新冠肺炎疫情於西元2020年在全球爆發，過往多篇文獻已經發現疫情對各國勞動市場造成負向衝擊的證據。台灣因西元2021年4月台北市爆發社區群聚感染，自此全台進入了三級警戒長達兩個多月之久，對台灣社會經濟活動造成極大的衝擊。因此本研究想要探討西元2021年疫情爆發對台灣各縣市勞動力市場與平均每人可支配所得的影響。
研究方法主要根據Cerqua and Letta (2022) 所使用的Machine Learning Control Method (MLCM)以及該論文所採用的變數來進行分析。資料來自台灣各縣市西元 1993年至西元2022年的相關數據，應用了隨機森林和迴歸樹的方法來預測在無疫情影響下的情境。研究結果顯示，相比於疫情沒有爆發的情況下，首先，多數縣市的勞動參與率在疫情時有所下降，但平均每人可支配所得卻意外上升，這一增長或可歸因於通貨膨脹上升、全球對晶圓需求擴張、台灣股市表現優異以及台商回流帶來的投資增加。其次，研究發現疫情對不同行業工業就業人口的影響存在地區上的差異。北部縣市工業就業人口的增加來自於電子零組件業、電腦電子及光學製品業等製造業在疫情期間的就業人數呈現正成長；而中部縣市工業就業人口的減少來自於化學材料及肥料業、食品及飼品業等行業就業人數的負成長；而台南市、高雄市工業就業人口的增加來自於電子零組件製造業、金屬製品製造業等行業就業人數的正成長。而根據迴歸樹分析，不同縣市因產業結構、扶養比等條件的差異，勞動參與率受影響的程度亦存在地區上的差異，因此各縣市政府未來若面對類似新冠肺炎大規模疫情時，或許應可考量各自的產業環境、勞動市場的變化，提出因地制宜的相關政策。

The COVID-19 pandemic, which broke out globally in 2020. Many studies found that the pandemic caused negative impact on labor market. In Taiwan, a significant outbreak in Taipei in April 2021 led to a nationwide Level 3 alert that lasted over two months, severely disrupting economic activities. This study aims to investigate the impact of the 2021 outbreak on the labor market and per capita disposable income across various counties in Taiwan. Using the Machine Learning Control Method (MLCM) as employed by Cerqua and Letta (2022), this research analyzes county-level data from 1993 to 2022, applying random forest and regression tree methods to predict scenarios without the pandemic's impact. The results show a decline in labor participation rates in most counties during the pandemic, while average per capita disposable income surprisingly increased. This rise may be attributed to factors such as inflation, increased international trade, strong stock market performance, and increased investment due to the return of Taiwanese businesses. Additionally, the study finds regional variations in the impact on industrial employment, influenced by local industrial structures. These findings suggest that future pandemic responses should consider regional economic conditions and labor market characteristics when formulating policies.

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