Economics

It is important and informative to compare and contrast major economic crises in order to confront novel and unknown cases such as the COVID-19 pandemic. The 2006 Great Recession and then the 2019 pandemic have a lot to share in terms of unemployment rate, consumption expenditures, and interest rates set by Federal Reserve. In addition to quantitative historical data, it is also interesting to compare the contents of Federal Reserve statements for the period of these two crises and find out whether Federal Reserve cares about similar concerns or there are some other issues that demand separate and unique monetary policies. This paper conducts an analysis to explore the Federal Reserve concerns as expressed in their statements for the period of 2005 to 2020. The concern analysis is performed using natural language processing (NLP) algorithms and a trend analysis of concern is also presented. We observe that there are some similarities between the Federal Reserve statements issued during the Great Recession with those issued for the 2019 COVID-19 pandemic.

Decision theorists propose a normative theory of rational choice. Traditionally, they assume that they should provide some constant and invariant principles as criteria for rational decisions, and indirectly, for agents. They seek a decision theory that invaribably works for all agents all the time. They believe that a rational agent should follow a certain principle, perhaps the principle of maximizing expected utility everywhere, all the time. As a result of the given context, these principles are considered, in this sense, context-independent. Furthermore, decision theorists usually assume that the relevant agents at work are ideal agents, and they believe that non-ideal agents should follow them so that their decisions qualify as rational. These principles are universal rules. I will refer to this context-independent and universal approach in traditional decision theory as Invariantism. This approach is, implicitly or explicitly, adopted by theories which are proposed on the basis of these two assumptions.

E-commerce provides an efficient and effective way to exchange goods between sellers and customers. E-commerce has been a popular method for doing business, because of its simplicity of having commerce activity transparently available, including customer voice and opinion about their own experience. Those experiences can be a great benefit to understand customer experience comprehensively, both for sellers and future customers. This paper applies to e-commerces and customers in Indonesia. Many Indonesian customers expressed their voice to open social network services such as Twitter and Facebook, where a large proportion of data is in the form of conversational data. By understanding customer behavior through open social network service, we can have descriptions about the e-commerce services level in Indonesia. Thus, it is related to the government's effort to improve the Indonesian digital economy ecosystem. A method for finding core topics in large-scale internet unstructured text data is needed, where the method should be fast but sufficiently accurate. Processing large-scale data is not a straightforward job, it often needs special skills of people and complex software and hardware computer system. We propose a fast methodology of text mining methods based on frequently appeared words and their word association to form network text methodology. This method is adapted from Social Network Analysis by the model relationships between words instead of actors.

Knowledge of consumers' willingness to pay (WTP) is a prerequisite to profitable price-setting. To gauge consumers' WTP, practitioners often rely on a direct single question approach in which consumers are asked to explicitly state their WTP for a product. Despite its popularity among practitioners, this approach has been found to suffer from hypothetical bias. In this paper, we propose a rigorous method that improves the accuracy of the direct single question approach. Specifically, we systematically assess the hypothetical biases associated with the direct single question approach and explore ways to de-bias it. Our results show that by using the de-biasing procedures we propose, we can generate a de-biased direct single question approach that is accu-rate enough to be useful for managerial decision-making. We validate this approach with two studies in this paper.

The purpose of this study is the design model of Islamic bank socialization in terms of four pillars (Business Institution, Formal Education, Islamic Scholar and Higher Education) through Synergy and Proactive. The location of the study was conducted in the Regency of Banyumas, Indonesia. The results of the survey on respondents obtained 145 respondents' answers that deserve to be analyzed. Data were analyzed using SEM models with Partial Least Squares approach, designing measurement models (outer models) and designing inner models. The results of the calculation outside the model of all measurements are more than the minimum criteria required by removing Formal Education from the model because it does not meet the requirements. While the inner model results show that the socialization model was only built by the Business Institution, Islamic Scholar and Higher Education through synergy and proactivity. All independent variables directly influence the dependent variable, while the intervening variables also significantly influence except the relationship of Islamic Scholar Islamic to Bank Socialization through Proactive.

This article introduces the Hedonic Metric (HM) approach as an original method to model the demand for differentiated products. Using this approach, initially, we create an n-dimensional hedonic space based on the characteristic information available to consumers. Next, we allocate products into this space and estimate the elasticities using distances. Our model makes it possible to estimate a large number of differentiated products in a single demand system. We applied our model to estimate the retail demand for fluid milk products.

Pursuing three important elements including economic, safety, and traffic are the overall objective of decision evaluation across all transport projects. In this study, we investigate the feasibility of the development of city interchanges and road connections for network users. To achieve this goal, a series of minor goals are required to be met in advance including determining benefits, costs of implement-ing new highway interchanges, quantifying the effective parameters, the increase in fuel consumption, the reduction in travel time, and finally influence on travel speed. In this study, geometric advancement of Hakim highway, and Yadegar-e-Emam Highway were investigated in the Macro view from the cloverleaf inter-section with a low capacity to a three-level directional intersection of the enhanced cloverleaf. For this purpose, the simulation was done by EMME software of INRO Company. The results of the method were evaluated by the objective of net present value (NPV), and the benefit and cost of each one was stated precisely in different years. At the end, some suggestion has been provided.

The Asian-pacific region is the major international tourism demand market in the world, and its tourism demand is deeply affected by various factors. Previous studies have shown that different market factors influence the tourism market demand at different timescales. Accordingly, the decomposition ensemble learning approach is proposed to analyze the impact of different market factors on market demand, and the potential advantages of the proposed method on forecasting tourism demand in the Asia-pacific region are further explored. This study carefully explores the multi-scale relationship between tourist destinations and the major source countries, by decomposing the corresponding monthly tourist arrivals with noise-assisted multivariate empirical mode decomposition. With the China and Malaysia as case studies, their respective empirical results show that decomposition ensemble approach significantly better than the benchmarks which include statistical model, machine learning and deep learning model, in terms of the level forecasting accuracy and directional forecasting accuracy.

We study how the quality dimension affects the social optimum in a model of spatial differentiation where two facilities provide a public service. If quality enters linearly in the individuals' utility function, a symmetric configuration, in which both facilities have the same quality and serve groups of individuals of the same size, does not maximize the social welfare. This is a surprising result as all individuals are symmetrically identical having the same quality valuation. We also show that a symmetric configuration of facilities may maximize the social welfare if the individuals' marginal utility of quality is decreasing.

This paper proposes a public-private insurance scheme for earthquakes and floods in Italy in which property-owners, the insurer and the government co-operate in risk financing. Our model departs from the existing literature by describing a public-private insurance intended to relieve the financial burden that natural events place on governments, while at the same time assisting individuals and protecting the insurance business. Hence, the business is aiming at maximizing social welfare rather than profits. Given the limited amount of data available on natural risks, expected losses per individual have been estimated through risk-modeling. In order to evaluate the insurer's loss profile, spatial correlation among insured assets has been evaluated by means of the Hoeffding bound for r-dependent random variables. Though earthquakes generate expected losses that are almost six times greater than floods, we found that the amount of public funds needed to manage the two perils is almost the same. We argue that this result is determined by a combination of the risk aversion of individuals and the shape of the loss distribution. Lastly, since earthquakes and floods are uncorrelated, we tested whether jointly managing the two perils can counteract the negative impact of spatial correlation. Some benefit from risk diversification emerged, though the probability of the government having to inject further capital might be considerable. Our findings suggest that, when not supported by the government, private insurance might either financially over-expose the insurer or set premiums so high that individuals would fail to purchase policies.