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Does going green pay off in the long run?
Journal of Business Research 65 (2012) 1636–1642
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Journal of Business Research
Does going green pay off in the long run? Chin-Chen Chien a, Chih-Wei Peng b,⁎ a b
Department of Accountancy, National Cheng Kung University, 701, Taiwan Department of Accounting and Information, Asia University, 413, Taiwan
a r t i c l e
i n f o
Article history: Received 1 March 2011 Received in revised form 1 July 2011 Accepted 1 October 2011 Available online 24 October 2011 Keywords: Pollution control investment Pollution prevention investment End-of-pipe pollution control investment Long-run financial performance Voluntary overcompliance
a b s t r a c t This study uses mandatory disclosures of environmental expenditures for public companies in Taiwan, to examine the impact of investment in pollution control on long-run financial performance. We break down pollution control investments into the following two categories by analyzing the content of the disclosures made in annual reports: (1) pollution prevention and (2) end-of-pipe solutions. Based on a sample of five major polluting industries in Taiwan from 1989 to 2006, we find that firms moving forward proactively with pollution prevention investments have significantly outperformed their counterparts who react sluggishly with end-of-pipe solutions. In addition to the notion that environmental expenditures are not necessarily detrimental to firms, our results also suggest that the often conflicting goals of financial reporting, namely representational faithfulness and macroeconomic growth, may be harmonized if the accounting standards embody the different features of pollution control investments. © 2011 Elsevier Inc. All rights reserved.
1. Introduction Researchers have extensively examined the relation between financial performance and pollution control investment, although the results have been inconclusive. In his seminal study, Spicer (1978) finds that firms with better pollution control records tend to have higher profitability, lower systematic risk, and higher price to earnings ratios than firms with poorer pollution control. However, various studies have drawn the opposite conclusions, such as Chen and Metcalf (1980), Smith and Sims (1985), Jaggi and Freedman (1992), Mathur and Mathur (2000), Judge and Elenkov (2005). The collective evidence from these studies suggests that investing in pollution control activities results in lower overall profitability, because it diverts resources to a fundamentally non-productive use. More recent studies have generally found a positive association between better pollution control and higher financial performance (Al-Tuwaijri, Christensen, & Hughes, 2004; Brammer, Pavelin, & Porter, 2006; Clemens, 2006; Clemens & Dougla, 2006; Dowell, Hart, & Yeung, 2000; and Menguc & Ozanne, 2005). One explanation for these mixed results is that prior research has not identified the effects of different kinds of pollution control investments. Pollution can be prevented either at the end-of-pipe, by capturing pollutants for disposal, or during the production process with upgraded facilities (Russo & Fouts, 1997). End-of-pipe solutions rely on external recycling and recovery of waste, and are typically applied after the production of goods or materials. This is the traditional
⁎ Corresponding author. E-mail address: [email protected] (C.-W. Peng). 0148-2963/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2011.10.023
approach, and one that has often been implemented by corporations in response to mandatory regulations. In contrast, pollution prevention processes are a relatively more recent phenomenon (Boer, Curtin, & Hoyt, 1998), and include (1) reducing waste and pollutants by modifying production systems, equipment and operations, (2) using different raw materials, (3) redesigning or reformulating products, and (4) using in-process recycling (Oldenburg, 1987). While it is clear that the costs incurred for end-of-pipe solutions eventually become a drag on financial performance, many multinational corporations (MNCs) claim that pollution prevention technologies may not only be less costly than end-of-pipe treatments, but in some cases may enhance financial performance. As a result, MNCs now have a tendency to move away from end-of-pipe approaches and toward pollution prevention systems. Environmental protection has risen dramatically as a public concern in many countries, and Taiwan, like many other newly developed nations, has considerable problems with pollution. In order to balance the need for both economic growth and environmental protection, the Taiwanese government enacted the Statute for Upgrading Industries in 1991. This statute not only contains very strict environmental protection policies, but also provides tax incentives for pollution control investments. Additionally, the Taiwanese Financial Supervisory Commission also requires publicly listed firms to disclose their environmental capital expenditure in their annual reports. We utilize the unique disclosure requirements stipulated by the Taiwanese authorities to investigate the association between investments in pollution control and long-run financial performance. To our knowledge, Sarkis and Cordeiro (2001) are the first to explore possible performance differences between pollution prevention and end-of-pipe approaches with regard to short-run financial performance, although
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they are unable to examine the long-run relation due to data availability limitations. However, they propose that a solution to this problem is to use data envelopment analysis (DEA) models to estimate both the pollution prevention and end-of-pipe ratios. Their empirical results, based on DEA, indicate that pollution prevention and end-of-pipe strategies are both negatively related to return on sales (ROS), and that this negative relation is larger and more significant for pollution prevention strategies. The availability of more data allows us to refine Sarkis and Cordeiro's (2001) work in two respects. First, we manually collect the related data to overcome the measurement errors that are associated with DEA models. Second, we switch from the focus on short-term performance to a longer time frame (up to five years). Our sample includes five highly polluting industries: cement, plastics, chemicals, paper and pulp, and iron and steel. The results indicate that: (1) firms tend to invest in pollution control facilities in prosperous years; (2) the investment in pollution control facilities is negatively associated with short- but not long-run financial performance; (3) the investment in end-of-pipe pollution control measures is negatively associated with short-run performance; and, most importantly, (4) a significant difference in both short- and long-run performance between firms investing in pollution prevention and end-of-pipe pollution control measures. In order to safeguard the external validity of using Taiwanese data, we compare U.S. GAAP, IFRS and Taiwanese standards and find that only U.S. GAAP (Emerging Issues Task Force (EITF) Issue No. 90–8 and EITF Issue No. 89–13) specifically requires the capitalization of environmental expenditures due to the long-lived nature of pollution control facilities, whereas both Taiwanese standards and IFRS have no special requirements in this regard. In Taiwan, firms usually capitalize their environmental capital expenditures due to the durability of pollution control equipment (based on Taiwanese Accounting Standard No. 1), which is consistent with IFRS (IAS 16). We also compare the definitions of assets, stipulated in Taiwanese Accounting Standard No. 1, the Framework for the Preparation and Presentation of Financial Statements of IFRS, and the U.S. Statement of Financial Accounting Concepts No. 6. We find a similarity among these standards, in that all require the expenditures to be recognized as assets only if they have future economic benefits to the entity. It is thus clear that there is no discrepancy in accounting practices in dealing with the capitalization of environmental expenditures among these three sets of standards. Consequently, we believe that the use of Taiwanese data does not adversely affect the external validity, and there is no reason to assume that our results cannot be generalized to other countries. The major contribution of this research is its finding that the impact of pollution control investment on financial performance is far more complicated than previous research has suggested. Our results have economic implications for management, governments, and the bodies that set accounting standards. First, although pollution prevention measures impose costs, managers should realize that they also improve productivity. Consequently, a strategy of overcompliance results in a win–win situation for both businesses and society as a whole. Second, governments can provide different tax credits for investments that mitigate and prevent pollution. Specifically, they can encourage firms to upgrade their production facilities instead of wasting their economic resources. Finally, we suggest that only investments in pollution prevention should be capitalized, because of their positive long-run effects on financial performance. In contrast, investments in end-of-pipe pollution control should be expensed per se. If accounting standard-setting bodies integrate the implications of our empirical findings then managers may be encouraged to upgrade production facilities with the twin aims of greater efficiency and producing less pollution. An amended accounting standard based on the implications of our findings would produce a significant outcome of harmonizing some of the generally conflicting objectives of financial reporting, namely, representational faithfulness and macroeconomic growth. For example, the
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economic consequences of accounting standards cannot be overlooked, as evidenced by the impact of R&D accounting on U.S. global competition during the 1980s and the reclassification of financial assets under IFRS in the 2008 financial crisis. The rest of this paper is organized as follows. Section 2 presents the research issues. Section 3 describes the research method. Section 4 presents the empirical results, and Section 5 concludes this study with recommendations for managers and suggestions for future research. 2. Background and research questions Two theories compete in the literature with regard to the association between pollution control investment and financial performance. Traditional economic theory asserts that investing in pollution control activities results in lower overall profitability by diverting economic resources to a fundamentally non-productive use (Chen & Metcalf, 1980; Jaggi & Freedman, 1992; Judge & Elenkov, 2005; Mathur & Mathur, 2000; Smith & Sims, 1985). In contrast, the overcompliance theory suggests that economic benefits can arise from better pollution control investment (Arora & Gangopadhyay, 1995; King & Lenox, 2001; Porter & van der Linde, 1995; Salop & Scheffman, 1987). We believe that dividing investment activities into prevention and end-of-pipe solutions is able to better distinguish between the two theories. It is evident that traditional economic theory applies most directly to end-of-pipe pollution control investments, which usually entail expensive, non-productive equipment to achieve compliance with existing environmental regulations. Such investments are seen to be detrimental to firm performance, because they incur costs and utilize resources for non-productive activities in order to comply with regulatory standards for controlling pollution and protecting public health (Hart, 1995; Russo & Fouts, 1997; Sarkis & Cordeiro, 2001). However, support is growing for the overcompliance theory, as evidenced by the many MNCs that have successfully implemented pollution prevention measures, such as 3M's Pollution Prevention Pays (PPP) and Dow's Waste Reduction Always Pays (WRAP) programs, which have produced substantial cost savings (Hart, 1995). However, a common feature of prior studies is that they do not distinguish whether a specific pollution control investment is intended to replace obsolete facilities or equipment or to alleviate end-of-pipe problems. We thus extend this line of research by examining the impact of these two approaches to pollution control and their effects on financial performance by utilizing the pollution control investment disclosures requested by Taiwanese authorities. These disclosures allow us to identify whether the pollution control investment is for end-of-pipe or prevention purposes, and we can examine whether the two approaches have different impacts on long-run financial performance. We also develop an appropriate statistical method that applies factor analysis to integrate different financial performance measures into a single index to prevent conflicting results arising from different accounting indicators, such as return on assets (ROA), return on equity (ROE), earnings per share (EPS), and cash flows to assets (CFA), which have all been used in prior research to measure financial performance. Based on the research topic, we investigate two questions: (1) is the impact of pollution control investment on financial performance significant? More importantly, (2) is the impact of pollution prevention solutions on financial performance significantly different from that of end-of-pipe solutions in the long-run? The first issue has been examined extensively in the literature, although with mixed results, and we believe our research methods and data set resolve the apparent contradictions in the previous work. However, the second issue, to the best of our knowledge, has only been investigated by Sarkis and Cordeiro (2001). Due to the limitations of data availability, they employ a two-stage approach of (1) calculating DEA efficiency scores, and (2) using these scores as dependent variables for a multiple regression model that incorporates firm characteristics and performance. They find a negative association between environmental performance and
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short-term financial performance, and that pollution prevention is more negatively associated with short-term performance than endof-pipe investment. However, they also provide certain caveats about the measurement problem of employing DEA, and broach the intriguing subject of extending this line of research to a longer-term (five-year) horizon. Therefore, we provide a direct extension of their pioneering work in this area. 3. Research method 3.1. Sample information Our sample includes firms in five highly polluting industries: cement, plastics, chemicals, paper and pulp, and iron and steel. We obtain the financial variables and market value data from Taiwan's SFI (Securities and Futures Institute) database and the Taiwan Economic Journal (TEJ) database for the period 1989–2006, and manually collect the environmental capital expenditure information, such as the date, amount, purposes, and the expected benefits of each pollution control investment from annual financial reports. We categorize the investments into pollution prevention and endof-pipe pollution control according to the information disclosed in the annual reports. We apply the principle of proportionality to classify a firm into one of these two categories in years it made both types of investments. In the end, we have 106 observations for firms with pollution prevention investments, and 53 for firms with end-of-pipe pollution control investments. Overall, the sample suggests that firms tend to overcomply with the environmental protection regulations. The Appendix details our classification procedures. In robustness tests, we exclude those firms that made both types of investment from our sample to avoid ambiguities. 3.2. Empirical model We follow Jaggi and Freedman (1992) in measuring financial performance using four accounting indicators, namely, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and cash flows to total assets (CFA). However, unlike many studies that use individual indicators to measure performance, we perform factor analysis with Varimax rotation to identify the factor patterns. The factor analysis model can be stated as follows. Yi ¼ μ þ Lfi þ εi ; i ¼ 1; 2; …; n;
ð1Þ
where Yi is a set of p observable random variables, (f1, f2,.., fn) are the factor scores for each observation and the L's are unobservable factor loadings. We wish to find the vector of common factors for subject i, or f^i , by minimizing the sum of the squared residuals. In matrix notation the solution is expressed as: 0 −1 0 f^i ¼ L L L ðYi −μ Þ:
Perf ormancei ¼ β0 þ β1 Prevent þ β2 MTB þ β3 Size þ β4 Beta þ ε
ð4Þ
We perform factor analysis to generate factor scores from four accounting variables (ROE, ROA, EPS and CFA). We then follow the approach proposed by Megginson et al. (1994) to measure financial performance with three different windows. Let Year(t) be the year of investment, Performance0 then denotes the difference in a firm's factor score between Year(t + 1) and Year(t − 1), Performance1 denotes the difference in the average factor score of Year(t + 1) to Year(t + 3) and Year(t − 3) to Year(t − 1), and Performance2 denotes the difference in the average factor score of Year(t + 1) to Year(t + 5) and Year(t − 5) to Year(t − 1). Prevent is an indicator variable that is set to a numerical value of 1 for firms with pollution prevention investments, and 0 for firms with end-of-pipe pollution control investments. MTB denotes the ratio of the market value of equity to the book value of equity. Size denotes the natural logarithm of total assets. The last independent variable, Beta, denotes the systematic risk and is obtained from running the regression of the market model. Table 1 summarizes the definitions of the key variables employed in this study. Earlier studies generally used market-to-book ratio, firm size, and unexpected earnings as control variables (Al-Tuwaijri et al., 2004; Sarkis & Cordeiro, 2001.) Following Fama and French (1992), we replace unexpected earnings with Beta because (1) unexpected earnings has been consistently insignificant in these earlier studies, and (2) the Fama-French model has drawn much attention with regard to the issue of anomalies in returns. A wealth of research also suggests that financial performance can be measured by accounting variables as well as stock returns, which further justifies using the Fama-French variables. 4. Empirical results
ð2Þ
We substitute our estimated factor loadings into this expression as well as the sample mean for the data, as follows: −1 0 0 L^ ðYi −y Þ: f^i ¼ L^ L^
performances of the matched samples. We use a three-year window (from one year preceding the investment to one year subsequent to it) to measure the short-term performance, while a seven-year window (three years before through three years after the investment) and an eleven-year window (five years before through five years after) to measure the long-run performance. We then calculate the average factor score for each firm over the pre- and post-investment windows. More specifically, for the seven-year window, we calculate two sets of the average factor scores, that is pre-investment: years −3 to −1 and postinvestment: years +1 to +3. The same procedure also applies to the eleven-year window. We then apply the conventional t-test, the nonparametric test and multiple regression models to examine possible long-run impacts of pollution control investment on financial performance under various scenarios. It is also worth noting that the windows we use to measure long-run performance are those that are commonly adopted in the literature (for example, Carter, Dark, & Singh, 1998, among others.; Loughran & Ritter, 1995) The multiple regression model is specified as follows:
ð3Þ
The factor score is a standardized number with a mean of zero and a standard error of one. The factor analysis yields only one factor (with an eigenvalue greater than one) that captures the commonality among these four variables, and its factor score essentially represents an index that integrates these four performance measures. We then follow the approach of Megginson, Nash, and Van Randenborgh (1994), who compare the short- and long-run financial
Table 2 reports descriptive statistics for the variables used in this study. The means of Performance0, Performance1 and Performance2 are 0.11, −0.03, −0.02, respectively. The mean of Pollution (indicator variable) is 0.22, suggesting that about 22% of sample firms mention pollution control investment disclosures in their annual reports. The mean of Prevent is 0.67, suggesting that the majority of the pollution control investments are for prevention purposes. The means of MTB, Size, and Beta are 1.43, 15.62, and 0.75, respectively. Table 3 presents univariate comparisons of financial performance of firms with pollution control investments and those who do not have pollution control investments in different subperiods. The first three rows indicate no significant differences in financial performance between these two groups before they make pollution control investments. However, we find that mean and median Fsc(t) are
C.-C. Chien, C.-W. Peng / Journal of Business Research 65 (2012) 1636–1642 Table 1 Definition of variables. Variables
Definition
Dependent variables Performance0 We perform factor analysis to generate factor scores from four Performance1 accounting variables (ROE, ROA, EPS and CFA). We then follow the Performance2 approach proposed by Megginson et al. (1994) to measure financial performance with three different windows. Let Year(t) be the year of investment, Performance0 then denotes the difference in a firm's factor score between Year(t + 1) and Year(t − 1), Performance1 denotes the difference in the average factor score of Year(t + 1) to Year(t + 3) and Year(t − 3) to Year(t − 1), and Performance2 denotes the difference in the average factor score of Year(t + 1) to Year(t + 5) and Year(t − 5) to Year(t − 1). Independent variables Pollution Pollution is an indicator variable that is set to a numerical value of 1 for firms with pollution control investments, and 0 for otherwise. Prevent Prevent is an indicator variable that is set to a numerical value of 1 for firms with pollution prevention investments and 0 for firms with end-of-pipe pollution control investments. MTB MTB is the ratio of a firm's market value of equity to its book value of equity. Size Size is the natural logarithm of a firm's total assets. Beta Beta is estimated by running an OLS regression of individual stock returns on market returns. Prior1 Prior1 is the difference in factor scores between the investment year and Avg(− 3,−1). Prior2 Prior2 is the difference in factor scores between the investment year and Avg(− 5,−1).
significantly higher for firms with pollution control investments than those who do not have pollution control investments at T = 0 (0.21 vs. −0.12 with a t-statistic of 3.90, and 0.08 vs −0.17 with a z-statistic of 4.24.) Both the t-statistic and Wilcoxon rank-sum z-statistic are significant at a one-percent confidence level. The results suggest that firms tend to make pollution control investments in periods in which they experience better economic performance. Nevertheless, we also find that mean Fsc(t + 1) is significantly lower for firms with pollution control investments than those who do not have pollution control investments at T = 1 (−0.24 vs 0.05 with a t-statistic of −2.80.) The results suggest that pollution control investments may drag down financial performance in the subsequent period. The mean and median Performance0 are significantly lower for firms with pollution control investments than those who do not have pollution control investments (−0.22 vs. 0.19 with a t-statistic of −2.89, which is significant at a one-percent confidence level, and −0.10 vs. 0.12 with a z-statistic of −2.04, which is significant at a five-percent confidence level). The results are consistent with Chen and Metcalf (1980), Smith and Sims (1985), Jaggi and Freedman (1992), and others, in that pollution control investment has a negative short-run impact on financial performance. Table 4 reports the empirical results of a more intriguing comparison. Specifically, Table 4 compares the financial performance of firms with pollution prevention investments to the financial performance of
Table 2 Descriptive statistics. Variable
N
Mean
Std. dev.
Minimum
Maximum
Performance0 Performance1 Performance2 Pollution Prevent MTB Size Beta
717 671 596 717 159 717 717 717
0.11 − 0.03 − 0.02 0.22 0.67 1.43 15.62 0.75
1.32 0.88 0.75 0.42 0.47 1.01 1.12 0.31
− 8.43 − 3.14 − 3.06 0 0 0.07 13.20 − 0.45
5.58 3.26 2.16 1 1 9.20 19.55 1.57
Note: definitions of the variables appear in Table 1.
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those with end-of-pipe pollution control investments. While we find no significant differences in financial performance between these two groups of firms during the pre-investment period, the end-of-pipe pollution control investments appear to hinder the mean financial performance more drastically than the pollution prevention investments at T = 1 (Fsc(t) is −0.63 vs. −0.05 with a t-statistic of 2.35, which is significant at a five-percent confidence level). Table 4 further indicates that the mean differences in Performance0 and Performance1 between these two groups are both significant at a five-percent confidence level (0.02 vs. −0.69 with a t-statistic of 2.26, and 0.14 vs. −0.16 with a t-statistic of 2.05). The preliminary results are consistent with our postulation that firms that make investments in pollution prevention significantly outperform those that make investments in end-of-pipe pollution control. Table 5 reports the empirical results of Eq. (4) investigating the association between future performance and investment type. Specifically, Eq. (4) regresses the dependent variables Performance1 and Performance2 on Prevent, MTB, Size and Beta, respectively. Prevent is an indicator variable that is set to a numerical value of 1 for firms with pollution prevention investments, and 0 for firms with end-ofpipe pollution control investments. Panel A of Table 5 indicates that the coefficients of Prevent are 0.35 and 0.27, with a t-statistic of 2.56 and 2.07, and these are significant at one-percent and five-percent confidence levels, respectively. The results suggest that pollution prevention investment is more positively associated with long-run financial performance than end-of-pipe pollution control investments. Our next research question examines whether pre-investment performance affects post-investment performance. More specifically, it is open to question whether firms with better pre-investment performance tend to overcomply with the regulations. To investigate this in more detail, we include a pre-investment financial performance variable (Prior1 or Prior2) in the regression models as control variables. Prior1 denotes the difference in factor scores between the investment year and Avg(−3,−1), and Prior2 denotes the difference in factor scores between the investment year and Avg(−5,−1). Panel B of Table 5 indicates that the coefficients of Prevent for Performance1 and Performance2 are 0.33 and 0.25, with a t-statistic of 2.65 and 2.16, respectively. The former is significant at a one percent confidence level, while the later is significant a five percent confidence level. The results are consistent with Panel A. 5. Robustness tests We further conduct robustness tests to enhance internal validity. Firstly, we employ Tobin's Q as an alternative financial performance measure, because it reflects the market's expectation regarding the cash flows that a firm's asset can generate (Amit & Wernerfelt, 1990). We exclude Tobin's Q from the factor analysis because it is different from the four other accounting variables in nature. More specifically, ROA, ROE, EPS, and cash flows to total assets (CFA) are reported in financial statements, while Tobin's Q is a number that can be estimated using different models. Panel A of Table 6 indicates that Prevent has a significant positive effect on Tobin's Q (coefficient = 0.40, p b 0.05), which is consistent with the previous findings. Secondly, we exclude firms that have made both pollution prevention and end-of-pipe pollution control investments, without applying the proportionality principle, in order to lessen the confounding effect. Panel B of Table 6 indicates that the coefficients of Prevent1 are 0.49 and 0.32, which are significant at the one-percent and fivepercent confidence levels, respectively. Finally, we use stock return as a proxy for financial performance. Panel C of Table 6 indicates that the coefficient of Prevent for Return1 (3 years) is 0.63, which is significant at a five-percent confidence level. However, the coefficient of Prevent for Return2 (5 years) is 0.82, which is only significant at a ten-percent confidence level. Although using stock return as a proxy for financial performance lowers
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Table 3 Comparison of the financial performance between firms with/without pollution control investments. Pollution control investments Firms have pollution control investments
Significance tests of the difference
Firms do not have pollution control investments
Period
N
Mean
Median
N
Mean
Median
t-statistic
z-statistic
Avg(− 5,−1) Avg(− 3,−1) Fsc(t − 1) Fsc(t) Fsc(t + 1) Avg(+ 1,+3) Avg(+ 1,+5) Performance0 Performance1 Performance2
136 150 159 159 159 150 136 159 150 136
− 0.09 − 0.01 − 0.02 0.21 − 0.24 0.02 − 0.01 − 0.22 0.03 0.08
− 0.12 − 0.02 − 0.08 0.08 − 0.12 0.05 − 0.04 − 0.10 0.04 0.03
460 521 558 558 558 521 460 558 521 460
− 0.01 − 0.02 − 0.14 − 0.12 0.05 − 0.06 − 0.05 0.19 − 0.04 − 0.04
− 0.05 − 0.04 − 0.17 − 0.17 − 0.06 − 0.09 − 0.09 0.12 − 0.08 − 0.01
− 1.75 0.34 1.23 3.90⁎⁎ − 2.80⁎⁎ 1.47 0.75 − 2.89⁎⁎
− 1.37 0.21 1.55 4.24⁎⁎ − 0.61 2.24⁎ 0.97 − 2.04⁎
0.85 1.68
1.32 0.39
Note: 1. Let Year(t) be the year of investment. Avg(− 5,−1) denotes the average factor score of Year(t − 5) to Year(t − 1). Avg(− 3,−1) denotes the average factor score of Year(t − 3) to Year (t − 1). Fsc(t − 1) denotes the factor score 1 year before the investment. Fsc(t) denotes the factor score in the year of investment. Fsc(t + 1) denotes the factor score 1 year subsequent to the investment. Avg(+ 1,+3) denotes the average factor score from Year(t + 1) to Year(t + 3). Avg(+ 1,+5) denotes the average factor score from Year(t + 1) to Year(t + 5). Performance0 then denotes the difference in a firm's factor score between Year(t + 1) and Year(t − 1), Performance1 denotes the difference in the average factor score of Year(t + 1) to Year(t + 3) and Year(t − 3) to Year(t − 1), and Performance2 denotes the difference in the average factor score of Year(t + 1) to Year(t + 5) and Year(t − 5) to Year(t − 1). 2. We use Satterthwaite t-statistics to test the means for unequal variances, and the pooled t-test otherwise. 3. The Wilcoxon rank sum test (z-statistic) is used to examine the statistical significance level of the differences between two medians. 4. ⁎⁎ and ⁎ represent significance at the 1% and 5% levels with the two-tailed test, respectively.
end-of-pipe pollution control investments, and this suggests that managers should avoid adopting only such solutions. Our results also indicate that pollution prevention investment is positively associated with long-run financial performance, which suggests that pollution prevention investment can not only lower a firm's negative environmental impacts, but can also improve its bottom-line income by triggering innovations and ultimately lowering the costs of compliance. Accordingly, firms should be aware that paying attention to environmental concerns not only benefits the wider society, but also strengthens their own financial performance. Since our research indicates that pollution prevention measures have economic as well as environmental advantages over more conventional end-of-pipe solutions, it is perhaps not surprising that they have been warmly received in many newly developing countries.
the significance of the difference, the results still suggest that overcompliance with regulations is the best strategy for firms to adopt.
6. Conclusions The relation between pollution control investment and a firm's financial performance is a complex issue, one that has been examined extensively for several decades with conflicting results. We exploit the mandatory disclosure of pollution control investments made by the public listed companies in Taiwan to further explore this intriguing issue. The results suggest that pollution control investments, taken as a whole, decrease short-run financial performance. However, we find that this poor short-run financial performance is largely caused by the
Table 4 Comparison of the financial performance between firms with pollution prevention investments and firms with end-of-pipe pollution control investments. Pollution control investments Pollution prevention investments Period Avg(− 5,−1) Avg(− 3,−1) Fsc(t − 1) Fsc(t) Fsc(t + 1) Avg(+ 1,+3) Avg(+ 1,+5) Performance0 Performance1 Performance2
N 88 95 106 106 106 95 88 106 95 88
Mean − 0.14 − 0.06 − 0.07 0.13 − 0.05 0.08 0.02 0.02 0.14 0.16
End-of-pipe pollution control investments Median − 0.14 − 0.08 − 0.09 0.05 − 0.06 0.07 − 0.01 − 0.05 0.20 0.08
N 48 55 53 53 53 55 48 53 55 48
Mean − 0.01 0.09 0.06 0.35 − 0.63 − 0.07 − 0.08 − 0.69 − 0.16 − 0.07
Median − 0.01 0.01 0.08 0.35 − 0.16 − 0.01 − 0.07 − 0.24 − 0.10 − 0.15
Significance tests of the difference t-statistic − 1.48 − 1.50 − 0.71 − 1.34 2.35⁎ 1.26 1.22 2.26⁎ 2.05⁎ 1.78
z-statistic − 1.79 − 1.52 − 0.89 − 1.57 0.78 0.84 0.36 1.12 1.52 1.79
Note: 1. Let Year(t) be the year of investment. Avg(−5,−1) denotes the average factor score of Year(t− 5) to Year(t− 1). Avg(−3,−1) denotes the average factor score of Year(t − 3) to Year (t− 1). Fsc(t − 1) denotes the factor score one year before the investment. Fsc(t) denotes the factor score in the year of investment. Fsc(t +1) denotes the factor score one year subsequent to the investment. Avg(+1,+3) denotes the average factor score from Year(t +1) to Year(t+ 3). Avg(+1,+5) denotes the average factor score from Year(t+ 1) to Year(t + 5). Performance0 then denotes the difference in a firm's factor score between Year(t+ 1) and Year(t− 1), Performance1 denotes the difference in the average factor score of Year(t+ 1) to Year(t + 3) and Year(t− 3) to Year(t −1), and Performance2 denotes the difference in the average factor score of Year(t+ 1) to Year(t+ 5) and Year(t − 5) to Year(t− 1). 2. We use Satterthwaite t-statistics to test the means for unequal variances, and the pooled t-test otherwise. 3. The Wilcoxon rank sum test (z-statistic) is used to examine the statistical significance level of the differences between two medians. 4. ⁎⁎ and ⁎ represent significance at the 1% and 5% levels with the two-tailed test, respectively.
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Table 5 Regressions of long-run financial performance. Panel A: regressions of long-run financial performance with control variables Dependent variable: Performance1 (N = 150)
Dependent variable: Performance2 (N = 136)
Independent variables
Parameter estimates
t-value
p-value
Independent variables
Parameter estimates
t-value
p-value
Intercept Prevent MTB Size Beta F-statistic (p-value) Adjusted R2
−3.27⁎⁎ 0.35⁎⁎ −0.18⁎⁎ 0.21⁎⁎
−3.39 2.56 −2.61 3.42 1.01 6.75 (p b 0.01) 0.14
b 0.01 0.01 0.01 b 0.01 0.31
Intercept Prevent MTB Size Beta F-statistic (p-value) Adjusted R2
−2.23⁎ 0.27⁎ −0.15⁎⁎ 0.15⁎⁎
−2.35 2.07 −2.22 2.48 0.41 4.02 (p b 0.01) 0.08
0.02 0.04 0.03 0.01 0.68
0.21
0.08
Panel B: regressions of long-run financial performance with pre-investment windows financial performance variable Dependent variable: Performance1 (N = 150) Dependent variable: Performance2 (N = 136) Independent variables
Parameter estimates
Intercept Prevent MTB Size Beta Prior1 Prevent*Prior1 F-statistic (p-value) Adjusted R2
−3.13⁎⁎ 0.33⁎⁎ −0.31⁎⁎ 0.22⁎⁎ 0.01 0.33⁎⁎ 0.01
t-value
p-value
−3.56 b 0.01 2.65 b 0.01 −4.58 b 0.01 3.95 b 0.01 0.02 0.99 3.60 b 0.01 0.01 0.99 10.68 (p b 0.01) 0.28
Independent variables
Parameter estimates
t-value
p-value
Intercept Prevent MTB Size Beta Prior2 Prevent*Prior2 F-statistic (p-value) Adjusted R2
−2.36⁎⁎ 0.25⁎ −0.30⁎⁎ 0.18⁎⁎
−2.82 2.16 −4.67 3.32 −0.68 2.76 1.28 10.51(p b 0.01) 0.30
b0.01 0.03 b0.01 b0.01 0.50 b0.01 0.20
−0.11 0.26⁎⁎ 0.14
Note: 1. Definitions of the variables appear in Table 1. 2. ⁎⁎ and ⁎ represent significance at the 1% and 5% levels with the two-tailed test, respectively. 3. The maximum VIF is 1.21, which is much less than 5, and thus multicollinearity is not a major concern.
Our research has some limitations that offer suggestions for future work in this area. First, this study provides indirect evidence consistent with the benefits of voluntary environmental overcompliance, in that firms with better pollution records can improve their long-run financial performance. However, future research may uncover more direct evidence to validate the theory of overcompliance. Another limitation of this study is that the sample firms are drawn from five highly polluting industries in Taiwan. Hence, the conclusions of the present study may not fully generalize to other countries, even though Taiwanese accounting
standards in dealing with investments in pollution control facilities are in compliance with both the U.S. GAAP and IFRS. Acknowledgments The authors thank two anonymous referees and F. J. Shiue, President of the Taiwan Accounting Association for their invaluable advice. We also wish to thank Arch Woodside and special issue co-editors for their cordiality and encouragement through the review process. Research
Table 6 Additional Tests. Panel A: regressions of Tobin's Q with control variables
Panel B: regressions of long-run financial performance with control the mis-classification of firms
Panel C: regression of long-run stock returns with control variables
Dependent variable: Tobin's q
Dependent variable: Performance1
Dependent variable: Return1
Dependent variable: Return2
Independent variable
Independent variable
Independent variable
Parameter estimates
Independent variable
Parameter estimates
Intercept Prevent MTB Size Beta
5.77⁎⁎ 0.63⁎ 1.13⁎ − 2.33⁎⁎ − 1.05
Intercept Prevent MTB Size Beta
2.41⁎ 0.82 − 0.60 − 0.03 − 1.85⁎
Parameter estimates
Intercept − 2.61⁎ Prevent 0.40⁎ Size 0.15⁎ ROE − 3.50⁎⁎ SG 0.01 Prior1 0.56⁎⁎ 0.03 Prevent ∗ Prior1 F(p-value) = 6.20 (p b 0.01), Adj. R2 = 0.16, N = 159
Dependent variable: Performance2 Parameter estimates
Intercept − 4.98⁎⁎ 0.49⁎⁎ Prevent1 MTB − 0.29⁎⁎ Size 0.34⁎⁎ Beta − 0.01 Prior1 0.34⁎⁎ Prevent1 ∗ Prior1 − 0.09 F(p-value) = 9.16 (p b 0.01), Adj. R2 = 0.32, N = 105
Independent variable
Parameter estimates
Intercept − 3.79⁎⁎ Prevent1 0.32⁎ MTB − 0.33⁎⁎ Size 0.28⁎⁎ Beta − 0.21 Prior2 0.28⁎⁎ Prevent1 ∗ Prior2 − 0.01 F (p-value) = 7.34 (p b 0.01), Adj. R2 = 0.29, N = 94
F(p-value) = 20.39 (p b 0.01), Adj. R2 = 0.33, N = 156
F (p-value) = 2.56 (p = 0.04), Adj. R2 = 0.05, N = 130
Note: 1. We follow Chung and Pruitt (1994) who define Tobin's Q as (Market Value of Equity + Preferred Stocks + Debt) / Total Assets. Let Year(t) be the year of investment. Performance1 denotes the difference in the average factor score of Year(t + 1) to Year(t + 3) and Year(t − 3) to Year(t − 1), and Performance2 denotes the difference in the average factor score of Year(t + 1) to Year(t + 5) and Year(t − 5) to Year(t− 1). Return1 denotes the cumulative three-year buy-and-hold returns. Return2 denotes the cumulative fiveyear buy-and-hold returns. Prevent is an indicator variable that is set to a numerical value of 1 for firms with pollution prevention investments and 0 for firms with end-of-pipe pollution control investments. Prevent1 is for firms with only pollution prevention or only end-of-pipe pollution control investments, and an indicator variable with the value of 1 for firms with pollution prevention investments and 0 for firms with end-of-pipe pollution control investments. Size is defined as the natural logarithm of total assets. ROE is defined as the net income to average equity. SG is defined as a sales growth percentage from the prior five year's sales. MTB is the ratio of a firm's market value of equity to its book value of equity. Beta is estimated by running an OLS regression of individual stock returns on market returns. Prior1 represents the difference in factor scores between the investment year and Avg(−3,−1). Prior2 represents the difference in factor scores between the investment year and Avg(−5,−1). 2. ⁎⁎ and ⁎ represent significance at the 1% and 5% levels with the two-tailed test, respectively. 3. The maximum VIF is 3.42, which is less than 5, and thus multicollinearity is not a major concern.
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support from the National Science Council, Taiwan (grant # NCS 830301-H006-030) is gratefully acknowledged. All errors are our own. Appendix A A pollution control investment is categorized as end-of-pipe if it is used to treat pollutants or waste water to comply with existing environmental regulations, and as pollution prevention if it can also enhance productive efficiency. The principles of categorizing investment in pollution prevention for these five highly polluting industries are summarized as follows: i. Cement industry: the major concern of the cement industry is dust emissions in the production process. Investment in dust pollution control equipment is categorized as prevention if it not only reduces atmospheric emissions, but also collects and recycles dust particles into non-poisonous and useful raw materials. ii. Plastics industry: waste plastic recovery equipment recycles and converts waste plastics into valuable raw materials. The exhaust heat recovery equipment used in this industry recycles heat energy with flue gas. This increases the combustible air temperature, avoids the loss of heat energy and reduces industrial air pollution. These investments are categorized as prevention investments. iii. Chemical industry: the exhaust heat recovery equipment serves a dual role as processing equipment and air pollution control equipment, and is considered as a prevention investment. iv. Paper and pulp industry: the waste recovery equipment and equipments for prevention and control of pollutant emissions are categorized as prevention investment. v. Iron and steel industry: the steel industry uses cupola and induction furnaces to melt and decompose pig iron and scrap by utilizing fluidized bed combustion technology. The equipment for reducing dust pollutants and also making improvements in the production and manufacturing processes is categorized as a prevention investment. References Al-Tuwaijri SA, Christensen TE, Hughes KE. The relations among environmental disclosure, environmental performance and economic performance: a simultaneous equations approach. Accounting, Organizations and Society 2004;29(5–6):447–71. Amit R, Wernerfelt BB. Why do firms reduce business risk? Academy of Management Journal 1990;33(3):520–33. Arora S, Gangopadhyay S. Toward a theoretical model of voluntary overcompliance. Journal of Economic Organi 1995;28(3):289–309.
Boer G, Curtin M, Hoyt L. Environmental cost management. Manage Account 1998;80(3):28–30. Brammer SJ, Pavelin S, Porter LA. Corporate social performance and geographical diversification. Journal of Business Research 2006;59(9):1025–34. Carter RB, Dark FH, Singh AK. Underwriter reputation, initial returns and the long-run performance of IPO stocks. Journal of Finance 1998;53:285–311. Chen KH, Metcalf RW. The relationship between pollution control record and financial indicators revisited. The Accounting Review 1980;55(1):168–77. Chung KH, Pruitt SW. A simple approximation of tobin's q. Financial Management 1994;23(3):70–4. Clemens B. Economic incentives and small firms: does it pay to be green? Journal of Business Research 2006;59(4):492–500. Clemens B, Dougla TJ. Does coercion drive firms to adopt ‘voluntary’ green initiatives? relationships among coercion, superior firm resources, and voluntary green initiatives. Journal of Business Research 2006;59(4):483–91. Dowell G, Hart S, Yeung B. Do corporate global environmental standards create or destroy market value? Management Science 2000;46(8):1059–74. Fama EF, French KR. The cross-section of expected stock returns. Journal Finance 1992;47(2):427–65. Hart SL. A natural-resource-based view of the firm. Academy of Management Review 1995;20(4):986-1014. Jaggi B, Freedman M. An examination of the impact of pollution performance on economic and market performance: pulp and paper firms. Journal of Business Financ Accoun 1992;19(5):697–713. Judge WQ, Elenkov D. Organizational capacity for change and environmental performance: an empirical assessment of Bulgarian firms. Journal of Business Research 2005;58(7):893–901. King AA, Lenox MJ. Lean and green? An empirical examination of the relationship between lean production and environmental performance. Production and Operations Management 2001;10(3):244–56. Loughran T, Ritter JR. The new issues puzzle. Journal of Finance 1995;50:23–51. Mathur LK, Mathur I. An analysis of the wealth effects of green marketing strategies. Journal of Business Research 2000;50(2):193–200. Megginson WL, Nash RC, Van Randenborgh M. The financial and operating performance of newly privatized firms: an international empirical analysis. Journal of Finance 1994;49(2):403–52. Menguc B, Ozanne LK. Challenges of the “green imperative”: a natural resource-based approach to the environmental orientation–business performance relationship. Journal of Business Research 2005;58(4):430–8. Oldenburg KU. Preventing pollution is no end-of-pipe dream: the way to turn the environmental spending curve down is to generate less of what is regulated. Across the Board 1987;24(6):11–5. Porter ME, van der Linde C. Toward a new conception of the environment competitiveness relation. The Journal of Economic Perspectives 1995;9(4):97-118. Russo MV, Fouts PA. A resource-based perspective on corporate environmental performance and profitability. Academy of Management Journal 1997;40(3):534–59. Salop SC, Scheffman DT. Cost-raising strategies. Journal of Industrial Ecology 1987;36(1):19–34. Sarkis J, Cordeiro JJ. An empirical evaluation of environmental efficiencies and firm performance: pollution prevention versus end-of-pipe practice. European Journal of Operational Research 2001;135(1):102–13. Smith JB, Sims WA. The impact of pollution charges on productivity growth in Canadian brewing. The Rand Journal of Economics 1985;16(3):410–23. Spicer BH. Investors, corporate social performance and information disclosure: an empirical study. The Accounting Review 1978;53(1):94-111.
Contents lists available at SciVerse ScienceDirect
Journal of Business Research
Does going green pay off in the long run? Chin-Chen Chien a, Chih-Wei Peng b,⁎ a b
Department of Accountancy, National Cheng Kung University, 701, Taiwan Department of Accounting and Information, Asia University, 413, Taiwan
a r t i c l e
i n f o
Article history: Received 1 March 2011 Received in revised form 1 July 2011 Accepted 1 October 2011 Available online 24 October 2011 Keywords: Pollution control investment Pollution prevention investment End-of-pipe pollution control investment Long-run financial performance Voluntary overcompliance
a b s t r a c t This study uses mandatory disclosures of environmental expenditures for public companies in Taiwan, to examine the impact of investment in pollution control on long-run financial performance. We break down pollution control investments into the following two categories by analyzing the content of the disclosures made in annual reports: (1) pollution prevention and (2) end-of-pipe solutions. Based on a sample of five major polluting industries in Taiwan from 1989 to 2006, we find that firms moving forward proactively with pollution prevention investments have significantly outperformed their counterparts who react sluggishly with end-of-pipe solutions. In addition to the notion that environmental expenditures are not necessarily detrimental to firms, our results also suggest that the often conflicting goals of financial reporting, namely representational faithfulness and macroeconomic growth, may be harmonized if the accounting standards embody the different features of pollution control investments. © 2011 Elsevier Inc. All rights reserved.
1. Introduction Researchers have extensively examined the relation between financial performance and pollution control investment, although the results have been inconclusive. In his seminal study, Spicer (1978) finds that firms with better pollution control records tend to have higher profitability, lower systematic risk, and higher price to earnings ratios than firms with poorer pollution control. However, various studies have drawn the opposite conclusions, such as Chen and Metcalf (1980), Smith and Sims (1985), Jaggi and Freedman (1992), Mathur and Mathur (2000), Judge and Elenkov (2005). The collective evidence from these studies suggests that investing in pollution control activities results in lower overall profitability, because it diverts resources to a fundamentally non-productive use. More recent studies have generally found a positive association between better pollution control and higher financial performance (Al-Tuwaijri, Christensen, & Hughes, 2004; Brammer, Pavelin, & Porter, 2006; Clemens, 2006; Clemens & Dougla, 2006; Dowell, Hart, & Yeung, 2000; and Menguc & Ozanne, 2005). One explanation for these mixed results is that prior research has not identified the effects of different kinds of pollution control investments. Pollution can be prevented either at the end-of-pipe, by capturing pollutants for disposal, or during the production process with upgraded facilities (Russo & Fouts, 1997). End-of-pipe solutions rely on external recycling and recovery of waste, and are typically applied after the production of goods or materials. This is the traditional
⁎ Corresponding author. E-mail address: [email protected] (C.-W. Peng). 0148-2963/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2011.10.023
approach, and one that has often been implemented by corporations in response to mandatory regulations. In contrast, pollution prevention processes are a relatively more recent phenomenon (Boer, Curtin, & Hoyt, 1998), and include (1) reducing waste and pollutants by modifying production systems, equipment and operations, (2) using different raw materials, (3) redesigning or reformulating products, and (4) using in-process recycling (Oldenburg, 1987). While it is clear that the costs incurred for end-of-pipe solutions eventually become a drag on financial performance, many multinational corporations (MNCs) claim that pollution prevention technologies may not only be less costly than end-of-pipe treatments, but in some cases may enhance financial performance. As a result, MNCs now have a tendency to move away from end-of-pipe approaches and toward pollution prevention systems. Environmental protection has risen dramatically as a public concern in many countries, and Taiwan, like many other newly developed nations, has considerable problems with pollution. In order to balance the need for both economic growth and environmental protection, the Taiwanese government enacted the Statute for Upgrading Industries in 1991. This statute not only contains very strict environmental protection policies, but also provides tax incentives for pollution control investments. Additionally, the Taiwanese Financial Supervisory Commission also requires publicly listed firms to disclose their environmental capital expenditure in their annual reports. We utilize the unique disclosure requirements stipulated by the Taiwanese authorities to investigate the association between investments in pollution control and long-run financial performance. To our knowledge, Sarkis and Cordeiro (2001) are the first to explore possible performance differences between pollution prevention and end-of-pipe approaches with regard to short-run financial performance, although
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they are unable to examine the long-run relation due to data availability limitations. However, they propose that a solution to this problem is to use data envelopment analysis (DEA) models to estimate both the pollution prevention and end-of-pipe ratios. Their empirical results, based on DEA, indicate that pollution prevention and end-of-pipe strategies are both negatively related to return on sales (ROS), and that this negative relation is larger and more significant for pollution prevention strategies. The availability of more data allows us to refine Sarkis and Cordeiro's (2001) work in two respects. First, we manually collect the related data to overcome the measurement errors that are associated with DEA models. Second, we switch from the focus on short-term performance to a longer time frame (up to five years). Our sample includes five highly polluting industries: cement, plastics, chemicals, paper and pulp, and iron and steel. The results indicate that: (1) firms tend to invest in pollution control facilities in prosperous years; (2) the investment in pollution control facilities is negatively associated with short- but not long-run financial performance; (3) the investment in end-of-pipe pollution control measures is negatively associated with short-run performance; and, most importantly, (4) a significant difference in both short- and long-run performance between firms investing in pollution prevention and end-of-pipe pollution control measures. In order to safeguard the external validity of using Taiwanese data, we compare U.S. GAAP, IFRS and Taiwanese standards and find that only U.S. GAAP (Emerging Issues Task Force (EITF) Issue No. 90–8 and EITF Issue No. 89–13) specifically requires the capitalization of environmental expenditures due to the long-lived nature of pollution control facilities, whereas both Taiwanese standards and IFRS have no special requirements in this regard. In Taiwan, firms usually capitalize their environmental capital expenditures due to the durability of pollution control equipment (based on Taiwanese Accounting Standard No. 1), which is consistent with IFRS (IAS 16). We also compare the definitions of assets, stipulated in Taiwanese Accounting Standard No. 1, the Framework for the Preparation and Presentation of Financial Statements of IFRS, and the U.S. Statement of Financial Accounting Concepts No. 6. We find a similarity among these standards, in that all require the expenditures to be recognized as assets only if they have future economic benefits to the entity. It is thus clear that there is no discrepancy in accounting practices in dealing with the capitalization of environmental expenditures among these three sets of standards. Consequently, we believe that the use of Taiwanese data does not adversely affect the external validity, and there is no reason to assume that our results cannot be generalized to other countries. The major contribution of this research is its finding that the impact of pollution control investment on financial performance is far more complicated than previous research has suggested. Our results have economic implications for management, governments, and the bodies that set accounting standards. First, although pollution prevention measures impose costs, managers should realize that they also improve productivity. Consequently, a strategy of overcompliance results in a win–win situation for both businesses and society as a whole. Second, governments can provide different tax credits for investments that mitigate and prevent pollution. Specifically, they can encourage firms to upgrade their production facilities instead of wasting their economic resources. Finally, we suggest that only investments in pollution prevention should be capitalized, because of their positive long-run effects on financial performance. In contrast, investments in end-of-pipe pollution control should be expensed per se. If accounting standard-setting bodies integrate the implications of our empirical findings then managers may be encouraged to upgrade production facilities with the twin aims of greater efficiency and producing less pollution. An amended accounting standard based on the implications of our findings would produce a significant outcome of harmonizing some of the generally conflicting objectives of financial reporting, namely, representational faithfulness and macroeconomic growth. For example, the
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economic consequences of accounting standards cannot be overlooked, as evidenced by the impact of R&D accounting on U.S. global competition during the 1980s and the reclassification of financial assets under IFRS in the 2008 financial crisis. The rest of this paper is organized as follows. Section 2 presents the research issues. Section 3 describes the research method. Section 4 presents the empirical results, and Section 5 concludes this study with recommendations for managers and suggestions for future research. 2. Background and research questions Two theories compete in the literature with regard to the association between pollution control investment and financial performance. Traditional economic theory asserts that investing in pollution control activities results in lower overall profitability by diverting economic resources to a fundamentally non-productive use (Chen & Metcalf, 1980; Jaggi & Freedman, 1992; Judge & Elenkov, 2005; Mathur & Mathur, 2000; Smith & Sims, 1985). In contrast, the overcompliance theory suggests that economic benefits can arise from better pollution control investment (Arora & Gangopadhyay, 1995; King & Lenox, 2001; Porter & van der Linde, 1995; Salop & Scheffman, 1987). We believe that dividing investment activities into prevention and end-of-pipe solutions is able to better distinguish between the two theories. It is evident that traditional economic theory applies most directly to end-of-pipe pollution control investments, which usually entail expensive, non-productive equipment to achieve compliance with existing environmental regulations. Such investments are seen to be detrimental to firm performance, because they incur costs and utilize resources for non-productive activities in order to comply with regulatory standards for controlling pollution and protecting public health (Hart, 1995; Russo & Fouts, 1997; Sarkis & Cordeiro, 2001). However, support is growing for the overcompliance theory, as evidenced by the many MNCs that have successfully implemented pollution prevention measures, such as 3M's Pollution Prevention Pays (PPP) and Dow's Waste Reduction Always Pays (WRAP) programs, which have produced substantial cost savings (Hart, 1995). However, a common feature of prior studies is that they do not distinguish whether a specific pollution control investment is intended to replace obsolete facilities or equipment or to alleviate end-of-pipe problems. We thus extend this line of research by examining the impact of these two approaches to pollution control and their effects on financial performance by utilizing the pollution control investment disclosures requested by Taiwanese authorities. These disclosures allow us to identify whether the pollution control investment is for end-of-pipe or prevention purposes, and we can examine whether the two approaches have different impacts on long-run financial performance. We also develop an appropriate statistical method that applies factor analysis to integrate different financial performance measures into a single index to prevent conflicting results arising from different accounting indicators, such as return on assets (ROA), return on equity (ROE), earnings per share (EPS), and cash flows to assets (CFA), which have all been used in prior research to measure financial performance. Based on the research topic, we investigate two questions: (1) is the impact of pollution control investment on financial performance significant? More importantly, (2) is the impact of pollution prevention solutions on financial performance significantly different from that of end-of-pipe solutions in the long-run? The first issue has been examined extensively in the literature, although with mixed results, and we believe our research methods and data set resolve the apparent contradictions in the previous work. However, the second issue, to the best of our knowledge, has only been investigated by Sarkis and Cordeiro (2001). Due to the limitations of data availability, they employ a two-stage approach of (1) calculating DEA efficiency scores, and (2) using these scores as dependent variables for a multiple regression model that incorporates firm characteristics and performance. They find a negative association between environmental performance and
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short-term financial performance, and that pollution prevention is more negatively associated with short-term performance than endof-pipe investment. However, they also provide certain caveats about the measurement problem of employing DEA, and broach the intriguing subject of extending this line of research to a longer-term (five-year) horizon. Therefore, we provide a direct extension of their pioneering work in this area. 3. Research method 3.1. Sample information Our sample includes firms in five highly polluting industries: cement, plastics, chemicals, paper and pulp, and iron and steel. We obtain the financial variables and market value data from Taiwan's SFI (Securities and Futures Institute) database and the Taiwan Economic Journal (TEJ) database for the period 1989–2006, and manually collect the environmental capital expenditure information, such as the date, amount, purposes, and the expected benefits of each pollution control investment from annual financial reports. We categorize the investments into pollution prevention and endof-pipe pollution control according to the information disclosed in the annual reports. We apply the principle of proportionality to classify a firm into one of these two categories in years it made both types of investments. In the end, we have 106 observations for firms with pollution prevention investments, and 53 for firms with end-of-pipe pollution control investments. Overall, the sample suggests that firms tend to overcomply with the environmental protection regulations. The Appendix details our classification procedures. In robustness tests, we exclude those firms that made both types of investment from our sample to avoid ambiguities. 3.2. Empirical model We follow Jaggi and Freedman (1992) in measuring financial performance using four accounting indicators, namely, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and cash flows to total assets (CFA). However, unlike many studies that use individual indicators to measure performance, we perform factor analysis with Varimax rotation to identify the factor patterns. The factor analysis model can be stated as follows. Yi ¼ μ þ Lfi þ εi ; i ¼ 1; 2; …; n;
ð1Þ
where Yi is a set of p observable random variables, (f1, f2,.., fn) are the factor scores for each observation and the L's are unobservable factor loadings. We wish to find the vector of common factors for subject i, or f^i , by minimizing the sum of the squared residuals. In matrix notation the solution is expressed as: 0 −1 0 f^i ¼ L L L ðYi −μ Þ:
Perf ormancei ¼ β0 þ β1 Prevent þ β2 MTB þ β3 Size þ β4 Beta þ ε
ð4Þ
We perform factor analysis to generate factor scores from four accounting variables (ROE, ROA, EPS and CFA). We then follow the approach proposed by Megginson et al. (1994) to measure financial performance with three different windows. Let Year(t) be the year of investment, Performance0 then denotes the difference in a firm's factor score between Year(t + 1) and Year(t − 1), Performance1 denotes the difference in the average factor score of Year(t + 1) to Year(t + 3) and Year(t − 3) to Year(t − 1), and Performance2 denotes the difference in the average factor score of Year(t + 1) to Year(t + 5) and Year(t − 5) to Year(t − 1). Prevent is an indicator variable that is set to a numerical value of 1 for firms with pollution prevention investments, and 0 for firms with end-of-pipe pollution control investments. MTB denotes the ratio of the market value of equity to the book value of equity. Size denotes the natural logarithm of total assets. The last independent variable, Beta, denotes the systematic risk and is obtained from running the regression of the market model. Table 1 summarizes the definitions of the key variables employed in this study. Earlier studies generally used market-to-book ratio, firm size, and unexpected earnings as control variables (Al-Tuwaijri et al., 2004; Sarkis & Cordeiro, 2001.) Following Fama and French (1992), we replace unexpected earnings with Beta because (1) unexpected earnings has been consistently insignificant in these earlier studies, and (2) the Fama-French model has drawn much attention with regard to the issue of anomalies in returns. A wealth of research also suggests that financial performance can be measured by accounting variables as well as stock returns, which further justifies using the Fama-French variables. 4. Empirical results
ð2Þ
We substitute our estimated factor loadings into this expression as well as the sample mean for the data, as follows: −1 0 0 L^ ðYi −y Þ: f^i ¼ L^ L^
performances of the matched samples. We use a three-year window (from one year preceding the investment to one year subsequent to it) to measure the short-term performance, while a seven-year window (three years before through three years after the investment) and an eleven-year window (five years before through five years after) to measure the long-run performance. We then calculate the average factor score for each firm over the pre- and post-investment windows. More specifically, for the seven-year window, we calculate two sets of the average factor scores, that is pre-investment: years −3 to −1 and postinvestment: years +1 to +3. The same procedure also applies to the eleven-year window. We then apply the conventional t-test, the nonparametric test and multiple regression models to examine possible long-run impacts of pollution control investment on financial performance under various scenarios. It is also worth noting that the windows we use to measure long-run performance are those that are commonly adopted in the literature (for example, Carter, Dark, & Singh, 1998, among others.; Loughran & Ritter, 1995) The multiple regression model is specified as follows:
ð3Þ
The factor score is a standardized number with a mean of zero and a standard error of one. The factor analysis yields only one factor (with an eigenvalue greater than one) that captures the commonality among these four variables, and its factor score essentially represents an index that integrates these four performance measures. We then follow the approach of Megginson, Nash, and Van Randenborgh (1994), who compare the short- and long-run financial
Table 2 reports descriptive statistics for the variables used in this study. The means of Performance0, Performance1 and Performance2 are 0.11, −0.03, −0.02, respectively. The mean of Pollution (indicator variable) is 0.22, suggesting that about 22% of sample firms mention pollution control investment disclosures in their annual reports. The mean of Prevent is 0.67, suggesting that the majority of the pollution control investments are for prevention purposes. The means of MTB, Size, and Beta are 1.43, 15.62, and 0.75, respectively. Table 3 presents univariate comparisons of financial performance of firms with pollution control investments and those who do not have pollution control investments in different subperiods. The first three rows indicate no significant differences in financial performance between these two groups before they make pollution control investments. However, we find that mean and median Fsc(t) are
C.-C. Chien, C.-W. Peng / Journal of Business Research 65 (2012) 1636–1642 Table 1 Definition of variables. Variables
Definition
Dependent variables Performance0 We perform factor analysis to generate factor scores from four Performance1 accounting variables (ROE, ROA, EPS and CFA). We then follow the Performance2 approach proposed by Megginson et al. (1994) to measure financial performance with three different windows. Let Year(t) be the year of investment, Performance0 then denotes the difference in a firm's factor score between Year(t + 1) and Year(t − 1), Performance1 denotes the difference in the average factor score of Year(t + 1) to Year(t + 3) and Year(t − 3) to Year(t − 1), and Performance2 denotes the difference in the average factor score of Year(t + 1) to Year(t + 5) and Year(t − 5) to Year(t − 1). Independent variables Pollution Pollution is an indicator variable that is set to a numerical value of 1 for firms with pollution control investments, and 0 for otherwise. Prevent Prevent is an indicator variable that is set to a numerical value of 1 for firms with pollution prevention investments and 0 for firms with end-of-pipe pollution control investments. MTB MTB is the ratio of a firm's market value of equity to its book value of equity. Size Size is the natural logarithm of a firm's total assets. Beta Beta is estimated by running an OLS regression of individual stock returns on market returns. Prior1 Prior1 is the difference in factor scores between the investment year and Avg(− 3,−1). Prior2 Prior2 is the difference in factor scores between the investment year and Avg(− 5,−1).
significantly higher for firms with pollution control investments than those who do not have pollution control investments at T = 0 (0.21 vs. −0.12 with a t-statistic of 3.90, and 0.08 vs −0.17 with a z-statistic of 4.24.) Both the t-statistic and Wilcoxon rank-sum z-statistic are significant at a one-percent confidence level. The results suggest that firms tend to make pollution control investments in periods in which they experience better economic performance. Nevertheless, we also find that mean Fsc(t + 1) is significantly lower for firms with pollution control investments than those who do not have pollution control investments at T = 1 (−0.24 vs 0.05 with a t-statistic of −2.80.) The results suggest that pollution control investments may drag down financial performance in the subsequent period. The mean and median Performance0 are significantly lower for firms with pollution control investments than those who do not have pollution control investments (−0.22 vs. 0.19 with a t-statistic of −2.89, which is significant at a one-percent confidence level, and −0.10 vs. 0.12 with a z-statistic of −2.04, which is significant at a five-percent confidence level). The results are consistent with Chen and Metcalf (1980), Smith and Sims (1985), Jaggi and Freedman (1992), and others, in that pollution control investment has a negative short-run impact on financial performance. Table 4 reports the empirical results of a more intriguing comparison. Specifically, Table 4 compares the financial performance of firms with pollution prevention investments to the financial performance of
Table 2 Descriptive statistics. Variable
N
Mean
Std. dev.
Minimum
Maximum
Performance0 Performance1 Performance2 Pollution Prevent MTB Size Beta
717 671 596 717 159 717 717 717
0.11 − 0.03 − 0.02 0.22 0.67 1.43 15.62 0.75
1.32 0.88 0.75 0.42 0.47 1.01 1.12 0.31
− 8.43 − 3.14 − 3.06 0 0 0.07 13.20 − 0.45
5.58 3.26 2.16 1 1 9.20 19.55 1.57
Note: definitions of the variables appear in Table 1.
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those with end-of-pipe pollution control investments. While we find no significant differences in financial performance between these two groups of firms during the pre-investment period, the end-of-pipe pollution control investments appear to hinder the mean financial performance more drastically than the pollution prevention investments at T = 1 (Fsc(t) is −0.63 vs. −0.05 with a t-statistic of 2.35, which is significant at a five-percent confidence level). Table 4 further indicates that the mean differences in Performance0 and Performance1 between these two groups are both significant at a five-percent confidence level (0.02 vs. −0.69 with a t-statistic of 2.26, and 0.14 vs. −0.16 with a t-statistic of 2.05). The preliminary results are consistent with our postulation that firms that make investments in pollution prevention significantly outperform those that make investments in end-of-pipe pollution control. Table 5 reports the empirical results of Eq. (4) investigating the association between future performance and investment type. Specifically, Eq. (4) regresses the dependent variables Performance1 and Performance2 on Prevent, MTB, Size and Beta, respectively. Prevent is an indicator variable that is set to a numerical value of 1 for firms with pollution prevention investments, and 0 for firms with end-ofpipe pollution control investments. Panel A of Table 5 indicates that the coefficients of Prevent are 0.35 and 0.27, with a t-statistic of 2.56 and 2.07, and these are significant at one-percent and five-percent confidence levels, respectively. The results suggest that pollution prevention investment is more positively associated with long-run financial performance than end-of-pipe pollution control investments. Our next research question examines whether pre-investment performance affects post-investment performance. More specifically, it is open to question whether firms with better pre-investment performance tend to overcomply with the regulations. To investigate this in more detail, we include a pre-investment financial performance variable (Prior1 or Prior2) in the regression models as control variables. Prior1 denotes the difference in factor scores between the investment year and Avg(−3,−1), and Prior2 denotes the difference in factor scores between the investment year and Avg(−5,−1). Panel B of Table 5 indicates that the coefficients of Prevent for Performance1 and Performance2 are 0.33 and 0.25, with a t-statistic of 2.65 and 2.16, respectively. The former is significant at a one percent confidence level, while the later is significant a five percent confidence level. The results are consistent with Panel A. 5. Robustness tests We further conduct robustness tests to enhance internal validity. Firstly, we employ Tobin's Q as an alternative financial performance measure, because it reflects the market's expectation regarding the cash flows that a firm's asset can generate (Amit & Wernerfelt, 1990). We exclude Tobin's Q from the factor analysis because it is different from the four other accounting variables in nature. More specifically, ROA, ROE, EPS, and cash flows to total assets (CFA) are reported in financial statements, while Tobin's Q is a number that can be estimated using different models. Panel A of Table 6 indicates that Prevent has a significant positive effect on Tobin's Q (coefficient = 0.40, p b 0.05), which is consistent with the previous findings. Secondly, we exclude firms that have made both pollution prevention and end-of-pipe pollution control investments, without applying the proportionality principle, in order to lessen the confounding effect. Panel B of Table 6 indicates that the coefficients of Prevent1 are 0.49 and 0.32, which are significant at the one-percent and fivepercent confidence levels, respectively. Finally, we use stock return as a proxy for financial performance. Panel C of Table 6 indicates that the coefficient of Prevent for Return1 (3 years) is 0.63, which is significant at a five-percent confidence level. However, the coefficient of Prevent for Return2 (5 years) is 0.82, which is only significant at a ten-percent confidence level. Although using stock return as a proxy for financial performance lowers
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Table 3 Comparison of the financial performance between firms with/without pollution control investments. Pollution control investments Firms have pollution control investments
Significance tests of the difference
Firms do not have pollution control investments
Period
N
Mean
Median
N
Mean
Median
t-statistic
z-statistic
Avg(− 5,−1) Avg(− 3,−1) Fsc(t − 1) Fsc(t) Fsc(t + 1) Avg(+ 1,+3) Avg(+ 1,+5) Performance0 Performance1 Performance2
136 150 159 159 159 150 136 159 150 136
− 0.09 − 0.01 − 0.02 0.21 − 0.24 0.02 − 0.01 − 0.22 0.03 0.08
− 0.12 − 0.02 − 0.08 0.08 − 0.12 0.05 − 0.04 − 0.10 0.04 0.03
460 521 558 558 558 521 460 558 521 460
− 0.01 − 0.02 − 0.14 − 0.12 0.05 − 0.06 − 0.05 0.19 − 0.04 − 0.04
− 0.05 − 0.04 − 0.17 − 0.17 − 0.06 − 0.09 − 0.09 0.12 − 0.08 − 0.01
− 1.75 0.34 1.23 3.90⁎⁎ − 2.80⁎⁎ 1.47 0.75 − 2.89⁎⁎
− 1.37 0.21 1.55 4.24⁎⁎ − 0.61 2.24⁎ 0.97 − 2.04⁎
0.85 1.68
1.32 0.39
Note: 1. Let Year(t) be the year of investment. Avg(− 5,−1) denotes the average factor score of Year(t − 5) to Year(t − 1). Avg(− 3,−1) denotes the average factor score of Year(t − 3) to Year (t − 1). Fsc(t − 1) denotes the factor score 1 year before the investment. Fsc(t) denotes the factor score in the year of investment. Fsc(t + 1) denotes the factor score 1 year subsequent to the investment. Avg(+ 1,+3) denotes the average factor score from Year(t + 1) to Year(t + 3). Avg(+ 1,+5) denotes the average factor score from Year(t + 1) to Year(t + 5). Performance0 then denotes the difference in a firm's factor score between Year(t + 1) and Year(t − 1), Performance1 denotes the difference in the average factor score of Year(t + 1) to Year(t + 3) and Year(t − 3) to Year(t − 1), and Performance2 denotes the difference in the average factor score of Year(t + 1) to Year(t + 5) and Year(t − 5) to Year(t − 1). 2. We use Satterthwaite t-statistics to test the means for unequal variances, and the pooled t-test otherwise. 3. The Wilcoxon rank sum test (z-statistic) is used to examine the statistical significance level of the differences between two medians. 4. ⁎⁎ and ⁎ represent significance at the 1% and 5% levels with the two-tailed test, respectively.
end-of-pipe pollution control investments, and this suggests that managers should avoid adopting only such solutions. Our results also indicate that pollution prevention investment is positively associated with long-run financial performance, which suggests that pollution prevention investment can not only lower a firm's negative environmental impacts, but can also improve its bottom-line income by triggering innovations and ultimately lowering the costs of compliance. Accordingly, firms should be aware that paying attention to environmental concerns not only benefits the wider society, but also strengthens their own financial performance. Since our research indicates that pollution prevention measures have economic as well as environmental advantages over more conventional end-of-pipe solutions, it is perhaps not surprising that they have been warmly received in many newly developing countries.
the significance of the difference, the results still suggest that overcompliance with regulations is the best strategy for firms to adopt.
6. Conclusions The relation between pollution control investment and a firm's financial performance is a complex issue, one that has been examined extensively for several decades with conflicting results. We exploit the mandatory disclosure of pollution control investments made by the public listed companies in Taiwan to further explore this intriguing issue. The results suggest that pollution control investments, taken as a whole, decrease short-run financial performance. However, we find that this poor short-run financial performance is largely caused by the
Table 4 Comparison of the financial performance between firms with pollution prevention investments and firms with end-of-pipe pollution control investments. Pollution control investments Pollution prevention investments Period Avg(− 5,−1) Avg(− 3,−1) Fsc(t − 1) Fsc(t) Fsc(t + 1) Avg(+ 1,+3) Avg(+ 1,+5) Performance0 Performance1 Performance2
N 88 95 106 106 106 95 88 106 95 88
Mean − 0.14 − 0.06 − 0.07 0.13 − 0.05 0.08 0.02 0.02 0.14 0.16
End-of-pipe pollution control investments Median − 0.14 − 0.08 − 0.09 0.05 − 0.06 0.07 − 0.01 − 0.05 0.20 0.08
N 48 55 53 53 53 55 48 53 55 48
Mean − 0.01 0.09 0.06 0.35 − 0.63 − 0.07 − 0.08 − 0.69 − 0.16 − 0.07
Median − 0.01 0.01 0.08 0.35 − 0.16 − 0.01 − 0.07 − 0.24 − 0.10 − 0.15
Significance tests of the difference t-statistic − 1.48 − 1.50 − 0.71 − 1.34 2.35⁎ 1.26 1.22 2.26⁎ 2.05⁎ 1.78
z-statistic − 1.79 − 1.52 − 0.89 − 1.57 0.78 0.84 0.36 1.12 1.52 1.79
Note: 1. Let Year(t) be the year of investment. Avg(−5,−1) denotes the average factor score of Year(t− 5) to Year(t− 1). Avg(−3,−1) denotes the average factor score of Year(t − 3) to Year (t− 1). Fsc(t − 1) denotes the factor score one year before the investment. Fsc(t) denotes the factor score in the year of investment. Fsc(t +1) denotes the factor score one year subsequent to the investment. Avg(+1,+3) denotes the average factor score from Year(t +1) to Year(t+ 3). Avg(+1,+5) denotes the average factor score from Year(t+ 1) to Year(t + 5). Performance0 then denotes the difference in a firm's factor score between Year(t+ 1) and Year(t− 1), Performance1 denotes the difference in the average factor score of Year(t+ 1) to Year(t + 3) and Year(t− 3) to Year(t −1), and Performance2 denotes the difference in the average factor score of Year(t+ 1) to Year(t+ 5) and Year(t − 5) to Year(t− 1). 2. We use Satterthwaite t-statistics to test the means for unequal variances, and the pooled t-test otherwise. 3. The Wilcoxon rank sum test (z-statistic) is used to examine the statistical significance level of the differences between two medians. 4. ⁎⁎ and ⁎ represent significance at the 1% and 5% levels with the two-tailed test, respectively.
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Table 5 Regressions of long-run financial performance. Panel A: regressions of long-run financial performance with control variables Dependent variable: Performance1 (N = 150)
Dependent variable: Performance2 (N = 136)
Independent variables
Parameter estimates
t-value
p-value
Independent variables
Parameter estimates
t-value
p-value
Intercept Prevent MTB Size Beta F-statistic (p-value) Adjusted R2
−3.27⁎⁎ 0.35⁎⁎ −0.18⁎⁎ 0.21⁎⁎
−3.39 2.56 −2.61 3.42 1.01 6.75 (p b 0.01) 0.14
b 0.01 0.01 0.01 b 0.01 0.31
Intercept Prevent MTB Size Beta F-statistic (p-value) Adjusted R2
−2.23⁎ 0.27⁎ −0.15⁎⁎ 0.15⁎⁎
−2.35 2.07 −2.22 2.48 0.41 4.02 (p b 0.01) 0.08
0.02 0.04 0.03 0.01 0.68
0.21
0.08
Panel B: regressions of long-run financial performance with pre-investment windows financial performance variable Dependent variable: Performance1 (N = 150) Dependent variable: Performance2 (N = 136) Independent variables
Parameter estimates
Intercept Prevent MTB Size Beta Prior1 Prevent*Prior1 F-statistic (p-value) Adjusted R2
−3.13⁎⁎ 0.33⁎⁎ −0.31⁎⁎ 0.22⁎⁎ 0.01 0.33⁎⁎ 0.01
t-value
p-value
−3.56 b 0.01 2.65 b 0.01 −4.58 b 0.01 3.95 b 0.01 0.02 0.99 3.60 b 0.01 0.01 0.99 10.68 (p b 0.01) 0.28
Independent variables
Parameter estimates
t-value
p-value
Intercept Prevent MTB Size Beta Prior2 Prevent*Prior2 F-statistic (p-value) Adjusted R2
−2.36⁎⁎ 0.25⁎ −0.30⁎⁎ 0.18⁎⁎
−2.82 2.16 −4.67 3.32 −0.68 2.76 1.28 10.51(p b 0.01) 0.30
b0.01 0.03 b0.01 b0.01 0.50 b0.01 0.20
−0.11 0.26⁎⁎ 0.14
Note: 1. Definitions of the variables appear in Table 1. 2. ⁎⁎ and ⁎ represent significance at the 1% and 5% levels with the two-tailed test, respectively. 3. The maximum VIF is 1.21, which is much less than 5, and thus multicollinearity is not a major concern.
Our research has some limitations that offer suggestions for future work in this area. First, this study provides indirect evidence consistent with the benefits of voluntary environmental overcompliance, in that firms with better pollution records can improve their long-run financial performance. However, future research may uncover more direct evidence to validate the theory of overcompliance. Another limitation of this study is that the sample firms are drawn from five highly polluting industries in Taiwan. Hence, the conclusions of the present study may not fully generalize to other countries, even though Taiwanese accounting
standards in dealing with investments in pollution control facilities are in compliance with both the U.S. GAAP and IFRS. Acknowledgments The authors thank two anonymous referees and F. J. Shiue, President of the Taiwan Accounting Association for their invaluable advice. We also wish to thank Arch Woodside and special issue co-editors for their cordiality and encouragement through the review process. Research
Table 6 Additional Tests. Panel A: regressions of Tobin's Q with control variables
Panel B: regressions of long-run financial performance with control the mis-classification of firms
Panel C: regression of long-run stock returns with control variables
Dependent variable: Tobin's q
Dependent variable: Performance1
Dependent variable: Return1
Dependent variable: Return2
Independent variable
Independent variable
Independent variable
Parameter estimates
Independent variable
Parameter estimates
Intercept Prevent MTB Size Beta
5.77⁎⁎ 0.63⁎ 1.13⁎ − 2.33⁎⁎ − 1.05
Intercept Prevent MTB Size Beta
2.41⁎ 0.82 − 0.60 − 0.03 − 1.85⁎
Parameter estimates
Intercept − 2.61⁎ Prevent 0.40⁎ Size 0.15⁎ ROE − 3.50⁎⁎ SG 0.01 Prior1 0.56⁎⁎ 0.03 Prevent ∗ Prior1 F(p-value) = 6.20 (p b 0.01), Adj. R2 = 0.16, N = 159
Dependent variable: Performance2 Parameter estimates
Intercept − 4.98⁎⁎ 0.49⁎⁎ Prevent1 MTB − 0.29⁎⁎ Size 0.34⁎⁎ Beta − 0.01 Prior1 0.34⁎⁎ Prevent1 ∗ Prior1 − 0.09 F(p-value) = 9.16 (p b 0.01), Adj. R2 = 0.32, N = 105
Independent variable
Parameter estimates
Intercept − 3.79⁎⁎ Prevent1 0.32⁎ MTB − 0.33⁎⁎ Size 0.28⁎⁎ Beta − 0.21 Prior2 0.28⁎⁎ Prevent1 ∗ Prior2 − 0.01 F (p-value) = 7.34 (p b 0.01), Adj. R2 = 0.29, N = 94
F(p-value) = 20.39 (p b 0.01), Adj. R2 = 0.33, N = 156
F (p-value) = 2.56 (p = 0.04), Adj. R2 = 0.05, N = 130
Note: 1. We follow Chung and Pruitt (1994) who define Tobin's Q as (Market Value of Equity + Preferred Stocks + Debt) / Total Assets. Let Year(t) be the year of investment. Performance1 denotes the difference in the average factor score of Year(t + 1) to Year(t + 3) and Year(t − 3) to Year(t − 1), and Performance2 denotes the difference in the average factor score of Year(t + 1) to Year(t + 5) and Year(t − 5) to Year(t− 1). Return1 denotes the cumulative three-year buy-and-hold returns. Return2 denotes the cumulative fiveyear buy-and-hold returns. Prevent is an indicator variable that is set to a numerical value of 1 for firms with pollution prevention investments and 0 for firms with end-of-pipe pollution control investments. Prevent1 is for firms with only pollution prevention or only end-of-pipe pollution control investments, and an indicator variable with the value of 1 for firms with pollution prevention investments and 0 for firms with end-of-pipe pollution control investments. Size is defined as the natural logarithm of total assets. ROE is defined as the net income to average equity. SG is defined as a sales growth percentage from the prior five year's sales. MTB is the ratio of a firm's market value of equity to its book value of equity. Beta is estimated by running an OLS regression of individual stock returns on market returns. Prior1 represents the difference in factor scores between the investment year and Avg(−3,−1). Prior2 represents the difference in factor scores between the investment year and Avg(−5,−1). 2. ⁎⁎ and ⁎ represent significance at the 1% and 5% levels with the two-tailed test, respectively. 3. The maximum VIF is 3.42, which is less than 5, and thus multicollinearity is not a major concern.
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support from the National Science Council, Taiwan (grant # NCS 830301-H006-030) is gratefully acknowledged. All errors are our own. Appendix A A pollution control investment is categorized as end-of-pipe if it is used to treat pollutants or waste water to comply with existing environmental regulations, and as pollution prevention if it can also enhance productive efficiency. The principles of categorizing investment in pollution prevention for these five highly polluting industries are summarized as follows: i. Cement industry: the major concern of the cement industry is dust emissions in the production process. Investment in dust pollution control equipment is categorized as prevention if it not only reduces atmospheric emissions, but also collects and recycles dust particles into non-poisonous and useful raw materials. ii. Plastics industry: waste plastic recovery equipment recycles and converts waste plastics into valuable raw materials. The exhaust heat recovery equipment used in this industry recycles heat energy with flue gas. This increases the combustible air temperature, avoids the loss of heat energy and reduces industrial air pollution. These investments are categorized as prevention investments. iii. Chemical industry: the exhaust heat recovery equipment serves a dual role as processing equipment and air pollution control equipment, and is considered as a prevention investment. iv. Paper and pulp industry: the waste recovery equipment and equipments for prevention and control of pollutant emissions are categorized as prevention investment. v. Iron and steel industry: the steel industry uses cupola and induction furnaces to melt and decompose pig iron and scrap by utilizing fluidized bed combustion technology. The equipment for reducing dust pollutants and also making improvements in the production and manufacturing processes is categorized as a prevention investment. References Al-Tuwaijri SA, Christensen TE, Hughes KE. The relations among environmental disclosure, environmental performance and economic performance: a simultaneous equations approach. Accounting, Organizations and Society 2004;29(5–6):447–71. Amit R, Wernerfelt BB. Why do firms reduce business risk? Academy of Management Journal 1990;33(3):520–33. Arora S, Gangopadhyay S. Toward a theoretical model of voluntary overcompliance. Journal of Economic Organi 1995;28(3):289–309.
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