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〈論文〉新型コロナ感染者(人口あたり)の都道府県別差異の経済的背景
https://kindai.repo.nii.ac.jp/records/21969
https://kindai.repo.nii.ac.jp/records/21969ee0cd782-a522-4615-9d57-94abc9d869b3
名前 / ファイル | ライセンス | アクション |
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AA1196034X-20210731-0017.pdf (857.2 kB)
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Item type | ☆紀要論文 / Departmental Bulletin Paper(1) | |||||
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公開日 | 2021-08-26 | |||||
タイトル | ||||||
タイトル | 〈論文〉新型コロナ感染者(人口あたり)の都道府県別差異の経済的背景 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | <Articles> Economnic Causes of Significant Differences by Prefecture in COVID-19 Cases per Population in Japan | |||||
著者 |
安孫子, 勇一
× 安孫子, 勇一 |
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言語 | ||||||
言語 | jpn | |||||
キーワード | ||||||
主題 | 新型コロナウイルス, 感染率, 都道府県別, 三密の指標, 人的交流の指標 COVID-19 cases, infection rate, differences by prefecture, indicators of “three Cs”, indicators of human exchange |
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資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | departmental bulletin paper | |||||
著者(英) | ||||||
en | ||||||
Abiko, Yuichi | ||||||
著者 所属 | ||||||
近畿大学経済学部; 教授 | ||||||
著者所属(翻訳) | ||||||
Kindai University | ||||||
版 | ||||||
出版タイプ | NA | |||||
出版タイプResource | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 | |||||
出版者 名前 | ||||||
出版者 | 近畿大学経済学会 | |||||
書誌情報 |
生駒経済論叢 en : Ikoma Journal of Economics 巻 19, 号 1, p. 17-32, 発行日 2021-07-31 |
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ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 24333085 | |||||
抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | [概要]日本でも新型コロナウイルスの感染者がみつかって1年あまりが経過した。この間,都道府県別の感染者数などが毎日発表されているが,人口10万人あたり感染者数(累計)をみると,都道府県別に非常に大きな違いがみられる(2020年3月末時点で最高約30倍の違い)。具体的には,三大都市圏と沖縄県,北海道,福岡県が上位を占める一方,東北の一部や山陰に低い県がみられる。このような地域別の差異がなぜ生じたのか,様々な地域経済データとの関連を探ってみた。人口10万人あたりの累積感染者数を被説明変数として回帰分析を行ったところ,人口密度,宿泊業・飲食業従事者の割合,県外就業率(他の都道府県への通勤者の割合),空港の旅客数などが有意にプラスの影響を与えている一方,第一次産業従事者の割合がマイナスの影響を与えているとの推計結果が得られた。本稿の知見が今後の感染症対策を検討する際の一助となることを期待したい。 [Abstract] It has been 15 months since the national government found the first COVID-19 case in Japan. According to each prefecture’s daily data, there are significant differences in the total number of COVID-19 cases per population among the prefectures. This paper tries to explain the causes of the differences using economic data. The regression analysis conducted shows population density, ratios for occupations employment rates outside the prefecture, and the number of annual passengers at airports in each prefecture. These serve as explanatory variables ranging from 1% to 5% levels of significance at the end of March 2021. The author hopes that these findings contribute to the planning for countermeasures in the event of other new infectious diseases. |
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フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |