The moral–emotional foundations of political discourse: a comparative analysis of the speech records of the U.S. and the Japanese legislatures

There is a growing body of research that focuses on the supposedly close association between an individual’s moral–emotional behavior and his/her political ideology. A prominent example is Haidt’s “moral psychology,” which claims that political liberals and conservatives draw on mutually different sets of moral foundations. However, this and other arguments, which have mostly been advanced in the social context of the United States, lack a comparative perspective. In this study, we examine these arguments in broader spatio-temporal settings by way of a comparative analysis of public deliberations in the U.S. and Japanese legislatures. More specifically, with the help of well-established moral- and emotional-word dictionaries, and employing advanced computational techniques for systematic data collection, we analyze a large volume of speech data that records floor debates over decades in the U.S. Congress and the Japanese Diet to derive longitudinal moral–emotional dynamics. We then use multilevel modeling to regress the derived moral–emotional patterns of legislative deliberations in each country on various covariates to locate possible drivers of these patterns. The results of these analyses reveal more qualified relationships between a moral–emotional framework and political ideology than preceding arguments have suggested, casting serious doubt on the widespread tendency in the literature to quickly rely on an ideological explanation. The findings suggest the need for a more comprehensive approach to handling moral–emotional phenomena in political science.     

Does energy substitution affect carbon dioxide emissions – Income relationship?

Considerable discussion has taken place during the last decade regarding the role of economic growth in determining environmental quality. Using data from 30 OECD countries for the period 1960–2003 and the nonparametric method of generalized additive models, which enables us to use flexible functional forms, this paper examines the environmental Kuznets curve hypothesis for carbon dioxide (CO₂). We find that the reduction of coal share in energy use has a significant effect on CO₂. Our results imply that economic growth is not sufficient to decrease CO₂ emissions.     

On statistical inference on the mean-variance efficient set

A proof of the asymptotic distribution of the estimated mean-variance frontier is given. A Bayesian prediction interval is derived for the capital asset pricing model. Numerical illustrations show that the prediction intervals for the CAPM are smaller than those for the constant mean model, if the fit of the CAPM is better than that of the constant mean model.     

Customer behavior analysis using motion pictures： Research on attractive flier design method

In this report, the authors would like to propose CMP-FDM （Customer Motion Pictures-Flier Design Method） to increase the willingness of consumers to visit the automobile dealership. CMP-FDM is a method of creating attractive fliers while using customer behavior analysis with videos. According to the analysis of how customers see fliers, the authors classify their behavior into three types and create attractive designs so that each guarantees each customer＇s satisfaction. Finally, the authors integrate the three types of fliers into one attractive to all customers by organizing the design features （design elements）, and then validate the method.     

The development of Japanese business, 1600–1980 :

The marginal propensity to consume in a simple Keynesian model is treated as a random coefficient. This gives rise to the problem of quotient of random variables, i.e., the Fieller-Creasy problem. The Bayesian and maximum likelihood estimators are compared in sampling experiments. The Bayesian estimators have smaller mean squared errors than the maximum likelihood estimators. Marginal posterior probability density functions for a given sample are also presented.     

A bayesian analysis of a random coefficient model in a simple keynesian system

The marginal propensity to consume in a simple Keynesian model is treated as a random coefficient. This gives rise to the problem of quotient of random variables, i.e., the Fieller-Creasy problem. The Bayesian and maximum likelihood estimators are compared in sampling experiments. The Bayesian estimators have smaller mean squared errors than the maximum likelihood estimators. Marginal posterior probability density functions for a given sample are also presented.     

Japanese–American Joint Restructuring of National Steel Corporation

In 1984, NKK corporation, a leading Japanese steel maker, acquired from the National Intergroup Inc. (NII), an American holding company, its 50% equity interest in National Steel Corporation (NSC), the fourth largest integrated steel firm in the US. In spite of the US economic recovery that began in 1992, NSC continued to operate in deficit and in June 1994 NKK brought in a new management team consisting of six American executives and one Japanese executive. Since the third quarter of 1994, NSC has turned its financial performance around. In this paper, using Bayesian estimation procedures, we estimate the join point or the time at which the productivity of NSC's major production lines experienced an upward shift; if the join point occurs after an appropriate gestation period of the new management team, NSC's recovery may be said to be due to the efforts by the new management team. After confirming that indeed this is the case, we discuss how the management team achieved the turnaround. The organization of the paper is as follows. The next section presents the background. In the third section Bayesian procedures for estimating a join point are presented. In the fourth section we discuss restructuring at NSC and the Japanese Total Quality Control system. Conclusions are given in the final section. © 1997 John Wiley & Sons, Ltd.     

A Bayesian estimation of macro and micro CES production functions

The paper presents a Bayesian estimation of CES production functions. The proposed estimation method is easier to use than methods so far developed and it allows a direct comparison with the maximum likelihood estimator. A Bayesian highest posterior density interval inference is made to examine the validity of the Cobb–Douglas representation. The method is applied to Japanese macro data and to micro data on two Japanese manufacturing plants. The results indicate that the current practice of employing the Cobb–Douglas form a priori ought to be corrected. In addition, the micro data present results paradoxical to the commonly held view of capital intensity.