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The search for flexible models has led the simple multinomial logit model to evolve into the powerful but computationally very demanding mixed multinomial logit (MMNL) model. That flexibility search lead to discrete choice hybrid choice models (HCMs) formulations that explicitly incorporate psychological factors affecting decision making in order to enhance the behavioral representation of the choice process. It expands on standard choice models by including attitudes, opinions, and perceptions as psychometric latent variables.

In this paper we describe the classical estimation technique for a simulated maximum likelihood (SML) solution of the HCM. To show its feasibility, we apply it to data of stated personal vehicle choices made by Canadian consumers when faced with technological innovations.

We then go beyond classical methods, and estimate the HCM using a hierarchical Bayesian approach that exploits HCM Gibbs sampling considering both a probit and a MMNL discrete choice kernel. We then carry out a Monte Carlo experiment to test how the HCM Gibbs sampler works in practice. To our knowledge, this is the first practical application of HCM Bayesian estimation.

We show that although HCM joint estimation requires the evaluation of complex multi-dimensional integrals, SML can be successfully implemented. The HCM framework not only proves to be capable of introducing latent variables, but also makes it possible to tackle the problem of measurement errors in variables in a very natural way. We also show that working with Bayesian methods has the potential to break down the complexity of classical estimation.

The purpose of this paper is to examine the effect of a change in minimum wage on hours worked of paid employment in Indonesia. This study used the Indonesian Labor Force Survey (Sakernas) data from 1996 to 2003.

This study employs Bourguignon-Fournier-Gurgand two-step procedure of sample selection corrections based on a multinomial logit model for a potential selection bias from a non-random sample. This study extends the specification by examining the effects of minimum wage on hours worked of paid employment separately across individuals in different groups of gender (male-female workers) and residences (urban-rural areas).

This study generally found that an increase in the minimum wage increases hours worked of the existing paid employees. The effects of the minimum wage on hours worked are stronger for female workers than male workers particularly in urban areas due to that female workers, particularly in urban areas, are mostly employed in industries which contain more low-wage workers. Comparing residences, the minimum wage coefficient in rural areas is slightly higher because of the structural transformation in Indonesia marked by a shift in employment from the agriculture sector to the other sectors that require more working hours.

The empirical studies of the effect of minimum wage on hours worked in developing countries are very limited. This study contributes to the literature by employing the sample selection corrections based on a multinomial logit for a potential selection bias from a non-random sample This study also extends the hours worked specification by analyzing the effects of minimum wage on hours worked separately across individuals in different groups of workers, in terms of gender (male-female workers) and their residences (urban-rural areas).

We examine the roles of sample initial conditions and unobserved individual effects in consistent estimation of the dynamic binary response panel data model. Different specifications of the model are estimated using female welfare and labor force participation data from the Survey of Income and Program Participation. These include alternative random effects (RE) models, in which the conditional distributions of both the unobserved heterogeneity and the initial conditions are specified, and fixed effects (FE) conditional logit models that make no assumptions on either distribution. There are several findings. First, the hypothesis that the sample initial conditions are exogenous is rejected by both samples. Misspecification of the initial conditions results in drastically overstated estimates of the state dependence and understated estimates of the short- and long-run effects of children on labor force participation. The FE conditional logit estimates are similar to the estimates from the RE model that is flexible with respect to both the initial conditions and the correlation between the unobserved heterogeneity and the covariates. For female labor force participation, there is evidence that fertility choices are correlated with both unobserved heterogeneity and pre-sample participation histories.

Purpose – This chapter gives an overview of methods for defining and analysing crash severity.

Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling.

Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling.

Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling.

Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.

The purpose of this study is to examine tax stamp evasion at Twifu Atti-Morkwa and Hemang Lower Denkyira districts in the central region of Ghana.

A cross-sectional survey design was adopted to sample 305 micro-taxpayers through the use of multi-stage sampling technique. Primary data were collected from the micro-taxpayers using structured interview. Binary and multinomial logit regression models were used to regress the tax stamp evasion on economic and non-economic factors.

The study found that the likelihood of micro taxpayers to evade tax stamp is predicted by age, application of sanctions, guilt feeling, transportation cost to tax office and rate of tax audit. Thus, the study found partial support for expected utility, planned behaviour and attributory theories in explaining tax evasion behaviour of micro-taxpayers.

There are several measures of addressing tax evasion behaviour of micro taxpayers. Evasion behaviour can be deterred by enforcement strategies such as application of sanctions and regular tax audit, establishment of more tax offices in the districts and writing normative messages on the faces of tax stamp stickers.

This study helps explains the tax evasion behaviour of micro-taxpayers of a developing economy like Ghana using a special type of tax design meant to capture such taxpayers in the tax bracket. To the best of our knowledge, the study is unique in terms of the means of measuring tax evasion and the methodologies used.

Purpose – Information collected from police crash reports has long been the primary source of data for the analysis of factors that determine the likelihood of a crash (crash frequency) and its resulting severity (measured in terms of the extent of injuries to vehicle occupants). Proper cross-sectional analyses techniques, covered in this chapter, are important for guiding safety policy and countermeasures.

Methodology – This chapter provides an overview of some of the more commonly used cross-sectional statistical and econometric methods, and discusses the nuances and their limitations with regard to how they are applied to typical crash-report data.

Findings – The wide variety of analytic methods available to safety researchers makes the selection of appropriate methods critical. This chapter provides important guidance for safety researchers in their choice of methodological approach.

Implications – Understanding the importance of proper model specification, unobserved heterogeneity, endogeneity and other factors covered in this chapter is extremely important in analysing safety data and must be given full consideration before any results are finalised.

Fast food (FF) consumption increased dramatically through the 1990s in the USA, accounting for nearly 35.5 percent of total away‐from‐home expenditures in 1999. Given dramatic changes in food consumption, and heightened public concern about health and obesity, there is a considerable need for research to understand better the factors affecting US FF consumption. This paper aims to fill this gap.

In this paper, logistic regression is applied to analyze the socioeconomic and demographic factors influencing the likelihood of consuming FF using United States Department of Agriculture data from the Continuing Survey of Food Intakes by Individuals from 1994 to 1996 and the Supplemental Children's Survey of 1998.

In general, the expected likelihood of FF consumption increases until around 20‐30 years of age and then decreases; increases as household income grows until about $50,000‐60,000 and then decreases; and decreases as household size grows. Further, males from the Midwest and South regions that live outside central cities in Metropolitan Statistical Areas have the highest likelihood of consuming FF.

While much literature has addressed key questions about expenditure on food away from home, this study complements previous work by focusing on food items consumed from FF facilities in the 1990s. In addition, the results find highly significant and important (statistically and economically) interactions between the likelihood of FF consumption and age, income, and household size.

To investigate the extent to which disability discourages an individual from going on the job market, using data from an Italian survey.

We use an extended definition of labour force participation based on being employed or currently seeking work even if the persons declare themselves as housewives, students, retired or in any other condition otherwise. We use probit, sequential and multinomial logit models for analysing labour force participation and outcomes. We distinguish between the impact of disability in its strict sense and chronic illness explaining the difference.

In all variants we find that chronic illness is a stronger deterrent for labour force participation than disability. Women are more discouraged compared to men. Intellectual disability is the strongest barrier and hearing the least influential. In a sequential decision-making process, we find that disability affects both labour force participation decision and the ability to be employed but not so much the choice between part-time and full-time.

Policies providing tailored solutions for improved access to education and health care for disabled persons will enhance their work opportunities.

Data set is cross-sectional and characterised by attrition. It would be interesting to compare results with a longitudinal and more representative data set.

We have a unique data set from a survey which was specifically targeted at people who were identified as disabled in a previous survey. The Italian context is also special due to its high legal employment quotas and noncompliance sanctions.