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The drivers of energy efficiency investments: the role of job flexibility

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Abstract

The goal of this paper is to identify the characteristics of firms that drive the adoption of energy efficiency investments. Particular attention is given to the distortive effect of the adoption of fixed-term job contracts on firms’ orientation toward the investment in energy savings. From a panel data analysis, three regularities emerge. First, extensive use of job flexibility determines a lower incentive for firms to make an energy efficiency investment. Second, firm performance significantly ameliorates the expenditure in energy savings. Third, substantial differences in energy efficiency investment are present within sectors, international regions, regional areas, and firms of differing size.

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Notes

  1. Potential emission reductions—which result from efficiency improvements—may be offset by the “rebound effect,” that is, an increase in demand for energy in response to price decreases (Khazzoom 1980). This rebound effect could be sufficiently high that it offsets efficiency gains completely, resulting in an overall negative effect (the Jevons paradox).

  2. The International Energy Agency (IEA) has estimated that energy efficiency improvements providing an abatement of approximately 60% of GHG pollutants are necessary to achieve the targets defined by the 20 20 20 strategies (IEA 2009 and 2013).

  3. This phenomenon, known as the “carbon leakage effect,” has been studied after the issuing of the European Emissions Trading Scheme (Allevi et al. 2013).

  4. With regard to emerging countries, Taylor et al. (2008) described the empirical benefits of increased industrial energy use efficiency in several ways. In particular, these include increasing production in ways that do not require additional energy supplies (which are usually limited in these countries) and can decrease poverty, as well as reducing energy prices and, more generally, increasing the environmental sustainability of human activities. Cantore et al. (2016) noted that policies favoring energy efficiency are profitable for firms and enhance the economic growth of developing countries.

  5. The present paper focuses on a category of innovation—energy efficiency improvement—that acts as an indicator for all energy efficiency process innovations (Porter and Van der Linde 1995). The dependent variable includes the following process innovations: replacing electric motors with green technology, adopting energy-saving light bulbs, and using heat insulation.

  6. Fixed-term contracts affect the welfare from different perspectives. In particular, the reforms introducing temporary jobs have determined a higher turnover rate, without a substantial reduction in unemployment duration (Blanchard and Landier 2002). Moreover, workers on temporary contracts experience (in)satisfaction, insecurity, and stress and in turn it lowers workers’ well-being (Green 2011; Caroli and Godard 2016; Chadi and Hetschko 2015).

  7. For instance, Costa-Campi et al. (2015) analyzed how much importance the firms attach to the objective of energy efficiency innovation.

  8. Montalvo (2008) has argued that surveys conducted at the firm level to study the determinants of clean investment are limited by two main factors. First, there is a lack of longitudinal data, which precludes the study of the dynamics of the possible diffusion processes. Second, this type of sample usually focuses only on a specific industry. Therefore, the findings cannot be extended to the whole economy.

  9. Specifically, the SYS-GMM estimator addresses the endogeneity of the explanatory variables by using their lags as instruments, which in turn are uncorrelated with the error term.

  10. We adopt the two-step estimators in which the standard covariance matrix is robust to panel-specific autocorrelation and heteroskedasticity.

  11. More specifically, the sample is constructed by the Bank of Italy research department, which collects data annually to directly represent a substantial share of Italian firms (approximately 80% of industrial firms and 64% of non-financial service firms), in terms of its composition by firm size, sector, and geographical location. The population of the survey is divided into strata, and from each one, a certain number of firms is extracted on a random basis. These firms make up the sample to be observed (one-stage stratified sample design). The strata are combinations of branch of activity, size class (in terms of number of employees), and the regional location of the firm’s head office.

  12. For more details, see Cameron and Trivedi (2005).

  13. The variable of interest has been reported in the Survey on Businesses and Services conducted by the Bank of Italy starting from 2009. The last dataset was made available at the end of February 2016.

  14. Firm energy efficiency investment is encoded by the Bank of Italy as “V059,” here renamed EEI.

  15. See IEA 2008 for a definition of energy-saving technology investment.

  16. Labor market flexibility can be subdivided into three dimensions of flexibility: (1) numerical flexibility (further classified as external or internal, depending on labor mobility (inter- and intra-firm, respectively)); (2) functional flexibility; (3) wage flexibility (see, e.g., Wachsen and Blind 2016). Our paper analyzes only internal numerical flexibility because TJob belongs to this category given that the BIRD dataset does not allow analyzing the other two types of labor flexibility.

  17. For the US economy, a positive relationship has been observed between innovation and barriers to employee layoffs (Acharya et al. 2014). Similarly, for the Italian economy, the negative effects of a high share of fixed-term contracts leads to a reduction in the levels of investment (Lotti and Viviano 2012).

  18. The variables belonging to this group are all time-invariant and so cannot be included in both the WFE and SYS-WGMM.

  19. The BIRD dataset adopts the NACE 2007 classification of economic activities.

  20. We selected this control variable, as in our preliminary analysis and in the literature (Horbach et al. 2012; Costa-Campi et al. 2015); firm age was never significant.

  21. The conflicting results, in terms of the effects of Tjob and Productivity on EEI, may highlight an interaction effect. Accordingly, we have explored the likely link between flexibility and labor productivity, which would dampen the negative impact of flexibility on EEI. More specifically, we amended the specification to consider an interaction effect between Tjob and Productivity. However, the interaction term did not approach statistical significance across all the econometric methodologies used. Therefore, there is insufficient evidence that the crowding-out effect of productivity offsets the negative impact of labor flexibility on EEI.

  22. Because EEI and Productivity are presented in logarithm form, the estimated coefficient β2 represents elasticity.

  23. We report only the Hansen J test because this test is robust against heteroskedasticity in the residual terms that are commonly present in longitudinal data (Roodman 2009b).

  24. As recommended by Roodman (2009b), the number of instruments used in a dynamic GMM estimator should be low and smaller than the number of observations. In our analysis, we used 48 instruments. Therefore, in both cases, the number of instruments is small and less than our 10,226 observations. The “optimal” number of instruments was achieved by collapsing the instrument matrix. Finally, we also performed alternative estimations by reducing the number of instruments further. Nonetheless, these further reductions worsened the diagnostic test results (specifically, they resulted in a lower Hansen p value), indicating that our selected number of instruments is reasonably “optimal.”

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Acknowledgments

We are grateful to the research department team of Bank of Italy for their support in the various remote elaborations. All errors are our own. The authors thank three anonymous referees for their critical comments and suggestions. Both authors contributed equally to each section of the article.

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  1. Department of Economics, University of Messina, Via dei Verdi 75, 98122, Messina, Italy

    Antonio Fabio Forgione & Carlo Migliardo

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Appendix

Appendix

This empirical analysis has been carried out using the Bank of Italy’s remote processing system BIRD. It allows researchers to process data collected through its Survey of Industrial and Service Firms while maintaining the confidentiality of the individual data. In detail, access to a dataset is possible exclusively through submission of a set of commands in one of the packages supported (SAS or Stata) and before passing the batch program on to the package parser, a legitimacy check is performed by the Bank of Italy officers. Therefore, we have carried out our statistical analyses without having direct access to the microdata. For more detail regarding how the data can be accessed, see https://www.bancaditalia.it/statistiche/basi-dati/bird/imprese-industriali-e-servizi/imprese/2_database_utilisation.pdf?language_id=1

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Fabio Forgione, A., Migliardo, C. The drivers of energy efficiency investments: the role of job flexibility. Energy Efficiency 12, 1203–1217 (2019). https://doi.org/10.1007/s12053-018-9736-3

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