Choice Model Research Articles

The impact of pension reform on employment, retirement, and disability insurance claims

We evaluate the 2011 comprehensive reform of Norwegian early retirement institutions using a parsimonious random utility choice model. Conditional on employment at age 60, we estimate a three-state conditional logit model to explain the realized labor market state at age 63 among the alternatives of employment, retirement, and disability program participation. The reform radically changed work incentives and/or pension access age for some (but not all) workers, such that the influence of economic incentives can be identified based on reform-generated variation only. We find that improved work incentives caused employment rates to rise considerably at the expense of early retirement and exit through disability insurance. Improved liquidity through a lower age to access own pension funds on actuarially neutral terms caused a small increase in employment and a large drop in disability program participation. Properly designed pension reforms thus need to take the interplay between old-age pension and disability insurance programs into account.

Social preferences and the variability of conditional cooperation

AbstractWe experimentally examine how incentives affect conditional cooperation (i.e., cooperating in response to cooperation and defecting in response to defection) in social dilemmas. In our first study, subjects play eight Sequential Prisoner’s Dilemma games with varying payoffs. We elicit second mover strategies and find that most second movers conditionally cooperate in some games and free ride in others. The rate of conditional cooperation is higher when the own gain from defecting is lower and when the loss imposed on the first mover by defecting is higher. This pattern is consistent with both social preference models and stochastic choice models. In a second study subjects play 64 social dilemma games, and we jointly estimate noise and social preference parameters at the individual level. Most of our subjects place significantly positive weight on others’ payoffs, supporting the underlying role of social preferences in conditional cooperation. Our results suggest that conditional cooperation is not a fixed trait but rather a symptom of the interaction between game incentives and underlying social preferences.

Long-Run Behavior and Convergence of Dynamic Mean Field Equilibria

AbstractWe study the behavior of dynamic equilibria in mean field games with large time horizons in a dynamic consumer choice model. We show that if the stationary equilibrium in the associated infinite horizon game is unique, the dynamic equilibria of the finite horizon games converge to the stationary equilibrium of the infinite horizon game as the time horizon tends to infinity. If the stationary equilibrium is not unique, however, the situation becomes more involved. In this case, we show that in addition to convergence to the stationary equilibria, in the long run, the dynamic equilibria circle around randomized stationary equilibria for certain choices of boundary data.

Investigating the e-learning choice under the learners’ perspective using demand driven learning model: insights from Vietnam

Over the past four decades, e-learning has experienced rapid global growth, revolutionizing higher education. In Vietnam, universities have increasingly turned their attention and resources toward e-learning development, adapting to specific contexts and needs. This study investigates the drivers of e-learning choice within the framework of the demand-driven learning model (DDLM), with a primary focus on three core factors: quality content, delivery, and service. Employing a quantitative approach and the PLS-SEM technique, this research uncovers significant findings. The results of the study highlight the pivotal role of quality content, which exerts the most potent influence on e-learning choice, evidenced by a robust path coefficient of 0.400. Service and delivery, with direct path coefficients of 0.183 and 0.173, respectively, also play substantial roles in shaping e-learning decisions. Moreover, mediate role of delivery in the the e-learning choice model has been confirmed. Quality content leads to delivery, and delivery, in turn, leads to e-learning choice. Similar pathway has been found with service. A higher level of service increases delivery, which positively impacts on e-learning choice. These findings hold critical implications for the formulation of e-learning development policies in Vietnam’s higher education institutions. Universities should prioritize the continuous development of high-quality, engaging, and up-to-date educational content that aligns with industry needs and student interests. Additionally, emphasis should be placed on providing a supportive e-learning experience, characterized by responsive customer service, accessible technical support, and efficient issue resolution mechanisms. Moreover, universities should consider the implementation of user-friendly and interactive content delivery platforms and methods that actively engage learners. In essence, this research serves as a guide for universities in Vietnam, enabling them to enhance their e-learning offerings by ensuring the content quality, support services, and delivery methods, ultimately fostering a dynamic and accessible learning environment that meets the evolving needs of their students and the demands of the modern educational landscape.

Are random price generators useful for health policy processes and analysis? effectiveness of random price generators in health policy processes and analysis

This paper discusses why using random price generators in an economic model for medical markets may be useful. It summarizes the different steps for the development of the model and describes an original contribution of the physicians’ choice model with random drug prices applied to Type 2 diabetes in the US. Pharmaceutical markets have been supply-driven to boost life science and medical technologies; however, with the widening inequalities inside national health systems and the global agenda for universal health coverage, more economic research is being done to strengthen the analysis of demand for healthcare services. Research, especially, examines more disaggregated levels of demand systems to understand the heterogeneity of physicians’ choices and better capture patient needs. It can also be an approach to calibrate supply and demand adjustments in medical markets. This paper argues that choice modeling is particularly relevant, using random price generators in a structural model where a demand approach is useful. If validated by additional experimental studies, this first study by Professors Huttin & Hausman (2021) could be used in advanced value assessment frameworks. Random price generators on medical markets could also be tested with additional models that fit oligopolistic market structures (e.g., models for differentiated product markets such as the Berry–Levinsohn–Pakes model). It may also help with a policy analysis process that addresses major disruptive transformations of market dynamics, evolving boundaries in science, fast digitalization, and artificial intelligence-based information systems.

Факторная модель личностного выбора соучастия в экстремальном добровольчестве и его диагностика

The article is devoted to improving psychological support for volunteering in extreme conditions. The emphasis is placed on the establishment of predictors of personal choice of participation in volunteerism, the development of an integrative model and diagnostic tools for assessing readiness for participating activities. The subject of the study is a model of personal choice of participation in extreme volunteerism. The aim is to substantiate an integrative model of personal choice of participation in extreme volunteerism, to develop an appropriate diagnostic methodology based on it. The methodological basis is based on axiological, holistic, activity-based and subjective approaches. Semantic analysis of diagnostic constructs, as well as expert assessment and psychological testing were used (questionnaire «Scale of dispositional egoism» (K. Muzdybaev); questionnaire «Motivation for help» (S.K. Nartova-Bochaver); questionnaire of value orientations (adaptation of M. Rokich's methodology); indicative questionnaire «Personality orientation» (V. Smekal, M. Kucera adaptation by N.M. Peisakhov); questionnaire on motivation of volunteer activity (K.V. Gil); methodology for diagnosing achievement motivation (A. Mehrabian); methods of diagnosing personal maturity (V.A. Ruzhenkov); test of resilience (S. Muddy, adaptation by D.A. Leontiev, E.I. Rasskazova); questionnaire «Coping behavior» (R. Lazurus, S. Folkman, adaptation by S. Hobfall); value questionnaire (Sh. Schwartz); original author's methods – a questionnaire for assessing the propensity to extreme volunteering, an assessment of manifestations of professional personality deformation, a questionnaire for evaluating the choice of participation in extreme volunteerism). The sample size is 218 people. Applied: descriptive statistics, factor analysis, meta-analysis of data. According to the results of the study, the 16 factor structure of the choice of complicity is justified, which includes: «Prosocial value orientations», «Ideality», «Integrity of worldview», «Voluntary sense orientation», «Motivation», «Interest in volunteerism», «Propensity to extreme activity», «Dedication», «Critical thinking», «Extreme perceptual congruence», «Emotional stenicity», «Self-control», «Activity in extreme conditions», «Extreme vitality», «Self-organization», «Reliability». The novelty lies in the development of an integrative model and a questionnaire for evaluating the choice of participation in volunteerism. The results can be applied by psychologists of law enforcement agencies and volunteer organizations in conducting psychological selection and training of volunteers.

Using discrete choice modeling to understand the drivers of reproductive delay in the United Kingdom

The ideal family size in the UK is, on average, two children. However, there is a fertility gap, the difference between wanted and achieved family size, of around one in three children, which is largely caused by extended delays to reproduction. Standard quantitative methods used to identify what causes these delays have limitations, often relying on macro-level data which conveys little about individual needs, or applying longitudinal methods that produce outdated results because only once people have completed their reproductive years is it possible to infer which life-course factors led to later-age childbearing. This study is the first to overcome these challenges by applying a discrete choice experiment to reveal the barriers that people are facing right now. This innovative methodology allows the estimation of the relative importance of the barriers and the distance between them measured in months of reproductive delay. Among other things, the results show that for men and women, partner support was more important than finances or housing, although support means different things depending on the level of education with more educated women prioritizing hands-on fathers and household gender equality, whereas less-educated women strongly desired stable partnerships. Men favored partner readiness and neighborhood quality. These, and the other findings shown here, provide detailed insight into the contemporary causes of delayed fertility in the UK.

Determinants of receiving government support by small and medium-sized enterprises in Russia

Government support measures help companies overcome various restrictions impeding their growth and develop ment, which is of special relevance for small and medium-sized enterprises (SMEs). These measures, therefore, must reach the target companies and ensure efficient allocation of resources. The article reveals key characteristics of SMEs in Russia that increase the probability of them receiving government support. The research methodology is based on public interest theory, public choice theory and the concept of economic freedom. The research method is a two-step regression analysis, in which the binary choice model is first evaluated, and then the LASSO model is used to assess the impact of the textual content of the companies’ websites. The dataset of the study is represented by data on 2,176 Russian metallurgical enterprises for 2020–2023, of which 57% were receiving government support. The results of the first step of evaluation showed that the organization’s age and size meas ured by revenue were the key factors associated with the probability of receiving government support. At the second step, we found that the mention of certain goods or materials on the websites, the enterprise’s location and orientation towards foreign stakeholders were significantly related to the likelihood of getting state support. The study offers tools for analyzing the determi nants of government support for SMEs and highlights the importance of this analysis for more efficient allocation of government resources.

The effect of trust in neighbourhood on parents' decision to drive their children to school: Insights from a hybrid choice model

IntroductionThe growing trend of children being driven to school has become a cause of concern in developed countries, with parents' perception of trust in neighbourhoods emerging as a key determinant. Previous research has mainly investigated the influence of either the mother's or the father's perception on children's school travel. While the contribution of each parent's perception to the final decision, and the factors affecting this contribution, remains unclear. MethodsThis study leverages data from the Longitudinal Study of Australian Children (LSAC) to examine the distinct roles of mothers' and fathers' perceptions of trust in the neighbourhood on their children's school travel mode. It proposes a hybrid choice model (HCM) to measure the contribution of each parent's perception as a function of their socio-economic status (SES) in the school travel mode decision. ResultsThe results indicate that a higher parental perception of trust in the neighbourhood significantly reduces the likelihood of using private car to take children to school. The study also identifies several factors that influence parental perception of trust, highlighting differing impacts on mothers and fathers. Neighbourhood SES has a more pronounced positive impact on mothers’ perceptions of trust, whereas homeownership and urban residency exhibit stronger effects on fathers' perceptions. The analysis further delineates the contribution of individual parents to the overall parental perception of trust, demonstrating that higher levels of income decrease this contribution. ConclusionThis study highlights the significant role of parental perception of neighbourhood trust in children's school travel mode, with both mothers' and fathers' perceptions playing crucial roles. Neighbourhood SES, homeownership, urban residence, and ethnic diversity shape parents' perceptions, with differing effects on mothers and fathers. Future policy initiatives should focus on fostering trust and social inclusion through community events, multicultural programs, and social activities to enhance neighbourhood cohesion and support sustainable school travel choices.

Welfare measurement with income-dependent discrete choice

The expectation of the random compensating variation is the welfare change measure that is used in discrete choice models. The expectation of the equivalent variation is equally founded theoretically. When choices are income independent and income enters utilities linearly, the two measures are identical. The case of income-dependent choices remains an area for exploration. The paper provides the equivalent variation counterparts of the formulas that are available for the expectation of the compensating variation.

Designing employee benefits to optimize turnover: A prescriptive analytics approach

Employee turnover significantly impacts organizations, particularly those with substantial investments in training their workforce. To mitigate these effects, we propose a Prescriptive Human Resources Analytics approach that optimizes employee benefits to minimize total costs, focusing on turnover management The methodology models employee decision-making using a discrete choice model, with parameters estimated through maximum likelihood. We solve the resulting nonlinear optimization problem with a heuristic tailored to the problem’s complexity. We applied this methodology to a hospital case study, which was used to enhance the transportation system as an employee benefit, considering the associated turnover costs. The results demonstrate that our approach can reduce total costs, optimize the usage level of the designed benefits, and increase employee satisfaction. This research provides a robust framework for data-driven decision-making in HR, offering practical tools for improving employee retention strategies.

Consumer preference and willingness-to-pay for formal recycling of electric vehicle batteries: a discrete choice experiment study

The burgeoning electric vehicle (EV) market poses a substantial challenge to battery recycling systems, yet understanding EV battery recycling behavior from the demand side remains limited. Previous studies have analyzed perceptual or attitudinal factors, neglecting the observable attributes of EV battery recycling. To this end, we proposed a discrete choice model to investigate the differences between formal and informal recycling behaviors, identifying consumer preferences and willingness to pay. By analyzing 1190 sample data collected from Chongqing, China, we find that: (1) The formal recycling market exhibits greater sensitivity to prices compared to the informal recycling market. (2) The formal recycling market favors recycling by EV battery producers, whereas the informal recycling market shows the least preference for recycling by automobile producers. (3) Door-to-door recycling services are the most effective in facilitating the transition from informal to formal recycling markets for EV batteries. (4) Capacity subsidy policies outperform one-time fixed subsidy policies in incentivizing formal recycling. (5) The formal recycling market for EV batteries necessitates "traceability to the recycling outlet", as opposed to being untraceable. (6) The high-awareness group exhibits greater sensitivity to government policies compared to those with lower environmental concerns and less knowledge of EV battery recycling.

Urban Transportation Mode Choice Model in Bali

Urban Transportation Mode Choice Model in Bali

Approaches to assessing the socio-economic effects of the implementation of strategic priorities in regional economic systems: The Yamalo-Nenets Autonomous Okrug case

Subject. The article deals with a review and formation of approaches to the assessment of strategic priorities of resource-oriented regions. Objectives. The study aims at preparation of systems approaches to the assessment of strategic priorities of resource-oriented regions and regional economic systems in general, for further implementation in the adapted model of interperiod choice. Methods. The study rests on the theory of strategy and methodology of strategizing by V.L. Kvint, a foreign member of the Russian Academy of Sciences, and employs econometric, statistical, and cognitive methods. Results. I presented an overview of existing approaches to assessing strategic priorities, formulated proposals for formalizing and systematizing the priority assessment system based on specific and inclusive approaches. The paper shows how the results of this analysis can be implemented in the model of interperiod choice. Conclusions. The findings can be used by public authorities when devising strategies for socio-economic development, while theoretical insights can be adapted for use not only in regional economic systems, but also in the analysis of strategic development of other systems.

Assessing the Impact of Distance Modelling Approaches on Active Travel Predictions

Trip distance is widely considered a key determinant of active travel choice, although distance is handled in choice modelling in various ways. This study examines how linear, categorical, quadratic, and piecewise representations for distance influence mode choice. The piecewise approach demonstrates superior fit and provides more stable elasticity estimates, ranging from -1% to -3%, compared to the broader range of -0.17% to -36% observed in categorical treatments and occasional positive values in quadratic treatments. These results underscore the piecewise model’s advantage in capturing critical behavioural shifts and offering more reliable insights to inform policy.

Gravity support from a robotic exoskeleton increases spontaneous use of the non-dominant upper extremity during a choice reaching task.

The objective was to determine if gravity support for the left arm of right-handed participants would increase left arm use during a 3D reaching task in virtual reality. Twelve healthy control participants each completed 630 reaching movements broken into 6 blocks. The majority of targets were placed close to the mid-sagittal plane at three heights and participants were free to use either limb when reaching for targets. The hand had to stay in the target for a prescribed dwell time before the target disappeared. For all reaching tasks within a block, the left arm gravity support was set to either 0% or 75% of full arm support. The blocks also varied in the dwell time (2, 4, or 6 sec). The order of blocks was balanced across participants in terms of gravity support level and dwell time. EMG level in the left medial deltoid decreased with increasing gravity support (p<.001) and was higher for higher targets compared to lower targets (p<.001). The odds of using the left arm were 1.95 times higher with gravity support compared to no support (p<.001). With gravity support, we expected greater shifts towards the left arm in tasks that were more energetically demanding. This was not the case, as the increased use was evenly distributed across all target heights, and use decreased or remained unchanged with increasing dwell time. Results are discussed relative to current models of limb choice and the potential use of robotic gravity support to overcome learned non-use in stroke patients.

Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models

Abstract In nonlinear panel data models, fixed-effects methods are often criticized because they cannot identify average marginal effects (AMEs) in short panels. In contrast with that criticism, we prove the point identification of different AMEs, including causal effects of changes in the lagged dependent variable or the last choice's duration, in a panel dynamic logit model for T as small as three. Our proofs are constructive and provide simple closed-form expressions for the AMEs in terms of probabilities of choice histories. We illustrate our results using Monte Carlo experiments and with an empirical application of a dynamic model of consumer brand choice.

Urban Air Mobility Passengers’ Profiling: Evidence from Milan Airports, Italy

Urban air mobility (UAM) is expected to offer new travel options for passengers to and from airports in the near future, despite uncertainties associated with regulatory issues, environmental concerns, and societal impacts. This paper analyzes the socioeconomic and behavioral factors that could influence users’ modal choices (including UAM services) for accessing and egressing airports. Using revealed and stated preference data collected at Milan airports (Italy), mixed logit and hybrid choice modeling specifications are estimated and compared with profile potential UAM passengers. Our findings suggest that the level of service, socioeconomic factors, and trip-related variables explain passengers’ choices better than latent traits such as fear of flying, propensity for technological advances, and expectations about the safety of UAM services. In other words, the additional complexity of hybrid choice modeling is not justified by the slight gain in likelihood compared with the estimated mixed logit model. The results also indicate that high-income individuals traveling for business purposes are the most likely demand segment to use UAM services, at least initially. Moreover, highly educated individuals and employees who have their travel expenses reimbursed for work trips are less likely to choose UAM services for airport access or egress, preferring traditional ground taxis.

On the Selection of Workplace in the COVID-19 Era Using Attitudinal Factors

After two years of living with the threat of COVID-19 in Ontario, Canada, pre-pandemic circumstances returned. During the pandemic, we relied on ICT-based tools to carry out our daily tasks, and now we have reached a tipping point. Should we keep our new routines to benefit us in the future? Or should we return to our routines before the pandemic? This study utilized a travel survey to examine the impact of the COVID-19 pandemic on work activity-travel behavior and the persistence of new traditions in the post-pandemic era. The data for this study comes from a sample of 1,000 Greater Toronto Area residents who participated in a web-based survey in July, 2021, when Ontario began the third phase of reopening. This paper investigates work activity and workplace selection in more depth. Using factor analysis, a collection of latent attitudinal variables was identified. An integrated choice and latent variable model estimated the influential systematic and latent variables on the perceived workplace selection in the post-pandemic condition. The results showed that 71% of responders want to continue working from home at least once weekly after the pandemic. The preferred frequency of telecommuting in the post-pandemic period was positively correlated with education level, positive feelings about telecommuting experience, and certain occupation types, while negatively associated with age.

Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations.

Surrogate optimisation holds a big promise for building energy optimisation studies due to its goal to replace the use of lengthy building energy simulations within an optimisation step with expendable local surrogate models that can quickly predict simulation results. To be useful for such purpose, it should be possible to quickly train precise surrogate models from a small number of simulation results (10-100) obtained from appropriately sampled points in the desired part of the design space. Two sampling methods and two machine learning models are compared here. Latin hypercube sampling (LHS), widely accepted in building energy community, is compared to an exploratory Monte Carlo-based sequential design method mc-intersite-proj-th (MIPT). Artificial neural networks (ANN), also widely accepted in building energy community, are compared to gradient-boosted tree ensembles (XGBoost), model of choice in many machine learning competitions. In order to get a better understanding of the behaviour of these two sampling methods and two machine learning models, we compare their predictions against a large set of generated synthetic data. For this purpose, a simple case study of an office cell model with a single window and a fixed overhang, whose main input parameters are overhang depth and height, while climate type, presence of obstacles, orientation and heating and cooling set points are additional input parameters, was extensively simulated with EnergyPlus, to form a large underlying dataset of 729,000 simulation results. Expendable local surrogate models for predicting simulated heating, cooling and lighting loads and equivalent primary energy needs of the office cell were trained using both LHS and MIPT and both ANN and XGBoost for several main hyperparameter choices. Results show that XGBoost models are more precise than ANN models, and that for both machine learning models, the use of MIPT sampling leads to more precise surrogates than LHS.