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This paper aims to estimate the global income distribution during the nineties using limited information. In a first stage, we obtain national income distributions considering a model with two parameters. In particular, we propose to use the so-called Lamé distributions, which are curved versions of the Sigh-Maddala and Dagum distributions. The main feature of this family is that they represent parsimonious models which can fit income data adequately with just two parameters and whose Lorenz curves are characterized by only one parameter. In a second stage, global and regional distributions are derived from a finite mixture of these families using population shares. We test the validity of the model, comparing it with other two-parameter families. Our estimates of different inequality measures suggest that global inequality presents a decreasing pattern mainly driven by the fall of the differences across countries during the course of the study period that offsets the increase in disparities within countries.

This paper considers a parametric model for the joint distribution of income and wealth. The model is used to analyze income and wealth inequality in five OECD countries using comparable household-level survey data. We focus on the dependence parameter between the two variables and study whether accounting for wealth and income jointly reveals a different pattern of social inequality than the traditional “income only” approach. We find that cross-country variations in the dependence parameter effectively account only for a small fraction of cross-country differences in a bivariate measure of inequality. The index appears primarily driven by differences in inequality in the wealth distribution.

This article explores the size‐growth relationship for a panel of large US credit unions, using the panel unit root tests of Im et al. (2003) and Maddala and Wu (1999). The reference point is Gibrat’s Law, or the Law of Proportionate Effect, according to which firm growth rates are independent of firm sizes. The panel unit root tests are applied to the log as sets and log membership series of a sample of 997 surviving credit unions which reported data over the period 1993 to 2002. In each case the panel unit root tests fail to reject the null hypothesis of non‐stationarity in the logarithmic size series for all credit unions. The implication is that credit union sizes follow random walks, producing a tendency for industry concentration to increase in the long term. With many of the largest institutions currently offering portfolios of products and services similar to those of commercial banks and other financial institutions, these implications of the panel unit root test results appear consistent with observed patterns within the sector in recent years.

In M&A markets, acquirers face a hold-up problem of losing the value of investments they make in due diligence, negotiations, and post-acquisition planning if targets would pursue the options of waiting for better offers or selling to an alternative bidder. This chapter extends information economics to the literature on M&A contracting by arguing that such contracting problems are more likely to occur for targets with better outside options created by the information available on their resources and prospects. We also argue that acquirers address these contracting problems by using termination payment provisions to safeguard their investments. While previous research in corporate strategy and finance has suggested that certain factors can facilitate an acquisition by reducing a focal acquirer’s risk of adverse selection (e.g., signals, certifications), we note that these same factors can make the target attractive to other potential bidders and can exacerbate the risk of hold-up, thereby leading acquirers to use termination payment provisions as contractual safeguards.

Introduction: Financial failure is a concept that may arise from many internal and external factors such as operational, financial, and economic items and may incur serious losses. Over-indebtedness arising from managerial misjudgments may cause high financial distress, insufficiency, and bankruptcy. In this regard, determination of effects of capital structure decisions on financial failure risk is crucial.

Aim: The main purpose of this study is to explore the relationship between capital structure decisions and financial failure risk. For this purpose, data from Borsa İstanbul (BIST) for listed food and beverage companies for the period from 2004 to 2019 is used. Another purpose of this study is to compare the financial failure models considering capital structure theories.

Method: In the study, capital structure decisions are associated with five different financial ratios; while the financial failure risk is proxied by financial failure scores of Altman (1968), Springate (1978), Ohlson (1980), Taffler (1983), and Zmijewski (1984). Therefore, five different panel data models are used for testing these hypotheses.

Findings: The results of panel data analysis reveal that capital structure decisions have statistically significant effects on financial failure risk for all models; however, those effects vary from one financial failure model to another. Also, the results show that in the models in which financial failure risk is proxied by the Altman (1968) and Taffler (1983) scores, the aggressive financial policies increase the financial failure risk. However, regarding the models in which financial failure risk is proxied by the Springate (1978), Ohlson (1980), and Zmijewski (1984) scores, aggressive financial policies decrease the financial failure risk.

Originality of the Study: To the best of our knowledge, this chapter is original and important in terms of revealing the effects of capital structure decisions on the financial failure risk and comparing the financial failure models.

Implications: The results revealed that the risk of financial failure models represented by Altman (1968) and Taffler (1983) scores are found to be statistically stronger and more successful in meeting theoretical expectations compared to other models. Therefore, it would be more appropriate to refer Altman’s (1968) and Taffler’s (1983) financial failure models in financial failure risk measurements.

This paper examines the gender differences of expenditure distribution within the last decade in Spain. In particular, the Lorenz dominance is tested using expenditure distributions as approximated by the Dagum model. The sensitivity of the results to some conceptual choices, such as the equivalence scale or the gender reference, is also analysed.

Perhaps one of the most important points Shalev makes is that Przeworski and Tuene's (1970) admonition to comparative political analysts to replace the proper names of cases with concepts and variables, or to pursue what Ragin (1987) dubs variable-oriented research, has gone too far. In Shalev's view, cases (typically nation states for the purposes of this discussion) have become all but invisible. This is particularly troublesome in Shalev's mind because, at least as far as comparative analysis of developed democratic capitalist systems is concerned (and we can say the same for political research on Latin America, Africa, or Asia), the cases are few enough to know quite well and bring to the forefront of sophisticated analysis.¹ In addition, Shalev makes the distinct point that the theories we seek to test in comparative political research entail complex and often non-linear causal sequences: causes of particular political outcomes are commonly contingent on the presence of other forces, or conjunctural with temporally and spatially bound forces and contexts. In fact, in comparative theory, it is fair to argue (as Shalev does) that causal explanations of important political outcomes are often put forward in terms of complex configurations of multiple factors. Moreover, in theory and in practice, we are often confronted with the prospect of multiple configurative paths of causation of the same outcome. In the end, Shalev believes that the linear and additive logic of general MR analysis, as well as the more sophisticated versions with non-linear specifications and interaction terms, cannot adequately test our complex theories.

This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.