Pregibit: A Family of Discrete Choice Models

The pregibit discrete choice model is built on a distribution that allows symmetry or asymmetry and thick tails, thin tails or no tails. Thus the model is much richer than the traditional models that are typically used to study behavior that generates discrete choice outcomes. Pregibit nests logit, approximately nests probit, loglog, cloglog and gosset models, and yields a linear probability model that is solidly founded on the discrete choice framework that underlies logit and probit.post-secondary education, probit, logit, asymmetry, discrete choice, mortgage application

Diagnosing the Productivity Effect of Public Capital in the Private Sector

Does public capital contribute to the productivity in the private sector? If so, which part of the private sector benefits most? Is public capital a substitute for or a complement of labor and private capital? This paper addresses these questions with both cost and profit function models estimated on U.S. time series data of the private sector and two of its subsectors. It pays special attention to nonstationarity in the data, to endogeneity in the price variables, and to the statistical and economic significance of the public capital effect.

Testing for IIA with the Hausman-McFadden Test

The Independence of Irrelevant Alternatives assumption inherent in multinomial logit models is most frequently tested with a Hausman-McFadden test. As is confirmed by many findings in the literature, this test sometimes produces negative outcomes, in contradiction of its asymptotic χ² distribution. This problem is caused by the use of an improper variance matrix and may lead to an invalid statistical inference even when the test value is positive. With a correct specification of the variance, the sampling distribution for small samples is indeed close to a χ² distribution.multinomial logit, IIA assumption, Hausman-McFadden test

On the distribution of job characteristics: an analysis of the DOT data

We analyze the information in the Dictionary of Occupational Titles to characterize the structure of labor demand. Two dimensions, an intellectual factor and a dexterity factor, capture two-thirds of the variance in job requirements; the remaining (co-)variance cannot be easily structured. Simple linear relationships go a long way in describing the matching between job activities and required worker qualities (Intellect for complex relations to Data and to People, Dexterity for complex relations to Things). There is no dichotomy between mathematical and verbal required skills. Poor working conditions are not restricted to workers in low level jobs; we find strong support for compensating wage differentials. At more intellectual jobs, men receive less wage compensation for working conditions, while in jobs requiring greater dexterity they receive more. Such a relationship is absent for women