政府推動科學園區至今超過40年，吸引國內外企業投入經營，形成各領域的產業聚落。本篇研究以傾向分數配對法(propensity score matching, PSM)結合雙重差分法(difference-in-differences, DID)來分析西元1991年至2020年科學園區產業聚落對於園區內電子業企業經營績效的影響，並控制了僱員人數、廠齡等企業特徵，如此便可減少集群政策對於企業選擇有關的內生問題，確定科學園區產業聚落與目標企業間的因果關係。本文將企業績效衡量指標集中在財務績效與營運績效兩項構面上，以銷售額、資產報酬率、股東權益報酬率、員工薪資支出、Tobin’s Q、勞動生產力、研究發展費用率七項指標衡量企業之經營績效。實證結果顯示科學園區內之企業僱用較多的員工，且廠齡也較園區外之企業年輕，而在績效表現方面，資產報酬率及股東權益報酬率兩項財務績效指標並沒有表現出明顯優勢，原因可能與園區之產業特性相關，另一方面園區內之企業相較於園區外企業成立年份普遍較晚，需要花費更多的金錢在資本投入或研究發展上，經營風險相對較高，然而科學園區產業聚落發展整體上仍然有利於企業其他績效指標的表現，特別是在研發及投資活動上尤為明顯。

The government has been promoting science parks for more than 40 years, attracting domestic and foreign enterprises to operate and form industrial clusters in various fields. This study uses propensity score matching (PSM) combined with difference-in-differences (DID) to analyze the impact of industrial clusters in the Science Park on the business performance of electronics enterprises from 1991 to 2020, and controls the characteristics of enterprises such as number of employees, plant age, etc. In this way, the endogenous problem of cluster policy on firm selection can be reduced, and the causal relationship between industrial clusters and target firms in the Science Park can be determined. In this paper, we focus on financial performance and operational performance, and measure the performance of enterprises by seven indicators: sales, return on assets, return on shareholders' equity, employees' salary, Tobin's Q, labor productivity, and research and development cost rate. The results show that the companies in the Science Park employ more employees and have younger plants than the companies outside the Science Park, but in terms of performance, the financial performance indicators of return on assets and return on equity do not show significant advantages. However, the clustering of industries in the Science Park is still beneficial to the performance of enterprises in general, especially in R&D and investment activities.

林惠玲, 陳麗旭, 羅美蘭, & 劉訓華. (2016). 產業聚集與廠商生產力之關係: 以台灣電子產業為例. 應用經濟論叢, (100), 1-34.

Acs, Z. J., Audretsch, D. B., & Feldman, M. P. (1992). Real effects of academic research: comment. American Economic Review, 82(1), 363-367.

Audretsch, D. B., & Feldman, M. P. (1996). R&D spillovers and the geography of innovation and production. American Economic Review, 86(3), 630-640.

Audretsch, D. B., & Feldman, M. P. (2003). Small-firm strategic research partnerships: The case of biotechnology. Technology Analysis & Strategic Management, 15(2), 273-288.

Beck, T., Levine, R., & Levkov, A. (2010). Big bad banks? The winners and losers from bank deregulation in the United States. Journal of Finance, 65(5), 1637-1667.

Blomström, M., & Kokko, A. (1998). Multinational corporations and spillovers. Journal of Economic Surveys, 12(3), 247-277.

Breschi, S., & Lissoni, F. (2001). Knowledge spillovers and local innovation systems: a critical survey. Industrial and Corporate Change, 10(4), 975-1005.

Caliendo, & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31-72.

Chen, C. J., Wu, H. L., & Lin, B. W. (2006). Evaluating the development of high-tech industries: Taiwan's science park. Technological Forecasting and Social Change, 73(4), 452-465.

Chen, C. P., Chien, C. F., & Lai, C. T. (2013). Cluster policies and industry development in the Hsinchu Science Park: A retrospective review after 30 years. Innovation, 15(4), 416-436.

DeCarolis, D. M., & Deeds, D. L. (1999). The impact of stocks and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry. Strategic Management Journal, 20(10), 953-968.

Dimick, J. B., & Ryan, A. M. (2014). Methods for evaluating changes in health care policy: the difference-in-differences approach. Jama, 312(22), 2401-2402.

Dimitrovová, K., Perelman, J., & Serrano-Alarcón, M. (2020). Effect of a national primary care reform on avoidable hospital admissions (2000-2015): A difference-in-difference analysis. Social Science & Medicine, 252, 112908-112911.

Gilbert, B. A., McDougall, P. P., & Audretsch, D. B. (2008). Clusters, knowledge spillovers and new venture performance: An empirical examination. Journal of Business Venturing, 23(4), 405-422.

Glaeser, E. L., Kallal, H. D., Scheinkman, J. A., & Shleifer, A. (1992). Growth in cities. Journal of Political Economy, 100(6), 1126-1152.

Goss, E., & Vozikis, G. S. (1994). High Tech Manufacturing: Firm Size, Industry and Population Density. Small Business Economics, 6(4), 291-297.

Heyman, F., Sjöholm, F., & Tingvall, P. G. (2007). Is there really a foreign ownership wage premium? Evidence from matched employer-employee data. Journal of International Economics, 73(2), 355-376.

Koltai, J., Raifman, J., Bor, J., McKee, M., & Stuckler, D. (2022). COVID-19 vaccination and mental health: A difference-in-difference analysis of the understanding America study. American Journal of Preventive Medicine, 62(5), 679-687.

Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483-499.

Lechner, M. (2011). The estimation of causal effects by difference-in-difference methods. Foundations and Trends in Econometrics, 4(3), 165-224.

Lee, W. H., & Yang, W. T. (2000). The cradle of Taiwan high technology industry development—Hsinchu Science Park (HSP). Technovation, 20(1), 55-59.

Löfsten, H., & Lindelöf, P. (2002). Science Parks and the growth of new technology-based firms—academic-industry links, innovation and markets. Research Policy, 31(6), 859-876.

Oerlemans, L., & Meeus, M. (2005). Do organizational and spatial proximity impact on firm performance? Regional Studies, 39(1), 89-104.

Öhman, M., Woodford, J., & von Essen, L. (2021). Socioeconomic consequences of parenting a child with cancer for fathers and mothers in Sweden: a population‐based difference‐in‐difference study. International Journal of Cancer, 148(10), 2535-2541.

Owen-Smith, & Powell, W. W. (2004). Knowledge Networks as Channels and Conduits: The Effects of Spillovers in the Boston Biotechnology Community. Organization Science, 15(1), 5–21.

Porter, M. E. (1990). The competitive advonioge of notions. Harvard Business Review, 73-91.

Sakakibara, M., & Porter, M. E. (2001). Competing at home to win abroad: evidence from Japanese industry. Review of Economics and Statistics, 83(2), 310-322.

Siegel, D. S., Westhead, P., & Wright, M. (2003). Science parks and the performance of new technology-based firms: a review of recent UK evidence and an agenda for future research. Small Business Economics, 20(2), 177-184.

Tassey, G. (2010). Rationales and mechanisms for revitalizing US manufacturing R&D strategies. Journal of Technology Transfer, 35(3), 283-333.

Tallman, S., Jenkins, M., Henry, N., & Pinch, S. (2004). Knowledge, clusters, and competitive advantage. Academy of Management Review, 29(2), 258-271.

Tsai, M. T., & Tsai, C. L. (2010). Innovation capability and performance in Taiwanese science parks: exploring the moderating effects of industrial clusters fabric. International Journal of Organizational Innovation, 2(4), 80-103.

Wennberg, K., & Lindqvist, G. (2010). The effect of clusters on the survival and performance of new firms. Small Business Economics, 34(3), 221-241.

Xu, J., Haris, M., & Liu, F. (2022). Intellectual capital efficiency and firms' financial performance based on business life cycle. Journal of Intellectual Capital. https://doi.org/10.1108/JIC-12-2020-0383