ABSTRACT A LINEAR PROGRAMMING ANALYSIS OF SMALL SCALE INDUSTRIES IN SIERRA LEONE By Enyinna Chuta Most African governments are becoming aware of the importance of rural and urban small scale industries. Recently, Sierra Leone government emphasized the need to assemble data on small scale indus- tries in order to evolve a detailed development program. This study is a direct response to the request of the Sierra Leone government. From March 1974 to August l975, a two-phased survey of small industries was undertaken in Sierra Leone. The first phase of the survey enabled the statistical estimation of the underlying popula- tion of Sierra Leone small scale industries. In phase ‘II, a twelve months survey was undertaken to assemble daily input-output data from 366 small scale industrial firms. The results of the phase I survey revealed that the small scale industrial subsector of Sierra Leone consists of about 50,000 estab- lishments, employing about 89,000 people and dominating the industrial sector of Sierra Leone. The dominant small scale industry is tailor- ing, followed by blacksmithing, carpentry, bakery, gara dyeing, etc. The bulk of the small scale industrial establishments (95 percent) are located in the rural areas. 0n the basis of the phase II survey data, a linear programming model was built, first, for evaluating the efficiency of resource use among the five major small scale industries, and second, to test the effects of alternative policies on the small n a): a 7" :«W |"" O A n n - ‘ :3' It.” 4- ”a c)! rib O .l :‘3’. :0; suddu vb :‘ 4 al a v1.5 8- \- . l ‘p..~“ '1: 1.1 e i .‘n :P’W h; + [C 'Ii "eg of . ‘o ‘5‘ C‘r ' wiF L ‘ « fir}? . W ,,_ U Enyinna Chuta scale industrial subsector. Two significant results emerged from the base run of the lin- ear programming model. First, the results indicated inefficiencies in the existing patterns of resource allocation and highlighted in- adequate knowledge about foreign markets. Second, due to the high cost of electricity, workshop rentals and wages, urban small scale industrial firms have absolute cost disadvantages when compared to firms in rural towns or villages. The results of the sensitivity analyses of the open model re- vealed that on the aggregate, increasing the rate of interest on capital resulted in a decline in output, employment and profits of the small scale industry subsector. Also, higher level of capacity utilization altered the comparative advantage of small scale indus- tries. While customs duty rebates increased the profits and output of the subsector, raising the tariffs on competing imports distorted the patterns of comparative advantage, resulting in a decline of aggregate output and gara exports. Increasing the supply of labor to the subsector resulted in doubling the value of output, employ- ment and profits. With respect to specific industries, increasing the rates of interest and granting customs duty rebates resulted in increased out- put, employment and profits of the gara industry. Although higher rates of interest resulted in increased labor utilization, output also declined in the bakery industry. Finally, the analysis revealed that the blacksmithing industry will eventually become exclusively rural industry. The policy implications of the results of the base run are that $. ,Af an O: —'i v . vi 5 I. I) ‘ . O. f ’55 9":- I. 1 .- Pris“ 1'- Eta. Enyinna Chuta first, an export market should be promoted for gara cloth; second, means should be found to transfer surplus iron scrap and bars in urban areas to blacksmiths in rural areas; and third, steps should be taken to reduce the high cost of electricity, housing and wage bills of the widely dispersed urban small scale entrepreneurs. The results of the sensitivity analyses indicate that increas- ing the demand for small scale industry products and supply of trained manpower, and granting customs duty rebates on intermediate inputs are effective ways of increasing output, employment and profits of small scale industries in Sierra Leone. Raising interest rates and tariffs on capital and competing imports respectively cannot by them- selves accomplish the desired results. Further research efforts needed for improving the findings of this study include computations of demand elasticities for competing imports and labor, tracing the employment and growth implications of reinvested profits and examining the interaction of farm-nonfarm activities within the small scale industrial subsector. A LINEAR PROGRAMMING ANALYSIS OF SMALL SCALE INDUSTRIES IN SIERRA LEONE By Enyinna Chuta A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Economics 1977 © Copyright by Enyinna Chuta l977 DEDICATION To my beloved father, late J.w. Chuta, who laid the foundation for my education but did not live to see the results of his efforts. ii :....,J.S, tl I“ 011' a a. F. .A :21}: .9 P, r.‘ |~ 'l‘P-r‘ ”mm! In 3”“ Ally, I 6 Denise f0 I exte Ezkjlént R ACKNOWLEDGMENTS I wish to express my thanks to the Departmentof Agricultural Economics, the chairman and Graduate Committee, for financing my graduate program at Michigan State University. I also gratefully acknowledge the assistance given to me by members of my Guidance and Thesis Committees: Derek Byerlee, Carl Eicher, Stephen Harsh, Glenn Johnson, Carl Liedholm, Vernon Sorenson and Robert Stevens. Particularly, I am grateful to my major pro- fessor, Carl Eicher, for stimulating my interest in graduate work and empirical research. His critical comments on my draft thesis, resulted in fundamental revisions and improvment of this disserta- tion. I also appreciate the role of my thesis supervisor, Carl Liedholm, in reading my manuscripts and making useful suggestions. Finally, I am indebted to Stephen Harsh whose expertise was always . available for building my linear programming model. I extend my gratitude to the following: the African Rural Employment Research Project, which, through its AID Research contract, financed my field research; the University of Sierra Leone for pro- viding a base for my research; the staff of the Department of Agri- cultural Economics and the Rural Employment Research Project members at Njala University College for support and encouragement; Dr. Dunstan Spencer, the project director, for guidance in the field and invalu- able contributions toward the building of my linear programming model; iii .,.‘.._ . ,3; (1' Cu F" U - ‘9‘ and very important, the enumerators, coders, supervisors, research assistants, respondents and vehicle drivers who participated in the field work. The excellent cooperation of Mr. M.A. Tunis, the Head of the Small Scale Industry Division, Ministry of Trade and Industry, Sierra Leone and the assistance of his office staff contributed immensely to the success of our field research in Sierra Leone. I am also grateful to all the traditional rulers and other government officials through whom we gained access to our respondents. I am indebted to Pamela Marvel, Linder Buttel, and Jacqueline Grossman, whose skills in computer programming, patience and avail- ability, facilitated data processing and analysis. The study was completed mainly because of the support, patience, sacrifice and understanding of my family. Therefore, I am grateful to my wife, Chijindu and our children, Ihuoma, Ijeoma and Kelechi. iv LIST OF TABLES ........................ LIST OF CHAPTER I TABLE OF CONTENTS FIGURES .~ ...................... INTRODUCTION ..................... l.l The Problem Setting ............... l.2 Objectives of the Study ............. l.3 Literature Review ................ l.3.l Theoretical and Analytical Developments . l.3.2 Sectoral Analysis ............ l.3.3 Demand .................. 1.3.4 Supply .................. 1.4 Review of Empirical Findings on Small Scale Industries ................... l.4.l Field Surveys .............. l.4.2 Demand .................. l.4.3 Supply .................. l.5 Outline of Small Scale Industry Research in Sierra Leone .................. l.6 Plan of Thesis ................. 1.7 Summary ..................... RESEARCH METHODOLOGY ................. 2.l Introduction .................. 2.2 Purpose and Scope of the Survey ......... 2.2.l Phase I Survey .............. 2 Phase II Survey ............. le eDesign .................. .l Sample Size ............... .2 Choice of Primary Units ......... .3 4 2.3 Choice of Elementary Units ........ Selection of Firms in the Enumeration Areas .................. rincipal Features of the Sample ........ Input-Output Measurements ............ 2.5.l Labor Input ............... 2.5.2 Capital Input .............. 2.5.3 Material Input and Output ........ 2.6 Summary ..................... 2 Sa 2. 2. 2. 2. p NN 01-h Page X CHAPTER 3 AN OVERVIEW OF SMALL SCALE INDUSTRIES IN SIERRA LEONE ........................ 3.1 Introduction .................. 3.2 The Extent of the Small Scale Industry Subsec- tor ...................... 3.3 The Relative Magnitude of the Small Scale Industry Subsector ............... 3.4 The Composition of the Small Scale Industry Subsector ................... 3.5 Seasonal Variations in Output ......... 3.6 Relative Magnitudes of Types of Labor and Capital .................... 3.7 Returns to Proprietor and Economic Profit Rates ..................... 3.8 The Problems of Small Scale Industry Entre— preneurs .................... 3.8.1 Lack of Capital ............. 3.8.2 Scarcity and High Cost of Materials . . . 3.8.3 Lack of Demand ............. 3.9 Summary .................... DELINEATION OF REPRESENTATIVE FIRM TYPES ...... 4.1 Introduction .................. 4.2 Broad Categories of Representative Firm Types 4.3 Detailed Delineation of Representative Firm Types ..................... 4.4 Annual Resource and Output Requirements for the Representative Firm Types ........... 4.5 Resource Efficiency Among Representative Firm Types ..................... 4.6 Summary .................... THE STRUCTURE OF THE LINEAR PROGRAMMING MODEL . . . . 5.1 Introduction .................. 5.2 The Objective Function ............. 5.3 The Activity Set ................ 5.3.1 Transportation Activities ........ 5.3.2 Transportation Costs .......... 5.3.3 Export Activities ............ .3.4 Import Activities ............ e Constraint Structure ............ 4.1 Derivation of Constraint Levels ..... .4.2 Demand Constraints ........... 4.3 Labor Constraints ............ 5.4.4 Capital Constraint ........... The Linear Programming Tableau ......... Summary .................... 5.4 010'! 0301 vi Page 96 99 101 102 105 107 108 111 111 112 114 V‘U 71777 CHAPTER 6 Page RESULTS OF THE LINEAR PROGRAMMING BASE RUN ...... 124 6.1 Introduction .................. 124 6.2 Number of Firms ................. 125 6.3 Employment Patterns ............... 128 6.3.1 Labor Utilization ............ 128 6.3.2 Labor Types ............... 130 6.3.3 Capital ................. 132 6.3.4 Limiting and Unlimiting Resources . . . . 134 6.4 Gross Output .................. 136 6.5 Production and Trade Patterns .......... 138 6.5.1 Intraregional Trade Patterns ....... 138 6.5.2 Interregional Trade Patterns ....... 140 6.5.3 Foreign Trade .............. 140 6.6 Regional Comparative Advantage ......... 142 6.6.1 Factors Determining Regional Comparative Advantage ................ 144 6.7 Summary ..................... 147 SHORT RUN POLICY ANALYSIS .............. 151 7.1 Introduction .................. 151 7.2 Policy Variables ................ 151 7.3 Policy Analysis with a Closed Model ....... 154 7.4 Results of the Short Run Policy Analysis with the Closed Model ................ 155 7.4.1 Effects of Levels of Interest Rate . . . . 155 7.4.2 Effects of Levels of Capacity Utiliza- tion ................... 156 7.5 Results of the Short Run Policy Analysis with an Open Model ................... 157 7.6 Effects of the Levels of Interest Rate ..... 157 7.6.1 Firm Numbers and Choice of Production Techniques ................ 157 7.6.2 Output and Employment .......... 161 7.6.3 Output and Employment Trade-Off ..... 164 7.6.4 Profits and Trade Patterns ........ 165 7.6.5 Elasticity Coefficients ......... 167 7.7 Effects of the Level of Capacity Utilization . . 167 7.7.1 Choice of Production Technique ...... 167 7.7.2 Number of Firms ............. 169 7.7.3 Output and Employnent .......... 171 7.8 Effects of Trade Policies on Output and Employ- ment . . . .‘ .................. 174 7.9 Summary ..................... 175 LONG RUN POLICY ANALYSIS ............... 178 8.1 Introduction .................. 178 8.2 The Effects of the Rate of Interest ....... 180 8.2.1 The Effects of Different Levels of Interest Rate on the Choice of Production Technique ................ 180 CHAPTER Page 8.2.2 Output Values and Employment Effects of Changes in the Rate of Interest ..... 185 8.2.3 Profit Effects of Changes in the Rate of Interest ................. 187 8.2.4 Effects of Interest Rates on Trade Patterns ................. 187 8.3 The Effects of Trade Policies .......... 190 8.3.1 Introduction ............... 190 8.3.2 Number of Firms and Choice of Production Technique ................ 190 8.3.3 Profits ................. 191 8. 3. 4 Output and Employment .......... 192 8. 3.5 Trade Patterns .............. 193 8.4 The Effects of Differential Rates of Growth in Labor Supply .................. 195 8.4.1 Introduction ............... 195 8. 4. 2 Marginal Value Productivities of Labor . . 195 8. 4. 3 Growth Rates of Firm Numbers, Output, Employment and Profits .......... 196 8.5 Summary ..................... 197 9 SUMMARY AND POLICY IMPLICATIONS ........... 200 9.1 Introduction .................. 200 9.2 The Small Scale Industrial Subsector of Sierra Leone ...................... 200 9.3 Results of the Base Run ............. 201 9.4 The Results of the Short Run Policy Analysis with the Closed and Open Models ........... 203 9.4.1 Introduction ............... 203 9.4.2 Aggregate Results ............ 203 9. 4. 3 Specific Industry Results ........ 204 9.5 The Results of the Long Run Policy Analysis . . . 205 9.5.1 Introduction ............... 205 9. 5. 2 Aggregate Results ............ 206 9.5.3 Specific Industry Results ........ 207 9.6 Policy Implications ............... 208 9.6.1 Introduction ............... 208 9. 6. 2 Implications for the Subsector ...... 208 9. 6. 3 Location Specific Implications ...... 211 9. 6. 4 Industry Specific Implications ...... 212 9.6.5 Further Research Needs .......... 214 9.7 Conclusion ................... 216 APPENDICES 2.1 PHASES I AND II QUESTIONNAIRES FOR THE SURVEY OF SMALL SCALE INDUSTRIES IN SIERRA LEONE. 1974/75 ...... 218 2.2 ORGANIZATION OF FIELD NORK, DATA COLLECTION, HANDLING AND PROCESSING .................... 234 viii 'unp‘ ' va" . - I C. "I 90A V‘ u.--c- APPENDICES Page 5 THE LINEAR PROGRAMMING TABLEAU ............ 247 BIBLIOGRAPHY ......................... 262 ix A .rh H./‘ ‘11! ‘11 Q‘U A." .11“ 1V O.‘ .1411. a: .11» ‘VJ 3.7 4.1 4.2 .40 Table 2.1 LIST OF TABLES Initial Distribution of Establishments in the Random Sample of Small Scale Industries in Sierra Leone, ' 1974 Survey . . . . . . . . . . . . . . ....... The Distribution of Sample Firms Used for Analysis Source of Initial Capital by Size of Location for Small Scale Industrialists in Sierra Leone, 1974 Distribution of Industrial Establishments in Sierra Leone by Location and Size, 1974 .......... Estimates of Value Added by Large and Small Scale Industry in Sierra Leone .............. Distribution of Small Scale Establishments and Employment by Industrial Categories and Size of Location, 1974 ................... Value Added by Small Scale Industrial Categories and Location, 1974/75 ................ Annual Mean Return to Proprietor and Economic Prifit Rate by Major Process and by Major Small Scale Indus- try Category, 1974/75 ................ Annual Returns to Proprietor by Major Industry and Location for Traditional Processes Only ....... Problems of Small Entrepreneurs in Sierra Leone, 1975/75 ....................... General Characteristics of the Representative Firm Types for Region I ................. General Characteristics of the Representative Firm Types for Region II ................. General Characteristics of the Representative Firm Types for Region III ................ Page 32 36 42 47 50 53 54 64 65 68 86 87 88 01- nPi - an. AH! ‘14 Q. . R./.b Alxlv '5 psi! 1‘ Q.‘ 3 If, 10 9Q 1. IG I.~ II nli 1.. Fc- pl: .Alw phi PF! FIJI PAH General Characteristics of the Representative Firm Types for Region IV ................. Population Weight Distribution of Industrial Firms by Representative Firm Types ............ Annual Resource and Output Coefficients by Rep- resentative Firm Types for Region I ......... Annual Resource and Output Coefficients by Rep- resentative Firm Types for Region II ....... Annual Resource and Output Coefficients by Rep- resentative Firm Types for Region III ........ Annual Resource and Output Coefficients by Rep- resentative Firm Types for Region IV ........ Resource Efficiency of Representative Firm Types Imputed Transportation Costs Between Regions Minimum and Maximum Number of Each Representative Firm Type Allowed in the Model ........... Constraint Levels of Product Final Demand by Indus- try and Location, 1974/75 .............. Resource Constraints by Industry, Labor Type and Location, 1974/75 .................. Per Hour Costs (in Leones) for the Different Kinds of Labor by Industry and Location, 1974/75 ..... Predicted Number of Firms by Industrial Process Type and Location for the Base Run, 1974/75 ....... Comparison of Actual and Predicted Number of Firms by Industry and Location, 1974/75 .......... Comparison of Actual and Predicted Patterns of Labor Utilization by Industry and Location, 1974/75 . . . . Predicted Percentage of Hours by Labor Types, Indus- try and Location, 1974/75 .............. Comparison of Actual and Predicted Values of Capital Services Utilization by Industry and Location . . . . xi Page 89 90 92 93 94 95 98 110 115 118 120 121 126 127 129 131 133 Table 6.6 Divergencies Between Per Hour Labor Costs and Pre- dicted Values of Marginal Productivities of Labor by Industry and Location (in Leones), 1974/75 . . . . Comparison of Actual and Predicted Values of Gross Output by Industry and Location, 1974/75 ...... Predicted Production and Trade Patterns by Industry Product and Location (in Leones), 1974/75 ...... Predicted Per Unit Costs of Production by Industry and Location, 1974/75 ................ Predicted Resource Requirements by Industry and Location as Bases for Explaining Comparative Advan- tage, 1974/75 .................... Predicted Aggregate Gross Output Values, Labor and Capital Services Utilization and Profits at Differ- ent Policy Levels for the Closed Model, 1974/75 . . . Predicted Number of Firms at Different Levels of Interest Rate by Industry and Region, 1974/75 . . . . Predicted Magnitude and Direction of Change in Firm Numbers by Industry and Location, Resulting From a Rise in the Rate of Interest from 20 Percent to 35 Percent, 1974/75 .................. Predicted Gross Output, Labor and Capital Services Utilization at Different Levels of Interest Rate by Industry and Location, 1974/75 ........... ‘ Predicted Changes in Output, Labor and Capital Ser- vices Utilization Resulting from Increasing the Rate of Interest by Region, 1974/75 ........... Predicted Patterns of Subsector Profits and Foreign Trade at Different Levels of Interest Rate, 1974/75 . Predicted Number of Firms at Different Levels of Capacity Utilization by Industry and Location, 1974/75 ....................... Predicted Changes in the Number of Firms at 100 Per- cent Level of Capacity Utilization by Industry and Region, 1974/75 . . ................. Predicted Changes in Capital Productivities Due to Different Capacity Utilization Levels 1974/75 . . . . xii Page 135 139 143 145 156 158 159 162 163 166 168 169 170 Ala as: .lv Table 7.10 8.1 8.2 8.3 8.4 8.5 8.6 A.2 Predicted Output Values, Labor and Capital Services Utilization at Different Levels of Capacity Util- ization by Industry and Region, 1974/75 ....... Projected Number of Firms at Different Levels of Policy Variables by Industry and Region, 1985 . . . . Projected Unit Costs of Production by Industry Rep- resentative Firm Type and Location (Leone), 1985 Projected Gross Output Values, Labor and Capital Services Utilization at Different Levels of Policy Variables by Industry and Location, 1985 ...... Projected Patterns of Interregional and Foreign Trade at Different Levels of Policy Variables, 1985 . . . . Projected Shadow Prices for Different Categories of Labor Resource and at Different Levels of Growth Rate of Labor Supply, 1985 ............. Projected Growth Rates of Firm Numbers, Output Values, Employment and Profits for Alternative Rates of Growth in the Level of Labor Supply, 1985 A Schedule for the Distribution of Workload for Two Hypothetical Months ................. xiii Page 173 181 182 186 189 196 198 238 LIST OF FIGURES Figure Page 2 Small Scale Industry Localities and the Enumer- ation Areas Which They Service ............ 30 3 Index of Monthly Output Values of Major Small Scale Industries in Sierra Leone, 1974/75 ......... 57 xiv CHAPTER I INTRODUCTION The focus of this thesis is on the use of a linear program- ] industrial subsector2 in ming model to analyse the small scale Sierra Leone. This study concentrates on the analysis of five maj- or small scale manufacturing industries in Sierra Leone: tailor- ing, gara dyeing or tie-dyeing, carpentry, blacksmithing and bakery, which engage in the production and repair of manufactured goods.3 According to the results of phase I survey of small scale industries 4 these industries constitute 80 percent of the em- in Sierra Leone. ployment and 67 percent of the value-added by the small scale indus- trial subsector of Sierra Leone [Chuta and Liedholm, 1975, p. 29; Liedholm and Chuta, 1976, p. 12]. Specifically, the linear 1In this thesis, small scale is defined to include those estab- lishments employing less than 50 persons. Staley and Morse [1965, p. 14] have various definitions for "small scale." Georgia Institute of Technology has found at least fifty different definitions of "small scale" in seventy-five countries. 2The manufacturing industrial sector constitutes about 7% of Sierre Leone gross domestic product and the small scale inddstry sub- sector contributes about 42% of total industry value-added (Liedholm and Chuta, 1976, p. 13). 3The scope of manufacturing industries adopted in this study exclude mining, construction, trading, transportation, financial, social and personal services and agricultural processing. For fur- ther details, see Liedholm [1973. Pp. 6-8]. 4See Chapter 2 on research methodology. 1 . AA" ’1'. bit (I) a. 0‘ ’0 1“ «I, P‘;1 ‘4 vi- 4 n. V.‘.. v I in. 0 gr. D. :n n programming model will trace the short-run and long-run effects of changing some policy variables on output, employment, profits and trade patterns within the small scale industrial subsector of Sierra Leone. 1.1 The Problem Setting Four major considerations serve as background for this study. First, most development models have not been based on solid micro foundations [Hayami and Ruttan, 1971, p. 25]. For example, the usual dichotomy of the economy into two sectors--agricu1ture and in- dustry or traditional and modern--has ignored the intersectoral and intrasectoral dynamics that constitute the key to understanding the problems of migration, employment, and income distribution. In addition, the closed1 economy models in the development literature have underplayed the potentials of the small scale industrial sub- sector as a source of output and employment. This research will depart from the conventional theoretical framework and focus on the nonfarm, urban and rural small scale industries. Moreover, the analytical framework for this thesis incorporates a trade sector. Such an approach will allow small scale industrial firms to reap the benefits from foreign demand for their products, while "import in- dustries," which introduce the imports of products of small scale industries, enhance the competitive performance of domestic small industries. Second, development economists have asserted that rates of 1Closed economy models would normally exclude the Foreign Trade Sector. See, for example, Harris and Todaro [1970]. a) .0: i. F;- V. .4 '5 u .u n... A; an. p ~ .‘v .u .w: 111 i ‘ 1 economic growth could be maximized by pursuing strategies to pro— mote large-scale, urban-based, capital intensive industrial projects [Ewing, 1968, p. 43]. Recently however, most developing countries have discovered that these policies over the past ten to twenty years have not yielded the desired results. In addition, manufactur- ing employment not only failed to keep pace with manufacturing output growth, it also lagged behind the rate of growth of population and the rate of rural to urban migration. For example, while the rates of urban population growth in Africa are typically about six to eight percent, nonagricultural employment grew at the rates of -l.O, -O.5, -O.7, 0.1, 3.0, -O.4, -O.l and -O.9 percent for Cameroon, Kenya, Malawi, Nigeria, Sierra Leone, Tanzania, Uganda and Zambia respectively between 1955-1964 [Frank, 1967]. In view of the disappointing results of the industrialization policies pursued in the previous past decades, some African govern- ments and scholars have started to assess the role of small scale in- dustries in both urban and rural development. Particular attention has been focussed on rural manufacturing as an important component in rural development strategies [Norman, 1973; Government of Tanzania, 1974; Abban 1975; Ntim and Powell, 1975]. International donor agencies [United Nations; 1974] have also started reassessing their lending strategies for industrial development. Third, since about 1970, there has been intense debate over the effects of alternative development strategies on output, employ- ment and income distribution objectives [Stewart and Streeten, 1971; Morawetz, 1974]. Resolving this controversy requires systematic em- pirical studies of both small scale and large scale industries. However, little research has been carried out on these problems in Africa [Steele, 1974]. Of special interest to this research is the case of Sierra Leone. While pursuing an import substitution strategy, in which policies were designed to encourage large-scale, urban-based, capital-intensive industries, Sierra Leone achieved a manufacturing growth rate of only 2.8 percent per year from 1966/67 to 1971/72. During the same period, large-scale manufacturing employment declined at a rate of 3.5 percent [Chuta and Liedholm, 1975]. Moreover, by 1971, unemployment in the urban areas had grown to about 14 percent [Byerlee, Tommy, Fatoo, 1976]. After much disenchantment with its previous industrialization policies, Sierra Leone recently set out a new industrialization strategy in which priority is given to small scale, agro-based, labor-intensive industries [Government of Sierra Leone, 1974]. Fourth; although there is growing interest in assessing the role of rural small scale industries in economic growth and develop- ment, steps have not been taken to generate adequate data for a rig- orous analysis of small scale industries in most African nations. For example, in Africa, there is no systematic study of factor pro- portions, output-factor ratios, production processes, scale economics, elasticities of factor substitution, locational advantage and profit- ability of rural small scale industries. Recently, the need to gen- erate reliable data was recognized by the Sierra Leone government when it stressed that "an immediate task relating to the [small scale industry] subsector, is to conduct an economic survey to assemble data on its size, composition, structure of inputs and outputs, de- velopment problems and potential. The survey is essential for D) i -\o 5 evolving a detailed development programme...." [Government of Sierra Leone, 1974, p. 185]. This study is a direct response to the needs of the Sierra Leone government. It will provide detailed information on the composition and characteristics of small scale industries in Sierra Leone.1 1.2 Objectives of the Study In order to make policy suggestions for the development of small scale industries, it is necessary first, to evaluate the existing pattern of resource allocation among small scale industires. Second, it is important to examine the effects of alternative policy strate- gies on the small scale industrial subsector. 0n the basis of the results of these analyses, some implications for small scale indus- tries in Sierra Leone can be discussed. The objectives are as follows: 1. To construct representative firm types, by industry and location in order to aggregate the linear programming results; 2. To evaluate the efficiency of resource use among small scale industries in Sierra Leone; 3. To examine the short-run and long-run effects of changing some policy variables on techniques of production, output, empolyment and trade among small scale industries in Sierra Leone and 4. To discuss the implications of the policy analysis for small scale industries in Sierra Leone. 1See Chapter 3 of this thesis. Also see Chuta and Liedholm, 1975; Liedholm and Chuta, 1976 for more details of the composition and economic characteristics of small scale industries in Sierra Leone. .A- ... ”I d V .1 O b s 5 Au. P... .41 .V1. . 1 av: .n[ P l. n .1 «a‘ g 5 F. 1. .«v 6 9... .1 F 1. 1- P .- 6. .ss .. v t . .us I. I. D n O u n u o u ,u Iii .tI. .k. .8 x. 1 I’m - I .l.‘ 1.3 Literature Review The purpose of this section is to review studies of small scale industries in developing countries. The theoretical and an- alytical developments relating to such research and their empirical findings will be reviewed in some detail. l.3.l Theoretical and Analytical Developments Most of the theoretical work on small scale industries can be classified at least into three categories: sectoral analysis, supply and demand analyses. 1.3.2 Sectoral Analysis Dual sector models as presented by scholars such as Lewis [1954], Fei and Ranis [1964], Harris and Todaro [1970] and Mellor and Lele [1972], focused on the traditional (agricultural) and modern (indus- trial) sectors. The modern sector is assumed to be the source of dynamism within the economy while the traditional sector slowly de- clines. These models did not explicitly incorporate the small scale industries in either the rural or urban areas, and they failed to deal effectively with rural-urban migration and employment generation in the economy as a whole. Due to such serious limitations of dual sector models, economists began to produce more comprehensive models for sectoral analysis. | The first theoretical model to include nonagricultural activ- ities in an agrarian economy was produced by Stephen Hymer and Stephen Resnick [1969]. In their model, rural households not only engaged in agricultural production but also are involved in nonfarm productive activities called "z-good" activities. Also, an I.L.O. mission to It» 7 Kenya [1972] recognized the need to go beyond the two sector models. While still maintaining the agricultural and industrial (nonagri- cultural) sectors' framework, the I.L.O. study in Kenya introduced formal, government-related and informal nongovernment-related sub- sectors. Oshima [1971] developed a trisector model in which the nonagricultural sector was split into labor-intensive and captial- intensive subsectors with agriculture remaining as a labor intensive sector. Also, he recognized a fourth sector, the government sector, even though he did not include this last sector in his theoretical analysis. Finally, Byerlee and Eicher [1972] developed a four sec- tor model of the economy, made up of urban large-scale, urban small scale, rural nonfarm and agricultural production sectors. l.3.3 Demand One of the key theoretical issues on the demand side centers on the nature of demand facing the products of small scale industries. Hymer and Resnick [1969] have argued that the only source of demand for the products of small scale industries is farm income. Moreover, they contend that the products of small scale industries are ”in- ferior" goods so that as farm income increases, the demand for the production of small scale industry products will decline. Bautista [1971] has developed a theoretical model in which "Z-goods" could be consumed or used as intermediate capital input for agricultural pro- duction. Liedholm [1973, pp. 23-26], has suggested that the Z-good con- cept is too general and needs disaggregation into three categories-- nontraded home production for own use, traded production undertaken 8 as a secondary occupation and traded production undertaken as a pri- ‘ 'mary occupation. Second, he suggested that the model should be modified to take account of the potential urban or foreign based demand for rurally produced goods. .Third, Liedholm emphasized that the "Z-good" concept cannot be restricted only to consumer goods, but should be expanded to include intermediate goods. Finally, Liedholm pointed out that the Hymer-Resnick model could be modified to explicitly take account of the backward and forward linkages be- tween the rural nonfarm activities and the agricultural sector. Apart from such linkages, Staley and Morse [1965, p. 46] point out a different kind of linkage that exists between rural artisal pro- duction and urban industries, especially where both perform comple— mentary functions. They cited wholesale and retail shops, shoe production and shoe repairing as examples of such complementarity. In addition, Huddle and Ho [1972] have argued that international demand for traditional goods produced by small scale industries is quite substantial. Little work has been carried out to assess the effects of the level of effective demand on the activities of small scale indus- tries. A numberof scholars recognize that a low level of aggregate demand could affect the growth of employment demand in these indus- tries [Steel, 1973; Stewart and Weeks, 1975, p. 93]. But the mag- nitude of such effects have not been empirically ascertained. This study will yield elasticities of output and employment which would focus attention on specific fiscal policies that could favor the growth of relatively labor-intensive industries; policies that spe- cifically affect the domestic and foreign demand for the products n 1’ ' 'v P Our; oi o 1 1.. up. ..' ) r1 (1 .d. (D . a?" of particular small scale industries; and policies that affect sec- . toral incomes and thus the level of aggregate demand [Furtado, 1975; Schydlowsky, 1970, p. 82]. 1.3.4 SuppLy One obvious consequence of lack of "Effective demand" is the low level of captial utilization on the supply side.] Most theoret- ical work on the supply side emphasize the creation of more jobs without considering that the existing labor force needs to be more fully employed.2 Lack of full capacity utilization in industrial production makes it difficult to use the concept of factor propor- tions [Winston, 1976, p. 1314], and difficult to determine the choice of efficient production processes, given relative factor prices. Thus, in order to isolate the effects of factor prices on choice of technique of production, different levels of capacity utilization ought to be incorporated in any meaningful economic analysis [Steel, 1976, p. 14]. The analysis of scale effects and factor substitution possibil- ities is of considerable theoretical significance. The neoclassical production function maintains that all firms, irrespective of their size, are on the same production function, given constant returns to scale. Therefore, theoretically, a proportionate change in capital and labor (which leaves the capital-labor ratio constant) will not alter the magnitudes of average product of both capital and labor. 1See Winston, 1971, p. 38 for other causes of excess capacity. Excess capacity in our context, means the difference between expected and actuallevels of utilization. . 2See Chapter 4 for the various levels of excess capacity. 31:1 case 1 we in c.‘ :‘ca;ita1 a 12:11-15“ 12657811 5: “"23 sake 1 23221: El; tithe Sieri and Ch. 719 de fisdtstltu‘. .11.” + 7"»; - .118 1. 1., | '“3' .9311: 3‘93; the .‘ 10 But a case of economies of scale will exist if a proportionate in- crease in capital and labor (for example) leads to increasing values of capital and labor productivities and possibly a decline in the captial-labor ratio. In other words, if scale economies exist in the small scale industries, it would mean that relatively larger firms make more economic use of resources than relatively smaller firms. Since the linear programming analysis will be utilized in this study, constant returns to scale is assumed. The validity of this assumption rests on the fact that both the Cobb-Douglas and the Constant Elasticity of Substitution production functions, when fitted to the Sierra Leone data, revealed constant returns to scale [Lied- holm and Chuta, 1976]. The degree of labor intensity depends partly on the elasticity of substitution between labor and capital (a). The larger the a, the greater the substitutability between labor and capital, and the great- er the income distribution effect from labor intensive production so that labor's share of value added is maintained or increased. The analytical approaches for estimating the elasticity of factor sub- stitution varies from the Cobb-Douglas function where a is equal to 1; the Leontief fixed inputs coefficient function where a is equal to zero; the more general version, the Constant Elasticity of Sub- stitution (C.E.S.) function [Arrow gt_a1,, 1961; Drhymes and Kurz, 1964] which varies from zero to infinity and of which the Cobb- Ibuglas and Leontief functions are special cases. Under conditions of the smooth neoclassical production func- tion and flexible factor prices, continuous process and factor sub- stitution is possible since in fact, an infinite number of production F1318 prob? RiTfing a; 11'; ; . Lidties he H“ A “I; DMIOJ “571315 of [933% on [N . “~15 ibre 71 The; . fl ‘ .- N fw‘ “LOJ’E ”L ar Er-L , r. :94:nce ~31: . ‘, g‘l‘ve “r Nail u '91 11 processes is assumed. But, with the Leontief fixed inputs coeffic- ient function, neither processrun~factor substitution is possible because only one process is assumed to exist. A representative firm linear programming model will be utilized to examine factor substitution possibilities among Sierra Leone major small scale industries. Although input ratios are assumed fixed, the delinea- tion of a finite number of production processes induces factor sub- stitution through process substitution in analysis of choice of tech- nique problem. The linear programming and even the nonlinear pro- gramming approaches for anlaysing labor-capital substitution possi- bilities have gained attention quite recently [Chenery and Raduchel, 1971; Duloy and Norton, 1973; Goreur, 1973], though not in the analysis of small scale industries. But all these efforts have focused on highly aggregated macro economic data. This study will focus more on the individual firm as a decision-making unit. The potential advantage of small scale industry is not limited to employment. Output gains might also result from such technolog- ical shifts. However, serious questions have arisen recently as to whether policies designed to increase employment would decrease out- put. Recently, Stewart and Streeten [1971] have distinguished be- ‘tween static and dynamic tradeoffs between output and employment and have clarified much ambiguities that surround the concepts of "output" and "employment." In a purely static sense, the presence (Jr absence of tradeoffs have been shown to depend on the relevant pn:int, given any positive real wage, on the neoclassical production function [Stewart and Streeten, 1971, pp. 147-148; Pack 1973, Mora- wetz, 1974]. For example, given one fixed factor, capital, and a lar‘fasle re refs. of We the ; 2: 112's ser ‘1creased 1 Cf‘fte in 01 its. [1911: 331.5 to a ‘ ‘ ‘-;“: a kl.....,[,r.‘ U 12 variable resource, labor, and one product, provided the marginal product of labor is positive, every increase in employment will raise the productivity of capital and therefore increase output. In this sense there cannot be a conflict. But if employment is increased to the point where the extra labor input leads to a de- cline in output, then a tradeoff can exist. Moreover, Peacock and Shaw [1971] have shown that the employment output conflict boils down to a fiscal problem of transferring income from the output- maximizing sector to engage surplus labor in a static framework. In a dynamic sense, Stewart and Streeten [1971] distinguish between a situation where less production and more employment now leads to future increased output and secondly, a situation where current low level of employment and high level of output leads to increased future employment. Focusing on aggregate savings ratio, they1 pointed out that if the savings ratio is determined by the distribution of income between wages and profits and if such dis- tribution is in turn determined by the level of employment, a con- flict might arise between maximizing the growth of employmnent and income, and such conflicts worsen with increases in wage level. They also pointed out that if a given savings ratio can be invested in either a capital goods industry or consumer goods industry, a higher proportion allocated to the capital goods industry will not lead to a trade off between employment and growth, provided labor requirement is the same in both sectors.2 But if labor requirement 1Stewart and Streeten [1971, p. 161] recognize the weaknesses of the assumptions of this model. 2Here, an invalid closed economy model is assumed. 1.! “‘"Or 1 1 rIV b' a . er: at 3m) in “row-6 \ \_ an’ we l'vvb‘ . '2'"? M1 “I .9 e +10 (1'! l 9 Eu, "r14 .74‘ P "I‘d ”M 4 .. A 19.1 ;. I.‘ Cat K“ 0; an s. :qui J "1 Sta‘ ‘1 £3, ‘ h: an N: V E'fi-i 13 is higher in the consumer goods industry, then a conflict between employment and growth arises. Such a conflict arises because an optimal investment allocation to maximize income growth may be in- consistent with the optimal investment allocation required to max- imize employment growth. Stewart and Streeten also distinguish between increase in labor productivity that results from technological developments (improved managerial performance) and increased labor productivity that results from other kinds of technical developments such as the installation of new machines. Whereas, in the former where output increases faster than employment, the technique or techniques which maximize output growth also maximize employment growth; in the latter, the trade-off might exist since the choice of more cap- ital intensive techniques over labor intensive ones might lead to a fall in employment. This study is designed to verify under what circumstances an output-employment tradeoff might or might not exist among small scale industries in Sierra Leone. 1.4 Review of Empirical Findings on Small Scale Industires An attempt will be made to examine first, the kinds and con- tent of actual field surveys that have taken place with respect to small scale industries. Second, an attempt will be made to summar- ize the results of such field surveys in so far as they affect the supply of and demand for output and employment in small scale industries. 14 1.4.1 Field Surveys Most of the previous data on small scale industries are quite rudimentary. Nevertheless, those studies do indicate the magnitude of employment that is involved in the small scale industrial sub- sector. For example an I.L.O. study indicates that in rural Western Nigeria, 27 percent of employed males had their primary occupation in the rural nonfarm sector and 14 percent of the employed males were secondarily engaged in nonfarm activities.1 Thus, 41 percent of employed males were engaged in rural nonfarm activities. Sim- ilarly, the data presented by H.A. Lunning [1967, p. 77] reveal that 48 percent of employed males had either primary or secondary occu- pation in rural small industries.2 In addition, David Norman's data, generated from a survey of three villages in Northern Nigeria, show that approximately 47 percent of the average male working time in the major village of Dan Mahawayi was spent on off-farm activi- ties.3 Also, available fragmentary data reveal large seasonal var- iations in labor allocation between agricultural production and nonfarm activities. For example, Lunning's study [1967, p. 77] reveals that while 65 percent of males in rural Sokoto Province were primarily engaged in nonfarm occupations during the dry season, only 6 percent were primarily engaged in this sector during the wet $835011 . 1Liedholm [1973, pp. 3-4] computed these figures from a re- cent I.L.O. report [1972, p. 117]. 2 3See Norman [1973, p. 29]. Each family adult spent 122.6 days per annum on off-farm activities out of a total of 262.7 days worked per family adult. See Liedholm [1973, p. 3]. 101915 :a'BEEIEWt S waif men“ Etifi'l’lc C15 3313] stud‘ 5.cifig 36 1 5.121 worke' i]:1cu;fi tns me lural I! "dairies 1 0‘. NE , ecom 1V I_ h1thr 15 However, since these studies were essentially rural or farm- management surveys, the small scale industries were not given ade- . quate attention with respect to the composition, content and the economic Characteristics of these small industries. Kilby's Study. of Small Industry in Eastern Nigeria [1962] and the survey of Small Scale industries in the Western Nigerian Urban centers of Ibadan, Oyo and Iwo [1970] were attempts to assemble data relating to the economic characteristics of small industries. Recently, Child [1973] studied 120 rural firms in the four regions of Kenya, in- cluding 36 different market centers. Industries surveyed included cloth workers, carpenters, bakers, tinsmiths, and blacksmiths. Although the firms surveyed were biased in favor of the clients of the Rural Industral Development Centres it was discovered that small industries were more profitable than the modern sector; that credit was not a major bottleneck and that there was need for improving managerial skills. Also, a recent study in Ghana by Steel [1977], carried out a detailed analysis of small scale industires in urban areas. The study conducted a complete enumeration of Aburi and Nsawam, localities that represent the smallest and medium sized localities in Ghana, and 10 percent sample survey of Acra, which is one of the two largest cities. In the first phase of the study, a frame was assembled which enabled the selection of firms for de- tailed study in a subsequent phase. This study, apart from reveal- ing that small industries constituted between 60-75 percent of industrial firms in urban Ghana, has enabled a detailed analysis crf the economic characteristics of these firms. Although these recent studies have made important K'I'let' S€"3'.S 1‘ 151.1 and csu‘ac' not 2'2; sca’ ‘ u 10133011. (I) (_i r. O ‘1. 16 contributions to the study of small scale industries, they have serious limitations. The most serious limitation is that both the Kenya and Ghana studies were one contact surveys. Thus, they could not reveal the patterns of seasonal variations that existed among small scale industries with respect to industrial types and location.1 Another limitation of the Ghanaian and Nigerian studies is that they focused too much attention in the urban areas to the neglect of the rural areas. Empirical evidence from Sierra Leone reveals that the small scale industries in the localities of less than 2,000 contributed about 90 percent of total small scale indus- trial establishment, 78 percent of employment [Chuta and Liedholm, 1975, p. 14] and 57 percent of value added [Liedholm and Chuta, 1976, p. 12] of the entire subsector in Sierra Leone. 1.4.2 Demand Until now, very little data were available on the demand for the products of small scale industries in developing nations. Yet, the question of the growth of small scale industries depends to a great extent on the nature of demand that faces those industries. Liedholm [1973] had shown that evidence with respect to the growth of rural small industries was inconclusive and conflicting. For example, whereas the studies of Stephen Resnick [1970] and Montoya and Villalba [1969] indicate that these industries decline over time, those of Arthur Gibb [1972] in the Philippines, and E. Gerken [1973] in Paktia, Afganistan, revealed an increased rate of labor absorption 1See Chapter 3' for some details on pattern of seasonal var- iation existing among small scale industries in Sierra Leone. 17 in these industries over time. Evidence exists for the linkages and employment potential for urban registered and unregistered small industries in India. Jan Herre van der Veen [1972], in his case study of Gujarat State, found that "unregistered” industries have a higher employment-output ratio than "registered" ones. His study, however, was limited to urban firms employing six or more persons. Thus, the rural and very small urban firms were not ex- amined. One major contribution of the Sierra Leone project [Byerlee and King, 1976], is that evidence now exists to dispell the notion that the products of rural small scale industries are "inferior" goods as portrayed in the Hymer and.Resnick model [1969]. The consumption survey of the Sierra Leone Project was designed to obtain a detailed breakdown of household expenditures on individual nonfood items by origin of production. The data generated from a sample of 220 rural households revealed that the expenditure elasticity coefficient for all small scale industries was 1.60. For the major small scale in- dustries, the elasticity ranged from 1.22 for tailoring to 1.90 for carpentry.1 These coefficients indicate that these industries will not decline as per capita incomes are increased in the rural economy. The small scale industry‘survey in Sierra Leone has also revealed some important sectoral linkages.2 In addition, foreign markets do exist for the products of gara industry in Sierra Leone. The impor- tant linkages, together with the income elasticity coefficients, 1The standard errors for the aggregate coefficient, tailOring and carpentry are .17, .22, and .31 respectively. 2The details of these linkages have been spelled out in Chap- ter' 3 of this thesis. 18 validate some previous studies that pointed to the growth potential of rural small scale industries. 1.4.3 [Supply Due to the paucity of systematic field surveys of African small scale industries, empirical evidence with respect to the supply of output and employment is scanty [Pack, 1976, p. 45]. Recent studies by Child in Kenya [1973], and Steel in Ghana [1977], while focusing on the economic characteristics of small-scale industrial firms, failed to examine the production functions of those firms and did not derive the relevant supply parameters. Pack's recent survey of 42 plants in Kenya [1976] which employ over 50 workers, is not very relevant for the study of small scale industries. Moreover, that study did not also estimate production function parameters. It is only the recent work of Liedholm and Chuta [1976] that did come out with detailed analysis of the production relations of small scale industries in Sierra Leone. According to their study, both the Cobb-Douglas and the C.E.S. production functions revealed that Sierra Leone small scale industries exhibit constant returns to scale. Thus, large firms are not more efficient than the small firms in resource utilization. In addition, their analysis revealed that the elasticity of qustitution was not significantly different from one .99 for tailoring, .93 for gara dyeing, .98 for carpentry, 1 .80 for blacksmith and .70 for bakery industries. According to these parameters, any proportionate change in factor price ratio 1Computed from the Estimated substitution parameters [Liedholm and Chuta, 1976, p. 78]. ,3) d 1' r L 13*" IE'1 ‘13 1' e 7e I“ I111 fr ,. ’1‘!— 1‘4: A 3 t.» ”1” \nM .11 z ‘1. .V1. A.) 111 C‘. IV1 .I’, ‘1‘ its 19 will tend to lead to the same proportionate change in factor ratio, thus indicating the possibility of factor substitution. Recently, Roemer [1975] carried out a time-series analysis of Ghanaian manu- facturing. Using the C.E.S. production function, he also found that the elasticities of substitution for five manufacturing industries in Ghana were close to one. Those industries included bakeries, and clothing. Although evidence is beginning to emerge about scale and sub- stitution parameters, the relevance of these estimates for practical policy purposes is often questioned. First, it is usually pointed out that the use of value added in the estimating formula excludes material inputs as a productive resource. Thus, the estimating model is incompletely specified. Also the constant omission of work- ing capital is another case of model misspecification [Bhalla, 1970]. Second, the econometric models do not differentiate between levels of managerial skill. This omission tends to mask an upward bias in the measurements of elasticity. Third, the econometric procedure does not differentiate between capital of different levels of vin- tage and technology. Such distinction may be very important from a point of view of near-future investment decisions [Roemer, 1975, p. 80]. Fourth, the econometric technique fails to take account of existing situation such as market imperfections, and capacity under utilization. Because of these limitations of econometric models, it is necessary to exercise caution while interpreting parameters that have been estimated from highly aggregated econometric models. .- ‘15 at va.ly'ns y. Oar-Ip‘;_ h =u|ys l-R .32". nas 3'7: C’lara ,. V5}? 1 r '" 0 LQK 20 1.5 Outline of Small Scale Industry Research in Sierra Leone As a result of the field survey1 of small scale industries in Sierra Leone, two joint papers have been published.2 Chapter 3 of this thesis will attempt to present some aspects of those publi- cations that describe the extent, composition and economic charac- teristics of small scale industries in Sierra Leone. Recently, a report has been written for the World Bank, highlighting the econ- omic characteristics of rural and urban small scale industries in Sierra Leone deserving the World Bank's attention. This thesis will attempt to accomplish three major objectives. First, a representative firm linear programming model of the small scale industrial subsector of Sierra Leone will be built. The model will serve as a major tool of analysis in this thesis. Second, some sensitivity analysis will be undertaken to examine the consequences of changes in some policy variables among the small scale industries in Sierra Leone. Third, the implications of the policy analyses for the development of small scale industries in Sierra Leone will be discussed. 4 Currently, further research is being undertaken for the World I. . lThe details of the field survey of small scale industries in Sierra Leone are presented in Chapter 2. 2See Chuta and Liedholm, 1975; Liedholm and Chuta, 1976. 3The Liedholm and Chuta [1976] World Bank report on small scale industries is confidential. 4This research is an extension of MSU Agricultural economics and World Bank Research contract whereby Carl Liedholm and Enyinna Chuta consult for the World Bank on rural and urban small scale industries. :11"; 52311 s 1:?de'ines c n1. It is 4,.1 ,A - ‘Iq‘ ‘ I ace lngjgf FILT’Ent a O. ‘F Ii ...1nthe the 44:: ‘ J11|erer :UE11near 21 Bank on: (1) a generalizable research methodology for carrying out base-line surveys of small scale industries, monitoring and evalua- ting small scale industry bank projects and programs; and (2) brief guidelines on analytical procedures for appraising the Bank's project work. It is anticipated that over the next one year, further analyt- ical work will be undertaken on case studies of the individual small scale industries. Also, an attempt will be made to analyze the employment and income distribution effects of farm-nonfarm interac- tion in the rural labor markets. Finally, the growth patterns of the different small scale industries will be traced, using a recur- cive linear programming framework. 1.6 Plan of the Thesis In order to undertake a linear programming analysis of small scale industries in Sierra Leone, Chapter 22 of this thesis will be devoted to a discussion of the research procedure utilized for ob- taining primary data. Thus, a descriptioncrfthe objectives and scope of the survey, the sample design, and how the various inputs and outputs have been measured will be presented. . In Chapter 3 an overview of small scale industries in Sierra Leone will be discussed.: Some details on the extent, composition, the relative magnitude and the economic characteristics of small scale industries will be outlined. Chapters 4, 5 and 6 contain detailed descriptions of the delineation of the representative firm types, the specification of the linear programming model and an analysis of the results of the linear pro In C sciicy ana tie basis :evelopin; the 5131.“ r ._. P“‘A fl May-CI. ‘3 The 5“? *Iode' 22 linear programming base run respectively. In Chapters 7 and 8, both the short-run and long-run policy analyses of small scale industries will be presented. On the basis of such policy analysis, suggestions will be outlined for developing small scale industries. These suggestions, together with the summary of the research findings will constitute the topics of Chapter 9. 1.7 Summary The objective of this thesis is to develop a linear program- ming model of the small scale industrial subsector in order to ex- amine the impact of alternative policies on small scale industries in Sierra Leone. Although primary data concerning small scale industries in Africa are still inadequate, this thesis will be based on daily input-output data that were, for the first time, generated over a one year period, on both rural and urban small scale industries in Sierra Leone. The policy analyses that will be undertaken in this thesis are of both theoretical and practical relevance. First, changes in the rate of interest will highlight the possibilities of labor absorption and employment-output tradeoffs among small scale indus- tries. Second, changes in the levels of effective demand and capac- ity utilizations will reveal the directions and magnitudes of changes in output, resource productivities and employment among small scale industries. Third, the policy analyses will verify the impacts of foreign trade and different rates of labor supply on the activities of small scale industries. r J 11.1 5C0 AL ‘ U E ta A red 1 $0 i 0L \AH E49 150‘ 6 ml CHAPTER 2 RESEARCH METHODOLOGY 2.1 Introduction The purpose of this chapter is to describe 1) the objectives and scope of the survey, 2) the survey design, 3) the methods of data collection, handling, storage and processing, 4) the procedures for data analysis and 5) how the various inputs and output have been measured for analysis. 2.2 Purpose and Scope of the Survey Specifically, the purpose of the survey was to generate the primary input-output data required for the detailed economic analysis of small scale industrial activities in Sierra Leone. In order to achieve this objective, it was necessary to organize the survey in two phases. In Phase I of the survey, an attempt was made to collect data that would enable a statistical estimation of the population of small scale industrial establishments in Sierra Leone and obtain additional information concerning the characteristics of the under- lying population of these industries. The Phase 11 survey involved the collection of detailed input-output data from a selected sample of small scale industrial firms, over a one year period, in order to undertake some rigorous economic analysis relating to those indus- tries. Each of the surveys will now be discussed in further detail. 23 rIO: 'Ubdln tr) 1. 24 2.2.1 Phase I Survey Phase I survey of small scale industries in Sierra Leone took place between March and June 1974. In order to accomplish the pur- pose of Phase I survey, a one-page questionnaire was constructed (See Appendix 2.1) and a simple manual was written to guide the admin- istration of the questionnaire. The questionnaire was designed to obtain the following information: 1. Type of enterprise, 2. Identification or address of firm, 3. Workshop description (whether temporary; built of mud or cement walls; operates in the open; thatch or zinc roof, etc.), 4. Types of labor (proprietor, hired, apprentices), 5. Number of each type of laborer, 6. Kinds of machines being used (power driven or manually operated), and 7. Number of machines being used. Due to budgetary and time constraints, it was not possible to carry out a complete census of industrial establishments in Sierra Leone. Also, since previous studies have revealed that both the magnitude and composition of industrial activity varies with size of settlements,1 a stratified sampling procedure based on locality size was adopted. First, a total establishment enumeration was 1u1dertaken in the 18 localities, including Freetown, with a popula- titan in excess of 20,000. Secondly, one-half of the 42 localities W1 th 2,000-20,000 inhabitants were randomly selected for total lLiedholm, 1973, p. 4. 1 1 W16 ”fill .1403. a: {5.1 I. .1“ P 61." .‘b b I . at. A: ,1.) I 1191 a v S 25 1 each establishment enumeration. Finally, 24 "enumeration areas," with less then 2,000 inhabitants and constituting the smallest samp- ling unit were randomly selected and enumerated. With a team of 12 enumerators, a Volkswagon bus, and well- planned logistical support field trips were made to the different 10- calities or groups of localities to carry out the survey. A dawn-to-I dusk, street-by-street enumeration was undertaken throughout the per- iod. Fortunately, excellent cooperation was obtained from the tradi- tional rulers and government officials, who, in most cases, provided the enumerators, not only with food and lodging, but also local per- sonnel who assisted in locating the various establishments. Indeed since most of the industrial establishments did not have signs and were often hidden behind other buildings, these local informants were vital in ensuring that an accurate enumeration of firms was Obtained. In Freetown, the capital city, a comprehensive up-to-date street map. obtained from the Ministry of Land and Survey, ensured a complete coverage of that city. The farm level2 enumerators, who already were stationed in the enumeration areas before our Phase I study was launched, also assisted in identifying various establishments. The Phase I survey was used to assemble a sample frame for the selection of firms for the Phase II study. The research findings of Phase I survey were published in 1975 [Chuta and Liedholm, 1975]. 1"Enumeration areas" are groups of villages that were artifi- cially grouped together by the central office of statistics for the 1963 Sierra Leone population census. Each such enumeration area con- tains an estimated 200 families [Spencer,l972, p. 8]. 2For the component parts of the Rural Employment Research Pro- ject in Sierra Leone, see p. 29 below. 2.2.2 Pt: The started 01 Mime tl 26 2.2.2 Phase 11 Survey The Phase II survey of small scale industries in Sierra Leone started on August 1, 1974, and ended on July 31, 1975. In order to achieve the purpose of Phase 11 survey, three kinds of primary data were collected. First, stock data on buildings, tools, equipment and furniture; inventories of material input and output stock were collected both at the beginning and end of the survey period. Secondly, detailed input-output flow data were gathered over a one year period. Such input-output data included labor input by hours and categories of proprietor,-family, hired and appretice labor; daily output data including the disposition of such output and destination of daily output sales; daily material input purchases and monthly financial expenditures relating to production. Finally, a one contact interviewing was undertaken to carry out in-depth 1 studies of the entrepreneurs and apprentices. All the questionnaires have been included in Appendix 2.1. 2.3 Sample Design In this section the procedures adopted for choosing the units of study for the Phase II survey will be described. In general, a two stage (cluster) sampling procedure was used in selecting the firms that were enumerated in detail. In the first stage, a sample of localities/enumeration areas (primary units) were selected while in thersecond stage, a sample of industrial firms (elementary units) were chosen from within the primary units. 1Results of Phase I study revealed that both proprietor and apprentice labor constituted 90 percent of employment in the small scale industries . PM“ 5 P" van. ‘5 .._. ‘ 11 mi. .v C I 1'" ‘11P. c. 11:1 tr 27 2.3.1 Sample Size Usually in any sampling procedure, the choice of sample size is a critical issue. Ideally for a two stage cluster sampling pro- cedure, the sample sizes for both the primary units and elementary units have to be statistically predetermined. Two important kinds of information are required to determine sample size. First, one has to specify the desired limit of error in the measurement of im- portant variables. Such limits of error or degrees of precision are usually stated in the form of standard errors or coefficients of variation. Secondly, the equation that connects the sample sizes with the degrees of precision has to contain advance variance esti- mates of the items to be measured. In order to estimate the optimal number of elementary units to be selected per primary unit in our survey, we would have needed variance estimates among primary units and within primary units. Also, it would have required detailed budgeting procedures to deter- mine the cost of access to the primary unit relative to the cost of obtaining data from any element within the primary unit. The fol- lowing equation sheds light on these issues [Cockran, 1963, p. 280]: S2 ”t ' /§ET_§I714'(m Where: Mopt = optimum number of elementary units 5? = variance between primary units 53 = variance within primary units M = total number of elements within primary units c1 cost of gaining access to primary units Due t infaratmr scale indus er‘arce es tire and b, a: :4 '1Juy In or 28 c2 = cost of obtaining information from each element within the primary unit Due to insufficient leadtime, it was not possible to obtain information on c1 and c2. Also, since no detailed study of small scale industries had been undertaken anywhere in Africa, no advance variance estimates were available at the time of study. Because of time and budget constraints, it was not possible to organize a pilot study in order to obtain the variance estimates of the important items. Some authors [Deming, 1960; Cockran, 1963] do recommend the use of variance estimates obtained by guesswork about the structure of the population and assisted by already existing mathematical re- sults. Thus, s2 = pqh2 (in the case of binomial distribution), 52 = O.O83h2 (with a rectangular distribution), and s2 = 0.042h2 (with a distribution like isoscles triangle), where s2 = variance estimates p = the probability of a success = 0.5 q = (l-p) = 0.5 h = range of the distribution. Unfortunately little or nothing was known about the structure of the population of the key variables to be measured in the survey. Any guess as to the distribution and range of values for such var- iabfles such as daily hours of labor input by labor types and daily (Hrtput by enterprise and locality size would have been misleading. What.fOllows therefore are the procedures that enabled the determin- ation of sample sizes at the levels of both primary and elementary 311* it- . .- ~11¥ 29 units. 2.3.1 Choice of Primary Units In order to take advantage of the integrated nature of the Sierra Leone project1 it was decided to choose localities that ser- viced the 24 randomly selected enumeration areas that were already being used for the farm level study.2 As a working defintion, a locality was defined to "service" an "enumeration area" if the in- habitants of the enumeration area purchased any of their goods and services in that particular locality. On the basis of the prelim- inary returns from the rural consumption portion of the overall project, it was subsequently determined that the following thirteen localitites serviced at least one enumeration area: Freetown (with excess of 100,000 people; Bo; Kenema, Makeni (each falling within the population range of 20,000-100,000) and Port Loko, Segbwema, Rotifunk, Pendembu, Pujehun, Mattru, Kabala, Kamakwie, and Rokupr (localities with 2,000-20,000 inhabitants). Koidu was also added to the sample because it ranked next to Freetown in terms of industrial concentration. Figure 2 shows the location of the sample localities and the enumeration areas which they serviced. 1The Integrated Rural Employment Research Project at Njala University College, University of Sierra Leone, consisted of the fellowing studies: a) Farm Level Study; b) Agricultural Processing and Marketing Study; c) Migration Study; d) Consumption Study and; e) Non-farm Small Scale Industry Study. 2See the map on next page. 30 | —- I. — II. _.____—_-1’:—— ______._ ______ I I O ”_ -,_-_---—--‘. Io' "- w” I ’.a. ’ - \.~." I ‘II ‘.‘. C I. Sick-mu ‘. .l' x" ’ .2 .. oKana . 1 I! 1.. 7 \L I ‘1‘: 12 m f. Imus: \ an. I 1.35 \‘I (.~\.:./ I "I . I. i, y .1." 0 0191605”: K. h“. ’1— I I'r}..‘t.1,r“-" .-\_ u (_ Alumna” [If .\ I. a? f— mami -' ‘3‘” . . /.- ‘. ‘2: ( Muklnl' I. !\ m 1 x 4 «at. m 1 -. ./ A " . ololdu I .2 1 r, ' 5‘ . . mug.- Ir- ’- :°’ \ ' [ll 1- “lua‘fl'a 4’ I7 ”“137 ,L/ '°\ \..A..’ §;\."' (1. . \ '-\ I) \- .. huh... I') \a 5‘ ""60.le ’I'TJ 8 g \: H-bul I‘ f . . / Sudan“ | h 5““ ' ' an I Tani-ulna “ ‘2': l . a ‘: - A2 6 I. "V "mu . Q, Jr: -( I" Q Maura] ‘. j ’I \ I: [5“,] . . .l' ' huh“ 'J'( .’ V “Qt-I 'u‘nhun _ ’I/ ’I‘. 3 )- LI’ ‘-’"' J \ - I. liuuru luglml Boundary. . . 9":31 "y: Intarnninml Boundary - -- - . 5° “Ill" Ital-o ) F—;_-1"‘/‘ (II-CIOUU Mu: ........ v 0 l. ‘0 CO ”Uh-nun h] Men's. M,- u) ......... Small. scale imfv [090141—95 no | . _ 13' w n F1gure 2' Small Scale Industry Sample Localities and the Enumeration Areas Which They Service. '\' G A, LA) 1 .‘\F— - 1“ ub‘ Q C 31 2.3.3 Choice of Elementary Units Having chosen l4 localities and being constrained by the budget to hiring only nine full-time enumerators, it was possible to deter- mine how many firms to study in each of the 14 localities. On the basis of the results obtained when the questionnaire was pretested, it was discovered that one full-time enumerator could interview a total of thirty establishments per week. Thus, a total of 270 firms could be interviewed in these localities by the nine full-time enum- erators assigned to the small scale industries. In actual fact, the small scale industry study had 18 enumerators on a half time basis, because the same enumerators also worked half time on the marketing and rice processing studies. Two sampling procedures were adopted in choosing the actual firms to be studied in each of the fourteen localities. Based on the sample frame that was assembled during the Phase I survey, two- thirds of the establishments chosen for each locality were selected on a completely random basis. This random sample ensured a reason- able representation of the underlying population of small scale in- dustries in Sierra Leone. Secondly, one-third of the establishments selected in each locality were chosen purposively. The purposive selection enabled the collection of information on the various techniques of production being utilized and different kinds of in- dustrial activities that were skipped during random selection.1 Table 2.1 shows that 180 establishments were chosen randomly out of 1A superior alternative to the combination of 2/3 random and 1/3 purposive sample would have been a simple random selection of firms from the frame stratified by location, industry and techniques of production. \l a.) 03.: a..\\.. \~. ...~.. .1..-..- .c-‘t’. ...:.? .h. to. ._ .73) .l.....--.: .n.~ . .e. ....\~ . .a.~.s \..~ h\-§ .‘~ .~0‘\ 32 00H m. m. m. m. m. m H w H N m N H N N 0 HH .NH 03 H33. mo N oNN H H H H H o m NN .N m m m m o n NH om mm moH H33. H H H H H H H H w 333. «H H N H N H N H N m neosawom mH N H H N N N m xcstuom 2 H H H H N H H N 33.3— mH m H H .N N c aazofiam mH N H H H H H N c 3.3 388 H H N N n N 225388 H N H H H N H m .N 3.5qu mH H H H H H .N o 3.5—mama H H N H N N N :33. cm H w H H H H H m m OH .233: on H N H H H H m .N 0H 95ch cm m H H H H H m n «H on on H H H H N H H H m .N NH 33300.5 NuHHmUOH moHaEmm stomonpam vcm Eovcmm Luom :H mucoacmHHnwumm mo coHuanuumHo HmHuHcH 00H m. m. m. N N n m. m. .N m. N N c n «H on H33. mo N omH H H H q n a H H N N .N m N m N HOH HouoH. oH H H w 3.33. S H N N H H H 538... oH H N N n xcstuom oH H H H H H m uqaxox OH 3 N a .5525 OH H H H H 0 33 tom oH H H H N 5950258 oH H H H n .N Shaun: S a a 3335. oH H N H o :33. oN m H H H H m OH .2833. ON H N H H N 2 60:3. oN N H H H m NH on oN H H N H H H H NH c3300.; dfllawom .318. $3.30 1%: mafia: 3le 3131...: ImmHl. a Imam 3% lwfll 1311 ate: m5 wfixoa 1&1 1% 331m alum a Guiana—Sm H.003 13H; uom 1.3m uHHH. 1:53 1H: 193 mmouu iuHunom 132—om Hana: 1:35» 1095 123.5 1.3.36 0.3:: IHHmH. £08 iwou 10: nun: 0:63— :33» a 1:3. 130a ixoaHm iusm 12: 9:33 a UHUHzo> 83:08 1H5 .3;qu nNNHNoH .ocowH 93on :H meuumdeH uHaum HHS—Hm mo oHnEmm Eovcmm on» CH mucoaanHDMumm no coHuanuumHa HoHuHcH H.N aHnnH selectec not to 1 se'for ( H A 1.1.46 1 x 43‘2”." ti ,"1 33 a sample size of 270. Out of the 180, 70 firms, representing 3 per- cent of industrial establishments in the "urban" localities and 110 firms, representing 4 percent of industrial establishments in "rural" localities of Sierra Leone, were selected.1 In addition, a replace- ment sample of 33 percent of the original sample size was randomly 2 However, the enumerators were advised selected for each locality. not to replace firms unless they were permitted to do so by the senior officials of the research project. 2.3.4 Selection of Firms in the"Enumeration Areas" In order to take advantage of the integrated nature of the Sierra Leone project, it was necessary to select establishments for detailed study from the 24 "enumeration areas" that had previously been randomly selected for the farm level study. Thus, it was pos- sible to utilize the services of the farm level enumerators in col- lecting input-output data on small scale industries. The decision as to how many firms to be studied in each enumeration area was based on the information contained in the enumeration area sample frame (household listings). Such village household listings indicated that approximately one-sixth of the combined farm and nonfarm house- holds were primarily engaged in industrial activities. Since twenty 1Our cut-off line for "rural" localities is consistent with the United Nations definition of "rural" as towns with less than 20,000 inhabitants. However, the definitions of rural and urban areas vary from country to country. 2If the rate of nonresponse or attrition among respondents is high, the use of a replacement sample can lead to a severe reduc- tion in sample size. Warwick and Linninger [1975, p. 112]suggest an upward adjustment in sample size to take care of the rate of attri- tion or nonresponse. fan?- museho 5011' it "a indiStria] a 111119 phase 1111511111 a 13:11 their f thei’lvout t 2.0119 the Since 1:11 be 1139‘ bath the W himsehtat' 2.73 firms "1 licah'ties 1 he sahme : tuistances. tiaisition seek the n that end r01 tapping 1:11 My four 11 icloseby t1 hark in ' Eta c011ec item] ing 34 farm households were already chosen randomly for the farm level study, it was decided that four households primarily engaged in industrial activities would be randomly selected for detailed study in the Phase II survey. Ninety-six households primarily engaged in industrial activities were selected in the enumeration areas and both their farming and industrial activities were studied in detail throughout the one year period. All the relevant information con- cerning the methods of data collection is in Appendix 2.2. 2.4 Principal Features of the Sample Since the linear programming model using representative firms will be used to estimate output and employment, a combination of both the random and purposive samples will be used to delineate the representative firm types. As was shown on Table 2.1 a total of 270 firms were initially chosen in the combined sample for those localities with above 2,000 people. At the end of the survey period, the sample size dropped to 225 due to the following unavoidable cir- cumstances. First, in one locality, Kabala, due to the multiethnic composition of that locality, we needed an enumerator who could speak the relevent three native languages, and also be familiar with that environment. Our inability to find such an enumerator, led to dropping this locality. Secondly, in Pendembu, we had worked for only four months when our enumerator stole valuable properties from a closeby tenant. Thus, we considered it inappropriate to continue. to work in that locality. Thirdly, after a period of six months of data collection in Rotifunk, we had fired two enumerators due to general incompetence. A total of 45 sample firms were already lost in $11951 11110111 tries 11 01 ~‘01 _.a. CS. _. l H-a. 'H' —._o. . . _, 35 in these three localities, thus reducing the overall sample size to 225, or 177 for the five major small scale industries, namely, 1 indus- tailoring, tie-dyeing, carpentry, blacksmithing and bakery, tries in eleven localities (see Talbe 2.1). Out of the 177 firms, 12 months data were obtained from l05 firms. Twenty-four out of the l77 firms closed down or changed their line of activity, 30 changed residence, and 14 refused to give out information. For the first two categories of dropouts, it was not pOSsible to obtain complete l2 months data since they dropped out at some point during the survey period. In the enumeration areas, out of the 96 firms initially ran- domly selected, 37 in the category of tailors, carpenters and black- smiths provided information. Out of the 37, only 23 had 12 months data that could be used for constructing the representative firms. The reduction in the sample size from the original 96 was due to problems relating to asSembling the sample frame at the initial stage of the survey. When households were being listed, heads of house- holds were asked about their primary occupation. Their responses tended to coincide with the on-going seasonal activity. Since the household listing was undertaken during the period of low farming activity, rural householdSLgave a biased report of their primary occupation. Thus, our input-output data revealed that household heads who were assumed to be nonfarm households, turned out to be 1According to Phase I survey, tailoring, carpentry, gara dye- ing, baking and blacksmighing industries accounted for 80 percent of the employment and 67 percent of the value-added in the small scale industry subsector of rural Sierra Leone. (See Chuta and Liedholm, l975, p. 29; Liedholm and Chuta, 1976, p. l2.) 36 farming households. Table 2.2 provides a summary of the sample sizes of industrial firms, by industry and by location, used in the actual construction of representative firm types. Firms that did not have complete twelve months data have been excluded from this table. Al— together, l28 firms were used for constructing representative firm types in the four regions and for the five major small scale indus- tries in Sierra Leone. Table 2.2 The Distribution of Sample Firms Used for Analysis ocalities Less than 2,000-20,000 20,000 - Over Total Industries 2,000 100,000 l00,000 Tailoring 7 25 29 6 67 Gara Dyeing - 2 4 4 l0 Carpenters 6 4 6 3 l9 Blacksmiths l0 5 3 1 l9 Bakers - 4 6 3 13 Total 23 40 48 17 l28 Source: l974/75 Small Scale Industry Survey in Sierra Leone 2.5 Input-Output Measurements In this section, it will be necessary to define the units in which our various inputs and outputs have been measured. In doing this, some issues relating to such measurements will be discussed. The first variable that will be described is labor. 37 2.5.] Labor Input During the survey, labor input datatwere gathered on a daily basis throughout the period of one year. Such labor input data were measured in hours actually worked in production process [Mor- awetz, l974, p. 497] as distinct from hours that firms planned or expected to work. This procedure of measuring labor input was chosen because it not only produces the more accurate factor services input for production function analysis but also provides an indication of an establishment's excessive capacity.] In addition, labor hours of services were collected in all possible categories of labor--pro- prietor, male family, female family, child2 family, apprentices and hired workers. In this study, the first four labor categories were lumped together as proprietor and family labor. Also, no attempt has been made to convert labor hours of the female and child labor into adult male equivalents. This is because child labor constitutes only .09 percent of total labor input in the five major small scale industries, and female family labor con- stitutes about 3 percent in the four locality sizes. Proprietor labor, male family labor, apprentice and hired labor types are of the magnitudes of 26 percent, 6 percent, 4l percent and 24 percent, respectively.3 Limited amounts of child labor were observed in the 4 gara industry where "stick ironing" of dyed fabric is sometimes l 2 See page 9l for our definition of excess capacity. The cut—off point for a child is ten years. 3Computed from survey data. 4See Chuta and Steacy, l975. dine b occasi 3 per: tries Sivir ‘ n [$179 h 38 done by children and in the balcksmithing industry where children occasionally operated the bellows for their fathers. Although 3 percent of labor hours utilized for the five major small indus- tries in Sierra Leone is attributed to female family labor, it is important to remember that in the gara industry alone, female labor input accounts for a magnitude of over 70 percent of total hours input in that industry. This is because approximately 80 percent of all gara proprietors are women.1 2.5.2 Capital Input Whereas labor input data were collected on a flow basis, i.e. , annual service flows in hours for the different labor types, data on capital were collected on a stock basis. Thus, information was collected on the original cost of buildings, tools, equipment and furniture, and working capital, i.e., inventories of material in- puts and finished output held by the enterprise.2 For purposes of more adequate valuation and accounting procedure [Livingstone and Burns, l97l, p. 33], all capital stock values were converted into annual capital service flows. The advantage of such a conversion is that the untenable assumption of the proportional relationship between capital service flow and capital stock data was avoided. As Yotopoulos [l967] and Nihston [1974] pointed out, such a pro- portionality exists, only if capital stocks are of the same durabil- ity and vintage. The variations in durability and vintage, prevailing 1Computed from survey data. 2Because information with respect to cash on hand and bank savings were difficult to obtain from respondents, our working capital definition is very restrictive. 39 within the Sierra Leone capital stock data, will now be discussed. In order to identify the range of durability existing for the stocks of tools, equipment and furniture, capital stock items were grouped into seven categories--machines, iron tools, non-iron tools, metal tools, wooden tools, furniture and others. For these categor- ies of equipment, durability1 ranged as follows: l2-25 years, 9-35 years, 3-ll years, 5-10 years, 20-25 years, l0 years and 8-l5 years, respectively. With respect to vintage, the capital stock data also revealed a wide variation. For example, in the bakery industry, there are the traditional ovens, built of mud, the modern French and German "peel oven," the outmoded British dough brake, and the Italian automatic oven. In the carpentry industry, equipments ranged from the earliest saws and hammers to the recent Italian electrical "combining" machines. Thus, in view of the wide variations in durability and vintage among our inventories of capital equipments, and the presence of a wide range of excess capacity,2 all capital stocks data were converted into annual service flows. The procedure for such conversions will be described next. Since the flow of current services from capital is approximated in a perfect market by the rental price of the capital assets per unit of time, times the units worked in the year [Yotopoulos, l967; Crouch, l972, p. 69], annual capital services flows were calculated 1During the field interviewing, respondents were asked first how long each equipment had lasted in use. Then secondly, they were asked to estimate how much longer they expected each equipment to last in useful employment. From both responses, it was possible to compute the average expected life of each group of equipment items. 2See page 9l. as fOHOW iiiiied t1 ffm. F01 ‘131 mnti used as r1 3 .htal, 1 SlIliar k to W315 , 40 as follows. For working capital, an appropriate discount rate1 was applied to obtain the opportunity cost of working capital for each firm. For buildings, the annual rental values, which were calculated from monthly rental values observed during the field survey, were used as rental price on buildings. Where rentals were not reported, a rental value was inputed on the basis of what was observed for a similar kind of industrial firm in the same region. With respect to tools, equipment and furniture, the following formula was applied:2 R = rV l -(l + r)‘n Where R = Constant annual capital service flow V = Original acquisition cost of the asset r = Discount rate n = Expected life of capital3 Thus, the annual capital service input for each firm is the sum of the opportunity cost on working capital, annual rental on buildings and the constant annual capital service flow (R). The yet unclarified issue is what the appropriate discount rate should be. Ideally, the appropriate discount rate required for any economic analysis should be that rate that truly reflects the opportunity cost of capital. Such opportunity cost of capital 1See page 43. zlt is important to remember that R is the discounted value 0f the asset's expected future services [Hayek, 1934; Hawtrey, 1937; Dunar, l953; Boulding, I955; Livingstone and Burns, l97l; Crouch, 1972]. 3See footnote on page 39. has to b Sierra L Th Accordin [Sierra O’Cmfnej :utfonal Cut of t 10 nonir lr: ~ 141:“ :- Ls‘. Erra 4] has to be considered from the nature of capital market existing in Sierra Leone during the survey period. The capital market in Sierra Leone is highly fragmented. According to a pilot survey of farmers' credit in Sierra Leone [Sierra Leone, l969], 80 percent of farmers in Sierra Leone obtained credit from institutional (commercial banks) and noninsti- tutional sources (friends, relatives, traders, fellow farmers). Out of the 80 percent that obtained credit, 91 percent had access to noninStitutional sources. In a recent fisheries study in Sierra Leone [Linsenmeyer, l976], 52 percent of traditional fishermen ob- tained credit from noninstitutional sources, while 2 percent obtained credit from commercial banks. Table 2.3 shows the various sources from which the Sierra Leone small scale industry entrepreneurs ob- tained their initial captial. The data reveal that our small scale entrepreneurs also obtained capital from both institutional and non- institutional sources. Although about 62 percent of the 185 propri- etors got their initial capital from past savings, about 1 percent obtained initial capital from commercial banks, 7 percent from friends and relatives, 13 percent whose initial capital were out- right gifts from parents, l percent from money lenders and l7 per- cent from other sources. Thus, while l percent of the proprietors got their initial capital from institutional sources, 38 percent obtained from noninstitutional sources. Whereas, the maximum discount rate charged by commercial banks in Sierra Leone is l2 percent [Sierra Leone, l975], non- institutional interest rates vary from zero to lOO percent [Sierra Leone, l969, p. 22']. Also, although the maximum interest Tabie 2.3 Ssurces of Initial Ca; —_— Savings frr AgricultL Savings frr Trade an: Loans from Comercie Lsans from Banks Friends anc Farily Sifts Mey mm 314913 731a} Me!“ of c .\ Elite: Cc th aThes tions far e [site Charge we“, th and a “We 42 Table 2.3 Source of Initial Capital by Size of Location for Small Scale Industrialists in Sierra Leone, 1974/75 Localities Sources of . . . less than 2,000- 20,000- over All ‘ I"‘t‘a‘ cap‘ta‘ 2,000 20,000 100,000 100,000 Localitiesa % % % % % Savings from Agriculture 38 21 8 - 18 Savings from Trade and Industry 26 43 48 64 44 Loans from Commercial Banks - - 2 - .7 Loans from Government Banks - - - - Friends and Family 3 9 5 12 7 Gifts 6 10 20 12 13 Money Lenders 3 - 2 - l Others 24 17 15 12 17 Total 100 100 100 100 100.7 Number of Observations 34 69 65 17 185 Source: Computed from the random sample observations obtained during the 1974/75 survey of small scale industries in Sierra Leone. aThese percentages have been weighted by the number of observa- tions for each locality size group. rate charged by the commercial banks in Sierra Leone is about 12 percent, the actual rate paid by two of our respondents (a gara dyer and a carpenter) during the survey is about 17 percent.1 Thus, it is . 1The respondents gave information with respect to how much they borrowed, the duration of loan and how much they were going to pay back to the banks. likeiy tha‘ above the 1 rates, Bot ternnatio 1a to con airroeoly p cast of le :renears i ensts to 3130 chars 1373; Ing' rurai 1en 3'» wereen 43 likely that commercial banks do allow for risk premium over and above the official rate of interest. With respect to rural interest rates, Bottomley [1963a, 1963b, 1975], has pointed out that any de- termination of the opportunity cost of capital from rural sources has to consider administration premium, risk (default) premium and monopoly profits.1 The major way of reducing the administrative cost of lending to a large number of widelycfispersedrural entre- preneurs is through rural cooperatives. But evidence exists to show that banks, while lending to rural cooperatives, also charge a commission to cover administrative costs [Von Pischke, 1973; Ingle, 1973]. In addition, default rates2 on commercial and rural lendings in most less developing countries, is in excess of 50 percent [Bottomley, 1975, p. 285]. With respect to monopoly profits, traders in Gambia, Sudan and Sierra Leone charge rates that range from 50 percent to 300 percent [Bottomley, 1975, p. 287; United Nations, 1963, pp. 30-31]. The implication of the foregoing is that the 12 percent rate of interest currently charged by the commercial banks in Sierra Leone may not reflect the true social cost of capital. But, it is equally likely that the interest rates prevailing in the noninstitu- tional credit sources are above the true social cost of capital. Due to these two extreme circumstances, a 20 percent rate of inter- est was subjectively chosen for the purpose of discounting the cap- ital stock values. It is hoped that a differential of 8 percent 1 2Linsenmeyer [1976] observed about 17 percent default rate from fishermen that use outboat engine. See Bottomley, [1975] fbr excellent discussions of these topics. above ably '\J (1" LA) Jn 1’11 [I -9‘ (‘9 m 3139 [11' out 11 ior ta “$1175 tee Pr Safier 3'13in \a FIT-OS V~C3nd 44 above the commercial rate of interest in Sierra Leone will reason- ably cover the necessary administrative and risk premiums. 2.5.3 Material Input and Output All material inputs have been valued at their purchase price. On the other hand, gross output which includes all daily sales, gifts to outsiders, consumption within the establishment, and stor— age have been valued at the sales price at the firm. 2.6 Summary The survey of small scale industries in Sierra Leone was carried out in two phases. In Phase I, an attempt was made to generate data for the estimation of the population of small scale industrial estab- lishments. On the basis of the sample frame that was assembled in the Phase I survey, the second phase of the survey was undertaken to gather input-output data from a selected number of firms in order to analyse the economics of small scale industries. Since previous studies have revealed that both magnitude and composition of industrial activity vary with size of settlement, the whole country was stratified into localities of different popula— tion sizes and about 40 localities were randomly selected for the Phase I survey. In the Phase II survey, a two-staged stratified (cluster) sampling procedure was adopted. First, 13 localities which primarily services the 24 enumeration areas were selected. Secondly, 270 firms were both randomly and purposively selected from the 13 localities and 96 firms were randomly selected from the enum- eration areas, for the purpose of collecting input-output data. Two major kinds of data were collected during the Phase II survey. ings, to iai inpu mmof and dail sation 1 scaie i1 45 survey. Firstly, stock data included information on build- ings, tools, equipments, furniture, inventories of mater- ial input and output. Secondly, flow data were collected in the form of daily hours input by labor types, material input purchases and daily output. In order to ensure adequate accounting and val- uation procedures for the anticipated economic analysis of small scale industries, all stock data were converted into flow data. All the input-output data were collected over a one-year per- iod. The one-year coverage was important to test the hypothesis that extensive seasonality existed among small scale industries. A one-shot survey would fail to capture any seasonality that might exist, both within industries and location. Also, since most respon- dents did not keep records of their transactions, a method of twice weekly visits was adopted for interviewing respondents. Finally, the data that were used for the construction of rep- resentative firm types were those obtained from 128 industrial firms. Such data were considered reliable and extended over a lZ-month period. AN The | gmmdinfo [he extent 1 l‘Irst, foll lien of the All, seaso the small 5 CHAPTER 3 AN OVERVIEW OF SMALL SCALE INDUSTRIES IN SIERRA LEONE 3.1 Introduction The purpose of this chapter is to outline some general back- ground information on the small scale industries of Sierra Leone.1 The extent of the small scale industrial subsector will be examined first, followed by discussion on the relative magnitude and composi- tion of the small scale industrial subsector in Sierra Leone. Fin- ally, seasonality, resource use, profitability, and problems among the small scale industries in Sierra Leone will be disucssed. 3.2 The Extent of the Small-Scale Industry Subsector One of the important findings of the research is the discovery that the industrial sector in Sierra Leone is much more extensive than had been previously recognized. This result is apparent from Table 3.1, which presents distribution of industrial establishments and in- dustrial employment by location and size. These figures reveal that in 1974 approximately 50,000 industrial establishments employed almost 93,000 individuals.2 The Government of Sierra Leone, on the other 1For more details-see Chuta and Liedholm [1975] and Liedholm and Chuta [1976]. 2The revised figures are somewhat higher than the initial re- sults presented in African Rural Employment Paper No. 11 [Chuta and Liedholm, 1975] where 47,000 establishments and 87,000 individuals were estimated for small-scale industry. 46 E” [(1) H L)? 47 Table 3.1 Distribution of Industrial Establishments in Sierra Leone by Location and Size, 1974 Location and Firm Size Number of Establishments Percent Employment Percent A. Small-scale industry 1. Localities with population less than 2,000a 2. Localities with population from 2,000-4,999b 3. Localities with population from 5,000-20,000 4. Localities with P0P- ulation from 20,000- 100,000 (Bo, Kenema, Koidu, Makeni) 5. Localities with population over 100,000 (Freetown) Total small-scale B. Large-scale industry Total large-scale Total large- and small-scale industry 45,000 1,704 834 1,189 1,408 50,135 28 50,163 89.7 3.4 1.7 2.3 2.8 99.9 100.0 73,000 4,164 1,995 4,368 5,039 88,566 4,111 92,677 78.8 4.5 2.2 4.7 5.4 95.6 4.4 100.0 SOURCE: Small-scale industry data collected during Phase I of small- scale industry component of African Rural Employment Project, Njala University College. Data for large-scale industry were obtained from employment lists of the Ministry of Labor for December 1973, supple- mented by data collected by the authors. aEstimate based on projection from preliminary data received from twenty-four sample enumeration areas. These data include those indi- viduals who engaged in industrial activities on a part-time basis. bThe actual establishments and employment figures obtained were doubled since only half the localities in this size range were examined. hand, had this sect that thei :1 three- Th lfse’ehts ll.‘ firsts . 5341 to 5825.2 U! l‘35-‘171ehts 16185.3 A ”the otl tries €310] [Ami/er, S7’311'Scal 'i' ' 48 hand, had assumed that only 52,000 individuals would be employed in this sector in 1974.1 Thus, the Phase I survey results indicated that their industrial employment estimate should have been increased by three-fourths. 3.3 The Relative Magnitude of the Small Scale Industry Subsector The surveys also reveal that the small-scale industrial estab- lishments dominated the industrial sector in terms of both the number of firms and total employment. "Small scale" was defined in this study to include those establishments employing less than fifty per- sons.2 Using this definition, there were only 28 large-scale estab- lishments in Sierra Leone and those firms employed only 4,111 indiv- iduals.3 In those localities with populations in excess of 2,000, on the other hand, there were approximately 5,000 small-scale indus- tries employing approximately 15,000 individuals (see Table 3.1). Moreover, it was projected that there were approximately 45,000 small-scale establishments employing 73,000 persons in those local- ities with populations less than 2,000.4 Thus, these results indicate 1Government of Sierra Leone [1974, p. 27]. 2For a more complete discussion of the small-scale industry definition used in this study, see Chuta and Liedholm [1975, p. 9]. 3Data obtained from employment list of the Ministry of Labor for December 1973 and supplemented by data on eight large-scale firms not included on this list. 4These estimates were obtained by multiplying the actual num— ber of industrial establishments and industrial employment in each of the 24 sample village "enumeration areas" by a figure reflecting the representation of that "enumeration area" in that particular ag- ricultural region. It should be further noted that individuals en- gaged in industrial activities on a part-time basis are included in these data. that Sr at? Jyr 'eose u em? oye DTJTEd frdiaic 49 that small—scale establishments account for over 95 percent of the employment in Sierra Leone's entire industrial sector. The average size of the industrial establishment in Sierra Leone was very small. The “average" industrial firm, for example, employed only 1.8 workers.1 Indeed, 98.9 percent of the firms em- ployed less than 5 individuals, 0.7 percent employed from 5 to 9 individuals, 0.3 percent employed from 10 to 49 individuals and only 0.1 percent employed over 50 individuals.2 Thus, in terms of employ- ment the vast majority of firms in Sierra Leone were concentrated at the lowest end of the small-scale industry size continuum. Another useful indicator of the relative importance of small- scale industry is "value added." In the Sierra Leone study, the annual value added of the small-scale industrial establishments has been estimated from the output and cost data collected from firms on a twice-weekly enumeration. The mean value added for each of those firms in thesmall-scale industrial category in each locality group was calculated and then blown up according to the number of establish- ments in each area.3 The resulting estimates for small-scale industry as well as the various estimates for small- and large-scale industry prepared by the Government of Sierra Leone are summarized in Table 3.2. Table 3.2 reveals that small industry in 1974-75 accounted for 1Computed from Table 3.1. 2The average size of firm, in terms of the number employed, is larger in Freetown (3.5 workers) than in the enumeration areas (1.6 workers). 3The breakdown of the individual mean value added is described in detail below. 3;;mx ‘ a 30765 1. v.‘ 1 0._‘ 50 1 or 2.9 percent of Sierra Leone's Gross approximately Le 13 million Domestic Product. This estimate is surprisingly close to the value added estimate prepared by the Central Statistics Office for the National Accounts, an estimate which was not based on an extensive survey and which was admittedly "very rough" [Government of Sierra Leone, 1973, p. 31]. Table 3.2 Estimates of Value Added by Large and Small Scale Indus- tries in Sierra Leone Government Authors' Estimate Estimate 1970/1971 I Percent 1974/1975 Percent GDP GDP (Leones Million) Small-scale industry 11.1 2.9 13.2 2.9 Large-scale industry 8.5 2.3 17.43 3.9 Total industry 19.6 5.2 30.6 6.8 Gross domestic product 375.3 450.4 SOURCE: 1970/1971 data from Government of Sierra Leone [1973]. 8The large-scale industry value added figure for 1974/1975 was obtained by subtracting all indirect taxes from the large-scale indus- try value added estimate presented in the Second Annual Plan [Govern- ment of Sierra Leone, 1975, Chapter X]. When the value added for small-scale industry is compared with the latest estimate of the value added of large-scale industry (Le 17.4 million or 3.9 percent of Sierra Leone's Gross Domestic Product 1Le (Leone) = $1.10 U.S. during the survey period. 51 in 1974-75), the small-scale establishments are revealed to con- tribute approximately 43 percent of the entire industrial sector's value added. Although the value added percentage of small-scale (43 percent) is much lower than the percentage employed in small- scale (i.e., 95 percent) relative to large-scale industry the study shows that small-scale establishments are a significant component of Sierra Leone's industrial sector. Moreover, thefsmall-scale component of Sierra Leone's indus- trial sector appears to be relatively large when compared with other developing countries. Data on the very small industrial firms in most countries are not generally collected and thus, of necessity, only a limited number of comparisons are possible. Small-scale in- dustry employing less than 50 workers accounted for 95 percent of the manufacturing employment in Sierra Leone, but only 70 percent in Nigeria [Aluko, 1973], 71 percent in Tunisia [IBRD, 1974], 79 percent in the Philippines [ILO, 1974] and 69 percent in Colombia [Berry, 1972].1 The corresponding contribution of small-scale industries to value added in manufacturing, on the other hand, was 43 percent in Sierra Leone, 32 percent in Colombia, and only 14 percent in the Philippines. In these countries the percentage contribution of small-scale industries to value added was thus substantially less than employment; indeed, the value added contribution was less than one4half that of employment.2 In terms of both employment and value added, however, the 1In the more industrialized countries, the share is lower and ranges from 12 to 34 percent [Morawetz, 1974]. 2For the explanation for these results, see above. rela ('9- C) tr 52 relative importance of small-scale industry in Sierra Leone appears to be somewhat greater than elsewhere. This result is perhaps traceable, in part, to the extensive effort made in our Sierra Leone survey to include the small rural establishments, which are often overlooked and thus are underestimated in surveys. In addition, there may also be economic reasons, such as the small size of the Sierra Leone domestic market, that account for the relatively large numbers of very small-scale industrial enterprises in Sierra Leone. 3.4 The Composition of the Small Scale Industry Subsector An examination of the composition of the industrial activities undertaken in Sierra Leone can provide additional insights into the nature of the industrial sector. The number of establishments, the number of employed, and value added are used in this chapter to portray the distribution of industries by industrial category as well as by size of locality. Although the preliminary results of this distribution by employment and enterprises were presented in African Rural Emplpyment Paper No. 11 [Chuta and Liedholm, 1975], the final results have now been obtained and consequently are pre- sented in Table 3.3. The industrial composition by value added is presented in Table 3.4. One of the most salient findings is the dominant position of tailoring activity in Sierra Leone's industrial sector. Tailoring accounted fbr 31 percent of the employment, 33 percent of the estab- lishments and 37 percent of the value added within the small-scale _industrial sector. This finding is consistent with studies of small- scale industry in other African countries and elsewhere, which have .«.\Q.. ...._~.. a. .....-..X..:: —.-..ou.._:— )2 .... t......._:..- u..-.u 1...... .~2-—. use}... .~—: I... —-..:.... s: .‘u...--lw: o ... .,‘~.~...~. 53 1&0 mums; mwugwumm 05H. .:mmoum ccuumumsaco: mHaEmm Enemixucmzu «Lu Scum vmchUno mumu Eouu ceauuwnoua co comma mumsaumm annum .:muunuo: cu vovsfiocuu .cowmmu fimuaufiaofiuwm umfisuwuuma umcu :H :mmum cowumumE:cm: umzu mo cauumucmmwuawu osu mauuuwfiuuh mouswfiu >5 :mmum sewumnmsacm: some :« acmEmoHan dmfimumzncw new mucmscmufinmumo Hmupumavcu uo sagas: Hmsuum mzu wa«x~n«-=§ an vmcumu a .coHumofiwummmao Hmauunavcu tumucmum guacaumcuouch .mwmfiaoo zuumuo>wcs mfimmz .uommoum ucoe>o~asm Nahum cmofiuw< we ucocoaeoo xuumaucfi mamomIHHmEm mo H ommcm mcwusv wouuoauoo mama "mumDOm o.oo~ o.oo~ ocm.wm mma.0m omo.m mos.a mom.q omH.H moo.H «mm qu.a qu.H ooo.MN coo.mc NmuOH N.e o.m mmN.m oaq.N moo moN MNN ooH mN Nm ass 66H ooo.N ooo.N muscuo moon N.o N.o «ma ooH 0N on 06 mN NN ma 0H 0H u o noun: «Nno o.H m.o Nwm.H 00H mum co moo cm Nofi oN mm oN u o maufinu> mama N.o H.o HmH on #0 oN as ma «a N Na ON u o aqua: Nana moofi>uon nummoz Hmm N.o ~.o moH Nm om mg cm a N m ON 0 o o wcquuuw vac wcuvaoz oamn ~.mH N.NH on.mH wNH.o NM NH ca 6H no wN owH on coo.mH ooo.o wcfinuuamxuuam dawn o.m H.m oNo.N mNm.H cm oN Ne NH ms «N Nm NN oom.N oom.H chLUNEmvaoo oNnm anus: mnaNm » (J q.o~ ~.NH Omm.qH mmo.o mam mN wNo mMH Hon NHH coo eNN 00m.NH cow.n xuucoauuo onm w.H ~.H NHe.H mmm a m mN q 6H s 0N «N 00m.H 00m mau>umo «Hmm woo: mm q.~ m.N CNN.“ NcH.H ANN Hm Nm «N on HN mg cg ooo.H ooo.H uumaou new messes seem oaNn m.~m «.mm ooN.NN ANN.c~ 0mm.N cam omm.~ one mNo Nmo wow.H NwN ooo.HN ooo.<~ nauseaquh oNNm N.~H o.N~ ooo.o~ ooo.m o o o o o o o o ooo.oH oco.m mauxqa an: aHNn a.H N.o eee.~ oem oss ON one ems ass om oea own a u assess sumo nHHNN o.o N.m qu.o moH.v o o N N 0N mH oN~ om ooo.o ooo.c mau>moa ecu weaseaam o ooo.oo~1¢oo.oN oo¢.oNIooo.n coo.maooo.N cash anA >uu>uuu< mo ucwuuom Hauoa moHuHHmooq mouuuamooa moquaauooq nowuuanuoq noauuaaooa uo cube u.u.u.m.n «Nod .CONumuog uo ouum was moauowouau AuauumavCH an ucoaxoanam van nucuasmuanmumu uauom «NwEm no newuanauuawo n.n «dank fi.\3—\.\\ 3~ .::—,,.-...:v._ 5...: I..—-~:.u4.-.u-...sv ~.-1-.T.:~u:.~ .d~:.u.f.|-.2:.r,.. >23 -.u~u~u< r..:.N:> sx.-~..1 .uN..-...~. 54 .mmfiuumsvaw :umnuo: anus vmvsauch .omeHoo >uamum>ficm mamfiz .uomhoum uaoahoanam Hmudm amuwuwdw wnu mo uawcoaaou huumnfidfi mamumlHHmBm 9.5 NO N mmflfim wflfihfiv kuumHHou wqu "mogom Asaav ANHHV ANNHV ANmmv Hausa ooH coo.me~.MH ooo.amw.a ooo.mam.~ coo.mm~.~ ooo.oa~.~ mm ooo.ea~.a ooo.-a ooo.~m~ ooo.ome ooo.~eo.m uuauo a ooo.osm ooo.om coo.meH ooo.mem a assume suds m ooo.mae ooo.ae~ ooo.n~a ooo.am~ a messes OH ooo.ew~.~ ooo.-H ooo.aH~ ooo.mmm ooo.mmm suesuaudu NH ooo.e~m.fi ooo.a ooo.aa ooo.- ooo.N~e.H weasusaaxuaflm Rm ooo.m~a.a ooo.~ma coo.HNm coo.smm coo.emm.~ mssuofisaa Amocomqv Anaouomuhv Awsmxm: .mEmcox muamuwnmnaa ooo.oo~ asses .omv ooo.~ uuso ooo.ooHaooo.o~ ooo.o~1ooo.~ saga aaua ucmuuom Hauoe huwamoon mmfiuwamooa mowuwamuoa momeHw> HmwuumavaH q mNaH\aNaH .soeusuog sea ausuomuuao HaeuuaaesH uHaumuflaaam Na euee< usHa> a.m eases perc both with tail 301i FOre 631111 the 1 Feta? 55 also revealed that tailoring is the single most important small- scale industry.1 Blacksmithing, which accounted for 12 percent of the sector's value added, and carpentry, which accounted for 10 percent, were the next most important industrial activities, but both f01lowed tailoring by a large margin. The composition of industries, however, varied importantly with the size and location of settlements in Sierra Leone. Although tailoring is ubiquitous in Sierra Leone, it is relatively more im- portant in Freetown than in the smallest villages. Indeed, the more "traditional" crafts such as blacksmithing, weaving, and mat making are relatively more important in the smallest villages, while the more "modern" activities such as tailoring, vehicle repair, and metal welding are more important in Freetown.2 These results reflect the importance not only of including location in the analysis of industry, but also provide support for distinguishing between "rural" and "urban" small-scale industries. Although any division between rural and urban is arbitrary, 20,000 inhabitants has been adopted as the dividing line in this study. Thus, Freetown, Koidu, Kenema, Bo, and Makeni, localities with more than 20,000 inhabitants (see Spencer, May-Parker and Rose [1976]), have been classified as urban, while all other localities have been classified as rural. Using this classification scheme, 95 percent of the industrial establishments in Sierra Leone are located in rural localities, and account for 86 percent of the employment in 1 2 See Liedholm [1973] for a review of these studies. Chuta and Liedholm [1975] and Tables 3.3 and 3.4. the i the s rathe tahce in 51 these are ' for 3 mm Eagte 56 the industrial sector.1 Finally, 75 percent of the value added by the small-scale industries in Sierra Leone is generated in the rural 2 These results thus reflect the impor- rather than the urban areas. tance of rural industries and the need to include these enterprises in studies of the industrial sector of Sierra Leone. Moreover, these results also point to the need to ensure that rural industries are incorporated into industrial studies in developing countries.3 3.5 Seasonal Variations in Output The annual value added and output data, however, mask important variations in the level of activity over the year. Indeed, one of the key reasons that data were gathered for one year in the present study was to permit these seasonal variations to be more precisely quantified. The mean monthly variation in output of the major small-scale industry groups located outside the villages are portrayed in Figure 3.1. The graph reveals that all the major industrial groups were subject to large monthly fluctuations in the level of activity. In- deed for all the major industries, the mean output in the peak 1 2Computed from Table 4. Rural small-scale industries account for approximately 33 percent of the total industry value added. 3Due to the lack of comparable data in other countries, it is difficult to assert that the Sierra Leone results about the relative importance of rural industry will be found in other developing coun- tries. A similar unpublished survey undertaken by the authors in Eastern Nigeria, however, yielded results similar to those found in Sierra Leone. Moreover, the indirect evidence relating to rural non- farm activity reported in Liedholm [1973, p. 2] points to the relative importance of these rural industrial activities. Clearly, however, more comparative research on rural industries is needed. Computed from Table 3.1. gUi‘g OUTPUT INDEX Figure 3 57 220- 210» - - Blacksmiths ‘ -— Tailors --— Bakers Carpenters I90- ITO- 160»- I50- I40- 130- I20r- IIO- MONTHS Source: Survey data Note: 9The indexes for tailors and carpenters reflect the mean output value for all localities while the indexes for blacksmiths and bakers are limited to those localities with 20,000 ' 100,000 inhabitants. Index of Monthly Output of Major Small-Scale Industries, 1974-75 (November = 1009) 58 month was at least twice the mean output in the lowest month. The blacksmithing industry exhibited the largest variation in output over the year, while tailoring exhibited the least. The seasonal pattern of output, however, is not the same for all the major industrial groups. Tailoring, the most important of the small-scale industries, for example, is most active during the last quarter of the year, the period when the major harvesting activ- ities are occurring. The seasonal peak for tailoring may be trace- able not only to the several important religious festivals and hol— idays that occurred during this period, but also to the fact that the farmers, who are the major customers for tailoring products in the more rural localities, earn much of their cash income at this time. The blacksmithing and baking industries, on the other hand, attain their peak levels of activity in the second quarter of the year. The seasonal peak for bread may be due to the fact that rice, which is a partial substitute for bread, may be less available and relatively more costly during this period. The main explanation for the seasonal peak for blacksmithing activity is that the farmers, who are the major demanders of blacksmithing services, engage in land clearing during the second quarter and thus require large quantities of blacksmiths' products and services at that time. In summary these results thus reflect the importance of incorporating seasonal variation into any analysis of small-scale industry and point to the potential dangers of surveys limited to one month. and p ise o Sireac Niger' Taber [1912, 3F$F9r educat dies v Of app DerCen DErcen b15Ck$1 DErcen. 59 3.6 Relative Magnitudes of Types of Labor and Capital The labor used by the small scale entrepreneur consists of his own or family labor, paid employees and apprentices. In the small scale industrial establishments in Sierra Leone, the appren- tices represent the largest component of the labor used by the firms in those localities with 2,000 or more inhabitants. Indeed, appren- tices accounted for 42 percent of the labor used, while paid employees and proprietors accounted for 17 and 41 percent respectively. The use of apprentices in Sierra Leone appears to be somewhat less wide- spread, however, than in other African countries. In rural Western Nigeria, for example, over 56 percent of the small scale industrial labor (including proprietors) consisted of apprentices (I.L.O. [1972, p. 87]). The reasons for the relatively smaller use of apprentices in Sierra Leone, particularly in view of the nonformal educational services it provides, must be further studied. The relative importance of the use of apprentices, however, does vary markedly from industry to industry. The highest percentage of apprentices is found in such industries as vehicle repair (53 percent) and carpentry (58 percent), while correspondingly smaller percentages are found in such industries as tailoring (42 percent), blacksmithing (37 percent); gara dyeing (31 percent) and baking (17 percent). Finally, it should be noted that the use of apprentices varies by size of locality. In urban Freetown, for example, 45 percent of the labor used were apprentices while in rural localities with 2,000 to 5,000 inhabitants, the percentage declined to 36 percent. The rural-urban distinction once again thus proved to be important. aver, Dowerr We 1 :anual 11663; [OF ex ll tho pErcgn furthe: [lira] ‘. drags, 00"her i ,4 ”Ustrie Gar tsunamic 60 The amount of physical captial used by the small scale indus- trial establishments in Sierra Leone provided to be quite small. Data collected during Phase I of the study reveal that 63 percent of the small scale establishments possessed at least one machine. The use of machinery was concentrated, however, in the tailoring industry, because virtually every establishment in that particular industry possessed at least one sewing machine. Since 55 percent of the industrial establishments were engaged in tailoring, it is clear that the use of machines elsewhere was indeed minimal. More- over, the majority of machines used by this sector was manually powered. In those establishments in localities with 2,000 or more inhabitants, 16 percent were power driven and 84 percent were manually powered. Finally, it should be noted that the use of machines was less widespread in the rural than in the urban areas. In urban Freetown, for example, 57 percent of the establishments used machines while in those rural localities with 2,000 to 5,000 inhabitants only 48 percent of the establishments used machines. Indeed, it should be further noted that none of the establishments in these smaller rural localities used any power—driven machines. In these rural areas, it will thus be important to ascertain whether the lack of power is an effective constraint on an expansion of small scale in- dustries. 3.7 Returns to Proprietor and "Economic" Profit Rate The return to the small-scale industrial proprietors and the economic profit rate within the various small scale industries will 61 be examined in this section. The return to the proprietor parameter is important because it provides not only a measure of the real in- come earned by the proprietor, but also, to the extent that the pro- prietor is a scarce factor of production, an indication of the rela- tive economic viability of the various industries and processes. The economic profit rate provides an additional measure of the relative economic viability of the various small-scale industries in Sierra Leone. Before proceeding to examine the returns to the proprietor and the economic profit rate, it will first be necessary to specify more precisely how these measures were obtained. Since one of the primary objectives of the study was to examine the economic viabil- ity of small-scale industries, measures of the economic rather than the financial returns were required. Consequently, it was important that all inputs be valued at their opportunity rather than the actual costs. Thus, the returns to the proprietor measure for each firm was obtained by subtracting from the firm's value added figure the opportunity cost of its annual capital services and its annual non- family labor services. The capital services figure are the annual user or rental cost of capital estimated at a discount rate of 20 percent, the assumed opportunity cost of capital in Sierra Leone.1 The estimate of the opportunity cost of the proprietor and family labor was obtained by combining the assumed opportunity costs of hired and apprentice labor. The opportunity cost of the hired labor was assumed to be equal to the actual money wage paid, since the earlier 1See pages 38-44 for a discussion of the opportunity cost of capital. 62 production function analysis had indicated that the marginal product and the wage rate of hired labor were quite similar. The production function analysis, on the other hand, indicated that the marginal product of apprentices was higher than the nominal remuneration they received; thus, in this study, the apprentices' labor was valued in terms of the estimated value of their marginal product in each in- dustry.1 The "economic" profit rate measure, on the other hand, was designed to provide an indication of the return generated by the industrial firm when all inputs, including the proprietor's input, had been valued at their opportunity cost. On this basis, the "marginal," yet viable, firm would thus generate zero returns or a zero economic profit, while a firm generating a positive return would 2 The be earning a pure surplus or positive "economic" profit. "economic" profit figure for each firm was obtained by subtracting from.the returns to the proprietor, the opportunity cost of the proprietor's labor. For lack of a better indicator of this parameter, the opportunity cost of the proprietor's labor was measured in terms of the approximate value of the proprietor's marginal product in 1Thus, based on the previous marginal productivity analysis, tailoring apprentices were valued at Le .08 per hour worked, black- smithing apprentices at Le .05 per hour worked, and gara, carpentry and baking apprentices at Le .05 per hour (assumed to be the same as tailors in the absence of a good estimate). 2The economic profit must be kept distinct from the accountant's concept of profit which usually includes the opportunity cost of the return on capital and proprietor's labor. 63 each industry.1 Finally, for comparative purposes, the "economic" profits figures have been expressed in rate terms as a percentage of the total value of firm's capital stock. The ruturns to the proprietor are summarized in Table 3.5. The table reveals the wide variation in the returns to the propri- etor both by process and by industry. The returns, for example, vary from a high of Le 10,601 per year for "modern" bakers to a low of -Le 850 per year for "modern" blacksmiths. At the major in- dustry level, bakers generate the highest proprietor's return, while tailors and blacksmiths generate the lowest return. One of the most striking results presented in the table, how- ever, is the indication that often the proprietor's return varies markedly between processes in the same industry. Indeed, analysis of variance procedures applied to these data revealed that except for gara dyeing and carpentry, these variations were statistically significant.2 The more "modern" technique in each industry, it should be noted, did not necessarily yield the highest return to the proprietor. Although in tailoring and baking the more "modern“ techniques produced the highest returns, in carpentry and blacksmiths the highest returns did not accrue to those proprietors using the more "modern" technique. The returns to the proprietor, however, also varied by location. IThus, based on previous marginal productivity analysis, tailor- ing proprietors were valued at Le .35 per hour, gara proprietors at Le .84 per hour, carpenters at Le .35 per hour (assumed to be the same as tailors in the absence of a good estimate), blacksmiths at Le .30 per hour and bakers at Le .54 per hour. 2At the 10 percent significance level. 64 Table 3.5 Annual Mean Return to Proprietor and Economic Profit Rate by Major Process and by Major Small-Scale Industry Category Industrial Annual Returns Economic Profit Categories8 to Proprietors as a Percent of Total Capital Stock (Leones) (Percent) Tailoring "Traditional" 377 23 "Modern" 982 46 Gara "Traditional" 1,572 190 "Mbdern" 1,463 170 Carpenters "Traditional" 2,062 169 "Modern" 2,060 21 Blacksmiths "Traditional" 745 75 "Modern" —850 -25 Baking "Traditional" 1,476 106 "Mbdern" 10,601 35 SOURCE: Survey data. 8Includes both randomly and purposively sampled firms in these localities with 2,000 or more inhabitants. This fact is demonstrated in Table 3.6 where the returns to those proprietors using the more "traditional" processes are arranged by locality size. The mean proprietor returns are generally highest in the intermediate size localities and lowest in the villages. For example, the tailoring proprietors engaging in ordinary sewing in those localities with from 20,000 to 100,000 inhabitants generate 65 Table 3.6 Annual Returns to Proprietor by Major Industry and Location for "Traditional" Processes Only (Leones per Year) Industrial Locality Categorya Villages 2,000- 20,000“ More Than Less Than 20,000 100,000 100,000 2,000 Tailoring 164 316 465 365 Gara dyeing -- -- 1,572 -- Carpentry 33 1,365 2,680 1,514 Blacksmithing 199 779 1 645 -- Baking -- 1.443 1,564 —- SOURCE: Survey data. aIncludes all randomly and purposively selected "traditional" firms; for definition of term "traditional", see text. an annual return of Le 465, while those tailoring proprietors in the villages generate only Le 164 per year. The relatively low return to the proprietor in the villages is undoubtedly due to the fact that they also engage in farming activity. Unfortunately, it has not yet been possible to determine the income generated by the pro- prietor's farming activity; this interaction, however, will be the subject of a subsequent study. The returns to the proprietor for firms in localities with 2,000 or more inhabitants, however, do shed some light on the rela- tive income position of the proprietors. The mean annual return to the proprietor from the randomly selected firms, for example, was 66 Le 672. The unskilled urban worker receiving the minimum wage earned approximately Le 250 per year, while the farm proprietor received a mean annual income in 1974/75 of approximately Le 475 [Spencer and Byerlee, 1976]. The mean return of small scale industry proprietors was thus higher than that earned by individuals in these two groups. The determination of the exact relative income position of the small scale industry proprietors must await more complete supporting data.1 An examination of "economic profit" rates of the small scale industries can provide additional insights into the economic viabil- ity of these activities. The estimated "economic" profit for these firms, expressed as a percentage of their capital stock, has conse- quently been estimated and summarized in Table 3.5, where the data have been presented by major process and major industry. The most striking result is that, with the exception of "modern" blacksmith- ing, all the processes and industries generated a positive "economic" profit; thus, if the assumptions underlying the analysis have been correctly specified, all the major types of small industrial activi- ties, except "modern" blacksmithing, must be considered economically viable. The "economic" profit rate varied widely, however, both by process and industry. Baking, gara dyeing and carpentry, for example, generated the highest rates of economic profit, exceeding 100 percent in several instances, while tailoring and blacksmithing generated IByerlee, Tommy and Fatoo [1976] report that those migrants who are self-employed earn an income almost one-third higher than that of the average employed migrant. 67 economic profit rates that were significantly lower.1 Moreover, the "ecomomic" profit rates differed significantly by major processes within each industry.2 It was the more "traditional" rather than Ethe more "modern" processes that generally produced significantly higher "economic" profit rates in these major industries. Indeed, only in the tailoring industry, did the more "modern" processes, embroidery, generate an economic profit rate exceeding that generated by the more "traditional" process. Even the traditional tailors, however, earned a positive "economic" rate of profit. The analysis thus indicates that the "traditional" processes used by small scale industries in Sierra Leone are economically viable at the present time. 3.8 The Problems of Small Scale Industry Entrepreneurs As Table 3.7 shows, 60 percent of the 213entrepreneurs3 who provided information said that the lack of capital was a major prob- lem facing their enterprise; 23 percent of the respondents felt that high cost of material inputs and/or scarcity of materials were major problems hindering the progress of their business; 9 percent of those respondents believed that the lack of effective demand was a major bottleneck facing them, while 6 percent of the proprietors thought that troubles with repairing equipment and obtaining the necessary spare parts were major problems they had to face occasionally. In 1Annalyses of variance indicated these were different statis- tically at the 1 percent level. 25 percent level. 3This figure includes the respondents in both the random and purposive sample. - order to place these problems in proper perspective for a later 68 analysis, each of these problems will be duscussed in greater de- tail. Table 3.7 Problems of Small Entrepreneurs in Sierra Leone, 1974/75 f Tailor Gara Carpen Black- IAdl Kinds 0 Problems , ' dye- - smith- Bakery Others n us- ing ing try ing triesa Z Z Z Z Z 1. Lack of demand 15 28 6 - - 6 9 2. Lack of Tech— . nical advice - - - - 5 - 0.5 3. Troubles with equipment 4 - 3 5 5 - 3 4. Lack of skilled labor - - 3 5 - - 0.9 5. Poor trans- portation - - 3 - - - 0.5 6. Lack of capital 64 43 66 52 40 66 6O 7. Scarcity of materials - - 3 l4 5 10 5 8. Scarcity of spare parts 4 - - 5 - 4 3 9. High cost of materials 13 29 l6 19 45 14 18 Total 100 100 100 100 100 100 100 Number of observations 80 7 32 21 22 51 213 Source: Computed from the entrepreneurship survey ‘aThese percentages are weighted averages. l The survey data revealed also that out of the 270 respondents that were selected for study in regions I-III, 9 percent dropped out 69 3.8.1 Lack of Capital Although most of Sierra Leone small scale entrepreneurs feel that lack of capital was their major problem, a recent analysis of the Sierra Leone data reveals that profitability in business is nega- tively correlated with the level of initial capital [Liedholm and Chuta, 1976]. However, the survey data also reveal that 80 percent of the funds used to establish small scale industries come from personal and family savings, and that 90 percent of funds used for expansion were reinvested profits [Liedholm and Chuta, 1976]. 3.8.2 Scarcity and High Cost of Materials Table 3.7 further reveals that the industries affected most by the high cost of materials are the gara dyers and bakers. How can we explain such a rise in the costs of material inputs? With respect to the gara dyers, the prices per cwt. of synthetic organic dyestuffs and caustic soda (sodium hydroxide)1 rose at annual rates of 42 percent and 4.4 percent (Freetown C.I.F.) between the period, 1969 and 1973 (Quarterly Trade Statistics). Also, during the per- iod of the survey, 1974/1975, our material input purchases data re- vealed a price increase of about 20 percent for each of these inputs. Such price increases could be explained partly by the scarcity of the materials from which the inputs are traditionally synthesized. A case in point is the scarcity of petroleum in recent years. of the sample due to such problems as lack of capital, lack of effec- tive demand and scarcity of material inputs. 1The three ingredients necessary for forming the dye solution are the synthetic organic dyestuff, the caustic soda (sodium hydroxide) and soda ash (sodium dydrosulphite). These ingredients constitute about 14 percent of the total material input purchases in gara dyeing. 70 In addition to the rapidly increasing import prices, the imp port duty on dyestuffs and other dyeing ingredients, which is in the magnitude of 36 1/2 percent ad valorem adds to the cost burden. However, another possible reason for the high cost of these ingre- dients to small producers is the small amounts in which they were purchased. Although, the Major and Company in Freetown, that deals on the Iandanthren vat dye-stuffs, allowed a rebate of one leone for each pound weight of dyestuff purchased, such economies of large scale purchasing did not accrue to small individual producers, some of whom bought dyeing ingredients in spoonfulls rather than in large volumes. In Gara dyeing, expenditures on imported textile fabrics are one major item of material input purchases, accounting for about 80 percent of total material input purchases needed for gara dyeing.1 Although textile fabrics, available to gara dyers in Sierra Leone, range from shirting and poplin, costing about 45 cents and 65 cents a yard respectively, to the very expensive cotton fabrics, costing Le 1.2 per yard for the "Omega," "Santos" and "Satin" brands and Le 3.6 per yard for the "Super" brand, most gara dyers prefer to pur- chase the medium price of fabric.2 The prices of these fabrics have increased due, not only to increases in the world prices of Egyptian cotton and overseas production costs, but also due to a customs duty 1Computed from Survey data. 2"Santos," "Omega" and "Super" brands were sold in widths of 50" to 52”, "Satin” in 36" widths, poplin in 31" to 32" width and shirting in 27" to 28" widths. The quoted prices are relevant for the 1974/75 survey period. 71 of about 22 percent ad valorem.1 The scarcity of the medium brand fabric always forced dyers to purchase the costliest brand, where it was available, and this sitution inflated the domestic production costs. The bulk of the purchased intermediate inputs in the bakery industry is flour which constitutes about 50 percent of total value of output and between 90 percent to 95 percent of total value of material input purchases needed for baking. The high cost of flour is associated with the high cost of domestic production that results from the Sierra Leone government's import - substitution policy of the 19605. Since the flour mill was installed in 1967, flour imports have been officially banned and bakers have to pur- chase the locally milled flour at a high cost. In fact, based on the price of wheat flour quota imports, the implicit tariff on wheat flour in Sierra Leone is about 167 percent.2 Another industry affected by the scarcity and high cost of material input is the blacksmithing industry. Local blacksmiths de- pend on car springs, iron rails or scraps, and railway sleepers for manufacturing cutlasses, axes and hoes respectively, items that are important for agricultural production. Our survey reveals that the price of car springs has increased by about 100 percent in recent years. Since rural blacksmiths do not have access to iron scrap or car springs that are found in large cities, most of the time, these 1The 22 percent ad valorem equivalent was computed from the specific duty. 2The price of imported flour was about Le 6 (Freetown, C.I.F.) while locally produced flour cost Le 15 per cwt. in Freetown during the survey period. 72 material inputs are not available. The industries least affected by the rising cost of material inputs are tailoring and carpentry. Nevertheless, these industries are also affected by import duties, but only to the extent of the import content of the material inputs. In the tailoring industry, the purchases of textile fabric varies from 21 percent in region I, 82 percent in region II to 33 percent in region III.1 Whereas the import duty on textile fabric is about 22 percent ad valorem, the import duty on the other inputs such as needles and buttons is 36 1/2 percent. In the carpentry industry, the bulk of the material input pur- chases is timber, constituting on the average about 32 percent of total material input purchases. According to our survey, all the timber utilized by the sample firms is locally produced. Another category of material input purchase in the carpentry industry is plywood. All plywood utilized by our sample firms was imported and at a cost of an import duty of 36 1/2 percent ad valorem. However, plywood purchases of one respondent constituted 67 percent2 of the total cost of this material input purchases. For the others, ply- wood purchases constituted only 4 percent to 9 percent3 of total material input purchases. The third category of material input purchases subject to about 35 percent import duty includes nails, formica, polish and sandpaper. Since nails are manufactured in 1Computed from Survey data. 2Computed from Survey data. 3Computed from Survey data. 73 Sierra Leone, some firms purchased only imported nails while others combined the use of imported and locally produced nails. Most car- penters in region I used imported nails only. 3.8.3 Lack of Demand The problem of lack of demand is most severe among the gara and tailoring industries. The presence of excess capacity among these firms1 is one indication of the low level of demand for the products of these industries. With the exception of the gara indus- try, the small scale industries depend on the domestic market for the purchasing of their goods. Sierra Leone has good export pros- pects for gara cloth, not only among African countries, but also in America and Europe. Our survey reveals that about 18 percent of total value of gara cloth production was exported in 1974/75. 3.9 Summary The survey results reveal that the small scale industrial establishments in Sierra Leone dominate the industrial sector in terms of both the number of firms and total employment. Tailoring occupies the dominant position in the Sierra Leone's small scale industrial subsector, accounting for 31 percent of the employment, 33 percent of the establishments and 37 percent of the value added. The Sierra Leone survey reveals seasonal variations in industrial activity among the small scale industries. Although small scale in- dustry entrepreneurs utilize the services of family labor, hired workers and apprentices, apprentices accounted for 42 percent of 1See pages 92-94. 74 the labor used. All the major types of small scale industrial activ- ities, except "modern" blacksmithing can be considered economically viable. The data reveal that the bulk of the small scale entrepreneurs (60 percent) feel that the lack of capital was a major problem fac- ing their business. Other perceived problems include, the lack of effective demand for the products of small scale industries, scarcity and high cost of material inputs, troubles with equipment and the lack of spare parts. Available time series and secondary data reveal that much of the high cost of the material inputs used by small scale intrepreneurs is due to both price increases in recent years and high import duties on imported intermediate inputs. CHAPTER 4 DELINEATION 0F REPRESENTATIVE FIRM TYPES 4.1 Introduction In order to focus on the small scale industrial firm, as a decision making unit, representative firm types or production pro- cess types [Dorfman, l953-a] will be delineated. Since the pro- cedures for delineating and weighting the representative firms for the purpose of aggregating the linear programming results in- volve errors of aggregation bias, the criteria will be spelled out ' It will now for grouping firms into representative firm types. be necessary to present, first, a broad descriptive categorization of representative firm types, and second, a detailed breakdown of representative firm types, which will be used for our linear pro- gramming analyses. 4.2 Broad Categories of Representative Firm Types Most analytical procedures tend to delineate production pro- cess types as "traditional," "modern," or "intermediate." Sometimes, "labor-intensive" and "capital-intensive" processes are used synon- ymously with the "traditional" and "modern" techniques of production 1For the purpose of this thesis, the definition of the repre- sentative firm as the average or modal firm is inadequate [Day, ’ 1963]. For a classical definition of a representative firm, see Marshall [1920, p. 317]. 75 76 1 In defining the broad production methods used in respectively. the Sierra Leone small scale industry, these concepts, though inade- quate, will be utilized to differentiate the production processes. In the tailoring industry, treadle sewing machines are wide- spread. But most treadle machines can be operated by electricity in localities where electricity is available. Since electricity is not available in most rural towns and villages, sewing machines are operated manually. In addition, there are sewing machines which do simple sewing, whereas other more complicated types are capable of putting embroidery designs on sewn fabrics. These differences in the type of tasks performed are also reflected in cost differences-- Le 100 to Le 110 for simple sewing machines of the Chinese "butterfly" brand or Japanese "Hedjazi" and Le 230 to Le 300 for the embroidery machines of the Italian "Necchi" brand or the Japanese "Zenith" or "Hijaz." But more important is the fact that within the embroidery process type, there exists a wide range in the cost structures of machines - ranging from Le 230 or Le 300 (as already pointed out) to Le 1000 for the British "Cornely" brand of embroidery machines.2 Within the gara3 industry, there is a clear distinction between "traditional" firms which dye fabric with the native indigo dye and the "modern" firms which use synthetic vat dyes such as the lBhalla [1975] points out that the dichotomy of labor-intensive and capital-intensive techniques is "confusing" and "inappropriate." 2These prices were observed for the survey period, 1974/75. 3"Gara" refers to the leaves of a Leguminonous plant (Loncho- carpus Cyanescens) that grows in the Northern Province of Sierra Leone. The native indigo dye solution is made out of those gara leaves in com- bination with other local leaves, nuts, roots, and tree bark. (See Chuta and Steacy, 1975). 77 Indanthren and Caledon vat dyes that are imported form the German Federal Republic and United Kingdom respectively. Whereas the native 1 in 44-gallon-size indigo dye takes seven to fourteen days to vat drums, the synthetic dyestuff takes only 10 to 15 minutes to vat in small dye basins. In addition, whereas the native indigo dye yields only the dark blue or dark green color when combined with Kola nut ("Kola acuminata"), the synthetic dye has a large number of color variations [Chuta and Steacy, 1975]. There are, of course, firms which combine both the native indigo dye and the synthetic dyestuffs, even on one fabric, in their dyeing operations. In the carpentry industry, traditional firms employ simple hand tools like saws, planes, hammers, and operate in thatch-roofed workshops. On the other hand, the "modern" carpenters, in addition to using simple tools, employ sophisticated equipment such as the Italian electrical combining machines, which contain wood planer, wood mortiser, vertical router, retractile circular saw, square, knife, and sharpener and costing about Le 4000 (Freetown C.I.F.). In addition, modern carpenters incur higher workshop rental costs since they operate in cement buildings with zinc roofs. Within the blacksmithing industry, the "traditional" firms use simple tools like hammers, bellows, anvils while their "modern" counterparts use electric forges, drilling machines, and welding equip- ment. Whereas the majority of "traditional" blacksmiths operate in thatch-roofed sheds and mud houses, the "modern" ones invest in cement 1The vatting process involves reducing the dye particles into their "Leuco" compounds whereby they become soluble in caustic a1- kalies. 78 houses with zinc roofs that command much higher rental values than the traditional type of workshops. Finally, the bakery industry in Sierra Leone contains at least three distinct firm types. The "traditional" firms mix, knead, and cut their dough manually and bake their bread in mud ovens costing about Le 50. On the other hand, the modern firms use electrical mixers, kneaders, cutters, rollers, and ovens. The electrical ovens vary from the French and German "peel" oven, to the British “reel" or "rotary" oven and the Italian "automatic" oven. These modern equipments cost between Le 30,000 to Le 60,000 (Freetown C.I.F.). Due to the differences in the types of workshops being used, the "traditional firms" incur a rental cost of about Le 24 per year, while the "modern" firms incur an average annual rental of about Le 2400. One bakery firm of the intermediate technology type uses a wood-firing iron oven imported from Britain some years ago. 4.3 Detailed Delineation of Representative Firm Types There are at least three considerations--definitional, empir- ical, and analytical--that necessitatea more detailed breakdown of our firm types. First, for definitional reasons, processes are said to be different so long as resources are consumed in different pro- portions, even while using the same resources and producing the same output [Dorfman, l953-b, p. 14]. Secondly, the Sierra Leone small scale industry data have revealed that, given one technique of pro- duction, there exist several ranges of resource combinations involved in producing an industrial output category. The wide variations in resource combinations nay be due to such factors as cost differen- tials, excess capacity levels which also vary with locations, 79 industry, and managerial ability. Thirdly, for purely analytical reasons, representative firm models present serious problems that could render the solutions they yield meaningless. Some of these problems relate to the manner in which the typical firms are sel- ected; others arise because of the type of constraints that are im- posed on the representative firm types; finally some of the problems are purely errors due to aggregation bias. The details of these problems and the empirical handling of those problems in the Sierra Leone small scale industry study will now be duscussed. The first kind of problem that is associated with the repre- sentative firm model is connected with the selection of the repre- sentative firm types [Sharples, 1969]. The purpose to be served by the representative firms determines the procedure for selecting the typical firms. (If the objective is supply response, then a procedure of obtaining representative firms by simple averaging of data will yield unrealistic results [Lee Day, 1963]. It has been pointed out that responsive firms are located not at the middle but at the tails of distributions [Barker and Stanton, 1965]. Thus the procedure that seeks to estimate supply response by focusing on the mean values misses the responsive managers. Barker and Stanton [1965] also point out that another source of bias for the represen- tative firm approach stems from the fact that, in sampling, units are grouped into relatively homogenous strata and equal number of elements are selected from each stratum. If indeed supply response is the primary objective of using the representative firm approach, then the relative concentration and flexibility of firms and resources have to be taken into account. Thus more elements should be selected 80 for study from those areas where firms and resources are most responsive to price changes and the selection of representative firms based on those elementary units. In the Sierra Leone small scale industry study, the ultimate selection of representative firm types was fully considered at the stage when respondents were being selected for the field survey.1 First, by adopting both a random and purposive procedure for sel- ecting respondents, we ensured that not only average firms but the more "progressive" ones were selected [Chuta and Liedholm, 1975]. Secondly, by selecting a larger number of firms for study from towns of larger population concentration and income levels, we allowed more firms in these areas a greater chance of being included in our representative firm types [Chuta and Liedholm, 1975]. These procedures ensure that firms with different levels of economic incentive and risk-bearing capabilities are represented in the study. Another serious problem of the linear programming representative firm approach is externalities [Sharples, 1969, p. 355]. This approach obtains aggregate supply by summing the weighted representa- tive firm supply functions while assuming that input costs and physical transformation rates of individual firms remain constant at all levels of aggregate production. In reality, aggregate production could have a major impact on input prices which in turn affect supply functions. It all depends on the elasticities of supply of various kinds of input categories. In order to overcome the problem of externalities, some studies have limited the major inputs on a 1See page 5 31 -34. 81 regional or subregional level for the different representative firm situations rather than limit those inputs to the level of the repre- sentative firm's statistical averages [Sharples, 1969; Buller, 1965]. The use of regional or subregional restraints allows the represen- tative firms enough capacity to compete for input supply. Under this circumstance, it is valid to assume that input supply is per- fectly elastic while demand is fixed. 0n the other hand, if inputs are constrained at the level of statistical averages, competition among the representative firm types might exhaust the prescribed level of those inputs. In such a circumstance, the assumption of perfectly elastic input supply function is not tenable [Lard, 1963; University of Minnesota, 1965]. The small scale industry study will specify resource constraints at four regional levels for the differ- ent kinds of resources. Finally, one fundamental problem that is involved in selecting representative firms for linear programming models is the aggrega- tion error. Richard Day [1963] points out that the question to be answered is "to what extent the component firms be alike in order for a single model to represent the aggregate of the individual decision problems without distortion." Day further specified what is usually referred to as the sufficient conditions for exact aggre- gation of linear programming models. Using the duality theorem of dual linear programs, Day specified that for exact aggregations to obtain, the following conditions must be satisfied. They are that: Bi = Bj = B (1) Ci 1- : 11>0; Ai» O 2i = in : Yi > 0; Yi < l; Zyi = l (3) Z = Zaili : 011. _>_ 0; 2011. = 1 (4) Eaiy‘. = l (5) Where: th B. = Technical coefficient matrix of the i firm. B = Technical coefficeint matrix of the aggregate firm and the equality of Bi's with B ensures the condition of technical homogeniety. th = Constraint vector of the i firm. th = The expected net return vector of the i firm. C C = Constraint vector of the aggregate firm. Z Z = The expected net return vector of the aggregate firm. a. = Weighted average of the individual firm's objective function. A. = Vector of proportionality, i.e., the proportion of each region's resources which the ith firm possesses. y. = The proportion of the value of the aggregate objective th function, possessed by the i firm. Homogeneity1 of the technical coefficients implies that all the individual firms that constitute a representative firm or produc- tion process type require the same amount of resources in producing an additional unit of output. Thus, if the sum (If the vectors of proportionality are greater than one, i.e., 2 Ai > 1, it suggests 1Day admits that the homogeneity of technical coefficients includes cases of proportional variation in the input-output struc- tures of firms that make up a representative firm type. 83 that some firms within a representative firm group are more pro- ductive than the average firm, and hence, would be more optimistic in responding to changes in some exogenous incentive parameters. Likewise, if 2 hi > 1, some firms within the representative firm group are earning net returns that are greater than the average and thus would possess different investment and growth patterns. In these cases, where 2 xi > 1 and 2 Vi > 1, the linear programming models will tend to predict output, profit, and investment patterns that are less optimistic than the actual, the difference between tha actual and predicted values being errors due to aggregation bias. The redundancy of equation (5), which states the condition that 2 “i Yi = 1, i.e., that the sum of the product of weights of the firm objective functions and of the proportionality vectors for the net revenue vectors be equal to one, has been proven by Marenco [1969]. The redundancy is based on the fact that when once the “i is known, the Yi is automatically determined [Marenco, 1969, p. 686]. Many scholars have considered Richard Day's specifications (that is, homogeneity/proportional heterogeneity for coefficient matrices and proportionality for constraint and net return vectors) too restrictive for empirical work [Lee Day 1963, Miller, 1966]. Recently, Paris and Rausser [1973] have pleaded for conditions of aggregation less binding than those specified by Day. They have therefore suggested and proven mathematically that an assumption of "equal dimensionality of all the firms' problems," is enough to ensure exact aggregation. In other words, provided that all the firms that are to be aggregated into a representative firm type have equal number of activity columns and rows, exact aggregation 84 can be carried out. For example, "equal dimensionality" condition will invalidate an attempt to aggregate bakery firms that produce only bread with those that produce both bread and cakes and/or bis- cuits. In order to aviod serious errors of aggregation,1 both the restrictive and relaxed aggregation conditions have served a useful guide in delineating the representative firm types. Allowing for some narrow variations in resource ratios, the Sierra Leone small scale industrial firms have been grouped into relatively homogenous firm types. The major criterion for grouping those firms into specific firm types is the individual firm's labor-capital ratio (which incorporates output - labor and output - capital ratios). But, since labor-capital ratios vary widely from industry/to indus- try, and from location to location, firms have been stratified by location and industry, before aggregating individual micro firms into homogenous firm types on the basis of labor-capital ratios. Analysis of variance procedure confirms that the variations of labor- capital ratios by industry and location are statistically signifi- cant at the 1 percent level and 10 percent level, respectively [Liedholm and Chuta, 1976, pp. 52-53.]. On the whole, 43 representative firm types have been identified, as shown in Tables 4.1 through 4.4. As the third columns of Tables 4.1 - 4.4 reveal, the range of variation of labor-capital ratios within groups of firms that have been aggregated is very narrow. This 1H. Theil [1957, p. 122] pointed out that "the aggregation bias of small industries, i.e., industries with relatively small total outputs, tend to be small in an absolute (not relative) sense...." 85 ensures the near-homogeneity of output and resource vectors of indi- vidual firms that constitute a representative firm type. The last two columns of Tables 4.1 - 4.4 yefifld additional information that explains the variations in labor-captial ratios between representa- tive firm types. For example, whereas the individual firms that con- stitute representative tailoring firm type, Plll on Table 4.1, have a labor-capital ratio of 8-9, those of P112 have a labor-capital ratio of 4-5. This variation in labor-capital ratio is due to the fact that the firms constituting P111 and possessing only one or two sewing machines, are less capital-intensive than the individual firms constituting P112, and having between two and five sewing machines, including one expensive embroidery sewing machine. Fin- ally, Table 4.5 is an attempt to assign weights to the different representative firm types as they are considered to exist in the pop- ulation. Those weights will be used for aggregation purposes in the linear programing model, after they have been adjusted for their Upper and lower bolids.1 4.4 Annual Resource and Output Requirements for the Representative Firm Types Tables 4.6, 4.7, 4.8 and 4.9 show the annual resource and output rmluirements for each industrial representative firm type in the respective regions. The resource and output coefficients have been C”Minted with the objective of carrying out the analysis of the base. run of the model and also some sensitivity analysis. As colunm one of Tables 4.6 - 4.9 shows, variables with an \ 1See page 113. 86 moafisupa woos onus was awawvafian acoaou Haowuuooao chavoa swung m ale Hamm auoxam owuow «mouuuuoam wounufiam woos onus was unavawan uaoaau moawnowa swung a N Hana ixuaam moon uawu van waqvawan uouaoo «magnuwa swung . a m Nacm nonaxuon huouoaaoa mHOOu Hausa N mmiem Nasm muucoaumu .oaunau o>waaonxo mood manna codaswiec 01H use 233% H3333: our: 523 use H H on 22 .oaunsw o>wmconxo mood was unauuohv uauonuaha was: nuance amp m H oN Nana .uwupau o>anconxo waaoav >uo> can uuaumazv uuuanuchm mom: magma; ohm m H OH Hana sumo .waqsau unwuauuo on .ocanoufl zuovuouaau guano >Ho>au iaaou oao ovsaoaw meniscus mausom maaasoaa wausom m.N N HMIQN mafia .OOu unison unmuauum on .onqnuaa auovuouaao o>amcon ixo use ovsaoaw aoawnoua wau3om moawnuma wowsom miN N mus NHHm ”sebum unwuohum made on maawnoaa magnum NIH N «am Adam mawuoHNsH muowouau unannouu luau o>wuauaouounoa ucuanuavu was mHOOH a u :a album cuuau Hauaaao alsz ovoo sham huuaavau ocu nu «spam we sawunuuumoa Macao hand: no onum uo\vaa onus ouuaum no .oz iuonaa mo amend o>wuouaomouaom H cosmos sou 39¢ a: 333.83qu «5 no 32333828 3.856 as. 623“ 87 moo» nuuwnu .wcavaqsn no: co>o magnumivooa vs: N mmimm MNwm moon onus .mwcavauaa ucoaou co>o mauuwu .vooaicouu N 9N NNmm «caucus! moon ucfiu .mwcfivaaan ucaaoo NaouuuuoNo shoves swung m QIN NNwm auuxsn «whoa Nauuuuoauo Noah and» .mcwvawan ucuflou can dosages! swung N N, NNmm . sausages nonnxuoa aunuoaloa uNOOu madam N ominN NNmm ixuaan woos onus “maavauan usuaoo mocqsuna owned N «1N mNem nozuxuoa auauoaloh aqccu Hanan N No NNom aosaxuo: auauomloa mecca Nun-m N nnuam NNem anacoauao .uwunau o>wncomxo naoN on: .uuauuoav auscuuuvauu on» no on: moon was «mounoav can Hahn lacunae no on: Manson» a onus-co noNNwwioc N .acuuan axe N N oNNlNo MNmm .oauaau o>uucanuu uaoN wasuv van unauaoav Naaoauuvnuu non: aoNNaNiqq 01H .caman ohm N N am NNmm .uwuaau o>wnconxo ”swamp one: one manuaohv uwuonunhu mom: acumen mac n N 0N NNnm sumo .oou amazon unwuauuu on .ocazoaa huovaoualo oao uuaoa an ovaNocu mucusuua sow macaroni mcqaoa his a Q1N «Nam .OOu newton unquuuo on .oaazuaa huovaouplo 0:0 gamma an «233.: «25:03 95:3 amazon... magnum MIN n 0.73 MN: median unwuuuua ammo on «unusual mambo» NIN «N ONIQN NNNm nausea uswwauuo huco on macaroni unison Nun o Nun NNNm nauuoauaa - huouuuau wanaaouu luau o>auuucooounom unalnuavm can sneak a u a I.» sauna Nauunau «In: avou luau huuasvau onu :« ulna“ no :oNuANHOQOO noguo Hana: no auum nexvaa ache adulimuuo .WM aboaaa mo aunax 0>Nuaun0aouna¢ NH :oNuom new macaw sham a>uunuaoaounax mo nouunuuauuauunu Nuuanou N.¢ sun-h 88 moon nouanu .mNHma vs: ca>o wcauwwvooz us: N «N Nmmm muoxmm monmxuoz humuoaaoh mNOOu Namam N HNHIom Nmmm wcwnuaam aosoxuoa suouoaaoa aHoou Seam m 218 :2 used; .uonma moau Icouaam uo om: nuns .uonmN owns mHoou o: .woou onus .wcavauan ucoaoo Hanan can mocwnoma swung N «NH mmqm monmxuos zumuoaaoa mHOOu Hamam N MNN qum nonmxuoa huauoaaoe mHOOu Hamam N mMIom qum anacoaumo owunmw wcwoxv amono auo> om: .omv uwuonuazm om: mcfimmn amp N N Noise Hmmm sumo .00» waabom uawfimuum on .ocanumfi huavwounao oco unsoa um ovaaucw moafinuma weasom mocwnoaa newsom mIN a «NINH «mam >Nco amazon unwwauum seasons waaaam N o wNImN mmam mace wawaom unwumuum mocwnuma wawaom NIH NH NNIHH Nmam mace waaaom unwasuum ocfinuma wcwaom H N mIN Hmam mcwuoaqma muowoumu magnaouu sham a>aumuaomounom uaaaaaavm was macaw o a may oNumm Naufiaao osmz ovoo sham huum: a saw ca «spam no sowuawuumoo wonuo scum: mo ouwm uo\vca onza ONMMQmNuo .M“ Incas; mo annum o>wuauaomauaom v H NNH acawom now momma sham o>Huauaouauaox mo mowumwuouumumno Nauocou m.c «Name 89 32:56.83 3 as: No uaoouoa mIN mouo>on mNOOu NNaam N mIN mama chNuNamxuaNn ou uaNu mo uaouuoa ONION mouo>an mNOOu NNmam o ONION Nona . maNnuNauxuaNa ou . maNu mo uaouuon NOINO mauo>oo mNOOu NNmEm N «OINm Ncnm wcwuwwflm auuaoauau ou oaNu mo ucoouom mN Gaza mmoN mouo>on mNOOu NNaam N N mean muuaoaumu ou oaNu mo uaoouom mN many amoN mouo>on mNOOu NNmam N m Noam hwy—39.30 ca can we ucoouom nN can» mmON mouo>oo mNoou NNmam N .mNInN Ncom zuuaoquau chHONNmu cu oaNu mo unmouoa mN mouo>oO moaNnomB waNaom N m ONIO McNm maNHONNau cu oaNu mo unwouoa Om mouo>on oanuaa waNaom N m NNINN Nqu wsNHONNau cu oaNu mo unmouoa NN mouo>on oaNnoma waNaom N N an Nqu chHONNmH huomouao chanuu auNm a>Nuaucomouaom uaoaaNavm can oNooa a a map ONuom NauNnao oaaz ovou auNm huum: :4 any :N mauNm mo aoNunNuumoO uosuo uofiaz mo auNm uo\v:s mask : aN Nm IuonsN «o annum o>Nuauaonounom e t . oNnasm no .02 >N :onom new wonha ath o>Nuaucamounom wo.moNumNuouuouano Nauoaoo c.¢ oana table 3.5 III 90 Table 4.5 Population Weight Distribution of Industrial Firms by Representative Firm Type Industry Tailors Gara Dyeing Carpentry Blacksmithing Bakery Region Type L/K No. Type L/K No. Type L/K No. Type L/K No. Type L/K No. I P111 8-9 514 P311 10 3 P411 54-55 56 P511 2 12 P811 6-9 11 P112 4—5 139 P312 20 16 P412 5 19 P113 24-31 163 P313 50 1 Total 816 20 75 12 II P121 5-7 138 P321 10 13 P421 31-33 50 P521 15—30 14 P821 2-4 6 P122 14-30 315 P322 50 13 P422 91 51 P522 2 1 P822 16 1 P123 10-16 112 P323 92—176 74 P423 2-4 34 P823 35-38 10 P124 7-8 92 Total 657 100 135 15 17 III P131 7-9 99 P331 64-92 120 P431 30-35 163 P531 20-23 80 P831 24 144 P132 11-21 644 P432 123 179 P532 99-111 26 P133 25-28 297 P433 114 1 P134 11-24 198 Total 1,239 230 343 106 144 IV P141 34 1040 P441 13-13 1666 P541 52-65 1375 P142 11-17 10920 P442 9 1666 P542 10-30 2750 P143 6-10 1040 P443 2 1666 P543 1-5 1375 Total 13,000 4,998 5,500 Note: L/K means range of labor-capital ratio. Source: The total figures were computed from Phase I survey data. (See Chuta and Liedholm, 1975, pp. 18-19.) 91 "a" footnote indicate resource and output coefficients computed at the capacity utilization level existing in the l974/75 survey per- iod. 0n the other hand, variables with a "b" footnote indicate re- source and output coefficients computed at the 100 percent level of capacity utilization.1 The adjustments of resource and output coef- ficients for l00 percent level of capacity utilization was based on the observed level of excess capacity as shown in the first rows of Tables 4.6 - 4.8. For example, in Table 4.6, in order to allow the representative firm type P111 to operate at 100 percent level of capacity, the annual output coefficients at existing level of cap- acity utilization were increased by l8 percent, i.e., from Le lOll output value to Le ll93. Likewise, proprietor hired and apprentice hours of labor services were increased from l4l7, 0, and 500 to 1672, 0 and 590 respectively. Also, material input costs were increased by l8 percent, from Le 382 to Le 451. Finally, variables with "c", "d" and "e“ footnotes in Tables 4.6 - 4.9 refer to capital coeffic- ients computed at l0 percent, 20 percent, and 35 percent rates of interest. The coefficients that are relevant for the initial run of the linear programming model in Chapter 5 are those that relate to the 1974/75 survey period level of capacity utilization and with capital 'coefficients computed at 20 percent rate of interest. Those variable coefficients bear the footnote "a" and "d" on Tables 4.6 - 4.9. The rest of the coefficients will be utilized for the sensitivity analysis yet to be undertaken in Chapters 6 and 7. IRepresentative firms in Region IV have not been adjusted for l00 percent capacity utilization since those firms also actively en- gage in agricultural production. 92 Table 4.6 Annual Resource and Output Coefficients by Representative Firm Types for Region I Ind:::;ial Tailoring Gara Dyeing Carpentry 3:128:83 Bakery Types Variables Unit P111 P112 P113 P331 P312 P313 P411 P412 P511 P811 Excess capacity Z 18 56 30 17 O 17 0 27 0 38 Output8 . 1e 1011 1073 3285 6591 8077 3316 4567 9492 1008 118569 Outputb 1e 1193 1674 4271 7711 8077 3880' 4567 12055 1008 163925 Proprietor labora hrs. 1417 980 1945 1815 1380 1286 1711 1375 466 3672 Proprietor laborb hrs. 1672 1529 2548 2124 1380 1505 1711 1746 466 5067 Hired labora hrs. 0 815 41. 805 20 422 o 3918 0 48486 Hired laborb hrs. 0 1271 57 942 20 494 0 4976 0 66911 Apprentice labora hrs. 500 9 5045 0 44 0 17124 0 775 0 Apprentice laborb hrs. 590 14 6559 0 44 0 17124 0 775 0 Material Inputa 1e 382 536 469 3348 6320 2263 1365 7749 560 86148 Material Inputb le * 451 836 610 3917 6320 2648 1365 9841 560 118884 Capitalc 1e 215 314 221 137 48 10 260 853 434 5207 Capitald 1e 225 408 243 264 72 18' 345 1088 552 7168 Capitale 1e 250 563 283 453 106 27 494 1473 736 10476 Source: Computed from the survey data of small scale industries in Sierra Leone. 8coefficient at existing capacity utilization bcoefficient at 100 percent capacity utilization ccapital cost at 10 percent rate of interest dcapital cost at 20 percent rate of interest 8capital cost at 35 percent rate of interest I! - .manL‘ mums umwumucfi ucmuuoa mm as mumou Hmuaamuo mums amououcw acouuoa ON um mumou Hmuuamuv mums amououcu unwound 0H um mumoo Hmuaanou :0wumnwauu: zuwomamu ucouuoa ooH um ucowuwwwooun :ofluouaafiua Auwomamu wowumaxo um ucowuwuwooom .ocooq muumwm :« mofiuumsccw ofimum Hanan mo sumo >o>u3m onu aouu vousaaou "ouusom 93 and ONNH mmmoH omma qu mowm Hon CNN um um mmq «we saw mm ANA mu manuaamu «Ha Nam ammo oNoH ONH OmoH oqm NoH ma . em «on men owa on Hod ma vamuuaou Nod mac came mam oHH onma mad NMH . NH . oH nnm MNN find mm mm 6H uamauaou fioqm manna mm~o~ mom mod amom owoo ONHH anmm mmofi mohm one 0mm ama NNH ma nusacu Hmaumum: mcnm mmnma mmmma mom oma seem omoo mom oo- cam down eon nae Add mm 0H ousncu Howuouoz o o mmma mom HoHH Hmmm omooa Hunm o o o mmma smma Non ooH .mu; nuonna ouwusouaa< o o “No awn mmo mem ommmd owom o o o NNHH O¢o com onH .mun muonma ouaucouna< mowm mmmqa Hosea mmmfi mm qnma cum NMH sac mmH Nam m c an 0 .mp: nuonma pupa: oq~n mmqu mmmn wmmd oh swag own Ha NNo mad mow H 0 mm o .mu; mucosa topaz oomfi an mood 00H mesa chm omoa mama mema mmoN o-~ nmca Omma MMHH Nos .mun nuonma nouoauaoum NmoH mm oqu ooH QNHH mam owed mama omma ow- mHmH “No mead mmm «so . .mun muonma acuouuaoum Onao oaswm mmmoq «cod oHNH omnoa caom omooH omwm memN Hams omen mNmN oaoA owm ma auaduso oHom oaqwm wmmmm wqu . mno coco caom ammo oanm mHmN Home aqmm ofiwu Hun amo 0H nusnuao MN o sq o mN as o ms NH NH as mm mm on «N N assumamu umauxm nmmm mama «Nwm mmmm Hmmm mmcm mmqm amen mmmm mama Hana «Nam mmam NNHm HNHm own: moanmuun> moazh anoxmm wWHMWMMm anacoaumu wswo5a sumo wcwuoauaa Hofinwmavcu HH newwom ecu moaxh sham o>wumucomouaox an mucoauquwoou uaauno mam ouuaonom Hmsoa< u.c wanna -~ Ccuxfiz L35 ZUR>F Eh-L ¥>uugbtUlahi3K k4 IUCQuUuNhaCU U:QUJQ 3:: QULIQIQK ~§3CC< Q.V 3~€Eh 94- Numb Numb Ouflh cosuousswus ammuoucs ucouuoa mm um umou Hausamoo amououcs acouuoa ON um umou amusamu v amououos acouuoa os um umou amusnoou xusumamu ucouuoa 00H um powwowuwoou n oesumusasua husomamo wcwumwwo um ucowosmuooom .ocoos muuosm as mosuumsvos osoum Hanan mo oumv >o>h=m may soum vousaaoo souusom ea oe ees eae me ewe on ems me as cm as asausaau em em ses wee em eon es hos es an se as esausaao Ne es we ass .ss ees es ea me we on as assesses asas Na ems seoe owe wees seas eon ass New em as eesaas sasuaeaz sens sh was ease moe News aees mom was ees ee as assess sasuouax sms sees sens eease sees seem se mess ee oem ems .eue euoeas mosecwuaa< mm shes ases essee saoe ssme Ne sea as mes oos .mae apoems casuaauaa< esss sees mums esea use emss sans News eses eeos ass .8“: euoeas uoeowuaoum esms moms mass esse sen oeas mass ssos Ness owe mam .eun auoeas nouosuaoua mace mam eses eaesm emsm seen seas escs esa esa wee as euaauao sess ems emms comes asas mess sens ese ese eme ees as assauao se on on as es es es oe as me em a sesoaaeu eeuuxm seem seem smea seem seem seem snma emsa mesa «as» smsa use: maseasua> monxa zuoxmm wwwmwmmm auuoonumo mHHMMo washouaoa Hosuuwmvoa HHH ceswom you moaxa Bush o>sumucomoudom an mucosusuuooo usauao van oouaomom Ho==a< w.< manna \ >H CCHdUZ hCu TDQAF Ehuk U>VUEUCUEQHQQI %fl TUCUNUNMHQCU UJQUJO 3C5 Ubkzomflz HQJCC< $.Q @NQEF 95 mums umououcH ucouuma mm on umou HmuHaoo v mums uwououcH unmouoa om um umoo HmuHaouu moon uwououcw unmouoa OH um umou HouHamo a aowumNHHHuo >uHuommu waHumem um uamHonmooom .mcomH msuon :H mmHuumsvcH onum HHmam mo mama %o>u:m Scum mmusaaoo "mousow no me on mm mm as mm mm mm mH vumoo HmuHmmo ee se em me me es en cm as as oueoo sausamu mm on s~ an em es an as ss as eeeoo sausamu H q oH H o Hm mH 0H NH oH mmumoo uaaaH HmHuoumz OHH Hum anH um Hoe wmq Heq nme «Hm .mu: mHOAMH hHHamm mm «on mmm cc oq qu mcH qu one oH musauoo mmqm qum qum mqqm ween Hqcm Mqu Nme HHem uHcD moHanum> moazfi w: u am on huucmmum w: Ho m auHm H: H x Hm u H HH H HmHuumomaH >H GOHwom sow moa>9 EhHm 0>Humucomounom hp muaoHUHmmooo usauao mam oousomom Hmsca< o.q oHan 96 4.5 Resource Efficiency Amgng;Rgpresentative Firm Types In order to examine the relative efficiency of resource use among the different representative firm types, attention will be f0cused on three key economic ratios-~gross output--labor ratio, gross output--capital ratio and labor-capital ratios. The first two ratios yield the values of the average products of labor and capital respectively while the labor-capital ratio gives the quan- tities of labor hours that combine with a Leone worth of capital in the production process. Thus, while the ratios throw light on the average productivities (if labor and capital, they also highlight the labor intensity or capital intensity of each production process. These three ratios need to be considered together since an isolated treatment might lead to inconsistent results.1 In examining the relative efficiency of the representative firm types, it will also be necessary to do so within the framework of our linear programming analysis. Thus, the first question that arises is, which of the representative firm types, within each in- dustry and within each region, will likely be dropped out of the solution. With respect to the linear programming problem, each rep- resentative firm has an opportunity cost in terms of the amount of income sacrificed due to a marginal decrease in the number of such firms and a marginal increase in the number of alternative firm types. If such opportunity cost is less than the revenue generated by a marginal increase of an alternative activity, then profit will 1Bhalla [1975, p. 28] points out that if capital-labor and capital-output ratios are considered in isolation, a technique could be identified as labor-intensive as well as capital-intensive. be ir maxim actii or cc costs reduc prom 97 be increased [Heady and Candler, 1969, p. 64]. Thus, in a profit maximization problem, the solution will include a combination of activities which yields the greatest revenue from given resources or costs. Activities with the largest positive marginal costs will be dropped from the solution since their inclusion will reduce total profits by that large amount. I If one assumes that capital is the scarce resource in most less developed countries, the relevant economic ratios for examin- ing efficiency become output-capital ratio (O/K) and labor-capital ratio (L/K). For each industry and in each region, the least effi- cient firms will be those firms having the smallest 0/K and L/K ratios. Thus, in the tailoring industry, and as shown in Table 4.10 production processes such as P112, P131, P143 in Regions 1, III and IV, respectively, could be dropped from the solution. In the gara industry, the least efficient firms are P311 and P321 in regions I and II respectively. Among the carpentry firms, the processes that might likely be dropped are P412, P423, P431 and P443, while in the blacksmithing industry, processes such as P521, P531 and P543.are the least efficient in terms of resource utiliza- tion. Finally in the bakery industry process P281 in region II is the least efficient. : 0n closer examination, it becomes clear that the dichotomy of labor-intensive and capital-intensive production processes, as a procedure for identifying efficient techniques of production can be misleading. For example in Table 4.10 and in region III, where- as types such as P431 (labor-intensive, traditional carpentry firms) is relatively inefficient, process type P433 (a modern carpentry lie—ssh. .Ih u... no). .I ~.-.¢l.~.~..i= NC 8.3:... ,1»:- .18.... :23! DH .1 .I~.ql.h 98 m.~ me m.om s.~ m.o o.ms s.w m.eH ~.nn essay Hausaouuuonoq ~.~ 90 MH s.H no. m.m o.~ n.o o.n~ swans Hnanouuusnuao om. em. m~. an. oH. on. Nm. we. oh. oHuou toeasnuaauao 3 menu News Hens mean «can Heem neHm NeHm HeHm o.m~ oHH o.oH mHH o.-H ¢.nn «.mn o.oH o.n~ «.0H ~.o oHuou .Hqusneuuuonqs o.o~ o.o~ N.o n.5m on m.oH qu n.o o.HH m.oH o.q oHunu \Hausaouuuaauao H.H oH se. am. No. Hn. o.H on. no. «o. oo. oHu-u nonoHluaauso HHH Hnom Nana snnm mnem ~nem Hmem Hana anHm nnHm NmHm HnHm n.0n <.oH e.H qo.~ mH ~.~ H.Hm mm NoH o.ow oH ~.o o.~H o.oH n.n oHuau Hausaauuuonua as ~.Hn m.c No.H H.o a.e H.oe o.~e ewH mos nN w.o e.oH H.MH n.o oHuon . Hnannuuuanuno N.H o.s n.m an. we. o.H es. n.s o.H q.H n.~ H.H no. no. No. oHuau uonoHauaauao HH nwmm «Nam Hump NNmm Hwnm m~¢m mwom HNem nwnm -nm H~mm qum nuHm «NHm HNHm N.“ ~.~ ~.m mm NoH m.o~ oH n.a~ a.e a.» oHuuu 1 Hauuaooluonos n.oH o.H «Ho ~.nH «QH mHH mN n.nH o.~ n.c oHuou iHouHQQUIusnuao n.~ mm. m.H an. o.H o.n m.~ «a. mm. mm. oHqu won-Hiuaaaao H Hme HHnm ~qu HHeA anm «Ham HHnm nHHm NHHA HHHm immAflwddmi xuoxan ucszquoxuoHn zuuconuou ncsoan undo ucHuoHth ILHh >Juuwn acumen HosuunsvaH IOQAH lush 0>Huaucoaouau¢ «o zucoHUHuuu cannons; OH.¢ oHnaH 99 firm) proves more efficient. It has to be remembered that the O/K and L/K ratios observed are a function of the existing capital util- ization levels, the existing factor prices, and effective demand levels. It is possible that changes in our independent variables could lead to different patterns of resource efficiency and there- fore different patterns of choice of production processes. Such an analysis will be undertaken in Chapters 6 and 7. 4.6 Summary The purpose of Chapter 4 was to lay out the procedure for and delineate the representative firm types. In order to avoid serious errors of aggregation bias, firms were first stratified by industry and location. Further stratification was carried out by grouping firms with approximately the same labor—capital ratios, as shown in Tables 4.1-- 4.4. On the whole, 43 representative firm types were identified. As Table 4.5 shows, weights have been assigned to the representative firm types. Tables 4.6 - 4.9 present annual resource and output.coeffi- cients for each representative firm type. As column one of those tables indicates, variables with "a" and "d" footnotes identify coefficients which will be used for the base run analysis of the model while those with "b," "c" and "e" footnotes indicate coeffi- cients that will be used for the sensitivity analysis of the model. On the basis of the base run coefficients, iLe., coefficients belonging to variables with "a" and "d" footnotes in Tables 4.6 - 4.9, some economic ratios such as output-capital ratios (0/K), output- labor ratios (0/L) and labor-capital ratios (L/K) have been 100 constructed for each representative firm type and presented in Table 4.10.. Since the O/K and 0/L ratios indicate the average productivities of capital and labor by representative firm type while the L/K ratios indicate labor intensities of representative firm types, these ratios might be useful in highlighting why some representative firm types enter the linear programming final solu- tion, while others drop out. CHAPTER 5 THE STRUCTURE OF THE LINEAR PROGRAMMING MODEL 5.1 Introduction This study uses a linear programming (LP) model to evaluate the efficiency of production, resource utilization and trade patterns during the 1974/75 survey period, and to carry out sensi- tivity analysis of the effects of some policy variables on output, employment, capital services utilization and profits among small scale industries in Sierra Leone. Although the input-output coef— ficients, resource and demand constraints have been specified at re- gional1 levels, the model is an interregional LP model. This inter- regional LP framework allows interregional feedbacks through the mechanism of trade flows within and between regions. The mechanism of trade flows, based on the prinicple of comparative advantage, affects regional production and distribution of final products and determines patterns of resource utilization. 1In the literature of regional economics, the two basic prin- ciples of regionalization are uniformity and function [Hoover, 1971, pp. 122-124; Morrill, 1970, pp. 184-185]. In this study, regions will be demarcated by grouping localities, though not geographic- ally contiguous, have a uniform range of population size. Thus, re- gion I will be made up of localities with an excess of 100,000 inhabitants. This region is constituted by greater Freetown, the capital territory of Sierra Leone. Regions II, III and IV will be made up of all localities with population ranges of 20,000-100,000; 2,000-20,000; and less than 2,000 respectively. 101 102 The model is also a representative firm LP model because the process of aggregating the LP results will be achieved through the representative firm types which have been constructed by industry and region.1 The model is open because it incorporates the foreign trade section in the basic linear programming model. Finally, the model is used to carry out both short-run and long-run policy analyses. In the short-run model, flexibility restraints will be imposed on the number of firm types, so that adjustments within the flexibil- ity limits will determine the approximate number of representative firm types, as they exist in the population. But, in the long-run model, such flexibility restraints will be removed. The basic linear programming model is discussed in detail in the subsequent sections of this chapter. The three important as- pects of the linear programming model are: l) the objective func- tion, 2) the constraint structure, and 3) the activity set. Each of these aspects will now be disucssed in fuller detail. 5.2 The Objective Function The linear objective function of the model will be to maximize the profits of the small scale industry subsector. The decision to maximize the profits of the subsector is based on the finding that 58 percent of the 226 proprietors in the random and purposive sample said that they joined their business in order to make profits. One weakness of profit maximization as a rationale for decision-making 1The details of delineating the representative firm types have been outlined in Chapter 4. 103 is that security goals,1 which ranked second in importance (16 per- cent) to profit goals, is not taken into account in the objective function. Day [1963] and Norman [1973, pp. 38-44] have shown that farmers maximize net incomes subject to the satisfaction of house- hold fOOd consumption requirements. Such a "constrained" type of profit maximization [Day, 1963] will not be undertaken in this study due to lack of relevant data. Therefore, it is necessary to interpret the values of the objective function with caution. In mathematical notation the linear objective function used in the study can be stated as follows: Maximize n = 3: E i ijg :93!“ — E? I): (cg) LU.k _ §i(cm) Mjk — §E(CZ) ij — griK' (Cy) Ykk' ng' (ct) Gkg' + X (cg) Ge —- $ (Cr) Ym m _. $m 9:. (Ct) Ym (1) The objective function can be defined as maximizing the difference between the sum of the value of gross outputzof all representative firms and incomes from gara dyeing exports on the one hand, and total expenditures from purchasing labor services, material inputs, 1The survey data also revealed that 7 percent, 6 percent, 1 percent and 16 percent of the 226 respondents joined business be- cause of family enterprise, father's occupation, prestige and se- curity respectively. .Eleven percent joined business for other reasons. 2Due to the heterogenous nature of the output categories of small scale industries, only values of output will be utilized in the model. Thus, physical quantities of industrial output and corresponding prices will not be explicitly shown in the model. 104 capital services, costs of competing imports and costs of transpor- ting goods within Sierra Leone and between Sierra Leone and the outside world, on the other hand. defining the The symbols, subscripts and superscripts that have been used in 6Y ij£ jki objective function equation (1) are defined as follows: = annual Leone value of the y-th output of the j-th pro- duction process in the k-th region and of the fl-th representative firm, number of the j-th production process in the k-th region and of the fi-th representative firm, where j represents: ijk Mjk ij Ykk' .., r; l, ... l for tailoring production processes. 3 for gara production processes, 4 for carpentry production processes, 5 for blacksmith production processes, 8 for bakery production processes; and k = l, 2, 3, and 4 regions and 2 = 1, ..., n; 1, .... q; , s; l, ..., t categories of representative firm type. level of the i-th labor type utilized by the j-th pro- duction processes in the k-th region where i represents hours of the following categories of labor: F - proprietor and family labor H - hired labor A - apprentice labor; level of the material input costs utilized by the j-th production processes in the k-th region; level of capital utilized by the j-th production pro- cesses in the k-th region; level of the y-th output values transported from region k to k' where values of Y represent the following output categories: where: 105 T - tailoring output 6 - gara output C - carpentry output B - blacksmith output K - bakery output; Gkg' = level of the 6 output values transported for exports from region k to 9' (port of shipment); Ge = level of the gara output values exported outside Sierra Leone; 'Ym = level of Ym output values imported into Sierra Leone; and ng' = level of the Y-th output imported into Sierra Leone and reshipped from g (the port of entry) into the k' region. 5.3 The Activity Set The full activity set is denoted: {Plkl, ..., Plkn, P3kl, ..., P3kq; P4k1, ..., P4kr, P5k1, ..., P5ks, P8k1, ..., P8kt; Lij, Lij, LAjk, Mjk, ij, Tkk', Gkk', Ckk', Bkk', Kkk', Gkg', Ge, TM, TMgk', BM, BMgk'} k = regions 1, 2, 3, and 4; k f k'; g = g' = either port of shipment or port of entry for Sierra Leone; j = 1 for tailoring production process. 3 for gara dyeing production process, 4 for carpentry production process, 5 for blacksmithing production process, 8 for bakery production process; Plkl, ..., Plkn are activities involving tailoring production processes in the k-th region, with l to n representative firms; 106 P3kl, ..., P3kg are activities involving gara dyeing production processes in the k-th region with the l to q representative firms; P4kl, ..., P4kr are activities involving carpentry production processes in k-th regions with l to r representative firms; PSkl, ..., P5ks are activities involving blacksmithing pro- duction processes in the k-th regions, with l to s representa- tive firms; P8kl, ..., P8kt are activities involving bakery production processes in the k—th regions, with l to t representative firms; Lij are activities involving the purchases of proprietor labor services in the k-th region for producing each of j-th production process goods; Lij are activities involving the purchases of hired labor services in the k-th region for producing each of j-th production process goods; LAjk are activities involving the purchases of apprentice labor services in the k-th region for producing each of j-th production process goods; Mjk are activities involving the purchases of material inputs in the k-th region and for producing each of j-th produc— tion process goods; and ij are activities involving the purchases of capital services in the k-th region and for producing each of j-th produc- tion process goods. Tkk' are activities involving the transportation of tailoring output values from k-th region of production to k'-th re- gion of demand; Gkk' are activities involving the transportation of gara dye- ing output values from k-th region of production to k'-th region of demand; Ckk' are activities involving the transportation of carpentry output values from k-th region of production to k'-th re- gion of demand; 107 Bkk' are activities involving the transportation of blacksmith— ing output values from k-th region of production to k'-th region of demand; Kkk' are activities involving the transportation of bakery output values from k-th region of production to k'-th region of demand; Gkg' are activities involving the transportation of gara dye- ing output values from k-th region of production to g'-th port of shipment to the outside world; Ge is the gara dyeing export activity; TM is the tailoring products import activity; TMgk' are activities involving the transportation of imported. tailoring product values from g-th port of entry to the k -th region of demand; BM is the blacksmithing products import activity; BMgk' are activities involving the transportation of imported blacksmithing product values from g-th port 01’ entry to the k'-th regions of demand. Now that the activity set has been outlined, further details concern- ing some specific activities need to be described. The relevant activities are therefore transportation, export, and import activi- ties. Effort will be made to outline the rationale for including those activities in the model and explanations will be given concern- ing the derivation of all the coefficients that have to deal with those activities. 5.3.1 Transportation Activities In order to integrate the regions, the overall model contains a transportation section. This section of the linear programming model will activate interregional shipments of output values. Such shipments will highlight comparative advantage and trade patterns within a general equilibrium framework [Moses, 1960; Hsiaso, 1971]. 108 We assume that products of each industry, irrespective of region of origin, are homogenous. Therefore, a given commodity is a perfect technical substitute for the same commodity from another region and regional commodity trade flows will depend only on economic consider- ations. In order to introduce some realism in the model, we have re- stricted the interregional transportation of regional output in two respects. First, since all the blacksmithing output in region 1 were observed during the survey to be in the nature of industrial services, we have not allowed region 1 to transport blacksmithing output into regions 2, 3 and 4 which demand essentialTyagricultural implements. Secondly, we have not allowed region 4 to export any of its products to other regions. Our survey data reveal that region 4 imports 2.5 percent of tailoring outputs of regions 1, 2 and 3, 1.3 percent of gara output and 10 percent of carpentry output from those regions and finally, 25 percent of blacksmith output of regions 2 and 3. Thus, given the limitations placed on the interregional flow of goods and services, the model will determine the trade pat- terns for the industries included in the model. 5.3.2 Transportation Costs The objective function coefficients of the transportation matrix will be the imputed costs of transporting a unit value of the output of a particular industry from the region of production to other re- gions. Most of the task of distributing the output of the small scale industries is not borne by the proprietors of those industries. However, the bakery industry and to some extent, the tie-dye industry I. I‘ III ll'ill 1|: '- ‘ 109 are exceptions. Most of the modern bakeries distribute bread in delivery vans and thus incur transportation costs. But most of such deliveries are done within the locality. Producers of tie-dye cloth ship their products to other localities and regions where friends, relatives or fancy shops serve as retail outlets. Here, the cost of shipping out tie-dye cloths across regions will be borne by the pro- prietors. In the tailoring, blacksmiths and carpentry industries, customers traditionally travel across regions to place orders and pick up their final products and thus, the costs of transporting such products will be borne by the customers. Nevertheless, for purely theoretical and analytical reasons, we shall assume that transportation costs are involved in the spatial distribution of the final products of each industry from one region to another. We shall also assume that transportation costs are pro- portionally related only to the distances over which commodities are conveyed. Thus, relative weights and volumes (if différent commod- ities will not be considered in imputing the transportaion costs. Based on the Sierra Leone Road Transport Corporation (SLRTC) 1 2 1 3 2 3 estimates, the average distance from U to U . U to U , U to U , u2 to R, U3 to R, and u1 to R are l80, 212, 195, 24, 15 and 227 miles respectively] (as shown in Table 5.1). Also, using the SLRTC freight rates for 1975, the average cost of transporting a ton of 1 2 1 3 to u , u to u , u2 rice (costing about Le 452.0 in 1975) from U 2 to R, U3 to R and U1 to R, given the above regional dis- to U3, U tances, are Le 9.0, Le 11.0, Le 9.75, Le l.2, Le .75 and Le 12.0 respectively. Therefore, the costs of transporting a Leone value 1U1, U2, U3, and R refer to urban region 1, urban region 2, urban region 3 (small rural towns) and rural areas respectively. Also see footnote on page 101. 110 of rice from u1 to uz, U1 to U3, u2 to U3, u2 to R, U3 to R and u1 to R become L02¢, .02¢, .ozo, .003¢, .002¢ and .03¢ respectively. Table 5.1 Imputed Transportation Costs Between Regions Average Cost of Average Cost Of Transporting a Average Distance Transporting Regions in Miles a Ton of Rice Leone Value of Rice 1 2 U ot U 180 Le 9.0 .02 ¢ U1 to U3 212 Le 11.0 .02 ¢ 2 3 U to U 195 Le 9.75 .02 ¢ u2 to R 24 Le 1.2 .003¢ U3 to R - 15 Le .75 .002¢ u1 to R 227 Le 12.0 ' .03 ¢ These imputed transportation costs will be used for transport- ing the output of the small scale industries. It has to be mentioned that these transport costs do not include the personal transport costs of a customer who travels to pick up his final product from the establishment. The justification for such an exclusion is that customers do not make single trips for the sole purpose of picking up items from the establishments.’ Rather, such trips are made with multiple purposes in mind, such as selling or purchasing other items as well, seeing friends or relatives, or even transacting other business. Therefore, there is no way of allocating personal transportation costs among various purposes. 111 5.3.3 Export Activities Since our survey data revealed that 20 percent of gara cloth produced by our sample firms was exported from Sierra Leone, the over- all model includes an export section. In this section, activities will be included to transport gara cloth from each region to the port of shipment and to the outside world, subject to regional demands for gara cloth. The objective function coefficient for the gara export activity is the difference between the foreign price of one Leone value of gara cloth in Chicago [Nahlman and Chuta, 1977] and the domestic price of a Leone value of gara cloth at the region, plus the costs of handling and transporting a Leone worth of gara cloth from the region of production to the point of shipment (Free- town), and from Freetown to Chicago. 5.3.4 Import Activities Since Sierra Leone imports the products of small scale industries from other ' countries, we decided to allow imports to enter the model. This approach assumes that these foreign imports are perfect substitutes for their domestic counterparts and that their "produc- tion" and distribution patterns will be determined by the optimiza- tion scheme, subject to specified objective function coefficients and transportation costs.: The two major imports are products of tailors and blacksmiths. Using the five-digit S.I.T.C., the relevant categories of tailoring products would be men's and boys' outer garments, other men's and boys' outer garments, blouses for females and infants, female and infant outer garments, and shirts of textile fabric. The categories 112 of imported blacksmith products are matchetes and axes and hatchets. The average annual values (Freetown C.I.F.) of tailoring and black- smithing products imports used as technical coefficients in the basic model are Le 1,028,386 and Le 42,728 respectively] (Quarterly Trade Statistics). The objective function coefficients for the import activities are the Freetown C.I.F. values plus tariff markups-~40 percent and 2 3/4 percent ad valorem on tailoring and blacksmith pro- ducts, respectively. Thus, it will cost Le 1,439,740 and Le 43,903 to import Le 1,028,386 and Le 42,728 worth of tailoring and black- smithing products, respectively. 5.4 The Constraint Structure Equation (1) is maximized subject to the following constraints: Pj z 3.0 (2) where j l, 3, 4, 5 and 8 industrial production process categories, k = l, ..., 4 regions, 2 = l, ..., n; l, ..., q; l, ..., r; l, ..., s; l, ..., t industrial representative firm categories, (the number of each representative firm type cannot be negative); t Z i=1 i . . a jk ijR : le (3) (the sum of the i-th labor hours utilized in the j-th production pro- cess in the k-th region and using the 2 representative firms should not exceed the maximum amount of the i-th labor type specified in the model for the k-th region); 1Figures are averages over the five-year period 1969-1973. 113 t 111 X 8. i=1 J" ijt _>_ o (4) (the sum of the total material input costs used in the j-th produc- tion processes in the k-th region and with 2 representative firms has to be positive); t z z y. ij£< z (5) ._ jk —- k j-l (the sum of all capital costs used in the j-th production processes in the k-th region utilizing the i representative firms should not exceed the maximum amount of capital services specified in the model for the k-th region); 2.8ng iji - z Ykk' + 2 Yk'k - z Gkg' + z Ymgk' = Dky (6) J kfk' kfk (total production of Y-th output values from j-th production processes in the k-th region minum domestic exports of the Y-th output values from the k-th region to k' plus domestic imports of the Y-th output values from k to the k'-th region, minus foreign exports of the gara output values plus foreign imports of the Y-th output values must equal the maximum demand level specified for the Y-th output values in the k-th region); ijit_<_ (l + Ej)‘ Ajk9._<_ mm (7) and ijl: (l - gj) Ajki_>_ sin (8) 1 ... In regions 1, 2 and 3, 8° = gj = 10 percent. Since 5 percent and 7 percent were observed as the maximum exit and growth rates re- spectively for small scale industries in the sample, 10 percent was chosen as the worst or the most optimistic annual rates of decay or growth that can be allowed in the model. In region 4, 83 = 20 percent since rural firms have the alternative of switching their time to ag- ricultural production. But Bj was maintained at 10 percent. 114 (number of each representative firm type should lie between the lower and upper limits specified for each representative firm type as shown in Table 5.2); where: “Aka annual amount of the i-th labor type required by the J j-th process type with the fi-th representative firm in order to produce Y‘5 in the k-th region; m jk Y5 Bjkl amount of material input costs required to produce jk by j-th production process in the k-th region; ng2 amount of annual rental price of capital required to J produce Ys a: mnuHmeumHm mumusomumu ii muoHHma conom mes am mm s ems mes eem esm eme eon mos sassxoz oes em me s ses ees mom was hem owe me gasses: seem meem seem eeem meem seem seem eesm mesa mesa sesm sss mumxmm mnuHmeuon muoucomumu muoao snow muoHHme conom ss s e s es em ee ee Pm es es sos ems eve mes sassxaz e s e s es se ea ea me ss H— mm sos «mm ems gasses: emem mmem smem mmea smem emem mmem smem emem mmem smem emsm mmsm smsm - ss msmxmm mnussmxoon msousomsmo muoao oumu muoHHoB cosmom ms es sm me s mm m mes eee sassxoz es ss as oe s as e ems eee gasses: ssem ssem msem ssem esem msem ssem essm mssm sssa s msmxmm mnuHmeoon muoucomumu whoao sumo muoHHoa conom Hmvoz was CH wm3oHH<.ma>H BuHm m>HumucmmosamM comm we sonaaz adamez mam asawaH: ~.m oHLMH H 116 representative firm types has been presented.1 We shall now describe how the levels of regional demands, labor and capital constraints were determined. 5.4.2 Demand Constraints In order to specify the final demand constraints for the output of each region,the daily sales of each firm's output were utilized. Since the ultimate destination of each firm's daily output sales was re- corded, it was possible to determine the regional allocation of the total output of each firm and therefore each industry. Daily output that was consumed by members of the establishment was allocated within the region where the firm was located. Output that was an outright gift was allocated to the region where the recipient was reported to be residing at the time. Each of the region's final demand for each industrial output can be defined as follows:2 Dk = qk _ ek + mk y y y y where: D: = final demand of y-th output in region k q: = output of the y-th industry in region k e: = domestic exports of the y-th output in region k m: = domestic imports of y-th output in region k 1 See footnote on page 113. 2An alternative procedure of estimating final demand levels would have been to use the per capital consumption coefficients. But these do not exist for Sierra Leone localities with population exceeding 2,000. 117 Thus, for our four regions, say, U1, U2, U3, and R or even in- cluding an X region (outside Sierra Leone), the following demand matrix was specified and applied to our daily output sales data, in order to estimate our final demand constraints. Thus: 1 l l 2 l 3 1 1 l = + + + + qy Uy Ry Uy Uy Uy Uy Uy Xy Uy Uy 2 2 2 3 2 2 l 2 2 = + + x + + qy Uy Ry Uy Uy Uy y Uy Uy ”y Uy 3 3 3 2 3 3 1 3 3 = + '1' + + qy Uy Ry Uy Uy Uy Xy Uy Uy Uy Uy k 2 3 l = + + + + qy Ry Uy Ry Uy Ry xy Ry Uy Ry Ry where superscripts represent regions, subscripts represent y-th out- put, U1,U2, U3, and R and X represent urban regions 1, 2, and 3, rural region and outside Sierra Leone respectively. Ignoring the regional output shipments to itself, i.e., U1 U1 yy’ 2 2 3 3 . . Uy Uy,Uy Uy and Ry Ry, it 15 easy to see that the sum of the row values is equal to each region's domestic exports to other regions. Thus: 1 l 1 2 l 3 l . = + + + : ey Uy Ry Uy Uy Uy Uy Uy Xy sum of domestic exports 1 from region U and column values are interregional imports, thus, 1 2 1 3 1 1 O O my - Uy Uy + Uy Uy + Ry U‘y " SUIT] 01 domestic imports lrom other regions into region U]. Table 5.3 shows the regional demand levels for each industrial out- put as determined with the formulation already presented. 118 Table 5.3 Constraint Levels of Product Final Demand by Industry and Location, 1974/75 Industry Region . Final Demand (1e) Tailoring I 1,817,814 II 1,092,948 III 623,948 IV 2,856,016 Gara . I 46,180 II 300,000 III 222,000 IV 25,000 Carpentry I 415,440 II 721,771 III 1,693,836 IV 347,892 Blacksmith I 12,588 II 14,667 III 54,705 IV 1,591,890 Bakery I 1,173,833 II 246,426 III 283,658 IV 244,474 Source: Sierra Leone Small Scale Industry Survey, 1974/75. 5.4.3 Labor Constraints Each category of labor-proprietor, hired or apprentice, is constrained at the regional level. The actual level of each regional labor constraint is the sum of the products of the average quantity of labor services of each category utilized by each representative firm type and the number of such representative firm type existing 119 in the population, across all the industries. The industry and total hours of labor services by type are presented in Table 5.4. In addi- tion, Table 5.5 presents the per hour costs of each category of labor prevailing in the various industries during the 1974/75 survey period. Given the present levels of capacity utilization among Sierra Leone small industries, the total labor hour figures should be the accurate available numbers. The availability of entrepreneurs and hired workers is limited by the fact that it takes about two to five years to acquire the necessary skills, depending on the type of industrial skill required [Liedholm and Chuta, 1976, p. 35]. The supply of apprentices is limited by the fact that youths look to the modern sector for employment at the existing legal minimum wages. Also, during the field interview, some proprietors complained that their apprentices left before completing their training. 5.4.4 Capital Constraint Capital services have also been constrained by region. The actual level of each regional capital Services constraint is the sum, across all industries, of the product of the average value of cap- ital services utilized by each representative firm type and the number of each representative firm type existing in the population during the 1974/75 survey period. Such aggregates have also been presented in Table 5.4. The aggregate levels of capital services constraint are reason- able approximations of the amount of capital services available for the fOllowing reasons. Firstly, not only are commercial banks skep- tical about lending to small entrepreneurs, small entrepreneurs are 120 .mN\ean .>m>u=m xuumsccH onom HHmam mcoos msuwsm "ouusom eeo.eoe . coo.eom eem.ees . oee.sse as sausaao soo.eeo.ms . ems.ese.e esm.mee.s . oes.ems.e .aoe ooeas soaasaaooa >s cosmae meo.ses eeo.ms eem.es eme.em ome.s eem.mm as sausaau eee.eme.m mae.es eoe.eas sse.oes.m oee.m eee.oee .aoe aoeas aosaaaaaa< eee.eme.s mee.mmm eeo.ees eee.eee oes.ees eae.emo.s .aae aoeas ooaasaaoom . , sss cosmos mem.esm eee.me oom.m oee.ee mee.m eem.ee as sausaao mso.mee.s mee.e sse.es osm.see.s o eee.ese .aoe aoeas aosaeaaaa< ese.ses eem.ee ese.m aee.ee emo.mm mee.es .aoe aoeas eaasm woe.see mee.es eme.es ems.sms mem.mm mms.eee .aoe aoeas ooaasoaoam ss cosmos mee.eee eee.em eme.e maa.ee mea.s sme.ssm - as seasaao eee.eeo.m o ooe.e eea.eee eom eee.oeo.s .aoe aoeas aosaaaoaae eee.eem eee.eee o mee.em oes.e ooe.ees .aae aoeas eaasm ese.eem.s mee.oe mee.e see.sms sse.em ems.mee.s .aoe ooeas uoaasaaoaa H :OHwom Hmuoe >umxmm m%%flMme xuusmaumo mumc onMOHHmH uHoD mN\qnoH .coHumUOH mam mamH poems .muumsvoH an muchuumaoo mouoomom ¢.m oHnoH 121 Table 5.5 Per Hour Costs (in Leones) for the Different Kinds of Labor by Industry and Region 1974/75 Tailoring Gara Carpentry Blacksmith Bakery Region Labor type Industry Industry Industry Industry Industry I Proprietor and Family Labor .35a .84a .51c .30a .54a Hired Labor .10 .15 .20 .15 .15 Apprentice Labor .02 .01 .12 .08 .08 II Proprietor and Family Labor .353 .84a .51C .30a .54a Hired Labor .10 .15 .08 .13 .15 Apprentice Labor .02 .01 .02 .08 .08 III Proprietor and Family Labor .35a .84a .51c .30a .54a Apprentice Labor .02 .01 .02 .02 .02 IV Proprietor and . b b b Family Labor .09 -- .09 .09 -- a C Values of the marginal productivities of labor by industry (without regional breakdowns) as estimated from Cobb-Douglas production function (LiedhOlm and Chuta, 1976, p. 73). Per hour wage for male hired worker in the enumeration areas. (Spencer and Byerlee, 1976). Average of values of marginal productivities for family labor in tailoring, gara, blacksmithing and bakery industries. Carpentry labor coefficient was statistically insignificant when computed with the Cobb-Douglas function. All the other values were the observed wage payments per hour by industry and by location. 122 also reluctant to apply for loans from commercial banks. Reluc- tance on the part of conmercial banks to grant loans to small entre- preneurs could be due to lack of adequate collateral, and unduly high administrative costs per unit of credit to be made available to a widely dispersed small entrepreneurs. According to the survey data, whereas accessibility to commercial bank credit was highest with the bakery industry, followed by gara dyeing and carpentry in- dustries, failure to obtain credit, even among these industries, was high and in the magnitude of 67 to 100 percent. 5.5 The Linear Programming Tableau Appendix 5 is a matrix presentation of the linear programming tableau of the entire model. The component parts of the tableau have already been described in most sections of this chapter. The dimension of the entire matrix is 209 rows by 184 columns. Pages 247-249, 250-252, 253-255 and 256-257 of Appendix 5 refer to the regional submatrices. Pages 258-269 present the transportation section of the entire matrix, while part of page 260 and page 261 contain the foreign trade section of the matrix. The matrix notations are the same as those used in the model presentation. Income transfer activities, cYk, and income transfer rows Rk’ have been included in the regional submatrices to facilitate the aggregation of gross output values in the objective function. 5.6 Summary This study uses an interregional representative firm, linear programming model to analyse output, employment and trade aspects of Sierra Leone small scale industries. The model is used first to 123 simulate the 1974/75 survey period, and second, to analyse the effects of changes in some policy variables. I The three important aspects of the model, i.e., the objective function, the activity set and the constraint structure, have been discussed. Since the model is open, bothiékport and export activities have been incorporated in the model. The transportation section of the model serves the purpose of integrating the economic activities of small scale industries between regions. The resource constraints in the model are specified at region levels. The actual level of each regional industry resource constraint is the sum of the products of the average quantity/value of the ser- vices of each resource utilized by the representative firm type, and the corresponding weights of the representative firm types. Each region's industry final demand constraint is expressed as a function of regional output, minus both domestic and foreign exports, plus domestic and foreign imports. Finally, Appendix 5 is a matrix presentation of the entire model. The component parts of the tableau have already been described in this chapter. CHAPTER 6 RESULTS OF THE LINEAR PROGRAMMING MODEL BASE RUN 6.1 Introduction The purpose of this chapter is to establish' some bases for evaluating the patterns of resource allocation, production and trade among small scale industries in Sierra Leone during the 1974/75 sur- vey period. Since our linear programming model is an "optimising"1 model, it reveals the "best" combination of production processes or the "largest" possible output or value-added obtainable for every input combination [Baumol, 1965, p. 271]. However, such optimum‘ (best) solutions are obtained under the assumptions2 of perfect know- ledge concerning both resource and product market situations. Al- though these perfect situationsckinot exist in real life, the best results of the model, when compared with actually observed data, do highlight the existing level of efficiencies among small scale indus- tries. On the basis of such comparisons, suggestions can be made to guide decision makers in formulating policies that would enable small 1According to Richard Day [1974], "optimising" is finding a best choice among possible alternative choices. 2Since firms are assumed to be perfectly aware of and have access to the cheapest input and most profitable product markets, they con- tinue to combine their inputs, say labor and capital, until the ratio of their marginal products is equal to the ratio of their prices. In the product market (domestic and foreign) the marginal cost of selling a unit value of output will be equated to its marginal revenue [Braff, 1969. PP. 269-272]. 124 125 scale entrepreneurs to come closer to their objectives, i.e., max- imum profit [Baumol, 1965, pp. 4-5]. 6.2 Number of Firms On the aggregate, our model predicts a total of 26,877 indus- trial firms that are in the Sierra Leone Small Scale Industrial sub- sector during 1974/75 (see Table 6.2). This predicted number is 2 percent less than the actually observed number. Thus, our model pre- dicts that a total of 528 most inefficient industrial firms ought to be dropped fdfim the subsector so that the profit of the subsector as a whole can be maximized. Although the aggregate number of firms declined by 2 percent, there are variations in the changes of number of firms by region, in- dustry and production process type. In region I, and with the excep- tion of the blacksmithing firms, all firms increased above the actual numbers as shown in Table 6.2. The sole blacksmithing firm type in region I, entered the model solution at the level of 12 firms, as was observed in 1974/75. Increases in the number of firms have been observed also in region II's tailoring, and carpentry industries, as shown in Table 6.2. But, the number of gara dyeing, blacksmithing and bakery firms, declined by 11 percent, 13 percent and 12 percent respectively. Such declines in firm numbers could be explained by a further examination of process types. Accordingly, as can be seen from Table 6.1, the blacksmithing firm, P522, did not enter the basic solution. Although ' this firm adopts modern production methods, it has very low output— capital and labor-capital ratios (as shown in Table 4.10), thus (all-I'll‘l'flulllllll.i 126 Hmoo hbmv mmm.NH Honouasm anamomcH MHmH mNom NHmH mmmH MMMH HHvH nmm ammoH vaH mama Nemm Hemm mvvm vam vam mch NQHm HQHm omau EuHm mcwnuesmxomHm huucomuoo mcHHOHHoB moHuumanCH NoooaN omnu mmoH eoHuHHmoosv >H c0H_oM NMH mm mom oVN VHHH Honounnm auumamcH NMH MN Nb H hmH 05H ovN th hoN 0mm mm Hmwm Nmmm Hmmm mmqm Nmem Hmcm Hmmm «MHm mmHm NMHm HNHm emu» Bush huoxom mascuHmeomHm auucomumu mCHoho snow mcHsoHHoa moHuumoch sooo.omnooo.m aasassaoosv sss :oseam es ms «as mm mee souounsm essences H m 0 NH mm mm mm no HH HH HOH MNH mmN NmH mem NNmm HNmm NNmm HNmm MNam NNem HNem MNmm NNmm HNmm «NHm mNHm NNHm HNHm emu» auHm huoxmm massuesmeMHm asucomuou mCHmwo mumu mCHsOHHma moHsumoch sooo.oosnooo.Om aaswssaoose ss ammeae NH NH mm NN ham Hmuounsm anamSch NH NH HN No H mH m mbH mmH mmm HHmm HHmm NHcm HHem mHmm NHmm HHmm MHHm NHHm HHHm omen Essa msoxom mcHnuHmeuon muoucomuoo mcHth ouou msHuoHHoB moHuumaocH Hooo.oom so>o moHusHmuoav H cOHwoM mN\QNmH egm OWGQ USU How COfiquOA Ufim ”GRAB mwQUOHm HdfihumOUflH RAD madman NO #09352 UUUUHUme Hoe UHDGH 127 .>o>u:m mHon mN\qan wcHssm voumuwcow oumv osu Eosm wousaaoo coon o>mn moustm Hmsuu< s aoos woos rem «em as am am on we we mamouaaouam eassaoos aoos woos mae.em eoa.em eee.mm ooe.em eees meom eea mom emo.s wee sauce re. re. mes mms . i . mes «as es ms ms ss saaxom rem asm ses.e eee.e seo.e ooe.e ea eos es es ms ms mesrassaxoasm was aom mes.e eee.e ase.a ooo.e wee ewe was oms ea es suaaamaoo as as _eee oee . . evm oem ea oos mm om sesame ammo see see eso.es msm.es eee.ms ooo.es asss aems mee mee new esm mcsuossaa vouoHooum Hopuom vouoecoum Hmsuod pouosmoum Hosuom vouonoum Hoouum couuecoum Hmsuom vouoevoum Ho5904 aaooaaaoaaa sauce ooo.am can» aaas ooo.omuooo.m. ooo.oos-ooo.om ooo.oos uaso emperors auumsocH moHuHHouos moHuHHmooq mosuHHoooq moHuHHoooq mm\quH .cOHumuos mom zoomswcH he msuHm mo sonaoz wouUHpoum mom H Hmsuu< mo somHumdaou N.o oHpoH 128 indicating a very low efficiency performance. In the bakery indus- try, while the decline in firm numbers could be due to the relative inefficiency of the "modern" process type firm P821, the decline in the number of the traditional bakery firm types, P823, and the three gara firm types could be due to other factors such as levels of effec— tive demand, regional comparative advantage, capacity utilization, forces that will be more thoroughly analysed in the next chapter. In region III, Table 6.2 also reveals that the number of firms in the gara dyeing and carpentry industries increased. But the firm numbers in the tailoring, blacksmithing and bakery industries declined by 10 percent, 10 percent, and 8 percent respectively. Also, in region IV, while firm numbers in the tailoring and carpentry indus- tries declined by 5 percent and 8 percent respectively, the number of firms in the blacksmithing industry increased. 6.3 Emplgyment Patterns Table 6.3 reveals that on the aggregate, the predicted quantity of labor hours utilized by small scale industries exceeds the actually observed data by only one percent. In addition, both the model pre- dictions and actually observed data indicate a wide variation in re- source utilization by industry and location. Such variations exist for both the use of different kinds of labor and capital. 6.3.1 Labor Utilization Table 6.3 reveals that out of a total labor service input of 24,255,222 hours predicted by our model f0r the year 1974/75, the 129 .mN\¢an .mm>u:m asumsvcH «Hmuw HHmEm ocooq muuosm osu mo oHaEmm Eovcmu ecu Esau oumcswsbo moustw Hmsuu< H OOs OOs as ea cm as ms m as ss aeaaeaoaaa oOs abs mmm.eem.em eea.mse.em see.maa.ss oce.aas.ss aee.eem.e oee.emm.e ees.esa.m aee.eee.s ame.ese.e emm.sem.m sauce a e. oeo.maa moa.OR . - eeo.eem . aao.ea moo.0s eee.eme . muaxae sm es mee.eao.e see.eee.a eee.eem.e oce.sao.e mes.eoe oee.ese sse.sm eea.em eem.es mee.es ecsresEasoase em as ese.eea.e ses.ees.e eeo.mse.s ooo.oee.s aem.eee.m see.eoe.e mme.eae.s oee.eoe ema.esm.s . muaeaaaao m e. esm.eoe bee.OOm - . eso.aoe - ees.ees oce.OOm oee.ee . eesamo aaao as ea see.e0s.0s eee.ame.ss aem.sea.e ooo.mes.e eon.esm.s ame.OOe.s ses.0ea mes.oee eom.ese.m mso.smm.m eesoossae .mp2 .mp5 .mp5 .mp5 .mp2 .mp5 .mun .muc .mu: .muc vmuUHvosm Hmsuu< vouUHvoum Hmsuu< vouosvosm Hmsuo< vouquoum Hmsuo< mouoHpOLN Hmsuum vmuquosm Hmzuu< . enumsvcH aaeaeaaooam saaos coo.m care aaas coo.Om - ooo.m ooo.oos . opc.om ooo.oos aaso moHuHHmuos mmHuHHmoos moHuHHmUOH moHuHHooos mm\c~oH .coHumuoq mam ebumzvcH he coHummHHHu: booms mo mauouumm vouosvobm mom H Hmauu< uo comHumdaoo «.0 oHnmh 130 tailoring industry contributed 44 percent, the highest hours input, followed by the carpentry, blacksmithing, bakery and gara dyeing in- dustries which accounted for 29 percent, 21 percent, 4 percent and 2 percent respectively. The table also reveals that the bulk of the total labor input in the subsector is located in the rural areas. Thus, while 49 percent of labor services of the subsector is accoun- ted for by localities with less than 2,000 people, regions I, II and III account for 19 percent, 12 percent and 49 percent respectively. These predicted patterns are close to the actually gbserved patterns. 6.3.2 Labor Types A first glance at Table 6.4 will seem to indicate that the pro- prietor and family labor is the most important labor type utilized in the small scale industry subsector. This bias stems from the fact that in region IV, the proprietor and family labor is the dominant labor type and accounts for 100 percent of the labor services utilized by all industries located in this region. However, the data also reveal that if region IV is ignored, apprentice labor seems to be the most important labor type contributing, 50 percent, 61 percent and 58 percent of the total labor services in regions I, II and III respectively. Next to the apprentice labor type is the proprietor family labor type which accounts for 33 percent, 32 percent and 42 percent of the total labor services of regions I, II and III respec- tively. The least important labor category is the hired type which only accounts for 17 percent, and 7 percent labor input in regions I and II. Region III virtually does not use hired labor services. Apart from the locational variation in labor type utilization, (I II. (II. 1‘1 III‘ II A III 131 HonoH mHHsmm moo nouoeumoum mo muse: u an ouoz HOQMH ooHucoummm mo muao: u Hm sonoH none: mo assoc u a: cos. 1 . oo cos me 1 ma oos se a me oos oe as me Masmaosocs ssa oos s me me u n i n cos e 1 ea oos e or .es cos r em a season oos a 0 ea cos n 1 cos oos me 1 ma cos «a e ee cos me 1 .me massasamxoasm oos me m om oos 00 cos em 1 es oos ea e ss oos. as .e .ss enacaouao oos e a me i u n cos we r em cos r em as oos m a me masaeo ammo ,oos mm s as cos i 1 oo oos .am r em oos em m me oos ea e ,roe masuossoe sauce sa so so sauce s4 so se sauce so s: wo sauce sa so so sauce so so so managers aasassaoos ssa ooo.m aofi aaas sass ooo.om-ooo.m 55 80.87898 0853 Page fiss .. aasassaoos aasassaoos .aasassaoos aasassaoos aosuaoos nN\qmoH .ooHumUOH mam huumovcH an momha noan en unqu musom mo mwmuooouom wouUHmoum «.9 oHan 132 Table 6.4 reveals a remarkable variation in labor type utilization by industry. Whereas the apprentice labor is crucial for the car- pentry industry, accounting for 79 percent, 86 percent and 85 percent in regions I, II and 111 respectively, the hired labor is of critical importance in the bakery industry accounting for 93 percent and 80 percent of total industry hours input in regions I and 11 while pro- prietor and family labor clearly dominates labor input in gara dyeing, accounting for 89 percent, 77 percent and 95 percent of labor input in regions I, II and III respectively. These patterns of variation in labor utilization by industry and location as predicted by our model correspond with the actually observed data [Liedholm and Chuta, 1976, p. 35]. 6.3.3 Capita1 Table 6.5 reveals that the value of capital services utilized by the small scale industries exceeded the actually observed data by only 9 percent. However the patterns of industrial and locational variations in the utilization of capital services remain the same. Out of a predicted total capital services of Le 1,547,155 utilized by the firms in the small scale industrial subsector for the year 1974/75, the tailoring industry accounted for 50 percent of total capital services as shown in Table 6.5. The gara industry, which used the least amount of the subsector capital services accoun- ted for only .7 percent of the total capital services. These pre- dicted patterns also synchronize with the actual situations that were observed in Sierra Leone during the survey period. The pattern of capital services utilization varies both by 133 .nN\qN¢H .>o>u=m auumavcH onum HHmBm ocooH ouuon «cu wo demam Eovcmh «nu Eouw oumcHwHuo moustu Hmsuuo eoHuHHoUOH noHuHHmuoq moHuHHwUOH moHuHHooos m~\man .coHumuoq vac zuumsucH hp :OHuanHHuD moUH>uom HouHomo mo moaHn> vouquoum and H Hdsuu< mo cOnHumaBou n.o oHnoh 134 industry and location. Whereas the bulk of the annual capital ser- vices are utilized by the tailoring industry, carpentry, blacksmith- ing and bakery industries utilize 25 percent, 16 percent and 9 percent of the total subsector capital services respectively. Also, as Table 6.5 reveals, the bulk of the subsector annual capital services is consumed in the rural areas - localities with less than 2,000 people. Thus, while region IV utilizes 52 percent of total annual capital services, region I accounts for 24 percent while regions 11 and III account for 14 percent and 10 percent respectively. 6.3.4 Limiting and Unlimiting Resources In a linear programming framework, a resource is limiting if it earns a positive value of marginal product (VMP). Such resource VMPs thus indicate possible gains/losses in income through the acquisition/salvage of scarce resources, given the level at which those resources were initially constrained. The results of our model reveal that all our labor categories earned postive VMPs, thus in- dicating that labor is a scarce resource. In regions I and IV, the VMPs of the different categories of labor are equal to the per hour labor costs originally stipula- ted in the model.1 But in regions 11 and III, the VMPs reflect different scarcity levels for the different labor categories as shown in Table 6.6. Nhereas hired labor is most constraining in region 11, both proprietor and family labor and apprentice labor types are very constraining in region 111. Thus, the VMPs of hired labor in regions II and III exceed two Leones while the VMPs of apprentice labor in region 11 range between Le 7 and Le 2.1. l See page121. 13S mooH>uom use: noan ooeucosmmo ursm mooH>uom soon sonoH moses n Hm .mooH>uom soon HOQMH >HHEom pom HouoHHmoum u so "meoz e. mo. e. mo. a. 8. s. so. s.m 8. so 898 I ooo.N m.N vm. v.N om. o.N Hm. m.N vm. v.N mm. Hm moHuHHmUOH HHH mo. mo. mo. mo. No. NH. .0 Ho. No. No. am a ooo OCH 0 s.m es. s.m es. s.m om. s.m es. s.m 2. so 1 8o om moHUHHmoos vm. vm. 0m. 0m. 0m. Hm. vm. vm. mm. mm. Hm HH mz> umou mz> umou mz> umou mz> umoo mz> umoo soon mom H50: mom noon mom soon mom soon now auoxmm mcHnuHSmxoon auuoomuoo ocHo>o muow UCHHOHHMB mafia conom .aooas uman mo zuH>Huu=wosm Hmonsmz mo em\emas .saaooas use oOHumUOH mom muumovoH %n monm> vmuoHcoum mam mumoo soan usom Mom coo3umm moHoamwsm>Hn v.0 mHan ll‘l Iliailllll-IIT‘ [loll Ill '1 '1 Illil 136 One explanation for the relative scarcities of labor in re- gions II and III could be the relative low net migration rates in these regions as distinct from the high net migration rates in region I [Byerlee, Tommy, Fatoo, 1976, p. 38]. If region I receives a large proportion of net flow of migrants in Sierra Leone, then one would expect labor to be less constraining there, than in regions where the rate of out migration is high. In addition, labor is less constrain— ing in region IV, the rural localities, because not only does a large proportion of the entire population live there, but only about .5 per- cent, an insignificant percent, of the rural population change resi- dence in a year [Byerlee, Tommy and Fatoo, 1976, p. 42]. Our analysis reveals that capital is not a limiting resource both by region and industry. This is so because capital services always earned a zero VMP. This important finding lends support to our earlier finding that lack of capital is no barrier to the success of small entrepreneurs [Liedholm and Chuta, 1976, p. 97]. 6.4 Gross Output As Table 6.7 reveals, our model predicts an annual gross output of Le 13,401,447 for the five major industrial categories of the small scale industrial subsector. This figure is greater than the actually observed figure of Le 9,618,400 for the value of annual gross output of the subsector. Our model prediction is thus 39 percent higher than the actually observed data. Out of the total gross output predicted, the tailoring industry (contributes 42 percent while the carpentry, bakery, blacksmithing and gara dyeing industries account for 24 percent, 15 percent, 12 percent 137 .mN\QNmH .%o>u:m muumzch onum HHmam ocooH muoon ocu mo oHaamm Sousa» ecu Ecom oumchHuo mooawHw Hmduu< s OOs o0s ee ma mm mm as ms em as aeaeeaooaa oos oos mee.soe.es ooe.ese.e mes.mee.e ooo.mse.e mee.sem.e eme.seo.m mee.moe.m mee.aee.s emm.ooe.e emo.eee.s sabos es a. see.eea.s mmm.ee . - cse.aem . eem.eem mmm.ee. eme.mea.s - moaaae ms es som.soe.s see.eOe.s eem.eee.s ooo.eee.s eem.mss eam.ams eem.ms oae.Os eee.ms eoo.ms masewseaxoase em as eee.mms.e ess.eee.s eee.mee ooo.0ae eeo.ess.s emm.eo~.s eae.eem.s oee.mem eee.mee . mobcaoaou e a soo.eso.s ooe.eee - - eem.eee . sse.mme ooe.eee eme.ees - mesasa aaeu me se ems.eee.e eea.mme.e eso.eee.m ooo.aes.m sem.eme eee.eem ceo.sam oee.mea aae.eme.s ema.mee.s ecsaossao . .ms: .mo; .mu: .mo; .mp5 .mp5 .msn .msn .mus .mo; mouquoum Hmsuo< wouoHvoom Hmsuo< vouoHpoom Hmsuo< vmuUHpoum Hanuo< nouosvoum Hmsuu< vouUHvoum Hmsuu< xoumsvcH assesses emerge. eerie... seems“. mH\¢NmH .coHuomuoq pom moumavcH up bamboo mmouo uo mosHm> pouosvoum use Hmsuu< no cOmHomosoo n.o oHDMH H 138 and 18 percent of total output respectivley. Apart from the in- dustrial variation in gross output, there is quite a variation by location. Whereas the rural localities, region IV, accounts for over one third of total gross output of the subsector, i.e., 35 percent, regions I, II and III contributed 25 percent, 19 percent and 28 percent of total gross output, respectively. The predomin- ant share of the tailoring industry and the rural localities in the total value of gross output is predicted by our model and con- firmed by the observed data. 6.5 Production and Trade Patterns In this section of Chapter 6, an attempt will be made to des- cribe the patterns of intraregional, interregional and foreign trade as revealed in our model. In addition, the predicted patterns of trade will be compared with the observed patterns. 6.5.1 Intraregional Trade Patterns Table 6.8 shows the overwhelming importance of intraregional as opposed to interregional trade of the commodities of the five major small scale industries in Sierra Leone. Although on the aggregate, about 92 percent.of total regional production is consumed within the region of origin, some regional and industrial variations exist in the patterns of intraregional trade. For example, whereas region IV trades all its produce internally to satisfy regional de- mand, regions I, II and III consume only 87 percent, 79 percent and 62 percent of their entire industry production respectively. With respect to industrial variatidn in intraregional trade patterns, it Table 6.8 Predicted Production (in Leones), 1974/75 1139 and Trade Patterns by Industry Products and Location ( Percentage Region of intra- Productsi Regions of Destination of regional of Foreign Total trade 0:131“ 1 II III IV Exports Exports“ 2 1*“ a 1.323.399 -- -- -- -- 1,323.39 100 b 46,180 —- -- - 122,295 168.47 27 c 415,440 -- 67,046 -- -- 482,48 86 d 12,588 -- -- -- -- 12,5 100 e 1,173,833 10,173 -- 238,822 —- 1,422.82 82. II a -- 791,050 791.05 100 b -- 300,000 22,371 322,371 93 c -- 721,771 513,722 1,235.49 58 d -- 12,675 12,67 100 e -- 236. 253 236.25 100 III a -- 623,261 623,26 100 b -- 222,000 -2,629 363,616 588,24 38 c -- 1,113,068 1,113.06 100 d -- 54,705 63,057 117,76 46 e -- 283,658 5,652 289.31 98. IV a 2,856,016 2,856.01 100 b -- -- -- c 347,892 347.89 100 d 1 .458 .268 1,458.26 100 e -- -- Foreign Imports a 494,415 298,061 b c d 1.992 70,565 e Total Imports a 1,817,814* 1,092,948* 623,261 2,856,016 b 46.180 300,000 222,000 25,000* c 415,440 721,771 1,693,836* 347,892 d 12,588 14,667 54,705 1.591.890' e 1,173,833 246,426‘ 283,658 ' 244,474* Note * Commodities where regional production falls short of regional requirement. ** Total export in our context equals total commodity production. *** a, b, c, d, e correspond to tailoring, gara dyeing, carpentry, blacksmithing and bakery industries respectively. .(l'u'! All." l|.l 140 is clear from Table 6.8 that all of tailoring output is consumed within the region of origin. With the exception of region III, all of the blacksmithing output is produced to satisfy regional demands. 6.5.2 Interregional Trade Patterns Table 6.8 reveals that the bulk of the interregional trade is in carpentry, gara dyeing, bakery and blacksmithing outputs. Region I ships 14 percent and 17 percent of its carpentry and bakery out- put to regions III and IV respectively; region 11 ships 42 percent and 7 percent of its carpentry and gara output to regions III and IV respectively; and region III exports .4 percent and 2 percent of its gara and bakery products respectively to region IV. In addition, region III ships about 54 percent of its blacksmithing output to region IV. 6.5.3 Foreign Trade The model predicts that 45 percent of total gara production is exported as shown in Table 6.8. But the bulk of this export, orig- inates from region 111. But, while region I ships 75 percent of its total gara produce as foreign exports, region III ships about 62 percent of its production as foreign exports. With respect to imports, our model predicted increased importation of blacksmithing output above the stipulated annual average. The bulk of such imports, i.e., 97 percent, were shipped to region IV for use in agricultural production. On the other hand, the model predicted that only 77 percent of the stipulated annual tailoring imports came into the solution. Out of this, 62 percent of the tailoring imports were shipped to region I while 48 percent of the tailoring imports went 141 to region 11. When all the predicted trade patterns are compared with the observed data, it becomes clear that the model has made very reli- able predictions. First, whereas we actually observed that only 4 percent of all tailoring output is traded interregionally, our model predicted that regions consume 100 percent of their tailoring output. Secondly, whereas we observed about 10 percent interregional shipments of bakery output, our model predicted that 13 percent of the value of total bakery output was traded interregionally during the survey period. Thus our model predictions synchronize with actual field observations in confirming the fact that the consump- tion of both tailoring and bakery products is highly localized. Thirdly, our model prediction of 18 percent interregional shipment of total value of carpentry produce is comparable to the actually observed 15 percent. While there exists such synchronizations between actually ob- served data and our model predictions, there are some differences too. Whereas our model predicted that 2 percent, and 4 percent of the total value of output of the gara, and blacksmith industries respectively entered interregional trade, we actually observed 37 percent and 32 percent respectively for those industries. The great divergence between actual and predicted values of gara cloth traded interregionally deserves some attention. Under conditions of perfect knowledge with respect to product prices in international markets and perfect mobility with respect to resources and products, our model predicted profitable increases in gara production for the export market. However, such perfect conditions do not exist 142 in real life. Thus, whereas we observed an actual export of 18 percent of total value of gara production, our model predicted 45 percent. In connection with blacksmith products, the model pre- dicted that it is cheaper to import cutlasses and matchets from abroad than to produce them locally. For example, whereas one leone value of blacksmithing import‘costs 1.076 cents, it costs 1.31 cents to produce one leone value of blacksmithing output using process type P151. Hence the model predicted the importa- tion of Le 72,557 worth of matchets and cutlasses, about twice the import coefficient stipulated in the model, and not unusual for some previous years. 6.6 Regional Comparative Advantage In order to explain the pattern of interregional trade ob- served and predicted by our model for the year 1974/75 in Sierra Leone, the principle of comparative cost advantage will be useful. For convenience, we shall concentrate on the composite financial costs2 of producing one Leone value of output by industry and loca- tion as shown in Table 6.9. Accordingly, two commodities stand out clearly in terms of comparative cost advantages. Since region 11 is the least cost gara cloth producer (with a unit cost of .63 cents), it exports gara cloth to region IV. Although regions I and III produce gara for foreign exports, nothing can be said about 1See page 261 in Appendix 5. 2Composite costs include annual labor costs, material input costs, working capital costs, building and equipment rentals and fixed capital costs. Kreinin [1971, p. 199] illustrated comparative opportunity cost principle using "composite factor cost." 143 the comparative advantage of Sierra Leone in foreign gara markets since we do not know the production-cost schemes of other competi— tors. Secondly, region 111 has a comparative advantage in producing blacksmithing output when compared with regions I and II. With a unit cost of production of .23 cents, as compared to Le 1.1 and .66 cents in regions I and II respectively, region III is able to ship over Le 63,000 worth of agricultural implements to region IV. Table 6.9 Predicted Per Unit Costsa of Production by Industry and Location (in Leones), 1974/75 Industry Black- Region Tailoring Gara Dyeing Carpentry smithing Bakery 1 .45 .77 .59 1.1 .77 2 .31 .63 .45 .66 .66 3 .42 .71 .36 .26 .66 4 .18 - .63 .19 - aCosts include labor costs, material inputs and capital costs. The situation is not quite as straight forward when it comes to carpentry and bakery industries. Although region I has absolute disadvantages in producing all the four interregionally traded pro- ducts of the small scale industries, such absolute disadvantages are least in carpentry and bakery but greatest in blacksmithing in- dustries. Thus region I produces carpentry and bakery products where it has the least diSadvantage and ships these products out to regions III and IV respectively. Likewise, although region II has absolute disadvantages in producing carpentry and blacksmithing products, 144 having unit costs of .45 cents and .66 cents respectively when com- pared to region III, its costs disadvantage is greater in black- smithing than in carpentry products. Hence reigon II produces enough carpentry output, and exports these to region III which has a comparative advantage in blacksmithing industry. Finally, region III is a least cost producer of bread output and is able to supple- ment the bread needs of region IV. It is important to realize that although the unit cost of bread production is the same in regions 11 and III, region III enjoys a lower cost of transportation advan- tage over region II. Whereas it costs .003 cents to ship a unit value of bread output from region II to IV, it costs .002 cents between regions III and IV. 6.6.1 Factors Determining Regional Cgmparative Advantagg_ Table 6.9 revealed that region I has absolute disadvantages in the production of all the five categories of small scale industry products. Such absolute disadvantages are further highlighted by Table 6.10. With the exception of carpentry industry, almost all the firms in region I tend to be capital intensive (at least on the aggregate). In our definition, capital incorporates not only service inputs from tools and equipments, but also from working capital and buildings. Thus, for example, whereas building rentals constitute about 31 percent of total capital services in region I, it is only 16 percent for region II and for similar kinds of modern bakery firms. In addition, and with the exception of tailoring, all the other industries in region I tend to utilize large amounts of (material input, when compared with similar industries in the other 145 - me. se. me. -- s.vm s.m m.m season no. es. es. ee. m.om a.em m.ss m.m masrusaaxoasm mo. om. me. se. m.e e.aa e.es m.mm mouaaouoo - or. oe. ea. - e.em e.me m.ss sesame ammo eo. on. om. em. e.es m.ss a.os m.ss masoossae ooo.m ooo.om ooo.o0s ooo.oos ooo.m ooo.om ooo.oos - ooo.oos anemones care oaas -ooo.m -ooo.om maao cars amas -ooo.m -ooo.om hobo moHuHHooQH moHuHHmooq moHUHHmooq moHuHHmooq moHuHHmooq moHUHHmooq moHuHHmooa moHuHHmooq huHHmooq AmmoooHv usauoo mo osHo> uHaD pom unoaouHouom uoaoH HMHuoumz AHmuHamo mo oon> moomH you mssomv mowumm HmuHamoisoan em\emes .amaaaa>e< o>Humumaaou onchHaxm mom momma mm GOHumUOH pom enumavoH mp muooaouHovmm mousomom wouUvaum OH.c oHnt 146 regions as shown in Table 6.10. One would expect the cost of mater- ial inputs to be lower in region I, being the port of entry for the entire country. But, if one recalls that most of the firms in region I purchase the services of electricity, water, and telephone, the high input coefficients of region I becomes understandable. With the exception of gara production, region III has an ab- solute advantage in producing the four major products of small scale industries. As is also revealed in Table 6.10, region III firms utilize labor-intensive techniques as revealed by the high labor-capital ratios. But such absolute advantages that are due to favorable factor endownment tended to be counter-balanced by material input costs which are higher in region III than in region II. The fact that most material inputs used in region III are pur- chased from regions I and II and in uneconomical quantities means that material input costs are higher in region 111. However, since region III makes much use of cheap apprentice labor and virtually no hired labor as in regions I and II, region III still enjoys ab- solute cost advantages when compared to regions I and 11. As was revealed in Table 6.9, region 11 enjoys a comparative advantage in gara dyeing being the least cost producer of all the three regions. Such comparative advantage stems from the fact that not only is labor-capital ratio in gara dyeing high for region 11, material input costs are also the least. Also, region III's compar- ative advantage in blacksmithing is based on the fact that cheap labor is always utilized and material input costs are relatively low when compared to region I. Region III's comparative advantage in bread production is due, not to low cost of material input but 147 relatively abundant use of cheap labor as shown in Table 6.10. 6.7 Summary The purpose of this chapter is to assess the efficiency of resource allocation among small scale industrial firms in Sierra Leone. Thus, the actual levels of performance of those firms have been compared with the predicted levels needed to maximize the total profits of the small scale industrial subsector. Any differences between the actual and the expected levels of performance there- fore call for policy suggestions on how to bridge the gap. A significant result of this analysis is that inefficiencies in resource allocation exist among Sierra Leone small scale indus- 1 of tries. On the aggregate, the predicted average productivities labor and capital of .55 and 8.7 respectively, exceed the actually observed corresponding productivities of .39 and 6.8. These diver- gencies therefore require policies that are directed towards raising the productivities of resources in the small scale industrial sub- sector. Although the predicted resource productivities exceed the actually observed data, the patterns of variation in industrial and locational resource utilizations remain the same for both the pre- dicted results and observed data. For example, over 50 percent of small scale establishments are tailors and over 80 percent of these small establishments are located in rural areas, i.e., local- ities with less than 2,000 people. In agreement with observed data, the model predicted industrial and locational variations in regard to patterns of resource utilization. Thus, while the tailoring 1The productivity coefficients were computed from the aggregate results of Tables 6.3, 6.4, and 6.5. 148 industry alone contributed 44 percent of the total labor services utilized by the five major small scale industries, 49 percent of such labor services are also located in the rural areas. Also, whereas apprentice and hired labor types dominate total labor use in the carpentry and bakery industries respectively, proprietor family labor is the most important labor type in the gara dyeing industry. In addition, while the proprietor and family labor are the most important labor types utilized in region IV, apprentice labor dominates the proprietor/family and hired labor types in regions I, II and III. The preponderance of apprentice labor in large and small urban areas of Sierra Leone calls attention to the need for an evalutation of the apprenticeship system as a major vehicle for skill formation in small scale industry. The model predicted that the relative scarcity of labor re- source varies by region. Thus, labor is more constraining in regions 11 and III, as compared to regions I and IV, due to dif- ferent rates of net migration among the regions. Therefore, the MVPs of labor in regions II and III exceed the observed wages specified in the model. In regions I and IV, the MVPs of labor equaled wage specifications. Since the model predicted zero MVPS for capital services in all industries, capital is considered a nonlimiting resource among Sierra Leone small scale industries. This result collaborates with an earlier regression analysis re- sult which indicated that business success was negatively correl- ated with the level of initial capital [Liedholm and Chuta, 1976]. With respect to domestic trade, intraregional trade, which accounts for 92 percent of total regional production, far exceeds 149 the level of interregional trade. However, the products which dominate intraregional trade, as predicted by the model, are gara cloth and blacksmithing products, followed by carpentry and bakery products. The principle of comparative cost advantage has been utilized to explain the patterns of trade flows. The composition of costs included in the determination of regional comparative advantages includes costs of labor, capital and material inputs as shown in Table 6.10. On the basis of such cost comparisons, region I possesses absolute disadvantages in producing each of the five major small scale industry products. Such cost disadvantages are mainly due to high building rentals, wages and electricity charges. On the other hand, region II has absolute advantages in producing most of the products of small scale industries. Such absolute advantages are mainly due to the use of cheap apprentice labor. In spite of such absolute disad- vantages and advantages of regions I and III respectively, produc- tion and distribution of the products of small scale industries is based on comparative, not absolute cost advantages. In situations where two regions have the same comparative advantage in producing the same output, transportation costs played a major role in deter— mining which region produced and distributed to other regions. One significant outcome of this analysis of comparative advantage is that the cost disadvantages of the firms in region I, the capital territory of Sierra Leone, are exogenous to those firms. This cir- cumstance therefore calls f0r policy interventions that will reduce the high wages, rental and electrictiy charges of urban firms. The industries most affected by the foreign trade section and 150 whose foreign trade patterns deviated from observed data are gara dyeing and blacksmithing industries. Thus, the predicted increases in gara dyeing exports and blacksmithing imports exceed actually ob- served data. Nevertheless, the model indicates that if a ready market was available, it was profitable to produce gara cloth for exports. In addition, the increased imports of blacksmithing pro- ducts, indicate that foreign sources of those products are cheaper than some domestic sources. CHAPTER 7 SHORT RUN POLICY ANALYSIS 7.1 Introduction In the last chapter an attempt was made to compare the re- sults of the model base run with actually observed data in order to evaluate the efficiency of resource utilization among small scale industries. In this chapter, sensitivity analysis will be undertaken to test the short run effects of changes in some policy variables on techniques of production, output, employment, profits and the directions of both foreign and domestic trade of the small scale industrial subsector in Sierra Leone. The "short run" per- iod referred to in this thesis is a period of one or two years. Such a short run situation is forced to be effective in the model by the use of flexibility constraints. These constraints impose upper and lower limits on the number of representative firm types as was observed in the 1974/75 survey period. Thus, within such narrow limits of :_10 percent, there is not enough flexibility for any long run adjustments. 7.2 Policy Variables The policy variables include interest rates on capital, the levels of capacity utilization, effective demand and import duties on both intermediate inputs and some categories of final products 151 152 (competing imports). In order to analyze the effects of different levels of inter- est rate, the capital coefficients were computed at 10 percent, 20 percent and 35 percent rates of interest as previously shown in Tables 4.6 - 4.9. If there are possibilities of factor substitution among small scale industries, one would expect that raising the rate of interest from 10 percent to 35 percent (price of labor remain- ing constant), would progressively increase the cost of capital, especially to those firms which employ capital-intensive techniques of production. An increase in the cost of capital would imply that costs can be lowered by moving to a new point on the same iso- quant, so that more of the relatively cheaper labor-intensive techniques of production would be utilized. The ultimate result would mean increases in employment of labor services. Tables 4.6 - 4.9 also show the different rates of excess ca- pacity observed for the different representative firm types. The estimation of excess capacity is based on the difference between the number of hours each firm actually worked everyday and the maximum number of hours each firm thought it could work given its existing buildings, equipments and furniture and had no demand limitations. On the basis of such information, coefficients at 100 percent capac- ity utilization were recomputed for labor, output and material input costs as shown in Tables 4.6 - 4.9. It is assumed here than any increases in capacity utilization will generate proportional changes in labor utilizations, output and material input costs. Thus, al- though output-labor ratios remain constant (since both output and labor coefficients change in the same proportion), output-capital ‘..lllllllllll.‘lllllll|ll‘llll ...-III! I 153 ratios (capital productivities) increase due to higher rates of capacity utilization. In order to justify increases in the rate of capacity utilization'hithe model, regional demand coefficients have been increased also, by the regional average level of excess capac- ity observed for each industry [Liedholm and Chuta, 1976, p. 31]. If we assume that import duties on imported intermediate in- puts constitute additional costs to small entreprenuers, then gover- ment policies that grant import rebates on imported raw materials would have the effect of reducing the costs of imported inputs, thus rendering small businesses more profitable. As was pointed out earlier in this thesis,1 Sierra Leone small scale industries utilize imported raw materials, either directly or indirectly. The amount of imported raw materials varies, of course, from indus- try to industry. If we also assume that high tariffs on relatively cheap imported final products that compete with products of small scale industry, protect the small scale industries from declining, then policies that raise the tariffs on imported final products would tend to cause domestic production to be substituted for foreign imports. Such policies would therefore tend to encourage the util- ization of domestic resources, thus increasing output and employment. In order to examine the short run effects of certain trade policies on employment, output, domestic and foreign trade patterns and profits of the small scale industries, it was necessary to change the material input and import coefficients that were used for the initial run of the model. The alteration in the material input 1See pages 69-73. 154 coefficients was made on the representative firm basis, in order to take into account the regional location of the firm type, the scale of operation, the variations in input combination, the various import content1 of material input coefficient and the level of import duty that applies to the relevant component of the import content of the coefficient. Import duty rebates would necessarily reduce the value of the material input coefficient. The reductions were greatest for gara dyeing and least for blacksmithing industries. The reductions were also high for bakery since quota imports of wheat flour for the confectionery industry in Sierra Leone revealed an 2 In the model, implicit tariff of about 167 percent on wheat flour. imported final products were placed at a competitive disadvantage in the domestic market, by doubling the tariff import duties on the 3 products. Thus, import duty on imported relevant 5-digit S.I.T.C. tailoring and blacksmithing products were increased from 40 percent to 80 percent and from 2 3/4 percent to 6 percent respectively. 7.3 Policy Analysis with a Closed Model Since most analytical research on the possibilities of labor- capital substitution and effects of different levels of capacity utilization have been undertaken within the framework of the clas- sical and neoclassical production function [Arrow gt 31., 1961; Winston, 1974], it will be important to verify the differential impact IImport content here refers to the percentage of the total material input purchased items originating fromoutside Sierra Leone. 2 3 See page 71. See page 111. 'llllll-l'l' III I Ilall 1|I‘uI'l‘III-El {III’Il-l‘rll.'- I'I‘ ‘ I'll 155 of foreign trade on the sensitivity analysis. Thus, the short run policy analysis that will be undertaken in this chapter will be un- dertaken both in a closed model (without foreign trade) and in an open model (with foreign trade). The results of the policy analysis with the colsed model will now be presented. 7.4 Results of the Short Run Policy Analysis with the Closed Model 7.4.1 Effects of Levels of Interest Rate Table 7.1 reveals that at the existing levels of capacity utilization, increasing the rate of interest on capital from 10 percent to 35 percnet did not change the level of output, given the level of aggregate demand for the products of small scale industries. However, the quantity of labor services utilized increased from 24,903,010 hours to 25,056,401. Although employment of labor ser- vicés increased at higher rates of interest rate, the elasticities of employment with respect to the rate of interest are .6, zero and .241 between the ranges of lO-ZO percent, 20-35 percent and 10-35 percent rates of interest respectively. Table 7.1 also reveals that as the rate of interest was raised from 10 percent to 35 percent, subsector profits declined from Le 5,174,714 to Le 4,072,066. Thus the profit elasticity of interest rate over the 10 percent to 35 percent range of interest rate is -l.4. It can be concluded that while a one percentage increase in the rate of interest leads to 1The elasticity coefficients have been computed from Table 7.1. The formula used to compute the coefficients is: Employment elasticity = percentage change in labor services of the rate of interest percentage change in the rate of interest 156 a less than one percent increase in employment, profits decline by a greater than one percent increase in the rate of interest. Table 7.1 Predicted Aggregate Gross Output Values, Labor and Capital Services Utilization and Profits at Different Policy Levels for the Closed Model,a 1974/75 Gross 6...... 3.28322. 3:53:22, Profits Policy Variable Values v (leones) (hours) (leones) (leones) 1. Existing capacity utili- zation 10% rate of in- terest 13,784,399 24,903,010 1,282,649 5,174,714 2. Existing capacity utili- zation 20% rate of in- terest (base run) 13,784,399 25,051,310 1,598,074 4,864,787 3. Existing capacity utili- zation 35% rate of in- terest 13,784,399 25,056,401 2,389,475 4,072,066 4. 100% capacity Utiliza- tion 20% rate of in- terest 14,325,802 25,782,742 1,469,772 4,972,663 8In the linear programming closed model, both labor and capital services were absolutely uncontstrained. Thus, it was possible to verify the extent, if any, of labor-capital substitution possibilities, in the absence of competing imports. 7.4.2 The Effects of Level of Capacity_Utilization The effect of a higher level of capacity utilization with the closed model can be seen by comparing the results of policy variables (2) and (4) in Table 7.1. By enabling the small scale industralists to operate at 100 percent capacity utilization through an increase in the effective demand for their products, output values, labor services and profits increased from Le 13,784,399 to Le 14,325,802, 25,051,310 157 hours to 25,782,742 hours and Le 4,864,787 to Le 4,972,663 respec- tively. Thus output values, labor services and profits increased by 4 percent, 3 percent and 2 percent respectively. In addition to the gains in output, employment and profits, an 8 percent savings in capital services occurred, due to a higher level of capacity utiiization.‘ 7.5 Results of the Short Run Policy Analysis With an Open Model In this section, the results of the short run policy analysis with the open model will be presented. Discussions will include the effects of changes in the rate of interest, levels of capacity util- ization, and certain trade policies on the small scale industrial subsector. At the end of this chapter, an attempt will be made to summarize the significant results of the analyses with the closed and open models. 7.6 The Effects of Changing the Rate of Interest on Output, Emplpyment, Profits and Trade Patterns 7.6.1 The Number of Firms and Choice of Production Technique As Tables 7.2 and 7.3 reveaL the sensitivity of firm numbers to changes in interest rate of 20 percent and 35 percent varies by industry and by location. With respect to firm numbers, Table 7.3 shows that the tailoring industry in region IV has the greatest decline in firm numbers (62 percent fall), followed by blacksmith- ing industries in regions III and IV with declines of 54 percent and 20 percent respectively, and tailoring industry in region I,with a 1Computed from the capital services data of Table 7.1. 158 ooHH CONN MHmH MHmH mNOm MHmH MMMH nmmH momH MMMH MMMH momH hnm mva V¢HH hmw vaOH VVHH «mm aON cam «OH memo Nemm Hem» mvvm Neva vam mch NeHL Hch van umououcH mo Ho>oH 1 mascassasoasn >uucomuoo Senossao. omau suHu o >uumpOCH HOOO.N coca nmoH zuHa noHuHHnooHv >H oonum ---<-- mmH nN ON H hmH mbH HvN th NON omm mm «mm NMH MN Nb H hOH OhH ovN OhH th omm mm wON mam oOH was unououcH Hmmm Nmmm Hmmm mnem Nmem Hmcm Hmnm «NHm mmHm NMHm HmHm mo Ho>oq om>u >uoxmm maesuHmeuon >uucomumo mcHoxv sumo mcHuoHHma auHu huunocoH sooo.om-ooo.m res: nasassaoose sss aoseaa _ o H m O MH Hm mm mm he HH HH om MNH hmm VNH 'mm m H m O NH mm mm mm no HH HH HOH MNH me NmH «ON moo aOH ans umououcu MNmm NNOm HNmm NNnm HNmm mNem Nqu HNam mNmm NNmm HNmm «NHN mNHm NNHm HNHN Caron Esfisaaxoosn >Hucomuou mcHoav sumo mesuossao. scoo.oos-ooo.om ease aasassaoosv ss noseam HH HH ON No H OH n OOH mNH new _ omm NH NH HN «O H OH m th an mom «ON use ooH m— was unouuucn Hme HHmm NHem Hqu NHmm NHmm HHm nHHm NHHm HHHm uo Ho>oH . emu» >uoxom mcHsuHamxuon swuoomwou mcHo>v sumo moHuOHHoa BuHu no haunoooH 88.2: aa>o 5s: aasassaood s cosmos. mm\eNmH .conoz ban hounsvcH he mung unououcH «o nHo>oH uoououmHO um mamHm.wo umpasz mouUHvoum N.N oHnma 159 decline in firm numbers of about 16 percent. The only industries that showed increases in firm numbers are carpentry and bakery in- dustries in region III with percentage increases of 2.4 percent and 20 percent respectively, and tailoring industry of region II where the number of tailors rose by 1 percent. Some industries in certain locations did not change in firm numbers as shown in Table 7.3. Table 7.3 Predicted Magnitude and Direction of Change in Firm Numbers by Industry and Location, Resulting from a Rise in the Rate of Interest from 20 Percent to 35 Percent, 1974/75 Black- Tailoring Gara Dyeing Carpentry smithing Bakery Region I 16% fall No change 1% fall 8% fall 8% fall Region II 1% rise No change 1% fall No change No change Region III No change 2% fall 2.4% rise 54% fall 20% rise Region IV 62% fall - No change 20% fall - In the tailoring industry, the 16 percent and 62 percent de- clines in regions I and IV are due mainly to the low output-capital and labor-capital ratios of the firm types in those two regions as shown in Table 4.10. In region I, the number of firm types P111 and P112, (modern capital intensive embroidery firms) fell to their min- imum levels due to very low productivity of capital among those firms. Likewise, regions IV tailoring firm types (P142, P143), though traditional firms, fell to their minimum levels owing to low capital productivity. But, although, the number of firms in region 11 rose by 1 percent, it will be noticed that the number of 160 tailoring firm type, P124, declined by 5 percent while the number of tailoring firm type, P122, increased by 14 percent as shown in Table 5.1. As is revealed in Table 4.10, firm P124 is a modern tailoring firm, having the lowest output-capital and labor-capital ratios in the region, while P122 is a traditional tailoring firm with the high- est output-capital and labor-capital ratios. In the blacksmithing industry, the greatest declines in firm numbers occurred among firm types with low output-capital and labor- capital ratios. For example the firm types P511, P522, P531, P542, P543, declined in numbers due to their relative inefficiencies as revealed in Tables 4.10 and 7.3. In region IV where the number of blacksmiths fell by 20 percent, the number of P541 firm type, which has a relatively high output-capital ratio, increased to their max- imum level allowed in the program. While no substantial changes in firm numbers occurred in the gara dyeing and carpentry industries, the bakery industry of region 111 experienced a 20 percent increase in firm numbers as revealed in Tables 7.2 and 7.3. The explanation for the substantial increase in the number of traditional bakers in region III is the high capital productivity of the traditional bakery firm type, P831, as shown in Table 4.10. Thus, whereas the number of modern, capital-intensive, bakery firm types, P811, declined by 8 percent due to a 15 percent change in interest rate, the traditional labor-intensive firm type, P831 increased by 20 percent. It can be concluded that a change of interest rate from 20 percent to 35 percent resulted to the increase in the number of tra- ditional firms and a decline in the number of the modern capital 161 intensive ones. But, the result of the analysis also shows that traditional firms with low output-capital ratios also declined in number.1 7.6.2 Output and Employment If one assumes that capital is the scarce factor of produc- tion in most developing countries, then an increase in the price of capital would result in a substitution of labor intensive pro- duction process for the capital intensive one, so that on the aggre- gate, employment should increase. Such an increase in employment 2 As Tables 7.4 has not resulted from the sensitivity analysis. 'and 7.5 reveal, an increase of the rate of interest from 10 percent to 20 percent had no effect on either employment or output of the small scale industry sector. A further increase of the rate of in- terest from 20 percent to 35 percent, led to declines of 21 percent and 18 percent on subsector aggregate output and employment. In fact, as employment declined by 18 percent, the level of capital services utilized increased by 13 percent owing to theincrease in the rate of interest. Although both aggregate output and employment declined due to a change-of the rate of interest from 20 percent to 35 percent, the 1This would indicate that productivity is not only a function of factor proportions, but also a function of other variables like manage- ment, quality of resource, level of capacity utilization, etc. 2Since the capital variable is measured as the value of annual capital services in the model (see pp.38-44), the picture of labor- capital substitution possibility is distorted. Increases in the rate of interest are simply reflected in increases in the value of capital services utilized. Therefore, the model is incapable of revealing how much physical capital is substituted for by labor services due to in- creases in the rate of interest. nacoos cs usncs nous>uon HquHaau uzmcH nooH>hoa nonsH mucosa cH usauao No 03H¢> Houou I a .0 assoc sauce 0 a mo usHo> HqUOu I o can: 162 oasm seem ans Hsao ans ssuo oHN pesx any was“ on ssao on oeea onH ssao oHH Hsno oHN ansx vow mesa as osnsossnoz was: och HH-o as quo 0H Hsom oNH Hseu oen HHaL on one use can copiou- noocsnu amoucouuoe sno.ovh.H 55H.0NO.oH omn.an.HH ov~.omH Hmo.mHO.H OHH.mmm.H vmv.HON OvH.mmN.v th.mON.H oHH.Nom mam.mNo.h hmH.OhH.n HON.mH mHm.NOm mam.ooo.H nmn.moo vmo.oHO.n hum.VNo.n 0mm mmH.~¢m.H NNN.mmN.VN hvv.Hov.vH mov.HmH omo.Noo Hmm.mvo.H owH.OmN mow.smh.v va.Hoo.H HON.vnH msw.mmo.o moo.mnH.n HOH.OH nHm.mOm HO0.0bO.H NHo.mhh noo.vO>.OH 0Nh.nom.m OON vev.ssm.s mmm.eem.vm oav.sov.as sav.eos ono.mom soe.eao.s enn.vns eoe.soa.v vom.soe.s noe.eem ese.seo.n eea.nes.n ems.e ese.nOe soo.moo.s esv.roe oeo.voe.0s omo.noe.e obs sauce OO0.000 Hmh.hvo.h Hmv.moo.N www.0ON Hmo.noo.v coo.mmH.H NON.HmN OOO.NHH.H mom.th ONO.NmN vmo.O0m.N mmn.own.H own eno.oon son.moo.ss mns.see.e eea.amm ese.nas.a nem.nea.s esv.sns eeo.mse.s nae.eae ese.ene onm.eee.e eso.oen.m obm th.hno Hmm.hoo.HH NOH.Noe.v mm¢.OoH o~m.me>.v mON.mmv.H vHN.NmH on.NHn.H mom.ovH oom.vnn OON.hmm.m OH0.0mO.N 00H >H hem.vHN DNN.th.v Nnm.0nh.N moH.mH mHm.oHn mow.vvm Hob.v msm.ovH OBH.mv mHm.oo «HO.HON.N vmn.HvH.H OHN.5 va.NOn own.mhm mov.oo OOH.OHN.H HON.nNo 0mm nmo.mmH voH.owh.v svo.Hm>.m NvH.HH omo.mON on.omN Nno.HH NmH.vOH moh.hHH Oom.mm OOB.OOO.N mwo.mHH.H Ono.n mHo.OOn mVN.mmm 0mm.mo wOn.oHN.H HON.nNo oON Ono.voH v0m.m0h.v evo.Hm5.n vNN.w omo.moN OHm.omN Omm.O NmH.v0n Hwh.hHH mHn.mN oon.mwo.n moo.nHH.H va.N oH0.00H mVN.OOm mmw.hm OOH.OHN.H HON.nNo 00H HHH omo.OOn wHO.oHO.N omm.omm.N va.vm mmo.mo nmN.0mN mmm.H Hso.nN mbO.NH moo.nNH oso.Nmo.H How.wHN.H mmn.n VMH.voH Hon.NNm mnH.mHH omo.hvo OOH.n9h own Onm.mHN me.vHo.N Nem.nmm.N mmo.Om moo.mo nmN.oHN owm.H Hso.hN mnO.NH OHO.om th.noo.H www.mnN.H chn.v va.voH Hon.NNn coo.mm 50H.0nm OmO.Hmh oON moh.onH mmH.vHo.N Nvm.hmm.N mn>.mn moo.mm nmN.0nN hvm.H HHm.bN mh0.NH mvo.oo wa.moo.H www.mmN.H HNv.N vMH.vOH Hem.NNn vNH.0h 50H.Ono OmO.Hor 00H HH www.va non.ooN.v vvm.voH.n th.ONH vnm.How mmN.vOm.H OO0.0 Hmv.mH mmo.HH OHN.oo NvH.vNN.H voo.N>v VON.n oom.mm mnv.OoH HvH.nnN woo.HmN.N ONm.NVH.H can wvm.Nhn on.mnm.v Ooh.oov.n oON.wm mom.mNo ONO.NNv.H nom.o mvN.mH mmm.NH mnN.vv HNO.th.H omv.va ONO.N 00m.mn msv.moH ovo.nnN MON.mHO.N amn.nNn.H ooN va.NHn ONm.mnm.v ooh.m0v.m www.mo mom.mNo mNO.NNv.H OHv.m mvm.mH wmm.NH nmo.vn HNo.mbN.H wmv.va NOm.H owm.mm mhv.moH o~o.mON nON.OHc.N «an.NNn.H ooH H u. H O x H 0 v. a O K ..— O x H O K .H 0 31¢ anemones soaps enaxon ocsnasaexoasn >ascaonau ocsaso atao ocsnossae mo cesses Ho>oA «HvaOH .cossauoq van asumavcH as soon umouuucH Oo nso>oa scoooueHO um ecoHunusHsuO nooH>hom HIanuu van ponds .usauno nuouo vouoHvaum a.“ 0Hndh 163 effects of such a change vary by industry and location. The indus- try that shows the greatest decline (21 percent) in value of output is the blacksmithing industry as shown in Tables 7.4 and 7.5. Such a decline is due not only to the relative inefficiencies of black- smithing firm types in regions I, II and IV but also due to the com- petitive advnatage of the foreign imported blacksmithing products over the locally produced items. The percentage declines in the values of output of theother industries are not substantial. On the whole, both output and employment declined in the tailoring, gara dyeing and blacksmithing industries; both rose in the carpentry gindustry; but While output rose in the bakery industry, employment declined as Table 7.4 shows. Table 7.5 Predicted Changes in Output, Labor and Capital Services Utilization Resulting from Increasing the Rate of Inter- est by Region, 1974/75 AG AL AK Region I 9% Fall 8% Fall 14% Rise Region II 6% Fall .2% Rise 38% Rise RegionIII 46% Fall .4% Fall 38% Rise Region IV 38% Fall 34% Fall No Change Note: A0 a change in the Leone value of output AL 3 change in the hours of labor services AK - change in the Leone value of capital services Table 7.5 also reveals declines in output values, and employ- ment f0r regions I, III and IV. But whereas regions III shows the greatest declines in output values, region IV shows the greatest 164 decline in employment. According to Table 7.3, regions III and IV, experienced the greatest declines in firm numbers due to a 15 per- cent change of interest rate on capital. But a closer examination of regional variations in changes in output and employment reveals that in region 11 while output declined by 6 percent, employment rose by an insignificant .2 percent. 7.6.3 Ogtput-Emplgyment Tradeoffs A tradeoff between output and employment is predicted for the bakery industry. A closer examination of the bakery industry in Table 7.4 reveals that on the aggregate, output values declined by 3 percent while employment increased by 3 percent, given a 15 percent change in the rate of interest. A further examination of the bakery industries of regions I and II will throw some light on the changes predicted at the aggregate level. In region I bakery output fell by 8 percent and employment declined by 4 percent, in region 111 both output values and employment rose by 19 percent respectively. While a high interest rate led to a greater percen- tage decline in output than in employment with a modern capital in- tensive bakery firm, the same percentage change in interest rate led to equal percentage increases in both output and employment with a traditional labor intensive bakery firm type. On the aggregate, the net effect was a decline in output value and a rise in employ- ment. It should be recalled from Table 4.10 that whereas the repre- sentative type bakery firm, P811, in region I, has a high output- capital ratio (if 16.5 and a very low labor-capital ratio of 7.2, the representative firm type, P831, in region III, has both a higher 165 output-capital ratio (26.0) and a high labor-capital ratio (23.6). Bot, if it is remembered that the modern bakery firms in region I control 70 percent of the predicted total value of bread output in Sierra Leone,1 it will not be surprising that an 8 percent fall in the number of modern bakery firms in region I should have a great impact on total bread output value, hence the trade-off. In conclusion, although an increase in the rate of interest from 10 percent to 20 percent, had no impact in the open model, a further increase from 20 percent to 35 percent rate of interest had far reaching effects. The analysis also suggests that an increase in the price of capital from 20 percent to 35 percent has differential impacts on industries and locations. Among the industries which produce tradeable goods, like tailoring, gara dyeing and blacksmith- ing, both output and employment fell. This indicates that foreign trade could have some effects on the results that are to be expected from domestic policy actions. In the bakery industry, there was a tradeoff between output and employment. Thus, as output fell by 3 percent, employment rose by 3 percent. 1:6.4 Profits and Trade Patterns As Table 7.6 shows, the aggregate profits of the small scale industry subsector continuously declined as the rate of interest on capital was changed from 10 percent to 35 percent. Thus, an initial 10 percent change in interest rate led to a decline in pro- fits by 9 percent while a further increase of interest rate from 20 percent to 35 percent, brought about a profit decline of about IComputed from Table 7.4 166 130 percent. Table 7.6 Predicted Patterns of Subsector Profits and Foreign _ Trade at Different Levels of Interest Rates, 1974/75 Interest Profits Tailoring Blacksmiths Gara Rate Imports Imports Exports 10% 3,764,713 792,476 75,552 485,911 20% 3,434,122 792,476 75,552 485,911 35% -930,405 2,495,813 404,408 475,117 Percnetage changes between 20% and 35% 137% Fall 210% Rise 435% Rise 2% Fall With respect to trade patterns, since the same production patterns were predicted for the interest rates of 10 percent and 20 percent, foreign exports, foreign imports and domestic trade patterns remained the same at the interest rates of 10 percent and 20 percent. But, a change of interest rate from 20 percent to 35 percent, resulted in a reduction of gara exports by a two percent, and substantial increases of the relatively lower cost tailoring and blacksmithing imports by 210 percent and 435 percent respectively. Such increased importation of tailoring and blacksmithing output helped to fill the domestic demand for the products of those indus- tries where domestic production declined as a result of changing the interest rate from 20 percnet to 35 percent. 167 7.6.5 Elasticity Coefficients As a result of the predicted percentage changes in value of output, labor and capital services utilization and profits, some meaningful elasticity coefficients can be computed, given the rele- vant percentage change in the rate of interest. Thus, it will be possible to estimate the magnitude of change in a policy variable, given a one percentage change in the rate of interest, (over the interest rate range of 20 percent and 35 percent). Since our aggre- gate data on Table 7.6 reveals that changing the interest rate from 20 percent to 35 percent led to a decline of output, employment and profits by 21 percent, 18 percent and 130 percent respectively and an increase in the value of capital services utilized by 13 percent, the elasticity of output, employment, profits and capital, given a one percent increase in the rate of interest, over the relevant range of interest rate, (20-35 percent), becomes -l.4, -1.2, -8.7 and .9 respectively. Thus, the greatest adverse impact of the change in interest rate is on profits, followed by output and employment. It is important also to note that whereas the elasticity of profits with respect to interest rates is -8.7 over the 20-35 percent range of interest rate, the elasticity of profit coefficient is only -.6 over a lower nange of interest rate, i.e., lO-ZO percent. 7.7 'The Effects of Level of Capacity Utilization on Output, Employment and Resource Productivities 7.7.1 Choice of Production Techniqgo In general, the firm types which experienced the greatest de- cline in numbers were mostly capital intensive firms such as P411, P511, P811, P522, P321, P124, and P134 as shown in Table 7.7. These 1681 MHmH mNOM MHmH MMMH MMMH mOmH NMm VMMOH vVHH OCHuonm coHuomHHHuO Memo qum Hana Mvvm vam vam MOHo NOHN HeHo NuHummou make 0 Ho>oH ucngHmeoon huucomuou mcHuoHHoB auHm HoHuunsmcH AOOO.N oncu nnoH l moHuHHmoosv >H :OHwom OeH MN O H NmH NeH MO 0 NON OOn mm «OOH NMH MN NN H NmH ONH OVN ONH NON one mm mcHuonm )1) aosaansssab HMwo NMMN HMMN MMON NMOm HMON HMMm OMHN MMHN NMHm HMHN NuHuomoo meme 0 Ho>oq Nuoxmm mcHnuHmeoon huucomuoo mcHONO oumw mcHuoHHoa auHm HosuunsocH sooo.om-ooo.m - aasassaoosv sss cosmos a s s s es an em me me ss ss en sos cam ems aoos m H e O MH MM Om mm NO HH HH HOH MNH OmN NmH mcHumem coHuouHHHuO MNOm NNmm HNON NNnm HNmo Mqu NNON HNqo MNMm NNMN HNMN ONHm MNHN NNHN HNHm NuHoomou mm o Ho>oq Nsoxom ooHsuHmeuon mwucomuou mcHONo sumo mcHuoHHoa EuHm HoHuunovcH sooo.oos-ooo.om - aasassaoosv ss cosman m HH NH on. H OH. M NVH MNH MOO «OOH ms ms sm me s as e ems mes eee ocsaasxn :OHuoNHHHuO HHOm HHnm NHOm HHON MHMo NHMN HHMm MHHm NHHm HHHo Nusoomoo o Ho>oH Nuoxom OCHnuHEnxoon muucomuoo ocHONo sumo mswuoHHoa auHm . HoHuumsvnu HOOO.OOH uo>o moHuHHmoosv H cOHwom MN\ONmH .coHumuos can NoumsvcH Na coHummHHHuO NuHumomo mo mHm>oq acouoOOHO um mahHm no sonaaz mouoHOoum N.N OHan 169 firms, as Table 7.9 reveals, have very low output-capital ratios even at both levels of capacity utilization. It can thus be said that high rates of capacity utilization favors those firms with initial high output-capital ratios. Hence, high rates of capacity utiliza- tion has adverse effects even on traditional firms, which though having high rates of increases in capital productivity due to in- creases in capacity utilization, have very low initial levels of capital productivity. Examples of such traditional firm types are P412, P422, P431 and P531 as shown in Tables 7.7 and 7.9. 7.7.2 Number of Firms In the shortrun, the effect of increased rate of capacity util- ization on the number of firms is an overall decline of 18 percent as shown in Table 7.8. However, the rates of decline also vary by Table 7.8 Predicted Changes in the Number of Firms at 100 Percent Level of Capacity Utilization by Industry and Region, 1974/75 Black— Average Tailoring Gara Dyeing Carpentry Bakery Decline smithing ~ by Region Region . ' ' I 18% fall no change 19% fall 31% fall 25% fall 17% Region II 12% fall no change 17% fall 8% rise 13% rise 2% Region III 16% fall 74% fall 6% fall 76% fall 6% rise 33% Average Decline by Industry 15% 25% 14% 33% 6% 18% 1170 Table 7.9 Predicted Changes in Capital Productivities Due to Different Capacity Utilization Levels, 1974/75 Region I Industrial Tailoring Gara Dyeing Carpentry Blacksmithing Bakery P111 P112 P113 P131 P132 P133 P141 P142 P151 P181 Variable O/K at 3‘18““9.“Vel 4 3 14 25 112 184 13 9 2 17 of CapaCity Utilization O/K at 10\ Level 5 4 18 29 112 216 13 11 2 23 Percentage Change 258 33‘ 25‘ 164 04 178 04 22\ 04 35‘ Region II Industéiii Tailoring Gara Dyeing Carpentry Blacksmithing Bakery . pe P211 P212 P213 P214 P231 P232 P233 P241 P242 P243 P251 P252 P281 P282 P283 Variable O/K at mi'tinq.“v°1 6 13 10 7 25 105 184 43 40 5 8 1 5 31 44 of Capac1ty Utilization O/K at 1008 Level 8 18 14 11 29 123 216 62 4O 6 10 l 7 31 54 Percentage . Change 33‘ 384 40s 57‘ 168 174 174 448 04 204 25\ 04 408 05 23s Region III InduSt;::; Tailoring Gara Dyeing Carpentry Blacksmithing Bakery Variable P311 P312 P313 P314 P331 P341 P342 P343 P351 P352 P381 O/K at ”nun" ““1 4 10 12 6 149 10 76 57 9 21 26 of Capacity Utilization O/K at 1008 Level 8 16 16 10 172 12 87 67 12 27 37 Percentage Change 50‘ 608 338 67% 15‘ 208 14% 188 33s 29% 42$ 171 industry and location. Whereas, the gara dyeing and blacksmithing industries show the highest rates of decline, tailoring, carpentry and bakery industries show the least declines. Table 7.8 also re- veals that region 111 showed the greatest decline in the number of firms, followed by region I with a decline of 7 percent. Region II showed the least decline. In order to explain the predicted patterns of decline in firm numbers by industry and location, it is necessary to more closely examine Table 7.9. It will be discovered from Table 7.9 that both gara dyeing and blacksmithing firms showed the least percentage increases in capital productivities when compared with tailors or bakers, for example. In the three regions, the range of increases in capital productivities for gara firms is 15-17 percent and O-33 percent for the blacksmithing firms. For the tailoring firms, the rates of increases in capital productivities range between 25-33 percent for region I, 33-57 percent for region 11 and 33-67 percent for region 111. While in the bakery industry the rates of capital productivity increases is 35 percent for region I, 23-40 percent for region II and 42 percent for region 111, such rates in the carpentry industry are still higher than the rates computed for gara and blacksmithing industries. Thus, it can be said that the rates of increases in capital productivities were important in de- termining the rates at which industrial firms declined. 7.7.3 Output and Employment Another significant result of the sensitivity analysis which also stands contrary to existing theoretical postulate relates to 172 the effect of high rates of capacity utilization on output and employment. One would expect that any increases in the level of capacity utilization will bring about increases in aggregate output and also employment [Winston, 1971]. The result of the sensitivity analysis reveals that with an increase in the rate of capacity util- ization, both output and employment declined by .4 percent and .8 percent respectively as shown in Table 7.10. However, some variations by industry in output and employment do also occur. As has already been mentioned, increases in output and employment occurred both in tailoring and bakery industries. Output and employment in the tail- oring industry increased by 7percent and 5 percent respectively while in the bakery industry increases in output and employment of about .4 percent and 8 percent were predicted. In the gara dyeing industry, output and employment declined by 45 percent and 46 per- cent respectively while in the blacksmithing industry, output and employment also declined by 6 percent and 4 percent respectively. It can also be concluded that an increase in the level of capaCity utilization has the effect of increasing the capital pro- ductivities of all the production processes. But, the industries which suffer a decline in firm numbers are those which originally operated at near the level 0f full capacity. In such industries, the gainin increased capital productivities resulting from the higher rate of capacity utilization is minimal. Also the production process types which decline more in number are those which origin— ally had a relatively low level of output-capital ratio, irrespec- tive of whether such production process types are modern or traditional. 173 HHah 0H haunancH ssao sn. ssao ma. ssaa onm aasn an aasn oa. ssao om ssao a. ssao oe aasa aes aasm omm co ssao ems ssao .ev ssao sea ssao on aasa oe aasx am an nonzero ovaucoUMON oeN.nas.s eoo.s0s.em ses.ess.sH mmo.ee sea.eeo.s ems.eea.s esa.asm eoe.asa.a nme.eoe.s nmn.mme nse.aen.e sen.ems.e eas.e som.am~ ase.aee mem.eoe eas.aas.ss see.ena.e ooos cse.mes.s ese.ooe.vm eon.soa.ss sav.e0s oeo.maa sas.naa.s sen.sam ove.eao.e aam.soe.s msa.snm ave.sve.e can.ess.s ees.e sse.noe sao.aeo.s see.eme mea.aoo.os see.soe.e mesa-sun snubs anN.sNN moo.meo.ms mao.mee.a ese.amm ese.nve.a nem.nea.s ass.ees st.ess.s smn_mvs esN.ees enm.eeo.e sea.eee.m ocsaasxu >s ses.mss ees.aee.a oes.mee.m cma.os aem.o0e oem.aae nsn ase.ess ema.mm ass.se eeo.ess.m eme.eas.s eso ose.sa seo.mes nee.se mes.ees.s eas.sam ooos ase.aos ves.eee.a eao.see.m amm.n eeo.aem cse.aam ona.e mes.a0s see.ess ass.am aem.nee.m aeo.sss.s mev.m eso.e0s eam.nne eee.ee nos.asm.s sem.eme mesa-sun sss eve.ses aea.eoo.e eee.e0m.m esa.s ase.ses aao.sn oae.m aoo.ms Nan.es mse.sa see ses s ems.ses.s asv.s ooc.mas ees.mom m~a.ee ema.oms.s nee.oeo soos nee.ees nes.asa.m mae.rae.m ssm.es aao.na sem.esm mae.s ssa.rm ere.ss sve.oe Rmn.sae.s saa.esm.s sma.m vss.ves ses.mms ams.ee Nes.0ee oeo.sae unsaasxu ss see.sem eaa.aav.a Reo.snm.e aes.ve axe.eee ese.mev.s meo.e ssa.es eao.ss ase.0m emo.eeo.s eon.eem oeA.s ees.ss seo.oas eee.oes ves.eve.m eva.ens.s noes ess.~se esn.see.v eee.oea.s aea.me eon.eme nsa.msa.s asa.e nsm.es ene.ms sso.4e sme.amm.s ena.maa mee.s oee.es eea.aes emo.aom sam.ese.s aas.ems.s cess-sun s x s o x s o x s o x n o x n o u .s o cosuausssua Nusouma :30... Heaps Nuoxam masnusauxuasm Nuucomsso ocso>o sumo mcHuoHHok uo Ho>oa meenoH .coHOoa van shaman cw Na :oHuauHHHu: NuHoqneu Oo sHo>0H ucosouuHO as coHumuHHHuO Huannu vac Laban .aaasa> asoaao eaaosoaoo os.m aseao 174 Although the result of the sensitivity analysis in an open model revealed that both aggregate output values and employment de- clined, differential impacts of higher levels of captacity utiliza- tion on the five major industries is quite noticeable. For example, both output values and employment increased in the tailoring and bakery industires, while both declined in the gara dyeing industry. As was previously pointed out, since all the gara dyeing firms oper- ated near the level of full capacity utilization, they relatively gained less from increased rates of capacity utilization. 7.8 The Effects of Trade Policies on Output and Employment The result of the sensitivity analysis revealed no short run output and employment effects from policies of granting import re- bates or increasing the cost of foreign imports of competing final products. But the main impacts of those policies in the short run were reflected on profits. From the result of the initial run of the model, total mater- ial input costs and sectoral profits amounted to Le 4,030,797 and Le 3,434,122 respectively. With the removal of the duties on im- ported material inputs, material input costs fell to Le 2,510,926 while profits rose to Le 4,953,939. Thus profits increased by the same amounts that costs were reduced. Also, doubling the custom duties on imported final products that compete with domestically pro- 1 duced products did not reduce the value of imports. Rather, 1It has not been possible to incorporate in the model income elasticity coefficients for the competing imports or cross elasticity coefficients of domestically produced items vis-a-vis competing imports. Thus, a horizontal demand curve for foreign imports is assumed here. Due to such weaknesses of the model, the results have to be interpre- ted with caution. 175 sectoral profits fell to Le 3,156,224. The extra cost increases were absorbed in the objective function by the optimising scheme, thus indicating that it was still profitable to import those pro- ducts, even at the high tariff level. Although the model predicts increased profits due to the grant- ing of import rebates, the output and employment impacts of such policy measures could not be predicted by the model. A respecifica- tion of the model to include expenditure and investment decisions of the representative firm types over time will highlight the extent to which sectoral profits could be ploughed back in order to create more output and employment. Other policy measures such as subsidiz- ing the raw material input purchases of small entrepreneurs would have the same effect of increasing profits. On the other hand, the model predicts that policies such as raising tariffs on im- ported final products may not protect the small industry products in the domestic market. Depending on the income elasticities of demand for foreign imports, foreign products would still be imported only at a high cost and loss of material welfare to consumers. 7.9 Summagy The results of the short run policy analysis with the closed model have revealed that a one percent increase in the rate of in— terest will result in .24 percent increase in employmnet and 1.4 percent decline in the profits of small scale industries. Also, enabling the small scale industries to operate at full capacity utilization level through an increase in the effective demand for their products resulted in 4 percent, 3 percent, 2 percent and 8 176 percent gains in output values, employment, profits and capital savings respectively. However, the results of the same analysis carried out in an open model differ from those of a closed model due to the effect of foreign trade. The results of the short run policy analysis with the open model reveal that competing imports can have a neutralizing effect on domestic policies that are aimed at achieving increased employ- ment through the use of interest rates. Thus, by raising the rate of interest on capital from 20 percent to 35 percent, output values, employment and profits among small scale industries declined by 21 percent, 18 percent and 130 percent respectively. Although, on the aggregate both output and employment declined, the only case of in- creased employment of labor services occurred in the bakery indus- try where the number of modern capital-intensive firms declined while 1 But the result of this the number of traditional firms increased. increased employment was a decline in the value of bread output. The foreign trade sector also has a neutralizing effect on domestic policies that are aimed at raising the level of capacity utilization among small scale industries. Although higher level of capacity utilization increased capital productivities among small scale industries, it in effect altered the comparative advantage configurations of the different small scale industries, given the existing levels of capacity utilization. With inefficient firms still in existance, imports of competing goods displaced some 1The bakery industry of Sierra Leone is the only small scale industry with the most clean-cut distinction between traditional (labor intensive) and modern (capital-intensive) technologies. See Chapter 4 of this thesis. 177 domestic producers, but subsector output and employment remained almost the same, while the aggretate number of firms declined. The impacts of a policy of granting customs duty rebates on material inputs had the short run effect of increasing the profits of small scale industralists without any immediate effects on output and employment. The short run analysis also revealed that increas- ing the level of tariffs on competing imports nay not have the immed- iate effect of causing a substitution of domestic production for 'foreign importation of competing products. CHAPTER 8 LONG RUN POLICY ANALYSIS WITH AN OPEN MODEL 8.1 Introduction In order to carry out a long run policy analysis on the model, some modifications had to be made on the basic model. First, all the flexibility constraints1 that were meant to limit the growth in firm numbers to realistic annual levels were discarded. Second, in order to project the model results to a ten year period, the re- source, demand and trade coefficients that were used in the basic model were also projected to 1985. Such projections required making some assumptions with respect to some important parameters. The annual rate of growth in demand for each regional indus- trial product was obtained from the following equation: or = Pr + ed - %r where dr = annual rate of growth in demand, Pr = population growth rate, ed = income elasticity of demand and %r = annual rate of growth in per capita Gross Domestic Product (GDP) If it is assumed that Sierra Leone population grows at an annual 1See page 113. , 178 179 rate of 2.2 percent [Government of Sierra Leone, 1975; World Bank Atlas, 1975, p. 6], and that the annual growth rate in per capita GDP is 2.1 percent [Government of Sierra Leone, 1973], then, regional product demand constraints were projected to grow at annual rates of 4.8 percent, 5.16 percent, 6 percent, 2.5 percent and 5.45 percent for tailoring, gara dyeing, carpentry, blacksmithing and bakery pro- ducts respectively, over the next ten years. The income elasticity coefficients used for deriving the annual rate of growth in product demand are 1.22, 1.41, 1.90, .16 and 1.55 for tailoring, gara dyeing, carpentry, blacksmithing and bakery products respectively [Byerlee and King, 1976]. Each regional labor resource constraint was assumed to grow at the same rate as annual population growth rate (2.2 per- cent). Also, capital constraints were projected at the annual rate of growth of the Sierra Leone manufacturing sector, i.e., 3 percent [Government of Sierra Leone, 1975, p. 1]. Finally, since the value of the relevant 5-digit S.I.T.C.] blacksmithing imports increased at an annual rate of 25 percent while the value of the relevant 5-digit S.I.T.C. tailoring imports declined at an annual rate of 17 percent over the period, 1969-1973 [Quarterly trade statistics], the import coefficients that were used to project future trends in the model, were adjusted accordingly. What follows are the results of the long run sensitivity anal- ysis to test the effects of some policy variables on the choice of production techniques, output, employment, profits and trade patterns. Since the results that would soon be presented exaggerate the ‘See pages 111-112. 180 situations that might occur in the long run, it is important that these results be interpreted as indicating the probable directions that the small scale industrial subsector would go rather than what might actually occur. These results therefore, serve a useful guide to policy makers as to what can be expected in extreme cir- cumstances. 8.2 Effects of the Rate of Interest In the short run, the scope for substitution among produc- tion process types was limited by the narrow flexibility bounds within which firms can adjust in response to any policy interven- tion. With the removal of those~b0undsfor our long run analysis, it is anticipated that the policy variables will yield differen- tial impacts on output and employment in the long run, as distinct from the short run. 8.2.1 Effects of the Rate of Interest on the Choice of Production Technigue Table 8.1 shows theaproduction processes that would maximise the profits of the subsector by 1985. Also, Table 8.2 sheds light on the rationale behind the choice of those production techniques. Since in the model, labor and capital have been constrained, and since the model results indicated that the level at which those resources were constrained proved effective, i.e., labor and capi- tal resources earned positive value of marginal productivities, the costs of production per unit of output that have been presented in 1 Table 8.2 include only labor and capital costs. Indeed, the model 1Material input costs were not constrained in the model. 181 .::u some on» mH HOV oHnoHum> NUHHom c o .53 o o o 83 o o 2: 3268a sac: co OOHumH oHnsoo l O OOHO O O O HOHO O O NNHN mHmHu0umx 3mm comm Nana l O OOHO O O O HOHO O O NNHN mum: umououcH NnM l O OOHO O O O HOHO O O NNHN mum: unououcH NON l O OOHO O O O HOHO O O NNHN «can: unencucH NOH l MMON NmOm HmOm mOOm NOON HOON MHOo NHOo HHON qumHon> -wcH£uHmeuon Noncomomu -m:sooHth weak NuHHom auHm HaHLunavcH >H coHOom HOH O O O OON O O O O OMON O nuoovoum Hmch co uuHunH oHnsoO l ONN ONN O NnNH O O O O nHmHuoumx 3mm ovum Nana l ONN O O O MON O ON O O OMHN O comm umouaucH NnM l NNN O O O OON O O O O HMNN O puma unouuucH NON l OM NNN O O O O MONN O mum: unsuOucH NOH l Hmmo Nmmo Hmmm MONO NOmm HOMN Hmmm OHMN MHmo NHHN HHMm oHanun> Nuoxnm mcqusmeooHO muucmmsou wdsmmo mono mcHsoHHmN UQNH NUHHON s: cosmos. Eso sasnaassés O o N O O O O MMO O O O OHm O O MOO unsavoom Hoch :0 OOanN oHnsoa l o s as o o is o o 9: cme o o o asasaaaax as. aaoo .38 - O NH O O O O O OMO O O O HOO OM NNM O mums unassucH Nmn l O N m OMO O O MNH own O O O mums unassucH NON l O O NN O O O O NMO O O NH OOm O O O suns unassucH NOH l MONO NONo HwNo NmNo Hmmo MONO NONN HONN MHNN NMNm HMNo OHNN MHNo NHNN HHNm oHanun> abummm -w:H;sHmeonHm Noncomumu -m:HomO mono mcHuoHHMN OONH NUHHom ss 833. E: sasaaaaeas ON O O O O Omm O Nmn O O nuuavoum Hmcsm so uuHuOH poaoO l ON O O O O NHMH O O O O anHu0uqx and moan NusO l ON O O O O NHMH O O O O sand unauOucH NnM l ON O O O O NHMH O O O O ovum unassuaH NON l ON O O O O NHMH O O O O and: unouuuaH NOH l HOHN HMHN NOHN HOHN mMHm NHHN HHHo MHHN NHHN HHHN oHOOHon> Nooxmm mchuHamxunHm MGHUNO some MdHooHth UQNH NoHHom ahHm HoHuuaavcH H conwm mOoH .moHanLa> NuHHom no mHo>oH scubmmuHO um maon uo Lobeaz vmuoofioum H.O oHnoH Q-DUUU “.0 (ll-DU 0 N13 182 .nunoo HauHouo can noan ovaHuoH nunooo MM. NO. NO. H.H O.H MO. OO. OM. NH. numoo anO >H 33 men 32 ego meeo sOOo eeso meso seso aors. ears. NM. MO. Om. HN. OH. OO. MO. ON. NO. OM. NO. munoo uHca HHH HMOm NMmm HMMm MMON NMOm HMOm HMMH OMHm MMHm NMHm HMHN OONH auHm MN. HH. HN. M.H Om. HM. NM. OH. MM. HO. ON. ON. NM. MO. OM. numou uHoO HH MNON NNON HNOm NNMm HNMN MNOm NNOm HNON MNMm NNMm HNMm ONHN MNHm NNHN HNHm onNN ahHm OH. ON. NN. NN. MM. MH. ON. HM. ON. NN. nunoo uHoO H HHOm HHOm NHOm HHOm MHMN NHMO HHMN MHHm NHHm HHHm oONN auHm aoHOuO. Nuoan OaHnuHanxunHO Nuucoauno OoHoNO ounO OoHuoHHnH anuu , lmsvoH Hoooowv MOOH .coHuOUOH van OONH auHm o>Huouoonoumom .NuunsvoH NO coHuoavoum no omunoo uHoO vouuofioum N.O oHnah § 183 chose those production techniques for which average factor costs of production with respect to labor and capital were minimum, given the relevant rate of interest. For example, in the tailoring indus- try and at 10 percent and 20 percent rates of interest, the optimum firm types selected were P124 in region 11, P132 in region III and P141 in region IV with minimum unit factor costs of 29 cents, 50 cents, 17 cents in regions 11, III nad IV respectively. It will be noticed that in region 1, none of the tailoring production processes was chosen. This is because it was cheaper to fill the tailoring out- put demand in region I from foreign sources. It is important to notice that raising the rate of interest to 35 percent reduced the number of production process type P124 and introduced the firm types P122 and P123. Capital requirements per unit value of output are 7 cents and 9 cents for P122 and P123 respectively, whereas the pro- cess type P124 uses 14 cents of capital per unit value of output. Thus, the higher rate of interest had the effect of reducing the number of the relatively capital intensive production pgficesses. In the gara industry, the long run solution chose the produc- tion process types P312 and P321 at 10 percent and 20 percent rates of interest. As is shown in Table 8.2, these two firms have the min- imum costs of producing gara cloth in regions I and II respectively. But at an interest rate of 35 percent, the process type P321 dropped out of the solution, as shown in Table 8.1, and was replaced by P331. Whereas P321 uses 4 cents of capital to pr0duce a unit value of out- put, P33l, being relatively less capital intensive, utilized only .6 cents to produce one unit value of gara output. Table 8.1 reveals also that at interest rates of 10 percent, 184 20 percent and 35 percent, region I will not produce any carpentry output. As Table 8.2 reveals, unit costs of production are higher in region 1 than in regions II and III. In the latter regions, only process types P421 and P432, with unit factor costs of 18 cents and 10 cents will produce for distribution to other regions. In the blacksmithing industry, the only optimum-sized blacksmithing firm is P542, and is located in the rural areas, to satisfy most of the black- smithing output needs of region IV. In the bakery industry, the modern bakery firm type, P811, with a unit factor production cost of 14 cents entered the solution at the same level irrespective of the rate of interest, as shown in Tables 8.1 and 8.2. But, in region II, the same modern type of bakery firm with a higher unit cost of production (26 cents), de- clined in number but finally dropped out of the solution as the inter- est rate rose from 10 percent to 35 percent. At the 35 percent rate of interest, the bakery firm, P822, using an intermediate technology,1 with the minimum factor cost of production of 11 cents per unit value of output, and relatively less capital intensive, dominated the bakery production in region II. The traditional bakery firm P823, did not enter the optimum plan at any interest value, due to relatively higher production costs, as Table 8.1 shows. It can be concluded that in the long run, only the minimum cost (optimum size) firms, modern or traditional, will dominate pro- duction in the small scale industrial subsector. But, it is clear from Table 8.1 that higher rates of interest within the relevant 1See page. 185 range, will reduce the number of or eliminate the relatively capital intensive production process types, and substitute relatively more labor intensive firm types. The output and employment effects of such substitutions among production process types, resulting from changes in the rate of interest, will be considered next. 8.2.2 Output Values and Employment Effects of Changes in the Rate of Interest Table 8.3 reveals that, on the aggregate, a change of interest rate from 10 percent to 20 percent will have favorable effects on output values, but little or no effects on employment. Thus, gross value of output will increase from Le 29,230,884 to Le 29,845,694, 8 gain of only 2 percent while employment will not change. However, a further increase of interest rate from 20 percent to 35 percent 1 As Table has adverse effects on both output values and employment. 8.3 reveals, output will decline from Le 29,845,694 to Le 29,167,144 while employment falls from 27,400,186 hours to 27,397,691. In short, by raising interest rates from 20 percent to 35 percent, out- put will decline by 2 percent while employment will also decline but by an insignificant amount. While on the aggregate the output and employment effects of changing the rate of interest rate will not be favorable in the long 'run, the effects at industry level vary. As Table 8.3 reveals, the only industry with a favorable output and employment effects of changes in interest rate from 10 percent to 20 percent is gara industry. 1These predicted trends do not change even when labor supplies for regions I, II and III were projected at annual population growth rates of 6 percent, 7 percent and 5 percent respectively. See page 186 Table 8.3 Projected Gross Output Values, Labor and Capital Services Utilization at Different Levels of Policy Variables by Industry and Location, 1985 Region Policy Variable Tailoring Gara Dyeing Carpentry 0 K L k 0 L K I a) 101 Rate of Interest 0 0 0 10,597,024 1,894,028 62,959 0 O O b) 202 Rate of Interest 0 o 0 10,597,024 1,894,028 91,816 0 o o c) 352 Rate of Interest 0 o 0 10,597,024 1,894,028 139,035 0 0 o a) Duty Free Raw Materials 0 o 0 10,597,024 1,894,028 91.816 0 0 o c) Double Tariff on Final Products 1,813,320 3,883,396 134,158 4,313,118 77l.I08 37.381 0 0 0 II a) 102 Rate of Interest 1,184,546 1,340,715 175.388 79.092 31.857 1.666 3.004.992 2,291,601 56,977 b) 20: Rate of Interest 1,308,619 1.145.647 191,686 876,603 347,790 35.044 3,018,904 2,303,004 70,274 c) 351 Rate of Interest 1,394,108 1,422,879 249,219 0 0 0 3.018.904 2,303,004 95,538 a) Duty Free Raw Materials 1.217.320 1,066,564 178,444 955,695 378.604 38.149 3,088,464 2,359,022 71,984 e) Double Tariff on Final Products 1.476.040 1,389,997 219,886 0 0 0 3,011,948 2.298.859 70.148 III a) 102 Rate of Interest 1,636,020 2,539,179 131.763 0 o 0 2,067,248 3,310,636 16,546 b) 202 Rate of Interest 1,341,596 2,082,246 128,311 0 o 0 2.050.754 3,286,709 26,879 c) 351 Rare of Interest 1,281,400 1,989,499 165.612 186,342 98,057 2,343 2,048,005 3.281.474 33,545 a) Duty rree Raw Materials 0 o 0 4,197,473 2.205.257 28.115 1,979,280 3,169,178 25,918 e) Double Tariff on Final Products 1,443,280 2,248,346 138,547 0 0 0 2,059,001 3,295,407 26,950 IV a) 101 Rate of Interest 4,564,572 6,545,022 150,708 0 O 0 622,969 1,831.43- 100,353 b) 202 Rate of Interest 4.564.572 6.545.022 193,767 0 o 0 622.969 1.831.434 117,078 c) 352 Rate of Interest 4,564,572 6.545.022 265.533 0 o 0 622,969 1,831,434 192,342 d) Duty Free Raw Materials 4.564.572 6,545,022 193,767 0 o 0 622.969 1,831,434 117,078 e) Double Tariff on Final Products 4.564.572 6.545.022 193,767 0 o 0 622,969 1,831,434 ll7,078 Indul- try a) 101 Rate of Intereat 7,385,138 10,424,916 457,859 10,676,116 1,925,885 64,625 5.695.209 7,433,671 173,876 Total b) 201 Rate of Interest 7.214.787 9.772.915 513,764 11,473,627 2,241,818 126,860 5,692,627 7.421.l47 214,231 c) 352 Rate of Interest 7.240.080 9,957,400 680,364 10,783,166 1,992,085 141,378 5.689.878 7,415,912 321,425 a) Duty Free Raw Materials 5,781,892 7,611,526 372,211 15,750,192 4.477.889 158,080 5,690,713 7,359,634 214,980 e) Double Tariff on Final Products 9,302,212 14,066,761 686,358 4,313,118 771,108 37,381 5,693,918 7,425,700 214,980 Blacksmithing Bakery Regional Total 0 L x L x L K fil I 'St I 2; :3: :2:: if I:,:::,. 0 o 0 2,371,380 1,041,689 103,993 12,968,404 2,935,717 116,952 ) 35. “3,8 of Interest 0 0 0 2,371,380 1,041,689 143,158 12,968,404 2,935,717 234,974 C ‘ 0 0 0 2,371,380 1,041,689 209.225 12,968,404 2,935,717 348,260 R Hat rials ‘; 33:;IE';:Y,?: on :ina, 6,0duct, 0 0 0 2,371,380 1,041,689 143,158 12,968,404 2,935,717 234,974 I! e) 102 Rate of Interest 0 o 0 2,371,380 l.041.689 143,158 8,497,818 5,696,193 314,697 g) 201 Rate of Interest 0 o 0 878.526 247.084 126,634 5,147,156 3,911,257 358,999 ) 352 Rate of interest 0 0 0 349.680 114.814 63.660 5,551,806 3,911,255 360.664 C o o 0 341,028 182,878 15,908 4,754,040 3,908,761 360,665 R Materials :1 333:1:'§Z.-?? on Final Products 0 0 0 353,799 107.127 72.087 5,615,278 3,911,257 360.664 111 ) 10, Race of Interest 0 o 0 548,613 222.402 70.631 5,036,601 3,911,258 360,665 8) 202 Rate of Interest 0 0 0 85,059 79,0I9 2.424 3,788,327 5,928,834 150,733 c) 35: Rate 0, ln,,,,,, 0 o 0 604,137 559.879 23.271 3,996,487 5,928,834 178,461 0 0 0 601,956 559,803 26,591 4,117,703 5,928,833 228,091 d D t Fre Raw Materials 0; 626616 T:,,,, on F1nal p.04“... 0 U 0 597,594 554.397 23.043 6,774,347 5,928,832 77,076 IV a) 101 Rate of In.,,,,, 0 0 0 416,571 385,081 16,005 3,921,852 5,928,834 181,502 6) 202 Rate of Interest 2,139,456 6,247,924 243,110 0 0 0 7,324,997 14,624,380 494,171 c) 351 Rate of Interest 2,139,456 6,247,924 332,250 0 o 0 7,326,997 14,624,380 643,095 , 2.139.456 6.247.924 510.531 0 o 0 7,326,997 14,624,380 968,406 t F e Raw Materials ’ :1 gzuzlergarlff on Final Products 2.139.456 6.247.924 332,250 0 o o 7.326.997 14,624,380 643.095 2.139.456 6.247.924 332.250 0 o 0 7,326,997 14,624,380 643,095 lndus- 7 400 188 L170 855 2,139,456 6,247,924 243,110 3,334,965 1,367,792 233.051 29,230,884 2 . , , try ‘) £03 R“° °: int':::: 2,139,456 6,247,924 332,250 3.325.197 1,716,382 230,089 29,845,694 27,400,186 1417.194 Tatal b; 32; 22:: 2! 1:12:68: 2,139,456 6,247,924 510,531 3,314,364 1,784,370 251,724 29,167,144 27,397,691 4905.422 C . 2,139,456 6,247,924 332,250 3,322,773 1,703,213 238.288 32,685,026 27,400,186 L315,809 d; SZZIIZ'IZrIII 32t§1621°rraancrs 2,139,456 6,247,924 332,250 3,336,564 1,649,172 229,794 24,785,268 30,160,665 1499.959 2 Note: 0 - Leone value of total output L - hours of labor services input K - Leone value of capital services input The (b) policy variable is always the base run. I 187 Being the least capital intensive industry, the change in interest rate from 10 percent to 20 percent channelled resources into gara dyeing. Thus, while the number of gara dyeing firm type P321 in- creased from 12 to 133, tailoring firm type, P132 declined from 2745 to 2251 and the carpentry firm types also declined from 752 to 746 as shown in Table 8.1. With the exception of employment in the bakery industry, both output and employment declined in the rest of the four industries. In the bakery industry, output declined while employment increased--a tradeoff that was also observed for the short run analysis. 8.2.3 Profit Effects of Changes in the Rate of Interest By raising the rate of interest from 10 percent to 20 percent the value of the objective function declined from Le 12,549,259 to Le 11,929,840, a decline of about 5 percent and a profit elasticity1 of interest rate of -.5. A further increase in the interest rate from 20 percent to 35 percent brought about a decline in profits from Le 11,929,840 to Le 10,818,881, another decline of 9 percent and a profit elasticity of about -.6. Raising the rate of interest from 10 percent to 35 percent will lead to a 14 percent decline in profits and a profit elasticity of interest rate of -.93 over the entire range of interest rate. 8.2.4 Effects of Interest Rates on Trade Patterns By influencing production patterns, raising the rate of inter- est will have some effects on both domestic and foreign trade. As L ~ 1Profit elasticity of _ Percentage change in the_1eve1_ofprofits interest rate - Percentage change in the rate of interest 188 Table 8.4 reveals, raising the rate of interest from 10 percent to 20 percent will reduce the volume of interregional trade by l9 per- cent but gara exports into foreign markets will also increase by 8 percent. A further increase of the rate of interest from 20 per- cent to 35 percent will neither increase nor decrease the volume of interregional trade but will result to a decline in the export of gara cloth. It is important to realize from Table 8.4 that the rate of interest has no effect whatsoever on the domestic and foreign trade in balcksmithing output. At all levels of interest rate no blacksmithing output values are traded interregionally while Le llO,l47 worth of blacksmithing output will be imported from abroad. The industry whose interregional and foreign trade will be most affected by changes in the rate of interest is tailoring. By raising the rate of interest from 10 percent to 35 percent, interregional trade in tailoring output will fall from Le 640,002 to Le 285,830, a decline of about 55 percent. Also, raising the rate of interest from 10 percent to 35 percent will bring about an increase of foreign tailoring output imports from Le 2,578,312 to Le 2,970,228, an in- crease of about l5 percent. Thus, raising the interest rate from l0 percent to 35 percent will substitute fOreign tailoring imports for domestic production to the detriment of domestic production and employment. 189 .::u mama any ma Any manuaum> wowaom ona.ecn.m mmc.ouo.a neH.oHH wem.aoo Num.~on.m st.mmu o nc¢.mas.a .uus.wmm HHo.~n< muoavoun annum no muwuuu . cannon wmm.mnn. «you Hauoa unusuaauxuuan wawuoauma Honey huoxun wawnuwauxoman Anaconuuu muse mawuoawua howaom undue :waouom muons Haaowmouuouaw \ nwoa .moanmwum> zoaaom mo mHm>oA acououwwa um ovmuh cwaouom cam HmcowmouuouaH mo maumuumm wouuonoum c.w QHAMH AU Go AU 3 A” 190 8.3 The Effects of Trade Policies, Profits and Trade Patterns 8.3.l Introduction The two trade policy variables that were tested on the model projected run are import duties on raw material inputs and tariffs on competing small scale industry products. Such competing final products include the relevant S-digit S.I.T.C. tailoring and black- ] Thus some sensitivity analyses were carried smithing products. out to test the effects of removing import duties on raw materials and the effects of raising tariffs on competing imports on profits, output,employment and trade patterns among small scale industries. In order to identify the effects of trade policies on the optimum plan, it is necessary to compare the model results at 20 percent rate of interest, 1985 base run, with the model results duty-free or at higher tariff levels, as shown on Tables 8.1, 8.3 and 8.4. 8.3.2 Number of Firms and Choice of Technique of Production Nithkthe exception of gara industry the removal of custom duties on raw material inputs did not have any significant effect on the choice of production technique. In the gara industry, an addi- tional and relatively efficient process type, P331, was brought into the solution, and a tailoring process type P132 was removed from the solution as shown in Table 8.l. This adjustment is expected because the gara industry depends mostly on imported raw materials while the tailoring industry depends on imported intermediate inputs to a much lesser extent. Also,the nnst significant change in firm numbers took 1See page 19] place in the gara dyeing industry. As Table 8.l reveals, the num- ber of gara dyers increased from l445 in regions I and II to 32l4 in regions I, II and III, an increase of l22 percent. It should also be noted that because of the removal of custom duties, the level at which some existing production process types entered the solu- tion increased. Thus P421 increased from 434 to 444, and P821 in- creased from 9 to l0 as shown in Table 8.1. When the tariffs on imported tailoring and blacksmithing pro- ducts were doubled, the consequence was that new tailoring firm types Pll3 and PlZl entered the solution at the levels of 552 and 445 re- spectively, while the number of existing process type, P132 increased from 225l to 2430. In effect, the number of tailoring firms in- creased from 28l0 in regions I and II to 3937, an increase of 40 percent. Doubling the tariff on blacksmithing imports did not have any effect on the blacksmithing industry whatsoever. Moreover, al- though the number of tailoring firms increased, the number of firm types P3l2 and P83l declined drastically as shown in Table 8.l. The consequences of such adjustments would be discussed later on. 8.3.3 Profits The removal of custom duties on the imported raw material in- puts has a positive effect on the profit of the small scale indus- trial subsector. The elimination of import duties has the effect of increasing sectoral profits from Le ll,929,840 to Le l5,757,630, an increasetyf32 percent. 0n the other hand, doubling the tariff rates on competing tailoring and blacksmithing products has the adverse effect of reducing sectoral profits from Le ll,929,840 to 192 Le ll,442,568, a decline of 4 percent. The reduction in sectoral profits is due to the higher cost of all the tailoring and black— smithing output that were imported, despite the higher level of tar- iff.‘ 8.3.4 Output and Employment Table 8.3 reveals that on the aggregate, the elimination of custom duties on raw material imports has the effects of increasing output values from Le 29,845,694 to Le 32,685,026, an increase of 10 percent while leaving the level of employment unchanged at the level of 27,400,186 hours of labor services. The greatest contri- butor to the increased aggregate output value is the gara industry where output values increased from Le 11,473,627 to Le 15,750,192, an increase of 37 percent. Although no significant changes in output and employment took place in the carpentry, blacksmithing and bakery industries, output and employment did decline in the tailoring in- dustry. As Table 8.3 revelas, the tailoring output values declined from Le 7,214,787 to Le 5,781,892, a 20 percent decline and employ- ment also declined from 9,772,915 hours of labor services to 7,611,526, a decline of 22 percent. It can be verified from Table 8.l~that while the number of gara dyeing firms increased by 122 percent, tailoring firms declined by 81 percent. This situation arises because gara dyeing firms depend to a great extent on imported intermediate inputs and therefore gained most from the removal of custom duties. Doubling the tariffs on competing tailoring and blacksmithing products has the effect of reducing aggregate value of domestic 1See Table 8.4. 193 output from Le 29,845,694 to Le 24,785,268, a decline of 17 percent but employment increased by 10 percent from 27,400,l86 hours of labor services to 30,160,665. However, a closer examination of Table 8.3 shows some industrial variation in the output and employment effects. For example, whereas tailoring output values and employment increased, significant declines in output and employment occured in the gara dyeing industry but no significant changes occured in the carpentry, and bakery industries. While the tailoring industry experienced a positive production effect of the tariff, the blacksmithing industry experienced a neutral effect. This phenomenon could be due to the differential tariff levels imposed on the competing tailoring and blacksmithing products--80 percent for tailoring and only 6 percent for placksmithing products. However, the decline in the aggregate value of output could be due to the distorting effects of high tariffs on the competing tailoring products. Thus domestic alloca- tion of scarce resources were no longer carried out according to comparative advantage configurations due to the high tariff levels. 8.3.5 Trade Patterns The most significant effect of the elimination of custom duties on imported raw material inputs is the 41 percent increase in gara dyeing exports from Le 10,502,928 to Le 14,778,338 as shown in Table 8.4. However tailoring imports increased by 48 percent, from Le 2,997,824 to Le 4,429,843. The increasing profitability of pro- ducing for the export market, attracted resources away from the tailor- ing industry, thus leading to a decline in the number of tailoring firms, as was pointed out previously. With respect to interregional 194 trade, a substantial decrease of 81 percent was predicted by the model. Thus, the volume of interregional shipments of tailoring products decreased from Le 3,253,361 to Le 2,619,558. This decline in the vol- ume of interregional trade is due to the decline in the number of tailoring firms, resulting from a shift of resources into the gara dyeing industry. With a decline in the domestic tailoring output production and interregional trade, tailoring imports increased by 48 percent as was previously pointed out. One significant effect of doubling the tariffs on competing imports is that tailoring imports declined by 170 percent from Le 2,997,824 to Le 909,348. Such a decline in tailoring imports is due to the production effects of the tariff whereby domestic produc- tion of tailoring products increased to replace foreign imports. Another significant effect of the high level of tariff is the re- duction of gara exports by 68 percent, from Le 10,502,928 to Le 3,346,959. With such a high tariff (80 percent) on competing tailor- ing imports, the domestic price of tailoring products will tend to rise far above domestic production costs. Thus, the tailoring in- dustry will tend to look more profitable and resources will tend to move away from the gara industry into the tailoring industry. As Table 8.1 reveals, doubling tariffs led to a decline in the number of gara firms from 1445 in regions I and II to 534 in region I, a de- cline of 63 percent. Finally, Table 8.4 reveals that the black- smithing industry is insensitive to either the elimination of custom duties on imported raw materials or the-doubling of tariff levels on competing blacksmithing products. 195 8.4 The Effects of Differential Rates of Growth in Labor Supply 8.4.1 Introduction Since the results of the short run and long run policy anal- yses revealed that labor, not capital,1 is the limiting resource among small scale industries, it became necessary to test the effects of differential growth rates of labor supply, given the projected 1985 demand projections and current levels of money wages,2 and input-output coefficients. 8.4.2 Marginal Value Productivities of Labor Table 8.5 reveals that when labor supply was made to grow at an annual rate of 2.2 percent, all categories of labor earned mar- ginal value products which are greater than the money wages stip- ulated in the model.3 For example, in the Greater Freetown, the largest urban area, the MVP of proprietor and family and hired labor are Le 3.3 and Le 2.7 per hour respectively for 1985. For the localities with 20,000 - 100,000 people, the MVPs of hired and appren- tice laborers are Le 2.7 and Le 2.4 per hour respectively. These MVPs which are higher than the acquisition costs of labor, indicate that industrial firms will profitably employ more labor services,4 1 1In the short run and long run analyses, labor earned positive marginal productivities while capital earned zero MVP when capital was priced at either 10 percent or 20 percent rate of interest. 2Since money wages rise over the years due to inflation, the assumption of current levels of money wages can be unrealistic. But this assumption helps to bring into clear focus the results of the policy analyses. 3See page 196. 4See Glenn Johnson, 1972. pp. 185-196. 196 Table 8.5 Projected Shadow Prices for the Different Categories of Labor Resource and at Different Levels of Growth Rate in Labor Supply, 1985 Shadow Prices (Leones per hour) Locality Size Labor Type (Population) Growth Rate of Growth Rate of 2.2% 2.2% - 7%1 Over 100,000 Proprietor Labor 3.3 3.3 Hired Labor 2.7 O Apprentice Labor 0 0 20,000 - 100,000 Proprietor Labor .98 1.3 Hired Labor 2.7 .10 Apprentice Labor 2.4 0 2,000 - 20,000 Proprietor Labor 1 1.03 Apprentice Labor .99 .18 Less than 2,000 Proprietor Labor 0 0 1Based on the 1963 and 1975 census results, labor supply in the various size localities were made to grow at the following annual rates of population growth: over 100,000 - 8 percent 20,000 - 100,000 - 7 percent 2,000 - 20,000 - 5 percent less than 2,000 — 2.2 percent given the existing levels of money wages. But at higher rates of growth in labor supply, the proprietor and family labor still earned high MVPs while the employment of hired workers and apprentices reached a point where these labor categories earned low ar zero MVPs, owing to increased utilization of these labor services. 8.4.3 Growth Rates of Firm Numbers, Output, Employment and Profits The impact of differential growth rates of labor supply on the number of firms, output, employment and profits can be seen from 197 Table 8.6. By increasing the growth rates of labor supply from 2.2 percent to 5 percent, 7 percent and 6 percent for localities with the population of 2,000 - 20,000, 20,000 - 100,000 and over 100,000, the annual growth rates of firm numbers, output, employment and pro- fits almost doubled between 1974/75 and 1985. As Table 8.6 shows, the annual rate of growth of firm numbers, output, employment, and profits increased from -.8 percent, 12.3 percent, 1.3 percent, 24.7 percent to 9.3 percent, 21.5 percent, 3.1 percent and 50.8 percent respectively. The policy implications of the benefits from increased growth rates of labor supply, for small scale industry, will be dis- cussed in the next chapter. 8.5 Summary The long run policy analysis revealed that the choice of pro- duction methods involves a mixture of both modern firms and tradi- tional firms,1 given the projected levels of effective demand and competing imports. Thus, only the minimum cost firms (as shown in Table 8.2) were chosen to produce in the long run situation. Although in both the short run and long run situations, high rates of interest tended to induce a substitution of labor-intensive process types for the capital-intensive ones, higher interest rates increased the domestic costs of production thus improving the compe- titive position of imported products. As a result, output values, employment and profits declined while the imports of competing goods increased. The long run policy analyses results revealed that a policy of 1A similar result was revealed in the short run analysis. 198 .ocomg muuowm mo mamaoo aowumazaom mnma ram mama mo muasmmu mnu aouw voaanaou coon o>mn moumu nuaoum Hmfiuaouommwv mmonha Na.om NH.m NmHN Nm.m moan nosey» Hasaq< I o Nmom Nam Nmam Nmm mama I m5\o saga mofiumeooa How unmouma o Humo moumu nuzouw zaaasm Honda um any mwma vmuoofioum .0 osm.a~a.aa as muse: soo.msm.hm sac.mqm.¢~ as oom.m~ saaaum comma an moan nuaoum uaouuaa N.~ Hamum>o am am nan mmma vmuoohowm .m NNH.qm¢.m o4 muco: www.mnm.¢~ seq.aoq.ma ma moq.nm m5\qnma cam ommm .¢ mauwm muwwoum. moua>umm gonna moaam> uaauso mo .oz can muwaom mmaa .%Ham:m Honda aw nuaouo mo moumm o>aumcuoua< mom mufimoum cam uaoaxoaaam .mmaam> usmuao .mumnasz sham mo mmumm suzouo vouommoum o.m wanna 199 granting custom duty rebates on imported inputs has a favorable effect of increasing output values, gara cloth exports and profits without significant changes in employment. This long run policy result differs from the short run policy result where only profits increased as a result of eliminating custom duties. In the short run, doubling tariffs on competing imports re- sulted in reduced profits since imports still came in at the high tariff level. But in the long run, with the elimination of high cost firms, minimum cost firms increased in number, thus eliminating the importation of tailoring imports by 170 percent. Although employ- ment increased by 10 percent due to the substitution of domestic pro- duction for foreign imports, output values declined by 17 percent due to the distorting effects of the high tariff level. Another significant result of the long run policy analysis is that the blacksmithing industry will become a rural industry. Our analysis reveals that the blacksmithing firms in region IV are the least cost (in terms of labor and capital) producers of hoes, matchets and other services related to farming. Finally, the policy analysis of increasing the annual growth rates of labor supply has significant results for the small scale in- dustry subsector and the Sierra Leone economy as a whole. As Table 8.6 reveals, higher rates of growth in labor supply resulted in the increased rates of growth in firm numbers, output, employment and profits from -.8 percent, 12.3 percent, 1.3 percent and 24.7 percent to 9.3 percent, 21.5 percent, 3.1 percent and 50.8 percent respectively between 1974/75 and 1985. These results have policy implication which will be discussed in the next chapter. CHAPTER 9 SUMMARY AND POLICY IMPLICATIONS 9.1 Introduction This study utilizes a linear programming model to evaluate the efficiency 6f resource allocation among small scale industries in Sierra Leone and to analyse the effects of various policy variables on these industries. A one year sample survey of Sierra Leone small scale industries was undertaken in 1974/75 and detailed input-output data were generated on the daily economic activities of 366 firms. The results of this study will be summarized, followed by a discus- sion of the policy implications of the research findings. 9.2 The Small Scale Industrial Subsector in Sierra Leone The evidence generated by our study has revealed that the small scale industrial establishments dominated the industrial sector in terms of both the number of firms and total employment. Small scale establishments accounted for over 95 percent of the employment and 43 percent of the value added of the industrial sector in Sierra Leone. One of the most striking results of the survey, however, was the discovery that the vast majority of these firms were located in rural areas. Thus, 95 percent of the industrial establishments, 86 percent of the industrial employment, and 75 percent of the industrial value added was generated by rural small scale industries. 200 201 In terms of specific industries, tailoring was the most impor- tant activity, accounting for 31 percent of the employment and 37 percent of the value added within the small scale sector. Black- smithing, carpentry, baking, and gara dyeing followed tailoring in importance, but by quite a wide margin. The total labor input varies quite widely from industry to industry and by location, being significantly lower in the villages than in the larger urban locations. Apprentices and proprietors supply the vast majority of the small scale industry labor. With the exception for the "modern" blacksmiths,.all the major types of small scale industries in Sierra Leone must be considered to be economically viable. "Traditional“ and "modern“ gara dyers, "tra- ditional" carpenters, and "traditional" bakers generate the highest rate of economic profit, exceeding 100 percent in each instance. Nevertheless, small scale industries in Sierra Leone, perceive their major problems to include lack of capital, and effective demand for their products, and the scarcity and rising cost of material inputs. 9.3 Results of the Base Run The purpose of the base run is to obtain "optimal" solutions of the model in order to evaluate the efficiency of resource use among small scale industries inSierra Leone. Such an evaluation has im- portant policy implications which will be pointed out later on. One significant result of the analysis of the base run is that inefficiencies in resource allocation exist among the small scale industries in Sierra Leone. For example, the predicted average pro- ductivities of labor and capital are .55 and 8.7 leones per unit of 202 labor and capital services respectively as compared to the corres- ponding actual average productivities of .39 and 6.8. However, the patterns of variation by industry and location in the utilization of labor, labor types and capital are the same for both predicted re- sults and actual data. For example, the tailoring industry, and the rural areas were predicted to have contributed 40 percent respectively of total labor services utilized by the five major small scale indus- tries. In addition, while apprentice and hired labor types dominate the total labor use in carpentry and bakery industries respectively, proprietor and family labor proved to be the most important labor type in gara dyeing industry. Apart from industrial variation, there are locational variations in the type of labor utilized. Thus, while regions I-III rely mostly on apprentice labor, proprietor and family labor dominate in rural areas. The predicted patterns of interregional trade also conformed to the observed patterns. The principle of comparative advantage was utilized to explain the directions of trade. The analysis re- vealed that the firms in region I have absolute disadvantages in producing all the small scale industry products. Such absolute dis— advantages were due to high costs of electricity, workshop rentals and wages on hired labor. ‘Firms in region 111 enjoyed absolute ad- vantages in the production of almost all the small scale industry products. The basis of such an absolute cost advantage is the wide- spread use of cheap apprentice labor in region 111. 203 9.4 The Results of the Short Run Policy Analyses with Closed and Open Models 9.4.1 Introduction The purpose of the short run policy analyses was to test the effects of different levels of interest rates, capacity utilization and trade policies on output, employments, profits and trade patterns among the small scale industries in Sierra Leone. In order to iso- late the effects of foreign trade on the results of the analyses, it was necessary to utilize aclosed model (without foreign trade) and an open model (with foreign trade) in the analyses. The results of these analyses will now be summarized at the aggregate and spec- ific industry levels. 9.4.2 Aggregate Results The results of the short-run policy analysis with the closed model reveal that at a given level of output, employment increased by .6 percent when the rates of interest were raised from 10 percent to 35 percent. But, profits also declined by 21 percent due to the same change of the rate of interest. The result of our analyses with the closed model also reveals that by enabling the small scale in- dustries to operate at a higher level of capacity utilization, output values, hours of labor serVices and profits of the major small scale industries increased by 4 percent, 3 percent and 2 percent respec- tively. These results will now be contrasted with the results of the open model. The results of the open model reveal that by raising the rate of interest from 10 to 35 percent, aggregate values of gross output and employment declined by 21 percent and 18 percent 204 respectively. At the higher rate of interest, the cost structures of industrial firms increased. As a result, the value of imports of tailoring and blacksmithing products increased by 210 percent and 435 percent.respective1y while profits of the entire subsector de; clined by 137 percent. The effect of a higher level of capacity utilization in the open model also differs from the result of the closed model. In the open model domestic production of high cost firms was displaced by imports, even though capital productivities of individuals in- creased due to high rates of capacity utilization. Thus, the aggre- gate number of firms declined by 18 percent, the value of Output and employment remained almost the same. But, higher rates of capac- ity utilization resulted in a one percent savings in capital services. In the short run policy open model, the effect of granting im- port rebates on imported material inputs increased the profits of the entire subsector by 23 percent. On the other hand, doubling the tariffs on competing imports did not reduce the value of foreign imports as anticipated. Rather, sectoral profits declined due to the increased tariff level. 9.4.3 Specific Industry Results The summary of the results of the short run policy analyses relating to specific industries will be based on the open model. Al- though higher rates of interest resulted in a general decline in output and employment among small scale industries, the greatest decline in the value of output was predicted for the blacksmithing industry. This particular phenomenon is due to the relative 205 inefficiencies of blacksmithing firm types in general.1 The only case of increased employment at higher interest rates was predicted for the bakery industry. By increasing the rate of interest from 20 percent to 35 percent, the number of traditional labor intensive firms increased while the number of modern capital intensive firms declined. However, this increase in employment was accompanied by another unique result - a trade off between output and employment in the bakery industry. With the substitution of labor intensive bakery firms for the capital intensive ones, output values of the entire bakery industry declined while employment increased. The greatest impact of increases in the levels of capacity utilization was predicted for the tailoring and bakery industries where both output values increased by seven percent and eight per- cent respectively and employment increased by five percent only in the tailoring industry. But, the largest declines in output values and employment of 45 percent and 46 percent respectively were pre- dicted for the gara dyeing industry. Since gara firms operated closer to full capacity than tailoring and bakery firms, the gara dyeing firms were placed at a comparative disadvantage when all firms were made to operate at full capacity. 9.5 The Results of the Long_Run Policy Analysis (Open Model) 9.5.1 Introduction The purpose of the long run policy analysis is to project the consequences of alternative policies through 1985, while permitting A ‘See Table 4.10 of Chapter 4. 206 the necessary adjustments in the number of firms by industrial type and location. The results of the long run policy analysis of the effects of levels of interest rates, trade policies and differential rates of labor supply among small scale industries in Sierra Leone will now be summarized. 9.5.2 Aggregate Results The difference in the results of short run and long run effects of raising the rate of interest frOm 10 percent to 35 percent is a matter of degree. The predicted declines in output values, employ- ment and profits are only .2 percent, zero percent and 14 percent re- spectively, for the long run situation. Thus, the significant effect of higher interest rates in the longrun is the large decline in the profits of the subsector. The effects of trade policies in the long run analysis differ from those of the short run. For example, while the short-run policyflao< no 0929 son I552 ,. . .x In... II. III- lIIITIIII acemnosdnm no 0362 ..Il'IaII' ll ma05. Eafito zen memo» 29: our?” . w , omacohzm pacemmadg . 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Father's previous occupation 11. Mother's Major Occupation 12. Mother's previous occupation 13. Level of Education: (a) no schooling Yes No (b) level of primary school education completed (c) level of secondary school education completed (d) number of years of adult education (e) Other (specify) 14. 15. 16. 17. 18. 19 19. 20. '228 What other training did you have? (a) (b) Which year did you establish this business? (a) (b) (C) (d) (e) (f) (g) Other (specify) family enterprise? previous experience? Personal security? Other (specify) Were you apprenticed before? If yes, in which enterprise? And for how long? What form of ownership is your business? (a) individual (b) partnership (c) Cooperative (d) private company Trade school? Yes No What were your reasons for joining this business? hope of making more money (profits)? Yes No Yes No Yes -—-—-_ No Father's major occupation? Yes No Prestige among my people? Yes No Yes No Yes No Yes No _1 Yes No Yes No Yes No Yes No[::::] (e) family How much initial capital did you have to start this business? Le 21. 229 What were the sources of this initial capital? (a) (b) (e) (d) (e) Personal savings from ( 1) ( 11) ( iii) ( 1V) ( V) HOW‘much? Agriculture Le Government Employment Trade Business Other (specify) Loans from ( 1) Commercial Banks ( ii) Government Development Bank ( iii) African Development Bank ( iv) Friends ( v). Money Lenders ( vi) Family ( vii) Cooperatives (viii) OSUSU ( ix) Others (specify) Gifts Partnership Others (specify) 22. 23. 24. 25. 26. 230 Since you established this business, have you increased your original size? Yes No If yes, what were the sources of the funds used for the expansion and how much? Source How much? (a) Reinvested profits? Le (b) Got loans from government development bank? (c) Got loans from commercial banks? (d) Got loans from the cooperatives (e) Got loans from money lenders (f) Other (specify) Do you plan to expand this business in the next one year? Yes No From what sources do you expect to get the funds for this future expansion? (a) Reinvestment of profits (b) Loan from the government (c) Loan from commercial banks (d) OSUSU (e) Others (specify) How many hours do you work every day? If you had enough customers to purchase all your products and you had all the raw materials you needed, with your existing equipments and buildings: (a) How many more hours would you work? (b) How many more workers would you hire? 27. 28. 29. 30. 31. 32. 33. 231 Have you ever applied for government assistance? Yes If yes did you obtain the assistance? Into what uses did you put the assistance? (a) Buy new equipments (b) Provide working capital (c) Set up buildings (d) Other (specify) Yes Yes Yes Yes Yes No No No —— \ Nol Nol l No If you have not applied for government assistance, why not? What types of assistance do you think that the government should give to business men? same compared Yes Yes Yes Yes Yes (a) (b) (C) Has your output increased, decreased or remained the with: (a) Last one year? (b) Last five years? What of the following changes have taken place since established? (a) Established other branches? (b) Expanded your original size? (c) Rebuilt your establishment? (d) Bought better machines? (e) Introduced a new product? (f) Introduced better marketing arrangements? Yes your firm was No No No No No No 34. 35. 36. 37. 38. 39. 40. 41. 42. 232 What have been the difficulties in building up this business? (3) Insufficient demand (b) Lack of technical advice (c) Troubles with equipment (d) Lack of skilled labor (e) Poor transport facilities (f) Lack of capital- (g) Scarcity of materials (h) Scarcity of spare parts (1) High cost of equipments and materials (j) Other (specify) Which of the above is the greatest difficulty? - What is the duration of apprenticeship in this business? Do you charge any learning fee? Yes No If yes, how much? Le Has the number of firms in your industry in this locality increased, decreased or remained the same compared with: (a) Last one year? (b) Last five years? Do you keep record of your purchases and sales? Yes No If no, why not? If yes, what record books do you have? (a) (b) (e) 43. 44. 45. 46. 47. 48. 233 How often do you try to find out if you are gaining or losing in your business? (a) Everyday? (b) Once a week? (c) Once every two weeks? (d) Once a month? (e) Other (specify) (f) None of the above Do you have an account in any bank? If yes, what type of account? Savings account? Current account? Have you ever attempted to obtain loans from any bank? If yes, did you get the loan? If you have not attempted to borrow money from any bank, why not? Yes Yes Yes Yes Yes Yes Yes) Yes Yes Yes No No No No No No No No No No APPENDIX 2.2 ORGANIZATION OF FIELD WORK, DATA COLLECTION, HANDLING AND PROCESSING APPENDIX 2.2 ORGANIZATION OF FIELD WORK, DATA COLLECTION, HANDLING AND PROCESSING The purpose of this appendix is to describe how the field work of data collection for small scale industries in Sierra Leone was organized, and to explain how some of the logistical problems were overcome. Therefore topics that would be discussed include: (l) the recruitment and training of enumerators, (2) the placement of enumerators in the field, (3) frequency of visits to respondents, (4) distribution of work load, (5) supervision of field work, and (6) data handling and processing. Each of these topics will now be discussed in greater detail. 1. Recruitment1 and Training_of Enumerators A two stage procedure was used to select the enumerators. In the first stage, all candidates were interviewed and information re- lating to academic credentials, letters of recommendations, ability to speak native languages, previous job experience, and attitudes were considered; In most cases, a high school qualification was the minimum that was required for considering the recruitment of an enumerator. An enumerator must speak the language of the proprie- tor he is interviewing although it is not advisable that he be a resident of the same area. So long as they speak the local language. enumerators seem to have little difficulty being accepted by the host 1The recruitment and training of enumerators for the small scale industry study were undertaken along side the other studies in the Rural Employment Research Project (see footnote on page 29 ). 234 235 community. After this first stage of recruitment, successful can- didates were announced and admitted into a general training course. The general training period lasts for approximately two weeks. At the end of the general training period, the candidates were given a written test after which successful ones were assigned to the studies in which they performed best. These successful candidates were then given a final intensive and specialized training for an- other three to seven days before being assigned to the field. Since supervisors cannot always be available to enumerators, the enumer- ators were provided with reference manuals in which the survey in- struments and methods are explained in detail. 2. .Placement of Enumerators in the Field Usually, enumerators were placed in their respective locali- ties by the senior officials of the research project. On arriving at the locality, the enumerators were introduced to the paramount chief, the town chief or the town speaker. This introduction was followed by the assembling together of the section chiefs and elders to welcome the enumerator into the community. At this time, the senior research officer emphasized the advantages of the study and asked fOr the cooperation of the chief and his community. Most of the time, it became the responsibility of the chief and his men to provide accommodation for enumerators. After being settled in the'com- munity, the enumerator took responsibility for identifying his respon- dents, establishing rapport with them, and starting data collection. 236 3. Frequengy of Visits Each of the questionnaires that was used to obtain stock data was administered at the beginning and at the end of the survey per- iod. This procedure enabled us to obtain accurate information not only with respect to the stock of existing assets, but also concern- ing what changes (net investment) may have taken place over the one year period. Since it was considered at the time of the survey that most of our respondents traditionally did not keep records of their trans- actions, the length of memory recall became of primary consideration in collecting our daily input-Output data. On the basis of the re- sults derived from the pretesting of the questionnaire, we decided to interview respondents twice every week in order to gather data on daily hours input and output. On each day, data were obtained for the previous four days starting with the latest and leading on to the earliest date. This way, data were collected fbr eight days each week, with one day overlap. The one day overlap was neces- sary to check response consistency, and to necessitate the enumer- ator probing of the respondent, where inconsistent response was suspected. More frequent visits tended to disrupt the activities of the entrepreneurs and thus engender lack of cooperation. On the other hand, longer periods of memory recall tended to generate very un- reliable data, particularly, with respect to daily hours actually worked and output. Also, on the basis of the pretesting of questionnaires, we found that respondents could recall how much they had spent on all categories of intermediate inputs in the last one week. In 237 connection with financial transactions, we found that entrepreneurs could recall how much money was borrowed, lent out, saved, paid in wages, house and equipment rents, electricity bills, taxes and water rates in the last one month. Therefore, information concerning all intermediate inputs and financial transactions were collected weekly and monthly respectively. The weekly material input questionnaire was administered on the last day of visit each week to_each respon- dent while the monthly financial input questionnaire was administered on the first day of visit to each respondent during each month (see the accompanying schedule of monthly distribution of workload). 4. Distribution of Work-Load In each of the 24 enumeration areas, (localities with less than 2,000 people) each enumerator was responsible for gathering data from four small scale industry households in addition to the twenty farm households. This workload was considered to be adequate in view of the seasonal nature of farm activity and the supposedly lower volume of industrial production in the enumeration areas. In all localities with above 2,000 people, the distribution of work-load was different. In the four localities with greater than 20,000 people, we selected thirty industrial firms and placed two of our enumerators there.‘ Thus each of our enumerators worked half time with fifteen respondents. In each of the localities with 2,000 to 20,000 people, we selected fifteen respondents and placed one enum- erator to interview them, also on a half-time basis. Because of the integrated nature of the Sierra Leone project, our small scale industry enumerators also worked half-time collecting information 238 .nuaoa Hmofiuonuonhn vacuum can you Aumav amoHSumm no woo woo nuaoa umuuw osu pow Aumav anode: no uumum ohms xwa A v .oumv asbaoo unasofiuumn onu you wouoamaoo on on mm: ouamd:oau Imosv 3ou “masofiunma oau sownz new Hanson huoaofi mo Suwaoa onu momma unfiuomnsm Au .oumu AGEDHOOV umaoofiuumm m u0u oofiafim on ou mum mousmddoau Imosv A3ouv Hmaoofiuumm Sofia: MOM anon» swam umadofiuuma momma uonmnmam uawuomuoaom An .oumv Anabaoov umaoofiuumm o How mufimaaoaummsv Asouv smasoauuma m use HHHM momma x Am "ouoz muaovaoamou cogs nonwwanoumo ma uncanny unmauwwuom as soon m< a a a A a a ox ax ax ox ax 38: u a s a q u a a a ... a a M a q s a a s a » ox ax < can >< afi on N “IN N 45‘“ U Q m a x 4L? u-c D< m < X «1:? Rest o q >< m c x a?! u a x n q >< {5:7 Rest 0Q >< mQ 'v< ‘9’ U‘Q m Q >< S." UQ >4 m~a >< (Q U Q m Q >1 ‘3." UQ Uh >< O"! N wfl mQ so" FQQ ‘5? if ‘3? UQ muaumuumuomumgu Hmuuawaouaouucu mama“ waoamcqu mHnucox manna Howuoumu haxook «x unnuao mawmn «x uamcu Donna maumn xoOuw Hmfiuoums can uamuao Jacum unusauavw e08... 9.333 x — m m h H 3 h z m Ame xmwa an on an mN «N on nN cu mm um HN o~ ma ma sumo «H ma ouwmccowumozo AmmacfiuGOUV N.< 241 relating to rice processing and market prices. Since each enumerator was to interview fifteen small scale industry respondents, visiting each respondent twice weekly, the respondents in each locality were divided into three groups--A, B and C—-according to proximity. Thus, it was possible (as shown in the schedule) to visit each group of respondents twice out of 7 days of the week. Moreover this arrangement facilitated the super- vision of field work since it was easy to know which respondents would be interviewed on particular days of the month. During the early part of the field survey, schedules of monthly distribution of workload, (as has been illustrated in the schedule) were dis- tributed to all the enumerators to ensure that all respondents were visited according to schedule, and that the right questionnaires were being administered. 5. Supervision of Field Work The reliability of data collected during a field survey depends most importantly on the quality of field supervision that accompanies the process of data collection. The process of data collection for the small scale industry study was supervised within the framework of the overall supervisory arrangement of the Sierra Leone project. Different kinds of field supervision were done with different inten- sities and at different points in time. A In order to pay salaries and also correct the mistakes that occurred in the process of data collection, routine monthly visits were made to the different localities, where our enumerators were posted. In addition to routine monthly visits, surprise visits 242 were made to ensure that enumerators stayed in their respective localities, to do their jobs. Also, spot checks were carried out on enumerators to verify that the right kinds of questions were being asked. Finally, frequent visits were made to ensure contin- uity in the data collection process. Frequent visits were made during festive occasions, weekends and immediately after the payment of monthly salaries. At these times, most enumerators were highly tempted to travel out of their localities, quite to the detriment of data collection. Usually, enumerators could travel out of their localities provided they obtained prior approval from the head office. 6. Data Handling,_$torage and Processing_ According to our original estimation, a total of 2,970 sets of questionnaires were to be filled out for 270 respondents in the small scale industry study between August 1974 and June 1975. Each monthly set of questionnaires for every establishment contained ten sheets of paper. By our calculation, 2,970 sets of questionnaires were to generate a total of about 143,600 computer cards. Such an amount of data suggested making adequate arrangements for advance printing and collation of questionnaires, delivery of questionnaires to field enumerators, receiving completed questionnaires, sorting and stacking them at the head office for immediate editing, coding, keypunching, verification and magnetic taping of data for shipment to the United States. The amount of data anticipated, necessitated providing adequate office accommodations, equipment and materials, and strong logistical support. The different aspects of data hand- ling and processing will now be discussed. 243 -a. (Questionnaire Handling The delivery of sets of questionnaires to field enumerators, and the recovery of completed ones were done within the overall logistical arrangement of the project. Close to the end of each month, trips were planned to different parts of the country for the purpose of paying salaries of enumerators and checking the comple- ted questionnaires for errors. Before such trips, bundles contain- ing sets of questionnaires for each enumerator in each locality were prepared. During the trips, these bundles were distributed and completed questionnaires were recovered simultaneously and checked. On arrival at the office, questionnaires were sorted by localities from which they were collected and re-checked at the office to ensure that each set had the correct identification num- ber, and any errors that skipped field supervision were detected immediately. Control books were utilized to record any problems the enumerators might have had in the field with respect to supply of materials, problem of cooperation, etc. After completed ques- tionnaires were edited and corrected, they were stacked and ready for coding. b. The Coding Scheme The coding scheme that was adopted by the project and there- fore used by each study component uniquely identifies each computer card. Data were coded from completed and edited questionnaires straight on to coding sheets similar to the fortran coding forms with 80 columns. The coding was done by a clerical staff of six individuals, specially recruited and trained for the coding purpose. 244 The least qualified of the clerical staff possessed a high school certificate. All the coding was done under strict supervision. To facilitate the coding process, a special coding manual had to be written to describe very precisely what was to be done. In developing the codes, enough familiarity was established with items that were involved in each enterprise and for different localities. Thus codes were developed for localities, establishments, stock items, flow items, measurement units, sources of inputs, destination of output, types of recipients, etc. After assembling the item codes, tables were developed to identify each column of the card with item codes or quantities or values for each class of data. After the coding was done, random checks were undertaken to ensure that the correct codes were being used in the right columns, that each firm was correctly identified, and that all the data were being coded. After the edit work, coded sheets were submitted for keypunching in Freetown. Some of the keypunched data were automat- ically verified. Others were verified manually. Errors were cor- rected and omissions incorporated and re-verified. All keypunched data were put on magnetic tape and shipped to Michigan State Univer- sity. c. Data Processing The next stage in data handling was data processing to ensure that data were good enough for analysis. This involved writing edit programs to check for correct dates and item codes, missing quantities and values and identification numbers. It also involved matching dates and identification numbers for different data 245 categories to check for inconsistencies. For example, we tried to find out if there existed labor data for which there were no corresponding output data or stock data or material input data and vice versa. The edit programs were written by specialized staff of the computer center at Michigan State University. After the edit programs were run through the computer, all the necessary cor- rections were made on raw data listings and re-run to ensure that optimum consistency had been achieved. Several re-runs were done before this optimum was arrived at. Final stages of data proces- sing before the start of analysis included annualization of data. d. Annualization of Data The annulization of data was necessary for several reasons. First, for most firms, data were not collected for all days of the first and last months. Secondly, the survey did not run for com- plete twelve months of the year. Thirdly, there existed some ob- vious cases of missing data, the values of which had to be inputed. In order to obtain complete data for the first and the last months, daily averages had to be utilized for each firm based on when data collection began and ended for that particular firm. In order to substitute for missing data we relied essentially on monthly in- dices that were constructed from the input-output data of the firms that stayed on the survey for ten to eleven months. Separate in- dicies were constructed for specific regional and industry categor- ies. The data for July 1975 were obtained as follows. Respondents were asked by June l975 to indicate whether July input or output would remain the same or increase or decrease as compared to June 246 data. The answer varied of course depending on locality and type of enterprise. The problem here was how to determine the magni- tude of rise or fall. In order to solve this problem, the average monthly changes between April and June were used to scale each firm's June data up or down in order to obtain the July data. If July data was anticipated to remain the same as June data, then no adjustments were required. e. Standard Data Files After data processing had been completed, we created ten standard data files. Each such binary file provided a storage for all the information contained in each questionnaire. Every time each binary file was updated, corrected, indexed or adjusted, the old file was purged, thus, keeping each file in a current sta- tus. Relevant information could therefore be pooled from specified files in order to carry out the various kinds of analysis. APPENDIX 5 THE LINEAR PROGRAMMING TABLEAU 247 The Linear Programming Tableau - Region 1 Row Production Activities Number Black- Tailoring Cara Dyeing Carpentry smithing Bakery W Row Name Unit 9111 P112 P113 P311 P312 P313 P411 P412 P511 P811 1 Objective Function 1e. 0 O 0 0 O 0 O O O 0 2 F11 hrs. 1417 980 1945 3 H11 " o 815 a: 4 A11 " 500 9 5045 5 M11 1e. 382 536 469 6 211 la. 225 408 243 7 F31 hrs. 1815 1380 1286 8 H31 " 805 20 422 9 A31 " o 44 o 10 M31 le. 3348 6320 2263 11 231 1c. 264 70 18 12 F41 hrs. 1711 1375 13 H41 " o 3918 14 A41 " 17.124 0 15 M41 le. 1340 7749 16 Z41 1e. 345 1088 17 F51 hrs. 446 13 H51 " o 19 A51 " 775 20 M51 1e. 560 21 251 1e. 552 22 F81 hrs. 3672 23 H81 " 48.486 24 A81 " o 25 M81 1e. 86,148 26 281 1e. 7168 27 LFl hrs. 28 LHI " 29 LAl " 30 M1 1e. 31 21 1e. 32 Y1 1e. -1011 -1073 -3285 -6591 -8077 -3316 —4567 -9492 -1008 -118,569 33 5111 no. 1 34 Y111 " 1 35 $112 " 1 36 U112 " 1 37 $113 " 1 33 U113 " 1 39 E311 " 1 40 S312 " 1 41 U312 " 1 42 E313 " 1 43 S411 " 1 44 U411 " 1 45 S412 " 1 46 U412 " 1 a7 5511 " 1 43 U511 " 1 a9 5811 " 1 50 U811 " 1 51 011 1e. 1011 1073 3285 52 013 " 6591 8077 3316 53 014 " 4567 9492 54 D15 " 1008 55 018 " 118,569 (Centinued) 248 The Linear Programming Tableau - Region 1 Row Labor Hirigg Activities Number Column Name Row Nam Unit LFII LHll LAll LP31 LASl LFQI 1.1181 LAB). Objective 1 Function 1e. -.35 -.1 -.02 -.84 -.08 -.54 -.15 -.08 2 p11 hrs. -1 3 H11 " —1 4 A11 " -1 5 M11 1e. 6 Z11 1e. 7 F31 hrs. ' -1 8 H31 " 9 A31 " 10 M31 1e. 11 Z31 1e. 12 F41 hrs. 13 H41 " 14 A41 " 15 M41 1e. 16 Z41 1e. 17 F51 hrs. 18 H51 " 19 A51 " -1 20 M51 1e. 21 251 1e. 22 F81 hrs. -1 23 H81 " -1 24 A81 " -l 25 M81 1e. 26 Z81 1e. 27 LPl hrs. 1 1 1 28 LHI " 1 1 29 LA1 " 1 1 1 30 M1 1e. 1 1 1 1 1 1 1 1 31 21 1e. 32 Y1 1e. 33 5111 no. 34 Ylll " 35 5112 " 36 U112 " 37 $113 " 33 0113 " 39 E311 " 40 S312 " 41 U312 " 42 E313 " 43 S411 " 44 U411 " 45 S412 " 46 U412 " 47 $511 " 48 U511 " 49 $811 " 50' U811 " 51 011 la. 52 DI) " 53 014 " 54 015 " 55 018 " (Continued) 249 The Linear Programming Tableau - Region 1 Rov Haterinl Input Activities Capital Activities Income RHS Number Activity ‘ lumn Name Rov‘;;g:~“‘-~.‘_g Unit M11 M31 H41 H51 H81 211 231 241 251 281 CYl 1 Objective Function 1e. -1 -1 -1 -1 -1 0 O 0 0 0 1 - O 2 F11 hrs. 1 0 3 H11 " < 0 4 A11 " E O 5 M11 1e. -1 ' - o 6 211 1e. -1 5 0 7 F31 hrs. 3 0 8 H31 " ‘5 0 9 A31 " i 0 10 M31 1e. -1 - O 11 231 la. -1 i 0 12 F41 hrs. 1 0 13 H41 " < 0 14 A41 " E o 15 H41 1e. -1 - 0 16 241 1c. -1 _g 0 17 F51 hrs. 1 0 18 H51 " < 0 19 A51 " E 0 20 M51 1e. -1 = 0 21 251 la. -1 i 0 22 F81 hrs. : o 23 H81 " < 0 24 A81 1' z 0 25 ”31 1e. —1 - 0 26 Z31 1e. -1 1 0 27 LFI hrs. §_1,783,616 23 LHl " 1 745,448 29 LA1 " 1 2,049,534 30 H1 1e. 1 1 1 1 1 3 0 31 Z1 1e. 1 1 1 1 1 5 339,397 32 Y1 1e. 1 - o 33 5111 no. i 463 32. 1111 .. < 565 35 s112 .. 3 125 36 U112 .. I 153 37 5113 " E 147 33 0113 " < 179 39 12311 " 3 4o 40 S312 " l 41 41 U312 " i 18 42 E313 " - 1 43 S411 " Z 50 41. U411 " i 62 45 S412 " l 17 4.6 U412 " i 21 47 S511 " l 11 43 U511 " i 13 1.9 $811 " > 10 so U811 " Z 12 51 011 1e. - 1,817,814 52 013 " a 46,180 53 014 " - 415,440 54 DIS " - 12,588 55 P18 " = 1,173,833 250 The Linear Programming Tableau - Region II Num- ber 56 57 58 S9 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 78 79 81 82 83 85 86 87 88 89 9O 91 92 93 94 95 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 Column Name Row Name Objective Function F12 H12 A12 H12 212 P32 H32 A32 H32 232 F42 H42 A42 H42 242 F52 H52 A52 H52 252 F82 H82 A82 H82 282 LF2 LH2 LA2 H2 22 Y2 $121 U121 5122 U122 5123 U123 5124 U124 S321 U321 S322 U322 S323 U323 S421 U421 S422 0422 S423 U423 $521 U521 0522 $821 U821 £822 5823 U823 021 023 024 025 028 Unit 1e. hrs. II 9. 1e. 1e. hrs. M 1e. 1e. hrs. II 1e. 1e. hrs. H I. 1e. 1e. hrs. 1e. 1e. hrs. 0. 1e. 1e. 1e. no. 17 Production Activities Tailoring Gara Dyeing Carpentry Black- Bakery smithing P121 P122 P123 P124 P321 P322 P323 P421 P422 P413 P521 P522 P821 P822 P823 0 0 0 0 0 O 0 0 0 O 0 0 0 0 0 644 833 1148 927 130 266 990 1122 99 111 615 306 101 59 180 343 1815 1780 1286 805 118 422 o o 0 3201 916 2200 264 24 18 1238 1626 775 91 326 1127 3980 19956 3381 807 6080 2647 162 240 1950 1124 190 70 1338 953 589 130 520 120 1020 1040 72 1032 7223 14528 3140 927 0 0 16,362 14,356 2728 6824 912 114 -634 -771 -1870 -2341 -6591 -2518 -3316 -6956 -9614 -9496 -975 -1042 —32,538-28,419 -5016 1 1 port rip- F'H 634 771 1870 2341 6591 2518 3316 6956 9614 9496 1 975 1042 32.538 28,419 5016 (Continued) 251 The Linear Programming Tableau - Region 11 Row Num- ber 56 57 58 59 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 9O 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 Row Name P—_——-—— Column Name Objective Function F12 H12 A12 H12 212 F32 H32 A32 H32 232 F42 H42 A42 H42 242 F52 H52 A52 H52 252 F82 H82 A82 H82 282 LFZ LH2 LA2 H2 22 Y2 $121 U121 $122 0122 $123 U123 $124 U124 S321 U321 S322 0322 S323 U323 S421 U421 S422 U422 S423 U423 S521 U521 5522 $821 U821 £822 $823 U823 021 023 024 025 028 Unit 1e. hrs. 1e. 1e. hrs. 1e. 1e. hrs. 1e. 1e. LFll LHll LAll LF31 LH31 -.35 -.02 -.84 -.15 -1 -s1 -1 -1 -1 -1 .- Labor Hiring Activities LA31 LF41 LH41 LA41 LF51 LHSl -.01 -.51 -.08 -.02 -.30 -.13 -1 -1 -1 -1 -1 -.08 LA51 LF81 —.54 -1 -1 (Continued) LH81 LAB] -.15 -.08 -1 -l 252 The Linear Programming Tableau - Region 11 Row Haterisl Input Activities Capital Activities Income RHS Num- Column Name Activity bur Row Name Unit Ml_.?. H3 M42 H52 M82 212 232 7.42 Z32 Z83 CY2 Objecitve Function 1e. -1 -1 -1 -1 -1 0 O 0 0 0 1 - 0 56 F12 hrs. . i 0 57 H12 " i 0 58 A12 " :0 59 H12 1e. -1 I 0 60 212 1e. -1 i 0 61 F32 hrs. 5 0 62 H32 " _<. 0 63 A32 " 10 64 H32 1e. -1 - 0 65 232 1e. -1 :0 66 F42 hrs. 1 O 67 H42 " i 0 68 A42 " 1 0 69 H42 1e. -1 - 0 70 242 1c. —1 5 O 71 F52 hrs. 1 0 72 1152 " 10 73 A52 " 5 0 . 74 H52 1e. -1 - 0 75 7.52 1e. -1 10 76 982 hrs. 5 0 77 H8: " :0 78 A82 " :0 79 H82 1e. -1 - O 80 282 1e. -1 10 81 LF2 hrs. 1 841.608 82 LH2 " 1 191,515 83 LA2 " 1 1,667,017 84 H2 1e. 1 1 1 1 1 3 o 85 22 1e. 1 1 1 1 1 1 219.257 86 Y2 1e. 1 - 87 5121 no. i 124 88 U121 " < 152 89 5122 " :284 90 0122 " E 347 91 $123 " 3 101 92 U123 " £123 93 5124 " 3 83 94 0124 " 5101 95 S321 " 5 11 96 U321 " ? 14 97 S322 " 3 11 98 U322 " 1’ 14 99 S323 " E 67 100 U323 " < 81 101 S421 " _>- 45 102 L421 " I 55 103 S422 " I 46 104 0422 " I 56 105 S423 " 3 31 106 U423 " I 37 107 5521 " ? 13 108 U521 " ? 15 109 E522 " I 1 110 5321 " > 5 111 U821 " I 7 112 15822 " I 1 113 S823 " 1 9 114 U833 " 5_ 11 115 D21 1e - 1,092,948 116 023 " - 300.000 117 024 " - 721.771 118 925 " , - 14.667 119 028 " - 246,426 253 The Linear Programming Tableau - Region III Row Production Activities Num- Column Name Tailoring Gara Carpentry Blackp Bakery ber Row Name Dyeing smithing Unit P131 P132 P133 P134 P331 P431 P432 P433 P531 P532 P831 Objective Function 1e. 0 0 O O 0 0 O 0 O O 0 120 F13 hrs. 398 680 1162 1021 121 A13 " 100 245 40 763 122 H13 1e. 46 166 198 205 123 213 " 61 57 45 107 124 F33 hrs. 1193 125 A33 " 62 126 H33 1e. 1679 127 233 " 16 128 F43 hrs. 1960 331 8226 129 A43 " 8322 4071 45.116 130 H43 1e. 1262 603 5976 131 243 " 120 36 468 132 F53 hrs. 1173 1893 133 A53 " 1817 1971 134 H53 1e. 228 71 135 253 " 152 36 136 F83 hrs. 1928 137 A83 " 93 138 H83 1e. 1361 139 283 " 84 140 LF3 hrs. 141 LA3 " 142 H3 1e. 143 Z3 " 144 Y3 " -246 -596 -520 -678 -2389 -3203 -2749 -26.904 -1396 -750 -2181 145 S131 no. 1 146 U131 " l 147 $132 " 1 148 U132 " 1 149 5133 " 1 150 U133 " 1 151 $134 " l 152 U134 " 1 153 S331 " 1 154 U331 " l 155 S431 " l 156 U431 " 1 157 S432 " l 158 U432 " l 159 E433 " 1 160 $531 " 1 161 U531 " l 162 5532 " 1 163 U532 " 1 164 S831 " 1 165 U831 " 1 166 031 la. 246 596 520 678 167 033 " 2389 168 034 " 3203 2749 26.904 169 D35 " 1396 750 170 038 " 2181 (Continued) 254 The Linear Programming Tableau - Region III Row Labor Hirgng Activities Hump Column Name ber Rov Hams Unit LF13 LA13 bF33 LA33 LF43 LA43 LF53 LA53 LP83 LA83 Objective Function 1e. -.35 -.02 -.84 -.01 -.51 -.02 -.3O -.02 -.54 -.02 120 F13 hrs. -1 121 A13 " —1 122 H13 1e. 123 213 " 124 F33 hrs. -1 125 A33 " -1 126 H33 1e. 127 Z33 " 128 F43 hrs. -1 129 A43 " -1 130 H43 1e. 131 243 " 132 F53 hrs. -1 133 A53 " -1 134 H53 1e. 135 253 " 136 F83 hrs. -1 137 A83 " -1 138 H83 1e. 139 283 " 140 LF3 hrs. 1 1 l l 1 141 LA3 " 1 1 1 1 1 142 H3 1e. 1 1 l l 1 l 1 1 1 1 143 23 " 144 Y3 " 145 5131 no. 145 U131 " 147 $132 " 143 U132 " 149 $133 " 150 U133 " 151 5134 " 152 0134 " 153 S331 " 154 U331 " 155 S431 " 156 U431 " 157 S432 " 153 U432 " 159 E433 " 160 5531 " 151 U531 " 152 $532 " 163 U532 " 154 $831 " 165 U831 " 166 031 1e. 167 033 " 168 034 " 169 035 " 170 038 " (Continued) 255 The Linear Programming Tableau - Region III Num- ber _— 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 Haterial Input Activities Capital Activities Income RHS Column Name Activity Row Name Unit H13 H33 H43 H53 H83 213 233 243 253 283 CY3 Objective Function 1e. —1 -l -1 —1 -l -1 -1 -1 -1 -l 1 - 0 F13 hrs. (— 0 A13 " i 0 H13 1e. -1 - O 213 " -1 i 0 F33 hrs. 5 0 A33 " i 0 H33 1e. -1 - O 233 " -1 i 0 F43 hrs. 1 0 A43 " i 0 H43 1e. -1 ' 0 243 " -1 i 0 F53 hrs. 1 0 A53 " 1 0 H53 1e. -1 ' 0 253 " -1 i 0 F83 hrs. 1 0 A83 " i 0 H83 1e. -1 ' 0 283 " -1 i 0 - LF3 hrs. 5 1,975,399 LA3 " 1 2,678,383 H3 1e. 1 l l 1 1 1 0 23 " 1 l 1 l l ‘1 131,047 Y3 " l - 0 S131 no. 1 89 U131 " 3 109 $132 " 1 580 U132 " i 708 $133 " 3 267 U133 " i 327 $134 " 1 178 U134 " 1 218 S331 " 3 207 U331 " g 253 S431 " 3 147 U431 " i 179 S432 " 1 161 U432 " 1 197 E433 " - l 8531 " 1 72 U531 " 1 88 $532 " i 23 U532 " i 29 $831 " 1 130 U831 " i 158 D31 1e. = 623,261 033 " - 222.000 034 " - 1,693,836 035 " - 54,705 038 " - 283.658 [EFL 256 The Linear Programming Tableau - Region IV Rov Production Activities Num-I““-~‘£21323.E:::__ Tailoring l Carpentry I Blacksmithing her How Name Unit P141 P142 P143 P441 P442 P443 P541 P542 P543 Objective Function 1e. 0 0 0 0 O 0 0 O O 171 F14 hrs. 917 437 441 172 H14 18. 17 10 13 173 214 " 27 30 54 174 F44 hrs. 438 401 87 175 H44 1e. 21 0 1 176 244 " 28 43 42 177 F54 hrs. 1487 771 110 178 H54 1e. 19 4 1 179 254 " 26 41 44 180 LF4 hrs. 181 H4 1e. 182 24 " 183 Y4 " -636 -194 -l43 -l49 -4O -46 -338 -264 -98 184 8141 no. 1 - 185 U141 " l 186 $142 " 1 187 U142 " 1 188 $143 " 1 189 0143 " l 190 S441 " 1 191 U441 " l 192 S442 " 1 193 U442 " 1 194 S443 " 1 195 U443 " l 196 $541 " 1 197 U541 " 1 198 8542 " 1 199 U542 " l 200 $543 " l 201 U543 " l 202 041 la. 636 194 143 203 043 " 204 044 " 149 40 46 205 045 " o 338 264 98 206 048 " (Continued) 257 The Linear Programming Tableau - Region IV Row Column Name Labor Hiring Material Input Capital Income Num- Activities Activities Activities Activities HHS her Row Name Unit LF14 LF44 LF54 H14 M44 H54 214 Z44 254 CY4 .1--1-.,.- Objective Function 1e. —.09 -.09 —.09 -1 -l -1 -l -1 -l 1 - 0 171 hrs. —1 L o 172 814 1e. -1 - o 173 214 " -1 . 0 174 F44 hrs. -1 7 o 175 M44 19. -1 : 0 176 244 1' -1 . o 177 F54 hrs. -1 E o 178 854 1e. -1 - o 179 254 " -1 . 0 180 LF4 hrs. 1 1 1 1 1 1 3 12,038,001 181 H4 in 1 1 1 ; o 182 24 " 1 1 1 1 809.098 183 Y4 " 1 - 184 5141 no : 837 185 U141 " i 1144 186 5142 " 4 8736 187 U142 " < 12.012 188 5143 " 3 837 189 U143 " E 1144 190 5441 " i 1333 191 0441 " 1 1833 192 $442 " 1 1333 193 U442 " < 1833 194 S443 " 3 1333 195 U443 " E 1833 196 5541 " > 1100 197 0541 " 7 1513 198 $542 " 3 2200 199 0542 " ? 3025 200 $543 " 7 1100 201 0543 " F 1513 202 041 1e : 2.856.016 203 043 " - 25.000 204 044 " - 347.892 205 045 " - 1.591.890 206 048 " - 244.474 258 AquacHucoov .0H :m mew .0H :9 wow .0H 00 Row .oH men cow .oH men new .oH «on «cw H H H .oH men now H H H .mH Hen NON .oH can OHH .mH nan ooH .oH can moH H1 H1 H1 H H .oH nmn HoH H1 H1 H1 H H .uH Hnn @0H .0H QNQ oHH .uH nun mHH .oH «Na sHH H H1 H1 H1 H .oH nun oHH H H1 H1 H1 H .oH Hwn nHH .oH mHn mm .oH an cm .oH «Ha mm H H H1 H1 H1 .oH an mm H H H1 H1 H1 .0H HHa Hm ~oo.1 No.1 No.1 moo.1 No.1 No.1 no.1 No.1 No.1 «00.1 No.1 ~o.1 noo.1 No.1 ~o.1 no.1 No.1 No.1 .oH GOHuunsm H o>Huuonno and: tom «no Nmo Hmo «No mac HNo oHo mHu «Ho one «my Hna ¢~H mNH HNH «Ha nHa «HR and: nopasz noHuH>Huu< aoHuuuuoaoaouh uaauzo sumo nOHuH>Huu< :OHuauuomuaouH unnuao wcHuOHHoH UHaD uaaHou no: naOHwom HH< now uoHuH>Huu< uuoan can uuonxm .dOHunuuonoaaua any smoHan. wdguwoum .39—H.H 93. 259 Avoacuuaoov .OH rm ao~ .0H :9 wow .oH mu new .uH moo cow H H .oH non now H H H .oH «on «on .OH man now .oH Hen Now .uH man an H1 H1 H1 H .oH nna «0H H1 H1 H1 H H .oH on: mcH .oH nnn 50H .uH Hm: 00H .oH «Na oHH H H1 H1 H1 .uH nun wHH H H1 H1 H1 H .oH «Na NHH .oH nan oHH .oH HNQ nHH .uH «Ha mm H H .oH MHQ cm H H H1 H1 H1 .uH «H: mm , .oH nHa «m .0H HHn Hm ~oo.1 No.1 No.1 moo.1 No.1 ~o.1 ~oo.1 ~o.1 ~o.1 noo.1 No.1 No.1 no.1 «0.1 «c.1 .OH noHuunum H 0>Huounno ulna you «no umn Hnm can m~m HNn «no «no Hno «no nwo HNo «Ho «Ho «Ho mauz non-:2 muHuH>Huu< cOHuouuoaaooua unnuso nuHanuaHn uoHuH>Huo< :OHuuuuonuanuH usmuno unaccouno awn: aaaHoo to: ncOHmom HH< now ooHuH>Huu< uuoaaH van uuonum .aOHunuuomuauuH ash :aoHnnH mafiaadumoum unoaHH may 260 HvoscHucoov uoHuH>Huu< nOHucuuoaquuh uaauso sudden .oH an oo~ .oH :u acN H H1 H1 H1 .4H 00 Ham H H H .3 3a SN .oH men noN .oH «on HoH .uH Hen nou .uH Hen ~o~ H1 H1 H1 H H .uH wan cHH .oH an: aoH .oH can noH H1 .oH an: HoH .oH Han. ooH H H1 H1 H1 H .aH o~n. «HH .oH nun. oHH .oH «Na HHH H1 .oH HNH oHH .oH Hun nHH H H H1 H1 H1 .oH mHn an .oH an on .oH «Ha an H1 .oH an «m .0H HHa Hm w. No.1 No.1 Hoo.1 Noo.1 No.1 No.1 Hoo.1 No.1 No.1 nc.1 No.1 «c.1 ..H uoHuuusm H «5HuuoHao «can you 8 .m8 .m3 .wHu .5. 3. 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