要旨：This paper investigates how airport congestion pricing affects airline network choices. Highlighting a network that links three congestible public airports and adopting a vertical structure approach, we show that governments can use take-off and landing charges to lead an airline to choose between a hub-spoke (hub) and point-to-point (ptp) network that maximizes social welfare. Given that the hub network uses large aircraft, socially optimal charges set below airport operating costs can lead the airline to choose this network even when the (potential) hub airport is more congested than local airports. When the hub network is chosen, the total subsidy required is almost always smaller than that of a ptp network. This also yields that the hub network is socially preferable from the perspective of subsidies. In addition, asymmetric congestion degrees and complementary relationships among the airports can result in the optimal charges of some airports to be positive.

[Abstract]
　We derive the finite sample bias and mean squared error of the fully aggregated estimator (FAE) for the stationary AR(1) model with intercept, which is proposed by Han, Phillips, and Sul (2011, Econometric Theory). Our analytical results show why the FAE is less biased than the ordinary least square estimator in finite sample case and is not biased by non-normality of error distribution and by intercept term at least O(1/T ), where T is sample size. We also propose a second order unbiased FAE using the analytical result. Finally, we examine the Monte Carlo simulation and show that it is consistent with the theoretical results.

[Abstract]
　This paper analyzes impacts of an outdated but sticky economic circumstance in the Japanese private nursing home market. Under this circumstance, homes cover all longevity risks of the residents. To measure its impacts, I compare consumers’ lifetime payments with and without this circumstance. This analysis provides implications for consumer welfare as a policy evaluation and longevity risk premium that homes assume. For the prediction analysis, I construct a structural econometric model that fits industrial organization of the nursing home market. Then, I propose a flexible prediction technique using a nonparametric Bayesian approach. My prediction analysis finds an excess payment under the circumstance that can be compensate only if a consumer live longer than thirty years in a nursing home. This results indicate rich policy implications for sustainable policy of elderly care in Japan.

要旨：City size distributions are known to approximately follow Zipf’s law, or more generally a common power law (CPL), across many countries. The most popular explanation for this regularity has been given by random growth models (e.g., Gabaix, 1999; Duranton, 2006, Rossi-Hanseberg and Wright, 2007), in which the power law results from the common iid growth rate of all cities. Since each subsample of cities has exactly the same properties, it also follows the same power law asymptotically without any role played by geography. The objective of the present paper is to establish counter-evidence from the US and Japan that the geography does indeed matter for CPL to hold among different regions and countries. Specifically, we show that random subsamples of cities taken from geographically connected regions are significantly more consistent with CPL than fully random subsamples. Furthermore, the sets of cities in economically meaningful regions (based on interregional trade) are even more significantly consistent with CPL. Finally, we present preliminary evidence that our finding is consistent with a class of central place models (Hsu, 2013, Akamatsu et al., 2013) in which sizes and locations of cities are determined through the spatial coordination of many industries subject to different degrees of scale economies.