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Housing supply in dense cities depends on the ability of builders to substitute capital for scarce land. This margin is difficult to estimate because builders choose capital after observing microgeographic conditions that are only imperfectly observed by researchers. This paper develops a method for estimating revenue-based housing production functions in this setting. Because observed capital variation reflects both technological substitution and endogenous responses to latent local conditions, existing estimators can transmit unobserved heterogeneity into the estimated production function. The method treats the unobserved local conditions that affect capital choice as a scalar Markov state and combines the capital share equation with Markov moments implemented using repeated cross-sectional construction data. Monte Carlo simulations show that the estimator recovers capital and land elasticities under flexible production technologies when capital choices respond to latent local conditions. An application to newly constructed housing in Tokyo’s 23 special wards illustrates how the method can be implemented in a dense single-city setting. The results show that explicitly modeling latent local heterogeneity matters for estimated capital-land elasticities and implies returns to scale close to one.

This paper investigates the impact of the exit of regionally dominant establishments on the productivity of the remaining local firms. Using establishment-level data from the Japanese manufacturing sector, we estimate the net effect of these exits through a difference-in-differences analysis, focusing on the top 1% of establishments that exited between 1999 and 2010. Our findings indicate a significant negative effect on regional productivity: exits reduced the productivity of the remaining establishments by about 1% within five years post-exit and by 0.7-0.8% within ten years. However, most of these declines can be attributed to the decreased demand associated with exits. Controlling for the factor of change in demand associated with exits, the exits themselves have little impact on total factor productivity (TFP) in either the short or long term. These results suggest that the legacy of large establishments in improving local TFP largely persists even after their exit.

This study proposes a novel methodology for measuring the stringency of land use regulation, particularly height regulation. My stringency measure focuses on the divergence between building stories with and without height regulations. To estimate building stories without height regulations, I develop a dynamic discrete choice model of housing floor space supply and examine a counterfactual experiment to estimate building stories in the absence of height regulations. I apply this measure to data on residential buildings in the central city of the Tokyo metropolitan area. The primary findings are as follows: First, the absence of height regulations led to a 3.4% increase in the supply of building stories and a 4.1% increase in housing price. Second, the stringency of regulations tends to be strong near the central area. Third, static model tends to overestimate the stringency of height regulations compared with the dynamic model.

Is there a difference in resilience to crises between urban and rural areas? By using microdata of establishments in the manufacturing industry in Japan from 2007 to 2014, this study estimated how the productivity distribution of establishments in a region changed during two crises caused by different factors: the Global Financial Crisis of 2008 and the Great East Japan Earthquake in 2011. The results indicate the followings. (i) Although establishments in urban areas experienced a larger leftward shift in the productivity distribution than those in rural areas in both crises, their capacity to recover from crises was shown to be greater. (ii) There are several rural areas in which the productivity distribution did not change significantly as a result of crises, where productivity improvement had already stagnated. (iii) In a few rural areas, the distribution of productivity moved to the left during the crises and did not recover afterward, shifting to a different growth path compared to before the crises.

This study investigates the impact of economic shocks on regional employment in Japan using causal forest algorithms and compares the data-driven findings with estimates derived from standard ordinary least squares (OLS) regression. Our results reveal no significant difference in the magnitude of impact between small and large regions. Instead, regional differences in impact stem from variations in pre-crisis employment growth and the concentration of local employment. Specifically, regions with a high proportion of manufacturing jobs demonstrate resilience to recession-related job losses but recover slowly, while regions with pre-crisis employment growth exceeding 5% suffer steep declines during recessions but exhibit strong recoveries.

This paper presents an approach for estimating changes in firms’ productivity. We apply the quantile approach, which estimates the changes in the productivity distribution of surviving firms. Using this method, the paper clarifies productivity dynamics in terms of both average change and dispersion in Japan from 1987 to 2014. The main results of the analysis are as follows: During a boom or normal period, the productivity distribution shifts to the right and the productivity dispersion decreases. Conversely, during a recession, the productivity distribution shifts to the left and the productivity dispersion expands. The analysis also gives quantitatively significant results. During the historically rare global financial crisis of 2008, the weighted (simple) average of manufacturing productivity in Japan fell by only 0.2% (5.4%). We identified that this counter-intuitive result was due to a significant change in the shape of the productivity distribution and found that the crisis would reduce productivity by more than 22% if the effects of changing the shape of the distribution were adjusted.

This study examines the effect of improvement in public transportation on population and employment distribution in the Tokyo metropolitan area. Using an instrumental variable, my analysis reveals two noteworthy features, as follows: (1) capacity expansion by duplex line cause suburbanization of both population and employment, contrarily, new line by construction of train station have only negative effect on both population and employment distribution in suburb area, and no effect on ether population and employment in central city and subcenter. (2) in the central city, employment in insurance and real estate is decreasing, while it is increasing in sales because of capacity expansion.

This study examines the applicability of the agglomeration effect to tourism economics. As such, we test the hypothesis that tourists preferences include a “love of variety.” To test the hypothesis, we propose a theoretical model of tourists’ destination choices based on the Dixit-Stiglitz model of monopolistic competition, and use regional data on Japanese tourists for the empirical analysis. Here, we define the number of genres as the variety of tourism goods. The results show that the number of genres offered by destinations affects tourists’ destination choices. In other words, tourists’ preferences exhibit a love of variety.

限られた土地の中で効率的な生産活動を行うことは、可住地面積の狭いわが国における課題である。限られた土地を有効に利用する１つの方策として、高層ビルなどに代表されるような建築物を高くする、土地の高度利用があげられる。しかしながら、これまでの土地の高度利用に関する研究では、その基礎となる理論モデルの仮定を都市化の進む現代の都市圏に適用することが難しく、土地の高度利用の最適な制限を求めることは難しかった。こうした課題に対して現実の状況に合わせた理論モデルを構築し、実際のデータから土地の高度利用の最適な制限を明らかにすることが、本研究の目的である。

本研究の目的は、通勤における混雑が企業の立地選択や生産性の上昇率に与える影響を理論と実証の両面から明らかにすることにある。近年の都市化により都市圏内の混雑は無視できないほど大きなものとなっており、実際のデータから通勤における混雑を考慮する必要性があることが示唆されている。しかし、混雑が都市圏内の経済主体、特に企業の参入退出や生産性の変化などに与える影響は、あまり分かっていない。そこで、本研究は交通インフラの整備により発生した混雑が企業の行動にどのような影響を与えるのかを理論と実証の両面から明らかにする。

本研究の目的は、交通インフラの整備が人口や雇用の分布に与える影響を理論と実証の両面から分析することである。特に、渋滞などの混雑が消費者の居住地と職場を選択する行動に与える影響を理論モデルにより分析する。さらに、その理論モデルの妥当性を実際のデータから検証する。