Independence Of Irrelevant Alternatives Assumption Research Articles

Testing classic theories of migration in the lab

We test different classic migration theories by using incentivized laboratory experiments to investigate how potential migrants decide between working in different destinations. We assess theories of income maximization, skill-selection, and multi-destination choice, as we vary migration costs, risk, social benefits, and incomplete information. The standard income maximization model leads to a much higher migration rate and more negative skill-selection than occurs when migration decisions take place under more realistic assumptions. The independence of irrelevant alternatives assumption mostly holds when decisions just involve wages and moving costs, but breaks down once we add risk and incomplete information. Cost-minimization, rather than income maximization, seems to be key in some migration decisions. The deviations found in our study between migration decisions and the predictions of classic migration theories call for richer models of migration to explain global migration patterns.

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Introduction: Among the various available tools in the field of natural resources and environmental management, the payment for ecosystem services (PES) is one of the market-based methods that is considered worldwide to protect the environment and ecosystem. PES is an important method for effective management of natural resources and public goods and one of the tools for managing degraded ecosystems and related environmental and economic services. Considering that Sefidrood is considered as the most important and valuable source of agricultural water supply and aquatic environment in Guilan province, and also the water quality of this important river is in a bad and very bad condition, this study was conducted using PES economic tools through payments by rice consumers in Guilan province to rice farmers and thus encouraging them to take environmentally friendly measures (organic agriculture) to reduce pollution of the Sefidrood River. Materials and Methods: This research was conducted using a choice experiment method. In our CE, each PES alternative is described by a set of attributes that include distribution of payments, contract duration, implementing organization, monitoring times, possibility to cancel and payments. First, to investigate the effect of different attributes of PES scheme on rice consumers' willingness to pay and their marginal utility, a conditional logit model was used to compare the results of random parameter logit model and latent class models with a base model. Then, the RPL and LC model was used to further investigate the invisible heterogeneity that exists in the behavior of respondents. The RPL model is an advanced model that allows attributes coefficients to change randomly among respondents. Therefore, instead of estimating a fixed coefficient for each attribute, two coefficients are estimated, which together describe the distribution of heterogeneous preferences of the respondents for this attribute. Results and Discussion: To confirm the CL model, the independence of irrelevant alternatives assumption was performed using the Hausman-McFadden test. Given that the value of chi-square statistics has become large and significant, therefore, the CL model is not suitable for investigating the effect of attributes on consumer’s willingness to pay, and more advanced models should be used. For this reason, RPL and LC models are estimated. According to the results of the RPL model, the highest willingness to pay is related to the monitoring times therefor indicating that consumers are willing to pay 1347 Tomans for more monitoring. The amount of willingness to pay for the duration of contract and distribution of payments is equal to 1326 and 914 Tomans, respectively, which indicates if the contracts are short-time and also more payments are made to low-income rice farmers, the willingness to pay will increase to 1326 and 914 Tomans, respectively. Based on the results of the LC model, in the first class, except for the contract duration, all other attributes were not statistically significant. In the second class, the distribution of payments, the contract duration and the monitoring times with a positive sign and the implementing organization with a negative sign are significant. Class membership coefficients for organic rice consumers indicate that the likelihood of being in second class depends significantly on the respondents' age, gender, and level of education. Conclusion: The results of RPL and LC models confirm the existence of heterogeneity in the preferences of organic rice consumers. Therefore, appropriate methods can be used to differentiate organic products and thus improve the utility of consuming these products. Consumers were also more inclined to have a short-time and high monitoring scheme, this result is not unexpected due to the novelty of the scheme. Therefore, it is recommended to start short-time schemes with high monitoring. Consumers also tended to make more payments to low-income rice farmers, so it is recommended that lower-income rice farmers be given priority in implementing the PES scheme. The results of both model showed that the distribution of payments and monitoring times had the highest priority for consumers in choosing the PES scheme, respectively. Therefore, in order to increase the participation of consumers in such schemes, it is recommended to include these attributes in the schemes. Also, although PES is not designed as a tool to reduce poverty, it can increase the incomes of low-income rice farmers and help their livelihoods. Given that such schemes have not yet been implemented in Iran, it is suggested that in order to increase consumer participation, various levels of attributes should be provided to the respondents.

The role of local labour market conditions and pupil attainment on post-compulsory schooling decisions

Purpose The purpose of this paper is to assess the role of local labour market conditions and pupil educational attainment as primary determinants of the post-compulsory schooling decision. Design/methodology/approach Through the specification of a nested logit model, the restrictive independence of irrelevant alternatives (IIA) assumption inherent in the multinomial logit (MNL) model is relaxed across multiple unordered outcomes. Findings The analysis shows that the factors influencing schooling decisions differ for males and females. For females, on average, the key drivers of the schooling decision are expected wage returns based on youth educational attainment, attitudes to school and parental aspirations, rather than local labour market conditions. For males, higher local unemployment rates encourage greater investment in education. Originality/value The contribution of this paper to the existing literature is threefold. First, a nested logit model is proposed as an alternative to a MNL. The former can formally incorporate the structured and sequential decision-making process that youths may engage with in relation to the post-compulsory schooling decision, as well as relaxing the restrictive IIA assumption inherent in the MNL across multiple unordered outcomes, an issue the authors discuss in more detail in the Methodology section below. Second, the analysis is based on extremely rich socio-economic data from the Longitudinal Study of Young People in England, matched to local labour market data and administrative data from the National Pupil Database and Pupil Level Annual School Census, which provide a broad set of unusually high-quality measures of prior attainment. The authors argue that such high-quality data and an appropriate model specification allows identification of the determinants of the post-compulsory decision in a more detailed manner than many previous analyses. Third, the data have the scale necessary to consider whether the determinants of post-compulsory schooling decisions vary by gender, a particularly important issue given the differential education participation rates of males and females (e.g. in this cohort, females are about 10 percentage points more likely to go on to higher education in the UK than males), and the gendered choices of occupation (see, e.g. Bertrand, 2011). The work will, therefore, provide recent empirical evidence from England on gender differences in the determinants of education choices.

Preference Aggregation Functionals on Rich Admissible Sets and Independence of Irrelevant Alternatives

In this paper we present some results for preference aggregation functionals defined on rich admissible sets. Our results are concerned with an Independence of Irrelevant Alternatives assumption due to Alan D. Taylor suitably adjusted to be applicable in the framework of preference aggregation functionals. The initial step towards this adjustment is a version of Taylor’s property due to Prasanta Pattanaik. We obtain several results which all point to its non-compatibility with a combination of other well-known assumptions that are often invoked in the literature concerning aggregation of preferences. A novel feature of our analysis is the mild requirement on the admissible set of utility profiles on which the preference aggregation functionals are defined for several of our results.

A Bivariate Multinomial Probit Model for Trip Scheduling: Bayesian Analysis of the Work Tour

As tour-based methods for activity and travel participation patterns replace trip-based methods, time-of-day (TOD) choice modeling remains problematic. In practice, most travel demand model systems handle tour scheduling via joint-choice multinomial logit (MNL) models, which suffer from the well-known independence of irrelevant alternatives assumption. This paper introduces a random utility maximization model of tour scheduling called the bivariate multinomial probit. This specification enables correlations across TOD alternatives, both outbound and return (on a tour) and over time slots (in a day). The model is estimated in a Bayesian setting on work-tour data from the San Francisco Bay Area with 30-minute time slots at most times of day (for both outbound and inbound journeys). Empirical results suggest that a variety of individual, household, and tour characteristics have reasonable effects on scheduling behavior. For instance, older persons typically pursue work tours at earlier times of day, part-time workers pursue their work tours later, and those with additional activities and tours tend to arrive slightly later and leave much earlier than those undertaking only a single tour, everything else constant. The model outperforms a comparable MNL, while offering reasonable implications under a variety of road-tolling scenarios.

New Empirical Strategies for the Study of Parliamentary Government Formation

In recent years, a consensus has developed that the conditional logit (CL) model is the most appropriate strategy for modeling government choice. In this paper, we reconsider this approach and make three methodological contributions. First, we employ a mixed logit with random coefficients that allows us to take account of unobserved heterogeneity in the government formation process and relax the independence of irrelevant alternatives (IIA) assumption. Second, we demonstrate that the procedure used in the literature to test the IIA assumption is biased against finding IIA violations. An improved testing procedure reveals clear evidence of IIA violations, indicating that the CL model is inappropriate. Third, we move beyond simply presenting the sign and significance of model coefficients, suggesting various strategies for interpreting the substantive influence of variables in models of government choice.

Are there Gender-Specific Preferences for Location Factors? A Grouped Conditional Logit-Model of Interregional Migration Flows in Germany

The article analyses the question of whether women and men differ in their tastes for location factors. The question is answered by quantifying the impact of location characteristics on interregional migration flows across Germany. The analysis is based on a grouped conditional logit approach. We augment the framework by controlling for violation of the independence of irrelevant alternatives assumption and for overdispersion. As a result we find no differences in terms of direction of impact; however, the regressions confirm gender differences, mostly in terms of intensity, regarding regional wage levels and the availability of educational institutions. Moreover, even after controlling for place attributes women seem to be more migratory than men. <bold>Zusammenfassung</bold> Die vorliegende Arbeit untersucht geschlechtsspezifische Unterschiede in der Wertschätzung regionaler Standortfaktoren. Die Standortpräferenzen werden auf Basis einer Analyse von interregionalen Wanderungsströmen innerhalb Deutschlands analysiert. Die Untersuchung basiert auf einem Grouped Conditional Logit-Ansatz, wobei die Probleme der Unabhängigkeit von irrelevanten Alternativen und der Überdispersion berücksichtigt werden. Im Ergebnis zeigt sich zunächst, dass bei Frauen und Männern dieselben regionalen Charakteristika als Pull- oder aber als Push-Faktoren wirken. Geschlechtsspezifische Unterschiede werden aber hinsichtlich Stärke des Einflusses der einzelnen Faktoren sichtbar, vor allem im Hinblick auf das regionale Lohnniveau und das Angebot an Bildungseinrichtungen. Darüber hinaus zeigt sich, dass Frauen auch nach Kontrolle von Standortfaktoren mobiler sind als Männer. <italic>JEL Classification: C25, J61, J16, R23</italic> Received: August 20, 2009 Accepted: March 2, 2010

A Bayesian mixed logit–probit model for multinomial choice

In this paper we introduce a new flexible mixed model for multinomial discrete choice where the key individual- and alternative-specific parameters of interest are allowed to follow an assumption- free nonparametric density specification while other alternative-specific coefficients are assumed to be drawn from a multivariate normal distribution which eliminates the independence of irrelevant alternatives assumption at the individual level. A hierarchical specification of our model allows us to break down a complex data structure into a set of submodels with the desired features that are naturally assembled in the original system. We estimate the model using a Bayesian Markov Chain Monte Carlo technique with a multivariate Dirichlet Process (DP) prior on the coefficients with nonparametrically estimated density. We employ a latent sampling algorithm which is applicable to a general class of models including non-conjugate DP base priors. The model is applied to supermarket choices of a panel of Houston households whose shopping behavior was observed over a 24-month period in years 2004-2005. We estimate the nonparametric density of two key variables of interest: the price of a basket of goods based on scanner data, and driving distance to the supermarket based on their respective locations. Our semi-parametric approach allows us to identify a complex multi-modal preference distribution which distinguishes between inframarginal consumers and consumers who strongly value either lower prices or shopping convenience.

Testing for IIA in the Multinomial Logit Model

The multinomial logit model is perhaps the most commonly used regression model for nominal outcomes in the social sciences. A concern raised by many researchers, however, is the assumption of the independence of irrelevant alternatives (IIA) that is implicit in the model. In this article, the authors undertake a series of Monte Carlo simulations to evaluate the three most commonly discussed tests of IIA. Results suggest that the size properties of the most common IIA tests depend on the data structure for the independent variables. These findings are consistent with an earlier impression that, even in well-specified models, IIA tests often reject the assumption when the alternatives seem distinct and often fail to reject IIA when the alternatives can reasonably be viewed as close substitutes. The authors conclude that tests of the IIA assumption that are based on the estimation of a restricted choice set are unsatisfactory for applied work.

Accounting for Voter Heterogeneity within and across Districts with a Factor-Analytic Voter-Choice Model

In this study, we propose a model of individual voter behavior that can be applied to aggregate data at the district (or precinct) levels while accounting for differences in political preferences across districts and across voters within each district. Our model produces a mapping of the competing candidates and electoral districts on a latent “issues” space that describes how political preferences in each district deviate from the average voter and how each candidate caters to average voter preferences within each district. We formulate our model as a random-coefficients nested logit model in which the voter first evaluates the candidates to decide whether or not to cast his or her vote, and then chooses the candidate who provides him or her with the highest value. Because we allow the random coefficient to vary not only across districts but also across unobservable voters within each district, the model avoids the Independence of Irrelevant Alternatives Assumption both across districts and within each district, thereby accounting for the cannibalization of votes among similar candidates within and across voting districts. We illustrate our proposed model by calibrating it to the actual voting data from the first stage of a two-stage state governor election in the Brazilian state of Santa Catarina, and then using the estimates to predict the final outcome of the second stage.

Cox-McFadden Partial and Marginal Likelihoods for the Proportional Hazard Model with Random Effects

In survival analysis, Cox's name is associated with the partial likelihood technique that allows consistent estimation of proportional hazard scale parameters without specifying a duration dependence baseline. In discrete choice analysis, McFadden's name is associated with the generalized extreme-value (GEV) class of logistic choice models that relax the independence of irrelevant alternatives assumption. This paper shows that the mixed class of proportional hazard specifications allowing consistent estimation of scale and mixing parameters using partial likelihood is isomorphic to the GEV class. Independent censoring is allowed and I discuss approximations to the partial likelihood in the presence of ties. Finally, the partial likelihood score vector can be used to construct log-rank tests that do not require the independence of observations involved.

Multinomial logistic regression.

When the dependent variable consists of several categories that are not ordinal (i.e., they have no natural ordering), the ordinary least square estimator cannot be used. Instead, a maximum likelihood estimator like multinomial logit or probit should be used. The purpose of this article is to understand the multinomial logit model (MLM) that uses maximum likelihood estimator and its application in nursing research. The research on "Racial differences in use of long-term care received by the elderly" (Kwak, 2001) is used to illustrate the multinomial logit model approach. This method assumes that the data satisfy a critical assumption called the "independence of irrelevant alternatives." A diagnostic developed by Hausman is used to test the independence of irrelevant alternatives assumption. Models in which the dependent variable consists of several unordered categories can be estimated with the multinomial logit model, and these models can be easily interpreted. This method can handle situations with several categories. There is no need to limit the analysis to pairs of categories, or to collapse the categories into two mutually exclusive groups so that the (more familiar) logit model can be used. Indeed, any strategy that eliminates observations or combines categories only leads to less efficient estimates.

Some Preliminary Evidence on Sampling of Alternatives with the Random Parameters Logit

Random utility models rely on the properties of the logistic distribution for ease of estimation, but this distribution implies the independence of irrelevant alternatives (IIA). The random parameters logit model offers a means of avoiding the IIA assumption as well as greater heterogeneity among agents, recreational anglers or beachgoers in the current application. A problem often encountered in the estimation of random utility models with many alternatives is the necessity of sampling alternatives or otherwise reducing the number of choices. Research has shown that in the random utility model, such changes in choice set still lead to consistent parameter estimates. However, with the random parameters logit, there is greater need to sample but no theoretical evidence that sampling is justified. In this paper we show the impact of sampling in a random parameters logit model. We find that sampling does not appear to change the parameter estimates substantially. We investigate two data sets: a study of beac...

Efficiency and the logit model

This paper discusses model building for discrete choice problems in the simplest case - the logit model. General model building principles are formulated and used in evaluatingways of deriving logit choice probabilities. The principles include that models should representhuman behavior, and that the basic assumptions should be testable and refutable againstobservations. Three ways of deriving the logit model are considered; the Luce model, theadditive random utility maximizing approach, and the efficiency approach. The equivalenceof the logit formula and the independence of irrelevant alternatives assumption (the Lucemodel) is well known. It is also well known that the assumption of additive random utilitymaximization with extreme value distribution for the unobservable stochastic componentsimplies the logit formula. The opposite implication does not hold. The principal merit ofadditive random utility maximizing is its coherence with standard economic theory. Itsprincipal weakness is that the basic assumptions can not be tested, since the stochasticcomponents cannot be observed. The efficiency assumption - samples with higher totalobservable utility are more probable - is equivalent to the logit formula. The advantage ofthe efficiency approach lies in its simple, testable basic behavioral assumptions. The independenceof irrelevant alternatives assumption is equivalent to the efficiency assumption.The additive random utility maximization approach and the efficiency approach are notequivalent.

Rank‐ordered logit models: An empirical analysis of Ontario voter preferences

AbstractThis study provides a Bayesian investigation of rank‐ordered multinominal logit models employing conjugate priors for standard multinomial logit models as well as other priors. Also considered is a specification test of the independence of irrelevant alternatives assumption. The proposed techniques are demonstrated in an empirical investigation of Ontario voter preferences before the 1988 Canadian Federal Election.

Mother logit analysis of substitution effects in consumer shopping destination choice

The feasibility of new shopping centers is largely related to their relative location and spending power in the trade area. Commercial developers, retailers, and retail planners need information about the likely impact of new retail developments on consumer choice behavior. Several types of consumer choice models have been applied to assess the effects of retail developments. However, most of these models are based on the so-called independence of irrelevant alternatives assumption, and the variables of interest are included in the models in some indirect manner. The purpose of this article is to demonstrate how substitution effects in consumer shopping behavior can be estimated as a function of marketing strategies or planning scenarios. We focus on the universal or “mother” logit model in a decompositional framework. The mother logit model represents a generalization of conventional multinomial logit models in that the utility of alternatives depends not only on their attributes, but also upon the attributes of other alternatives in the choice set. These so-called cross-effects represent corrections on the utilities as predicted by the conventional multinomial logit model to account for substitution and other effects. The mother logit model was applied to predict the likely effects of a number of proposed actions to improve the attractiveness of some shopping centers in the Einhoven region, The Netherlands. Rather than using attributes of shopping centers, possible planning actions were used to specify the utility function of the mother logit model. The model was estimated using choice data observed in hypothetical situations, created according to the principles of experimental design strategies. The results indicated that some planning actions affect the utility of other shopping centers significantly. Competition between shopping centers seemed to be stronger among centers belonging to the same hierarchical level and weaker among centers of different hierarchical levels, indicating the existence of substitution effects on consumer shopping center choice behavior.

Mother logit analysis of substitution effects in consumer shopping destination choice

The feasibility of new shopping centers is largely related to their relative location and spending power in the trade area. Commercial developers, retailers, and retail planners need information about the likely impact of new retail developments on consumer choice behavior. Several types of consumer choice models have been applied to assess the effects of retail developments. However, most of these models are based on the so-called independence of irrelevant alternatives assumption, and the variables of interest are included in the models in some indirect manner. The purpose of this article is to demonstrate how substitution effects in consumer shopping behavior can be estimated as a function of marketing strategies or planning scenarios. We focus on the universal or “mother” logit model in a decompositional framework. The mother logit model represents a generalization of conventional multinomial logit models in that the utility of alternatives depends not only on their attributes, but also upon the attributes of other alternatives in the choice set. These so-called cross-effects represent corrections on the utilities as predicted by the conventional multinomial logit model to account for substitution and other effects. The mother logit model was applied to predict the likely effects of a number of proposed actions to improve the attractiveness of some shopping centers in the Eindhoven region, The Netherlands. Rather than using attributes of shopping centers, possible planning actions were used to specify the utility function of the mother logit model. The model was estimated using choice data observed in hypothetical situations, created according to the principles of experimental design strategies. The results indicated that some planning actions affect the utility of other shopping centers significantly. Competition between shopping centers seemed to be stronger among centers belonging to the same hierarchical level and weaker among centers of different hierarchical levels, indicating the existence of substitution effects on consumer shopping center choice behavior.

A Nested Logit Model of Space and Water Heat System Choice

This paper estimates a nested logit model for space heat and water heat choice using data from the 1980 Pacific Northwest Energy Survey (PNW). The estimated structure involves six alternative space heat systems and three alternative water heat fuel types. Operating and capital costs are predicted for each residence using an energy thermal model. To relax the Independence of Irrelevant Alternatives assumption we employ a family of nested logit models. We illustrate the two-step estimation method and use a Lagrange multiplier test for nested logit structure. Finally, we use the estimated models to forecast the market shares of alternative space heat systems under an assumed scenario for the course of relative fuel prices.

The Location and Employment Choices of New Firms: An Econometric Model with Discrete and Continuous Endogenous Variables

B USINESS location is a subject of great interest. Businessmen obviously want to know where to locate their plants. State planners want to know the best way to attract new employment to their state. Regional economists use business location to get an advance reading on the health of an economy. Newly locating plants are responding to current incentives in making their locational choices and are therefore a better barometer of a region's future than employment at existing plants whose decisions are obviously influenced by their prior locational decision. Despite all the interest, economists know very little about the factors influencing new business location. (See Carlton (1979) for a survey.) Part of the reason is undoubtedly the lack of data on new business formation. As far as I know, Dun and Bradstreet is the only systematic data source available for studying new business formation. However, there have been only a handful of studies using this data set. And only two, Schmenner (1975) and Carlton (1979), have econometrically attempted to model location by economic variables. Schmenner's study looked at location within an SMSA and failed to find much explanatory power for his economic variables. Carlton (1979) focused on interregional location of new single establishment (one plant) firms and was able to uncover some significant economic effects. I also examined the location of branch plants in that paper, but failed to model the size of the branch plant. Obviously, what is of interest is not only where new location will occur but how much employment will be generated. No one to my knowledge has linked the two. This paper simultaneously models both the location and employment choice of new branch plants across SMSAs. The important methodological contributions are showing that the two decisions are linked (via duality theory) and exploiting this link in the model estimation. A special bonus of the methodology is that it allows for direct testing of the independence of irrelevant alternatives assumption in a logit model. The study takes special care to use information on individual plants in narrowly defined industries (4 digit SIC code) and narrowly defined geographic regions (SMSAs). The use of such disaggregate data is a distinguishing feature of the work. Some of the specific findings of the study are the following: