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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 楊豐安 | zh_TW |
dc.contributor.advisor | Feng-An Yang | en |
dc.contributor.author | 黃子芸 | zh_TW |
dc.contributor.author | Tzu-Yun Huang | en |
dc.date.accessioned | 2023-07-19T16:15:40Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-07-19 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-06-13 | - |
dc.identifier.citation | Alcedo, J., Cavallo, A., Dwyer, B., Mishra, P., & Spilimbergo, A. (2022). E-commerce during COVID: Stylized facts from 47 economies (No. w29729). National Bureau of Economic Research.
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87747 | - |
dc.description.abstract | 本研究探討新冠疫情對臺灣總體經濟與重點產業之衝擊影響效果,使用臺灣動態可計算一般均衡模型,針對2020年至2021年新冠疫情期間的18個重點產業,建立歷史模擬與反事實模擬情境,進行事後分析。不同於事前分析,本研究使用政府公布之歷史數據,將需求面與供給面的歷年變動重現,再根據產業過去趨勢進行反事實模擬衝擊設定,推導出若是未發生疫情,臺灣的總體經濟表現會是如何。
本研究發現,即使有部分產業因疫情發生後的封鎖、供應鏈與消費中斷等因素受到負面影響,但在政府的紓困振興政策,以及資訊與通信科技產業(Information and Communication Technology,簡稱ICT產業)出口擴張的交互作用下,反而為我國2020年實質國內生產毛額帶來了0.22個百分點的正向效果,更於2021年帶來4.19個百分點的正向效果。而若將歷史模擬結果減去反事實模擬結果,2020年實質國內生產毛額的差異為416億元,2021年的差異則達8,739億元。 另外,在18個重點產業的模擬結果中發現,2020年損失金額最多的前三名為航空運輸業、餐飲業與汽車及其零件製造業。2021年大部分產業逐漸恢復,但旅行及相關服務業、軌道車輛運輸業、藝術、娛樂及休閒服務業、餐飲業與住宿業仍持續受到疫情的負面影響,且藝術、娛樂及休閒服務業和餐飲業的負面影響較2020年來得更為嚴重。相反的,隨著居家辦公、遠距教學以及企業加速自動化的興起,帶動了我國電子零組件、電腦電子光學產品製造業以及出口相關主力產品批發業之復甦,使這三個產業成為在2020至2021年兩年間累積獲益最多的產業。 總結本研究之分析,臺灣在新冠疫情影響下反而於2021年出現大幅經濟成長之原因有二,第一為迅速應對的防疫政策使得臺灣相較於其他疫情嚴峻的國家,仍可維持穩定的產能,自轉單效應中受惠;第二,臺灣ICT產業對實質國內生產毛額成長的貢獻很高,顯示ICT產業的大幅成長為帶動我國經濟成長之主要動力。 | zh_TW |
dc.description.abstract | This study uses a dynamic computable general equilibrium model to conduct an ex post analysis of the historical simulation and counterfactual simulation scenarios for the period from 2020 to 2021. As to examine the impact of the Covid-19 pandemic on Taiwan's overall economy and 18 key industries. In contrast to the ex ante analysis, this study uses historical data published by the government to reproduce the historical changes in demand and supply side, and then conducts a counterfactual simulation based on the past trends of the industries to solve for the macroeconomic status without the the Covid-19 pandemic.
This study found that even though some industries were negatively affected by the pandemic, the government's strong policies and the expansion of ICT industry brought a positive effect of 0.22 percentage points in real GDP in 2020 and 4.19 percentage points in 2021. If the historical simulation results are subtracted from the counterfactual simulation results, the difference in real GDP is $41.6 billion in 2020 and $873.9 billion in 2021. The top three industries with the most severe losses in 2020 are air transportation, food and beverage, and motor vehicles and parts manufacturing, according to the simulation results of the 18 key industries. The negative impacts on the art, entertainment and recreation services and food and beverage services were more severe in 2021. On the contrary, the rise of working from home and distance learning, as well as the accelerated automation of enterprises, has boosted the electronic component manufacturing, computer electronic and optical products, and wholesale export industries, the three industries that have accumulated the most benefits during 2020 and 2021. This study finds that Taiwan experienced significant economic growth in 2021 in spite of the pandemic. First, Taiwan's rapid response to the pandemic has enabled it to maintain stable production capacity and profitability compared to other competing countries. Second, Taiwan's ICT industry contributes significantly to real GDP growth, indicating that the significant growth of ICT industry is the main driver of Taiwan's economic growth. | en |
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dc.description.tableofcontents | 目 錄
口試委員會審定書 i 誌謝 ii 中文摘要 iii Abstract iv 目 錄 vi 圖目錄 viii 表目錄 ix 第一章 緒論 1 第一節 研究背景 1 第二節 研究動機與目的 5 第三節 研究方法與架構 7 第二章 文獻探討 9 第一節 公共衛生事件之經濟影響分析文獻 9 第二節 評估新冠疫情對經濟影響之相關文獻 12 第三節 我國評估新冠疫情經濟影響之相關文獻 17 第三章 理論模型 29 第一節 模型基本架構 29 第二節 模型資料結構 35 第三節 事後分析之概念與說明 36 第四章 模擬設定與資料處理 42 第一節 歷史模擬設定 42 第二節 反事實情境模擬設定 51 第五章 模擬結果與分析 54 第一節 歷史模擬結果 54 第二節 總體指標變動結果之事後分析 55 第三節 產業指標變動結果之事後分析 59 第六章 結論與研究限制 71 第一節 結論 71 第二節 研究限制及未來研究方向 73 參考文獻 74 附錄 81 | - |
dc.language.iso | zh_TW | - |
dc.title | 新冠疫情對臺灣經濟影響之事後分析 | zh_TW |
dc.title | An Ex Post Analysis of the Economic Impacts of COVID-19 on Taiwan | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.coadvisor | 張靜貞 | zh_TW |
dc.contributor.coadvisor | Ching-Cheng Chang | en |
dc.contributor.oralexamcommittee | 劉瑞文;林幸君;李金全 | zh_TW |
dc.contributor.oralexamcommittee | Ruey-Wan Liou;Hsing-Chun Lin;Kenneth Bicol Dy | en |
dc.subject.keyword | 可計算一般均衡模型,新冠疫情,事後分析,經濟影響, | zh_TW |
dc.subject.keyword | Computable General Equilibrium (CGE),Covid-19,Ex Post Analysis,Economic Impacts, | en |
dc.relation.page | 84 | - |
dc.identifier.doi | 10.6342/NTU202300990 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2023-06-13 | - |
dc.contributor.author-college | 生物資源暨農學院 | - |
dc.contributor.author-dept | 農業經濟學系 | - |
顯示於系所單位: | 農業經濟學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
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ntu-111-2.pdf 目前未授權公開取用 | 3.33 MB | Adobe PDF | 檢視/開啟 |
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