DSpace community: 經濟研究所

產業科技化程度對STEM與非STEM勞工薪資及薪資差異之影響 — 以臺灣為例;The Impact of Industry Technology Levels on STEM and Non-STEM Wages and Their Wage Differences: Evidence from Taiwan

title: 產業科技化程度對STEM與非STEM勞工薪資及薪資差異之影響 — 以臺灣為例;The Impact of Industry Technology Levels on STEM and Non-STEM Wages and Their Wage Differences: Evidence from Taiwan abstract: 隨著數位轉型與技術快速發展，臺灣作為以高科技產業為主的出口導向型經濟體，其勞動市場變化值得關注。本研究結合2020至2023年《人力運用調查》原始資料，與多項衡量科技化程度之資料建構成的相關產業科技化指標，運用普通最小平方法(OLS)、Heckman選擇性偏誤修正法及Blinder-Oaxaca薪資分解法，探討產業科技化程度對臺灣私部門STEM與非STEM勞工薪資影響與其薪資差異的來源。本文結果發現，產業科技化程度對勞工薪資均具正向效果，對非STEM勞工尤為顯著。STEM與非STEM勞工的薪資差異主要來自於個人特性與工作特性，數位化程度則有助於縮小兩者差距；但在藍領與女性勞工中，數位化程度反而擴大薪資差異。此外，機器自動化對電子產業的勞工薪資負面衝擊尤為明顯。因此，建議政府持續鼓勵與支持產業的研發創新，以提升STEM人力的生產力與薪資成長空間；對非STEM則應強化數位技能與跨域能力的培訓，並聚焦受負面衝擊較顯著的群體，建立人力影響評估與預警機制，以協助勞工適應數位轉型。;As digital transformation and technological advancement accelerate, Taiwan—an export-oriented economy driven by high-tech industries—faces noteworthy changes in its labor market. This study integrates data from the 2020 to 2023 Manpower Utilization Survey and various relevant indicators measuring industrial technological advancement that was constructed from different sources that to construct. Using Ordinary Least Squares (OLS), the Heckman Selection Bias Correction Model, and the Blinder-Oaxaca wage decomposition methods, this research examines the impact of industrial technological advancement on the wage determination of private-sector STEM and non-STEM workers in Taiwan and analyzes the sources of their wage differences. The results show that digitalization positively influences wages for all workers, with a particularly strong effect on non-STEM workers. Moreover, wage differentials between STEM and non-STEM workers are primarily explained by personal and job-related characteristics, while industrial technological advancement tends to narrow this gap. However, for blue-collar and female workers, digitalization appears to widen wage differentials. In addition, industrial automation has a notably negative wage impact on workers in the electronics industry. Based on these findings, this study recommends that the government continue to promote and support industrial R&D and innovation in order to enhance the productivity and wage growth potential of STEM workers. For non-STEM workers, it is crucial to improve digital skills and interdisciplinary training. Additionally, the government should also focus on groups more vulnerable to negative impacts by establishing workforce impact assessments and early warning mechanisms to help workers adapt to digital transformation.

台灣信用與金融情勢對經濟成長風險之影響評估

title: 台灣信用與金融情勢對經濟成長風險之影響評估 abstract: 本研究旨在透過分量迴歸模型探討台灣金融變數對實質經濟之不對稱影響，並且證實金融面影響的雙面性，即短期下金融寬鬆能正向影響工業生產成長率，但長期下則加劇實質經濟的下行風險。本文進一步將分量迴歸結合偏斜t 分布以評估下行風險，實證發現疫情前的下行風險（Growth-at-Risk, GaR）波動較不劇烈，然而疫情後所呈現的GaR 顯著增大，顯示金融市場對極端負向風險的敏感度上升。再者，本文將偏斜t 分布納入熵模型架構，建構更具前瞻性的風險評估工具，結果顯示，當經濟處於景氣低迷階段，下行熵值顯著上升。透過Granger 因果關係檢定，實證亦支持下行熵值可領先預測工業生產成長率之變動，展現其作為預警指標之潛力。最後，為檢驗實證結果之穩健性，本文採用分量向量自我迴歸（Quantile Vector Autoregression, QVAR）模型，以探討主要變數間在不同分位數下的動態關係。結果顯示，QVAR 與分量迴歸結果一致，金融變數皆是正向影響實質面經濟，且工業生產成長率對金融情勢指數與民間信用占比GDP 成長率不具內生性。衝擊反應分析進一步指出，金融情勢指數在較低分位時對工業生產成長率具更大的顯著影響。;This study examines the asymmetric effects of financial variables on Taiwan’s economy using a quantile regression framework. The results show that short-term financial easing supports industrial production growth but heightens long-term downside risks. To capture tail risk more precisely, the analysis incorporates a skewedt distribution into the model. The results show that Growth-at-Risk (GaR) remained stable before COVID-19 but increased significantly after, indicating greater sensitivity to adverse shocks. The analysis further integrates the skewed-t distribution into an entropy model to strengthen forward-looking risk assessment. Results show that downside entropy peaks in advance of economic recessions. Granger causality tests suggest it precedes and predicts future industrial production growth. To ensure robustness, a Quantile Vector Autoregression (QVAR) model analyzes the dynamic relationships across quantiles. The QVAR findings confirm those of the quantile regression, showing that financial variables positively affect the economy. Additionally, no evidence of endogeneity is found from industrial production growth to the Financial Conditions Index or credit-to-GDP. Impulse response analysis shows stronger effects of financial conditions at lower quantiles.

臺灣股票市場異常超額報酬之預測性;The Forecastability of Abnormal Returns in Taiwan’s Equity Market

title: 臺灣股票市場異常超額報酬之預測性;The Forecastability of Abnormal Returns in Taiwan’s Equity Market abstract: Fama (1970) 一文提出效率市場假說，認為有效率的資本市場沒有資產錯誤定價獲利的機會，然而這與現實情況有所不同，過去諸多文獻探討股票市場屬於何種效率市場，但其文獻多使用單一變數，本文同時使用總體經濟面、市場資金面與股價技術面變數為預測因子預測臺灣股票市場超額報酬率。本文實證結果發現，被篩選出最能有效預測臺灣股市超額報酬之變數以臺灣股票市場資金面與股價技術面預測因子為主。整體而言，預測模型中簡單組合法預測表現最為傑出，接著為組合彈性網路法與平均預測法，其中組合彈性網路法與簡單組合法之變異度表現最佳、預測累積年化報酬率以簡單組合法與主成分分析法最高。本文預測結果，機器學習預測模型可有效捕捉股票市場超額報酬，然而當前所擁有資訊隨著時間經過不斷進行校正，使得向前預測二個月與三個月之預測報酬率最高，然而隨著預測期數越長，資訊會逐漸反應於股價上，累積超額報酬率越低，當前所擁有資訊僅能於短期內獲取額外的超額報酬。;Fama (1970) proposed the efficient market hypothesis, arguing that an efficient capital market has no opportunity to gain any profit from mispricing. However, in the past, many literatures have discussed what kind of efficient market the stock market belongs to, but many literatures used the single part of variables, we are different from the past, simultaneously uses the macroeconomic, market capital, and technical variables as predictors to predict the excess return rate of the Taiwan stock market. The empirical results found that the selected variables that are most effective in predicting excess returns in the Taiwan stock market are mainly cash flow and technical variables. Overall, the simple combination method has the best forecasting performance among forecasting models, then the combination elastic net method and the average forecasting method, combined elastic network method especially had the best forecasting performance in recession. The most important results this article observes is that the information is continuously corrected over time, making the two-month and three-month forecast returns the highest. However, the longer ahead forecast period we predict, the more information will gradually be reflected in the stock price. In other words, the information just can only obtain additional excess returns in the short term.

美國公債殖利率期限結構之利率風險結構分析;The Term Structure of Interest-at-Risk for U.S. Treasury Yields

title: 美國公債殖利率期限結構之利率風險結構分析;The Term Structure of Interest-at-Risk for U.S. Treasury Yields abstract: 本研究旨在建構美國公債殖利率的風險結構，並評估其在不同經濟情境下的變化。相較於現有文獻中以 GDP 為核心的 Growth-at-Risk（GaR）架構，創新地提出「殖利率風險結構」（Term Structure of Interest-at-Risk）概念，將不同存續期間的美國公債殖利率視為應變數，試圖找出其上下行風險的來源與傳導機制。首先本文採用多種機器學習方法進行變數篩選，並以均方根誤差（RMSE）作為輔助依據，選出各期限殖利率下對解釋力最具貢獻的模型，將其挑選出之關鍵變數納入分量迴歸架構中，估計殖利率在不同條件分位下的風險結構，藉此捕捉利率反應於經濟極端情境的潛在異質性。實證結果指出，短端殖利率對通膨與工業生產等變數較敏感，而長端殖利率則更受市場風險偏好與中長期預期影響。部分變數在不同期限的殖利率中呈現方向相反的效果，顯示市場對相同訊號可能有期限上的不同詮釋。此外，殖利率對特定變數的反應在極端情況下或有不同，顯示風險評估應考慮條件性與非對稱性。本研究所建立之殖利率風險結構，有助於政策制定者與風險管理者掌握利率變動潛在風險，並可作為利率政策評估與資產配置的量化依據。;This study aims to construct the risk structure of U.S. Treasury yields and evaluate its variation under different economic conditions. In contrast to existing literature that centers on GDP within the Growth-at-Risk (GaR) framework, this paper introduces an innovative concept—the Term Structure of Interest-at-Risk—which treats Treasury yields of different maturities as dependent variables to identify the sources and transmission mechanisms of both upside and downside risks. First, this study applies various machine learning methods for variable selection and uses root mean square error (RMSE) as a supplementary criterion to identify the models with the greatest explanatory power across different maturities. The key variables selected are then incorporated into a quantile regression framework to estimate the conditional risk structure of yields and capture the potential heterogeneity in yield responses under extreme economic scenarios. Empirical results show that short-term yields are more sensitive to variables such as inflation and industrial production, while long-term yields are more influenced by market risk sentiment and medium- to long-term expectations. Some variables exhibit opposite effects across different maturities, suggesting that markets may interpret the same signals differently depending on the term. In addition, yield responses to certain variables may differ under extreme conditions, indicating that risk assessments should consider both conditionality and asymmetry. The risk structure of interest rates developed in this study offers policymakers and risk managers a quantitative basis for understanding potential interest rate risks, and serves as a useful tool for evaluating policy and guiding portfolio allocation.