The literature on energy efficiency provides numerous examples of apparently profitable technologies that are not universally adopted. Yet according to the standard neoclassical theory of investment, profit-maximizing firms should undertake all investments with a positive net present value. The standard theory also holds that the discount rate for computing the present value of a project should be the return available on other projects in the same risk class, and therefore should not depend on characteristics of the firm. This model as applied to energy-saving investments is tested by examining whether firms'characteristics influence their decision to join the Environmental Protection Agency's voluntary Green Lights program. A discrete choice regression is estimated over a large sample of participating and nonparticipating firms. Missing values in the data matrix are replaced with multiple imputations from a distribution estimated using the expectation—maximization algorithm. The results show that (1) substantial improvements in the power of hypothesis tests can be achieved through maximum-likelihood imputation of missing data, and (2) contrary to the conventional theory, the characteristics of firms do affect their decision to join Green Lights and commit to a program of investments in lighting efficiency.

This study provides new evidence of the importance of financing constraints for explaining the dramatic cycles in inventory investment. We compare the empirical performance of different financial variables (coverage ratio, cash stocks, and cash flow) used in previous research to test for the presence of financing constraints. The comparison is undertaken in a common framework with an identical sample and high-frequency (quarterly) firm panel data. Cash flow is much more successful than cash stocks or coverage in explaining the facts about inventory investment across firm size, different inventory cycles, and different manufacturing sectors.