《発表論文》

要旨：Older people are less mobile than young people are. Population aging thus means more people would be trapped in locations affected by a shock, preventing the economy from smoothing out spatial differences in labor market outcomes. However, the existence of a large share of immobile workers may mitigate their welfare effects by delaying the capital supply adjustment that would be caused by a flow of workers. In order to study how population aging affects the welfare effects of a local shock, this paper develops a dynamic spatial specific-factor model with demographics that change dynamically depending on fertility rates. Individuals decide where to live and whether to work. Their choices vary over the life cycle because the expected working lifetime and fundamentals (e.g., mobility costs) vary with demographic factors. Hence, aggregate labor adjustment depends on the economy’s age structure. Forward-looking landlords accumulate location-specific capital, and the dynamics of labor and capital interact with each other. I apply the model to Japan and find that population aging can mitigate the welfare loss of workers in a location affected by a negative shock.

要旨：This paper theoretically investigates the relationship between costs for telework and the location of firms and households in a city. I extend the model of Ogawa and Fujita (1980) by introducing two types of companies with different technologies; one is teleworking company hiring more teleworkers and the other is an office company hiring more on-site workers. Only on-site workers conduct face-to-face communication with other firms and incur communication costs.  This paper shows that (i) when telework cost decreases, the first teleworking companies appear in either of two candidate locations: the edge of the existing central business districts (called CBD fringe) or the edge of the residential districts (called urban fringe). If face-to-face communication cost is high, and commuting cost and the ratio of labor input for teleworkers in telework companies are low, then the first telework company is located at the CBD fringe;  otherwise at the urban fringe. (ii) When the telework cost further decreases, the number of telework companies, wage, and welfare increase. In contrast, bid rent and city boundaries decrease. Former empirical researches showed different evidence for the location of primary telework companies; one is nearby CBD and the other is in the suburban area. However, this is the first paper to demonstrate both results in a model and to show the difference depends on the several key parameters: face-to-face communication cost, commuting cost, and the ratio of labor input for teleworkers in telework companies.

《発表論文》

要旨：Economies transform at an uneven pace: San Jose’s meteoric rise coexists with Detroit’s slow decline. This paper develops a dynamic overlapping generations model of economic geography to explain variation in structural transformation across space and time. In the model, historical exposure to different industries creates persistence in occupational structure, and non-homothetic preferences and differential productivity growth lead to different rates of structural transformation. Despite the heterogeneity across locations, sectors, and time, the model remains tractable and is calibrated to match metropolitan area data for the U.S. economy from 1980 to 2010. The calibration allows us to back out measures of upward mobility and inequality, thereby providing theoretical underpinnings to the Gatsby Curve. The counterfactual analysis shows that structural transformation has substantial effects on mobility: if there were no productivity growth in the service sector, income mobility would be 6 percent higher, and if amenities were equalized across locations, it would rise by 10 percent.

要旨：We propose a scalable regression model with spatially and temporally varying co- efficients based on Moran’s eigenvectors and efficient computation algorithms. Regression models that consider spatiotemporal non-stationarity are important because many real-world datasets, such as housing prices, are tied to geographical and tempo- ral locations. Although geographically weighted regression (GWR) and its variants are widely used to model spatially varying coefficients, they cannot handle large datasets. We employ an alternative modelling method of spatially varying coefficients based on Moran’s eigenvectors and extend it to handle large spatiotemporal datasets. Additionally, we introduce a scalable learning algorithm that exploits the model structures based on the Kalman filter and the expectation—maximisation algorithm. Our scalable algorithm is efficient even for large datasets that cannot be handled by GWR. To evaluate the performance of the proposed model, we applied it to a housing market dataset collected in Tokyo, Japan. The results show that the predictive performance of the proposed model is comparable to that of GWR while increasing the computational speed. Moreover, larger datasets can accelerate the algorithm convergence.

要旨： We study the impact of telecommuting in a monocentric city which produces (i) a tradable consumption good using skilled and unskilled labor and (ii) a non-tradable consumer service provided by unskilled workers at the city center to the skilled workers. Commuting costs are proportional to wages. When the WFH share is low, the skilled reside near the CBD and all workers earn more under WFH. By contrast, a high WFH share lowers both wages and leads the skilled to reside in the suburbs. Telecommuting leads to lower urban costs in the latter case, but not in the former. We then consider two cities that have different productivities. WFH allows skilled workers of the more productive city to reside in the less productive city where housing is cheaper while keeping their job in the more productive city. The flow of this type of inter-city commuters first increases and, then, decreases with the WFH share. Likewise, skilled workers of the less productive city may take a job in the more productive city while keeping their residence in the less productive city. The flow of the second type of inter-city commuters increases with the WFH share. For these commuting patterns to arise, the two employment centers must be connected by a link that allows workers to travel at relatively low costs.

要旨：COVID-19が猛威を振るう昨今、感染拡大に寄与した要因を明らかにすることは喫緊の課題となっている。陽性者数や死者数の分析には、ポアソン回帰やその拡張手法が用いられてきた。しかしながら、ポアソン回帰には次の課題が残されている：(i)ゼロ値の多いカウントデータの場合は解が識別できず、この問題はモデルが複雑化するほど深刻化する；(ii)そもそも陽性者数や死者数がポアソン分布に従うとは限らない。そこで本研究では、課題(i)、(ii)に対処することで、多種多様な要因の影響を安定的に推定するための新たな回帰手法を開発する。同開発では、課題(i)に対処するための新たな対数線形近似を提案し、次に課題(ii)に対処するためのデータ分布の自動推定手法を提案する。最後に、開発した手法をCOVID-19の地理的要因の分析に応用することで、人流、世代、政策などの各種要因の感染拡大に対する影響を評価する。

要旨：所有権移転登記は、不動産の変化を捕捉する際、包括的かつ正確な情報を有している。本報告では不動産の流動に着目し、移転登記目的別の流動量の空間的傾向や人口増減などの要因との相関関係を考察する。全体的な傾向として、移転登記件数は都市圏規模に比例する一方で、大規模災害などの事象によりその流動性を増加させる自治体も存在した。さらに、移転登記と地域環境変数との対応から、登記目的によって両者の相関の程度が異なることが明らかになった。上記の知見に加えて、本報告では国勢調査小地域単位あるいは3次メッシュ単位での追加分析も加え、考察を深める。このような不動産流動の理解は、土地・建物の管理の限界や、国庫へ帰属すべき土地の検討などにも関連すると考えられ、更なる分析を通して政策的含意へとつながる研究へ発展させる予定である。

《参考資料》

馬塲弘樹, 眞木仁, 高野佳佑, 清水千弘. 2021. 所有権移転登記情報からみた不動産市場の流動性—不動産流動統計を用いた分析—. CSIS Discussion Paper, 173.

配布資料：論文, 付録

要旨：This paper revisits the core-periphery model of Krugman (1991) when his CES utility is replaced by a general additively separable sub-utility which captures the income effect and allows variable markups. New evolving paths and bifurcation patterns are created by the interaction between the agglomeration force generated by monopolistic competition, increasing returns to scale, trade cost and the dispersion force resulting from the pro-competitive effect. Autarky does not necessarily produce full dispersion and free trade may not correspond to agglomeration. Moreover, the demand parameter of manufactured goods is crucial to determining the firm location.