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Presents results from cross‐section and conditional logit models estimating the probability of participation by married women. One version of each specification uses potential experience and the other a measure of the number of years worked in the market since the age of 18. A series of cross‐section logit models, representing a threshold analysis of the decision to work in the market, appears to be inappropriate, when unmeasured characteristics influence the probability of participation. Hence, reports results from a conditional logit model controlling for fixed effects. These results confirm the cross‐section findings regarding the limitation of potential experience. The results suggest that potential experience reflects the negative effects of ageing on the probability of participation rather than the positive impact of training or tastes for market work.

The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment.

A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers.

The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice.

The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial.

Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation.

This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.

Public management researchers commonly model dichotomous dependent variables with parametric methods despite their relatively strong assumptions about the data generating process. Without testing for those assumptions and consideration of semiparametric alternatives, such as maximum score, estimates might be biased, or predictions might not be as accurate as possible.

To guide researchers, this paper provides an evaluative framework for comparing parametric estimators with semiparametric and nonparametric estimators for dichotomous dependent variables. To illustrate the framework, the article estimates the factors associated with the passage of school district bond referenda in all Texas school districts from 1998 to 2015.

Estimates show that the correct prediction of a bond passing increases from 77.2 to 78%, with maximum score estimation relative to a commonly used parametric alternative. While this is a small increase, it is meaningful in comparison to the random prediction base model.

Future research modeling any dichotomous dependent variable can use the framework to identify the most appropriate estimator and relevant statistical programs.

This paper investigates Chinese agricultural insurance agents willingness to offer (WTO) livestock insurance based on the variations of eight main attributes of livestock insurance.

This study implements discrete choice experiments (DCE) with actual insurance agents who design, sell and operate livestock insurance in China. The choice experiment of this study is based on the D-optimal approach, a six-block design, with 15 cards per block and two choices per card. The sample size was 211. Econometrics results are based on conditional and mixed logit models.

The authors find that the subsidy effect is enormous; a one level increase of subsidy leads to 3.166 times higher probability to offer. This subsidy effect is important as it confirms the endogenous structure between price and quantity in insurance offering, where subsidy does not only incentivize demand but also the supply. Another main factor of insurance investigated is the impact of different coverage types on agents' WTO. The authors find that agents prefer mortality insurance the most, followed by revenue insurance and profit insurance, while Index-Based Livestock Insurance (IBLI) is the least preferred to offer. Agents' knowledge about these newer types of insurance supports their WTO as well; thus, proper education is necessary to promote the more advanced types of livestock insurance.

A limitation is that in the presence of COVID 19, and administrative issues at the local level, the sample was not randomly drawn. Nonetheless, the authors believe that there is enough diversity across participants, insurers and provinces and have done sufficient robustness checks to support results and conclusions.

This study provides further validation for the DCE research method that could potentially be applied to different analyses: using choice experiments to study insurers and reveal their preferences, through combinations of various levels of core attributes for insurance products. The findings and contribution are critical to the reform and improvement of livestock insurance in China and for insurance markets more broadly. The authors find that insurers do not place equal weights or values on insurance product attributes and do not view types of insurance equally. In other words, while farmers may hold different preferences about the type of insurance they demand, the results suggest that insurers also hold preferences in the type of insurance they sell.

So far as the authors are aware, this is the first DCE designed around the supply of insurance products with the subjects being insurance agents, marketers and executives.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

In recent years, several methodologies for measuring portfolio credit risk have been introduced that demonstrate the benefits of using internal models to measure credit risk in the loan book. These models measure economic credit capital and are specifically designed to capture portfolio effects and account for obligor default correlations. An example of an integrated market and credit risk model that overcomes this limitation is given in Iscoe et al. [1999], which is equally applicable to commercial and retail credit portfolios. However, the measurement of portfolio credit risk in retail loan portfolios has received much less attention than the commercial credit markets. This article proposes a methodology for measuring the credit risk of a retail portfolio, based on the general portfolio credit risk framework of Iscoe et al. The authors discuss the practical estimation and implementation of the model. They demonstrate its applicability with a case study based on the credit card portfolio of a North American financial institution. They also analyze the sensitivity of the results to various assumptions.

The purpose of this paper is to fit a logit model for dry bulkers transporting grains through the Panama Canal versus alternative routes destined to East Asia, originating on the US Gulf and East Coast. This is with the purpose of better understanding the attributes.

In this paper, grain transits both through the Panama Canal and alternative routes, which are examined, and a logit model is developed to explain the route decision from a carrier/vessel operator point of view.

Transit draft is the most important attribute in the route decision process for grains according to this study. Also, Panamax bulkers are the preferred vessel size into China, especially through the Cape of Good Hope route, impacting Panama Canal’s market share for grains.

This research used only a full year of grain traffic data approximating fiscal year 2018 (October 1, 2017 to September 30, 2018). Data will come mostly from the Panama Canal transit data and observations using IHS’s Market Intelligence Network (MINT).

This paper is highly dependent on visual observations of grains vessels through alternative routes using AIS data from MINT software.

To identify predictors of corporate financial distress, using the discriminant and logit models, in an emerging market over a period of economic turbulence and to reveal the comparative predictive and classification accuracies of the models in this different environmental setting.

The research relies on a sample of 27 failed and 27 non‐failed manufacturing firms listed in the Istanbul Stock Exchange over the 1996‐2003 period, which includes a period of high economic growth (1996‐1999) followed by an economic crisis period (2000‐2002). The two well‐known methods, discriminant analysis and logit, are compared on the basis of a better overall fit and a higher percentage of correct classification under changing economic conditions. Furthermore, this research attempts to reveal the changes, if any, in the bankruptcy predictors, from those found in the earlier studies that rested on the data from the developed markets.

The logistic regression model is found to have higher classification power and predictive accuracy, over the four years prior to bankruptcy, than the discriminant model. In this research, the discriminant and logit models identify the same number of significant predictors out of the total variables analyzed, and six of these are common in both. EBITDA/total assets is the most important predictor of financial distress in both models. The logit model identifies operating profit margin and the proportion of trade credit within total claims ratios as the second and third most important predictors, respectively.

This paper reveals the accuracy with which the discriminant and logit models work in an emerging market over a period when firms face high uncertainty and turbulence. This study may be extended to other emerging markets to eliminate the limitation of the small sample size in this study and to further validate the use of these models in the developing countries. This can serve to make the methods important decision tools for managers and investors in these volatile markets.