{"updated":"2025-01-19T19:35:31.416091+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00205837","sets":["6504:10247:10248"]},"path":["10248"],"owner":"6748","recid":"205837","title":["機械学習を用いた上場企業の労働分配率および平均給与の説明可能性に関する検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-20"},"_buckets":{"deposit":"26cbfb05-1ba0-4047-9d43-7c7a33142433"},"_deposit":{"id":"205837","pid":{"type":"depid","value":"205837","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"機械学習を用いた上場企業の労働分配率および平均給与の説明可能性に関する検討","author_link":["510917","510918","510919"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いた上場企業の労働分配率および平均給与の説明可能性に関する検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"コンピュータと人間社会","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2020-02-20","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"慶大"},{"subitem_text_value":"慶大"},{"subitem_text_value":"慶大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/205837/files/IPSJ-Z82-7ZC-05.pdf","label":"IPSJ-Z82-7ZC-05.pdf"},"date":[{"dateType":"Available","dateValue":"2020-06-19"}],"format":"application/pdf","filename":"IPSJ-Z82-7ZC-05.pdf","filesize":[{"value":"719.3 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"6f8538f7-5219-4e4e-b166-0db768bb83f8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松本, 章宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菅, 愛子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高橋, 大志"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"2013年以降のアベノミクスの効果により、日本経済は回復の兆しを見せ始めている。企業業績は改善され、内部留保の金額は過去最高に至るまでとなっている。一方で、日本の労働分配率は戦後最低の値を示しており、労働分配率の低下は実体経済の弱体化と格差の拡大をもたらす大きな社会的課題として捉えられている。本研究では、日本の上場企業の公開財務データをもとに、機械学習を用いて日本の上場企業における労働分配率および平均給与の説明可能性について検討するとともに、資本市場から実体経済へのトリクルダウンを阻害している要因について検討した。結果、企業の時価総額の上昇率が高い企業群が労働分配率を下げ、トリクルダウンを阻害している可能性を示唆した。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"344","bibliographic_titles":[{"bibliographic_title":"第82回全国大会講演論文集"}],"bibliographicPageStart":"343","bibliographicIssueDates":{"bibliographicIssueDate":"2020-02-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"created":"2025-01-19T01:07:57.453059+00:00","id":205837,"links":{}}