Abstract
A longstanding question in the study of energy demand concerns the role of information as a determinant of home efficiency improvements. Although the provision of information via energy audits is frequently asserted to be an effective means for governments to encourage the implementation of efficiency-enhancing renovations, empirical support for this assertion is tenuous at best. Apart from endogeneity issues with respect to receiving an audit, two other factors have complicated attempts to measure their effect: First, the nature of the information provided by the audit is typically unobserved, and, second, the response to this information may vary over households. Using household data from Germany, we address both sources of heterogeneity by estimating a random-parameter model of four retrofitting alternatives. In addition to confirming the importance of costs and savings as determinants of renovation choices, our results suggest that the effects of consultancy vary substantially across households, with some households responding negatively to the provision of information.
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Notes
The audited households that do renovate, however, have a tendency to undertake several measures, which would reduce their average information costs on a per retrofit basis. Of the 293 households in the data who are audited, 70 do nothing, 65 implement a single measure, and 158 implement multiple measures.
In the statistics literature, the weighted average of distinct functions is called a mixed function, which explains the name of the mixed logit model, while the density that provides for the weights is called the mixing distribution.
For both the standard and the mixed logit, the error terms are assumed to be independently and identically distributed, obeying a Gumbel or Type I extreme value distribution with \(F (\varepsilon ) = e^{-e^{-\varepsilon }}\) being the cumulative distribution function. Differences \(\varepsilon ^*_{ijk} := \varepsilon _{ij} - \varepsilon _{ik}\) of two error terms then follow the logistic distribution: \(F (\varepsilon ^*_{ijk}) = \frac{\exp (\varepsilon ^*_{ijk})}{1 + \exp (\varepsilon ^*_{ijk})}\).
If \(\mathbf{x}_{ij}\) and \(\mathbf{z}_{ij}\) overlap, that is, some of the variables enter both vectors, \(\mathbf{x}\) and \(\mathbf{z}\), the coefficients of these variables can be considered to vary randomly with mean \(\varvec{\beta }\) and the same distribution as around their mean.
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Acknowledgments
We are grateful for invaluable comments and suggestions by Christoph M. Schmidt and inspiring discussions with Frank Wätzold. Furthermore, we thank Peter Grösche for very helpful research assistance. This work has been supported in part by the Collaborative Research Center “Statistical Modeling of Nonlinear Dynamic Processes” (SFB 823) of the German Research Foundation (DFG), within the framework of Project A3, “Dynamic Technology Modeling”.
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Frondel, M., Vance, C. Heterogeneity in the Effect of Home Energy Audits: Theory and Evidence. Environ Resource Econ 55, 407–418 (2013). https://doi.org/10.1007/s10640-013-9632-4
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DOI: https://doi.org/10.1007/s10640-013-9632-4