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The purpose of this paper is to examine the factors associated with three modes of firms’ exit (voluntary liquidation, involuntary liquidation and acquisition) in a mutually exclusive environment. In particular, three modes of exit are treated as independent events given that different causes and consequences exist for each exit mode. The data set is a panel of 4,408 US manufacturing firms spanning over the period 1976–1995.

The discrete choice model is used to establish a relationship between modes of exit and a set of explanatory variables, which are specific to the firm, industry and macroeconomic conditions. Use of panel data encourages us to estimate a random effects multinomial logistic regression model, which allows exit modes as mutually exclusive events and at the same time controls the firm-specific unobserved heterogeneity in the sample.

The analysis suggests that the determinants of voluntary liquidation are age, size, profitability, technology intensity and inflation level. The determinants of involuntary liquidation are size, leverage, profitability and inflation level. For acquisition, determinants are age, size, advertising intensity, Tobin’s q, GDP growth, inflation level and interest rate. The findings suggest that exit modes have a different set of determinants and the scale of effects of some common determinants such as age, size and profitability differs between exit modes.

The analysis presented in this study relies on data from US manufacturing firms only. Thus, there is a need to explore the determinants of exit modes in other countries as well using the proposed econometric model.

The findings presented in this paper are useful for managers and policymakers to design strategies/actions for avoiding particular mode of exit.

This study provides empirical evidence on the differences in factors associated with exit modes and confirms the existence of mutually exclusive nature of exit modes. Findings suggest that for future empirical studies on firm exit, the exit modes must be treated as a heterogeneous event.

The purpose of this paper is to examine the ability of hedge funds and funds of hedge funds to generate absolute returns using fund level data.

The absolute return profiles are identified using properties of the empirical distributions of fund returns. The authors use both Bayesian multinomial probit and frequentist multinomial logit regressions to examine the relationship between the return profiles and fund characteristics.

Some evidence is found that only some hedge funds strategies, but not all of them, demonstrate higher tendency to produce absolute returns. Also identified are some investment provisions and fund characteristics that can influence the chance of generating absolute returns. Finally, no evidence was found for performance persistence in terms of absolute returns for hedge funds but some limited evidence for funds of funds.

This paper is the first attempt to examine the hedge fund return profiles based on the notion of absolute return in great details. Investors and managers of funds of funds can utilize the identification method in this paper to evaluate the performance of their interested hedge funds from a new angle.

Using the properties of the empirical distribution of the hedge fund returns to classify them into different absolute return profiles is the unique contribution of this paper. The application of the multinomial probit and multinomial logit models in the fund performance and fund characteristics literature is also new since the dependent variable in the authors' regressions is multinomial.

This paper reviews the current literature on theoretical and methodological issues in discrete choice experiments, which have been widely used in non-market value analysis, such as elicitation of residents' attitudes toward recreation or biodiversity conservation of forests.

We review the literature, and attribute the possible biases in choice experiments to theoretical and empirical aspects. Particularly, we introduce regret minimization as an alternative to random utility theory and sheds light on incentive compatibility, status quo, attributes non-attendance, cognitive load, experimental design, survey methods, estimation strategies and other issues.

The practitioners should pay attention to many issues when carrying out choice experiments in order to avoid possible biases. Many alternatives in theoretical foundations, experimental designs, estimation strategies and even explanations should be taken into account in practice in order to obtain robust results.

The paper summarizes the recent developments in methodological and empirical issues of choice experiments and points out the pitfalls and future directions both theoretically and empirically.

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).

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.

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.

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.

This study examines the relationship between managerial overconfidence and corporate financing decisions by constructing proxies for managerial overconfidence based on the track records of earnings forecasts in Japanese listed firms. We find that managers have the stable tendency to forecast overly upward earnings compared to actual ones and that their upward bias decreases the probability of issuing equity in the public market by about 4.7 percent per one standard error, which economically has the strongest impact on financing decisions. This tendency is observed when we employ alternative measures for managerial overconfidence and other model specifications. However, in private placements, the choice to offer equity is not always avoided by managers. This implies that managers place private equity with the expectation of the certification effect

With this study we investigate the preferences of an important category of consumers of organic products (regular consumers of organic food or RCOFs) allowing for preference heterogeneity. A survey instrument was developed to elicit preferences for important qualitative and quantitative attributes of extra virgin olive oil. The survey was administered via questionnaire to a random sample of 198 RCOFs in organic food stores of Naples, Italy. The choice task was organised around a fractional factorial main effects orthogonal design. Each respondent made eight choices to rank‐order nine product profiles in terms of their individual preference. Product attributes included price, origin of production, type of certification and visual appearance. Interestingly, the set of observed responses appears to display significant preference heterogeneity for origin of production and price. Once heterogeneity and correlation among repeated choice by the same respondent are accounted for by means of random‐parameter panel logit models, the fit increases dramatically with respect to the more restrictive fixed‐parameter logit models. Results also suggest that price plays an important role as quality proxy, while visual appearance is not significant in preference modelling and the type of certification programme has a fixed effect.

Improved indigenous chicken is considered a sustainable agricultural practice with social, economic and environmental indicators. Therefore, the analysis of the choice of market channels is of considerable importance to farmers with reference to improved livelihoods and poverty alleviation in developing countries. The purpose of this study is to investigate the factors that influence market channel choices among improved indigenous chicken farmers in Baringo County and to rank the determinants according to their level of importance in influencing farmer's choice of marketing channels.

A multistage sampling technique was employed to collect data from 209 households for the study conducted between April and July 2019, out of which, 198 useful responses were obtained. Multinomial logit regression and neural network models were used to analyze the factors influencing market channel choice based on socioeconomic, demographic and farm characteristics.

It was established that group membership, education, market distance, transport costs, farm size, cost of information and bargain costs were statistically significant in the choice of market channels (wholesaler, brokers, processors and supermarkets). With the direct consumer as the base market choice. The cost of transport had the highest normalized importance in the prediction of a farmer's selection of market channels for both radial basis function (RBF) and multilayer perceptron (MLP) neural networks. However, flock attributes and age of household head had the least normalized importance in MLP and RBF, respectively.

Due to the insufficiency of resources and time, this study only focused on a small part of the country (Baringo County). However, improved indigenous chicken farming is widely practiced in Kenya. Further studies can be carried out in other counties to validate the results of this study.

The outcome can be used in policy implementation involving improved indigenous chicken production in Kenya.

This study suggests the methods aimed at enhancing poultry sector in other counties in Kenya as well as other developing countries.