A note on the linear, logit and probit functional form of the labour force participation rate equation

The commonly used specification in regional economic research on labour force participation is the linear probability function. An important alternative recommended in the Handbook of Regional and Urban Economics in the contribution of Isserman et al. (1986) on `Regional Labor Market Analysis' is the logit probability function. Their argument for the logit probability function is as follows. Given that economic theory on labour force participation does not suggest to pick one functional form over another and that the parameters of the logit probability function are estimable by OLS under the usual assumptions about the error term, the benefit of the logit probability function is that any estimated value for L lies within the logical bounds [0,1]. This feature is particularly desirable in a forecasting context when out of sample data might otherwise potentially yield absurd labour force participation rates. In this note two counter-arguments are gathered against using the logit probability function which are lacking in the Handbook of Regional and Urban Economics. Furthermore, it is shown that the logit probability function in this discourse can be replaced by the probit probability function equally well. Keywords: logit, probit, labour force participation rate.

Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable

This paper surveys panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable. In particular, it focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the fixed coefficients model and the random coefficients model. This survey should prove useful for researchers in this area.

Unconditional maximum likelihood estimation of dynamic models for spatial panels

This paper hammers out the estimation of a fixed effects dynamic panel data model extended either to include spatial error autocorrelation or a spatially lagged dependent variable. To overcome the inconsistencies associated with the traditional least squares dummy estimator, the models are first-differenced to eliminate the fixed effects and then the unconditional likelihood function is derived taking into account the density function of the first-differenced observations on each spatial unit. When exogenous variables are omitted, the exact likelihood function of both models is found to exist. When exogenous variables are included, the presample values of these variables and thus the likelihood function must be approximated. Two leading cases are considered: the Bhargava and Sargan approximation and the Nerlove and Balestra approximation. As an application, a dynamic demand model for cigarettes is estimated based on panel data from 46 American states over the period 1963 to 1992.

Effects of Transport Improvements on Commuting and Residential Choice

This paper develops a commuter location model able to explain and simulate residential location changes of commuters that result from transport improvements. The core model is based on the assumption of constant commuting time, while two extensions incorporate substitution possibilities having an upward effect on total commuting time. Estimation errors of the residential location of the working population with the existing transport system are limited to 7%. With the extended model, the impacts on commuting and residential choice are investigated for six higher speed rail connections between Amsterdam, located in the urban core of the Netherlands, and Groningen, located in its rural periphery. The model outcomes strongly influenced the public policy debate in the Netherlands.

Evidence of Competition in Research Activity among Economic Department using Spatial Econometric Techniques

Despite the prevalence of both competitive forces and patterns of collaboration within academic communities, studies on research productivity generally treat universities as independent entities. By exploring the research productivity of all academic economists employed at 81 universities and 17 economic research institutes in Austria, Germany, and German-speaking Switzerland, this study determines whether a research unit’s productivity depends on that of neighboring research units. The significant negative relationship that is found implies competition for priority of discovery among individual researchers, as well as the universities and research institutes that employ them. In addition, the empirical results support the hypotheses that collaboration and the existence of economies of scale increase research productivity.Research productivity, Competition, Collaboration, Negative spatial autocorrelation, Geo-referenced point data

A note on the linear, logit and probit functional form of the labour force participation rate equation

The commonly used specification in regional economic research on labour force participation is the linear probability function. An important alternative recommended in the Handbook of Regional and Urban Economics in the contribution of Isserman et al. (1986) on `Regional Labor Market Analysis' is the logit probability function. Their argument for the logit probability function is as follows. Given that economic theory on labour force participation does not suggest to pick one functional form over another and that the parameters of the logit probability function are estimable by OLS under the usual assumptions about the error term, the benefit of the logit probability function is that any estimated value for L lies within the logical bounds [0,1]. This feature is particularly desirable in a forecasting context when out of sample data might otherwise potentially yield absurd labour force participation rates. In this note two counter-arguments are gathered against using the logit probability function which are lacking in the Handbook of Regional and Urban Economics. Furthermore, it is shown that the logit probability function in this discourse can be replaced by the probit probability function equally well. Keywords: logit, probit, labour force participation rate