Three Essays on Industrial Network Analysis

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滕人傑(Jen-chieh Teng) 查詢紙本館藏

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論文名稱

(Three Essays on Industrial Network Analysis)

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摘要(中)

產業之間的網絡結構(network structure)分析是近年文獻日益關注的主題。透過投入產出表中投入產出交易的進一步研究，可以將投入產出表的應用範圍擴大到產業間網絡結構的各項特徵的實證應用。
本論文第二章探討產業網絡結構對總體經濟波動的影響。傳統文獻多關注在產業間投入產出連結所引導的向前及向後關聯效果。本章透過將向前及向後關連與有向圖(directed graph)的概念結合，建立了在產業網絡關係中衡量產業做為中間財供給者權重的出度指標(outdegree index)，以及衡量產業做為中間財消費者權重的入度指標(indegree index)。在眾多針對總體經濟波動的文獻研究中，網絡效果是最新的研究發現。透過上述出度指標及入度指標的衡量，並參考最新文獻的理論架構，可以將網絡效果對總體經濟的波動影響量化分析。本文使用的資料為世界投入產出資料庫(World Input–Output Database)中，各開發中與已開發國家的35部門產業的投入產出表。實證結果顯示，在一個經濟體的網絡結構中，若某些產業的中間財供給者權重越大，由供給方驅動的網絡效果對總產出及投資波動有正向效果。若某些產業的中間財消費者權重越大，由消費方驅動的網絡效果對總產出、投資及最終消費波動有正向效果。
第三章探討產業的網絡結構與產業價值鏈的關係。最新文獻將投入產出中的向前關聯交易，透過原始數學公式的引申，提出了產業上游度(industrial upstreamness)的衡量指標。上游度指標衡量某產業與所屬經濟體系中所有最終財消費的中間交易距離。若某產業上游度指標值越高，代表該產業距離各個最終財消費的距離越遠，也就是該產業所貢獻的直接加間接中間財投入價值鏈越長。實證結果顯示，若某產業的中間財供給者權重越大，該產業的上游度指標值越高；也就是在網絡建構的產業價值鏈中，處於越上游的位置。反之，若某產業的中間財消費者權重越大，該產業的上游度指標值越低；也就是在產業價值鏈中處於越下游的位置。然而，若以個別產業的跨國比較來分析，不同產業的網絡結構與產業價值鏈的關係，也根據各產業中間投入結構的不同，而呈現出與整體不同的樣貌。譬如，各國電子及光學設備產業的跨國比較顯示，無論該產業在各國的網絡結構中，是屬於中間供給權重值越高或中間消費權重值越高的情況，該產業都會朝向該國產業價值鏈的上游端移動。
第四章探討各國研究發展活動與中間產品跨國交易的關聯。最新文獻顯示，兩國之間的知識擴散，可透過兩國之間的中間財貿易達成。同時，產業發展的實際經驗也顯示，透過增加研發能力達成進口替代的開發中國家，替代過程中對更高階的關鍵零組件產品與生產設備的進口需求也會增強。本章透過各國的製造業研發密集度與中間產品進口資料的兩階段迴歸分析，在考慮內生性的影響下，發現各製造業部門使用於自身的進口中間投入，與該部門的國內研發密集度有顯著的正向關聯。此一發現也間接證實跨國間知識擴散與中間財跨國貿易之間的確存在一般性的關聯。

摘要(英)

This dissertation elucidates the novel aspects of network analysis from both the input–output linkage and value-chain perspectives.
Chapter 2 discusses the effect of the industrial network on aggregate fluctuations at the country level. The input–output linkages between industries construct a network system. By combining the concepts of backward and forward linkages with the directed graph, I provide novel outdegree and indegree indices to exhibit industry’s network effect on aggregate fluctuations. Within the literature, researchers have discussed several hypotheses regarding the mechanism between aggregate fluctuations and micro-level shocks; the network effect is one of the newest theories in this field. In this dissertation, I provide cross-country empirical evidence demonstrating that the structure of the industrial network can affect aggregate fluctuations by using World Input–Output Database (WIOD). I further distinguish between the demand- and supply-driven network effects on various aggregate fluctuations, and obtain several findings regarding the network effects on different aggregate fluctuations, such as the fluctuations of investment and consumption.
In Chapter 3, the characteristics of industrial upstreamness are described. Input–output linkages reveal information about the value chain or the “upstreamness” of each industry and here I determine the relationship between upstreamness and degree indices and other variables regarding the structure of the production network among domestic industries. For example, considering both the direct and indirect effects, when one industry plays a stronger role as a “supplier” of intermediate goods and services in the network, it tends to have an upper position in the value chain. Conversely, if one industry has more weight as a “consumer” in the network system, it tends to have a lower position in the value chain. However, the relationships regarding the value chain position and network connection between different industries are various under a country-by-country comparison. For example, for the industry of electrical and optical equipment, intensive connections for both a supplier and consumer of intermediate inputs are related to an upper position in the value chain.
In Chapter 4, I turn to the topic of R&D and testify the effect of imported intermediate inputs on domestic R&D intensity. Empirical evidence is demonstrated that intermediate imports have a positive effect on the domestic R&D intensity under a wide range of cross-country and cross-industry comparison. Furthermore, the 2SLS empirical evidence in Chapter 4 mainly specifies the imported self-use intermediate inputs for manufacturing industries. The finding also indirectly supports the hypothesis that international diffusion of knowledge is systematically related to cross-border trade relationships.

關鍵字(中)

★ 投入產出分析
★ 網絡結構
★ 產業上游度指數
★ 中間財交易
★ 總體經濟波動

關鍵字(英)

★ input–output analysis
★ network structure
★ industrial upstreamness
★ intermediate transactions
★ Aggregate Fluctuations

論文目次

摘要 i
Abstract iii
誌謝辭 v
Contents vi
List of Tables viii
List of Figures ix
1. Introduction 1
2. The Network Effect on Aggregate Fluctuations 5
2.1 Introduction 5
2.2 The Theoretical Framework and Index measurement 7
2.2.1 The Concept of Network Effects on Aggregate Fluctuation 7
2.2.2 The Measurement of Industrial Network Structure 10
2.3 Data and Regression Specification 16
2.3.1 The Relationship between the Network Structure and GDP Fluctuations 16
2.3.2 The Studies on Aggregate Volatility and Its Source 18
2.3.3 Data and the Regression Specification 19
2.4 Empirical Results of Network Effects on Aggregate Fluctuations 24
2.4.1 Empirical Results and Robustness 24
2.4.2 Further Issues about Network Effects on Aggregate Fluctuations 30
2.5 Conclusions 37
3. The Relationship between Industrial Upstreamness and Network Structure 39
3.1 Introduction 39
3.2 Theoretical Review 42
3.2.1 Upstreamness and the Decay of Input Coefficient 42
3.2.2 Relationship between Upstreamness and Degree Indices 48
3.3 Data and Regression Model 50
3.3.1 Industrial Upstreamness Data Summary 50
3.3.2 Regression Model and Data Description 55
3.4 Empirical Results 64
3.4.1 Results of Basic Models 64
3.4.2 Results of Net Degree Effect 65
3.5 Conclusion 67
4. The Effect of Intermediate Inputs on Domestic R&D Intensity 69
4.1 Introduction 69
4.2 Data Description and Regression Model 71
4.3 Empirical Results 78
4.4 Conclusion 81
5. Conclusion 82
Reference 84
Appendix A: the WIOD industry classification 89
Appendix B: the WIOD and OECD STAN Industrial classification concordance 90
Appendix C: the WIOD and OECD STAN Industrial classification concordance for R&D intensity 91

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指導教授

徐之強

審核日期

2017-1-11

推文