Willingness-to-Pay for Energy Conservation and Free-Ridership on Subsidization – Evidence from Germany

terns by layering the error components on top of the random-parameter logit
model.

2.2 Specification of Utility

Recognizing the preceding discussion about heterogeneous adoption behavior, we
assume that the household’s utility V_ij is negatively effected by the investment
cost C_ij, and positively effected by the decline of the building’s annual primary
energy demand ∆Q_ij, measured in megawatt hours (MWh), both of which are
associated with a specific retrofitting alternative j . We control for the economic
background of the households by including annual disposable income into the
analysis.³ Further, we expect that the level of the household’s energy consump-
tion influences the decision of whether to renovate, either positively because a
household with a high energy consumption level is more inclined to lower its
energy cost, or negatively because a high level reflects low energy awareness.
Moreover, because there is a quality differential between the building stocks in
western and eastern Germany, a binary variable indicates whether the household
lives in the eastern part of Germany. Finally, we include a measure of the ac-
cessibility of information on home retrofits within the immediate vicinity of the
household. This variable is intended to proxy for the transaction costs of infor-
mation acquisition, and is defined as the relative availability of certified home
auditors within a 20 kilometer radius of the household’s location.⁴

³As is typical for survey data, information on income is missing for a large share of the
households - roughly 20%. To impute these missing values, we employ the expectation-
maximization algorithm recommended by King et al. (2001). The employed algorithm can
be implemented using a program compatible with the statistical software R, and is download-
able from http://gking.harvard.edu.

⁴To derive this measure we drew upon a list of certified home auditors and their addresses
published by the German government. We read the data as a map-layer into a Geographical
Information System and overlaid this with a layer of household locations. We then created a
circular buffer around each household having a radius of 20 kilometers and generated a count

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