Draw Policy Implications Research Articles

Improving Regression Models

This paper illustrates cut-and-try techniques that point to appropriate transformations of variables and to the selection of sets of variables for an equation that may improve understanding of a social process. The substance of the research reported — the relation of mental test results to state population, cultural, and school resource indexes (Walberg and Rasher, 1974) — illustrates typical problems of behavioral data: multi-colinearity, outliers, abnormal distributions, and the lack of a consensually-validated, explicit theoretical model. Despite these problems, data originally collected for purposes other than the investigator’s may yield tentative confirmations or cautions about prior findings and provisional indications for theory or policy; such inferences may be at least partially checked by cross-validation on independent or semi-independent sets of data. After discussing the sequence of analyses and the results, we conclude by mentioning a number of uncertainties and reservations about drawing substantive or policy implications.

The electricity measure of capital utilization

Not having access to detailed establishment-level data on hours, days, and intensity of use of capital equipment, most serious investigations of industrial capital utilization have been forced to rely on the ‘electricity measure’ of utilization. Electricity studies in a variety of countries find almost unanimously that utilization rates are remarkably low, usually ranging from 15 to 25%. The present paper (a) examines the theoretical bases of the electricity measure, and finds that it is likely to be biased downward; (b) compares the electricity measure with an ‘objective’ measure based on a body of newly available survey data, and confirms a severe downward bias; (c) tests two hypotheses concerning the causes of this bias; (d) draws policy implications for governments wishing to collect periodic data on capital utilization.