A new measure of economic voting : priority heuristic theory and combining sociotropic and egocentric evaluations

It is well-known that a voter's retrospective economic evaluations influence vote choice. A classic debate within the literature on retrospective economic voting concerns whether voters are sociotropic or egocentric when evaluating an incumbent's economic performance. Each side assumes a voter independently considers two perceptual dimensions of economic health, the national economy and one's household financial situation. However, the extant studies overlook the fact that two economic perceptions are correlated. Consequently, our current understanding often fails to account for how sociotropic and egocentric economic evaluations interactively affect vote choice. Moreover, theoretically, voters tend to simplify decision-making when confronted with several alternatives. This notion suggests that voters jointly use sociotropic and egocentric evaluations in a scale rather than use them separately to assess incumbents. Like assuming that voters use a unidimensional scale of ideology though there are several different items that reflect an individual's or a political party's ideology, it is plausible that voters use a unidimensional scale of economic evaluation. On the basis of this notion, this dissertation proposes an improved interval measure of economic evaluation to capture a voter's economic assessment in a single dimension and to provide a comparable economic voting measurement across elections. To construct the new unidimensional measure, this dissertation proposes a lexicographic or priority ordering of economic evaluations on the basis of priority heuristic theory, which provides a convincing prediction of how voters jointly use two different criteria of economic evaluation. PH theory argues that decision makers place alternatives along a single dimension by primarily using the first-priority criterion and then using the next priority to supplement the first. In the case of retrospective economic voting, voters may judge the incumbent mainly according to the economic evaluation they value more and use the other to supplement the decision. According to this theoretical expectation, this dissertation proposes a new unidimensional scale of a voter's economic evaluation. By treating this ordinal variable as nominal in a logistic regression model to predict the probability of an incumbent vote, this dissertation tests its theoretical expectation that voters use the two economic evaluations in a combined way on the basis of priority heuristics. This theoretical expectation is tested with survey data from the elections of five countries (the United States, Britain, Canada, South Korea, and Taiwan). The empirical findings of this research show that voters order economic perceptions and prioritize in sequence, thus merging sociotropic and egocentric retrospective evaluations. A logistic regression model demonstrates that the order of probabilities in voting for the incumbent corresponds with the ordinal measure. Throughout recent elections in five countries, voters used sociotropic and egocentric economic assessments jointly for making vote decisions. Voters depend primarily on sociotropic evaluations as a component of vote choice. However, they incorporate an egocentric perspective as a complementary criterion. This is a universal voting behavior of economic voters examined in this dissertation. This confirms the theoretical expectation on the basis of priority heuristics. This dissertation proposes an economic voting heuristic, an innovative unidimensional measurement combining sociotropic and egocentric assessments that is theoretically stronger than, and empirically as strong as, traditional retrospective voting models

The effects of voters’ relative evaluations of government response to COVID-19 on government trust, satisfaction of democracy, and national pride in South Korea, Japan, and Taiwan

The spread of COVID-19 has impacted on political support of voters. This research analyzes the effects of voters’ relative evaluations of government response to COVID-19 on their government trust, satisfaction of democracy, and national pride. For this analysis, this research analyzes survey data conducted in South Korea, Japan, and Taiwan. According to results, it is commonly observed in all three countries that voters’ relative evaluations of government response to COVID-19 have significant impacts on their government trust, satisfaction of democracy, and national pride. The voters, who perceive their government response to COVID-19 is better than other advanced countries’ responses, have higher levels of government trust, satisfaction of democracy, and national pride. It implies that the government’s ability to manage health disasters like COVID-19 become a new salience issue that decide a voter’s political support

Do the welfare benefits weaken the economic vote? A cross-national analysis of the welfare state and economic voting

Comparative economic voting studies have found great instability in economic voting across countries and over time. In explaining this instability, we highlight the role of welfare systems because strong welfare protection attenuates voters’ incentives to base their vote on government economic performance. By analyzing 174 legislature elections in 31 Organisation for Economic Co-operation and Development (OECD) countries from 1980 to 2010 and by taking into account clarity of responsibility, we find that welfare protection weakens the linkage between macroeconomic outcomes and incumbent electoral fortunes. This result implies that strong welfare protection enables politicians to avoid blame for economic failures

Who is responsible? The effect of clarity of responsibility on voter turnout

Does voters’ ability to discern who is responsible for policy outcomes affect voter turnout? Although particular institutional arrangements which influence this ability – known as clarity of responsibility – appear to affect how voters form retrospective judgements, existing literature is less informed about its role on voter turnout. This article argues that voters tend to turn out less if they cannot discern who is responsible for policy outcomes. This lack of clarity hinders the process of retrospective evaluations, makes the electoral stakes less profound, and dampens the voters’ political efficacy. Using 396 elections in 34 established democracies between 1960 and 2015, it is found that lower clarity of responsibility is associated with lower voter turnout. This study highlights the importance of clarity of responsibility, as it enhances democratic accountability, not only by encouraging retrospective voting, but also by increasing political participation

Artificial intelligence for the prediction of tensile properties by using microstructural parameters in high strength steels

Artificial intelligence is widely employed in metallurgy for its ability to solve complex phenomena, which are associated with the learning process of previously obtained experimental data. Although numerous physical modeling techniques have been implemented for the prediction of mechanical strength using equations, several empirical efforts are necessitated to evaluate specific constants for different models. To address this issue, numerous recent studies have employed artificial neural networks for the prediction of mechanical properties based on the material composition and process conditions; however, majority of these works have been limited applications due to the extensive number of input parameter combinations of chemical compositions. Microstructure is a good feature to understand mechanical properties because it incorporates the effects of material composition and process conditions. The complex combination of material composition and process parameters determines the microstructure of steel. In this study, the information on microstructural volume fraction is utilized for the prediction of tensile strength, yield strength, and yield ratio via artificial neural networking. Various combinations of PF (polygonal ferrite), AF (acicular ferrite), GB (granular bainite), BF (bainitic ferrite), and M (martensite) are investigated for the prediction of yield strength, ultimate tensile strength, and yield of high strength steel via back-propagation linear regression and neural network based algorithm. The effects of each microstructure on the three mechanical properties were successfully predicted by employing back-propagation linear regression. A deep learning algorithm with hyper-parameter tuning and cross-validation enabled high accuracy in predicting experimental data with mean absolute percentage errors of 6.59% and 10.78% for the validation and test sets, respectively. These studies can open a new avenue for applying the microstructural design effects to find optimum yield strength, tensile strength, and yield ratio of high strength steel. ?? 2020 Acta Materialia Inc