Random Utility/Multinomial Logit Model Literature
Amemiya, Takeshi. 1977. “On a Two-Step Estimation of a Multivariate Logit Model.” Journalof Econometrics, Volume: 8, Issue: 1978, Pages: 13-21. Keywords: two-step estimate,logit models, asymptotically variance.
Bockstael,-Nancy-E.; McConnell,-K.-E.; Strand,-I.-E. “Random Utility Model for Sportfishing:Some Preliminary Results for Florida.” Marine-Resource-Economics; 6(3), 1989, pages
245-60.
The gray literature in the field of nonmarket benefit measurement has made extensive useof the random utility (or discrete choice) model in recent years, but few applications appear in theliterature. This article provides such an application, illustrating the technique with preliminary
results from a regional study modeling east cost sportfishing behavior. The article discusses someof the strengths and weaknesses of the random utility model. It also illustrates how data regularlycollected by the National Marine Fisheries Service can be supplemented with economic survey
data to estimate these discrete choice behavioral models.
Bockstael,-Nancy-E.; Hanemann,-W.-Michael; Kling,-Catherine-L. “Estimating the Value of
Water Quality Improvements in a Recreational Demand Framework.”
Water-Resources-Research; 23(5), May 1987, pages 951-60.
Cameron,-Trudy-Ann; Englin,-Jeffrey. “Welfare Effects of Changes in Environmental Quality
under Individual Uncertainty about Use.” RAND-Journal-of-Economics; 28(0), Special
Issue 1997, pages S45-70.
The authors adapt the theoretical state-preference model to value nonmarket public goods
under individual uncertainty about use, illustrating with an assessment of willingness-to-pay toprevent acid rain lake damage in the northeast United States. Individual usage uncertainty ismodeled via probabilities of participation in trout fishing. Changes in environmental quality arevalued using a random utility model to explain yes/no responses to a contingent valuationquestion. The authors produce quantitative welfare measures: individual fitted and simulated
passive- and active-use values, individual expected consumer surplus, option price, option value,and complete individual willingness-to-pay loci.
Clark,-Stephen-A. “The Random Utility Model with an Infinite Choice Space.”
Economic-Theory; 7(1), January 1996, pages 179-89.
This essay presents a measure-theoretic version of the random utility model with nosubstantive restrictions upon the choice space. The analysis is based upon DeFinetti's Coherency
Axiom, which characterizes a set function as a finitely additive probability measure. The central
result is the equivalence of the random utility maximization hypothesis and the coherency of the
choice probabilities over all allowable constraint sets.
Cropper, Maureen L.; Leland Deck; Nalin Kishor; Kenneth McConnell. 1993. “Valuing ProductAttributes Using Single Market Data: A Comparison of Hedonic and Discrete ChoiceApproaches.” The Review of Economics and Statistics, Volume: 75, Issue: 2, Pages:
225-232. Keywords: multinomial logit model, hedonic model, welfare evaluation.
The paper compares estimates of welfare measures derived from a hedonic price modelwith those from the random utility model for given known household preferences. After
simulating an equilibrium in an Baltimore housing market, the paper uses the resulting hedonic
price and individual choice data to estimate both a hedonic price model with its marginal bid
functions and a random utility model. The random utiltiy model provided more accurate
estimates of the known welfare measure than the hedonic price model for non-marginal changes inhousing attributes.
Feather,-Peter-M. “Sampling and Aggregation Issues in Random Utility Model Estimation.”
American-Journal-of-Agricultural-Economics; 76(4), November 1994, pages 772-80.
Measurement of nonmarket values often involves subjective judgments. Since thesejudgments may influence results, they should be carefully considered. The author focuses on an
aspect of subjective choice relating to the estimation of random utility models. Such models
require specification of each recreationalist's choice set. Whether an individual perceives his
choice set as composed of all possible alternatives, a few popular alternatives, or collections ofspatially aggregated alternatives is an important judgment affecting the conclusions.
Greene, Gretchen, Charles B. Moss, and Thomas H. Spreen (1997). "The Demand for
Recreational Fishing in Tampa Bay, Florida: A Random Utility Approach."Marine Resource Economics, forthcoming, May.
An estimation of demand for recreational fishing in Tampa Bay, Florida, can facilitate theenvironmental management of the bay. A nested random utility travel cost model is used toestimate access values. Results suggest average annual values for the bay alone are $18.14 and$0.048 for participants and nonparticipants, respectively.
Hanemann, W. Michael (1984). "Discrete/Continuous Models of Consumer Demand."Econometrica, 52(3):541-561.
This paper develops a unified framework for formulating econometric models of
discrete/continuous consumer choices in which the discrete and continuous choices both flowfrom the same underlying (random)utility maximization decision. As a special case a number ofmodels suitable for empirical application are developed where the discrete choice is among
different f=brands of a commodity. Since these brands are essentially substitutes, the consumer
prefers to buy only one brand at any time; the discrete choice is which brand to select and thecontinuous choice is how many units to buy.
Hausman, J.A. and David A. Wise. 1978. “A Conditional Probit Model for Qualitative Choice:Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences."
Econometrica. 46:403-426.

Hausman, Jerry A. and Daniel McFadden (1984). "Specification Tests for the Multinomial
Logit Model." Econometrica, 52(5):1219-1240.
Discrete choice models are now used in a variety of situations in applied econometrics.
By far the model specification that is used most often is the multinomial logit model. Yet it is
widely known that a potentially important drawback of the multinomial logit model is theindependence from irrelevant alternatives property. While most analysts recognize theimplications of the independence of irrelevant alternatives property, it has remained basically amaintained assumption in applications.
In this paper we provide two sets of computationally convenient specification tests for themultinomial logit model. The first test is an application of the Hausman (1978) specification test
procedure. The basic idea for the test here is to test the reverse implication of the independence
from irrelevant alternatives property. The test statistic is easy to compute sine it only requires
computation of a quadratic form that involves the difference of the parameter estimates and thedifferences of the estimated covariance matrices.
The second set of specification tests that we propose is based on more classical test
procedures. We consider a generalization of the multinomial logit model that is called the nested
logit model. Since the multinomial logit model is a special case of the more general model when agiven parameter equals one, classical test procedures such as the Wald, likelihood ratio, andLagrange multiplier tests can be used.
The two sets of specification test procedures are then compared for an example whereexact and approximate comparisons are possible.
Horowitz, Joel L. ; Denis Bolduc; Suresh Divakar; John Geweke; Fusun Gonul; Vassilis
Hajivassiliou; Frank Koppelman; Michael Keane; Rosa Matzkin; Peter Rossi; Paul Ruud.
1994. “Advances in Random Utility Models.” Marketing Letters. Keywords: Conjoint,
Stated Choice.
Special Issue On Workshop Reports From the Duke Invitation Conference on Consumer
Decision Making and Choice Behavior.
Kaoru, Y. 1988. "A Discrete Choice Benefit Analysis of Marine Recreational Fishing: Does Site
Definition Matter?" Unpublished mimeo. Marine Policy Center, Woods Hole, MA. 35 pp.
Kaoru,-Yoshiaki; Smith,-V.-Kerry; Liu,-Jin-Long. “Using Random Utility Models to Estimate
the Recreational Value of Estuarine Resources.”
American-Journal-of-Agricultural-Economics; 77(1), February 1995, pages 141-51.
In this paper, the authors describe a model using a household production framework tolink measures of nonpoint source pollution to fishing quality and a random utility model todescribe how that quality influences sport fishing parties' decisions in North Carolina. The results
provide clear support for using a model that evaluates the effects of pollution on the activities anddecisions associated with the fishing activity once a trip is taken. Site selection decisions are thenconditioned on the anticipated quality of fishing sites. The framework also has the advantage oflinking the spatial, technical, and economic information required to evaluate the management
plans required for estuaries under the National Estuarine Program.
Kaoru,-Yoshiaki. “Measuring Marine Recreation Benefits of Water Quality Improvements by the
Nested Random Utility Model.” Resource-and-Energy-Economics; 17(2), August 1995,
pages 119-36.
This paper discusses and evaluates three issues to which the analyst has not paid muchattention when describing recreational site choices and implementing the nested random utility
model (RUM) for recreation benefit measurement: (1) the effects of party composition onrecreational decisions; (2) the implications of estimating a nested RUM on the functional structureof the underlying indirect utility function; and (3) the variation in benefit estimates across quality
improvement scenarios. A three-level nested RUM is estimated and the benefits of water quality
improvements are measured for marine recreational fishing in the Albemarle-Pamlico Estuary ofNorth Carolina.
Kaoru,-Yoshiaki; Smith,-V.-Kerry; Liu,-Jin-Long. “Using Random Utility Models to Estimate
the Recreational Value of Estuarine Resources.” Resources for the Future Discussion
Paper: 94-04, December 1993, pages 29.
This paper describes an integrated model using a household production framework tolink measures of nonpoint source pollution to fishing quality and a random utility model todescribe how that quality influences the decisions of sport fishing parties in the
Albemarle-Pamlico estuarine system in North Carolina. The results provide clear support for
using a model that evaluates the effects of pollution on the activities and decisions associatedwith the fishing activity once a trip is taken. Site selection decisions are then conditioned on the
anticipated quality of fishing trips at each site. The framework also has the advantage of linking
the spatial, technical, and economic information required to evaluate the management plans
required for estuaries under the National Estuarine Program.
Kaoru, Yoshiaki ; V. Kerry Smith. 1990. “"Black Mayonnaise" and Marine Recreation:
Methodological Issues in Valuing a Cleanup.” Resources for the Future, Octoberr 1990.
Keywords: random utility model, marine recreation, benefits of pollution control.
Kling,-Catherine-L. “The Reliability of Estimates of Environmental Benefits from Recreation
Demand Models.” American-Journal-of-Agricultural-Economics; 70(4), November 1988,
pages 892-901.
Recreation demand models are commonly-employ ed tools of economists interested in
valuing improvements in environmental amenities. Despite their importance, little comparative
work has been undertaken to examine the ability of the models to accurately estimate welfarechanges. A simulation study designed to compare the reliability of estimated welfare measures(compensating variation and consumer surplus) from several commonly-employed recreation
demand models is presented. Results of the study indicate that choice of functional form andmodel specification are important determinants of the result ing estimates of benefits.

Kling, Catherine L. and Joseph A. Herriges (1995). "An Empirical Investigation of theConsistency of Nested Logit Models with Utility Maximization." American
Journal of Agricultural Economics, 77(4):875-884.
Global conditions under which nested logit models are consistent with utility maximization
are provided by Daly and Zachary and by McFadden. Recently, Borsch-Supan and Herriges andKling have provided conditions under which a nested logit model is locally consistent. However,previous work has not discussed implementation of these conditions. Here, three alternative
approachers to checking and formally testing for the consistency conditions using classicalstatistics are investigated. In addition, a Bayesian approach to interpreting and imposing the local
consistency conditions is provided. The application is based on anglers’ choices regarding
sportfishing alternatives in southern California.
Lin,-Pei-Chien; Adams,-Richard-M.; Berrens,-Robert-P. “Welfare Effects of Fishery Policies:
Native American Treaty Rights and Recreational Salmon Fishing.”
Journal-of-Agricultural-and-Resource-Economics; 21(2), December 1996, pages 263-76.
Severe declines in Pacific Northwest salmon stocks, coupled with increasing recreational
demands, and judicial decisions supporting Native American fishing rights create challenges forfishery agencies. This article explores the welfare effects on recreational anglers of alternative
salmon allocation policies to meet Native American treaty rights. A discrete choice random utility
model, coupled with a poisson trip frequency model, is used to analyze these welfare effects. The
model is fit to survey data from the Willamette River spring chinook fishery, an important
recreational fishery in Oregon. Management options have dramatically different welfare effects.
McConnell,-K.-E. “Consumer Surplus from Discrete Choice Models.”
Journal-of-Environmental-Economics-and-Management; 29(3), Part 1 Nov. 1995, pages
263-70.
The budget-constrained random utility model (RUM) gives utility-consistent measures of
welfare, but requires the length of the planning period be specified. An alternative is to treat the
RUM probabilities as behavioral and calculate consumer surplus. This paper shows that suchcalculations lead to the same welfare measures as RUM calculations. The paper provides support
and an alternative justification for the standard welfare measured. (c) 1995 Academic Press, Inc.
McConnell,-Kenneth-E.; Strand,-Ivar-E.; Blake-Hedges,-Lynne. “Random Utility Models of
Recreational Fishing: Catching Fish Using a Poisson Process.”
Marine-Resource-Economics; 10(3), Fall 1995, pages 247-61.
This paper presents a Poisson model of expected angler catch during a sportfishing trip
and employs the expected catch in a random utility model of site choice. The approach permits
greater heterogeneity in expected catch and in individual welfare estimates from policies such as
creel limits.
McFadden, D. 1978. "Modelling the Choice of Residential Location." Spatial Interaction

Theory and Planning Models. ed. A. Karlquist, L. Lundquist, F. Snickars and J.L.
Weibull. Amsterdam: North Holland.
McFadden, Daniel (1974). "Conditional Logit Analysis of Qualitative Choice Behavior."
In Zarembka, Paul (ed.) (1974). Frontiers in Econometrics. Academic Press, NewYork.
This paper outlines a general procedure for formulating econometric models of population
choice behavior from distributions of individual decision rules. A concrete case with usefulempirical properties, conditional logit analysis, is developed in detail. The relevance of thesemethods to economic analysis can be indicated by a list of the consumer choice problems to which
conditional logit analysis has been applied: choice of college attended, choice of occupation, labor
force participation, choice of geographical location and migration, choice of number of children,
housing choice, choice of number and brand of automobiles owned, choice of shopping travel
model and destination.
McFadden, Daniel (1987). "Regression-Based Specification Tests for the Multinomial
Logit Model." Journal of Econometrics, 34:63-82.
Diagnostic tests for omitted variables or functional misspecification in the multinomial
logit (MNL) model can be performed conveniently by testing the significance of auxiliary
regressions of residuals on included and excluded variables. In particular, Lagrange Multiplier(LM) and Hausman-McFadden (HM) tests of the Independence from Irrelevant Alternatives (IIA)
Property on the MNL model can be carried out by the regression method using suitably defined
excluded variables. Using this method, it is straightforward to test jointly against several sourcesof misspecification and to diagnose the structure of deviations from the MNL model.
Milon,-J.-Walter. “A Nested Demand Shares Model of Artificial Marine Habitat Choice by Sport
Anglers.” Marine-Resource-Economics; 5(3), 1988, pages 191-213.
There is a growing public interest in the development of artificial habitats to enhance anddiversify coastal marine resources for recreational and commercial uses. In this article, ahierarchical discrete choice model of recreational demand for artificial habitat is presented using anested multinomial logit analysis of artificial and natural habitat site choice by sport anglers. The
model can be used to evaluate the effects of site characteristics and socioeconomic attributes ofindividual sport benefits of new artificial habitat. An empirical application using survey data fromsport anglers in southeast Florida is reported. The model parameters are used to estimate theexpected use benefits and distributional implications of alternative new artificial habitat sites.
Extensions and limitations of the model for artificial habitat planning are considered.
Morey, Edward R.; Donald Waldman; Djeto Assane; Douglas Shaw. 1995. “Searching for a
Model of Multiple-Site Recreation Demand that Admits Interior and Boundary Solutions.”
American Journal of Agricultural Economics, Volume: 77, Issue: 1, Pages: 129-140.
Keywords: travel cost, repeated nested-logit model, multinomial share model, KuhnTucker model.

This paper critiques eight recreation demand models in terms of their ability toaccommodate boundary solutions. Three models are recommended for consideration when there
are multiple sites and the data set includes a significant number of boundary solutions: a repeatednested-logit model, a multinomial share model, and a Kuhn-Tucker model.
Morey,-Edward-R.; Shaw,-W.-Douglass; Rowe,-Robert-D. “A Discrete-Choice Model of
Recreational Participation, Site Choice, and Activity Valuation When Complete Trip Data
Are Not Available.” Journal-of-Environmental-Economics-and-Management; 20(2),March 1991, pages 181-201.
A discrete-choice model of the demand for site-specific recreational activities is developedand estimated. It simultaneously predicts both how many trips the individual will take and whichsite will be chosen on each trip. The model is formulated to estimate demand when the data set
reports the total number of trips in a given time period, but the actual destinations for only asubset of the total. The model also includes a correction for sample-selectivity bias. The
application is marine recreational fishing. The consumer's surplus associated with any change insupply conditions is derived and used to assess the impact of chantes in species availability.
Morey, E.R., R.D. Rowe and D. Shaw. l987. "The Logit Model and Expected Consumer's
Surplus Measures: Valuing Marine Recreational Fishing.'' Unpublished mimeo.
Department of Economics, University of Colorado Boulder, CO. 31 pp.
Morey, Edward R.; Robert D. Rowe; Michael Watson. 1993. “A Repeated Nested-Logit Modelof Atlantic Salmon Fishing.” American Journal of Agricultural Economics, Volume: 75,
Issue: 3, Pages: 579-592. Keywords: Atlantic salmon fishing, nested-logit, travel costmodel.
Participation and site choice for Atlantic salmon fishing are modeled in the context of arepeated three-level nested-logit model. For comparison, six other travel-cost models are
estimated. These include restrictive cases of the nested-logit model, a partial demand model, andtwo single-site demand models.
Morey, Edward R. 1994. “Two RUMs Uncloaked: Nested-Logit Models of Site Choice and
Nested-Logit Models of Participation and Site Choice.” March 24, 1994, Monograph.
Keywords: Travel Cost Method
This paper lays out the theory behind the nested-logit model of site choice and the nestedlogit model of participation and site choice; and then provides derivations of the properties ofnested-logit models. Examples are used to tie the theory to recreation demand and benefit
estimation.
Needelman, Michael S.; Mary Jo Kealy. 1995. “Recreational Swimming Benefits of New
Hampshire Lake Water Quality Policies: An Application of a Repeated Discrete Choice
Model.” Agricultural and Resource Economics Review, April 1995, pages: 78-87.

Parsons,-George-R.; Hauber,-A.-Brett. “Choice Set Boundaries in a Random Utility Model of
Recreation Demand.” University of Delaware, Department of Economics, Working Paper
96/3, March 1996, pages 11.
Random Utility Models are commonly used the recreation demand literature to model the
choice among a set of qualitatively different recreation sites. Often, due to computational or data
constraints, analysts do not include all possible alternatives in the choice set. This research isconcerned with choice set boundaries. In particular, we ask, "how far away from a person'shome must a site be before it is no longer considered part of the choice set in estimation?" Weexamine this question by estimating a single model with varying choice set boundaries using adata set of recreational fishing activity in Maine. In this analysis, we show that there exists somethreshold distance beyond which adding additional sites to the choice set has negligible effects
on the estimation results.
Parsons,-George-R.; Needelman,-Michael-S. “Site Aggregation in a Random Utility Model of
Recreation.” Land-Economics; 68(4), November 1992, pages 418-33.
Random utility models are commonly used to model the choice among a set ofalternatives. Often, due to data or computational constraints, the analyst must use aggregatedalternatives to estimate the model. These aggregates are defined by averaging characteristics ofalternatives over prespecified groups. In this analysis, the authors demonstrate that unless somevery restrictive conditions hold, the use of aggregated alternatives will lead to biased results.
Then, using a data set of recreational fishing in Wisconsin, they show the effects that this bias canhave on the results estimated from the model.
Parsons,-George-R.; Kealy,-Mary-Jo. “Randomly Drawn Opportunity Sets in a Random Utility
Model of Lake Recreation.” Land-Economics; 68(1), February 1992, pages 93-106.
Random utility models are widely applied in studies of recreation demand. The model isparticularly useful when the number of recreation sites from which individuals may choose islarge. Yet, when the number gets too large, say in the hundreds, estimation becomesburdensome. The authors present an analysis suggested by Daniel McFadden (1978) for dealing
with large numbers of sites. They estimate a model using randomly drawn opportunity sets. The
authors use each person's chosen site plus a random draw of as few as eleven other sites (whenhundreds are available) to estimate a plausible behavioral model.
Parsons,-George-R.; Kealy,-Mary-Jo. “A Demand Theory for Number of Trips in a Random
Utility Model of Recreation.” Journal-of-Environmental-Economics-and-Management;
29(3), Part 1 Nov. 1995, pages 357-67.
We present a random utility model of recreation site choice that incorporates an aggregate
demand function for number of trips during a season. We derive the trip demand function using
conventional demand theory and use it to calculate seasonal welfare changes due to improvements
in site characteristics or addition of new sites. The model is based on Bockstael, et al.'s
participation function. (c) 1995 Academic Press, Inc.

Parsons,-George-R.; Kealy,-Mary-Jo. “A Demand Theory for Number of Trips in A Random
Utility Model of Recreation.” University of Delaware Department of Economics Working
Paper 94-1, January 1994, pages 13.
We present a simple Random Utility Model of recreation site choice that incorporates anaggregate demand function for number of trips during a season. We derive the trip demand
function using conventional demand theory and use it to calculate seasonal welfare changes due
to improvements in site characteristics or addition of new sites. The model is based on Bockstael,
Hanemann, and Kling's participation function.
Parsons,-George-R.; Kealy,-Mary-Jo. “Benefits Transfer in a Random Utility Model of
Recreation.” Water-Resources-Research; 30(8), August 1994, pages 2477-84.
Parsons,-George-R.; Kealy,-Mary-Jo. “Benefits Transfer in a Random Utility Model of
Recreation.” University of Delaware Department of Economics Working Paper 93-17,
December 1993, pages 16.
Time and other resource constraints often call for benefit-cost analyses of environmental
policies that rely on models from existing studies. When such analyses are done the benefit orcost assessment is said to be "transferred" from an existing study to a policy site. Little
economic analysis has been done to judge the viability of such transfers. This is unfortunate
given their widespread use. In this paper we present results of an experiment designed to help
judge the viability of transferring a Random Utility-Model (RUM) of recreation. Our experimentfollows the design of Atherton and Ben-Akiva(1976). We analyze a RUM of lake recreation in
the state of Wisconsin. The model is based on the survey results of 603 individuals who visitedlakes in the state. We divide the data set into two groups: (1) respondents living in Milwaukee
County and (2) respondents not living in Milwaukee County. Then we transfer a model
estimated with the non-Milwaukee sample to the Milwaukee sample. Our (hypothetical)
purpose throughout is to estimate the benefits of improving water quality for the 117 Milwaukee
residents. Since we have the same information for individuals in this sample as we have in the
non-Milwaukee sample, we have a means of judging the viability of transfer.
Schulze, William; McClelland, Gary; Doane, Michael; Balisteri, Ed; Boyce, Rebecca; Hurd, Brian;
Simenauer, Ronald. 1995. “An Analysis of Stated Preferences for Superfund Site
Cleanup.” Department of Agricultural, Resource, and Managerial Economics, Cornell
University. February 1995. Keywords: Conjoint Analysis.
This paper uses a random utility model of discrete choice to estimate the willingness topay for alternative cleanup options at superfund sites.
Small, K.A. and H.S. Rosen. 1981. “Applied Welfare Economics with Discrete Choice Models.”
Econometrica. 49: 105-130.
Swait, Joffre; Louviere, Jordan. 1993. “The Role of the Scale Parameter in the Estimation andComparison of Multinomial Logit Models.” Journal of Marketing Research, volume: 30,

Pages: 305-314. Keywords: conjoint, stated choice methods.
If two sources of data (revealed preference and stated preference) are the outcome of acommon choice process which differs only in the variability of the random component, utility
parameters estimated from each data source should be proportional. This technical article shows
how to estimate the ratio of the scale units in two or more data sets, and proposes a method torescale one set of data against the second and test for a common choice process.
Swait, Joffre; Wiktor Adamowicz. 1997. “The Effect of Choice Complexity on Random Utility
Models: An Application to Combined Stated and Revealed Preference Models.” Working
Paper, August 27, 1997. Keywords: conjoint analysis, choice complexity, random utility
models, combined stated and revealed preference models.
This paper presents ana analysis of context effects on choice. Task demands arecharacterized and incorporated into random utility models of choice. Results indicate that taskcomplexity significantly affects the variance of choice.
Thomson, C. l988. "Modelling Recreational Angler Decisions Regarding Fishing Area and Modein the Context of a Nested Logit Model." Unpublished mimeo. National Marine FisheriesService, La Jolla. CA. 18 pp.
Wegge, T.C., R.T. Carson and W.M. Hanemann. 1988. "Site Quality and the Demand forSportfishing in South Central Alaska." Unpublished mimeo. Jones and Stokes Associates
Inc. Sacramento, CA. 17 pp.
Wills,-Hugh. “A Note on Specification Tests for the Multinomial Logit Model.”Journal-of-Econometrics; 34(1/2), Jan.-Feb. 1987, pages 263-74.
Choice probabilities generated by D. McFadden's (1973) random utility model are both
multinomial logit and have the Independence of Irrelevant Alternatives (IIA) property. Various
authors have suggested specification tests of this property. This paper identifies the alternative
against which these tests are constructed and obtains classical specification tests. It is shown thatboth the null hypothesis and local asymptotic power of the classical tests is the same as for a
proposed Hausman test. Finally the discussion throws some light on the nature of the IIA
property and on the performance of the logit model in applications where one would expect the
IIA property to be violated but where the alternative set is fixed.