and Osaka from 1996 to 2007.

Table 1 shows a list of dependent and independent variables. The dependent variable is the logarithm of net sales over raw materials expense as a proxy for lnγQ and the independent variables are logarithm of wages for lnW, logarithm of the book value of tangible fixed assets for lnR, logarithm of raw materials expense for lnQ, a dummy that takes a value of 1 if at least one facility of a firm has ISO 14001 and 0 otherwise as a proxy for Sa, and the number of years from the time a firm adopts the initial ISO 14001 as a...
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