Abstract:
This paper provides novel nonparametric methods for estimation and uniform inference of optimal policy allocation rules and the resulting welfare. The original problem is formulated as a partially identified model, which entails non-standard asymptotics. We show that employing a surrogate risk function ensures point identification of a representing function, thereby enabling tractable asymptotic analysis based on Gaussian approximations. We propose a two-step cross-fitting procedure for estimating the nonparametric optimal policy, which achieves the standard nonparametric convergence rate and admits standard Gaussian approximations for inference. Leveraging these results and coupling principles, we develop a new method for nonparametric inference on both the optimal policy and the associated welfare. We also emphasize that the limiting distribution of the welfare estimator admits a Gaussian approximation, supported by the stochastic equicontinuity of the policy function estimator.

要旨：　Exclusion and shape restrictions are crucial for defining causal effects, understanding individual heterogeneity, and interpreting estimators in potential outcome models. This paper is concerned with characterizing the empirical content of such restrictions. To date, the testable implications of these restrictions have been studied on a case-by-case basis within a limited set of models. Using a novel graph-based representation of the model, we provide a systematic approach to deriving sharp testable implications of general support restrictions. We illustrate the proposed approach in simulations and an empirical application.

要旨：This paper develops new theoretical insights into how sectoral trade costs and demand patterns shape specialization and wage levels across locations. It presents two key results. First, locations with stronger demand for sectors with low trade costs command higher wage levels, conditional on productivity and population size. Second, contrary to conventional interpretations, higher wages—rather than larger market size—drive specialization in sectors with high trade costs. This pattern arises not from trade cost savings, but from stronger incentives for firms in low-trade-cost sectors to reduce production costs in order to compete across locations. Existing models have overlooked firms’ strategic incentive to locate production in smaller markets, where trade barriers weaken competitive pressures. These results arise under monopolistic competition and extend to models with perfect competition and external scale economies.

Abstract: We study a general doctors, hospitals and regions matching model with complex distributional constraints. Every hospital faces floor and ceiling constraints on the number of doctors, and every region which has several hospitals also faces its floor and ceiling constraints on the number of doctors. We examine how to assign doctors to hospitals and regions in an efficient, fair, stable, and strategy-proof way. We propose two mechanisms for finding such solutions, and examine their properties and policy implications.

*English follows Japanese.

＜Workshop on Search and Platform ご案内＞

日時：2025年7月8日（火）10:25 – 17:10
会場：京都大学経済研究所北館N101、N102（京都市左京区吉田本町 京都大学吉田キャンパス本部構内）
プログラムのURL：こちらをご参照ください。
参加費：無料
定員：50人
言語：英語 ≪同時通訳はありません≫
オーガナイザー：渡辺誠： CIGS 上席研究員

—————————————————————————-

＜登録方法＞
会議に参加希望の方は、以下の申込フォームからご登録ください。（締切：6月30日）
登録が完了すると、自動返信メールが届きます。
https://forms.gle/uVbyTCsQtTXi6XQJ9

メールが届かない場合は、迷惑メールフォルダをご確認ください。登録後に内容を変更したい場合は、詳細な情報をメールでお知らせください。

＊資料の配布はございませんので、必要な場合はご自身の端末をご持参いただき、上記プログラムのURLよりPaper/Slidesにアクセスしてください。　Slideは随時更新をしていきます。

よろしくお願いいたします。

南幸子
一般財団法人キヤノングローバル戦略研究所
〒100-6511　東京都千代田区丸の内1-5-1新丸の内ビルディング11F
TEL:03-6213-0550　FAX:03-3217-1251
minami.sachiko@canon-igs.org

——————————————————————————-

“The Workshop on Search and Platform”
Date: 2025, July 8th（Tuesday）10:25 – 17:10
Location: Room N101/N102, KIER North Building, Kyoto University, Yoshida hon-machi, Sakyo Ward, Kyoto, 606-8317
Details : Here is the URL to the agenda of the conference.
Participation Fee: Free
Capacity: 50 participants *The registration will be closed if we receive too many applications.
Organizer: Makoto Watanabe (CIGS senior researcher)

—————————————————————————-

If you wish to participate in this conference, please register through the following application form. (Application Deadline: June 30th)
https://forms.gle/uVbyTCsQtTXi6XQJ9

After your registration is complete, you will receive an auto-reply from the form.
If you do not receive the email, please check your spam mail folder.
If you want to change some items after your registration, please email us with the detailed information.

[CIGS Secretariat] (Reception hours: 9:00-17:30 on weekdays)
TEL: +81-3-6213-0550 (Representative)
Mail: minami.sachiko@canon-igs.org

アブストラクト：
独立な高次元確率ベクトルの和の成分の最大値として与えられる統計量の分布の近似は、高次元パラメータに対する仮説検定や一様信頼区間の構成を行う上で重要な役割を果たす。V. Chernozhukov, D. ChetverikovおよびK. Katoらによる近年の研究によって、そのような最大値統計量の分布に対する正規型の近似やブートストラップ近似は、次元がサンプル数よりもはるかに大きいような超高次元の設定においても適当なモーメント条件下で正当化できることが明らかにされた。一方で、データの歪度がある程度大きい場合、スチューデント化を行わない場合であっても、高次元の設定では3次モーメントまでマッチさせるようなブートストラップ近似の方が正規型の近似よりも有限標本でのパフォーマンスが優れていることが数値実験によって観察されているが、既存の理論的結果はこのことを説明できない。本報告では、漸近展開を用いることでこの現象が理論的に説明できることを示す。特に、母集団の共分散行列が一定の条件を満たす場合、次元がサンプル数よりも大きい状況では3次モーメントまでマッチさせるようなブートストラップ近似がスチューデント化せずとも2次の精度を持つというblessing of dimensionality phenomenonが現れる。