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The impact of management systems on technical change: the adoption of pollution prevention techniques

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Abstract

A firm’s capability to innovate is influenced by its organizational structure. We examine the effect of Total Quality Environmental Management (TQEM) on the adoption of innovative pollution prevention activities over the period 1992–1996, and show that the rate of innovation increases following the adoption of TQEM. However, the effect of TQEM on pollution prevention innovation “wears out” over time. Our analysis indicates that this is likely because pollution prevention undertaken in one year continues to be effective in future years. This, in turn, reduces the incentives for further innovation due to declining marginal returns, rather than because the institutional effectiveness of TQEM weakens. We provide corroborative evidence based on the time profile of pollution prevention of firms that adopted TQEM prior to the start of our sample, and also develop a stylized model with (partial) obsolescence of pollution prevention innovations that matches the empirical regularities we obtain. Our findings shed light on the importance of organizational structure on the pace of technical change.

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Notes

  1. In related research, we have investigated the importance of TQEM, relative to regulatory (and other) factors in the adoption of pollution prevention practices (Khanna et al. 2009), and how TQEM has affected the nature of pollution prevention activities (Deltas et al. 2014).

  2. The role of organizational structure on innovation has been examined outside of the environmental arena as well by Teece (1996), among others. More recently, Battisti and Stoneman (2010) established complementarities between organizational innovations and technological innovations among UK firms.

  3. In theory, the relationship between voluntary programs and innovation could go either way due to the nature of such programs which may enhance or limit incentives for innovation. Voluntary programs may provide firms the flexibility to set their own environmental goals (Sunnevåg 2000), allow for information exchanges that promote collaborative learning and reduce shared uncertainties (Aggeri 1999; Ashford 1999; de Vries et al. 2012; Skjærseth 2005). However, voluntary programs’ lack of stringency, a key factor in many environmental innovation studies (Brunnermeier and Cohen 2003; Jaffe and Palmer 1997; Lanjouw and Mody 1996), may curtail the incentive to undertake costly innovations that have uncertain outcomes (Cunningham and Clinch 2005).

  4. In this work, P2 is scalar-valued, and not distinguished by type. We recognize that there may indeed be synergies between P2s such that promoting one type using TQEM can enhance the adoption of others as well. Measuring such synergistic effects may require analysis akin to Battisti and Stoneman (2010). However, we cannot identify synergies separately from the returns to P2 with the data used in this study. The only way to identify synergies would be to distinguish P2 by type. If adding P2 of the same type does not involve any synergies (by assumption), then synergies can be distinguished by returns. Doing this disaggregated analysis is far beyond the scope of this work. It is also unclear how it would help us pin down the obsolescence parameter, which is the central question here.

  5. The myopic firm assumption also allows us to sidestep the issue of whether the adoption of TQEM is anticipated or un-anticipated. Consider a forward-looking manager who values both current and future profits, and anticipates adoption of TQEM in period t. Such a manager would reduce P2 innovation in period t-1, and would rationally allocate resources to pollution prevention research to the following year when the ability of identifying viable solutions would be enhanced due to the adoption of TQEM in that year.

  6. With a time-varying \({V}_{j,t}\), the steady state would be a distribution, not a point, and convergence would not deterministic. Both of these effects would lead to unnecessary complications to the exposition and analysis.

  7. The \({\epsilon }_{j,t}\) component of \({V}_{j,t}\), which represents random non-persistent variation, is unlikely to cause endogeneity issues, because the adoption of TQEM is a long-run decision and probably taken with some lag. Endogeneity would be a problem if \({V}_{j,t}\) exhibits systematic time variation across firms over time, i.e., its relative value across firms changes over time in a systematic way. This, however, is unlikely to be an issue in our panel, since it not a long one.

  8. The first year for which these data are reported is 1991. Pollution prevention activities for chemicals which have been added or deleted over the period 1991–1996 were dropped due to changes in the reporting requirements by the USEPA. This ensures that the change in pollution prevention activities in our sample over time is not due to differences in the chemicals that were required to be reported.

  9. The firm-reported TQEM data from IRRC are self-reported, i.e., they are not verified or certified by an external party. Non-survey response leads to some missing values of the TQEM variable, and results in an unbalanced panel. Since the decision to adopt TQEM is not likely to be made year to year and since even if a firm were to de-adopt TQEM, the culture and organizational practices are likely to persist, we assume that there is no de-adoption of TQEM during our sample period. This allows us to “fill-in” missing values for TQEM for 15% of the sample and affects an additional 4% of the observations for which transient “de-adoption” of TQEM was reported. Nonetheless, TQEM remains missing for 227 firm/year combinations.

  10. Synthetic control, a more recently developed method that generalizes our implicit “difference-in-difference” research design, cannot be credibly applied to our data given the very short duration of the panel.

  11. In a robustness check, we estimate separate trends for the firms that have adopted TQEM prior to the start of our sample, the firms that have adopted TQEM during our sample, and the firms that have never adopted TQEM.

  12. In robustness checks, not reported here for brevity, we have also dropped these observations from the linear model to ensure than any of the differences between the two models is not driven by the difference in observations. In these regressions, the TQEM treatment effect is slightly higher and the R-squared slightly lower.

  13. Alternatively, it could be that some firms are practicing the essence of TQEM as part of their standard way of doing business; thus, they may not find it necessarily to “officially” launch such a program.

  14. Observe that if all firms that adopted TQEM during our sample did it at the same year, the third subsample would not have been sufficient to identify the treatment effect. Moreover, if in addition no firms adopted TQEM prior to the start of our sample period, then the second subsample would not have been sufficient to identify the TQEM effect either.

  15. The results using yearly dummies in the other two samples are also similar and are omitted for brevity.

  16. In one specification, there is a tiny increase from year 3 to year 4, but it is also associated with an increase in the standard error of the year 4 effect. In fact, the year 4 effect is no longer as strongly statistically significant as the slightly smaller year 3 effect.

  17. Difference-in-difference estimates cannot be obtained in a model that estimates the time profile of TQEM effects using year dummies when limiting ourselves only to the firms that adopted TQEM during our sample period. This is because in such a sample no firms are treated in the first year, four distinct treatment effects need to be estimated (each requiring one year of data), and all firms are treated by the fifth year.

  18. Note that the first deviation is zero since it is used to pin down the value of \(\Delta \alpha\).

  19. These percentages are obtained by exponentiating the parameter estimates in Table 3, column 3, if these correspond to a Poisson and not a linear regression model.

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Acknowledgements

We thank two anonymous referees for comments that have improved the quality of the paper. Financial support from the EPA STAR program Grant No. R830870 is gratefully acknowledged. We alone bear responsibility for any errors.

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Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, 1407 W., Urbana, IL, 61801, USA

    George Deltas

  2. University of Vermont, 94 University Place, Burlington, VT, 05405, USA

    Donna Ramirez Harrington

  3. University of Illinois at Urbana-Champaign, 1301 W. Gregory Drive, Urbana, IL, 61801, USA

    Madhu Khanna

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  1. George Deltas
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  2. Donna Ramirez Harrington
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  3. Madhu Khanna
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Correspondence to George Deltas.

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Deltas, G., Harrington, D.R. & Khanna, M. The impact of management systems on technical change: the adoption of pollution prevention techniques. Econ Change Restruct 54, 171–198 (2021). https://doi.org/10.1007/s10644-020-09273-w

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