The Travel Cost Method for Valuing Recreational Fishing -- Issues of Sampling, Estimation, and WTP For Site Improvements (abstract)

Abstract only. The author refers readers to http://elsa.berkeley.edu/~mcfadden.This talk surveys methodological developments in the analysis of recreational fishing demand using the "travel cost method", in which the value of a fishing experience is inferred from the generalized travel costs incurred to reach desirable fishing sites. A first set of issues concern sampling anglers to obtain data on participation, avidity, and site selection, particularly the use of intercept surveys and panels recruited by intercept . A second set of issues deal with the specification and estimation of recreational fishing demand models, particularly the use of mixed multinomial logit models as a device for capturing the distribution of preferences for recreational fishing. The final set of issues concern the translation of estimated demand models into measures of willingness-to-pay (WTP) for improvements in fishing sites

The estimation of multivariate extreme value models from choice-based samples

We consider an estimation procedure for discrete choice models in general and Multivariate Extreme Value (MEV) models in particular. It is based on a pseudo-likelihood function, generalizing the Conditional Maximum Likelihood (CML) estimator by Manski and McFadden (1981) and theWeighted Exogenous Sample Maximum Likelihood (WESML) estimator by Manski and Lerman (1977). We show that the property of Multinomial Logit (MNL) models, that consistent estimates of all parameters but the constants can be obtained from an Exogenous Sample Maximum Likelihood (ESML) estimation, does not hold in general for MEV models. We propose a new estimator for the more general case. This new estimator estimates the selection bias directly from the data. We illustrate the new estimator on pseudo-synthetic and real data

The estimation of Generalized Extreme Value models from choice-based samples

We consider an estimation procedure for discrete choice models in general and Generalized Extreme Value (GEV) models in particular. It is based on a pseudo-likelihood function, generalizing the Conditional Maximum Likelihood (CML) estimator by Manski and McFadden (1981) and theWeighted Exogenous Sample Maximum Likelihood (WESML) estimator by Manski and Lerman (1977). We show that the property of Multinomial Logit (MNL) models, that consistent estimates of all parameters but the constants can be obtained from an Exogenous Sample Maximum Likelihood (ESML) estimation, does not hold in general for GEV models. We identify a specific class of GEV models with this desired property, and propose a new estimator for the more general case. This new estimator estimates the selection bias directly from the data. We illustrate the new estimator on pseudo-synthetic and real data

Trees Grow on Money: Urban Tree Canopy Cover and Environmental Justice

This study examines the distributional equity of urban tree canopy (UTC) cover for Baltimore, MD, Los Angeles, CA, New York, NY, Philadelphia, PA, Raleigh, NC, Sacramento, CA, and Washington, D.C. using high spatial resolution land cover data and census data. Data are analyzed at the Census Block Group levels using Spearman\u27s correlation, ordinary least squares regression (OLS), and a spatial autoregressive model (SAR). Across all cities there is a strong positive correlation between UTC cover and median household income. Negative correlations between race and UTC cover exist in bivariate models for some cities, but they are generally not observed using multivariate regressions that include additional variables on income, education, and housing age. SAR models result in higher r-square values compared to the OLS models across all cities, suggesting that spatial autocorrelation is an important feature of our data. Similarities among cities can be found based on shared characteristics of climate, race/ethnicity, and size. Our findings suggest that a suite of variables, including income, contribute to the distribution of UTC cover. These findings can help target simultaneous strategies for UTC goals and environmental justice concerns

Contract cheating & the market in essays

We conduct the first empirical economic investigation of the decision to cheat by University students. We investigate student demand for essays, using hypothetical discrete choice experiments in conjunction with consequential Holt-Laury gambles to derive subjects risk preferences. Students stated willingness to participate in the essay market, and their valuation of purchased essays, vary with the characteristics of student and institutional environment. Risk preferring students, those working in a non-native language, and those believing they will attain a lower grade are willing to pay more. Purchase likelihoods and essay valuations decline as the probability of detection and associated penalty increase

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47225/1/11002_2004_Article_138116.pd

The Fungal Cell Wall : Structure, Biosynthesis, and Function

N.G. is funded by the Wellcome Trust via a senior investigator award and a strategic award and by the MRC Centre for Medical Mycology. C.M. acknowledges the support of the Wellcome Trust and the MRC. N.G. and C.M. are part of the MRC Centre for Medical Mycology. J.P.L. acknowledges support from ANR, Aviesan, and FRM.Peer reviewedPublisher PD