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Building on recent advances in inverse probability weighted identification and estimation of counterfactual distributions, the authors examine the history of wage earnings for women and their potential wage distributions in the United States. These potentials are two counterfactuals, what if women received men’s market “rewards” for their own “skills,” and what if they received the women’s rewards but for men’s characteristics? Using the Current Population Survey data from 1976 to 2013, the authors analyze the entire counterfactual distributions to separate the “structure” and human capital “composition” effect. In contrast to Maasoumi and Wang (2019), the reference outcome in these decompositions is women’s observed earnings distribution, and inverse probability methods are employed, rather than the conditional quantile approaches. The authors provide decision theoretic measures of the distance between two distributions, to complement assessments based on mean, median, or particular quantiles. We assess uniform rankings of alternate distributions by tests of stochastic dominance in order to identify evaluations robust to subjective measures. Traditional moment-based measures severely underestimate the declining trend of the structure effect. Nevertheless, dominance rankings suggest that the structure (“discrimination”?) effect is bigger than human capital characteristics.

Since its introduction by Chari, Kehoe, and McGrattan (2007), Business Cycle Accounting (BCA) exercises have become widespread. Much attention has been devoted to the results of such exercises and to methodological departures from the baseline methodology. Little attention has been paid to identification issues within these classes of models. In this chapter, the authors investigate whether such issues are of concern in the original methodology and in an extension proposed by Šustek (2011) called Monetary Business Cycle Accounting. The authors resort to two types of identification tests in population. One concerns strict identification as theorized by Komunjer and Ng (2011) while the other deals both with strict and weak identification as in Iskrev (2010). Most importantly, the authors explore the extent to which these weak identification problems affect the main economic takeaways and find that the identification deficiencies are not relevant for the standard BCA model. Finally, the authors compute some statistics of interest to practitioners of the BCA methodology.

The purpose of this paper is to examine to what extent Finnish university students endorse entrepreneurial intent and the ways in which they position themselves in relation to entrepreneurship according to their self-perceived abilities or “ability self”.

The study was conducted by means of an e-survey, and the participants comprised the sample of students (n =1,819) from two Finnish universities, representing diverse fields of study.

It was found that a great majority of the students showed a relatively low intent to become an entrepreneur. The perception of abilities, such as innovativeness and ambitiousness-competitiveness, was positively related with entrepreneurial intent, whereas the perception of academic abilities and “conventional” employee skills indicated inverse associations.

The findings suggest that in terms of self-perceived abilities, entrepreneurship in an academic context is perceived as a rather restricted category to which only a few specific individuals have access. Accordingly, there is a certain tension between the tenets of entrepreneurship and corresponding abilities, and the ethos of universities and related high-valued abilities such as theoreticality and criticality.

Although employability and entrepreneur intent have been widely studied, little is known about students’ identification with entrepreneurship according to their ability perceptions. The present study contributes to the existing body of knowledge on university students’ “internal employability” that involves students’ self-assurance and views of work-related relevance with regard to supposed abilities.

This study aims to measure and further compare the countries in terms of the achievement in the degree of financial inclusion over the study period and between income groups considering 26 nations from Asia for the period 2013-2017.

While measuring the degree of financial inclusion, the study prepares an index using weighted arithmetic mean and the inverse of the Euclidean distance method. Further, comparison between the study period and between the income groups has been made using the dependent samples t-test as well as the Wilcoxon signed-rank test and independent samples t-test, respectively.

The study extends empirical insights by laying out the ranks for the countries considered for each of the study periods individually as well as in terms of mean financial inclusion scores for the study period. Further, comparison in terms of mean financial inclusion scores shows significant differences between the income groups, whereas the differences between the study periods turn out to be non-significant.

Less availability of intended variables over time restricts the predictive capability of sketching the phenomena in a true sense and claims further an exhaustive research to pursue in the future.

With the declining trend except for 2016-2017 in the achievement of financial inclusion scores over time, the study suggests emphasizing the initiatives targeted to include the excluded within the ambit of the formal financial system, which somehow seems unstable.

The novelty of the study lies in the portrayal of a measure that seems representative of the scale for development with deeper insight.

This paper aims to introduce a non-invasive and convenient method to detect a life-threatening disease called aortic dissection. A Bayesian inference based on enhanced multi-sensors impedance cardiography (ICG) method has been applied to classify signals from healthy and sick patients.

A 3D numerical model consisting of simplified organ geometries is used to simulate the electrical impedance changes in the ICG-relevant domain of the human torso. The Bayesian probability theory is used for detecting an aortic dissection, which provides information about the probabilities for both cases, a dissected and a healthy aorta. Thus, the reliability and the uncertainty of the disease identification are found by this method and may indicate further diagnostic clarification.

The Bayesian classification shows that the enhanced multi-sensors ICG is more reliable in detecting aortic dissection than conventional ICG. Bayesian probability theory allows a rigorous quantification of all uncertainties to draw reliable conclusions for the medical treatment of aortic dissection.

This paper presents a non-invasive and reliable method based on a numerical simulation that could be beneficial for the medical management of aortic dissection patients. With this method, clinicians would be able to monitor the patient’s status and make better decisions in the treatment procedure of each patient.

To reduce the heat load of a gas turbine blade, its surface is covered with an outer layer of ceramics with high thermal resistance. The purpose of this paper is the selection of ceramics with such a low heat conduction coefficient and thickness, so that the permissible metal temperature is not exceeded on the metal-ceramics interface due to the loss ofmechanical properties.

Therefore, for given temperature changes over time on the metal-ceramics interface, temperature changes over time on the inner side of the blade and the assumed initial temperature, the temperature change over time on the outer surface of the ceramics should be determined. The problem presented in this way is a Cauchy type problem. When analyzing the problem, it is taken into account that thermophysical properties of metal and ceramics may depend on temperature. Due to the thin layer of ceramics in relation to the wall thickness, the problem is considered in the area in the flat layer. Thus, a one-dimensional non-stationary heat flow is considered.

The range of stability of the Cauchy problem as a function of time step, thickness of ceramics and thermophysical properties of metal and ceramics are examined. The numerical computations also involved the influence of disturbances in the temperature on metal-ceramics interface on the solution to the inverse problem.

The computational model can be used to analyze the heat flow in gas turbine blades with thermal barrier.

A number of inverse problems of the type considered in the paper are presented in the literature. Inverse problems, especially those Cauchy-type, are ill-conditioned numerically, which means that a small change in the inputs may result in significant errors of the solution. In such a case, regularization of the inverse problem is needed. However, the Cauchy problem presented in the paper does not require regularization.