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系統識別號 U0002-2801200814412700
中文論文名稱 一般化共同邊界Malmquist生產力指數及其應用
英文論文名稱 The Generalized Metafrontier Malmquist Productivity Index and Its Applications
校院名稱 淡江大學
系所名稱(中) 產業經濟學系博士班
系所名稱(英) Department of Industrial Economics
學年度 96
學期 1
出版年 97
研究生中文姓名 陳谷劦
研究生英文姓名 Ku-Hsieh Chen
電子信箱 124684@mail.tku.edu.tw
學號 892510040
學位類別 博士
語文別 英文
口試日期 2007-01-17
論文頁數 110頁
口試委員 指導教授-楊浩彥
共同指導教授-廖惠珠
委員-陳忠榮
委員-楊志海
委員-黃璀娟
委員-陳明園
委員-楊浩彥
中文關鍵字 總要素生產力  規模效率  技術效率  技術進步  技術收斂  Malmquist指數  共同邊界 
英文關鍵字 Total factor productivity  Scale efficiency  Technocal efficiency  Technical progress  Technological convergence  Malmquist index  Metafrontier 
學科別分類 學科別社會科學經濟學
中文摘要 在總要素生產力的領域,Cave et al. (1982) 的Malmquist生產力指數 (MPI) 受到廣泛的重視;然而在應用上,當生產單位非屬同一技術集合,而面對不同的技術邊界,跨群組MPI的分析,將受限於共同比較基礎的缺乏,而無法進行。為突破此限制,Rao (2006) 引進共同邊界 (metafrontier) 的概念,在非參數法下,提出共同邊界Malmquist生產力指數 (MMPI) 的估計架構。本文針對Rao (2006) 的MMPI,進行三個層面的延伸:首先,考量生產力指數衡量的完全性,嚐試透過距離函數的推導,在非參數法下,將規模效率調整項 (SEC) 納入MMPI的構成項目中;其次,並在參數法下,提供一般化共同邊界Malmquist生產力指數 (gMMPI) 的拆解,與可行估計架構;另外,亦針對MMPI構成項目中的「追趕」,進一步再拆解為純粹技術追趕 (PTCU) 與邊界追趕 (FCU) 二項,其中,PTCU所捕捉的,為個別國家的技術追趕,而FCU則反應出整個群組的技術追趕,其意涵與可應用的範圍不同。
在實證的應用上,本文利用1980-2003年間,35個高所得國家 (HICs)、73個中所得國家 (MICs),以及48個低所得國家 (LICs) 群組,進行參數法的生產力推估與拆解。結果發現,在MMPI的架構下,平均而言,中所得國家的生產力成長高於高、低所得國家群組,而在gMMPI的架構下,高所得國家的生產力成長則高於中、低所得國家群組,此反轉的結果,說明了未考量SEC的MMPI,所潛藏的衡量不完全的風險。其次,再將焦點轉向個別國家的比較,可以發現各個國家生產力成長的排序,也有類似的現象。此外,本文並發現,在樣本期間中,台灣的生產力指數呈現每年衰退0.3173%的現象,相較其他亞洲四小龍國家、金磚四國,以及美、日而言,位居最後。探究其原因,發現台灣在生產效率與規模效率上,持續以來的表現並不理想,分別呈現平均每年2.1679%與1.1594%惡化的現象;然而,在此同時也可以發現,在樣本期間內,各國的技術多呈現進步的結果,其中,台灣的技術進步則以年成長幅度3.0470%,位居亞洲四小龍國家、金磚四國,以及美、日之首。對此極端的結果,本文認為台灣過去的生產力,存在著對技術進步高度的依賴,但卻忽視了效率的提昇;因此,如何維持技術的成就,同時有效地進行在生產與規模上效率的調整,使欠缺天然資源稟賦的台灣,能獲得進一步經濟發展的動力,是當前重要的課題。最後,本文亦利用所推估的PTCU項,進行技術收斂假說的實證,觀察是否技術水準較低的國家,技術追趕的速度較快。結果發現,在絶對收斂的設定下,技術收斂並不存在,然而在條件收斂的設定下,技術將呈現收斂的現象。推論原因,主要可能與技術取得的差異有關。
英文摘要 This study extends Rao’s (2006) Metafrontier Malmquist Productivity Index (MMPI) to three dimensions. First, given the consideration to the completeness of a productivity index measure, we attempt to subsume a scale efficiency change term (SEC) in the MMPI with inductions of distance function under a non-parametric context. Second, a feasible structure of generalized MMPI (gMMPI) is also proposed under a parametric context. Furthermore, the ‘catching-up’ term in MMPI is disintegrated into two sub-components: pure technological catching-up (PTCU) and frontier catching-up (FCU), for achieving a more meaningful decomposition. Where, the PTCU is more suitable for observing the catching-up dynamics of a specific country, while the FCU is more suitable for observing the catching-up dynamics of the group frontier. For demonstrating the feasibility of this gMMPI framework, an empirical study is then conducted using data based on three country groups: 35 high income level countries (HICs), 73 middle income level countries, and 48 low income level countries (LICs) during the period from 1980-2003 with the parametric context. Consequently, the results reveal the risk embedded in the MMPI if the effect of the SEC is not taken into account. When shifting the focus to specific countries, the inconsistency between the rankings of MMPI and gMMPI is also found to be existent. Furthermore, the empirical results also reveal that the technical efficiency and scale efficiency of Taiwan exhibit annual averages of 2.1679% and 1.1594% declines, respectively. However, on average the margin of technical progress of Taiwan is about 3.0470% annually, which is ranked as the first when compared to the other four Asian countries, the BRIC countries and Japan and the US. Ultimately, this study also inspects the hypothesis of convergence in technology by using the pure technological catching-up term to observe whether a country with a lower level of technology would exhibit a higher catching-up rate. The results show that the technological convergence does not exist in the specifications of absolute convergence but exists in the situation of conditional convergence.
論文目次 CONTENTS
1. RESEARCH BACKGROUND 1

1.1 Research Motivation 1
1.2 Research Purpose and Framework 3

2. TFP, MMPI AND CONVERGENCE HYPOTHESIS 7

2.1 Introduction to the Total Factors Productivity (TFP) Index 7
2.2 The Metafrontier Malmquist Productivity Index (MMPI) 12
2.3 Convergence Hypotheses 16

3. GENERALIZED MMPI AND MODEL SPECIFICATIONS 21

3.1 Extensions of Rao’s Technological Catching-up 21
3.2 Extensions of the Metafrontier Malmquist Productivity Index 25
3.2.1 Non-Parametric Extension of MMPI 30
3.2.2 Parametric Extension of MMPI 32
3.3 Model Specifications and variable constructions 36
3.3.1 Stochastic and Metafrontier Production Function Models 36
3.3.2 Models for Convergence in Technology 39

4. EMPIRICAL APPLICATIONS 45

4.1 Data and Its Arrangements 45
4.2 Estimations of the Stochastic and the Metafrontier Production Function 49
4.3 Estimations of MPI, MMPI and gMMPI 53
4.4 Tests for Hypothesis of Convergence in Technology 73

5. CONCLUDING REMARKS AND RESEARCH LIMITATIONS 84

5.1 Concluding Remarks 84
5.2 Research Limitations and Further Research Suggestions 89

6. APPENDIX 92

Appendix 1: Rao’s (2006) Decomposition of MMPI 92
Appendix 2: The Inductions of Decompositions of CRS Based GMPI 93
Appendix 3: The Inductions and Decompositions of CRS Based MMPI 94
Appendix 4: The Inductions of gMMPI under the Parametric Context 97
Appendix 5: The Further Decompositions of gMMPI 98

7. REFERENCES 102



LIST OF TABLES
Table 1. Average annual growth rates: gross domestic product, capital and labor 47
Table 2. MLE estimation of the group frontiers - stochastic frontier analysis model 50
Table 3. Estimation of metafrontier - linear programming approach 52
Table 4. Decomposition of the generalized metafrontier Malmquist productivity index: 1980-2003 (HICs, MICs and LICs) 54
Table 5. Decomposition of generalized metafrontier Malmquist productivity index: 1980-2003 (BRICs, Asian four countries, Japan and the US) 60
Table 6. Inspections of scale efficiency changes (BRICs, Asian four countries, Japan and the US) 67
Table 7. Inspection to hypothesis of absolute β convergence in technology 77
Table 8. Inspection to the hypothesis of conditional β convergence in technology 78
Table 9. Inspection to hypothesis of conditional β convergence in technology (some extensions for checking robustness) 83
Table A1. Country list of high, middle and low income countries 100
Table A2. Difference tests of the MMPI, gMMPI and its components of HICs, MICs and LICs 101



LIST OF FIGURES
Figure 1. Illustrations of the concept of Rao’s catching-up and the pure technological catching-up and frontier catching-up of this study 23
Figure 2. Illustrations of the importance of scale efficiency in a productivity measure and the concept of CRS and VRS technology 27
Figure 3. The average annual growth rates of output, capital and labor for the HICs, MICs and LICs 48
Figure 4. The dynamics of gMMPI and MMPI for the HICs, MICs and LICs 56
Figure 5. The dynamics of the TEC*, TC* and SEC* for the HICs, MICs and LICs 57
Figure 6. The dynamics of the PTCU, FCU and RCU decomposed from the gMMPI in this study for the HICs, MICs and LICs 59
Figure 7. The dynamics of the TEC* for the BRIC countries, Asian four countries, Japan and the US 64
Figure 8. The dynamics of the TC* for the BRIC countries, Asian four countries, Japan and US 65
Figure 9. The dynamics of the gMMPI for the BRIC countries, Asian four countries, Japan and US 69
Figure 10. The dynamics of the MMPI for the BRIC countries, Asian four countries, Japan and US 70
Figure 11. The dynamics of the SEC* for the BRIC countries, Asian four countries, Japan and US 72
Figure 12. The dynamics of the PTCU for the BRIC countries, Asian four countries, Japan and US 74
Figure 13. The dynamics of FCU for the BRIC countries, Asian four countries, Japan and US 75
Figure 14. The relation ship of technological level and technological catching-up for the HICs, MICs and LICs 81
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