Multinomial Choice Research Articles

Incorporating Product Choice into Transit Fare Policy Scenario Models

Customer fare product choices can affect both ridership and revenue, so they are strategically important for transit agencies. Nearly all major agencies offer choices between pay-per-use and pass products, and with each potential fare change, agencies face decisions about whether to modify pass “multiples”—the number of rides needed to “break even” on a pass purchase. However, the simple elasticity spreadsheet models often used to analyze the potential ridership and revenue impacts of fare changes make little or no adjustment for shifts in fare product choices. This paper reviews different options for incorporating product choice into fare policy scenario models, and it presents a ridership and revenue prediction procedure that combines a multinomial logit fare product choice model with the logic of an elasticity spreadsheet model. This combination facilitates evaluation of complex fare changes that are likely to alter fare product market shares while maintaining much of the flexibility and simplicity of a traditional spreadsheet model. Additionally, the proposed model uses only preexisting, revealed-preference automated fare collection data rather than requiring customer surveys. The proposed model is demonstrated using examples at the Chicago Transit Authority (CTA). The CTA experienced a large shift from passes to pay-per-use following a fare change in 2013, illustrating the potential value of accounting for fare product choices in fare scenario evaluation.

Job Flexibility and Occupational Selection in Ghana: An Application of Maximum Simulated Likelihood†

Abstract In many African labour markets, the vast majority of self-employed workers are female. It is often hypothesised that self-employment enables workers to balance income-generation with caring for children and other domestic tasks and, since responsibility for these activities is divided unequally in the household, this effect is stronger for women than men. However, testing whether ‘job flexibility’ matters is difficult because variables that proxy for domestic obligations—such as the number of dependents in the household—may be endogenous to occupational choice. In this paper, we build a new estimator using maximum simulated likelihood that allows us to use selection on observables as a guide to selection on unobservables within the multinomial choice problem individuals face when deciding their occupation. We apply this approach to detailed cross-sectional data from Ghana. Our results show that having extra dependents in the household pushes women towards own account self-employment substantially more than men, even under more conservative assumptions about the extent of endogeneity.

Immigration and electoral support for the far-left and the far-right

Immigration is one of the most divisive political issues in the United States, the United Kingdom, France, and several other Western countries. We estimate the impact of immigration on voting for far-left and far-right candidates in France, using panel data on presidential elections from 1988 to 2017. To derive causal estimates, we instrument more recent immigration flows by settlement patterns in 1968. We find that immigration increases support for far-right candidates. This is driven by low-educated immigrants from non-Western countries. We also find that immigration has a weak negative effect on support for far-left candidates, which could be explained by a reduced support for redistribution. We corroborate our analysis with a multinomial choice analysis using survey data.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

Estimating Multinomial Choice Models with Unobserved Choice Sets

This paper proposes a new approach to estimating multinomial choice models when each consumer’s actual choice set is unobservable but could be bounded by two known sets, i.e., the largest and smallest possible choice sets. The bounds on choice set, combined with a monotonicity property derived from utility maximization, imply a system of inequality restrictions on observed choice probabilities that could be used to identify and estimate the model. A key insight is that the identification of random utility model can be achieved without exact information on consumers’ choice sets, which generalizes the identification result of the standard multinomial choice model. The effectiveness of the proposed approach is demonstrated via a range of Monte Carlo experiments as well as an empirical application to consumer demand for potato chips using household scanner data.

Nonseparable multinomial choice models in cross-section and panel data

Multinomial choice models are fundamental for empirical modeling of economic choices among discrete alternatives. We analyze identification of binary and multinomial choice models when the choice utilities are nonseparable in observed attributes and multidimensional unobserved heterogeneity with cross-section and panel data. We show that derivatives of choice probabilities with respect to continuous attributes are weighted averages of utility derivatives in cross-section models with exogenous heterogeneity. In the special case of random coefficient models with an independent additive effect, we further characterize that the probability derivative at zero is proportional to the population mean of the coefficients. We extend the identification results to models with endogenous heterogeneity using either a control function or panel data. In time stationary panel models with two periods, we find that differences over time of derivatives of choice probabilities identify utility derivatives “on the diagonal,” i.e. when the observed attributes take the same values in the two periods. We also show that time stationarity does not identify structural derivatives “off the diagonal” both in continuous and multinomial choice panel models.

시나리오기법 및 선택실험법 기반 한국 전기차 시장 수요예측 연구

This study presents a scenario-based, long-term forecasting model of electric vehicle (EV) sales. A simulated choice experiment using the multinomial hybrid choice model was estimated, where consumer utility was dependent on vehicle type, total cost of ownership (TCO), and personal variables. Results show that lower TCO, lower range-anxiety, and greater environmental concern lead to greater choice probability for EVs. Using the parameters of the TCO variable and the intercept, an initial probability of choosing an EV over an internal combustion engine car was derived. To this initial estimate, macro factors were incorporated, including charging infrastructure, oil prices, battery technology, and government incentives. Charging infrastructure was modeled as the convenience factor; battery technology and government incentives were modeled as affecting vehicle costs; and oil prices as affecting the operating cost. Based on long-term changes in the macro factors, three scenarios were selected: the reference scenario, the low-battery cost scenario, and the high oil price scenario. The forecasting model shows that long-term changes in oil prices are much more important than long-term changes in battery cost in terms of determining the adoption of electric vehicles. This is because fuel cost, over time, has a bigger impact on TCO, whereas the importance of battery prices on TCO decreases. Results also indicate that convenience is the main inhibitor of EV adoption, implying that the government’s policy should transition from providing cash-back incentives to building better charging infrastructure.

Estimating Elasticities for Freight Transport Using a Network Model: An Applied Methodological Framework

This paper presents a general framework that can be used to estimate direct and cross elasticities for freight transport using a network model. This methodology combines operational research (network assignments in a geographical information system) with more classical econometrics (multinomial logit choice models). The application of the method to a real-world case is illustrated by a simple model that relies on the generalized cost of transport as the only explanatory variable in the utility function. The methodological framework allows, however, for the implementation of more complex functions. Beside the generalized cost functions for road, rail and inland waterways transport, the network model needs origin-destination matrixes and digitized networks. They are imported from ETIS Plus, a European transport policy information system. A set of direct and cross elasticities is presented. The estimated values are obtained using two methods: the first computes standard elasticities, while the second estimates arc elasticities. Figures are presented for Europe and for a large region around the Benelux countries, where more competition exists between the three modes of interest.

Finland: Venator – TiO2

Many strategic decisions made by firms involve a choice among several discrete alternatives. International Business (IB) scholars are often interested in modelling the factors that potentially influence these (multinomial) choices: these factors might include not only characteristics of the firms making the choices but also attributes of the alternative choices. This paper provides a succinct and intuitive introduction to the possible applications of multinomial choice models in IB research. We briefly outline the theory behind discrete choice modelling, and then explain how multinomial choice models may be estimated (including how the datasets need to be formatted) and how the significance of the coefficient estimates as well as the diagnostic statistics may be tested and interpreted.

Analysis of high-speed rail and airline transport cooperation in presence of non-purchase option

We study cooperation between the airline and high-speed rail (HSR) sectors by formulating their joint profit as a maximization problem using a multinomial logit choice model in a three-node setting. We allow the non-purchase option as an outside option available to consumers. The demand for each choice is not only a function of the price but also the service quality, such as the total trip time, frequency of service, and ease of connecting from the hub to a nearby HSR station. As a result, the following findings are presented. First, regardless of the service quality of either sector and the non-purchase option, cooperation decreases the total volume of the domestic market of a country. Second, when the attractiveness of the outside option is high, the HSR and air sectors can prevent a large reduction in the total volume by cooperation in the connecting market. However, this is not the case in the domestic market. Third, if the non-purchase quality in the domestic market is high, then cooperation increases the social welfare of the whole market. If the non-purchase quality is low, then cooperation increases the welfare of the whole market only in cases where the number of potential customers in the connecting market is relatively large. We also show the effect of improving air–rail service quality on each market share and on the total profit.

Reading between the Stars: Understanding the Effects of Online Customer Reviews on Product Demand

Problem definition: Many studies have examined quantitative customer reviews (i.e., star ratings) and found them to be a reliable source of information that has a positive effect on product demand. Yet the effect of qualitative customer reviews (i.e., text reviews) on demand has been less thoroughly studied, and it is not known whether (or how) the sentiment expressed in text reviews moderates the influence of star ratings on product demand. We are therefore led to examine how the interplay between review sentiment and star ratings affects product demand. Academic/practical relevance: Consumer perceptions of product quality—and how they are shared via customer reviews—are of extreme relevance to the firm, but we still do not understand how product demand is affected by the quantitative and qualitative aspects of customer reviews. Our paper seeks to fill this critical gap in the literature by analyzing star ratings, the sentiment of customer reviews, and their interaction. Methodology: Using 2002–2013 data for the US automobile market, we investigate empirically the impact of star ratings and review sentiment on product demand. Thus, we estimate an aggregated multinomial choice model after performing a machine learning–based sentiment analysis on the entire corpus of customer reviews included in our sample. We take advantage of a quasiexogenous shock to establish a causal link between online reviews and product demand. Results: We find robust empirical evidence that (i) review sentiment and star ratings both have a decreasingly positive effect on product demand and (ii) the effect (on demand) of their interaction suggests that the two components of reviews are complements. Positive sentiments in text reviews increase the positive effect of ratings when the effect of ratings is decidedly positive while they also compensate for the tendency of consumers to discount extremely high star ratings. Managerial implications: The firm should pay greater attention to quantitative and qualitative customer reviews so that it can better understand how consumers perceive the quality of its offerings.

Tracking the Impact of Media on Voter Choice in Real Time: A Bayesian Dynamic Joint Model

ABSTRACTCommonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents’ exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications, such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor consumers’ perceptual responses over time. This study based on a new data collection technique using cell-phone text messaging (called real-time experience tracking or RET) offers the potential to address these weaknesses. We propose an RET-based model of the impact of communications and apply it to a unique choice situation: voting behavior during the 2010 UK general election, which was dominated by three political parties. We develop a Bayesian zero-inflated dynamic multinomial choice model that enables the joint modeling of: the interplay and dynamics associated with the individual voter's choice intentions over time, actual vote, and the heterogeneity in the exposure to marketing communications over time. Results reveal the differential impact over time of paid and earned media, demonstrate a synergy between the two, and show the particular importance of exposure valence and not just frequency, contrary to the predominant practitioner emphasis on share-of-voice metrics. Results also suggest that while earned media have a reducing impact on voting intentions as the final choice approaches, their valence continues to influence the final vote: a difference between drivers of intentions and behavior that implies that exposure valence remains critically important close to the final brand choice. Supplementary materials for this article are available online.

Strict scalability of choice probabilities

This paper introduces the concept of strict scalability, which lies between the classical notions of simple scalability (Krantz, 1964; Tversky and Russo, 1969) and monotone scalability (Fishburn, 1973). For binary choices, strict scalability is precisely characterised by the well-known axiom of weak substitutability (at least for countable domains). We also introduce a multinomial extension of weak substitutability that characterises strict scalability for multinomial choice.

Estimating panel effects in probabilistic representations of dynamic decision trees using bayesian generalized linear mixture models

When collecting panel data, we need to acknowledge that responses do not represent independent measurements. The known apparatus in transportation research offers several opportunities to estimate panel effects for well-known and widely applied models such as hazard and dynamic logit models. However, the transportation research community is not endowed with a rich set of methods to account for panel effects in dynamic probabilistic decision trees, which have been used as a formalism for the representation of decision heuristics. Building on scarce prior work in statistics, we elaborate an approach to estimate panel effects in dynamic probabilistic decision trees with multinomial action states. Given that panel data naturally have a hierarchical structure with repeated measures nested within individuals, we implement a mixed-effects model that simultaneously accounts for population-level effects (fixed effects), between-individual variances (random effects), and within-individual variances (autocorrelations). The approach uses an iterative estimation procedure between CHAID-based probabilistic tree induction and Bayesian generalized linear mixture modeling (GLMM). When extracting the dynamic probabilistic decision trees, it is assumed that the random effects are known, while it is assumed that the fixed effects are known when estimating the Bayesian GLMM. This iterative process continues until convergence is reached. A Monte Carlo technique is used to navigate between aggregate choice probabilities and individual level multinomial choices. We also test the significance of temporal autocorrelation within individuals. The suggested approach is illustrated using charging station choice of users of Plug-in Electric Vehicles (PEV). Results support the potential value of the suggested approach.

Asymmetric, closed-form, finite-parameter models of multinomial choice

Class imbalance, where there are great differences between the number of observations associated with particular discrete outcomes, is common within transportation and other fields. In the statistics literature, one explanation for class imbalance that has been hypothesized is an asymmetric (rather than the typically symmetric) choice probability function. Unfortunately, few relatively simple models exist for testing this hypothesis in transportation settings—settings that are inherently multinomial. Our paper fills this gap.In particular, we address the following questions: “how can one construct asymmetric, closed-form, finite-parameter models of multinomial choice” and “how do such models compare against commonly used symmetric models?” Methodologically, we introduce (1) a new class of closed-form, finite-parameter, multinomial choice models, (2) a procedure for using these models to extend existing binary choice models to the multinomial setting, and (3) a procedure for creating new binary choice models (both symmetric and asymmetric). Together, our contributions allow us to create new asymmetric, closed-form, finite-parameter multinomial choice models. We demonstrate our methods by developing four new asymmetric, multinomial choice models. Empirically, most of our models strongly dominate the multinomial logit (MNL) model in terms of in-sample and out-of-sample log-likelihoods. Moreover, analyzing two policy applications, we find practical differences between the MNL and our new asymmetric models. Our results suggest that while asymmetric models may not always outperform symmetric ones, asymmetric choice models are worth testing because they might have better statistical performance and entail substantively different policy and financial implications when compared with traditional symmetric models, such as the MNL.

The political geography of government formation: Why regional parties join coalitions

Political parties differ in the geographic distribution of their support. This article argues that a regionalized distribution of a party’s votes facilitates its participation in government, because it produces a tendency to prioritize demands for locally targeted goods that are more conducive to the negotiation of reciprocal logrolling agreements with potential partners. Using a measure based on the Gini coefficient, I empirically evaluate the extent to which the geographic concentration of votes plays a role in the formation of governments, taking Spanish local elections from 1987 to 2011 as a test bed. With around 500 formation opportunities and 20,000 potential governments, multinomial choice models are estimated (conditional and mixed logits) and a very sizable effect is documented: A one-standard deviation increase in the electoral geographic concentration of the members of a potential government almost doubles the likelihood of its formation. These findings are relevant for students of government formation, regional parties, and political geography.

Choice of Enterprise Form: Spain, 1886–1936

Every new firm selects a legal form. Organizing as a corporation, a limited company, or a partnership shapes the firm’s access to capital markets, its governance arrangements and tax liabilities, and its treatment in bankruptcy. We use multinomial choice models to estimate the determinants of enterprise form using firm-level data on Spain for the period 1886–1936. Our results support hypotheses drawn from the corporate-finance and ownership literatures; entrepreneurs preferred the corporation for the largest firms and for firms vulnerable to holdup. In 1919, Spain introduced a new legal form, a limited company combining attributes of the corporation and the partnership. This Sociedad de Responsabilidad Limitada (SRL) displaced both corporations and partnerships, and was especially popular for small and medium-sized firms whose owners were unrelated. Counterfactual calculations suggest that few enterprises created prior to 1919 would have chosen the SRL even if it had been available.

Location factors and spatial dependence in household perceptions and adaptations to climate change: A case in the upper Blue Nile Basin

ABSTRACTStudies on perceptions of, and adaptation responses to, climate change have not paid sufficient attention to location and neighbourhood effects. Moreover, although one often regards perceptions as preconditions for adaptation, some direct and underlying factors may influence perceptions and adaptations in opposite ways. To assess determinants of perceived directions of climate change and adaptations by farmers, the paper formulates ordered response and multinomial choice models accounting for the location and neighbourhood effects. Relative to a survey of rural households in the Ethiopian Nile basin, perceptions of long-term climate trends are found to substantially vary across regions, altitudes, distances from markets, and neighbourhood characteristics, with climate reference scales being themselves non-uniform across respondents. The inclusion of proxies for local spatial dependence and clustering in multinomial logit regressions on adaptation decisions provides further insights, relative to individual- and farm-specific features. Adaptation measures turn out to be largely independent from degree of awareness of negative trends in climate changes. This highlights the importance of adaptive strategies coordinated at regional and national scale, such as preventive measures of control of pest damage risk due to increasing temperatures, and specific interventions for semi-arid agro-climatic zones.

Income Effects and Rationalizability in Multinomial Choice Models

McFadden's random-utility model of multinomial choice has long been the workhorse of applied research. We establish shape-restrictions under which multinomial choice-probability functions can be rationalized via random-utility models with nonparametric unobserved heterogeneity and general income-effects. When combined with an additional restriction, the above conditions are equivalent to the canonical Additive Random Utility Model. The sufficiency-proof is constructive, and facilitates nonparametric identification of preference-distributions without requiring identification-at-infinity type arguments. A corollary shows that Slutsky-symmetry, a key condition for previous rationalizability results, is equivalent to absence of income-effects. Our results imply theory-consistent nonparametric bounds for choice-probabilities on counterfactual budget-sets. They also apply to widely used random-coefficient models, upon conditioning on observable choice characteristics. The theory of partial differential equations plays a key role in our analysis.

Empirical welfare analysis for discrete choice: Some general results

This paper develops nonparametric methods for welfare‐analysis of economic changes in the common setting of multinomial choice. The results cover (a) simultaneous price‐change of multiple alternatives, (b) introduction/elimination of an option, (c) changes in choice‐characteristics, and (d) choice among nonexclusive alternatives. In these cases, Marshallian consumer surplus becomes path‐dependent, but Hicksian welfare remains well‐defined. We demonstrate that under completely unrestricted preference‐heterogeneity and income‐effects, the distributions of Hicksian welfare are point‐identified from structural choice‐probabilities in scenarios (a), (b), and only set‐identified in (c), (d). In program‐evaluation contexts, our results enable the calculation of compensated‐effects, that is, the program's cash‐equivalent and resulting deadweight‐loss. They also facilitate a theoretically justified cost‐benefit comparison of interventions targeting different outcomes, for example, a tuition‐subsidy and a health‐product subsidy. Welfare analyses under endogeneity is briefly discussed. An application to data on choice of fishing‐mode illustrates the methods. Multinomial choice general heterogeneity income effects compensating variation deadweight loss multiple price change elimination of alternative change in characteristics weak separability nonexclusive choice compensated program‐effects C14 C25 D12 D61 H22