14,431 research outputs found
Modeling the relation between income and commuting distance
We discuss the distribution of commuting distances and its relation to
income. Using data from Denmark, the UK, and the US, we show that the commuting
distance is (i) broadly distributed with a slow decaying tail that can be
fitted by a power law with exponent and (ii) an average
growing slowly as a power law with an exponent less than one that depends on
the country considered. The classical theory for job search is based on the
idea that workers evaluate the wage of potential jobs as they arrive
sequentially through time, and extending this model with space, we obtain
predictions that are strongly contradicted by our empirical findings. We
propose an alternative model that is based on the idea that workers evaluate
potential jobs based on a quality aspect and that workers search for jobs
sequentially across space. We also assume that the density of potential jobs
depends on the skills of the worker and decreases with the wage. The predicted
distribution of commuting distances decays as and is independent of
the distribution of the quality of jobs. We find our alternative model to be in
agreement with our data. This type of approach opens new perspectives for the
modeling of mobility.Comment: 9 pages, 3 figure
A TWO-STEP ESTIMATOR FOR A SPATIAL LAG MODEL OF COUNTS: THEORY, SMALL SAMPLE PERFORMANCE AND AN APPLICATION
Several spatial econometric approaches are available to model spatially correlated disturbances in count models, but there are at present no structurally consistent count models incorporating spatial lag autocorrelation. A two-step, limited information maximum likelihood estimator is proposed to fill this gap. The estimator is developed assuming a Poisson distribution, but can be extended to other count distributions. The small sample properties of the estimator are evaluated with Monte Carlo experiments. Simulation results suggest that the spatial lag count estimator achieves gains in terms of bias over the aspatial version as spatial lag autocorrelation and sample size increase. An empirical example deals with the location choice of single-unit start-up firms in the manufacturing industry in the US between 2000 and 2004. The empirical results suggest that in the dynamic process of firm formation, counties dominated by firms exhibiting (internal) increasing returns to scale are at a relative disadvantage even if localization economies are presentcount model, location choice, manufacturing, Poisson, spatial econometrics
Quantifying uncertainties on excursion sets under a Gaussian random field prior
We focus on the problem of estimating and quantifying uncertainties on the
excursion set of a function under a limited evaluation budget. We adopt a
Bayesian approach where the objective function is assumed to be a realization
of a Gaussian random field. In this setting, the posterior distribution on the
objective function gives rise to a posterior distribution on excursion sets.
Several approaches exist to summarize the distribution of such sets based on
random closed set theory. While the recently proposed Vorob'ev approach
exploits analytical formulae, further notions of variability require Monte
Carlo estimators relying on Gaussian random field conditional simulations. In
the present work we propose a method to choose Monte Carlo simulation points
and obtain quasi-realizations of the conditional field at fine designs through
affine predictors. The points are chosen optimally in the sense that they
minimize the posterior expected distance in measure between the excursion set
and its reconstruction. The proposed method reduces the computational costs due
to Monte Carlo simulations and enables the computation of quasi-realizations on
fine designs in large dimensions. We apply this reconstruction approach to
obtain realizations of an excursion set on a fine grid which allow us to give a
new measure of uncertainty based on the distance transform of the excursion
set. Finally we present a safety engineering test case where the simulation
method is employed to compute a Monte Carlo estimate of a contour line
Spatial evolution of the US urban system.
We examine spatial features of the evolution of the US urban system usingUS Census data for 1900 â 1990 with non-parametric kernel estimation techniques that accommodate the complexity of the urban system. We consider spatial features of the location of cities and city outcomes in terms of population and wages. Our results suggest a number of interesting puzzles. In particular, we find that city location is essentially a random process and that interactions between cities do not help determine the size of a city. Both of these findings contradict our theoretical priors about the role of geography (physical and economic) in determining city outcomes. More detailed study suggests some solutions that allow us to restore a role for geography but a number of puzzles remain.
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