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題名：三篇關於廠商生產力與所得不均之文章
作者：蘇奕璿
作者(外文)：Su, Yi-Hsuan
校院名稱：國立清華大學
系所名稱：經濟學系
指導教授：祁玉蘭
學位類別：博士
出版日期：2015
主題關鍵詞：產業群聚；總要素生產力；知識外溢；投入產出連結；勞動市場匯集；貿易自由化；技術進步；所得不均；文化；制度；Industrial agglomeration；Total factor productivity；Knowledge spillovers；Input-Output linkage；Labor market pooling；Trade liberalization；Technology；Income inequality；Cultures；Institutions
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本研究之三個章節主要涵蓋廠商生產力及所得分配不均兩大議題。第一章以理論模型及台灣製造業的實證研究，觀察產業群聚對廠商生產力的影響；第二章探索全球化、技術進步與所得分配不均的關係；第三章則進一步探討一國的文化因素對所得分配不均有何影響效果。
第一章之研究動機起始於產業群聚現象於全球各地日漸趨多及其在台灣經濟發展過程中所扮演的重要角色。Marshall (1890) 提及產業或廠商會因為三方面的好處而選擇群聚在特定地區進行生產活動，亦即知識外溢效果( knowledge spillovers)，投入產出連結( input-output linkage)，以及勞動市場匯集( labor market pooling)。有別於過往文獻關注單一群聚現象或程度與廠商生產力之間的關係，本研究將焦點擴展至形成產業群聚之三種關鍵驅動力對生產力的影響。我們首先嘗試建構一個納入三種群聚成因之理論模型，分析群聚成因與廠商生產力之間的理論意涵，並進一步利用台灣製造業1992至2004年之工廠營運校正資料進行實證分析。實證結果發現，三種形成產業群聚的關鍵驅動力對於廠商生產力，都帶來正向且顯著的效果。特別的是，當我們將總和知識外溢效果再進一步區分成相異產業間知識外溢以及相同產業內知識外溢兩個效果時，發現產業間知識外溢對廠商生產力有正向影響，但產業內知識外溢卻有減損生產力的傾向。此結果不僅呼應了Jacobs (1969) 所提群聚內產業多樣化的重要性，同時透露可能存在Bloom et al. (2013) 所強調來自產品市場中敵對廠商的負面競爭及剽竊效果。
第二章的研究著重在探究技術進步與貿易開放將如何影響長期受到關注的所得不均現象。藉由61國橫跨1975-2005年所組成的長期追蹤資料，運用有別於傳統計量工具的追蹤資料門檻迴歸模型，本研究分析全球化、技術發展以及所得不均之間的關係，試圖探究形成各國逐年擴大的所得不均程度，以及跨國所得不均呈現差異的原因，並討論是否誠如近期文獻所建議的，貿易開放與技術進步分別與所得不均存在非線性的關聯?實證結果顯示，在探討自由貿易與所得不均的關係時，技術進步不僅扮演著關鍵性的角色，並且存在門檻效果，也就是在科技發展程度相對較低的國家，自由貿易愈開放將使所得不均愈加惡化；相反地，在科技水準較先進的國家，愈多的自由貿易反而可以減緩所得不均的情形。
在第三章中，由於開始有學者提出(e.g. Acemoglu, 2003; Acemoglu et al., 2005)，過去文獻探討影響所得不均的重要因素，包括自由貿易、技術進步、甚或教育，充其量只是近因，真正影響所得不均的關鍵遠因乃是制度以及左右制度形成的文化背景及社會狀態，所以除了自由貿易、技術進步，我們更進一步加入文化因素的角色，並探究這些元素對所得不均帶來何種影響。我們選用Hofstede所建構的其中兩項國家文化向度指標，亦即權力距離指數(PDI: Power Distance Index)以及個人主義指數(IDV: Individualism)，來代表形塑出不同制度的文化背景，其中若權力指數愈高，則愈有可能制定出增加所得不均的制度，若個人主義指數愈高，則有助於制定出消弭所得不均的制度。我們計算2000至2009年各變數的10年平均值以建構出包含58個國家的跨國資料，利用橫斷面門檻迴歸法進行實證研究。實證結果顯示，個人主義指數在不同的社會情境下大致皆有助減緩所得不均，而權力分布越不平均的社會的確連帶提升所得不均，除非同時個人主義足夠盛行，所得不均情況才能得以舒緩。至於被視為近因的自由貿易與技術進步等因素，在加入國家文化的考量後，它們對所得不均的影響會依據不同的社會狀態及文化背景，呈現出迥然相異的結果。
This dissertation studies the topics on firms' productivity and income inequality. The first chapter aims at exploring the impacts of industrial agglomeration on productivity on the theoretical side as well as on the empirical side. In the following two chapters, we focus on the topic of income inequality. The second chapter attempts to investigate the relationship among trade, technology, and income inequality. The third chapter examines whether and how cultural attributes shaping institutions affecting income inequality.
In the first chapter, our study is motivated by the fact that agglomeration economies tend to be a distinguishing feature for fostering economic growth and firms’ productivity in manufacturing industries in Taiwan. The benefits that firms cluster in a geographical area are considered to include a variety of mechanisms such as the possibility of sharing suppliers with similar firms (IO linkage), the existence of thick labor markets facilitating matching (labor market pooling), and the possibility of learning from other firms’ experiences and innovations in the industrial agglomeration (knowledge spillovers). In this study, we attempt to construct a theoretical model embracing three driving forces of industrial agglomeration and implement an empirical test using a plant-level dataset to explore the impacts of agglomeration on productivity in Taiwan's manufacturing sector. Our empirical findings suggest that, by and large, all three determinants of agglomeration boost plants' productivity. Particularly, after the decomposition of total knowledge spillovers, the inter-industry knowledge spillovers are helpful for productivity, while the intra-industry knowledge spillovers turn out to depress plants' performances.
In the second chapter, we try to assess the effects of trade openness and technology on income inequality because many countries have experienced rising within-country inequality, technological development, and globalization since the 1990s. There has been an ongoing debate on the impact of changes in technology and globalization on income inequality in recent literature. To reconcile conflicting empirical findings, this study explores a nonlinear relationship between trade openness and income inequality across countries with a range of technological progress status. Using a panel of 61 countries over a 31 year period from 1975 to 2005, this study analyzes trade-inequality relationships by estimating panel threshold regression models. The threshold effects of technology and an inverted-U relationship are identified when examining the impacts of trade on income inequality. On the one hand, countries with less-advanced technologies tend to confront increases in income inequality when they become more globalized. On the other hand, trade openness is revealed to ease income inequality in countries with advanced technologies.
In the third chapter, we additionally incorporating the role of cultural attribute into the analysis of income inequality. It has been discussed that trade globalization, financial globalization, and technology are simply proximate causes rather than fundamental ones like institutions to explain income inequality. With proxies for cultural backgrounds of the emergence of different types of institutions, this paper combines trade, FDI, and technology with cultural attributes to investigate how worldwide variations of within-country income inequalities are shaped by those factors. Using the Standardized World Income Inequality Database and the well-known Hofstede's cultural dimensions, this study compiles a cross-country dataset of 58 countries for 10-year averages from 2000 to 2009. The threshold regression model is applied to show that individualism is consistently helpful for reducing income inequality. In addition, unequal power distribution deteriorates income inequality, and tends to ease inequality only when the members of a society highly praise individualism. As for conventional causes of income inequality, including trade, FDI, and technological progress, our empirical findings suggest that their distributional effects vary depending on the status of a society.
Chapter 1
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Chapter 2
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Hall, J.D., (2010). “The Diffusion of Technology, Education and Income Inequality: Evidence from Developed and Developing Countries.,” the 2009 Dynamics, Economic Growth, and International Trade (DEGIT) conference paper, Drexel University, Philadelphia.
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Helpman, E., O. Itskhoki, M. Muendler, and S. Redding, (2014). “Trade and Inequality: From Theory to Estimation,” NBER Working Paper No. 17991.
Helpman, E., O. Itskhoki, and S. Redding, (2010). “Inequality and Unemployment in a Global Economy,” Econometrica, 78(4), 1239-1283.
Hyytinen, A. and O. Toivanen, (2011). “Income Inequality and Technology Diffusion: Evidence from Developing Countries.,” The Scandinavian Journal of Economic, 113(2), 364-387.
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Chapter 3
Acemoglu, D., (1998). “Why Do New Technologies Complement Skills? Directed Technological Change and Wage Inequality.” The Quarterly Journal of Economics, 113(4), 1055-1089.
Acemoglu, D., (2003). “Root Causes: A Historical Approach to Assessing the Role of Institutions in Economic Development.” Finance & Development, June, 2003.
Acemoglu, D., S. Johnson and J. A. Robinson, (2005). “Institutions as a Fundamental Causes of Long-Run Growth.” Handbook of Economic Growth, Chapter 6.
Acemoglu, D. and M. Dell, (2010). “Productivity Differences between and within Countries.” American Economic Journal: Macroeconomics, 2(1), 169–188. American Economic Association.
Aghion, P. and P. Howitt, (1992). “A Model of Growth through Creative Destruction.” Econometrica, 60(2), 323-351.
Chan, K. S. (1993). “Consistency and Limiting Distribution of the Least Squares Estimator of a Threshold Autoregressive Model.” The Annals of Statistics, 21, 520-533.
Dreher, A. (2006). “Does Globalization Affect Growth? Evidence from a new Index of Globalization.” Applied Economics, 38(10), 1091-1110.
Grossman, G. and E. Helpman, (1991). “Quality Ladders in the Theory of Growth.” Review of Economic Studies, 58, 43-61.
Hall, J. D., (2010). “The Diffusion of Technology, Education and Income Inequality: Evidence from Developed and Developing Countries.” the 2009 Dynamics, Economic Growth, and International Trade (DEGIT) conference paper, Drexel University, Philadelphia.
Hansen, B., (2000). “Sample Splitting and Threshold Estimation.” Econometrica, 68(3), 575-603.
Helpman, E., O. Itskhoki, and S. Redding, (2010). “Inequality and Unemployment in a Global Economy.” Econometrica, 78(4), 1239-1283.
Helpman, E., O. Itskhoki, M. Muendler, and S. Redding, (2014). “Trade and Inequality: From Theory to Estimation.” NBER Working Paper No. 17991.
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Hyytinen, A. and O. Toivanen, (2011). “Income Inequality and Technology Diffusion: Evidence from Developing Countries.” The Scandinavian Journal of Economic, 113(2), 364-387.
Jaumotte, F., S. Lall and C. Papageorgiou, (2013). “Rising Income Inequality: Technology, or Trade and Financial Globalization?” IMF Economic Review, 61(2), 271-309.
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Melitz, M. J., (2003). “The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity.” Econometrica, 71, 1695-1725.
Raab, Marcel, M. Ruland, B. Schönberger, H.-P. Blossfeld and D. Hofäcker, (2007). “GlobalIndex-A Multidimensional Approach to Globalization Measurement.” TransEurope Project, http://www.transeurope-project.org.
Solt, Frederick, (2009). “Standardizing the World Income Inequality Database.” Social Science Quarterly 90(2), 231-242. SWIID Version 4.0, September 2013.
Willen, P., I. Hendel and J. Shapiro, (2004). “Educational Opportunity and Income Inequality” NBER Working Paper No. 10879.
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