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Workforce participation decisions involve multiple stages: search, screening and offer evaluation. Standard econometric practice focusses on these stages in isolation. We conceptualize the focal behaviours as separate sequential decision stages, and provide a stated preference measurement framework for online job search and choice with a behaviourally consistent modelling approach. We demonstrate this approach in an empirical application of 275 dentists who completed an online survey including two Discrete Choice Experiments: the first mimicked an online job search site in which dentists decided which jobs they would apply to and the second presented dentists with a job offer which they accepted or rejected. Modelling these tasks requires a two-stage econometric model that incorporates the likelihood of application (first stage) into the job offer choice (second stage). The model detects differences in preferences (hence behaviours) across stages, facilitating the differentiation of policy aimed at search and job choice behaviours. Job screening occurs during search and the marginal propensity to apply for a job-type differs from the offer stage. We suggest that the approach presented provides a valuable way to investigate how dentists particularly, and perhaps the health workforce more generally, respond at different stages of workforce participation decisions and discuss practical implications. • Propose a conceptual framework to separate stages of job search and evaluation. • Propose an econometric model that includes the likelihood of job search in choice. • Empirical application to understand dentist workforce supply decisions. • Job screening by attribute cut-offs occurs at the job search stage. • Survey mimics the process of online job search, application and offer evaluation. [ABSTRACT FROM AUTHOR]

This paper follows the review of empirical evidence on the existence of hypothetical bias (HB) in choice experiments (CEs) presented in Part I of this study. It observes how the variation in operational definitions of HB has prohibited consistent measurement of HB in CE. It offers a unifying definition of HB and presents an integrative framework of how HB relates to but is also distinct from external validity (EV), with HB representing one component of the wider concept of EV. The paper further identifies major sources of HB and discusses explanations as well as possible moderating factors of HB. The paper reviews methods of HB mitigation identified in the literature and the empirical evidence of their effectiveness. The review includes both ex-ante and ex-post bias mitigation methods. Ex-ante bias mitigation methods include cheap talk , real talk , consequentiality scripts , solemn oath scripts , opt-out reminders , budget reminders , honesty priming , induced truth telling , indirect questioning , time to think and pivot designs. Ex-post methods include follow-up certainty calibration scales , respondent perceived consequentiality scales , and revealed-preference-assisted estimation. It is observed that the mitigation methods and their preferred use vary markedly across different sectors of applied economics. The existing empirical evidence points to the overall effectiveness of mitigation strategies in reducing HB, although there is some variation. The paper further discusses how each mitigation method can counter a certain subset of HB sources. Considering the prevalence of HB in CEs and the effectiveness of bias mitigation methods, it is recommended that implementation of at least one bias mitigation method (or a suitable combination where possible) becomes standard practice in conducting CEs to ensure that inferences and subsequent policy decisions are as much as possible free of HB. • Definition and conceptualisation of hypothetical bias in choice experiments is revisited. • The relationship between hypothetical bias and external validity in choice experiments revisited. • Sources/explanations and moderating factors of hypothetical bias are identified. • Methods of hypothetical bias mitigation and the empirical evidence on their effectiveness reviewed. • Recommendations offered for adopting mitigation methods tailored to likely source(s) of hypothetical bias. [ABSTRACT FROM AUTHOR]

The notion of hypothetical bias (HB) constitutes, arguably, the most fundamental issue in relation to the use of hypothetical survey methods. Whether or to what extent choices of survey participants and subsequent inferred estimates translate to real-world settings continues to be debated. While HB has been extensively studied in the broader context of contingent valuation, it is much less understood in relation to choice experiments (CE). While the number of CE studies has rapidly increased, the critical issue of HB has been studied in only a small fraction of CE studies. This paper provides macro-scale insights into the literature of CE and reviews the empirical evidence for HB in CE in various fields of applied economics as well as experimental psychology and behavioural neuroscience. Results suggest mixed evidence on the prevalence, extent and direction of HB as well as considerable context and measurement dependency. While HB is found to be an undeniable issue when conducting CEs, the empirical evidence on HB does not render CEs unable to represent real-world preferences. While health-related choice experiments often find negligible degrees of HB, experiments in consumer behaviour and transport domains suggest significant degrees of HB. Environmental valuation studies provide mixed evidence. Also, across these disciplines many studies display HB in their total willingness to pay estimates and opt-in rates but not in their hypothetical marginal rates of substitution (subject to scale correction). Further, recent findings in psychology and brain imaging studies identify neurocognitive mechanisms underlying HB that may explain some of the discrepancies and unexpected findings in the mainstream CE literature. • Empirical evidence on the prevalence of hypothetical bias in choice experiments is reviewed. • The survey integrates findings of consumer, health, environmental and transport economics. • Evidence from experimental psychology partly explain mechanisms of hypothetical bias. • Hypothetical bias is found to be an undeniable issue in relation to choice experiments. • The prevalence of significant bias varies considerably across disciplines and contexts of choice. [ABSTRACT FROM AUTHOR]

Since the introduction of Discrete Choice Analysis, countless efforts have been made to enhance the efficiency of data collection through choice experiments and to improve the behavioural realism of choice models. One example development in data collection are best-worst discrete choice experiments (BWDCE), which have the benefit of obtaining a larger number of observations per respondent, allowing for reliably estimating choice models even with smaller samples. In SWDCE, respondents are asked to alternatingly select the 'best'/'worst' alternatives, until the choice set is exhausted. The use of BWDCE raises the question of decision-rule consistency through the stages of the experiment. We challenge the notion that the same fully compensatory decision rule is utilised throughout the experiment. We hypothesize that respondents may utilise one decision rule for selecting the 'best' and another for selecting the 'worst' alternatives. To test our hypothesis, we developed a model that combines the SBWMNL model for modelling best-worst data and the μRRM model that can account for variations in decision rules. Our results show that decision-rule heterogeneity does seem to be present in BWDCE: it is more likely that 'best' choices are made using a fully compensatory decision rule (maximising utility), whereas 'worst' choices are more likely made using a non-compensatory decision rule (minimising regret). Such behaviour is largely similar to how image theory describes the decision-making process in complex situations. Our findings give choice modellers new insight into the behaviour of respondents in best-worst experiments and allows them to represent their behaviour more accurately. • Adapted best-worst choice model that can account for decision rule heterogeneity. • Respondents do not use the same decision rule in best-worst choice experiments. • Successive choices in discrete choice experiments have a lower reliability. • The developed models do not produce significantly different parameter estimates. [ABSTRACT FROM AUTHOR]