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What change in the distribution of a population’s health preserves the level of inequality? The answer to this analogous question in the context of income inequality lies somewhere between a uniform and a proportional change. These polar positions represent the absolute and relative inequality equivalence criterion (IEC), respectively. A bounded health variable may be presented in terms of both health attainments and shortfalls. As a distributional change cannot simultaneously be proportional to attainments and to shortfalls, relative inequality measures may rank populations differently from the two perspectives. In contrast to the literature that stresses the importance of measuring inequality in attainments and shortfalls consistently using an absolute IEC, this chapter formalizes a new compromise concept for a bounded variable by explicitly considering the two relative IECs, defined with respect to attainments and shortfalls, to represent the polar cases of defensible positions.

We use a surplus-sharing approach to provide new insights on commonly used inequality indices by evaluating the underpinning IECs in terms of how infinitesimal surpluses of health must be successively distributed to preserve the level of inequality. We derive a one-parameter IEC that, unlike those implicit in commonly used indices, assigns constant weights to the polar cases independent of the health distribution.

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

This paper examines variable selection among various factors related to motor vehicle fatality rates using a rich set of panel data. Four Bayesian methods are used. These include Extreme Bounds Analysis (EBA), Stochastic Search Variable Selection (SSVS), Bayesian Model Averaging (BMA), and Bayesian Additive Regression Trees (BART). The first three of these employ parameter estimation, the last, BART, involves no parameter estimation. Nonetheless, it also has implications for variable selection. The variables examined in the models include traditional motor vehicle and socioeconomic factors along with important policy-related variables. Policy recommendations are suggested with respect to cell phone use, modernization of the fleet, alcohol use, and diminishing suicidal behavior.

The ascendancy of vertical marketing structures as total systems has brought about considerable interest in designing distribution systems. Various administered and co‐operative alignments have been successful in the marketplace by enabling lower total costs through central buying practices and by producing a more predictable demand pattern. As these planned systems begin to create de facto competition among individual firms, the need for methods of discovering optimal vertical arrangements will increase. One method of finding optimal arrangements of interacting phenomena is by modelling—and linear programming techniques have been found to be particularly useful. The transportation problem or distribution model was one of the first applied special cases of linear programming. Mathematical solutions to this “special case” of linear programming began appearing in the literature during World War II. Since that time, the management science literature has been replete with significant contributions in the transportation area such as those of Hitchcock, Dantzig, Chames and Cooper, and Orden.

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.

The purpose of this paper is to investigate the impact of different mathematical formulations of the problem of optimal design of electrical machines on the results obtained using a local optimization solver. The aim is to investigate the efficiency and reliability of standard local solvers when handling different mathematical formulations. This could provide guidelines for designers in practical engineering applications.

The paper proposes six equivalent mathematical formulations of the optimal design problem of a slotless permanent‐magnet electric rotating machine. The authors investigate the impact of these different mathematical formulations on the results obtained using a local optimization solver which is well‐known in the engineering community: MatLab's fmincon function. The paper first computationally compares the six proposed formulations with a fixed value for the number of pole pairs p, that gives continuous optimization problems, then discusses some results when p is free on three mixed‐integer formulations.

The paper shows that, even though the considered formulations are mathematically equivalent, their numerical performances are different when an optimization solver, such as the one proposed by MatLab in fmincon, is used. Thus, the designer must take care about the formulation of the design problem in order to make more efficient the use of these kind of algorithms.

In the context of engineering applications, one usually resorts to well known and easy to use optimization solvers. The same optimization problem can be often formulated in different ways. Furthermore, the formal description of optimization problems has an impact on the applicability and efficiency of the corresponding solution methods. This is usually not taken into account when optimization solvers are exploited. The originality of this paper is in building on the theory of reformulations in mathematical optimization to investigate and highlight the impact of formulation differences.

There are many problems in civil or mechanical engineering related to structural design. In such a case, the solution techniques which lead to deterministic results are no longer valid due to the heuristic nature of design problems. The purpose of this paper is to propose a computational tool based on genetic algorithms, applied to the optimal design of cross-sections (solid tubes) of 3D truss structures.

The main feature of this genetic algorithm approach is the introduction of a selective-smart method developed in order to improve the convergence rate of large optimization problems. This selective genetic algorithm is based on a preliminary sensitivity analysis performed over each variable, in order to reduce the search space of the evolutionary process. In order to account for the optimization of the total weight, the displacement (of a specific section) and the internal stresses distribution of the structure a multiobjective optimization function was proposed.

The numerical results presented in this paper show a significant improvement in the convergence rate as well as an important reduction in the relative error, compared to the exact solution.

The variables sensitivity analysis put forward in this approach introduces a significant improvement in the convergence rate of the genetic algorithm proposed in this paper.

The purpose of this paper is to present an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform.

Unlike the convention of developing a set of kinematic equations and then solving them, an alternative numerical algorithm is proposed in which the principal components of link lengths are used as a bridge to analyze the forward kinematics of a Stewart platform. The values of link lengths are firstly transformed to the values of principal components through principal component analysis. Then, the computation of the values of positional variables is transformed to a two‐dimensional nonlinear minimization problem by using the relationships between principal components and positional variables. A hybrid Nelder Mead‐particle swarm optimizer (NM‐PSO) algorithm and a modified NM algorithm are used to solve the two‐dimensional nonlinear minimization problem.

Simulation experiments have been conducted to validate the numerical algorithm and experimental results show that the numerical algorithm is valid and can achieve good accuracy and high efficiency.

This paper proposes an adaptive numerical algorithm for forward kinematics analysis of general Stewart platform.

This chapter focuses on the impact of national economic conditions and voters’ attitudes on the positioning of European national political parties with regard to the European Union (EU). We provide an empirical analysis based on data gathered through the Chapel Hill Expert Survey (CHES) covering parties from 14 European countries observed over the 1999–2010 time span. We perform a regression analysis where the dependent variable measures the position of political parties vis-à-vis EU integration and explanatory variables include a number of measures of national economic conditions, features of the national political and institutional framework and voters’ Euroscepticism. Fixed effect, ordered logit and fractional logit estimates provide the following main results. Compared with other parties, non-mainstream political parties and those acting in established economies are more prone to mirror citizens’ Eurosceptic sentiments. National economic conditions such as inflation as well as gross domestic product (GDP) growth affect mainstream party support for the EU. Smaller and ideologically extreme parties are, on average, less supportive of European integration.

The purpose of this paper is to investigate the role of acquirer's earnings quality on the choice of payment method in mergers and acquisitions (M&A).

The paper applies a simultaneous equations model to address the concern of endogeneity between earnings quality and payment method in corporate acquisitions. In addition, a propensity score matching model is used for robustness purpose.

Previous studies imply that short‐term accruals have a significant impact on the choice of payment method in M&A. In this study, This paper shows that acquisition financing is not significantly affected by short‐term earnings quality once control variables are considered. Instead, this paper finds that it is the long‐term earnings quality of the acquirer that matters. Acquiring firms with poor (good) long‐term earnings quality prefer lower (higher) cash payment in acquisitions. Their results are robust to different definitions of earnings quality.

Researchers should consider the effect of long‐term earnings quality in their future investigations.

Investors should be aware of this issue when evaluating corporate mergers.

This is the first study to examine the impact of long‐term quality of earnings on the choice of payment method in M&A.