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6 DEDICATION I would like to dedicate thi reearch work to my dear wife Keziah, my on David, Jame and Timothy and my Lovely Daughter Fiona. i

7 ACKNOWLEDGMENTS Firt and foremot, I would like to thank the Lord God Almighty whoe grace abound throughout thi work; I give all glory and honour to Him. I am o indebted to all thoe who have been intrumental in different way including financial, material and more o piritual upport in the coure of my tudy. Since I am not able to reach them all, I mention jut a few. Indeed, I might not have made it if it were it not for the irreitible and peritent upport from my dear wife Keziah; we pent leeple night together a he at beide me all night through. My dear on David, Jame and Timothy a well my Lovely daughter Fiona who were o patient with me even when I wa not available for them; I will never let them down. I am alo greatly indebted to my upervior Prof. Martin N. Etyang and Dr. Nelon H. Were Wawire whoe guidance, contructive criticim, recommendation and uggetion were invaluable in giving hape to thi thei; I am o grateful to them for committing their time, and for being o patient with me. I am alo very grateful to the Late Dr. Peter M. Makau for the wie guidance he accorded me a my upervior before the cruel hand of death took hi life. May the Lord ret hi oul in eternal peace. I alute all my colleague in the Department of Economic at Kenyatta Univerity whoe comment and criticim added value to my thei. I am pecifically grateful and I acknowledge the encouragement and upport I received from Dr. T. Kimani, Dr. D. Ngui, Dr. A. Obere, Mr. M. Kuuya, Dr. G. Koimbei, Dr. J. Njaramba, Mr. J. Kinyanjui, Dr. S. Okeri, Dr. E. Manyaa, Dr. C. Ombuki and Dr. J. Korir. A note of appreciation goe to Kenyatta Univerity for offering me the opportunity to purue my tudie. Much more appreciation to DAAD for giving me a partial ii

8 cholarhip to do coure work in the Center for Development Reearch (ZEF), Univerity of Bonn, Germany; and for funding my reearch toward my PhD. Long live DAAD, Long live Kenyatta Univerity. Special gratitude goe to Ezekiel Mucheru Mucheru (Ecky) for the effort he accorded to enure a ucce in thi work. He wa alway available for me whenever I needed hi pecial kill. Special appreciation alo goe to my begotten on and daughter Chri, Deni, Cate and Shiru. They were alway there for me upporting me piritually and challenging me never to give up. Word may fail me to expre how grateful I am to my dad the Late David Gachanja and to my mom Mr. Hannah Njeri Gachanja. I now know the value of having loving and committed parent. Without them, my achievement would only have remained a weet dream. I am alo grateful to my brother and iter for the moral upport they have accorded me through out. iii

9 About the Main Author Dr. Paul M Gachanja i a lecturer of Economic at Kenyatta Univerity. He received hi B.A (Economic)-Firt Cla Honour, MA (Economic) and PhD in economic from Kenyatta Univerity. He i alo a graduate of Center for Development Reearch, Germany under the DAAD exchange programme. Dr. Gachanja primary area of interet are Economic Theory and Quantitative Method with vat involvement in the review of the African Economic Reearch Conortium (AERC) Managerial Economic curriculum. He i currently the Chairman of the Economic Theory Department at Kenyatta Univerity, a conultant upervior for the graduate chool and ha co-authored everal article over the year. iv

10 TABLE OF CONTENTS Page DEDICATION... i ACKNOWLEDGMENTS... ii TABLE OF CONTENTS... v LIST OF TABLES... vii LIST OF FIGURES... ix OPERATIONAL DEFINITION OF TERMS... xi ACRONYMS AND ABBREVIATIONS... xii ABSTRACT... xiv CHAPTER ONE... 1 INTRODUCTION The concept of productivity The manufacturing ector in Kenya The tatement of the reearch problem Reearch quetion Objective of the tudy Significance of the tudy The cope and organiation of the tudy CHAPTER TWO LITERATURE REVIEW AND THEORETICAL FRAMEWORK Introduction Theoretical literature The production technology and ditance function Output ditance function Input ditance function Meauring productivity and productivity change Meauring productivity change and the Total Factor Productivity index Calculation and decompoition of productivity change uing frontier method Data Envelopment Analyi (DEA) The Stochatic Frontier method Overview of the theoretical literature General empirical literature Empirical literature pecific to Kenya Overview of the empirical literature Theoretical framework Introduction The malmquit TFP index CHAPTER THREE METHODOLOGY Introduction Reearch deign The empirical model Definition and meaurement of variable v

11 3.5 The data Data editing, coding, cleaning and refinement Validity and reliability of data Data analyi CHAPTER FOUR RESEARCH FINDINGS Introduction Policy epiode in Kenya indutrialization proce The tructure and compoition of the Kenyan manufacturing ector Total Factor Productivity change in the manufacturing ector Introduction TFP change, efficiency change and technical change in the manufacturing ector Table 4.1: Malmquit index ummary of annual mean Table 4.2: Summary of malmquit Indice per ub-ector TFP change, efficiency change and technical change in the individual ubector Table 4.3: Malmquit index ummary of annual mean for food ub-ector Table 4.4: Malmquit Index ummary of annual mean for textile and garment ubector Table 4.5: Malmquit index ummary of annual mean for wood and furniture ubector Table 4.6: Malmquit index ummary of annual mean for paper, printing and publihing ub-ector Table 4.7: Malmquit index ummary of annual mean for platic ub-ector Table 4.8: Malmquit index ummary of annual mean for metal ub-ector Table 4.9: Malmquit index ummary of annual mean for contruction material ub-ector Table 4.10: Malmquit index ummary of annual mean for chemical and Pharmaceutical ub-ector CHAPTER FIVE SUMMARY, CONCLUSIONS AND POLICY IMPLICATIONS Introduction Summary Concluion Policy implication and area for further reearch BIBLIOGRAPHY APPENDICES APPENDIX I: MANUFACTURING SECTOR SHARES Table A1: Total Number of Employee in Manufacturing Sector 2001 to Table A2: Structure, Contribution and Performance of Kenya Manufacturing Subector Table A3: Sectoral Share in the Real GDP, (Percentage) vi

12 Table A4: Percentage Growth in GDP and Percentage Growth in Manufacturing Contribution to GDP APPENDIX II: MALMQUIST INDICES Table A5: Malmquit Index Summary of Firm Mean Table A6: Malmquit Index Summary of Firm Mean (Food) Table A7: Malmquit Index Summary of Firm Mean (Textile) Table A8: Malmquit Index Summary of Firm Mean (Wood and Furniture) Table A9: Malmquit Index Summary of Firm Mean (Paper, Printing and Publihing) Table A10: Malmquit Index Summary of Firm Mean (Platic) Table A11: Malmquit Index Summary of Firm Mean (Metal) Table A12: Malmquit Index Summary of Firm Mean (Contruction Material) 153 Table A13: Malmquit Index Summary of Firm Mean (Chemical and Pharmaceutical) APPENDIX III: DEFLATORS Table A14: Output and Input Deflator for each Sub Sector in the 2000/ /2002 period Table No. LIST OF TABLES vii Page Table 4.1: Malmquit index ummary of annual mean Table 4.2: Summary of malmquit Indice per ub-ector Table 4.3: Malmquit index ummary of annual mean for food ub-ector Table 4.4: Malmquit Index ummary of annual mean for textile and garment ubector Table 4.5: Malmquit index ummary of annual mean for wood and furniture ubector Table 4.6: Malmquit index ummary of annual mean for paper, printing and publihing ub-ector Table 4.7: Malmquit index ummary of annual mean for platic ub-ector105 Table 4.8: Malmquit index ummary of annual mean for metal ub-ector 107 Table 4.9: Malmquit index ummary of annual mean for contruction material ub-ector Table 4.10: Malmquit index ummary of annual mean for chemical and Pharmaceutical ub-ector Table A1: Total Number of Employee in Manufacturing Sector 2001 to Table A2: Structure, Contribution and Performance of Kenya Manufacturing Subector Table A3: Sectoral Share in the Real GDP, (Percentage) Table A4: Percentage Growth in GDP and Percentage Growth in Manufacturing Contribution to GDP Table A5: Malmquit Index Summary of Firm Mean Table A6: Malmquit Index Summary of Firm Mean (Food) Table A7: Malmquit Index Summary of Firm Mean (Textile)

13 Table A8: Malmquit Index Summary of Firm Mean (Wood and Furniture)150 Table A9: Malmquit Index Summary of Firm Mean (Paper, Printing and Publihing) Table A10: Malmquit Index Summary of Firm Mean (Platic) Table A11: Malmquit Index Summary of Firm Mean (Metal) Table A12: Malmquit Index Summary of Firm Mean (Contruction Material) Table A13: Malmquit Index Summary of Firm Mean (Chemical and Pharmaceutical) Table A14: Output and Input Deflator for each Sub viii

14 LIST OF FIGURES Figure No. Page 1.1 Sectoral hare in the real GDP, (percentage) A comparion of trend in real GDP growth and real growth in manufacturing output ( ) Output ditance function and production poibility et Input ditance function and input requirement et Malmquit productivity indice Total number of employee in the manufacturing ector Ownerhip of Kenyan manufacturing enterprie Manufacturing ub-ector contribution to GDP, export and employment in 2005 (percentage) Efficiency change core for manufacturing ub-ector Technical change core for manufacturing ub-ector Total Factor Productivity change core for manufacturing ubector Efficiency, Technical and Total Factor Productivity change core for food ub-ector Efficiency, Technical and Total Factor Productivity change core for textile ub-ector Efficiency, Technical and Total Factor Productivity change core for wood and furniture ub-ector Efficiency, Technical and Total Factor Productivity change core for paper, printing and publihing ub-ector Efficiency, Technical and Total Factor Productivity change core for platic ub-ector Efficiency, Technical and Total Factor Productivity core for Metal ub-ector Efficiency, Technical and Total Factor Productivity change core for ix

15 contruction material ub-ector Efficiency, Technical and Total Factor Productivity change core for chemical and pharmaceutical ub-ector x

16 OPERATIONAL DEFINITION OF TERMS A Firm: refer to a deciion making unit that i engaged in the production of good and ervice. Manufacturing: refer to the tranformation of raw material (input) into finihed good for ale. A Manufacturing firm: refer to a deciion making unit engaged in the tranformation of raw material into finihed good for ale. An Indutry: refer to a group of manufacturing firm that produce a homogeneou product. Productivity: refer to the ratio of output that a firm produce to the input that it ue. Efficiency: refer to the effectivene with which a given et of input are ued to produce output, given the technology. Technology: refer to the plan or proce with which input are ued in production. Production Frontier: repreent the maximum output attainable from each level of input. Capacity utilization: refer to the ratio of actual output to the capacity available for production. xi

17 ACRONYMS AND ABBREVIATIONS ACP - African, Caribbean and Pacific Countrie AGOA - African Growth and Opportunitie Act COMESA - Common Market for Eatern and Southern Africa Creation CRS - Contant Return to Scale DEA - Data Envelopment Analyi DEAP - Data Envelopment Analyi Programme DMU - Deciion Making Unit EAC - Eat African Community EPZ - Export Proceing Zone EPZA - Export Proceing Zone Authority ERSWEC - Economic Recovery Strategy for Wealth and Employment EU - European Union Forex-C - Foreign Exchange Certificate GDP - Gro Dometic Product IMF - International Monetary Fund KAM - Kenya Aociation of Manufacturer LP - Linear Programming MUB - Manufacturing Under Bond NEMA - National Environment Management Authority OECD - Organization for Economic cooperation and Development PFP - Partial Factor Productivity R&D - Reearch and Development RPED - Regional Programme on Enterprie Development SAP - Structural Adjutment Programme SFA - Stochatic Frontier Analyi SME - Small and Medium Enterprie SSA - Sub-Saharan Africa xii

18 TE - Technical Efficiency TEC - Technical Efficiency Change TFP - Total Factor Productivity TP - Technical Progre VAT - Value Added Tax VRS - Variable Return to Scale WTO - World Trade Organization xiii

19 ABSTRACT Indutrialization ha been embraced by many developing countrie a a mean of achieving tructural tranformation of the economie. In Kenya, the goal to indutrialize ha long been held a a trategy for economic development. It ha received emphai a the main trategy for addreing the principal challenge of development in Kenya; employment creation and poverty eradication. While Kenya inherited a relatively well etablihed manufacturing ector at independence in 1963, the ector overall performance ha been rather dimal. The hare of the manufacturing ector in GDP, which account for over 70 percent of the indutry, ha changed little over the lat three decade. At the ame time, the ector which wa expected to play a leading role in the country development and growth proce ha not been dynamic enough to effectively play thi role. The tudy examined Kenya manufacturing ector to empirically analyze the total factor productivity change. The tudy ued the latet World Bank Regional Programme on Enterprie Development firm level data for the period to form a panel over the three year period 2000, 2001 and The total factor productivity change over the period wa meaured and decompoed into efficiency change and technical change. The tudy ued data envelopment analyi (DEA) to derive Malmquit productivity indice. The tudy revealed an overall decline in Total Factor Productivity (TFP) of about 8.3 percent. The decline reulted mainly from declining efficiency which dropped by about 17.8 percent over the period depite an overall technical progre of about 11.5 percent. In a far a the ub-ector were concerned; the tudy revealed that only the chemical and pharmaceutical ub-ector recorded a TFP growth of about 7.9 percent. The textile and wood and furniture ub-ector recorded an efficiency improvement of about 11.8 and 6 percent, repectively. Efficiency change wa revealed to be the major ource of TFP change. The viion 2030 enviage the development of a robut, diverified and competitive manufacturing ector. The xiv

20 overall goal for the ector for the next five year i to increae it contribution to GDP by at leat 10 percent per annum and moving Kenya to a middle income country by year The tudy concluded that, for the manufacturing ector to play the crucial role in employment creation and poverty eradication, the infratructural and intitutional bottleneck bedeviling the ector mut be addreed. Thee include; low capacity utilization, poor infratructure, lack of innovation, licening and ecurity. xv

21 CHAPTER ONE INTRODUCTION 1.1 The concept of productivity There i no univeral definition of the term productivity. Economit have defined it a the ratio of output to input in a given period of time. In other word, it i the amount of output produced by each unit of input. Buine manager, on the other hand, ee productivity not only a a meaure of efficiency, but alo connote effectivene and performance of individual organization. For them, productivity would incorporate quality of output, workmanhip, adherence to tandard, abence of complaint, and cutomer atifaction (Udo-Aka, 1983). Productivity can be computed for a firm, indutry group, the entire indutry ector or the economy a a whole. It meaure the level of efficiency with which carce reource are being utilized. Higher or increaing productivity will, therefore, mean either getting more output with the ame level of input or the ame level of output with fewer input. Productivity can be divided into two ub-concept, that i, Partial Factor Productivity (PFP) and Total Factor Productivity (TFP). PFP etimate the ratio of total output to a ingle input, uually labour. In mot tudie, epecially in economic, productivity i taken to be ynonymou with labour productivity. Thi i becaue it i a impler concept to etimate and it i a rough meaure of the effectivene with which labour i ued a the mot important factor of production. However, it i worth noting that productivity i 1

22 not determined by the effort of labour alone, but in combination with other factor epecially land, capital technology, management and the environment. TFP i the ratio of output to the aggregate meaure of the input of all the factor of production. Theoretically, thi i the true meaure of productivity a it incorporate the contribution of all the factor input. However, there are ome problem aociated with meauring total-factor productivity. For example, it i difficult to contruct an index number that will erve a the input. Thi i becaue thi will mean adding hour done by labour to unit of invetment, the contribution of land, technology, among other to get a ingle index. Moreover, even their quantification in monetary term would be very cumberome. In the conventional growth accounting approach to etimate TFP growth, there i an implicit aumption that the economie are producing along the production poibility frontier with full technical efficiency (Solow, 1957). Studie that have adopted the approach etimate TFP growth without ditinguihing between it two component: technical progre (TP) and technical efficiency change (TEC); TP i ynonymouly conidered to be the unique ource of TFP growth. Defined thi way, TFP growth i at bet a meaure of Hick-neutral diembodied technological change (Coelli et al, 2005). More importantly, a failure to take inefficiency and TEC into account may produce mileading and biaed TFP etimate. While high rate of TP could coexit with deteriorating technical efficiency, relatively low rate of TP could alo coexit with improving technical efficiency (Nihimizu and Page, 1982). Furthermore, different policy implication reult from different ource of variation in TFP. 2

23 1.2 The manufacturing ector in Kenya Kenya ha ince independence depended heavily upon the agricultural ector a the bae for economic growth, employment and foreign exchange generation. An etimated 80 percent of the population live in the rural area and depend on agriculture for their livelihood. In addition, the ector account for two-third of the country export. Hitorical experience ha however hown that indutry ha a higher potential for timulating economic growth. A a upplier of eential input to other ector a well a to itelf, and a a uer or proceor of the output of other ector, indutry repreent an effective timulant to the economic ytem. In thi regard, the growth of the indutrial ector raie productivity not only in the ector itelf, but alo in other ector of economy. Wherea agriculture continue to be the primary foundation of rapid and utained growth, indutry and more particularly the manufacturing ector, i more dynamic in accelerating thi growth (Republic of Kenya, 1997). Although under United Nation claification, the indutrial ector include four ector namely; mining, manufacturing, building and contruction, and public utilitie; manufacturing dominate. The ector contitute about 73 percent of the total indutrial output (Wagacha and Ngugi, 1998). Therefore, the indutrial ector ha become ynonymou with the manufacturing ector. The dynamim in manufacturing ector growth depend heavily on the ucce of agriculture, given the trong link between the two ector in Kenya. The indutry induce the development of agriculture; thi i through the proviion of market for it product via the proceing of agricultural output while at the ame time providing required agricultural input uch a fertilizer, peticide, farm implement and machinery. In addition, raiing agricultural output and income create a growing demand for manufactured product and a ource of aving needed to finance indutry. 3

24 In the light of the aid cenario, agriculture and manufacturing i a twin engine to rapid economic growth. However, agriculture uffer from vagarie of weather and agricultural commodity. By an equal meaure, export alo uffer from price and revenue intabilitie due to inelaticitie in their demand and upply. A uch, the deciion to conider the indutry a the leading ector in economic recovery i baed on the perceived vulnerability of agriculture to many factor outide policy-maker control, which reduce it reliability a a ource of utained growth. Indutry, on the other hand, ha hown remarkable reilience and ha potential for providing high and dynamic growth. In order to enure table and utainable economic growth therefore, the propect lie in the development of the manufacturing ector (Republic of Kenya, 1997). The Kenyan manufacturing ector i claified under three main ub-ector namely, agro-baed, engineering and chemical, and mineral (Republic of Kenya, 1994). The agro-baed ector ha developed on the bai of dometic reource activitie; contribute about 68 percent of manufacturing ector value added (KAM, 2002). Thi ub-ector conit of fruit and vegetable proceing, vegetable oil and fat, cereal and grain melting, bakery, alcoholic beverage, dairy, fih and water reource, tannerie and leather product, cotton, textile, wood, pulp and other micellaneou food product. It i notable that the engineering ub-ector relie heavily on imported raw material and contribute about 12 percent of manufacturing ector value added (KAM, 2002). The ub-ector conit of fabricated metal product, machinery and equipment, metal furniture and fixture, tructural metal product, electronic, tranport machinery and equipment and micellaneou machinery ector. The chemical and mineral ub-ector i reearch oriented and contribute about 20 percent of the manufacturing ector value added. The ub-ector include baic indutrial chemical, fertilizer, alt, peticide, paint varnihe, 4

25 pharmaceutical, oap, perfume, cometic, rubber and rubber product, platic and platic product, cement and lime product, ceramic, gla and gla product. 90 percent of the chemical import in the chemical and mineral ub-ector are intermediate input (KAM, 2002). The manufacturing ector play an important role with repect to the country indutrialization trategy. Hence, it i expected to be more dynamic in accelerating economic development through employment creation, linkage between firm and acro region, kill formation and export earning. The growth of the manufacturing ector ha long been conidered intrumental for economic development depite the fact that it i uually far from the larget economie in term of hare of total output and employment (Soderbom, 2001); the ector currently contribute about 10.5 percent of GDP. On the other hand, the agricultural and the ervice ector contribute about 24.2 and 65.3 percent repectively (Republic of Kenya, 2006). Figure 1.1 below compare the ectoral hare for three ector, agriculture, manufacturing and ervice. 5

26 70 Figure 1.1: Sectoral hare in the real GDP, (percentage) Agriculture Manufacturing Service Source: Republic of Kenya, Economic urvey, variou iue From the figure, the contribution of the manufacturing ector ha remained almot contant throughout the period at lightly over 10 percent. Thi wa depite the indutrialization trategy in Seional paper No.2 of 1996 that ought to tranform Kenya into an indutrialized nation by Further, the growth in manufacturing output ha been a key element in the ucceful tranformation of mot economie that have utained rie in their per capita income; the mot recent example i the newly indutrialized countrie and their uccee in exporting manufactured good (Soderbom, 2001). The effect of the growth in the manufacturing ector on overall economic growth cannot be over-emphaized a depicted in figure 1.2 below. 6

27 Figure 1.2: A comparion of trend in real GDP growth and real growth in manufacturing output ( ) Growth Rate Year Source: Republic of Kenya, Economic urvey, variou iue GDP Growth Manufacturing Growth Figure 1.2 clearly how a high level of correlation between the growth of the manufacturing ector and GDP growth in Kenya. Being a component of the GDP, the declining growth in the ector mut have contributed ignificantly to the falling growth in GDP until After the period of ubdued performance, the economy howed trong ign of recovery, regitering impreive growth of 4.8 percent in 2004, and to high of over 6 percent in The manufacturing ector ha been part of the recovery all through after 2000; it ha been regitering an impreive growth of cloe to 7 percent in Thi notwithtanding, the development in the Kenyan manufacturing ector ha been hampered by inefficiency of production, outdated technologie relative to thoe in ue by competitor, limited technological progre, underutilization of intalled capacitie and ub-optimal plant ize ince the mid 1970 (Lundvall and Battee, 1998; Lundvall et al, 2002). Thi can be partially explained by the unucceful import ubtitution trategy which aw the protection of indutrie from competition, encouraged capital intenive production technology and limited the potential of the indutrial manufacturing ector to generate employment. Several factor contributing to the unatifactory performance of 7

28 the Kenyan manufacturing ector, in addition to the import ubtitution policy, have been identified a both international and national factor (Lundvall and Battee, 1998). On the international level, the oil crie in the 1970 and the withdrawal of donor upport in the 1980 negatively affected the invetment climate. Thi wa reflected in the performance of the economy whoe real GDP growth declined from 5.6 percent in the period 1974 to 1979 to 4.1 percent, 2.5 percent and 2.0 percent in the period 1980 to 1989, 1990 to 1995 and 1996 to 2000, repectively. On the national level, the ector ha been contrained by a number of factor uch a hortage of technically trained peronnel, poor tate of the country infratructure, high electricity tariff and interet rate, lack of competitivene, general lowdown of the economy leading to depreed effective demand for manufactured product, credit rationing, inecurity and corruption (Lundvall and Battee, 1998; Republic of Kenya, 2002). Depite tructural reform undertaken, a cloe analyi of the manufacturing ector how that the upply repone to the policie have been lower than expected. The reliance on the import ubtitution trategy in the early year of independence created a generally inward looking ector, with limited technical progre. Depite a hift from import ubtitution to export promotion ince the mid-1980, and reforming the policy environment under the tructural adjutment programme (SAP), the performance of the manufacturing ector in Kenya remained poor mot of the 1980 and 1990; thi wa in regard to it hare in GDP, growth of output and the creation of employment and linkage with other ector of the economy (Republic of Kenya, 1997). In addition, it i notable that the average annual growth rate of real GDP for the manufacturing ector declined from 10 percent in the period , to 4.8 percent, 3 percent and 1.3 percent in the period , and , repectively. In 2001 however, the ector regitered an improved growth rate attributed to the improved power upply, agricultural production, favorable 8

29 tax reform and expanded outlet through African Growth and Opportunity Act (AGOA), Common Market for Eatern and Southern Africa (COMESA) and Eat African Community (EAC). However, the ector capacity utilization wa hampered by low conumer demand, lack of competitivene and inecurity in the country (Republic of Kenya, 2002). In 2005, the real value added grew by 5 percent from 4.5 percent in 2004; thi wa partly attributed to a table macroeconomic environment that prevailed during that year, improved acce to credit and increae in export demand particularly within the EAC and COMESA region (Republic of Kenya, 2006.) However, thi wa below the target of 8.6 percent per annum for the period Moreover, the hare of the manufacturing ector in GDP wa 10.5 percent, which wa below the projected growth of 15.7 percent in 2007 (Republic of Kenya 2003). Apart from the low output growth rate per annum, the manufacturing ector faced a problem of inadequate invetment partly caued by high cot of dometic fund and reduced foreign direct capital invetment. Invetment not only add to the productive capacity, but alo create new opportunitie for the acquiition of new and more efficient technology (Ronge and Kimuyu, 1997). According to Bigten (2002), growth in the ize of the manufacturing ector appear to have been driven by increae in factor input rather than improvement in efficiency and productivity. Productivity meaure the quality of buine management at all level, which i alo a reflection of how reource are utilized, and what they yield. A tudy by Gerdin (1997) found that the mean total factor productivity growth from wa percent per annum, while a report by International Monetary Fund in 1999 cited in Blattman et al (2004) indicated that productivity declined by 0.5 percent per annum between 1991 and 1998; thi wa in comparion to an increae of 2.5 percent per annum between 1981 and An analyi by Blattman et al (2004) uggeted that 9

30 there wa no viible improvement of productivity in the average firm between 1999/2000 and 2002/2003. According to the tudy, the low level of productivity probably accounted for the poor export performance in the average Kenyan firm. Lundvall et al (2002) noted that low productivity tranlated into high unit cot, which explained the competitive diadvantage in both dometic and foreign market. According to Lovell (1993:3), productivity varie due to difference in production technology, difference in the efficiency of the production proce and difference in the environment in which the production occur. 1.3 The tatement of the reearch problem Kenya ha a large volatile manufacturing and indutrial ervice ector whoe hare of GDP ha increaed very little over the pat three decade (ee figure 1.1). The ector ha not been dynamic enough to function a an engine of growth for the whole economy and ha not contributed ignificantly to the major challenge of employment creation and poverty eradication. The ector ha been facing low capacity utilization, declining productivity and limited technological progre (Republic of Kenya, 2002; KAM, 2006). The government recognize that employment opportunitie can only be created and utained through encouragement of efficient indutrie, which are internationally competitive and utilize the latet technologie in their production activitie (Republic of Kenya, 2007). After a period of ubdued performance in the late 1990, the Kenyan economy ha in the lat few year howed trong ign of recovery, regitering an impreive growth of over 6 percent in 2006 from -0.2 percent in The manufacturing ector ha been part and parcel of thi recovery, growing from a 10

31 negative rate of -1.5 percent in 2000 to about 7 percent in However, a cloe examination reveal that thi growth in the manufacturing ector ha been driven ubtantially by an increae in input and volume of output a oppoed to improvement in efficiency and productivity (KAM, 2006; Bigten, 2002). Yet thi growth led by increae in input, i not utainable owing to increaing cot of major input to production in the ector, a bulk of which are imported. Effort mut be diverted to productivity growth ourced from efficiency improvement and technical progre. Indeed, the goal of indutrialization ha been held a a trategy for economic development and it ha all along received emphai a the main trategy for addreing the principal challenge of development in Kenya: employment creation and poverty eradication (Republic of Kenya, 2002). The overall goal for the manufacturing ector by the year 2012 i to increae it contribution to GDP by at leat 10 percent per annum. To achieve thi goal, the ector productivity hould be improved to trengthen the local production capacity in order to increae dometically manufactured good and enhance Kenya competitivene globally (Republic of Kenya, 2007). The ub-ector in the manufacturing ector in thi tudy are given an individual approach unlike in mot of the ource-of-productivity tudie that focu on the aggregate economy. Studie carried out have either addreed general productivity in the economy, with little or no attention given to the productive ector (Kimuyu, 2007; Onjala, 2002; and Gerdin, 1997). Studie pecific to the manufacturing ector have concentrated on efficiency iue without any link of the efficiency or inefficiency change to productivity (Ngui, 2008; Lundvall, 1999; Lundvall and Battee, 1999). Other focu on other dimenion of manufacturing ector uch a input ue and ubtitutability (Onuonga, 2008). 11

32 Thi tudy addree itelf to the meaurement of total factor productivity change in the manufacturing ector and the ource of the productivity change thereby creating a richer policy environment. The tudy alo bridge the knowledge gap that exit in the undertanding of productivity in the manufacturing ector in Kenya. Only a few tudie exit and hence the tudy add on to the available work done on mot recently collected panel data. The methodology ued i alo relatively new and even though it ha gained popularity in productivity tudie, it ha not been applied widely in Kenya. Stochatic frontier analyi core highly a having been widely applied in the pat tudie. Moreover, thi tudy depart from the commonly ued parametric method to a non-parametric method that provide a different approach, and enriche the area of tudy for future reearch. 1.4 Reearch quetion The tudy ought to anwer the following quetion: (i) (ii) (iii) (iv) (v) What are the policy epiode in Kenya indutrialization proce? What i the tructure and compoition of the Kenyan Manufacturing ector? What change in total factor productivity have occurred in the Kenyan Manufacturing ector? What ha been the ource of uch change? What are the policy implication of the tudy finding? 1.5 Objective of the tudy The tudy aimed at examining the Kenyan Manufacturing ector environment for change in total factor productivity. Specifically, the tudy ought to: (i) Analye the policy epiode in Kenya indutrialization proce 12

33 (ii) (iii) (iv) (v) Decribe the tructure and compoition of the Kenyan manufacturing ector. Meaure the Total Factor Productivity change in the manufacturing ector. Etablih the ource of change in Total Factor Productivity. Propoe policy recommendation in the light of the reearch finding. 1.6 Significance of the tudy While Kenya inherited a relatively well etablihed indutrial ector, the ector overall performance ha been rather poor for mot of the pot-independence period with the exception of the period between 1963 and 1972 when it regitered an annual average growth rate of above 10 percent. The hare of the manufacturing ector in the GDP ha changed little over the pat three decade. At the ame time, the ector, which wa expected to play a leading role in the country growth and development proce, ha not been dynamic enough to effectively play thi role. Thi tudy come at a time when the country i on an ambitiou viion to tranform the economy by the year Attention i no doubt going to focu on manufacturing a one of the productive ector in order to achieve thi viion. Thi tudy will hed light on the productivity environment in the manufacturing ector and therefore, provide ome bai for policy intervention that would ee the ector break from the hitorical non performance. Thi tudy will alo be ueful to policy maker ince it will provide a rich environment for policy deciion that are more focued in order to realize the et target. The tudy alo contribute to the exiting literature on productivity iue in the manufacturing ector. Mot of the tudie dicu iue on efficiency without much regard to the other productivity ource. 13

34 1.7. The cope and organiation of the tudy While majority of the empirical tudie carried out in the Kenyan manufacturing ector focued on efficiency iue (Ngui, 2008; Mazumdar and Mazaheri, 2003; Lundvall et al, 1999; Lundvall and Battee, 1998; Bigg et al, 1995), thi tudy take a broader look at the ector Total Factor Productivity. Efficiency change i one of the major ource of growth in productivity. Technical change, which form the other major ource of productivity growth, ha not attracted much attention. From the few tudie on the performance of the Kenyan manufacturing ector, growth ha not been attributed to Total Factor Productivity growth probably uggeting the reaon why no attention ha been directed to efficiency and technology iue in policy, hence the un-competitive manufacturing ector. Therefore, thi tudy only addreed itelf to the examination of Total Factor productivity change and whether uch change reulted from efficiency change or technical change. Policy propoal are alo made that focued more on the ource of the TFP growth. One major obtacle in the tudy ha been the availability of very reliable data on the manufacturing enterprie. While the Kenyan manufacturing ector i largely informal, data available i from the formal manufacturing. The tudy ued a panel data of three year 2000, 2001 and Due to lack of adequate data on majority of the firm, and due to the need to enure homogeneity in the firm elected, few firm were ued in the analyi for ome of the ub ector, therefore poing challenge in the etimation. The tudy i organized in five chapter. The foregoing chapter introduced the tudy by highlighting it principal objective. Chapter two i devoted to reviewing the relevant literature and end by preenting the theoretical framework. Chapter three highlight the reearch deign and methodology ued 14

35 in the tudy. The tudy finding are preented and dicued in chapter four while chapter five conclude the tudy. 15

36 CHAPTER TWO LITERATURE REVIEW AND THEORETICAL FRAMEWORK 2.1 Introduction In thi chapter, both the theoretical and empirical work done on productivity and related iue are reviewed. The firt ection i a review of the method ued in the meaurement of productivity and productivity change. The other ection of thi chapter review empirical literature carried out elewhere, and pecific to Kenya, repectively. 2.2 Theoretical literature The material preented in thi ection wa drawn from microeconomic textbook and well preented in Ceolli et al (2005). The ection expoe the theoretical foundation that underlie the meaurement and decompoition of productivity. The theoretical repreentation of the production technology and ditance function are briefly decribed. The concept of a productivity index, with particular attention to the Malmquit productivity index i introduced. The ection alo briefly decribe the method ued to obtain etimate of TFP change, and decompoe thee meaure into the variou component, uch a technical change (TC) and technical efficiency change (TEC) The production technology and ditance function A technology et, S may be defined a: S = {(x,q): x can produce q}, Where x and q denote an N-dimenional input vector of non-negative real number and a non-negative M-dimenional output vector, repectively. Thi et conit of all input-output vector (x,q), uch that x can produce q. 16

37 The production technology defined by the et S, may be equivalently defined uing the output et, P(x), which repreent the et of all output vector, q that can be produced uing the input vector, x. The output vector i defined by: P(x) = {q: x can produce q} = {q: (x,q) S} The output et are referred to a production poibilitie et aociated with variou input vector, x. The variou output combination that could be produced uing a given input level form a production poibility et. The maximum output at variou level of input form the production frontier. Cloely related to production frontier i the concept of ditance function that are very ueful in decribing the technology in a way that make it poible to meaure among other thing, productivity. Ditance function allow one to decribe a multi-input, multi-output production technology without the need to pecify a behavioral objective uch a cot minimization or profit maximization. One may pecify both input ditance function and output ditance function. An input ditance function characterize the production technology by looking at a minimal proportional contraction of the input vector, given the output vector. An output ditance function conider a maximal proportional expanion of the output vector, given an input vector Output ditance function The output ditance function i defined on the output et, P(x), a: d 0 (x,q) = min { : (q/ ) P(x)}..2.3 o that if q belong to the production poibility et of x, i.e. q P(x), then 17

38 d 0 (x,q) 1; and if q belong to the frontier of the production poibility et, then d 0 (x,q) =1. Suppoe q 1 and q 2 are two output produced uing the input vector, x. For a given input vector x, the production technology i preented in figure 2.1. Fig 2.1 Output ditance function and production poibility et q 2 B q 2A A C PPC-P(x) P(x) q 1A 0 Source: Coelli et al (2005:48) q 1 The production poibility et, p(x) i the area bounded by the production poibility frontier, PPC-P(x), and the q 1 and q 2 axi. The value of the ditance function for the firm uing input level x to produce the output, defined by the point A i equal to the ratio =OA/OB. The ditance meaure i the reciprocal of the factor by which the production of all output quantitie could be increaed while till remaining within the feaible production poibility et for the given input level. Point B and C are on the production poibility frontier denoted by PPC-P(x), and hence would have ditance function value equal to 1. 18

39 2.2.3 Input ditance function The input ditance function, which involve the caling of the input vector i defined on the input et L(q) a: d i (x,q) = max { :(x/ ) L(q)}..2.4 Where the input et, L(q) repreent the et of all input vector x which can produce the output vector q. If x belong to the input et of q, i.e. x L(q), then d i (x,q) 1 and if x belong to the frontier of the input et (the ioquant of q), then d i (x,q)=1. Given two input x 1 and x 2 ued in producing output vector q, the production technology i preented in figure 2.2. Fig 2.2 Input ditance function and input requirement et x 2 x 2A A B C L(q ) Ioq-L(q) 0 x 1A x 1 Source: Coelli et al (2005:50) The input et L(q) i the area bounded from below by the ioquant, ioq-l(q). The value of the ditant function from the point A which define the production point where firm A ue x 1A of input 1 and x 2A of input 2 to produce the output vector q, i equal to the ratio =OA/OB. Output and input ditant function are ued in defining a variety of index number. They alo provide the conceptual under pinning for variou meaure among them, productivity meaure. Thee ditance function can be directly 19

40 etimated uing either econometric method or mathematical programming method. In thi tudy, ditance function decribed in thi ection are ued to define the Malmquit index that meaure the total factor productivity change uing Data Envelopment Analyi, a linear programming method a dicued later in ection Meauring productivity and productivity change. Productivity i eentially a level concept and meaure of productivity can be ued in comparing performance of firm at a given point in time. In contrat, productivity change refer to movement in productivity performance of a firm or an indutry over time. Productivity i often meaured uing partial productivity meaure uch a output per worker or per hour worked or output per hectare. Though commonly ued, partial productivity meaure are of limited ue and can potentially milead and mirepreent the performance of a firm (Coelli et al. 2005). Total factor productivity (TFP) meaure account for the ue of a number of factor input in production and therefore are more uitable for performance meaurement and comparion acro firm and for a given firm over time. In the preence of multiple output and input, TFP may be defined a a ratio of aggregate output produced relative to aggregate input ued. Aggregation of output and input give rie to index number problem. In thi tudy however, the Malmquit TFP index ued i contructed by meauring the radial ditance of the oberved output and input vector in different time period relative to a reference technology. A imple TFP meaure for firm with multiple output and multiple input i to look at the profitability of a firm, defined a the revenue of the firm divided by it input cot. Suppoe there are two firm producing output vector q 1 and q 2 uing input x 1 and x 2 repectively. Suppoe the correponding output and input 20

41 price vector are given by (p 1,p 2 ) and (w 1,w 2 ). Then the profitability ratio of firm 1 and 2 are given by: π p ' q M 1 1 m= 1 1 = = K w1 ' x1 k = 1 p m1 m1 w q x k1 k1 And π p ' q M p q m2 2 2 m= 1 2 = = K w2 ' x2 wk 2 k = 1 m2 x k A meaure of relative performance i given by the ratio, 2 / 1. Though 1 and 2 are calar meaure of total or multifactor productivity, a trict comparion of 1 and 2 i difficult ince the output and input price faced by thee firm are different. The only option here i to adjut the value aggregate in the equation above for difference in price level. Such an adjutment require that the value aggregate in the numerator of equation above are deflated by uitable price deflator or price index number. The ue of DEA in meauring productivity doe not require any price data (Coelli and Rao, 2001). In thi tudy however, accurate data on the quantitie of output and input wa not available and intead value were ued that required a deflator for the neceary adjutment. The deflator were contructed from the tatitical abtract over the data period Meauring productivity change and the Total Factor Productivity index In the cae of firm producing multiple output uing multiple input, change in productivity i repreented by the total factor productivity (TFP) index. There are everal imple and intuitive approache that can be ued in deriving meaningful meaure of productivity change. Irrepective of which approach i employed in meauring the TFP index, it i important that it atifie the following property. If a firm produce the ame quantitie in the two period,, and, t, but the input ue i decreaed by a certain proportion, the TFP index hould increae accordingly. If the input are reduced for example by 25 percent (output are produced with only 75 percent of the original input) then the TFP 21

42 index hould be equal to 1/0.75. Similarly, if the output are increaed by a given percentage, keeping the input fixed, the TFP index hould increae by the ame percentage. If all the output increae by 30 percent over the period,, to, t, with input ue remaining the ame, then the TFP index hould be equal to 1.3. Conider the problem of meauring productivity change for a firm from period,, to period, t, auming that the firm make ue of the tate of knowledge, a repreented by production technologie, S, and,s t, in period,, and, t. Suppoe the firm produce output q and q t uing input x and x t repectively. Where information on output and input price i available, then the output price vector and input price vector are p and p t, and w and w t, in period,, and t, repectively. Poible approache ued to meaure productivity change include: (a) Hick-Moorteen TFP (HM TFP) index The Hick-Moorteen index (ee Diewert, 1992) repreent a fairly imple TFP index that meaure the growth in output net of growth in input. If output growth and input growth are meaured uing output and input quantity index number, then the HM TFP index i given by: Growthinoutput Output quantityindex HMTFP Index = =.2.6 Growthininput Input quantityindex The HM index can be made operational once appropriate meaure of output and input growth are elected. A range of index number formulae are available for thi purpoe. Thi index i alo cloely related to the index that i baed on profitability ratio and the TFP index that i baed on the Cave, Chritenen and Diewert (CCD) approach, dicued later in thi ection. Though thi index i eay to meaure and interpret, it i quite difficult to identify the main ource of productivity growth. Moreover, the HM index doe 22

43 not have a conceptual framework that underpin a decompoition of TFP growth etimate unle the indexe are defined uing a malmquit quantity index. (b) TFP index baed on the profitability ratio Let R, R t, C and C t repectively, repreent the oberved revenue and cot of a given firm in period,, and, t. The data on input and output quantitie and their price are given by (x, q, p ) and (x, q, w ) for period and (x t, q t, p t ) and (x t, q t, w t ) for period t. The TFP index that i baed on the profitability ratio i meaured uing revenue and cot after adjuting for change from period t to period. Let R *, R t *, C * and C t * repreent revenue and cot for the firm in period,, and, t, repectively, after adjuting for price change from period,, to period, t. Then the TFP index i defined a: * * Rt / R ( Rt / R )/ output price index TFP index = = * * c / c ( C / C )/ input priceindex t t Where appropriate index formulae are ued in meauring price change from period,, to period, t. Since the TFP meaure in equation 2.7 above doe not contain any price effect, the main ource of TFP change over period,, and, t, can be attributed to technical change and efficiency change over thi period (Coelli et al (2005). (c) Malmquit TFP index The malmquit TFP index wa firt introduced by Cave, Chritenen and Diewert (1982a, 1982b). Cave, Chritenen and Diewert defined the TFP index uing Malmquit input and output ditance function, and thu the reulting index ha come to be known a the Malmquit TFP Index. The method of uing the ditance function a decribed in ection of thi tudy in defining the TFP index i due to the approach propoed by Cave, Chritenen and Diewert. 23

44 Malmquit TFP index i contructed by meauring the radial ditance of the oberved level of output and input vector in period,, and, t, relative to a reference technology. A the ditance can be either output oriented or input oriented, the Malmquit TFP indice differ according to the orientation ued. The output oriented productivity meaure focu on the maximum level of output that could be produced uing a given input vector and a given production technology relative to the oberved level of output. Thi i achieved uing the output ditance function. The period,, Malmquit productivity index may be given by the following equation: d o ( qt, xt ) mo ( q, qt, x, xt ) =..2.8 d ( q, x ) o Auming that the firm i technically efficient in both period, then d ( q x ) = 1 and o; o ( q, q, x, x ) = d ( q x ) m, t t o t t Equation (2.9) how that m ( q, q, x, x ) i the minimal output-deflation factor o t t uch that the deflated-output vector for the firm in period, t, q t / [ m 0 () ] and the input vector, x t, are jut on the production urface of the technology in period. If the firm ha a higher level of productivity than i implied by the period,, technology, then m () > 1. o An output oriented Malmquit productivity index can be imilarly defined baed on period, t, technology t t do ( ) ( qt, xt ) mo q, qt, x, xt = t d ( q, x ) o t If the firm i technically efficient in period t, then d ( q x ) = 1 Since the Malmquit productivity index can be defined uing period,, technology a well a period, t, technology, the Malmquit TFP index i defined a the geometric average of the two indice baed on period-t and period- o t t o 24

45 technologie. Thu the output oriented Malmquit productivity index i given by: [ ] 5 t m ( q q, x, x ) = m ( q, q, x, x ). m ( q, q, x, x ) o, t t o t t o t t It i noted that the Malmquit TFP index, defined in the equation above require the computation of four ditance function namely, t t d ( q, x ), d ( q, x ), d ( q, x ) and ( q x ) o o t t o t t d,. o In order to compute thee ditance function, the production technologie in period,, and, t, hould be decribed. If data i very limited, uch a only oberved output and input quantitie in period,, and, t, then the index number approach i ued. If data on a cro-ection of firm in period,, and, t, i acceible, then the data envelopment analyi (DEA) approach or the tochatic frontier analyi (SFA) are ued. The input oriented productivity focue on the level of input neceary to produce oberved output vector q and q t under a reference technology. Suppoe period,, technology i ued a the reference technology, then the period- input oriented Malmquit productivity index for period,, and, t, i defined a: m i d i ( qt, xt ) ( q, qt, x, xt ) = d ( q, x ) i Auming that the firm i technically efficient, in both period, then d i ( q x ) = 1and alo i ( q q, x, x ) d ( q x ) m, =, t t i t t Similarly, the input-oriented Malmquit productivity index, baed on period, t, technology i defined a: m t i ( q q, x, x ) t i t i ( qt, xt ) ( q, x ) d, t t = d 25

46 t If the firm i technically efficient in period t, then d ( q, x ) = 1 Since the Malmquit input oriented index can be defined uing period,, or period, t, technology a the reference technology, Cave, Chritenen and Diewert defined the input oriented Malmquit TFP index a: [ ] 2 1 t t m ( q q, x, x ) = m ( q, q, x, x ). m ( q, q, x, x ) i, t t i t t i t t To compute the Malmquit TFP index in the equation 2.15 above, four different ditance are computed that are involved in equation 2.12 and If the firm i aumed to be technically efficient, then only two ditance need to be computed. There are however problem in the calculation of the Malmquit TFP indice. That i, in order to compute thee indice, the ditance function a well a the numerical value of the relevant parameter or equivalently, a decription of the underlying technology need be known. Thi require firm level data on input and output in period,, and, t, a well a frontier method that do not require the aumption of technical efficiency of the firm oberved. i t t The Malmquit TFP index can give different numerical value depending on the type of orientation ued (output or input orientation). However, if the underlying production technology exhibit contant return to cale (CRS) in both period, then the input-and output-oriented Malmquit TFP indice coincide. In chooing the appropriate approach to elect, the purpoe of meauring productivity level and change; and the kind of data available are major conideration. If a ummary meaure of productivity change i required without any need to identify their ource, then the HM approach i recommended. If however panel data et are available on a large number of firm over ome period of time, then the Malmquit index approach i recommended. In that cae, the contant return to cale aumption i tenable, 26

47 27 and the Malmquit TFP Index i ufficient becaue it coincide with the index reulting from the ource-baed meaure of TFP growth Calculation and decompoition of productivity change uing frontier method The Malmquit TFP index meaure the TFP change between two data point by calculating the ratio of the ditance of each data point relative to a common technology. If the period, t, technology i ued a the reference technology, the Malmquit (output oriented) TFP change index between period,, (the bae period) and the period, t, can be written a: ), ( ), ( ),,, ( t t t t y t t x q d x q d x q x q m = Alternatively, if the period reference technology i ued, it may be defined a: ), ( ), ( ),,, ( t t y t x q d x q d x q x q m = In the above equation, the notation ), ( 0 t t x q d repreent the ditance from the period, t, obervation to the period,, technology, and all other notation i a previouly defined. A value of 0 m greater that one indicate poitive growth in TFP from period,, to period, t, while a value le than one indicate a TFP decline. The TFP index i defined a the geometric mean of the above two indice, according to Fiher (1922) and Cave, Chritenen and Diewert (1982b). That i, ( ) ( ) ( ) ( ) 2 1,,,, ),,, ( = t t t t o t t t t x q d x q d x q d x q d x q x q m

48 28 The ditance function in thi productivity index can be rearranged to how that the index i equivalent to the product of a technical efficiency change index and an index of technical change. Thi i given in equation 2.19 that follow: ( ) ( ) ( ) ( ) 2 1,,,, ), ( ), ( ),,, ( = t t t t o t t t t t t t x q d x q d x q d x q d x q d x q d x q x q m The ratio outide the quare bracket in the above equation meaure the change in the output oriented meaure of technical efficiency between period,, and, t. The remaining part of the index i a meaure of technical change. It i the geometric mean of the hift in technology between the two period, evaluated at t x and at x. The two term are: Efficiency change = ), ( ), ( 0 0 t t t x q d x q d and Technical Change = ( ) ( ) ( ) ( ) 2 1,,,, t t t t o t t x q d x q d x q d x q d.2.21 Thi decompoition i illutrated in figure 2.3 below.

49 Fig 2.3: Malmquit productivity indice (Output) Frontier in period t Y c Y t E Frontier in period Y b Y a Y D X X t (Input) Source: Coelli et al (2005: 75) The firm produce at the point D and E in period,, and, t, repectively. In each period, the firm i operating below the technology for that period. Hence, there i technical inefficiency in both period given by the meaured ditance in output between Y t and Y c for period, t, and between Y and Y a for period,. Therefore, the efficiency change and the technical change are given by; yt y Efficiency change = y y c a Technical change yt y yb = yt y yc 1 2 ya y b A number of additional poible decompoition of thi technical efficiency change and technical change component have been propoed by variou author. Fare et al. (1994) uggeted that technical efficiency change can be 29

50 decompoed into cale efficiency and pure technical efficiency component. Thi can only be done when the ditance function in the above equation are etimated relative to contant return to cale technology. Thi decompoition, involving cale efficiency ha been widely ued, and alo widely criticized. The decompoition involve taking the efficiency change meaure and decompoing it into pure efficiency change component, meaured relative to the variable return to cale (VRS) frontier. t dov( qt, xt ) i.e. pure efficiency change = d ( q, x ) The cale efficiency change component in equation 2.23 t d ov t dov t ( qt, xt )/ doc ( qt, xt ) t ( q, x )/ d ( q, x ) oc d d ov ov ov ( qt, xt )/ doc ( qt, xt ) ( q, x )/ d ( q, x ) oc i actually the geometric mean of two cale efficiency meaure. The firt i relative to the period, t, technology and the econd i relative to the period,, technology. The extra ubcript, v and c, relate to the VRS and CRS technologie repectively. If thi extra decompoition i ued, the ditance function would all need to be relative to a CRS technology. The above uggeted method of introducing a cale efficiency change component in the malmquit TFP index decompoition ha been the ource of coniderable debate in recent year (ee Fare et al, 1998; Ray and Deli, 1997). The main point of criticim i eentially that if there i cale efficiency change then, thi implie that the true production technology hould have VRS. However, the Fare et al (1998) decompoition report a technical change meaure that reflect the movement in a CRS frontier and not the VRS frontier. Ray and Deli (1997) point out thi inconitency and ugget an alternative decompoition that ha technical change meaure relative to VRS frontier and an amended cale change component that i no longer equivalent to the cale efficiency change. 30

51 Thee two alternative decompoition will be approximately equal if the rate of technical change i imilar at the oberved data point and at the correponding mot productive cale ize point, but will otherwie differ. The Ray and Deli (1997) decompoition i arguably a more internally conitent decompoition. However, the difference between the two approache will only be ubtantive when there are firm within the ample with ignificantly different cale and there are non-neutral rate of technical change acro the different ized firm. Furthermore, the Ray and Deli (1997) method can uffer from computational difficultie when DEA baed ditance function are ued becaue of infeaibilitie in ome VRS calculation. One important point that i cloely related to thi iue i that the return to cale propertie of the technology are very important in TFP meaurement. According to Grifell-Tatje and Lovell (1995), the Malmquit TFP index may not correctly meaure TFP change when VRS i aumed for the technology. Hence, it i important that CRS be impoed upon the technology that i ued to etimate ditance function for the calculation of that Malmquit TFP index, or alternatively, that an appropriate adjutment factor i included to correct for thi omiion. Orea (2002) uggeted the incluion of an additional cale change component in a Malmquit TFP index derived from a tranlog technology. Otherwie the reulting meaure may not properly reflect the TFP gain or loe reulting from cale effect. Thi tudy therefore, impoed CRS upon the technology in the etimation of the ditance function to calculate the Malmquit TFP index for the Kenyan manufacturing ector. There are a number of different method that could be ued to etimate a production technology and hence meaure the ditance function that make up the Malmquit TFP index. The mot popular method are; the DEA-like linear 31

52 programming method and the Stochatic Frontier method. The next ection briefly introduce the two method Data Envelopment Analyi (DEA) DEA i a linear programming methodology, which ue data on the input and output quantitie of a group of Deciion Making Unit (DMU) to contruct a piece wie linear urface over the data point. The frontier urface i contructed by a olution of a equence of linear programming problem, one of each DMU in the ample. The degree of technical inefficiency of each DMU (the ditance between the oberved data point and the frontier) i produced a a by-product of the frontier contruction method. One of the poible limitation of DEA i that when there are a few obervation and many variable, many of the firm will appear on the DEA frontier exaggerating the technical efficiency core (ee Coelli et al 2005). For example, a the ratio of the number of variable to the ample ize increae, the ability of DEA to dicriminate among firm i reduced. Hence, many firm are labeled a 100 percent efficient ince there are no firm or combination of firm againt which they can be compared (Roi and Ruzzier, 2004). DEA can be either input-oriented or out-put oriented. In the input-oriented cae, the DEA method define the frontier by eeking the maximum poible proportion reduction in input uage, with output level held contant, for each DMU. While in the output-oriented cae, the DEA method eek the maximum proportional increae in output production, with input level held fixed. The two meaure provide the ame technical efficiency core when a contant return to cale (CRS) technology applie, but are unequal when variable return to cale (VRS) i aumed (Coelli et al, 2005). The ue of DEA to calculate the required 32

53 ditance function ued to contruct the Malmquit TFP index in the current tudy i preented later in ection The Stochatic Frontier method The ditance meaure required for the Malmquit TFP index calculation can be meaured relative to a parametric technology. The method are baed upon the tranlog ditance function method decribed by Fuente, Grifell-Tatje and Perelman (2001) and Orea (2002). Focuing on a production frontier cae, which i a ingle output pecial cae of the more general multi-output ditance function, the tranlog tochatic production frontier i defined in equation 2.24: N N N N ln qit = β 0 + β n ln xnit + βnj ln xnit ln xnit + β tnt ln xnit + β tt + β ut + vit uit n= 1 2 n= 1 j= 1 n= 1 2 i = 1,2,..., I, t = 1,2,..., T, Where qit i the output of the i th firm in the t th year; xnit denote an n th input variable; t i a time trend repreenting technical change; the β are unknown parameter to be etimated; the vit are random error aumed to be independently identically ditributed 2 (i.i.d) and have (, ) N 0 σ v ditribution, independent of the it u ; and the uit are the technical inefficiency effect with appropriately defined tructure. The above model ha the time trend, t, interacted with the input variable which allow for non-neutral technical change. The technical efficiencie of each firm in each period can be predicted by obtaining the conditional expectation of exp ( u it ), given the value of eit = vit uit. Since u it i a non-negative random variable, thi technical efficiency prediction are between zero and one, with a value of unity indicating full technical efficiency. In thi parametric model, meaure of technical efficiency and technical change can be ued to calculate 33

54 the Malmquit TFP index uing the equation 2.19 to The technical efficiency meaure i: TE = ( exp ( u ) e ).2.25 it Where e it it it it = v u can be ued to calculate the efficiency change component. i.e it t by oberving that d 0( xit, yit ) = TEit and d o ( xi, yi ) = TEi the efficiency change index i calculated a: Efficiency change = TE / TE it i Thi meaure can be compared directly to equation The technical change index between period,, and, t, for the i th firm can be calculated directly from the etimated parameter. The partial derivative of the production function i firt evaluated with repect to time uing the data for the i th firm in period,, and, t. The technical change index between the adjutment period,, and, t, i then calculated a the geometric mean of thee two partial derivative. When a tranlog function i involved, thi i equivalent to the exponential of the arithmetic mean of the log derivative i.e. 1 ln yi ln yit Technical change = exp t Thi meaure may be compared directly with equation The indice of technical efficiency change and technical change obtained uing equation 2.26 and 2.27 can then be multiplied together to obtain a Malmquit TFP index, a defined in equation Some iue are worth noting. Firt, the above technical change meaure involve derivative calculation, which appear to contradict the earlier comment that thee indice are derived from ditance meaure. It can be eaily hown (for the tranlog cae in which a time trend i ued to repreent technical change) that for geometric mean of the ditance ratio in equation 2.21, are equivalent to the geometric mean of the derivative meaure. 34

55 One poible criticim of the above method i that, if cale economie are important, then the TFP index may produce biaed meaure becaue the productivity change are not captured. One poible olution to thi problem i to impoe CRS upon the etimated production technology. Another option i to ue the approach propoed by Orea (2002); it ue Diewert quadratic identity to derive a Malmquit TFP decompoition identical to that propoed above, and then uggeted that the cale iue can be addreed in a manner imilar to that ued by Denny, Fu and Waverman (1981). Thi involve the incluion of a cale change component to the TFP meaure, 1 2 N Scale change = exp [ ε nisfi + ε nitsfit ] ln( xnit / xni ), n= Where SF i = ( ε i )/ ε i, ε i = 1 N 1 ε ni n= 1 and ε ni ln q = ln x i ni Thi cale change index i equal to one if the production technology i CRS. That i, where the cale elaticity ( ε i ) i equal to Overview of the theoretical literature The reviewed theoretical literature revealed that the approache widely ued in the etimation of productivity change are broadly claified a traditional Pricebaed Index Number (PIN) approach and Frontier approache. The principal weaknee of the traditional PIN method are that price and quantity information i required but may be unavailable; and the method aume that the firm are technically efficient which i likely to be untrue. On the other hand, the method cannot be ued to meaure technical and efficiency change. The Frontier approach include Stochatic Frontier Analyi which i a parametric approach, and the Data Envelopment Analyi which i non- 35

56 parametric. The frontier approach i preferred to PIN for four reaon: it doe not require price information, doe not aume all firm are fully efficient, doe not need to aume a behavioral objective uch a cot minimization or revenue maximization, and it permit TFP change to be decompoed into component uch a technical change (TC), efficiency change (EC) and cale change (SC). The firt i a ditinct advantage becaue in general, input price data are eldom available epecially in the Kenyan manufacturing ector given it enormou informal nature and more o that the price data available from the formal ector may be ditorted due to the government intervention common in developing countrie. Stochatic Frontier Analyi (SFA) impoe a priori functional form to the frontier. When the functional form i pecified, the unknown parameter of the function are etimated uing econometric technique. The requirement to firt pecify a functional form make the SFA more computationally demanding. However, SFA account for noie in the data and can be ued to conduct conventional tet of hypothee. The non-parametric approache on the other hand are dominated by data envelopment analyi (DEA). The DEA method i computationally imple and ha the advantage that it can be implemented without pecifying the functional form of the frontier. However, it will not account for noie in the data. Lundvall (1999) applied the DEA method on the ame data that wa ued by Lundvall and Battee (1998) to check the enitivity of the SFA reult obtained by the latter. The reult obtained were largely conitent even though the inefficiency core yielded by DEA were lower than thoe yielded by SFA. 36

57 2.4. General empirical literature Solow (1957) howed that a compoite meaure, TFP growth could be iolated from individual input by decompoing output growth into that portion attributed to increae in factor input and a reidual. Thi reidual or TFP growth i what Abrahamovtz (1956) had earlier aptly labeled a a meaure of our ignorance and meaure the combined effect of pure technical progre and growth in the overall efficiency with which input are combined to produce output. The Solow growth model focued on the four variable: output (Y), capital (K), labour (L) and knowledge (A) or the effectivene of labour where the production function take the form:- () t F( K() t, A() t, L( T )) Y = Where t denote time. In the model, time did not enter the production function directly but only through Labour, capital and knowledge. That i, output change over time only if the input ued in production changed. In particular, the amount of output obtained from given quantitie of capital and labour roe over time- there wa technical progre - only if the amount of knowledge increaed. The model aumed that knowledge entered into the production function through labour implying that technical progre wa labor-augmenting or Harrod-neutral. In the article by Solow (1957) on growth in indutrialized countrie between 1909 and 1949, knowledge entered the production independently a a reidual or a meaure of the total factor productivity growth. Such technical progre i aid to be Hick-neutral. Solow found the reidue to be quite large and explaining almot 90 percent of US growth per capita while growth in capital per man accounted for ome 10 percent. In other indutrialized countrie, the 37

58 contribution of the reidue wa alo high. For the UK, Wet Germany and Japan, TFP growth accounted for 54, 56 and 55 percent repectively. The Solow model i a conventional growth accounting approach to etimate TFP growth without ditinguihing between it two component; technical progre and technical efficiency. Technical progre i ynonymouly conidered to be the unique ource of TFP growth. Failure to take account of inefficiency and technical efficiency change may produce mileading and biaed TFP etimate. Therefore, thi tudy etimate the TFP change and decompoe it into both technical change and technical efficiency change. Fare et al (1994) ued Data Envelopment Analyi (DEA) to decompoe total output growth into technical change and efficiency change in developed countrie. The decompoition involved taking the efficiency change meaure in equation 2.20 and decompoing it into a pure efficiency change component in equation 2.22 and a cale efficiency change in equation The ample covered 17 OECD countrie during the period 1978 to The tudy contructed a determinitic frontier from the ample and compared each country ditance from the frontier in the contant return to cale framework. The author ued ditant function to calculate the Malmquit index a an alternative meaure to TFP. The Malmquit index iolated the change in efficiency interpreted a catching up, from technological change, which i meaured by hift in the frontier. The efficiency factor further decompoe into pure technical efficiency change and change in cale efficiency (Coelli et al, 2005). Thi tudy borrow heavily from the methodology ued by Fare et al (1994) where the total factor productivity change wa decompoed into efficiency change (catch- up) and technical change. However, while Fare at al (1994) 38

59 decompoed the efficiency change further into pure efficiency change and cale efficiency change, thi tudy doe not. If there i cale efficiency change, then thi implie that the true production technology mut how VRS. However, the Fare et al (1994) reported a technical change meaure that reflected the movement in a CRS frontier and not the VRS frontier. For conitency thi tudy aume CRS for both efficiency and technical change. According to Grifell-Tetje and Lovell (1995) malmquit TFP index defined in equation 2.18 may not correctly meaure TFP change where VRS i aumed for the technology and hence the need that CRS be impoed upon the technology that i ued to etimate ditance function for the calculation of thi malmquit TFP index. While Fare et al (1994) analyzed productivity growth at the country level, thi tudy i a firm level analyi in the manufacturing ector. Mengitae (1996) ued an unbalanced panel data of 220 Ethiopian manufacturing firm to invetigate age-ize effect in productive efficiency. Both fixed effect and random effect model were etimated and the predicted technical efficiency core regreed on variou firm characteritic including firm age and firm ize. The tudy found that the age-ize effect detected in the growth of firm in the ample were matched by time-invariant inter-firm difference in technical efficiency. There were alo age-ize effect in efficiency where bigger firm were more efficient given age and older firm were more efficient given ize. Firm age and firm ize mainly proxied for owner human capital and location variable in a far a they explained efficiency core. There wa no evidence that efficiency depend on any one of pre-ownerhip, employment experience, occupational following of parent or prior vocational training. On the other hand, a tudy by Alvarez and Crepi (2003) found that owner characteritic uch a education or job experience were not related to efficiency; however, input quality variable uch a worker experience and capital modernization increaed efficiency. 39

60 While the tudy revealed ome important determinant of efficiency (catch up) in the manufacturing firm in Ethiopia, no focu wa made on the technical progre which i the other major component of productivity. Overall the tudy concentrated on the efficiency apect only. Thi tudy departed from thi partial approach to productivity and ought to conider both ource of productivity change that i efficiency change and technical change. However, Ethiopia being comparable to Kenya in that both are developing countrie, the determinant of production efficiency brought out in the Mengitae (1996) tudy could till explain a Kenyan cae. In term of data, thi tudy ha the advantage of uing a balanced panel for the period of tudy. Chirwa (2000) aeed efficiency of firm during the tructural adjutment programme period for four ub-ector in Malawian manufacturing indutry: food proceing, clothing and foot ware, pharmaceutical and oap. Auming the inefficiency component to be ditributed a both half normal and truncated normal, the tudy etimated tochatic production frontier for each ub-ector uing Cobb-Dougla and Tranlog pecification. Firm level inefficiencie were predicted uing panel data between 1984 and 1988 and regreed on firm pecific and indutry characteritic uing cenored Tobit regreion analyi. The tudy found that technical efficiency ignificantly declined with firm ize, dometic monopoly power and tariff while it wa a poitive function of factor intenity and kill of worker. While Chirwa (2000) focued on the manufacturing ector firm a i the cae in thi tudy, technical efficiency wa the focu with no attention given to technological iue in the Malawian manufacturing ector. It i however worth noting that while Mangitae (1996) found technical efficiency to increae with firm ize in Ethiopia, efficiency declined with firm ize in Malawi. 40

61 Battee et al (2000) carried out an empirical tudy of the technical efficiencie of firm in the Indoneian garment indutry uing panel data from the annual cenu of manufacturing indutrie during 1990 to The tudy ued different tochatic frontier module in five different region of Indoneia becaue of differing technologie involved. However, a tochatic metaproduction frontier wa applied to obtain alternative etimate for the technical efficiencie of firm in the different region. A meta-production, which wa firt introduced by Hayami (1969) can be regarded a the envelop of commonly conceived neo-claical production function. In thi context, it wa defined a the envelop of the production point of the mot efficient region. Uing a decompoition reult obtained by uing both the regional and the metaproduction frontier, the mean productivity potential of firm in a given region wa etimated. The technical inefficiency effect in the tochatic frontier wa aumed to have the time-varying tructure propoed by Battee and Coelli (1992). The tudy found that the productivity potential ratio played an important part in explaining the ability of the garment firm in one region to compete with other garment firm from different region at the national level. Thi ratio provided an etimate of the technology gap between the region and the country a a whole. The above tudy i another cae of a partial approach to productivity tudy. It concentrated on efficiency iue in only one ub-ector of the manufacturing ector. Furthermore, the tudy ued a parametric approach while our tudy ue a non-parametric approach. Deraniyagala (2001) examined the effect of technology accumulation on firm level technical efficiency in the Sri Lanka clothing and agricultural machinery indutry uing cro-ectional urvey data. A two-tep analyi of efficiency 41

62 wa applied on a Cobb-Dougla tochatic frontier function and etimated uing maximum likelihood method. Technical inefficiency wa aumed to follow an exponential ditribution. The tudy found that technology accumulation meaured by technical change and technical capabilitie had a ignificant and poitive effect on firm level technical efficiency. Furthermore, the analyi howed that firm uing broadly imilar technologie could achieve varying level of efficiency depending on the extent of incremental, minor technical change undertaken for adaptation and aimilation. Thi wa conitent with Bigg and Raturi (1997); they found out that all indicator of learning-related technological capabilitie that enhance firm capacity to build and augment human capital, had a poitive impact on productivity. The tudy introduced a new and intereting dimenion in that an attempt wa made to relate the two main ource of productivity change. Efficiency i hypotheized to be dependent on technology accumulation meaured by technical change and technical capabilitie. Our tudy however, take both efficiency and technical change to be complementary ource of productivity change. Chirwa (2001) tudied the relationhip between privatization and technical efficiency in Malawian manufacturing ector uing panel data between 1970 and Non-parametric production frontier (DEA) wa ued to derive the technical efficiency core. The tudy found that change in technical efficiency were higher in privatized enterprie compared to the tate owned enterprie and private enterprie. Controlling for other ource of technical efficiency, econometric reult howed that efficiency core were 25 percent higher in the period after privatization. Bottao and Sembenelli (2004) agreed with thi tudy that privatization bring efficiency gain. 42

63 However the tudy again focued on efficiency iue comparing how efficiency differ before and after privatization. While it may add value to a imilar tudy for Kenya, our tudy ue a broad approach to productivity change ourced from both efficiency change and technical change. However, our tudy, in a imilar manner ue a non-parametric approach though it take interet in both the efficiency and technical apect of productivity change. Coelli and Rao (2003) carried out a tudy on the TFP growth in agriculture of 93 countrie uing data of 20 year from 1980 to In the tudy, they ought to examine the level and trend in agricultural output and productivity in the 93 developed and developing countrie that accounted for a major portion of the world population and agricultural output. They ued data from the food and agriculture organization of United Nation. Due to the non-availability of reliable input price data, the tudy ued Data Envelopment Analyi (DEA) to derive Malmquit productivity indice. The tudy examined trend in agricultural productivity over the period; iue of catch up and convergence or in ome cae poible divergence in productivity in agriculture were examined within a global framework. Moreover, the tudy derived the hadow price and value hare that were implicit in the DEA baed Malmquit productivity indice, and examined the plauibility of their level and trend over the tudy period. The reult howed an annual growth in TFP of 2.1 percent, with efficiency change (or catch up) contributing 0.9 percent and technical change (or frontier hift) providing the other 1.2 percent. In term of individual country performance, the mot pectacular performance wa poted by China with an average annual growth of 6.0 percent on TFP over the tudy period. Countrie with trong performance were, among other, Cambodia, Nigeria and Algeria. The United State had a TFP growth rate of 2.6 percent where a India had 43

64 poted a TFP growth rate of only 1.4 percent. In term of region, Aia wa the major performer with annual TFP growth of 2.9 percent. Africa eemed to be the weaket performer with only 0.6 percent growth in TFP. Examining the quetion of catch up and convergence, there wa an encouraging reveral of negative productivity trend and technological regreion. Thu, the tudy clearly meaured the TFP change and decompoed the change into efficiency change and the technical change. Our tudy take a very imilar approach to decompoe the TFP change in the Kenyan manufacturing ector. While Coelli and Rao (2003) focued on country level data in the agricultural ector, thi tudy i caled down to firm level data in the manufacturing ector. Sharma and Margono (2004) etimated the technical efficiencie and the total factor productivity (TFP) growth in the food, textile, chemical and metal product indutrie during the period 1993 to 2000 in Indoneia uing the tochatic frontier model. The tudy alo analyzed the determinant of inefficiency and the TFP growth wa decompoed into technological progre, cale component, and efficiency growth. The reult revealed that the food, textile, chemical and metal product ector were on average 50.79, 47.89, and per cent technically efficient, repectively. The tudy alo noted that ownerhip contributed to technical inefficiency in the food ub-ector; location and ize contributed to technical inefficiency in the textile ector, wherea ize, ownerhip and age contributed to inefficiencie in the chemical and metal product ector. The etimate of TFP growth indicated that productivity in Indoneian manufacturing indutrie decreaed at the rate of 2.73, 0.26 and 1.65 percent in the food, textile, and metal product repectively, wherea in the chemical ector, it grew at a rate of 0.5 percent during the period of the tudy. The tudy revealed that TFP growth wa poitively driven by technical efficiency change a oppoed to technological progre. 44

65 Our tudy i quite imilar to the above tudy in that apart from emphai placed on technical efficiency, the tudy etimated the total factor productivity growth, decompoing the growth into technical efficiency and technical progre. However, the tudy ued a tochatic frontier model, a parametric approach, which impoe a priori functional form to the frontier and which make the methodology more computationally demanding (Coelli et al, 2005). Thi tudy however, ue a non-parametric approach (DEA), which i computationally imple and ha an advantage in that it can be implemented without pecifying the functional form of the frontier. Limam and Miller (2004) examined cro country pattern of economic growth by etimating a tochatic frontier production function for 80 developed and developing countrie; they decompoed output change into factor accumulation and production efficiency improvement. The tudy incorporated the quality of input in analyzing the output growth, where productivity of capital depended on it average age while the productivity of labour depended on it average level of education. The growth decompoition involved five geographical region i.e. Africa, Eat Aia, Latin America, South Aia and the Wet. The tudy found that factor growth, epecially capital accumulation, proved much more important than either improved quality of factor or TFP growth in explaining output growth. The quality of capital poitively and ignificantly affected output growth in all group. The quality of labour had a poitive and ignificant growth only in Africa, Eat Aia and the Wet. Labour quality had a negative and ignificant effect in Latin America and South Aia. Thi tudy focued on economic growth and identified TFP growth a one determinant of the economic growth at the country level. TFP growth turned not ignificant in explaining economic growth epecially in Africa. The reult revealed factor accumulation and labour mobility to explain growth epecially 45

66 in Africa probably explaining why little attention ha been given to factor productivity a a mean to economic growth. Thi i depite the fact that competitivene require efficiency and technical progre which together lead to TFP growth. Therefore, our tudy focued on TFP growth to give more attention to productivity growth a a mean to a competitive manufacturing ector in Kenya Empirical literature pecific to Kenya Bigg et al (1995) invetigated the technical efficiency level for four manufacturing ub-ector in three African countrie namely; Ghana, Kenya and Zimbabwe. The ub-ector were: food proceing, wood working, metal working and textile and garment. The tudy ued the Cobb-Dougla technology where the dependent variable wa defined a value added. The tudy ued firm level data for the period ; it found out that technological capabilitie, defined a kill and knowledge needed to et up and operate a modern indutry efficiently, wa a ignificant determinant of efficiency at all level, i.e. the firm, the ector and the country level. Thi tudy adopt Bigg et al (1995) definition of the dependent of variable. The tudy reviewed defined output a value added; our tudy conveniently borrow it to reduce the number of independent variable and increae the degree of freedom in etimation. Thi tudy alo ue the firm level data of the manufacturing ub-ector in the above reviewed tudy. However, Bigg et al (1995) focued on technical efficiency only leaving out technical change, which our tudy give equal emphai. Gerdin (1997) tudied productivity and growth in Kenya for the period In one of the chapter on the Kenyan manufacturing indutrie, the tudy 46

67 ued a panel of diaggregated data and pecified a tranlog model propoed by Baltagi and Griffin (1988) to etimate input elaticitie, return to cale, technical change and TFP. The tudy ued nine ub-ector of the Kenyan manufacturing. From the tudy, input elaticitie uggeted that intermediate input were the mot important while capital wa the leat important. Intermediate input were not only the dominant input but alo increaed it hare of output while the importance of labour and capital decreaed. Some input elaticitie were negative hence violating the monotonicity condition though none of the negative elaticitie wa ignificant. Thi indicated that at leat in a weak ene, the underlying technology would atify the monotonicity condition. There wa no particular trend in technical change and the technical change core ranged between 0.33 percent and percent. Technical change wa alo decompoed to pure and non-neutral technical change and it appeared to be of mainly a pure nature. Both TFP growth and technical change were very low. The mean TFP growth wa percent, technical change percent and cale change 0.06 percent. Even though TFP growth rate were generally negative for mot part of the period, there wa an increae during the coffee boom period , the dominant factor behind which appeared to be the cale change component. The tudy revealed that TFP change followed rather well the hock and boom that Kenya had experienced. The tudy made an attempt to tudy productivity change and it contribution to growth in Kenya. The reult of the tudy pointed to the dimal performance of the manufacturing ector in Kenya ince independence; thi i concerned the concern of thi tudy. The timing of our tudy i appropriate ince it ue data collected in the middle of a period when Kenya had tarted on ambitiou indutrialization trategy, compared to the reviewed tudy. 47

68 Lundvall and Battee (1998) ued an unbalanced panel of 235 Kenyan manufacturing firm in the wood, food, textile and metal ub-ector to invetigate the relationhip between age, ize and technical efficiency and to tet the Jovanovic (1982) theory that efficient firm grow and urvive while inefficient firm decline and fail. Their tudy ued a tranlog functional form of the Battee and Coelli (1995) model where the natural logarithm of output wa the dependent variable. The tudy found a poitive relationhip between ize and efficiency a potulated by the theory, implying that large firm were more efficient than mall firm. Specifically, the tudy found that the mean technical efficiency increaed in ize in all ector where ize wa defined a value of intermediate input. When the number of worker wa ued a a proxy for ize, the ize effect were ignificant in the textile ector only, implying that the reult were enitive to the definition of the firm ize and to the incluion of mall firm. Neverthele, the firm age wa inconitent with thi theory whoe effect on technical efficiency wa le ytematic than firm ize, but inignificant in all ector except textile. The tudy concluded that one of the main reaon for technical inefficiency in the manufacturing ector wa the large number of mall firm and the tudy recommended upport programme to timulate growth in ize rather than number. The tudy provided inight into the factor that influence technical efficiency clearly revealing firm ize a a major determinant. However, emphai a again given to only one apect of productivity (efficiency) leaving out technical progre. The tudy and our both place emphai on the firm level analyi of the manufacturing ector. 48

69 Lundvall (1999) applied the DEA method on the ame data ued by Lundvall and Battee (1998) to check the enitivity of the tochatic frontier model reult. The reult obtained were broadly conitent. However, the inefficiency core yielded by the DEA were lower than thoe yielded by the tochatic frontier model. Soderbom and Teal (2004) noted that the ubtantially lower core yielded by the DEA were conitent with preence of meaurement error in the dependent variable. Lundvall (1999) alo invetigated the factor intenitie and ubtitution in the Kenyan manufacturing ector uing an unbalanced panel of 195 firm and a total of 450 obervation. The etimated tranlog production function uggeted that large firm tend to be more capital intenive than mall one in Kenyan manufacturing. Thi i due to nonhomothetic technologie and to different input price. The tudy oberved that the relative marginal product of capital and labour exhibited tendencie to fall with firm ize uggeting a negative relationhip between the relative price for capital and firm ize. Skilled and unkilled labour exhibited higher ubtitutability between each other than with capital. Hence capital wa more likely to contrain the firm more than kill. In another tudy, Lundvall et al (1999) tudied the performance of four Kenyan manufacturing indutrie, food, wood, textile and metal. The performance wa analyzed in term of technical efficiency and productivity uing fixed-and random-effect Cobb-Dougla production function. Their tudy oberved that mall and informal firm were comparably inefficient. Food ector followed by metal ector, wa found to be the mot productive ector. The tudy alo found that growing firm are more productive than contracting one, uggeting that high turnover may increae overall ector productivity, including exporting, kill, acce to overdraft facilitie and foreign ownerhip. Textile appear to have experienced everal technological regree after the trade liberalization. 49

70 The tudie by Lundvall (1999) and Lundvall et al (1999) on the Kenyan manufacturing ector did hed light on how factor of production are ubtituted and attempted to analyze productivity on variou ub-ector of the manufacturing ector. However, there wa no attempt to explain the productivity in term of it ource; like mot of other tudie, emphai wa placed on technical efficiency. The ub-ector covered in the above tudie alo form part of the ub-ector included in our tudy. Their tudie etimated the technical efficiency and productivity uing a parametric method while thi tudy applie a non-parametric method. The data ued by Lundvall (1999) compried an unbalanced panel collected in the mid 1990 while our tudy applied a balanced panel data that wa uccefully collected between Bigten, et al (1999) tudied the Kenyan manufacturing ector eeking evidence of any difference between the formal and informal mall firm. The unbalanced panel compried of 276 firm in the food, wood, textile and metal ub-ector. The tudy aumed a common Cobb-Dougla technology with capital and labour. The tudy found that, although informal etablihment dominated the mall firm-egment in Kenyan manufacturing, there were alo formal enterprie with ditinct characteritic. Informal firm were younger, le capital-intenive, almot never run by Aian, paid le killed wage and no taxe, had poor acce to credit and had le educated manager. The mall firm inveted more often and were le efficient than Aian-managed formal firm, but more efficient than thoe managed by African. The tudy uggeted formality tatu, independent of ize, mattered. Important alo wa how ethnicity affected thee difference and the graduation of firm from the informal to the formal ector. The tudy clearly brought out a difference between the formal and the informal manufacturing ector enterprie in term of performance, feature and how the 50

71 firm were run. The difference in performance wa captured uing a parametric method, Cobb-Dougla production function. However, no attempt wa made to addre apect of productivity difference in the divide, which our tudy addree in the formal manufacturing ector. Productivity meaurement require particular and detailed data that i not available for the informal ector. Onjala (2002) carried out a tudy on TFP in Kenya and the link with trade policy. The tudy explored productivity ource in the manufacturing and agricultural ector uing aggregated data from 1960 to The tudy noted that, while economic growth can be viewed a a proce involving the entire economy output performance, it invariably depend on the productivity of the country in quetion. The ource of productivity growth over time and of productivity difference among countrie and region have emerged a a central unifying theme of growth and development. The tudy ought to etimate TFP in the agricultural and manufacturing ector and linked the productivity to the trade policy epiode. The tudy adopted a growth accounting approach baed on the procedure by Elia (1992), Shaaeldin (1989), Ritter (1988) and Chen (1977). The model aumed a neo- claical production function where time i an independent variable meauring the TFP. The tudy pecified a tranlog function in the etimation of the TFP core, which were then regreed againt trade policy variable. The trade policie were the real exchange rate, an import index to meaure the productivity effect of dometic preure brought about by import, and a meaure of productivity effect on export promotion. Import and export penetration ratio were ued to capture the effect of foreign competition and greater openne on productivity. The tudy found out that TFP growth contributed more to output growth in agriculture than in the manufacturing ector in Kenya. However, TFP formed a mall portion of growth in all ector. 51

72 In the manufacturing ector, output growth wa mainly explained by factor input. In a far a the trade policie were concerned; fluctuation in TFP growth appeared more trongly correlated with the real exchange rate followed by import penetration and by export penetration. The reult of the above tudy infer that TFP growth did not explain growth in the manufacturing ector output a it did in the agricultural ector. While our tudy focue on the manufacturing ector TFP change and explain the ource of uch change, the above tudy looked at the aggregate manufacturing ector TFP growth but no attempt wa made to decompoe uch growth into the variou ource. Moreover, the tudy ued a growth accounting approach which aumed that all firm in the ector are efficient. Our tudy addree the variou ub-ector of the manufacturing ector and tudie the ub-ector productivity in detail. The methodology ued meaure the productivity change and decompoe the change into efficiency ourced change and technically ourced change. Mazumdar and Mazaheri (2003) invetigated technical efficiency of manufacturing firm in Ghana, Kenya, Zimbabwe, Tanzania and Zambia uing the Battee and Coelli (1995) model. Their tudy found that correlation between firm ize and technical efficiency differed from country to country. Larger firm in both Kenya and Zimbabwe were found to be more efficient, while thoe in Tanzania uggeted an invere relationhip between firm ize and technical efficiency. Furthermore, the reult only upported a trong relationhip between age and technical efficiency in Zimbabwe. A in the Lundvall and Battee (1998) tudy, the poitive age-efficiency relationhip propoed by the Jovanovic (1982) learning model found little upport. Their tudy alo found that the firm that engaged in trade either a importer or exporter, and thoe with technology tranfer or foreign licening, were more efficient irrepective 52

73 of the country tudied. Thi finding wa conitent with Chirwa (2001) and Bottao and Sembenelli (2004) who found that ubidiarie of multinational firm were more efficient compared to tate owned firm; thi wa an indicating that foreign participation had a poitive implication on dometic production. According to the Mazumdar and Mazaheri (2003) tudy, firm gain experience a they grow and improve work practice, leading to efficiency improvement. Firm ize captured qualitative variable uch a learning by doing, organizational uperiority of larger firm and firt mover advantage of larger older firm. Firm ize wa meaured by the total number of employee in the firm in a given period. Moreover, firm ize wa further divided into three ize categorie namely mall, medium and large in order to invetigate whether mall firm were more efficient than larger one (Ngui, 2001; Soderbom, 2004). According to Bigg et al (1995), the relative efficiency between firm ize clae can help policy maker identify enterprie group with the highet potential to meeting planned economic target a well a provide ueful input into other government policie. The tudy wa yet another attempt to addre only one apect of productivity namely technical efficiency with the ame reult reported by other imilar tudie. The tudy ued the tochatic frontier analyi which required a pecification of the functional form of the model. Our tudy a tated earlier, tudie TFP change where technical efficiency i jut one of the ource of the change. The DEA method i ued with no need to pecify any prior functional form of the model. Soderbom (2004) tudied productivity, export and firm dynamic in the Kenya manufacturing ector over the period , uing Regional Program for Enterprie Development (RPED) urvey collected in 2003 covering the period 53

74 , in conjunction with data from the year The tudy aumed a Cobb-Dougla production function with two input, phyical capital and labour. The tudy found out that ignificant productivity difference exited acro ector, the mot productive ector being chemical and food, while the leat productive one being leather, wood and textile. There were however, ome ign that the textile ector had recovered omewhat in relation to the food ector over the period conidered. There were ignificant productivity advantage to being located in Nairobi, poibly due to external factor uch a relatively good infratructure. In line with mot other tudie, thi tudy found a trong ize effect on the deciion to export, upporting the notion that firm face ignificant fixed cot to entering the export market. The tudy found a poitive and ignificant time trend for international export, uggeting that firm were reponding to policy meaure deigned to pur export. A further probing of the data uggeted that a large part of the increae wa played by more firm in the textile and garment ector becoming export oriented during the period. On export, Kimuyu (2007) oberved that exporter are forced to remain on the look out for way of improving product quality, production and delivery chedule; thi i becaue they face tiff competition from foreign firm, and have to atify exacting demand from foreign cutomer. Bernard and Jenen (1999) howed that more efficient firm were likely to export, but exporting did not lead to change in efficiency. However, Tybout (2000) noted that while mot tudie found that exporter were more efficient than non-exporter before they tarted elling abroad, learning-by-exporting hypothei a evidenced by Bigten et al (2004) could not be ruled out ince firm in everal indutrie exhibited everal efficiency gain after becoming exporter. 54

75 Thi i one among the few tudie that have utilized the mot current RPED urvey in part of it tudy. The tudy ued a parametric approach to meaure productivity in the Kenyan manufacturing ector among other apect of the ector. The tudy hed more light on the link between productivity and export confirming a trong link conitent with other tudie. However, there wa no attempt to meaure productivity change which our tudy addree. Mulwa et al (2005) carried out a tudy on the productivity growth in mallholder ugarcane farming in Kenya uing the Malmquit TFP decompoition. The tudy noted that the Kenyan agricultural ector had undergone major tructural change ince independence in form of intitutional arrangement related to the land tenure ytem and marketing. Coniderable change related to the ue of intermediate factor of production uch a fertilizer, eed and machinery were evident. The tudy alo undercored the important role played by mall holder firm in Kenya. The tudy only conidered the plant-crop in two cycle, previou and current, for comparion. The main input and activitie that entered ugarcane production function were eed cane, fertilizer, labour (family and hired), and land preparation. The tudy oberved that Mumia ugarcane farmer continued to ue old technologie cauing declining technical change. However, the cheme wa efficient to ome extent. Chemelil cheme uffered both technical regre and efficiency decline. Similar reult were recorded for Wet Kenya ugarcane farmer. The tudy uggeted that one factor that hindered technical progre and efficiency improvement wa the continued land ubdiviion which brought with it divere management tyle. Our tudy i quite imilar to the above tudy in term of the methodology ued in meaurement and decompoition of TFP change. However, our tudy ue a 55

76 panel of firm level data with a focu on the manufacturing ector. The above tudy wa only a micro-tudy of ugar cane farming in wetern Kenya. Ngui (2008) carried out an empirical tudy on the efficiency of the Kenyan manufacturing ector. Specifically, the tudy analyzed efficiency difference, efficiency ditribution and efficiency relationhip with it determinant for three ub-ector namely: food, metal, and textile, during the tructural reform period. In addition, the tudy invetigated whether the technical efficiency point etimate were etimated with preciion and dicued the implication ariing from conidering the confidence interval etimate. The tudy ued the tochatic frontier analyi to etimate the technical efficiency and confidence interval were calculated uing Horrace and Schmidt (1996) procedure. The Caudill et al (1995) pecification wa applied on an unbalanced panel data for two period, and The tudy found that the width of confidence interval varied coniderably among the obervation for each ubector making it hard to eparate the firm into ditinct group of high, average and low technical efficiency uing the individual efficiency etimate. Furthermore, the confidence interval etimate for mot of the obervation overlapped making it difficult to identify obervation that were ignificantly le or more efficient than the average. Therefore, the tudy could only differentiate between et of firm that were efficient and inefficient roughly, a finding conitent with Fraer and Horrace (2003) on Autralian wool production, and Balcombe et al (2006) on Autralian dairy farm. On technical efficiency, the tudy revealed that the technical efficiency ditribution for all the ub-ector ignificantly changed during the period of analyi. The textile and food ub-ector howed an improvement while the metal ub-ector declined in efficiency. However, the food ub-ector firm largely remained relatively inefficient. An analyi on the determinant of 56

77 technical efficiency revealed that the ignificance and the relationhip between technical efficiency and it determinant were not only different acro the ubector in each period, but alo changed during the period of tudy. In repect to the firm ize, the tudy found that there wa a negative relationhip between firm ize and efficiency for the metal and textile ub-ector between 2000 and Thi wa attributed partly to the operation of exceively large firm driven by increae in factor input rather than improvement in productivity, a finding conitent with Bigten (2002). The tudy alo found a negative effect of firm age on technical efficiency acro both period. Thi, according to the tudy, could be explained by the poibility that relatively young firm utilized more recent technology while the older firm ued relatively abolute phyical capital. Sunk cot were ignificant and poitively related to technical efficiency for the food and textile ub-ector in the period, upporting the argument that capital intenive firm embody the mot advanced technology. The analyi, further revealed that mot of the foreign owned firm were exporting firm probably motivated by the incentive provided in the export promotion policie offered by the Kenya Government during the tructural reform period. Apparently, the exporting firm were le efficient in period than in the period, a finding that could be explained by participation in trade not necearily being related to higher firm efficiency, but with the export promotion policie adopted during the reform period. Even though thi tudy i in the category of tudie that focued on one apect of productivity in the manufacturing ector (technical efficiency), it hed light on the determinant of the efficiency change in three ector that have been included in our tudy. The tudy ued the tochatic frontier analyi, which i quite comparable to our tudy which ue the data envelopment analyi. The 57

78 meaured technical efficiency change in the tudy were quite conitent with the change reported in our tudy depite the different methodologie. The two tudie are alo quite comparable in that the ame data et wa ued. However, our tudy take a broad approach to productivity where technical efficiency i one of the ource of TFP change. Onuonga (2008) carried out a tudy on the factor that influenced energy utilization in Kenya manufacturing ector and determined the extent of ubtitution poibilitie between energy input and other non-energy factor of production within the manufacturing ector over the period. The tudy noted that the Kenyan manufacturing ector wa a major conumer of commercial energy, the econd larget uer of petroleum product, and the larget uer of electricity. According to the tudy, the analyi of price and non-price variable that affected the ue of energy within the ector wa neceary for deigning policy meaure that could lead to the energy conervation. Information on the degree of energy ubtitution wa important in predicting the effect of energy hortage on manufacturing output and indutrial employment. The tudy ued the tranlog model to analye total factor demand and the inter-fuel ubtitution. The tudy found out that price of energy, cro price, output, technology, price of capital and unexpected event influenced the ector ue of energy. The reult for inter-fuel model indicated that demand for electricity and oil in the Kenyan manufacturing ector were price inelatic and that oil and electricity were ubtitute. Limited ubtitution poibilitie between electricity and oil in thi ector were found. The demand for energy and labor were price inelatic while that of capital had a unitary elaticity. 58

79 The tudy concentrated on energy a the major factor of production in the manufacturing ector in Kenya. It indeed portrayed the manufacturing ector a an important ector that i a conumer of energy in it production and hence bringing the ector overall productivity in focu. While energy may not have been one of the variable in our tudy, it neverthele formed a major component in the computation of the value added ued a the dependant variable. Furthermore, all the component of energy highlighted in the above tudy are ued in our tudy. 2.6 Overview of the empirical literature In mot of the tudie carried out and epecially in Kenyan manufacturing ector, the focu ha been on the iue of efficiency, it meaurement and determinant. However, efficiency change i only one of the ource of total factor productivity growth. The tudie meaure and dicu efficiency without linking it abence or preence to the total factor productivity change. Even where TFP iue were examined, the focu wa on the manufacturing ector a a whole among other productive ector of the economy. Thi tudy i a micro level approach to the Kenyan manufacturing ector. The literature reviewed provide inight into the iue that influence production of manufacturing output. Mot of the tudie have ued variable that are conitent with economic theory and therefore, have been ueful in deciding the variable ued in our tudy. Thee variable include capital, labour, raw material and other indirect input which can be meaured either in phyical or monetary value term depending on the homogeneity of the variable. For the output variable, both gro output and value added have been ued; Soderbom and Teal (2004) found out that they yielded imilar reult. Thi tudy adopt the value added approach more o to avoid the lack of degree of freedom 59

80 epecially in ub-ector with relatively few firm. When obervation are few, but with many input and/or output, then many firm will appear on the DEA frontier, implying efficiency, which may be mileading (Coelli et al, 2005). The few tudie on the Kenyan manufacturing ector revealed that growth in the ector ha reulted from growth in input ue and not productivity change (Ngui, 2008; Bigten, 2002; KAM, 2006). Thi could probably explain why no attention ha been given to TFP growth in the ector. Studie done on TFP growth have actually revealed that TFP ha been declining (Gerdin, 1997). Thi tudy addree itelf to the total factor productivity change with pecific attention to the ource of uch change, including technological and efficiency change. Thi tudy adopt the DEA approach to calculate the Malmquit index core in the meaurement and decompoition of TFP growth in Kenya manufacturing ector. The approach ha rarely been ued in productivity tudie in Kenya. Out of the empirical literature reviewed, only Coelli and Rao (2003), Chirwa (2001), Fare et al (1994) and Mulwa et al (2005) ued the methodology, in which only one, Mulwa et al (2005) applie to Kenya though in the agricultural ector. Hence the tudy will provide a new approach to productivity analyi in Kenya. Furthermore, the tudy utilize the mot current firm level data with more ubector to allow more homogeneity among the firm. The tudy i alo quite timely in utilizing data that wa collected right in the middle of the phae one of an indutrialization trategy that could have een Kenya indutrialize by the year The tudy i crucial in evaluating the extent to which the trategy met the indutrialization goal a pecified in the eional paper No. 2 of Moreover, it pay attention to the leon that can be learnt to ee the manufacturing ector play the critical role in propelling the economy at a 10 percent growth rate, in line with the apiration of the Kenya viion

81 2.7 Theoretical framework Introduction Total Factor Productivity (TFP) change wa meaured uing the Malmquit index method decribed in Fare et al (1994) and Coelli, Rao and Battee (1998). Thi tudy adopted Data Envelopment Analyi (DEA) method to contruct a piece-wie linear production frontier for each year in the ample. The Malmquit TFP index wa contructed uing the DEA-like linear program decribed in ection below The malmquit TFP index The Malmquit index i defined uing ditance function. Ditance function allow one to decribe a multi-input, multi-output production technology without the need to pecify behavioral objective uch a cot minimization or profit maximization. There are two ditance function namely input ditance function and output ditance function. An input ditance function characterize the production technology by looking at a minimal proportional contraction of the input vector, given an output vector. Thi tudy arbitrarily chooe an output oriented cae and therefore only conider output ditance function in detail. However, input ditance function can be defined and ued in a imilar manner. Following Fare et al (1994), and given that uitable panel data are available, the required ditance meaure for the malmquit TFP index can be calculated uing DEA linear programme. For the i th DMU, the four ditance function in equation 2.18 to meaure the TFP change between two period,, and, t, are calculated. Thi require the olving of four linear programming (LP) problem. The analyi aume contant return to cale (CRS) technology (Fare et al (1994). Thi enure that the reulting TFP change meaure atify the 61

82 fundamental property that if all input are multiplied by the poitive calar uch aδ, and all output are multiplied by the non-negative calar uch aα, then the reulting TFP change index will equalα. Auming there are data on N δ input and M output for each of the I firm. For the i th firm, thee are repreented by the column vector x i and q i, repectively. The N I input matrix, X, and the M I output matrix, Q, repreent the data for all I firm. The required LP are: t [ d 0 (q t,x t )] 1 = max, 2.30.t - q it + Q t 0, x it X t 0, 0, [ d 0 (q,x )] 1 = max, t - q i + Q 0, x i X 0, 0, t [ d 0 (q,x t )] 1 = max, t - q i + Q t 0, x i X t 0, 0, and [ d 0 (q t,x t )] 1 = max, t - q it + Q 0, x it X 0, 0, Where i a calar and i a I 1 vector of contant. 62

83 In equation 2.32 and 2.33 where production point are compared with technologie from different time period, the parameter need not be greater than or equal to one, a it hould be when calculating output-oriented technical efficiencie. The data point could lie above the feaible production et. Thi will mot likely occur in equation 2.33, where a production point from period, t, i compared with technology in an earlier period,. If technical progre ha occurred, then a value of <1 i poible. It could alo poibly occur in equation 2.32 if technical regre ha occurred but thi i le likely. The and are likely to take different value in the four LP preented in equation 2.30 to Furthermore, the above four LP mut be olved for each DMU in the ample. Thu, if there are twenty (20) DMU and two time period, 80 LP mut be olved. A extra time period are added, one mut olve an extra three LP for each firm to contruct a chained index. If there are T time period, then (3T-2) LP mut be olved for each firm in the ample. Hence, if there are I firm, then there are I (3T-2) LP to be olved. For example, with I = 20 firm and T = 10 time period, thi would involve 20 (3 10-2) = 560 LP. 63

84 CHAPTER THREE METHODOLOGY 3.1 Introduction Thi chapter decribe the reearch deign in the tudy and preent the empirical model adopted for the tudy. The variable ued in the tudy are defined. To achieve the objective of the tudy, quantitative econdary data were collected and analyzed. The data, it ource, and the method ued in data analyi are explained. 3.2 Reearch deign The tudy ought to empirically analyze the total factor productivity change in the Kenyan manufacturing ector. The tudy come at a time when propect of enuring table and utainable economic growth lie more in the development of indutry. Policie that trengthen local production capacity of dometically produced product and enhance Kenya competitivene globally hould be put in place to realize thee goal. Therefore, thi created a need for the tudy to provide a richer policy environment. The firm and worker urvey formed the core of the tudy and therefore, firm level data were ought. The analyi of the manufacturing ector focued on the analyi of data collected in the 2002/03 urvey of 282 formal manufacturing firm. The urvey reulted from a partnerhip between Kenya Intitute for Public Policy Reearch and Analyi (KIPPRA) and the Regional Programme on Enterprie Development (RPED), in the African private ector group of the World Bank. The ample wa drawn from a cenu conducted by Central Bureau of Statitic (CBS) of nearly 2000 formal manufacturing firm employing more than 64

85 250,000 full time employee. In order to enure repreentation of all type of firm, the ample wa tratified acro location, ub-ector and ize in 148 cluter. Four location were identified namely, Nairobi, Eldoret/Kiumu, Mombaa and Nakuru. Nine manufacturing ub-ector were Agro-indutry, chemical/paint, contruction material, furniture, metal, paper/printing/publihing, platic, textile/leather and wood. Four ize clae were ued: mall (11-49 employee), medium (50-99 employee), large ( employee) and very large (500 and more employee). Three hundred and ixty eight (368) firm were elected randomly from the cluter repreenting roughly 20 percent of all formal firm. Due to nonrepone, perhap a a reult of urvey fatigue, only 282 firm completed the urvey. A detailed quetionnaire wa prepared to capture information from the firm on eight thematic area namely; entrepreneurhip and buine hitory, technology, trade, infratructure, buine environment, ale/ raw material/ product/ invetment, credit and finance, and labour and training. The relevant data for thi tudy wa in the thematic area of ale/ raw material/ product/ invetment. The data were analyzed baed on the tudy objective and the report prepared conitent with the objective. The data analyi technique are briefly explained later in thi chapter while the finding are reported in chapter four. 65

86 3.3 The empirical model In thi tudy, total factor productivity (TFP) growth wa meaured uing the malmquit index method decribed by Fare et al (1994) and Coelli, Rao and Battee (1998). Data Envelopment Analyi (DEA) method wa ued to contruct a piece-wie linear production frontier for each year in the ample. While DEA can be either input-oriented or output-oriented, in thi tudy, a contant return to cale (CRS) technology wa aumed to etimate the outputoriented DEA, ince the two meaure provide the ame technical efficiency core when a (CRS) technology applie (Coelli et al, 2005). Furthermore, Grifell-Tatje and Lovel (1995) ued a imple one-input, one-output example to illutrate that a malmquit TFP index may not correctly meaure TFP change when VRS wa aumed for the technology. Hence it wa important that CRS be impoed upon any technology that wa ued to etimate ditance function for the calculation of a malmquit TFP index. Otherwie, the reulting meaure might not have reflected the TFP gain or loe reulting from cale effect properly. Given that data were available for I firm in the tudy time period, the linear programming (LP) problem that wa olved for the i th firm in an output oriented DEA model wa a follow: max,.t - q it + Q t 0, x it X t 0, 0, 66

87 Where q it i a Mx1 vector of output value for the i-th firm in period t; x it i a Nx1 vector of input value for the i-th firm in period t; Q t i a IxM matrix of output value for all I firm in period t; X t i a IxN matrix of input value for all I firm in period t; i a Ix1 vector of weight; and i a calar. It wa oberved that will take a value greater than or equal to one and that ( - 1) wa the proportional increae in output that could be achieved by the i th firm, with input quantitie held contant. Note alo that 1/ defined a technical efficiency (TE) core which varie between zero and one. The above LP wa olved I time-once for each firm in the ample. Each LP produced a and a vector. The parameter provided information on the technical efficiency core for the i th firm and the vector provided information on the peer of the (inefficient) i th firm. The peer of the i th firm are thoe efficient firm that defined the facet of the frontier againt which the (inefficient) i th firm wa projected. The and were likely to take different value in the LP etimated. Furthermore, the LP were to be olved for each firm in the ample. Thu, if for intance there were I firm and two time period, 2 I LP were to be olved. A extra time period were added, an extra three LP for each firm were to be olved to contruct a chained index. If there were T time period, then (3T-2) LP were olved for each firm in the ample. Hence if there were I firm, then there were I (3T-2) LP to be olved. In the tudy, each ub-ector had a different number of firm I and the panel conited of 3 time period. For every ub-ector therefore, for T = 3, (7 I) 67

88 different LP were olved uing the appropriate computer programme. There were many piece of information from the computer output but for the purpoe of thi particular tudy, only the Malmquit index ummarie of annual mean and firm mean are reported in chapter four. Where the ub-ector were compared againt the whole manufacturing ector frontier, the above formulation applied, only that I repreented the number of ub-ector, and not the number of firm a in the ub-ector level. 3.4 Definition and meaurement of variable Output: the dependent variable wa meaured by value added in Kenyan hilling. It wa computed a the difference between the value of each firm output and the value of the intermediate input. The value of the firm output wa meaured by the total ale figure for the manufactured good a provided by the firm. The intermediate input included energy, raw material and indirect input. Energy compried of electricity, fuel and other energy related cot a provided by the firm. Raw material were meaured by the direct cot of raw material a provided by the firm, while indirect input were meaured by all the aggregate expene other than capital, labour, energy and raw material. The expene included the interet charge and financial fee in the proce of production. Labour wa defined a the phyical and mental effort put for wage and alarie. It wa meaured by the total cot of labour (wage bill) in the period of tudy. The wage bill component conited of direct labour cot and indirect labour cot. The direct labour cot component included wage and alarie, allowance, bonue and other benefit. The indirect labour cot component included the adminitrative cot. All thee cot a provided by the firm were ummed up to form a meaure of the labour input. 68

89 Capital denoted the intermediate good ued in the production of other good. The major component of capital were machinery and other equipment. It wa meaured by the replacement value of machinery and other equipment. The value wa obtained by ubtracting the new purchae in a particular year from the cot of replacing each item at the end of the year, and adding back the ale of the item during the year. The figure wa corrected for degree of capital utilization in a given period. For the data et ued in thi tudy, it wa meaured by the declared capacity utilization by the individual firm. 3.5 The data The empirical analyi wa baed on micro-data of the Kenyan manufacturing firm collected a part of the World Bank Regional Programme on Enterprie Development (RPED) urvey of 2002/2003. The data formed a balanced panel for the period 2000/ /2003 obtained from the firm that gave information for either one or two previou year uing the recall method. Thi left out all the firm that availed information for only one year in the 2002/2003 data et to enure completene and conitency of the data for the etimation. The data were collected on 282 formal manufacturing firm covering 13 ubector drawn from 5 urban center in Kenya namely Nairobi, Mombaa, Eldoret, Kiumu and Nakuru. The ub-ector included agro-indutry, bakery, chemical and paint, contruction material, furniture, metal, machinery, paper printing and publihing, platic, textile, garment, leather and wood. The original urvey touched on eight thematic area in the manufacturing ector. Thee were buine hitory, technology, trade, infratructure, buine environment, ale and production, credit and finance and labour and training. For the purpoe of thi tudy, the relevant data wa under the thematic area of ale and production 69

90 in which, ection data on each firm information on output and input wa contained. Out of the 282 firm included in the urvey, the tudy filtered a total of 119 firm and a total of 357 obervation. The data on the 282 firm contained quite a number of gap with ome vital information on the major variable miing. For thee reaon the tudy made a few, but neceary adjutment in the data to enure ubtantial degree of freedom for the etimation a follow: Some ub-ector that had too few firm with complete data were omitted from the tudy. Thee were rubber and gla ub-ector. Some ub-ector that had a few complete obervation but highly imilar were merged but enuring homogeneity a much a poible. The bakery ub-ector wa therefore merged with the agro-indutry in thi tudy referred to a the food ub-ector; wood ub-ector wa merged with furniture ub-ector in the tudy now referred to a wood and furniture ub-ector; and textile and garment ub-ector were merged in the tudy now referred to a textile ubector. The value added approach wa applied leaving only two input that were the independent variable. Thee were labour and capital. Therefore, the tudy focued on eight ub-ector namely; food (31), textile (19), wood and furniture (8), metal (22), platic (11), paper printing and publihing (11), contruction material (7) and chemical and pharmaceutical (10). 3.6 Data editing, coding, cleaning and refinement Every quetion in the quetionnaire had been allocated a code that identified the ub-ector, the firm, the period and the variable of interet. The data collected had been recorded under each of the code allocated and therefore it wa relatively eay to pick the neceary variable that formed the bai of the analyi in the tudy. 70

91 However, not all firm in each ub-ector had complete data et for all the required variable. In all cae where data on important variable wa not provided, the particular firm wa dropped culminating into a ample of firm with complete information in each ub-ector. For example, firm with zero output value in particular year were omitted from the analyi while for other variable, they were imputed uing the panel dimenion of the data et. 3.7 Validity and reliability of data While the ue of non-parametric method, uch a DEA, may reduce mot of the etimation problem aociated with parametric analyi, meaurement error and other noie may influence the hape and poition of the etimated frontier. Noie wa expected to ignal-ratio for the input and output due to; the unwillingne of the firm to reveal true value for fear that the information might reach the competitor or the tax authoritie, varying degree of market imperfection which might have caued bia in the value of output, inaccuracy in valuing the capital tock which conited of a wide range of machinery and equipment acquired over long period of time, converion of nominal value to real value in which cae the deflator were aumed to be equal acro different ub-ector depite the heterogeneity of the ample and inaccuracy in the value epecially in the data et ued where recall method wa utilized to acquire information on the previou period (Lundvall et al., 2002). To remove price effect from the variable, different deflator were ued for all input and output with 2000 being the bae year. Outlier might alo have affected the reult; they may have been caued by data recording or data entry error, heterogeneou et of one or two type of cae, one of which i much more frequent, and occurrence of extreme obervation 71

92 with greater frequency than expected for a normal ditribution (Judd and McClelland, 1989). The outlier preent were detected uing quared Mahalanobi ditance and omitted from the analyi. Squared Mahalanobi ditance point out to obervation for which the explanatory part lie far from that of the bulk of the data. The value of quared Mahalanobi ditance were then compared with 95 percent quartile of the chi-quare ditribution with (M- 1) degree of freedom where M repreented the number of independent variable (Roueeuw and Leroy, 1986). Too few obervation and many input and/or output may provide mileading reult where many firm appear on the DEA frontier uggeting all firm are efficient (Coelli, 2005). The ub-ector with too few firm were therefore omitted while other were merged to improve the reult. 3.8 Data analyi The tudy ought to addre four objective. The firt objective wa qualitative and involved a review of the tructure and compoition of the manufacturing ector. It therefore, involved a decriptive analyi with the help of table and chart. The third and fourth objective were met by empirically analyzing the data collected a decribed in ection 3.3. Data envelopment analyi (DEA) calculation were conducted uing Data Envelopment Analyi (computer) programme (DEAP verion 2.1). The application calculated indice of total factor productivity change and decompoed them into technological change and efficiency change. Thee index core are reported in chapter four of the tudy. Thi i followed by concluion and policy recommendation in the light of the tudy finding. 72

93 CHAPTER FOUR RESEARCH FINDINGS 4.1. Introduction Thi chapter preent the finding of the tudy. The policy environment that ha guided the tructure and performance of the manufacturing ector ha been traced from independence to the current, followed by the tructure and compoition of the ector. The empirical reult concerning the total factor productivity growth and ource in the ector are alo preented and dicued. 4.2 Policy epiode in Kenya indutrialization proce The econd objective of the tudy wa to analye the policy epiode in Kenya indutrialization proce, which have been undertaken in phae. To thi end, ix phae have been identified and analyed a follow. (a) The import ubtitution phae In the period immediately after independence, Kenya purued an import ubtitution trategy a a mean of promoting indutrialization. The trategy wa influenced by conventional widom prevailing during the period in many developing countrie. The objective of the trategy were rapid growth of indutry, eaing balance of payment problem, and generation of employment. To achieve the objective, the government relied on a variety of policy intrument including an overvalued exchange rate, high tariff barrier, import licening, foreign exchange control and quantitative retriction to protect local producer againt foreign competition (Bienen, 1990; Roemer, 1993; Hill, 1993). Foreign exchange meaure were extenively ued during thi phae to protect local manufacturer. The foreign exchange allocation committee were etablihed to adminiter foreign exchange quota for import for which a 73

94 limited quota had been etablihed to protect dometic producer. Foreign exchange control were ued to dicriminate againt certain import, promote foreign exchange earning indutrie and to conerve foreign exchange which wa a major contraint in the economy. Tariff were alo ued extenively a an intrument of protection. For mot of the time, the Kenya tariff tructure reflected the overall import ubtitution trategy. Tariff were generally high on import of final product relative to capital and intermediate good uch a machinery and building material. Until the collape of Eat African Community (EAC) in 1977, Kenya could not unilaterally change external tariff. However, due to a weak adminitrative capacity, quantitative retriction proved to be more effective in controlling import compared to tariff. Import licene requirement wa the main intrument for quantitative regulation, with input and certain product receiving preferential or priority treatment in the iuance of import licene. The import ubtitution phae and the policie that utained it had mixed reult. On the poitive ide, the country enjoyed a coniderably high rate of indutrial growth during the firt decade of independence with the manufacturing ector growing at an average rate of 8 percent compared with the rate of 5 percent in the 1980 and Indutrie that recorded rapid development during thi period were proceing of platic, pharmaceutical, teel rolling and galvanizing electrical cable, pepper, vehicle aembly, indutrial gae, rubber, ceramic, and battery manufacture. Some indutrie expanded from a few etablihment into indutrie with a wide range of product and a large number of employee. They included paper, textile and garment manufacturing, food proceing, leather tanning and footwear (Coughlin, 1988; KAM, 1998). 74

95 The impreive performance of the economy in general and indutrial ector in particular wa due to dynamim and prudent macroeconomic management. Invetment wa encouraged through high protection, a liberal attitude toward foreign invetor, an active role played by the government in promoting indutrialization through proviion of credit facilitie and other incentive and a relative table political and economic environment that wa attractive to both dometic and foreign invetor. The high growth rate wa alo facilitated by the economic dynamim normally aociated with the import ubtitution trategy in the initial tage. The commendable manufacturing ector performance benefited ubtantially alo from the expanding dometic demand partly due to riing agricultural income which wa timulated by modernization and diverification of the agricultural production for both export and dometic market. However, toward the end of the 1970, there wa a general deterioration in the country overall economic performance due to a number of factor. Indutrial production for export market lowed down ubtantially becaue the incentive tructure favored production for dometic market creating an inward-looking indutrial ector whoe potential wa everely limited by the ize of the dometic market. The ituation wa aggravated by the collape of the Eat African Community (EAC) in In addition, there wa an eroion of fical dicipline after the coffee boom in the late 1970, which wa worened by a deterioration in the country external term of trade following the econd oil hock in 1977 (Foroutan, 1993). The import ubtitution trategy wa alo in general trongly biaed againt export. Import ubtituting indutrie created too few job while many indutrie ued inappropriate capital-intenive technologie that created a manufacturing ector heavily dependent on imported equipment and raw material. Moreover, the 75

96 ector failed to develop trong linkage with the ret of the economy partly becaue of the undue emphai on production of conumer good at the expene of capital and intermediate good. Under the trategy, the indigenou population failed to control a ignificant portion of the manufacturing ector. Manufactured export thu formed a mall proportion of the country export while indutrial development wa concentrated in Nairobi and a few major town (Ikiara, 1988; Nyongo, 1988; Ogonda, 1990). There wa thu growing dienchantment with thi trategy by early 1980 due to thee and other hortcoming. It hould be pointed out that ome of the failure of the import ubtitution trategy were caued by external factor beyond the control of Kenya policy maker. Firt, the 1973 oil crie reulted in an ecalation of the cot of production and exerted preure on the balance of payment with advere effect on availability of imported raw material and equipment. Second, the collape of the regional market in the late 1970 forced manufacturer to depend on a much narrower market, making many of them operate with exce capacity and carrying high overhead which undermined their competitivene even with variou protective meaure. By the end of the 1970, Kenya had virtually exhauted opportunitie for further indutrial growth through ubtitution. Due to the abence of a well articulated indutrial policy, few meaure were implemented to move the economy to the next tage of the trategy which would have facilitated the production of manufactured export (Coughlin, 1988). (b) The export promotion phae (Early ) A a reult of increaing recognition of the economic realitie facing the country, the government made ome attempt to change the indutrial trategy from import ubtitution to export-led indutrialization. Some of thee 76

97 intention were evident from development plan and policy document publihed during the late 1970 and early The fourth development plan ( ), for intance, advocated a more open trategy for the indutrial ector. It outlined policie deigned to create an enabling environment for indutry through increaed reliance on market-baed incentive and le regulatory tructure. Thi wa to be done through a erie of reform in trade and indutrial regime. Firt, the quantitative retriction were to be gradually replaced with equivalent tariff. Secondly, the tariff rate were to be rationalized and reduced over time. Other recommended meaure included more liberal exchange rate policy and trengthening export promotion cheme (Foroutan, 1993). Depite the expreed need to promote export, there wa lack of commitment in the implementation of the recommended meaure. Thi poor record of implementation of policy meaure wa partly attributed to policy contraint facing the policy maker (Bienen, 1990). After the initial round of liberalization, the government temporarily revered the reform proce and re-introduced import control for ome item. Tariff were teeply increaed on ome item with rate over 100 percent in ome cae (Swamy, 1994). During the period , a number of intitutional and market oriented initiative were taken to re-orient the economy away from their import ubtitution trategy to export promotion. Thee included the export compenation cheme, Manufacturing Under Bond (MUB), import duty and VAT remiion cheme that were intended to improve export producer acce to imported input at world price. The export compenation cheme wa to compenate exporter for government taxe on input, while the manufacturing under bond programme wa meant to encourage manufacturing 77

98 for world market. Under the programme, which wa open to local and foreign invetor, input were imported duty free (Republic of Kenya, 1986). To attract foreign invetor into the export ector, an Export Proceing Act wa paed in 1996 providing for the development of the Export Proceing Zone Authority (EPZA). Thi led to the etablihment of the export proceing zone in Nairobi, Mombaa, Athi- River and Nakuru. Another cheme wa initiated in 1991 to promote export through duty and VAT exemption. The cheme alo introduced regulatory change deigned to make invetment in bonded factorie and export proceing zone more attractive. While traditional commodity product uch a food and beverage continued to dominate over the period, accounting for over 50 percent of the total export, there were ign of increaing diverification. The hare of the food and beverage product in total export had declined from 68 percent in 1986 to 52 percent by 1994 while that of fuel and lubricant had gone down by about twothird from 19 percent to 7 percent. Meanwhile the hare of indutrial upplie and conumer good categorie had, repectively rien from 15 percent to over 26 percent and 3.8 percent to 13.6 percent between 1984 and 1994 (Republic of Kenya, 1995). The main export detination for Kenyan export continued to be the European Union (EU) and Africa with both accounting for over 70 percent of the total export between 1985 and Little or no effort had been made to penetrate and expand new market uch a the Middle Eat and Eatern Europe which remained unimportant. It i however notable that by 1990, Africa wa emerging a an increaingly important market for Kenya, aborbing 44.6 percent of the country export in 1994 up from 26.1 percent in 1984 (Republic of Kenya, 1995). 78

99 Kenya indutrial ector generally remained inward looking throughout the 1980 and A number of factor contrained the country export growth: Firtly, the government not only wa low in implementing liberalization but alo did little to put in place effective export promotion policie. Inufficient exchange rate adjutment in the 1980 frutrated import liberalization while inefficient fical adjutment worked againt invetment. The end reult wa a peritent bia againt export depite the announced hift away from import ubtitution to an outward looking export trategy (Wignaraja and Ikiara, 1999). High tariff rate and burdenome adminitrative procedure contributed in dicouraging Kenyan exporter from vigorouly puruing export expanion programme a manufacturer found it more profitable to produce for the protected dometic market. Secondly, the government intitutional and adminitrative machinery continued to be biaed in favor of import ubtitution, leading to low and uneven implementation of export promotion policy reform. Depite the fact that policy maker clearly identified policy-related contraint to export growth, nothing much wa done to change the ituation. While many export promotion policie were regularly announced in variou government policy document, development plan and budget peeche, they were either not implemented at all or were implemented in uch a bureaucratic manner that their incentive value became eroded or eliminated all together. Thi wa partly due to the fact that ome of the policy announcement were largely a a repone to donor preure without genuine dometic ownerhip of the policie. Latly, both the public and private ector exhibited advere attitudinal tance that worked againt a ucceful puh to increae export of manufactured good. Depite government policy announcement in favor of export, exporter were often not given adequate upport by the government. Exporter frequently 79

100 experienced difficultie in obtaining foreign exchange to facilitate trade promotion trip and other activitie while refund of their export compenation claim were delayed. The private ector, for it part, wa often unwilling to take the tep neceary to raie their competitivene in international market (KAM, 1989). (c) The tructural adjutment phae ( ) The beginning of the 1990 marked the tart of weeping economic and political reform that included privatization of paratatal, liberalization of the financial and energy ector, price decontrol, and phaing out of import control. The main thrut of the adjutment programme wa to effect a hift from a highly protected dometic market to a more competitive environment that would facilitate increaed ue of local reource through outward oriented production policie that would promote employment creation and export expanion. However depite the official adoption of economic reform in the early 1980, the government did not eriouly implement the reform. In November 1991, the donor froze their quick diburing aid to Kenya a a reult of low pace in economic and political reform. Thi worened the country economic crii and balance of payment deficit. Thi wa to erve a a critical catalyt for radical economic and political reform oon after, and by the end of 1991, the government had introduced Foreign Exchange Certificate (Forex-C), which became an important ource of foreign exchange to the private ector. Thi marked an important firt tep in the liberalization of Kenya foreign exchange market (Univerity of Nairobi and Univerity of Gothenburg, 1994). The government continued with the reform and in the budget introduced a number of change relevant to the manufacturing ector, including further reduction of import dutie, retructuring of Value Added Tax (VAT), 80

101 introduction of an Eential Good Production Support Programme and increaed incentive for the Export Proceing Zone (EPZ) enterprie. A part of the policy to reduce government participation, there wa a need for privatization and retriction of government invetment to certain apect of infratructure and ocial ervice. Thi period of economic adjutment alo witneed increaed preure for reform in the political ytem from a ingle party regime to a more open, accountable and tranparent ytem for efficient management of public affair. The introduction of the weeping economic and political reform in early 1990 coincided with a particularly difficult period for the country economy. The period witneed a harp decline in major macro economic performance indicator. The GDP growth rate recorded a negative rate of -0.4 percent between 1991 and 1992, the lowet rate in pot independent period. The per capita GNP fell from US $ 350 in 1992 to US $ 270 in 1993 while the real annual growth rate of the manufacturing ector fell from 3.8 percent in 1991 to 1.8 percent in Inflation more than doubled to 46.8 percent from percent between 1991 and 1993 (Republic of Kenya, 1995). In general, the liberalization policie that tarted in 1980 had a number of weaknee. Firt, for a long time the country wa unable to attain the neceary peed, and the intenity of reform wa wanting. The reform were carried out rather gradually and without full ownerhip or commitment. The overall protection of the manufacturing ector continued to be high. During the firt two phae of liberalization, implementation moved lowly and intermittently, mainly due to little commitment on the part of policy maker and rampant rent eeking which wa rapidly becoming one of the eriou bottle neck in the country economic and ocial political development. 81

102 (d)the indutrial tranformation to the year 2020 ( ) The indutrialization trategy, a outlined in Seional Paper No. 2 of 1996, had the objective of achieving the tranformation of the Kenyan economy to a newly indutrialied country by the year The Eighth National Development Plan wa the firt five-year planned implementation programme baed on the longterm policy framework. It outlined pecific programme and policie to be purued over the firt 5 year ( ) of the 25-year indutrialization drive. The trategy emphaized elective encouragement of indutrie to produce for export and in the proce increae their employment potential. Two innovation, however, made the trategy different from pat trategie: indutry wa for the firt time taken to be the leading ector in economic development and pecific indutrie were for the firt time earmarked for government upport. The deciion to conider indutry a the leading ector in economic recovery wa baed on the perceived vulnerability of agriculture to many factor outide policy-maker control, which reduced it reliability a a ource of utained growth. Indutry, on the other hand, had hown remarkable reilience and had potential for providing high and dynamic growth. The concluion that followed from thi wa that to enure table and utainable economic growth, the propect lay more with the development of indutry and it wa therefore neceary to deign appropriate meaure that would enhance it development (Republic of Kenya, 1996). The indutrialization trategy outlined ome of the meaure to be implemented, to indutrialize over a two-tage period. In the firt phae, the government wa to electively encourage labour-intenive, reource-baed and light manufacturing indutrie, where the country enjoy comparative advantage. To be targeted in thi phae were primarily mall-cale indutrie that ue locally available raw material and imple labour-intenive technologie that were 82

103 capable of generating employment. Example were agro-baed indutrie uch a, textile, horticultural proceing, hide and leather, tea, coffee and ugar proceing, and building and contruction, uch a brick manufacturing. The growth rate of indutrial production during the firt phae wa expected to be between 8 to 10 percent per annum and the annual GDP growth rate wa expected to reach 6.8 percent by the end of the phae (Republic of Kenya, 1997). In the econd phae, policy wa to target intermediate and capital good indutrie that were more technology and capital intenive but that had to await the removal of infratructure, technological, human capital and aving contraint. Thee indutrie, which include metallurgical, non-petroleumbaed chemical, petro-chemical, pharmaceutical, and machinery and capital good indutrie were initially expected to produce for the dometic market with the export market being their eventual goal. The growth rate of indutrial production during thi phae wa expected to accelerate to between 12 to 15 percent per annum and the annual GDP growth rate to 10.6 percent by the end of the phae. If ucceful, thi trategy would have reulted in a diverified and dynamic indutrial bae by the year 2020 with a GDP per capita that i almot five time it 1996 level (Republic of Kenya, 1996). In 1999, output in the manufacturing ector in real term roe by a minimal 1.0 percent, a rate below the 1990 to 1999 average of 2.4 percent, and a growth target of 7.8 percent in the eighth National Development Plan. The low growth wa attributed to a number of factor prime among them being the general lowdown in the economy leading to depreed effective demand for manufactured product, high product price a a reult of high input cot, decline in invetment portfolio, power rationing and infratructure bottleneck. The quota allocation for Kenya garment to the USA and the fih export ban to 83

104 the European Union market curtailed growth in the textile and fih indutrie repectively. However, the hift by manufacturer from the traditional packaging material to platic material booted the platic product indutry and the activitie at the EPZ improved (Republic of Kenya, 2000). In 2000, real output growth in the manufacturing ector recorded a decline. The devatating effect of drought compounded the exiting tructural weaknee in the ector contributing to the poor performance. Metered power upply to the commercial and indutrial ector declined by 5.4 percent, leading to increaed ue of generator a an alternative ource of power. Conequently, there wa reduced plant capacity utilization leading to le output, lo of job and increae in product price. Growth in the manufacturing output declined by 1.5 percent in 2000 (Republic of Kenya, 2001). By midway of the firt phae of the indutrialization trategy (2001), the performance of the indutrial ector wa at it wort. The ector faced low capacity utilization, declining productivity and limited technological advancement. The manufacturing ector whoe contribution to GDP averaged 13 per cent declined from a growth rate of 3.7 per cent in 1996 to -1.5 per cent in 2000 ( Republic of Kenya, 2002). By 2002, economic growth had not only tagnated but the ground covered had been lot. For the firt time, bad governance in government and public ector among other, were cited a being the ource of the negative growth in the major ector of the economy, the manufacturing ector being one of the wort hit. It i againt thi background of overall economic decline and worening ituation of poverty and employment that a policy framework on recovery needed to be formulated. The government tated objective wa to create employment and eradicate poverty through accelerated economic growth baed on a utained indutrialization drive. Thi neceitated the introduction of the 84

105 economic recovery trategy for wealth and employment creation (ERS) (Republic of Kenya, 2003b). (e) Economic recovery trategy ( ) Kenya began to lay a olid foundation upon which to tart the journey of building a globally competitive and properou economy in A a repone to pat economic and ocial challenge, Kenya implemented bold economic and tructural reform a elaborated in the Economic Recovery Strategy. According to thi trategy, intervention in the manufacturing ector were to be built around an indutrial mater plan which lay the ground work for the firt phae of Kenya Indutrialization Strategy and retore the ector to a rapid growth path. The ERS wa anchored on three key pillar, namely; retoration of economic growth within the context of a table macroeconomic environment, enhanced equity and poverty reduction, and improvement of governance to enhance efficiency and effectivene in the economy. Thee three pillar were carefully choen to pull the economy out of a receion and to commence the journey toward a broad-baed equitable economic recovery underpinned by improved efficiency in public ervice delivery (Republic of Kenya, 2003b). The manufacturing ector had been een a the major ource of economic growth, and wa projected to grow during the recovery period at an annual average rate of 8.6 per cent compared to the meagre 1.2 per cent in Conequently the hare of the manufacturing ector in GDP wa expected to rie from 13 per cent in 2002 to 15.7 per cent in 2007, driven mainly by higher capacity utilization and reduced cot of production (Republic of Kenya, 2002b). 85

106 Indeed, the period after 2002 repreented the bet phae of utained economic growth in all ector of the economy notably manufacturing, agriculture, tourim, trade and telecommunication a well a the ocial ector. By 2007, the growth in the manufacturing ector fell hort of the targeted growth of 8.6 percent. It contribution to GDP remained around 10 percent and the 8.6 percent growth wa not achieved. However, the recovery trategy aw a remarkable growth in the ector from 1.2 percent in 2002 to cloe to 7 percent in 2007 (Republic of Kenya, 2007). The economic recovery trategy came at a time that Kenya experienced a political regime change from a perceived corrupt and inenitive regime to a renewed hope and high expectation in the new regime. Good governance and public ector reform took a center tage in the trategie that were uppoed to provide an enabling environment for growth in all ector of the economy. Furthermore, the trategy only covered the five year political term of the new regime. Thi meant that little attention wa given to the individual ector of the economy but were expected to reap the benefit of the reform that were undertaken by the new regime. Indeed, the period of the economic recovery trategy aw utained economic growth in all ector of the economy notably the manufacturing ector. However, the growth of the manufacturing ector wa till driven largely by increae in input and volume of output rather than by improvement in efficiency and productivity (KAM, 2006). If the ector i to play the critical role of propelling the economy growth, long-term trategie that are pecifically directed to the manufacturing ector among other productive ector are neceary. Beide the quantitative growth in the economy and the productive ector uch a manufacturing, improving efficiency and increaing total factor productivity (TFP) are alo critical to achieving growth target et in the viion 86

107 2030. The unveiling of Kenya Viion 2030 marked an important miletone in the country development coming after the perceived ucceful implementation of the ERS. (f) The Viion 2030 ( ) The Viion 2030 enviage a 10 per cent growth in the manufacturing ector that will conequently propel the economy expected growth rate of above 10 per cent and upport the country ocial development agenda through the creation of job, the generation of foreign exchange and attracting foreign direct invetment. To meet thee goal, the manufacturing ector ha to become more efficient and raie productivity per unit of input (epecially of labour and capital) cloer to thoe of Kenya external competitor (Republic of Kenya, 2008). Viion 2030 enviage that pecial economic cluter and mall- and mediumenterprie park will erve a eed bed of Kenya indutrial take-off. In the long run, the nation i expected to kip the moke tack aociated with rapid indutrialiation and move up the value chain once the more baic indutrial infratructure ha been developed. The manufacturing ector will play a vital role in booting growth in agriculture by timulating agro-proceing activitie. The barrier that have hampered the expanion and moderniation of thi ector will be addreed to make the manufacturing indutry more competitive both at the regional and global level. Thee include continued decline in invetment and overall lack of competitivene that have made it difficult for the ector to play a larger role in the economy. A a reult, many manufacturing companie in Kenya have truggled to thrive and ome key player have moved their operation to other countrie. The Viion 2030 identifie four factor that have contributed to the lack of competitivene in the ector. Thee are high input 87

108 cot, low productivity level, inefficient flow of good and ervice, and unfavourable buine environment (Republic of Kenya, 2008). The viion for the manufacturing ector i the development of robut, diverified and competitive manufacturing. The overall goal for the ector over the next five year will be to increae it contribution to GDP by at leat 10 per cent per annum. To realie thi growth rate, pecific goal will be purued. Thee are; trengthening local production capacity to increae dometicallymanufactured good by focuing on improving the ector productivity, raiing the hare of Kenyan product in the regional market from 7 to 15 percent, and developing niche product through which Kenya can achieve a global competitive advantage (Republic of Kenya, 2008). Kenya potential competitive advantage in manufacturing lie in agroindutrial export. To compete globally in thi ector, the country will have to increae the capacity of value addition in agro-baed indutrie. Thi ha to be done by attracting trategic invetor to boot agro-baed indutrie and increaed export, epecially in new market. The invetor will be offered attractive incentive and will be expected to bring new kill and technologie to the dometic economy. In addition, five cro-cutting trategie will be critical for uperior performance of the manufacturing ector a a whole, including trengthening Small and Medium Enterprie (SME) to become the key indutrie of tomorrow by improving their productivity and innovation, booting cience, technology and innovation in the ector by increaing invetment in reearch and development (R&D), improving critical infratructure, uch a port, energy ditribution ytem, rail and major highway; improving the buine environment in critical area, uch a licening and ecurity, and implementing efficiency-enhancing intitutional reform in the ector (Republic of Kenya, 2008). 88

109 The major challenge of the viion 2030 will be in it implementation. The tate of infratructure in Kenya poe the greatet obtacle to a globally competitive manufacturing ector. Although coniderable tep have been made in improving infratructure ince 2003, the importance of rehabilitating, building or expanding infratructure will till remain a priority. Other prioritie include technology and innovation. Competitivene in the global market require dynamim and flexibility in approach. New technologie, product and market will keep emerging and beide Kenya traditional market, the viion hould take tock of the unprecedented development that i in the competing world. Kenya economic ector hould trike out in the direction that may not be fully predicted preently, and uch i the eence of globally competitive market. Thi will call for a pragmatic approach to development, contant monitoring of both internal and external development and a political will, to make change rapidly o that the economy doe not loe ground. In order to develop a robut, diverified and competitive manufacturing ector, a favorable buine environment hould be created. Unfavorable buine environment arie from heavy regulation, weak trade agreement, lack of rigorou legal enforcement, incidence of inecurity, a well a limited acce to capital. In addition, heavy regulation that lead to complex and overlapping buine and invetment regitration affect both the eae and the cot of doing buine in the ector. Weak negotiating capability may impede the country ability to negotiate for favorable trade agreement and therefore create barrier againt Kenyan firm. Weak enforcement of tandard and tax law will lead to importation of ub tandard and/or counterfeit good into the dometic market making it hard for local manufacturer to compete. Thee and other challenge 89

110 may tand in the way of ucceful achievement of the viion To deliver on the ambitiou proce of national tranformation, it will require a fundamental hift from buine a uual to a new management philoophy. 4.3 The tructure and compoition of the Kenyan manufacturing ector A ditinctive feature of the manufacturing ector in Kenya i the co-exitence of the modern ector alongide a rapidly expanding informal ector. While the former comprie mainly of mall, medium and large enterprie, the latter conit of numerou open air mall and micro cale productive activitie in town and rural trading center. Traditional artian production in the informal ector i dominated by mall undertaking employing le than 10 worker. Thee are in mot cae unregitered and ue production method which require limited pecialization and management capacity. A large proportion of their output i directed toward atifying baic need namely, the proviion of low income conumer good and ervice. While data on thi ub-ector are not adequate, there i little doubt that it i one of the fatet growing ector and a major ource of employment in the country (KAM, 2006). The mall and medium cale enterprie, which form part of the formal economy, are characterized by ome degree of pecialization. Thee enterprie manufacture a wide range of item including food product, wood and furniture, metal product, gla and pottery, clothing and leather product, among other. Thee item are generally deigned to meet the dometic need of the low income houehold although part of thee i exported to neighbouring countrie, epecially Uganda and Rwanda (KAM 2006). Thi ub-ector employ about 20 percent of the country labour force. Figure 4.1 how the employment pattern between the formal private and public manufacturing ector and the informal manufacturing ector 90

111 Figure 4.1: Percentage of employee in the manufacturing ector 100% 80% 60% 40% 20% informal ector public ector private ector 0% Source of data: Republic of Kenya (2006) Economic urvey, Nairobi: Government Printer From figure 4.1, the role of the informal ector manufacturing cannot be over emphaized. It repreent over 80 percent of employment in the manufacturing ector and approximately 20 percent of total employment in all the year (KAM, 2006) With regard to ownerhip and management of firm in Kenya manufacturing indutry, there have been ome ignificant change in the year after independence. Multinational and paratatal dominate the large indutrie while Kenya buine people, mainly of Aian origin dominate the mall and medium one. Kenyan of African origin own mainly micro enterprie in the informal ector. Out of over 2300 regitered manufacturing unit, only cloe to 2000 are active including branche in the country. About 48 percent of thee enterprie are privately owned companie by Kenyan citizen, 46 percent are privately owned partnerhip between Kenyan and non-kenyan, about 2 percent between Kenyan, non-kenyan, and Government through paratatal, 91

112 while 4 percent are foreign owned (KAM, 2006). Figure 4.2 how a claification of thi ownerhip trend. Figure 4.2: Ownerhip of Kenyan Manufacturing Enterprie (2006) 100% foreign owned Partnerhip between Kenyan, nonkenyan and paratatal Private ownerhip between Kenyan and non-kenyan citizen Private ownerhip by Kenyan Citizen 0% 10% 20% 30% 40% 50% 60% Source: Regitrar of Companie, Minitry of Tourim and Indutry, Government of Kenya The tructure of Kenya manufacturing ector ha undergone minimal change depite hift in policie. Production i largely geared toward conumer good, the mot important ector being food proceing. The food proceing enterprie form the larget component of manufacturing ector enterprie with the larget turnover, employment contribution and export earning. Textile enterprie alo form a ubtantial hare in term of number and employment contribution in the manufacturing ector. Metal and allied ub-ector i a ignificant contributor in manufacturing ector epecially in the Jua kali enterprie which form the larget component of the informal manufacturing ector. Detail of the ub-ector contribution are preented in table A2 in appendix I. The manufacturing ector employ 254,000 people, which repreent 13 per cent of total employment. An additional 1.4 million people are employed in the informal ide of the indutry. The ector i highly fragmented with more than 92

113 2,000 manufacturing unit. The ector i divided into everal broad ub-ector (KAM, 2006). Figure 4.3 how each of the ub-ector hare contribution to three main component of the Kenyan economy namely, GDP, export and employment. Figure 4.3: Manufacturing ub-ector contribution to GDP, export and employment in 2006 (percentage) Other Furniture Publihing & printing Rubber & platic Fabricated metal Equipment Chemical Foret product Textile & leather Refined petrolium Food proceing employmnet export GDP Source of data: Republic of Kenya (2007) Statitical abtract, Nairobi: Government printer Figure 4.3 how that the bottom three manufacturing ub-ector namely, Food proceing, Textile and Leather, and petroleum product accounted for 50 percent of the manufacturing GDP, 50 percent of manufacturing export, while food proceing and textile accounted for 60 per cent of formal employment in the manufacturing ector. Nearly 50 per cent of manufacturing firm in Kenya employed 50 worker or le. Mot manufacturing firm were family-owned and operated. In addition, the bulk of Kenya manufactured good (95 per cent) were baic product uch a food, beverage, building material and baic chemical. Only 5 per cent of manufactured item, uch a pharmaceutical, were in kill-intenive activitie. 93

114 4.4 Total Factor Productivity change in the manufacturing ector Introduction Thi ection preent the Malmquit index core of 119 firm drawn from the food, textile, wood and furniture, metal, chemical, contruction material and paper, printing and publihing ub-ector of the manufacturing ector. The overall manufacturing ector reult are firt preented where each ub-ector performance i compared againt the manufacturing ector frontier during the period of tudy. Reult for each of the ub-ector are then preented and dicued. In both cae, the focu i on the change in the total factor productivity over the tudy period and the ource of the change TFP change, efficiency change and technical change in the manufacturing ector. The tudy period coincide with the time when the country wa regaining from the effect of liberalization of the trade regime in the early Beide, the introduction of power rationing in 2000 due to the drought caued devatating effect, compounding the exiting tructural weaknee in the ector. A combination of thee factor aw a decline in the real output in the manufacturing ector by 1.5 percent (Republic of Kenya, 2001). The reduced plant capacity utilization that led to reduced output, lo of job and increaed product price wa lightly revered by the favorable weather condition in Thi led to improved upply of raw material epecially to the agro-baed indutrie while the lifting of power rationing enured a table upply of power to manufacturer leading to increaed plant capacity utilization. Real output in the ector continued to expand in 2002 by 1.2 percent, attributed to the table macroeconomic environment, reduction of import duty to zero rate for the majority of indutrial intermediate input, government intervention in promoting export opportunitie for manufactured good, among other 94

115 (Republic of Kenya, 2003). The TFP change, efficiency change and the technical change reult preented here, therefore ought to cloely examine the manufacturing ector with a view to empirically explain where productivity gain were recorded in the ector and whether any tructural change wa recorded in term of technical progre (innovation) and/or improved efficiency (catch-up) during the tudy period. The tudy recognized the fact that the ub-ector were not homogeneou either among themelve or even acro the firm in each ub-ector. The productivity core therefore need to be interpreted with care. From the reult, the tudy make an attempt to compare the ub-ector performance with the overall manufacturing ector bet practice. Table 4.1 preent the ummary of the annual mean of Efficiency Change, Technical Change and TFP Change over the tudy period for the 119 firm. Table 4.1: Malmquit index ummary of annual mean Year Efficiency Technical TFP change change change Mean Source: DEAP output, The mean TFP change of implie that there wa a fall in total factor productivity of about 8.3 percent over the period 2000 to Thi wa depite the fact that technical progre wa recorded of about 11.5 percent whoe benefit were all eroded by a decline in efficiency by about 17.8 percent over the period. Thee reult eem to concur with the general performance of the 95

116 manufacturing ector (Republic of Kenya, 2001), during the period when any technical progre would not have yielded the much expected benefit epecially due to the drought that hit the country leading to power rationing. Energy i a major input in production in the ector, and lack of it reduced plant capacity utilization leading to decreaed output. The reult uggeted that the ector efficiency declined by 32.1 percent between 2000 and 2001 and by 0.5 percent between 2001 and Technical progre wa recorded of about 71.9 percent between 2000 and 2001 which wa completely revered between 2001 and 2002 to technical regre of about 27.6 percent. Between 2000 and 2001, the technical progre offet the effect of decline efficiency to record a TFP growth of about 16.8 percent. Thee gain were however revered by a declining efficiency and technical regre between 2001 and 2002 with the ector uffering a fall in TFP of about 28 percent. Thee reult were conitent with the tudy by Gerdin (1997) that revealed negative TFP growth in Kenya between 1964 and Even though covering different period, the current tudy eem to confirm that the ituation did not eem to have changed there after. A naphot look at table A5 in the appendix II ugget that majority of the firm under review recorded technical progre but a decline in efficiency. Very few individual firm recorded both technical progre and improved efficiency. Generally, mot of the firm that recorded growth in TFP progreed in technology but had a problem with efficiency. The power rationing problem might have had far reaching effect on the manufacturing ector a a whole. Table 4.2 i a ummary of the mean TFP change, efficiency change and technical change per ub-ector in the manufacturing ector. Thee figure are computed a geometric mean from the whole manufacturing ector DEA reult in table A5 in appendix II. 96

117 Table 4.2: Summary of malmquit Indice per ub-ector Sub-Sector Efficiency change Technical change TFP Change Food Textile Wood and Furniture Platic Metal Chemical and pharmaceutical Contruction material Paper, printing and publihing Mean NB. Thee are own computation from table A5 in the appendix II. From thee reult, all the ub-ector apart from textile, howed evidence of innovation through recorded technical progre. Improved efficiency wa only recorded in the textile which achieved an efficiency improvement of 15.3 percent but a technical regre of about 1.6 percent leading to a TFP growth of about 12.6 percent in the textile ub-ector. Thee reult eemed to confirm the tudy by Ngui (2008), which indicated efficiency improvement in the textile ub-ector. Soderbom (2004) alo howed that depite low level of productivity in textile ub-ector, there were ign of recovery in the ubector efficiency. Thee reult were expected ince during the period mot textile enterprie became export oriented. Wood and furniture ub-ector 97

118 recorded a modet growth in TFP of about 4 percent reulting from a 7.5 percent technical progre but a light fall in efficiency of about 3.2 percent. In the overall, there wa no evidence of catch-up in the ector ince efficiency wa on the decline. Majority of the firm in all the ub-ector recorded efficiency decline. Only wood and furniture and textile eemed to have a ubtantial number of firm that howed ign of catch-up over the period. Thi i preented in figure 4.4 that follow: Figure 4.4: Efficiency change core for manufacturing ub-ector Efficiency change Source: DEAP Output Firm Agro Textile Wood&Furniture Platic Metal Chemical Contruction PPP In all ub-ector, mot of the firm howed evidence of innovation with majority recording an index above one, except the textile that eemed to have had a ubtantial number of firm that regreed technologically a depicted in figure 4.5 that follow: Figure 4.5: Technical change core for manufacturing ub-ector 98

119 Technical change Source: DEAP Output Firm Agro Textile Wood&furniture Platic Metal Chemical Contruction PPP The effect of innovation among the firm however, could not be utained for mot of the firm due to lack of catch-up, with mot of the firm in all the ubector recording a drop in the total factor productivity. Thi i evident from figure 4.6: Figure 4.6: TFP change core for manufacturing ub-ector TFP change Source: DEAP Output Firm Agro Textile Wood&Furniture Platic Metal Chemical Contruction PPP From figure 4.6, mot of the firm in all ub-ector had an index below one, with only few firm in textile and wood and furniture recording an index above one. A cautioned earlier in thi ection, thee reult may not form a olid foundation for policy recommendation in the overall ector unle the ub-ector level reult point to the ame direction. Rather than dicu the ub-ector 99

120 reult a ummarized from the overall manufacturing ector reult in table 4.2 only, dicuion on the individual ub-ector reult wa preferred. Thi i done in the next ection that alo compare the two et of reult. Thee individual ub-ector reult how a higher degree of homogeneity of individual firm in term of the product and therefore form a trong bai for policy recommendation TFP change, efficiency change and technical change in the individual ub-ector After preenting the overall manufacturing ector reult, thi ection focue on the individual ub-ector where firm in one ub-ector are compared againt the bet practice in the ub-ector. (a) Food proceing ub-ector Thi wa the mot predominant ub-ector in the manufacturing ector during the tudy period. Food proceing contributed 18 percent to the number of manufacturing ector enterprie in Kenya, 70 percent of the manufacturing output and 23 percent of the manufacturing GDP in 2003 (KAM, 2006). The ub-ector alo accounted for 26 percent of the total formal employment in the country. The Malmquit core for 31 firm included in the tudy from the ubector are preented in table

121 Table 4.3: Malmquit index ummary of annual mean for food ub-ector. Year Efficiency change Technical change TFP change Mean Source: DEAP output, A mean TFP change of implie that productivity dropped by 16.3 percent during the period of the tudy. Thi drop in productivity wa due to both a fall in efficiency and technical regre of about 9.2 and 7.8 percent, repectively. There wa however productivity growth recorded between 2000 and 2001 of about 16.7 percent ourced from a 9.4 and 6.6 percent efficiency improvement and technical progre, repectively. The gain were however revered between 2001 and 2002 when efficiency dropped by 24.6 percent with a technical regre of 20.3 percent, leading to a productivity drop of about 40 percent. A cloer look at the individual firm elected in thi ub-ector (ee table A6 in the appendix II) uggeted that over 50 percent of the firm experienced an efficiency improvement with only 0.3 percent (one firm) experiencing ome technical progre of about 6.3 percent even though the ame firm experienced a fall in efficiency. The ub-ector wa a target of the indutrialization trategy purued in the lat decade where Kenya ha had comparative advantage. By it nature, the ub-ector enterprie are labour intenive and would achieve efficiency without overly advanced technology (Republic of Kenya, 1996). The above reult are conitent with Ngui (2008), where the food ub-ector remained largely inefficient. 101

122 (b) Textile and garment ub-ector The textile ub-ector wa the fourth larget ub-ector of manufacturing, contributing 11 percent to the number of manufacturing enterprie. During the tudy period, the ub-ector employed the econd highet number of the labour force after the food ub-ector, i.e 17.5 percent of total formal employment in the manufacturing ector by Thi repreented about 2.5 percent of the total economy employment (KAM, 2006). During the ame year, the ubector contributed about 2.9 percent of government revenue collected from cutom dutie. In term of export earning, the ector contributed about 1.3 percent of the export earning (KAM, 2006). The Malmquit core for 19 firm tudied are preented in table 4.4. Table 4.4: Malmquit Index ummary of annual mean for textile and garment ub-ector Year Efficiency change Technical change TFP change Mean Source: DEAP output, During the period under review, the ub-ector TFP on average declined by about 11.3 percent which reulted from technical regre of about 20.6 percent depite the improved efficiency which averaged about 11.8 percent over the period. Between 2000 and 2001, there were ign of catch-up after the TFP grew by 5.2 percent reulting from an efficiency growth of about 10.2 percent even though the ub-ector did not how evidence of innovation. Thi growth in the TFP could however not be utained between 2001 and 2002 depite a 102

123 further improvement in efficiency by about 13.4 percent. A 34 percent regre in technology eroded all the gain from the efficiency improvement. Thee reult eem to confirm the tudy by Soderbom (2004) who found little productivity in the textile ub-ector even though there were ome ign of recovery. (c) Wood and furniture ub-ector The wood product and furniture ub-ector contituted about 7 percent of the manufacturing enterprie in Kenya. The ector regitered a product turnover of 1.3 billion in 2004, accounting for 0.4 percent of production turn over in the manufacturing ector. The ub-ector contributed about 3 percent of the manufacturing GDP and 0.3 percent to the economy GDP. It alo contributed 5.7 percent of all job in the manufacturing ector while accounting for about 0.8 percent of export earning from the ector (KAM, 2006). The Malmquit core in term of TFP change and the ource of the change are preented in table 4.5. Table 4.5: Malmquit index ummary of annual mean for wood and furniture ub-ector Year Efficiency change Technical change TFP change Mean Source: DEAP output,

124 There were 8 firm out of the 119 firm in the tudy. On average, the ub-ector uffered a decline in TFP of about 8 percent over the tudy period. Thi wa a a reult of technical regre of about 13.2 percent even though the ub-ector recorded improved efficiency of about 6 percent over the period. The exaggerated TFP growth in the year two (2001) under review wa largely due to a large efficiency change core. The number of firm in the ub-ector wa quite mall and with uch few obervation, many firm would appear on the DEA frontier (Coelli et al, 2005). Thi i one of the weaknee aociated with DEA etimation and thi tudy take note and caution that the large efficiency growth and hence the TFP change could not have been poible. Soderbom (2004) found the ub-ector to be one of the leat productive which the current tudy eem to confirm. (d) Paper, printing and publihing ub-ector Thi ub-ector accounted for about 7 percent of manufacturing contribution to GDP in 2000 but thi declined to about 6 percent by 2003 (KAM, 2006). Over the ame period, it contribution toward the GDP fell to about 0.7 percent by 2000 and to about 0.6 percent by The Malmquit index core for 11 firm in the tudy are preented in table 4.6. Table 4.6: Malmquit index ummary of annual mean for paper, printing and publihing ub-ector Year Efficiency Technical TFP change change change Mean Source: DEAP output,

125 Thee reult confirm a urvey by (KAM 2006) that the ub-ector contribution to the larger manufacturing and the economy wa on the decline. The TFP fell by about 9.5 percent on the average between 2000 and Declining efficiency wa a major undoing for thi ub-ector over the entire period under review. On the average, efficiency declined by about 9.6 percent with the ub-ector recording a mere 0.1 percent technical progre. (e) Platic ub-ector The platic and rubber ub-ector regitered a contribution of 5.6 percent to the manufacturing ector in Kenya. The ub-ector employment accounted for 3.5 percent of all job in the manufacturing ector. In 2004, the ub-ector export tood at only 1.9 percent of the export earning from manufacturing. The ubector wa a net exporter where 89 percent of it raw material were imported (KAM, 2006). The Malmquit index core for the ub-ector are preented in table 4.7. Table 4.7: Malmquit index ummary of annual mean for platic ubector Year Efficiency change Technical change TFP Change Mean Source: DEAP output. From the reult, thi wa probably one of the ub-ector that uffered major decline in TFP. The decline wa about 18.9 percent. On the average, efficiency 105

126 in the ub-ector declined by about 10.9 percent while technical regre tood at about 9 percent. Depite ome evidence of catch-up between 2000 and 2001 of about 8.8 percent, TFP till declined by about 1.3 percent due to lack of innovation. A technical regre of about 9.3 percent during the period wa recorded. The period between 2001 and 2002 wa even wore with the ubector TFP declining further by 33.4 percent reulting from efficiency decline and technical regre of about 27 and 8.7 percent, repectively. The poor performance uggeted by thee reult eemed to contradict figure from government publication that uggeted a growth in output in the ubector (ee Republic of Kenya, 2003). However, thi tudy focued on total factor productivity rather than growth in total output which could till happen due to increaed ue of input and not necearily growth in TFP. (f) Metal ub-ector The ub-ector ha over the year contributed, on average, 13 percent of the manufacturing ector GDP. In addition, apart from it contribution to total employment within the formal manufacturing which tood at 4.9 percent in 2003, the ub-ector upported a further etimated 128,000 Jua Kali artian with their raw material requirement. It contributed 5.3 percent of the country export earning in 2004 and 5.6 percent to government revenue collected from import dutie in 2002 (KAM, 2006). The ub-ector production turnover declined from a contribution of 3.4 percent of total manufacturing production in 2002 to 3.2 percent in The Malmquit index core for 22 firm in the ub-ector are preented in table

127 Table 4.8: Malmquit index ummary of annual mean for metal ubector Year Efficiency change Technical change TFP change Mean Source: DEAP output, From thee reult, the ub-ector wa perhap the only ector that recorded a technical progre of about 12.8 percent on the average during the tudy period. However, the trend in term of TFP change wa not different from all other ubector in the manufacturing ector. The ub-ector uffered a TFP decline of about 5 percent on the average over the tudy period. Thi decline reulted from a decline in efficiency of about 15.8 percent. During the period between 2000 and 2001, the ub-ector recorded a poitive growth in TFP of about 17.2 percent that could be attributed to a 76.7 percent technical progre. Thi trend immediately revered between 2001 and 2002 when the ub-ector uffered technical regre of about 28 percent depite an improvement in efficiency of about 7 percent. TFP therefore declined by about 22.9 percent. Thee reult agree with Ngui (2008), who found the ub-ector to have uffered a drop in efficiency. (g) Contruction material ub-ector The ub-ector contributed 11.6 percent of the country total export earning from tangible export in 2000 and 14.5 percent in 2002, but declined to 4.9 percent in It contributed 14 percent to manufacturing GDP in 2001 and 12 percent in Thi wa a minimal 1.0 percent of the economy GDP during 107

128 that period (KAM, 2006). The Malmquit index core for the ub-ector 7 firm are preented in table 4.9. Table 4.9: Malmquit index ummary of annual mean for contruction material ub-ector Year Efficiency change Technical change TFP change Mean Source: DEAP output, From table 4.9, the ector recorded growth in technology that averaged 11.8 percent over the tudy period. However, on average the firm declined in efficiency by about 16.3 percent and conequently a negative growth in TFP of about 6.5 percent. (h) Chemical and pharmaceutical ub-ector Thee were two eparate ub-ector of the manufacturing ector but the tudy treated them a highly imilar in term of product and the procee. Furthermore, only a total of 10 firm from both ub-ector had complete data and hence were included in the tudy. While the chemical and allied ub-ector turnover roe from 4.7 percent of manufacturing ector turnover in 2002 to 6 percent in 2004, the pharmaceutical ub-ector production turnover declined by about 4.4 percent during the ame period. The pharmaceutical contribution to formal manufacturing ector 108

129 employment wa 1.3 percent between 2000 and 2002, and 1.2 percent in It overall contribution to total formal employment in the country tood at 0.2 percent. Employment in the chemical and allied ub-ector tood at 5 percent of all employment in the manufacturing ector (KAM, 2006). The Malmquit index core for the 10 firm are preented in table Table 4.10: Malmquit index ummary of annual mean for chemical and Pharmaceutical ub-ector Year Efficiency change Technical change TFP change Mean Source: DEAP output, The reult indicate that on average, the combined ub-ector recorded a growth in TFP of about 7.9 percent over the tudy period. Thi poitive growth reulted from technical progre of about 35.6 percent that offet the 20.4 percent decline in efficiency a uggeted by the core. 4.5 Source of Total Factor Productivity change The fourth objective of the tudy wa to etablih and explain the ource of TFP change. The total factor productivity change were to be decompoed into two main ource, technical change and efficiency change. Thi ection examine the trend in the TFP change and eek to identify whether the change were ourced from efficiency change (catch-up) or technical change (innovation). The reult are preented on ub-ector bai. 109

130 (a) Food proceing ub-ector The lack of innovativene and the evidence of catch-up among the firm in thi ub-ector are hown figure 4.7. Figure 4.7: Efficiency, Technical and TFP change core for the food ubector Effch, Techch, TFPch Source: DEAP Output Firm Efficiency change Technical change TFP change From the figure above, negative total factor productivity change were quite evident, where majority of the firm recorded a TFP change index below one. Thee TFP change were conitent with efficiency change. The reult uggeted that TFP growth in thi ector would be ourced from efficiency change. From the pattern, technical change did not eem to contribute to the TFP change. Thi obervation may probably explain the government reaoning that the food ub-ector if targeted could eaily achieve productivity growth from efficiency change without necearily overly advanced technology (Republic of Kenya, 1996). Ngui (2008) found that the food ub-ector wa largely inefficient, uggeting the negative influence thi had on TFP change depicted in figure 4.7. (b)textile and garment ub-ector Figure 4.8 below how the pattern of the TFP, Efficiency and Technical change in the textile and garment ub-ector. 110

131 Figure 4.8: Efficiency, Technical and TFP change core for textile ub-ector Effch,echch, TFPch Source: DEAP Output Firm Efficiency change Technical change TFP change From the figure, there wa evidence of a very trong poitive correlation between efficiency change and the TFP change uggeting that efficiency wa the ource of TFP change in the textile ub-ector. During the tudy period, the textile ub-ector wa jut recovering from the effect of market liberalization in the early 1990 which wa coupled with market retriction abroad leading to many textile enterprie cloing down. The poitive efficiency change in the ub-ector wa not urpriing given that more firm became export oriented compared to other ub-ector. The export promotion hypothei potulate that exporting firm are more efficient than non exporting one (Bigten et al, 2004). Hence, efficient firm elf elect into exporting and the competitive preure pur them to raie their performance. From figure 4.8, it i evident that majority of the firm in the ub-ector recorded efficiency growth (catch-up) but largely lacked in innovation leading to negative growth in TFP. It wa therefore evident from the pattern that TFP change in the ub-ector were driven mainly by efficiency change. Innovation in the ub-ector did not how evidence of contribution to the TFP change over the tudy period. Thee reult 111

132 agree with Ngui (2008) and Soderbom (2004) where both reported efficiency growth in the textile ub-ector. (c)wood and furniture ub-ector The wood and furniture ub-ector had the leat number of firm in the analyi. The efficiency, technical and TFP core in the ub-ector are how in figure 4.9: Figure 4.9: Efficiency, Technical and TFP change core for wood and furniture ubector Effch, Techch, TFPch Efficiency change Technical change TFP change Source: DEAP Output Firm Figure 4.9 how that mot of the firm under tudy appear to have been efficient, which i mot unlikely. The few firm in the ub-ector could have caued all firm to appear on the frontier. That fact not withtanding, the TFP change in the ub-ector eem quite conitent with both efficiency and technical change. It i evident that firm that uffered technical regre alo declined in TFP. Efficiency change eem to have had moderate impact on TFP change but only for ome firm. According to KAM (2006), the ub-ector faced challenge with regard to timber availability ince 1999, after the government introduced a ban on logging, unlicened ferrying and export of timber. The aim wa to protect the 112

133 limited foret reource. The logging ban forced a hift to plantation-grown oftwood which i generally of poor quality. (d) Paper, printing and publihing ub-ector Thi i one of the ector that howed no ign of either catch-up or innovation. The efficiency, technical and TFP core for the firm in the ub-ector are hown in figure 4.10: Effch, Techch, TFPch Figure 4.10: Efficiency, Technical and TFP change core for the paper, printing and publihing ub-ector Source: DEAP Output Firm Efficiency change Technical change TFP change From figure 4.10, it i evident that the technical, efficiency and TFP core are all below one. Even though a little evidence of innovation i evident, change in TFP were driven by efficiency change. There i a clear pattern that the firm that improved on efficiency recorded growth in TFP while thoe that declined in efficiency alo declined in TFP. According to Republic of Kenya (2002), real output in the ub-ector grew in 2001 due to available local and regional market for paper product even though printing and publihing declined. Thi i conitent with the index core in table 4.6, which how ome TFP growth of about 9.9 percent between 2000 and 2001, but mainly driven by technical progre of about 14.3 percent. Efficiency declined by about 3.9 percent during the tudy. 113

134 (e) Platic ub-ector Figure 4.11 how the efficiency, technical and TFP core for the 11 firm in the platic ub-ector. Effch, Techch, TFPch Figure 4.11: Efficiency, Technical and TFP change core for platic ub-ector Source: DEAP Output Firm Efficiency change Technical change TFP change The poor performance in productivity i evident where the efficiency change and technical change core for mot of the firm were below one implying no catch-up and lack of innovation leading to negative TFP growth in the ubector. The pattern depicted in figure 4.11 ugget ome neutrality in term of the driver to the TFP change. Even though the TFP change eem more conitent with efficiency change among the firm, there i ome evidence of neutrality where firm which improved in efficiency but regreed technologically did not experience TFP change. The ub-ector faced a major challenge from environment regulating bodie uch a NEMA and the Minitry of Environment which came up with threat to ban the ue of platic packaging in the country, due to problem of dipoal and recycling. The increaing trend in the ue of platic packaging ha reulted in an increae in wate generation, and ince the product are non-bio-degradable, thi ha preented a challenge regarding their dipoal and impact on the environment. Thi i an area that require the development of an efficient 114

135 national wate dipoal policy which encourage communitie to adopt a more reponive wate dipoal approach (KAM, 2006). (f) Metal ub-ector The figure 4.12 confirm the reult in ection 4.3(f) where the ector howed evidence of innovation but no catch-up over the tudy period. Figure 4.12: Efficiency, Technical and TFP change core for Metal ub-ector Effch, Techch, TFPch Source: DEAP Output Firm Efficiency change Technical change TFP change The reult how that almot all firm uffered efficiency decline, o much o that the effort to innovate did not revere the ituation leading to negative TFP growth during the period. The pattern therefore ugget that TFP change in the ub-ector reulted mainly from efficiency change. There i evidence from figure 4.12 that even where firm progreed technologically, declining efficiency largely drove the TFP change to record negative growth. The above reult were expected given the forward linkage the ub-ector ha had to the building and contruction indutry, which had been declining ince Thi wa coupled with a weak demand in the packaging indutry. Over the year, the metal ub-ector had the packaging and building and contruction ub-ector a the traditional conumer of it product. On account of manufacturer preference for cheaper platic packaging material and the 115

136 lowdown in the building and contruction ector, the metal ub-ector ha uffered immenely (Republic of Kenya, 2003). (g) Contruction material ub-ector Figure 4.13 how the efficiency, technical and TFP core in the contruction material ub-ector. Figure 4.13: Efficiency, Technical and TFP change core for contruction material ub-ector Effch, Techch, TFPch Source: DEAP Output Firm Efficiency change Technical change TFP change From the figure, there wa evidence of innovation but the TFP change were highly conitent with efficiency change with perhap technical progre having little or no effect on TFP. Thee reult are not urpriing and confirm the ituation in the metal ubector. The declining productivity in contruction material ub-ector led to negative growth in the metal ub-ector which ha trong forward linkage to the contruction material ub-ector. 116

137 (h) Chemical and pharmaceutical ub-ector The efficiency, technical and TFP change core for thi ub-ector are ploted in figure 4.14: Figure 4.14: Efficiency, Technical and TFP change core for chemical and pharmaceutical ub-ector Effch, Techch, TFPch Source: DEAP Output Firm Efficiency change Technical change TFP change It i quite evident that the TFP change are highly conitent with efficiency change depite the evidence of technical progre. The poitive effect of innovation are not ruled out but the ub-ector change in TFP eem ourced from catch-up. If the ector improved on it efficiency, total factor productivity growth would be certain given the trend above. It appear that the growth in productivity came from the chemical and allied ub-ector rather than the pharmaceutical, judging from the urvey conducted by KAM (2006). However, it might not be wie to make uch a concluion ince the turnover contribution wa meaured in term of output old which may not necearily imply growth in productivity. 117

138 118

139 CHAPTER FIVE SUMMARY, CONCLUSIONS AND POLICY IMPLICATIONS 5.1 Introduction Thi chapter ummarize the tudy finding and make the neceary concluion. The policy implication from the finding and area for further reearch are alo propoed. 5.2 Summary The purpoe of thi tudy wa to empirically analye the TFP change in the Kenyan manufacturing ector. The ector i expected to play a leading role in the country development and growth proce. The deciion to conider indutry a the leading ector in economic recovery i baed on the perceived vulnerability of agriculture to many factor outide policy-maker control, which reduce it reliability a a ource of utained growth. Manufacturing, on the other hand, ha hown remarkable reilience and ha potential for providing high and dynamic growth. To enure table and utainable economic growth, the propect lie more with the development of indutry and it i therefore neceary to deign appropriate meaure that would enhance it development. The manufacturing ector ha not been dynamic enough to function a an engine of growth for the whole economy and ha not contributed ignificantly to the major challenge of employment creation and poverty eradication. A cloe examination revealed that the ector growth ha not been driven by technical progre and efficiency but by growth in input ue, which i not utainable given the increaing cot of input. The tudy therefore ought to examine the tructure and compoition of the ector within a changing policy environment. The tudy further ought to examine the ector for change in total factor productivity with a view to identifying the ource of uch growth. 119

140 Specifically, the tudy meaured the total factor productivity change in variou ub-ector of the manufacturing ector and decompoed the TFP change into efficiency change (a movement to the frontier) and technical change (a hift of the frontier). Data Envelopment Analyi wa employed to calculate the Malmquit indice that were ued to meaure the TFP change and decompoed them into the variou component. The reult revealed that depite the tructural change that aw policy change from time to time, there wa a decline in total factor productivity during the tudy period. In the overall, the ector recorded a decline in TFP which wa attributed to lack of efficiency even though technical progre wa recorded. The reult from the individual ub-ector were quite imilar. All the ub-ector apart from chemical and pharmaceutical recorded a decline in TFP. Among the eight ub-ector included in the tudy, only textile and wood and furniture recorded ome efficiency improvement over the tudy period. Metal and contruction material ub-ector recorded ome technical progre while food, paper, printing and publihing, and platic did not record any growth in technology or efficiency. It wa quite evident that TFP change were driven more by efficiency change rather that technical progre. Seven ub-ector revealed a conitent and direct relationhip between TFP change and efficiency change. The metal ub-ector howed ome neutrality between the two ource of TFP change, but with efficiency change howing more conitency. 5.3 Concluion The tudy conclude that manufacturing ector did not experience any growth in total factor productivity. The ector i till inefficient and lack in innovation. 120

141 Thi confirm the reult of tudie done by KAM (2006) and Bigten (2002), that over the year, growth in the ize of the manufacturing ector ha been driven largely by increae in input and volume of output, rather than by improvement in efficiency and productivity. The manufacturing ector i yet to experience the big leap to high utainable growth in productivity. Although the ector current performance eem to be on an upward trend, there i no guarantee that there i any productivity growth. Productivity growth would be ourced from enhanced efficiency and innovation. The poor performance of the manufacturing ector wa not unexpected. Kenya enjoy a comparative advantage in the labour-intenive, reource baed and light manufacturing indutrie that include food proceing, textile, wood and furniture, to mention jut a few. Thee are primarily mall-cale indutrie that ue locally available raw material and imple labour-intenive technologie and are therefore capable of generating employment. However, thee ector recorded no growth in productivity and therefore the ector could not have experienced any growth. Thi confirm the importance of thee ub-ector in any effort to enhance the ector productivity and trengthen the local production capacity to increae dometically manufactured good. The viion 2030 apire to raie the hare of Kenyan product in the regional market and to develop niche product through which Kenya can achieve a global competitive advantage. The challenge lie in how efficiently the local reource are mobilized and how innovative the main takeholder will be to bring the viion to pa. Succeful implementation of the viion 2030 will have to be built on a common determination and heritage and the hope for a more properou nation offering a high quality of life to all it citizen. 121

142 5.4 Policy implication and area for further reearch In the light of the reearch finding, the Kenyan manufacturing ector uffer from declining productivity due to lack of efficiency and innovation. The implication of the low productivity are high cot of operation leading to lack of competitivene in the entire ector. To enhance productivity, effort hould be directed toward increaed efficiency in the ue of available reource and application of modern technologie that other competitor are uing. Kenya potential competitive advantage in manufacturing lie in the agroindutrial export. To compete globally in thi ector, the country will need to increae capacity of value addition in the agro-baed indutrie. Thi could be done by attracting trategic invetor to boot agro-baed indutrie and increae export, epecially in new market. The invetor hould be offered attractive incentive and hould be expected to bring new kill and technologie to the dometic market. In order for the ector to play a leading role in employment creation and poverty eradication, elective encouragement of indutrie hould be embraced. The target hould be the labour intenive reource baed and light manufacturing indutry, where the country enjoy comparative advantage. Thee include the agro-baed and textile ub-ector which are heavily dependent on locally available reource. To thi end, agriculture and manufacturing hould develop in tandem a ource of economic growth. Since the larget ub-ector of manufacturing i agro-indutry, the development of the agricultural ector which i the main ource of raw material i key. The Kenyan manufacturing ector i characterized by a large informal manufacturing ector. The formal manufacturing ector poor performance may not have provided the impetu for the informal ector to become formal and 122

143 ignificantly contribute to the country economic growth. Other bottleneck uch a credit availability could have hampered the growth of the informal ector. In thi regard, the government hould trengthen the Small and Medium Enterprie (SME) to become the key indutrie of tomorrow by enhancing their productivity and innovation. Such SME hould be encouraged in all urban center where the local authoritie could play a critical role in developing them. The relevant infratructure and ervice that make them attractive hould be provided. Different region of the country are uitable for different type of indutrial and manufacturing activitie. In order to harne the reource available and efficiently ue the ame, in different part of the country, regional pecific indutrial and manufacturing cluter hould be encouraged. The neceary infratructure and ervice hould be provided to timulate the development of uch cluter. Thi will not only addre the unemployment problem but will alo enure efficient ue of available reource baed on the regional reource endowment. It will alo bring about regional balancing in the ditribution of reource. A a major takeholder, the government hould boot cience, technology and innovation in the ector by increaing invetment in reearch and development. The government need to utilize the proviion of the World Trade Organization (WTO) to fund reearch and development activitie that benefit producer. Key ector like the textile and clothing would greatly benefit from reearch on eed varietie that produce better quality fibre, intead of relying on import. The government hould alo improve the critical infratructure uch a port, energy ditribution ytem, rail and major highway. The buine environment need to be improved in critical area uch a licening and ecurity. 123

144 According to KAM (2006), the ector face many external challenge, which include difficultie in penetrating COMESA and EAC market, a well a undertanding and exploiting the opportunitie ariing out of the variou proviion of WTO, EU-ACP tariff preference, and AGOA. The country hould therefore addre the marketing dimenion of the locally produced good. To thi end, intitution uch a the Kenya Invetment Authority Promotion Centre and Kenya Aociation of Manufacturer hould play a more active role, not only in creating invetment opportunitie, but alo in promoting the manufactured product both locally and internationally. Manufacturer alo need to addre their internal weaknee, for example, the ue of old and inefficient technologie. Thi would imply updating of technologie ued and retraining of labour o that the ector can increae it efficiency. The role of information in hedding light on the exiting bottleneck hould not be underetimated. To enure that a focued approach i ued to addre the contraint, a proper undertanding of the contraint, challenge, and opportunitie i required. Manufacturer have a duty to give correct and timely data which i then ued to make policy recommendation. Such information collected and analyed hould form a platform for action oriented dicuion, whoe end reult hould be to make Kenyan manufacturing a more competitive undertaking compared to competitor countrie. Finally, the tudy ued the latet firm level data et collected in Since then tatitic from government publication reveal that the manufacturing ector ha grown from a 0.2 percent growth rate in 2002 to cloe to 7 percent growth in A new urvey would be neceary and a imilar analyi carried out to etablih whether the growth reported ha led to any 124

145 poitive change in TFP through enhanced efficiency and/ or technical progre. In a far a the methodology i concerned; the tudy propoe that parametric ditance function uing the tochatic frontier analyi (SFA) could be ued, to tudy the robutne of the finding to the choice of methodology. 125

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157 APPENDICES APPENDIX I: MANUFACTURING SECTOR SHARES Table A1: Total Number of Employee in Manufacturing Sector 2001 to 2005 Manufacturing Category Private ector 183, , , , ,600 Public ector 33,500 33,400 31,400 31,000 29,600 Informal ector 1,039,400 1,121,000 1,199,300 1,281,000 1,386,000 Total 1,256,000 1,350,000 1,439,000 1,523,000 1,633,500 Manufacturing Total Economy 6,411,200 6,873,500 7,339,400 7,822,800 8,281,700 Source: Republic of Kenya (2006) Economic Survey, Nairobi: Government printer Table A2: Structure, Contribution and Performance of Kenya Manufacturing Sub-ector Sub Sector Food, Enterprie (2002) Production Turnover (2004) Employme nt (2003) Export (2004) No. of % Productio % Emp t % Export Enterpri n 2003 (Kh e turnover Mn) % 137

158 beverage and tobacco , , , Textile & Garment , , ,777 4 Metal & , , , Allied Leather , , ,860 8 Product Paper & paperboard , , ,026 2 Timber, wood & , , furniture Chemical & Allied , , , Building, contruction & mining , , , Platic & , , rubber Pharmaceutic al & medical , , ,394 5 Motor vehicle aembly , , Electric & electronic , , Other , , ,

159 Total 2, , , , Source: KAM, 2006 Table A3: Sectoral Share in the Real GDP, (Percentage) Year Agriculture Manufacturing Service Total Source: Republic of Kenya, Economic urvey, variou iue 139

160 Table A4: Percentage Growth in GDP and Percentage Growth in Manufacturing Contribution to GDP Year GDP growth Manufacturing growth Source: Republic of Kenya, Economic urvey, variou iue 140

161 APPENDIX II: MALMQUIST INDICES Table A5: Malmquit Index Summary of Firm Mean FOOD SUB-SECTOR Firm Efficiency Technical TFP change change change

162 TEXTILES SUB-SECTOR

163 PAPER PRINTING AND PUBLISHING SUB-SECTOR METAL SUB-SECTOR

164

165 PLASTICS WOOD AND FURNITURE MATERIALS SUB- SUB-SECTOR SUB-SECTOR SECTOR

166 CHEMICALS SUB-SECTOR Mean [Note that all Malmquit index average are geometric mean.] Source: DEAP output. 146

167 Table A6: Malmquit Index Summary of Firm Mean (Food) Firm Efficiency Technical TFP change change change

168 Mean [Note that all malmquit index average are geometric mean.] Source: DEAP output. 148

169 Table A7: Malmquit Index Summary of Firm Mean (Textile) Firm Efficiency Technical TFP change change change Mean [Note that all malmquit index average are geometric mean.] Source: DEAP output. 149

170 Table A8: Malmquit Index Summary of Firm Mean (Wood and Furniture) Firm Efficiency Technical TFP change change change Mean [Note that all malmquit index average are geometric mean.] Source: DEAP output. Table A9: Malmquit Index Summary of Firm Mean (Paper, Printing and Publihing) Firm Efficiency Technical TFP change change change

171 Mean [Note that all malmquit index average are geometric mean.] Source: DEAP output. Table A10: Malmquit Index Summary of Firm Mean (Platic) Firm Efficiency Technical TFP change change change

172 Mean [Note that all malmquit index average are geometric mean.] Source: DEAP output. Table A11: Malmquit Index Summary of Firm Mean (Metal) Firm Efficiency Technical TFP change change change

173 Mean [Note that all malmquit index average are geometric mean.] Source: DEAP output Table A12: Malmquit Index Summary of Firm Mean (Contruction Material) Firm Efficiency Technical TFP change change change Mean [Note that all malmquit index average are geometric mean.] Source: DEAP output 153

174 Table A13: Malmquit Index Summary of Firm Mean (Chemical and Pharmaceutical) Firm Efficiency Technical TFP change change change Mean [Note that all malmquit index average are geometric mean.] Source: DEAP output 154

175 APPENDIX III: DEFLATORS Table A14: Output and Input Deflator for each Sub- Sector in the 2000/ /2002 period Year 2000= Output Food Textile Wood & Furniture Paper Printing and Publihing Platic Metal Contruction Material Chemical & Pharmaceutical Input Capital CPI (Labour) Source: Own calculation from Government of Kenya (Statitical Abtract; 1995, 2003, 2006) 155

176

177

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