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題名:臺灣製造業中小企業考量地區特性影響下技術效率與生產力的變化:隨機性統計邊界法與資料包絡分析法之比較
作者:李東杰
作者(外文):Tung Chieh Lee
校院名稱:東吳大學
系所名稱:經濟學系
指導教授:李文福
學位類別:博士
出版日期:2001
主題關鍵詞:技術效率生產力變化外部操作環境資料包絡分析法隨機性統計邊界法地區特性製造業中小企業technical efficiencyproductivity changeexternal operating environmentData Envelopment AnalysisStochastic Statistical Frontier Approacharea characteristicssmall and medium manufacturing enterprises
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除勞動、資本等內部管理投入外,外部操作環境也會影響廠商生產績效,而外部環境的影響有其重要的政策涵義;因此本文首先從理論層面探討應用資料包絡分析法(DEA)與隨機性統計邊界法(SFA)時,如何將外部操作環境對績效的影響抽離出來,以獲得管理面的技術效率與生產力。本文進而以臺灣製造業中小企業為例,以不同的衡量模型實證研究各縣市之管理技術效率與生產力之相對表現。
囿於資料的限制,本文所選取的廠商外部操作環境僅包括四個面向:公路密度、工業用土地面積、高中以上程度人數、環保稽查人數,通稱為各縣市之地區特性變數。管理技術效率衡量則包括三個模型:DEA-FSY、SFA-ONE、SFA-FSY。DEA-FSY為Fried, Schmidt, and Yaisawarng (FSY, 1999)所提的考量外部操作環境變數下的無母數DEA方法;SFA-ONE為效率文獻中所指的一階段中立性母數隨機邊界法;SFA-FSY則為本文仿照FSY修正資料之程序所提出的母數隨機性邊界法。製造業實證資料係利用民國七十五年、八十年、及八十五年之工業普查資料,依縣市別(惟不含澎湖縣)整理成2欄位產業資料。
主要實證結果為:
1.在大部份產業上,上述剔除地區特性影響後的各模型中,SFA-ONE與SFA-FSY(即同為有母數方法比較)所得到的各縣市技術效率與生產力變化之排名,頗為一致;但SFA-ONE與DEA-FSY間、及SFA-FSY與DEA-FSY間 (即有母數與無母數方法間比較)所得到的各縣市之技術效率(或生產力變化)排名,則在一些產業上並不一致。
2.然而在大部份產業上,剔除地區特性影響後有關技術效率與生產力變化之變動幅度與方向,無母數方法(即DEA與DEA-FSY)間之比較均較有母數方法(即SFA分別與SFA-ONE、SFA-FSY間之比較)有明顯差異,原因可能是無母數衡量方法雖已摒除地區特性之影響,但卻仍受如氣候、地形等社會外部因素之影響,然而在有母數衡量方法上則連社會外部因素之影響亦均排除。
3.就各產業的技術效率受地區特性影響而言,在較科技性產業上僅電力及電子機械器材製造修配業在南部區域明顯較有利;在較污染性產業中則以化學材料製造業在西部各區域明顯受不利的影響。
4.就各產業的生產力變化受地區特性影響而言,在較科技性產業上機械設備製造修配業在各區域均受有利影響,而電力及電子機械器材製造修配業則除中部區域外,亦均受有利影響;在較污染性產業中,則以金屬製品製造業在西部各區域均受不利影響,而紡織業則除中部區域外,在其餘區域亦均受不利影響。
5.若上述地區特性之影響對產業如屬有利,則表該產業適於該縣市發展,但廠商須加強其PTE(純粹技術效率)與TFPch(總要素生產力變動);如屬不利影響,則可擬定改善地區特性之措施以增強該縣市廠商之PTE與TFPch。
6.在摒除地區特性影響前之排名方面,就PTE而言,中部地區與東部地區的縣市排名大致較佳,南部地區較差;就TFPch而言,則並無任何縣市的排名上均能名列前矛,但名次大致均落在後面者則為基隆市。
7.在摒除地區特性影響後之PTE排名上,並未改變位居較優與較差之縣市順序,仍是以臺北縣居冠,臺中縣其次,臺東縣、花蓮縣分居三、四名;而排名在後的縣市則仍為屏東縣、宜蘭縣;在摒除地區特性影響後之TFPch排名上,亦無任何縣市均能名列前矛,但名次大致均落在後面者則改為苗栗縣。因此由各縣市在摒除地區特性影響前後TFPch的排名變動情況,可知若某產業的排名呈上升(或下降),則表該縣市在此產業上之廠商相對其他縣市,是更具有(或不具有)競爭優勢。
Besides the internal controllable factors such as labor and capital, the external operating environment could influence the ability of the firm to transform inputs into outputs, and hence the effects of the external operating environment on firm’s performance has important policy implications. Therefore, the paper aims to study how to theoretically measure managerial technical efficiency and productivity by incorporating the exogenous environment characteristics under Data Envelopment Analysis (DEA) and Stochastic Statistical Frontier Approach (SFA). Furthermore, the paper empirically analyzes the technical efficiency and productivity of small and medium-sized manufacturing enterprises across prefectures and cities in Taiwan.
Being subject to the data availability, the paper only includes four area environment characteristics: highway density, lands available for industrial use, population with senior high school and above, and the official employees for environment protection. Managerial technical efficiency is measured and compared by three models: DEA-FSY, SFA-ONE, and SFA-FSY. DEA-FSY is a nonparametric DEA model proposed by Fried, Schmidt, and Yaisawarng (FSY, 1999); SFA-ONE is one-stage parametric neutral stochastic frontier approach (Battese and Coelli, 1995), and SFA-FSY proposed here adopts SFA and the application of FSY to adjust output data. The data used here is the panel data at two-digit industrial level as well as prefectures and cities level, which are compiled from the Industry, Commerce, and Service Census for the years 1986, 1991 and 1996.
The main empirical results are as follows:
1.In most industries, the two parametric models SFA-ONE and SFA-FSY produce the similar technical efficiency and productivity change rankings of prefectures and cities. But in some industries, the similarity does not exist between parametric and nonparameric models, i.e. between SFA-ONE and DEA-FSY or between SFA-FSY and DEA-FSY.
2.The rate and the direction of changes in technical efficiency and productivity change between the nonparametric models DEA and DEA-FSY are more significant than those between the parametric models SFA and SFA-ONE or between SFA and SFA-FSY. The reason for the difference may be that the nonparametric models incorporate area characteristics, but they still don''t account for the influence of some society external factors such as climate, topography and etc., while the parametric models incorporate area characteristics and society external factors into consideration.
3.As for the effect of area characteristics on technical efficiency, it is significantly favorable to the Electrical & Electronic Machinery Industry among technological intensive industries in southern Taiwan, while it is significantly unfavorable to the Chemical Matter Manufacturing Industry among pollution industries in western Taiwan.
4.As for the effect of area characteristics on productivity change, it is significantly favorable to the Machinery & Equipment Industry in all areas, and to the Electrical & Electronic Machinery Industry in all areas except central Taiwan. But it is unfavorable to the Fabricated Metal Products Industry in all areas except eastern Taiwan, and so it is to the Textile Mill Products Industry in all areas except central regions.
5.If the influence of area characteristics on technical efficiency or productivity change is favorable to an industry in a region, it indicates that the region has gained the advantage to further develop the industry. But if it is unfavorable, the local government has to improve the external operating environment for the firms to gain greater competitiveness.
6.Without incorporating the influence of area characteristics, the central and eastern regions are ranked better in technical efficiency in the three periods, but the southern region is ranked worse. As for productivity change, no prefectures or cities are ranked top throughout all periods, but Keelung City is almost in the lowest position over the periods.
7.With the incorporation of area characteristics, the technical efficiency ranking of prefectures and cities remains the same both in front and at the back of the ranking. Specifically, Taipei Prefecture, Taichung Prefecture, Taitung Prefecture, and Hualien Prefecture are ranked top, and Pingtung Prefecture, Ilan Prefecture are ranked low. As for productivity change ranking, Miaoli Prefecture becomes the lowest one. If the productivity change ranking is falling or rising for a specific industry, the firms of the industry become less or more competitive in the prefectures or cities.
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