Developing industrial clusters, found by Saxenian and conceptualized by Porter, is still an attractive way to revitalize regional economies and communities, even though some criticism has been expressed concerning specific targets or industrial and technological categories set artificially.　Following global movements, the Japanese national government has implemented cluster initiatives since the 2000s.　However, several studies that dealt with failures of cluster development activities have been reported and a lack of leadership or mismatching between the targets and regional, social and technological resources as factors.　Responding to rapid growth in the global market many regions including the national ministries started industrial cluster development targeting aerospace parts manufacturing in anticipation of the high-tech Aerospace Industry’s high value-added, high growth potential, and large spillover effects on other industries both economically and technologically.　However, in the real environment, the business condition is not easy at all and is far different from these expectations, especially for the new small- and medium-sized entrants to the market.　Quality management is strictly and highly required, absolute market size is far smaller than the automobile industries, cost-cut pressure from the Original Equipment Manufacturers is too strong, etc.　As of today at the beginning of the 2020s, the author cannot find successful aerospace industrial clusters with very few exceptions in Japan.　Therefore, some important questions must also be asked.　Were these difficulties and uncertainties discussed among all stakeholders in each region? Did they agree to the policy proposals to be implemented in the region and understand the negative side effects well enough?　Was a stiff consensus formed to allocate limited regional resources to a particular industrial category?

　　To clarify these questions, considering the Niigata Sky Project as a case comparing a similar activity implemented in Iida City, this study adopted partly participatory research and carried out specific interviews with company presidents, members of city councils, municipal employees, and individual persons.　At the same time, statistical data and the records of city councils were compiled and quantitatively and qualitatively analyzed.　Especially to analyze long term and large amounts of council records, the topic modeling method was applied, which was confirmed to be well fit for the qualitative analysis carried out by the author.

　　Finally, the research results, both qualitatively and quantitatively using a text-mining technique, showed the existence of unignorable possibilities in the lack of consensus formation in the region making efforts to form cutting-edge industrial clusters appear stagnant.　Consequently, the author concluded that consensus forming with not only leadership but building a thick followership layer based on the objective data including negative factors is essential for tackling the challenges.

JEL Classifications:O22, L52, L62, Z18

　　Half the municipalities in Japan might disappear because of depopulation by 2040.　The reasons for this include relocating from a hometown to the Tokyo area with getting employment or entering into university.　Therefore, it is considered that each municipality is required to understand the factors for local settlement and to promote local settlement through the U-turn or J-turn.　Customer satisfaction (CS) analysis is among the methods for searching improvement factors using CS space which has an axis of satisfaction degree of each evaluation item and axis of importance which are simple correlation coefficients between a comprehensive evaluation and each evaluation item.　But above-mentioned general CS analysis is problematic in which each importance of each evaluation item cannot be deemed a unique improvement effect.　Based on these backgrounds, we newly propose three-dimension CS space which enable CS analysis considering both the simple improvement effect and unique improvement effect of each evaluation item.　We also explore the improvement factors for the local settlement intention of natives of a local city in Japan focusing on the experience of relocating at entrance into university and a hometown area applying CS analysis based on the three-dimension CS space.

　　In this study, first, we defined a partial-importance CS space and three-dimension CS space.　Next, we conducted an attitude survey for entrance into university and local settlement intention in Japan, and compared 3 CS analysis results by means of general CS space, partial-importance CS space and three-dimension CS space, based on the survey.　Based on an analysis result, it was indicated that CS analysis based on the three-dimension CS space can explore improvement factors considering both the simple improvement effect including the relationships between each evaluation item and unique improvement effect excluding the effect from other evaluation items of each evaluation item.　Additionally, we analyzed the improvement factors for the local settlement intension of natives of a local city in Japan focusing on the experience of relocating at entrance into university and a hometown area applying CS analysis based on the three-dimension CS space to the results of that survey.　The results showed that “options of employment” and “regional attachment” are important factors for improving local settlement intension regardless of experience of relocating at entrance into university and a hometown area.

JEL Classifications:C44, C60, I28, R58

　　In this paper, we analyze the effect of corporate inhabitant tax on local public finance under the hometown tax payment system.

　　The model in this paper is built on the following assumptions.　There are two regions with different population sizes, and each local government aims to maximize tax revenue.　Residents of each region have an attachment to the area in which they live, and after comparing that attachment with the tax rate of each region, determine which region to pay their hometown tax.

　　As shown by Kato and Yanagihara （2022）, we consider the model in this framework.　In special, in this paper, we introduce a corporate inhabitant tax into the framework of Kato and Yanagihara （2022） to investigate the effects of this tax revenue on tax competition.

　　The main results of this paper are as follows.　First, if the attachment of residents to local region in which they live is equal to the attachment of residents to urban region in which they live, an equilibrium uniquely exists and hometown tax payments are made from the region with a large population to the region with a small population.　Second, the tax rates of local and urban governments decrease with an increase in the urban population.　Third, when the attachment of residents to local or urban regions in which they live become stronger, the tax rates of local and urban governments increase.

JEL Classifications:H31, H71, H77, N45

　　Prompt implementation of measures according to the industrial structure of a region is indispensable for the reconstruction of affected areas.　For that purpose, it is necessary to present the amount of damage at an early stage.　This requires estimates of the damage to capital stock and impacts on production based on limited damage information, but methods have not been established.

　　In this study, after a disaster occurred, the damage to private capital stock was estimated using data released by the disaster response headquarters, housing damage data and geographic information of flooded areas.　Next, the Cobb-Douglas production function was used to estimate the decrease in sales based on the capital stock damage by prefecture and industry.　Damage to private capital stock was determined according to the industrial composition of the flooded area and the number of employees.　In addition, the actual decrease in sales could be estimated by using the production function according to the industrial characteristics of the prefecture.　These estimates are based on general-purpose statistics and can be used even in the event of a disaster in another area.

JEL Classifications:D24, E22, H84, O15, R11