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Temporal and spatial variation in sectoral labor allocation during development

Pandit, Kavita K., Ph.D.

The Ohio State University, 1987

Copyright ©1988 by Pandit, Kavita K. All rights reserved.

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University Microfilms International TEMPORAL AND SPATIAL VARIATION IN SECTORAL

LABOR ALLOCATION DURING DEVELOPMENT

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Kavita K. Pandit, B.Arch., M.C.R.P., M.A.

The Ohio State University

1907

Dissertation Committeet Approved by

Krishnan Namboodiri/ Ph.D.

Burkhard von Rabenau, Ph.D.

Randy Smith, Ph.D. ______Emilio Casetti, Advisor Department of Geography ©1988

KAVITA K. PANDIT

All Rights Reserved DEDICATION

To my parents

- ii - ACKNOWLEDGEMENTS

I would like to express my sincere appreciation to Pro­ fessor Emilio Casetti for his guidance, encouragement and support throughout my years in the Department of Geography.

Over this period. Professor Casetti has been a teacher, an advisor, a colleague, and a friend. His deep concern for the welfare of his students, and the professionalism and integrity that underly his interactions with them have greatly impressed me. I look forward to our continued association.

I would also like to thank my dissertation committee members, Professor Gauthier, Professor Namboodiri, Pro­ fessor von Rabenau, and Professor Smith for their support.

I have benefited greatly through their courses and through personal interaction with them.

Finally, I would like to acknowledge the financial assistance provided by the Graduate School at Ohio State, which allowed me an uninterrupted year of work on my dis­ sertation, and by the Department of Geography at Ohio

State.

- iii - VITA

October 24, 1956 ...... Born, Bombay, India

1978 ...... B.Arch., Architecture, Bombay University, Bombay, India

1979-1981 ...... Teaching Associate, Department of Engineering Graphics, The Ohio State University, Columbus, Ohio

1981 ...... M.C.R.P., City and Regional Planning, The Ohio State University, Columbus, Ohio

1981-1982 ...... Project Planner, Centre for Development Studies and Activities, Poona, India

1983 ...... M.A., Geography, The Ohio State University, Columbus, Ohio

1982-1986 ...... Teaching Associate, Department of Geography, The Ohio State University, Columbus, Ohio

1986-1987 ...... Presidential Fellow, The Ohio State University, Columbus, Ohio

PUBLICATIONS

"Sectoral Allocation of Labor Force with Development and the Effect of Trade Activity," ECONOMIC GEOGRAPHY 62 (2), April 1986i 144-154.

"Service Employment During Development and Economic Dualism," in PROCEEDINGS OF THE RAPID II POPULATION POLICY FELLOWS WORKSHOP, Washington D.C.i U.S. Agency for International Development, 1986t 206-223.

"Changes in Tertiary Sector Employment During Development," MODELING AND SIMULATION 16 (1), April 1985i 373-377.

- iv - "A Catastrophe Model of Urbanization,” MODELING AND SIMULATION 15 (1), April 1984* 201-206, (with E. Casetti).

COSTS AND BENEFITS OF ANNEXATION IN LIMA, OHIO, Report submitted to the Joint Planning Commission of Lima, Ohio, 1980, (with Steve Gordon et al).

FIELDS OF STUDY

Major Field* Development

Studies in Population/Demography: Professors Casetti, von Rabenau, and Namboodiri

Studies in Regional Development* Professors Casetti and Gauthier

Studies in Urban Geography* Professors Casetti, L.A. Brown, and Smith

- v TABLE OF CONTENTS

PAGE

Dedication ...... 11

Acknowledgements ...... Ill

VITA ...... lv

LIST OF T A B L E S ...... ix

LIST OF FIGURES ...... xi

CHAPTER

I. INTRODUCTION ...... 1

1.1 PROBLEM STATEMENT ...... 2 1.2 LABOR FORCE SHIFTS AND ECONOMIC DEVELOPMENT ...... 6 1.3 ORGANIZATION OF THE DISSERTATION . . . 10

II. SECTORAL LABOR ALLOCATION DURING DEVELOPMENT . 12

2.1 DEFINITION OF THE SECTORS ...... 12 2.2 CHANGES IN SECTORAL LABOR ALLOCATION . 15 2.3 THEORETICAL DETERMINANTS OF SECTORAL LABOR SHIFTS ...... 17 2.3.1 Demand Determinants ...... 17 2.3.1.1 Household Demand ...... 17 2.3.1.2 Intermediate Demand ..... 21 2.3.1.3 Foreign Demand ...... 24 2.3.2 Production Determinants ...... 27 2.3.2.1 Differentials in Factor I n t e n s i t y ...... 29

- vi - 2.3.2.2 Differentials in Elasticities of Factor Substitution ...... 31 2.3.2.3 Differentials in Technological Growth Rates . 32 2.3.2.4 Differentials in Economies of S c a l e ...... 34

III. SECTORAL SHIFTS AND THE SERVICE SECTOR IN THE THIRD WORLD ...... 37

3.1 DIFFERENTIAL PATTERNS OF LABOR ALLOCATION IN THE LDC'S ...... 38 3.1.1 Sequence of Sectoral Shifts and Tertiary Hypertrophy ...... 39 3.1.2 Service Sector Dualism ...... 45 3.2 EXPLANATIONS OF TERTIARY SECTOR HYPERTROPHY AND DUALISM ...... 50 3.2.1 Service Sector Growth as R e s i d u a l ...... 51 3.2.1.1 Demand-Oriented Explanations ...... 52 3.2.1.2 Supply-Oriented Explanations ...... 57 3.2.2 Service Sector Growth as Demand Determined ...... 62 3.2.2.1 Long-term Economic Changes . 64 3.2.2.2 Government Demand ...... 67

IV. SECTORAL LABOR ALLOCATION - RECENT PATTERNS . . 69

4.1 WORLD PATTERNS ...... 69 4.2 MDC VS. LDC PATTERNS ...... 72 4.3 SECTORAL SHARES BY LDC REGION ...... 75 4.4 INTRA-REGIONAL VARIATION IN SECTORAL SHARES ...... 79

V. EMPIRICAL ANALYSIS OF SECTORAL SHIFTS ...... 82

5.1 SECTORAL LABOR ALLOCATION AND DEVELOPMENT ...... 82 5.1.1 The Sectoral Shifts Model .... 85 5.1.2 Data and Methodology ...... 87 5.1.3 Results ...... 88 5.2 VARIATION IN THE SECTORAL SHIFTS MODEL ...... 93 5.2.1 Expansion of the Sectoral Shifts Model ...... 95

- vii - 5.2.1.1 The Initial Model ...... 95 5.2.1.2 The Expansion Equations . . . 95 5.2.1.3 The Terminal Model ...... 97 5.2.2 Data and Methodology ...... 98 5.2.3 Results ...... 99 5.2.3.1 Temporal Variation ...... 99 5.2.3.2 Spatial Variation ...... 106

VI. SUMMARY AND CONCLUSIONS ...... 117

6.1 THE SECTORAL SHIFTS MODEL ...... 119 6.2 VARIATION IN THE SECTORAL SHIFTS MODEL ...... 120 6.2.1 Temporal Drift ...... 121 6.2.2 Regional Drift ...... 122

APPENDICES

A. LIST OF COUNTRIES AND DATA USED IN THE ANALYSIS ...... 125

B. LIST OF COUNTRIES BY DEVELOPMENT CLASSIFICATION ...... 141

C. LIST OF LESS DEVELOPED COUNTRIES BY REGION . , 143

BIBLIOGRAPHY ...... 145

- viii - LIST OP TABLES

List of Structural Changes ...... 3

Labor Force Structure in the U.K. and the USA t 1900-1980...... 7

Labor Force Structurei MDCs and LDC's 1900-1980...... 9

Sectoral Distribution of the World's Labor Force, 1960-1980 ...... 70

Sectoral Growth Rates of the World's Labor F o r c e ...... 72

Sectoral Labor Allocation, 1960-1980* MDCs v s . LDCs ...... 73

Growth Rates of Sectoral Labor Shares: MDCs v s . L D C s ...... 74

Sectoral Labor Allocation 1960-1980, by LDC Region ...... 76

Growth Rates of Sectoral Labor Shares by LDC Region ...... 78

Sectoral Labor Allocation 1960-1980, for Selected Countries ...... 80

Regression Statistics for the Sectoral Shifts Model ...... 89

Values of Variables Dl, D2, and D3 by Region . 97

Regression Statistics - Temporal Drift in Agriculture ...... 100

Regression Statistics - Temporal Drift in Manufacturing ...... 101

- ix - 15. ANOVA of Models with Dummy Variables: Agriculture ...... 106

16. ANOVA of Models with Dummy Variables: Manufacturing ...... 107

17. Regression Statistics - Spatial Drift in Agriculture ...... 108

18. Regression Statistics - Spatial Drift in Manufacturing ...... 109

- x - LIST OF FIGURES

FIGURE PAGE

1. Labor Allocation in Agriculture and Manufacturing ...... 90

2. Labor Allocation in Services ...... 92

3. Temporal Drift in Manufacturing Labor A l l o c a t i o n ...... 103

4. Temporal Drift in Service Labor Allocation . . . 105

5. Sectoral Labor Allocation - Latin America . . . Ill

6. Sectoral Labor Allocation - Africa ...... 112

7. Sectoral Labor Allocation - A s i a ...... 113

8. Sectoral Labor Allocation - MDC's ...... 114

- xi - CHAPTER I

INTRODUCTION

The change in a country's labor force structure during the course of economic development constitutes a well estab­ lished phenomenon with strong theoretical underpinnings.

Historical studies of the presently industrialized coun­ tries have shown that while relative employment in agricul­ ture declines with rising incomes per capita, that in manu­ facturing first rises and then falls, and that in services rises steadily. As a result, the relationship between labor force structure and economic development has attained a quasi-”law" status in the field of development economics.

Recent evidence from the contemporary developing coun­ tries has indicated that the historically observed patterns are not being replicated. It strongly suggests that the sectoral labor allocation patterns may not be constant over time and space. This dissertation explores, theoretically and empirically, this variation in the relation between sectoral labor shares and economic development. The dis­ sertation represents an application of the Expansion Method

Paradigm in that it directs attention to questions related

- 1 - 2 to the possible contextual variation of an important, theo­ retically grounded relationship, and then systematically searches for the theoretical basis and possible empirical occurence of such drift.

This chapter is organized as follows. The first section presents a statement of the problem addressed in this dis­ sertation, as well as the general philosophical orientation of the study. The second section provides a review of the structural changes in labor allocation and development relationship, and the evidence related to its spatial and temporal drift. The final section presents an overview of the dissertation.

1.1 PROBLEM STATEMENT

The reallocation of labor between the agricultural, manu­

facturing, and services sectors is part of a broader

restructuring of a country’s economy as it progresses along

the development continuum.1 Chenery and Syrquin (1975)

classified structural changes into accumulation processes,

resource allocation processes, and demographic and distri­

butional processes as indicated in Table 1. The theoreti­

cal justification for these extensively documented trends

is found in, for example, dual economy theories (Lewis,

1954; Jorgensen, 1961; Fei and Ranis, 1964), Engel’s Law of

1 Changes in a country’s social and political structure may also accompany development (Zuvekas, 1979i9). 3 household consumption, theories of balanced and unbalanced growth (Rosenstein-Rodan, 1943; Nurkse, 1959), and income inequality theories (Kuznets, 1955; Myrdal, 1957).

Table 1

List of Structural Changes

PROCESS STRUCTURAL CHANGE

Accumulation Investment

Government Revenue

Education

Resource Allocation Domestic Demand

Production

Trade

Demographic/Distributional LABOR ALLOCATION

Urbanization

Demographic Transition

Income Distribution

SOURCE) Chenery and Syrquin (1975*9)

The past few decades have seen a great deal of attention

directed to the study of structural changes and their 4 determinants, as a large number of Third World countries achieved political independence and began embarking upon their industrial development. Most studies havd adopted a uniform analytical approach; they have applied statistical techniques to different country samples and time periods in order to define and estimate a model best believed to describe a particular development process. Such an approach enables the identification of important relation­ ships between variables and can often shed light on the causal mechanisms at work. However/ since the end product of the exercise is a single regression model that is thought to fully describe the pattern of structural changes in all countries and time periods# these studies make an implicit# and unwarranted assumption of parameter stability in the model.

The assumption of spatio-temporal stability in develop­ ment processes overlooks an important dimension of such research. The various demographic and economic factors that influence structural changes do not necessairily oper­ ate uniformly over time or geographic region. As a result# the relationship between any given phenomenon and economic development is likely to vary. The examination of the tem­ poral change or the spatial variation of a theoretically justified relationship, and the reasons for this variation, it is argued here# is in itself an important research ques­ tion. 5

The exploration of contextual variation in structural changes during development is likely to have greater policy significance for the Third World countries than traditional research. These countries, in many cases, have only the past experiences of the more developed countries to antici­ pate the changes in their own economies. Given that the environment in which they are embarking upon their indus­ trialization differs significantly from that which existed at the time of the Industrial Revolution, it seems much more appropriate to rely upon analyses that are sensitive

to the temporal and spatial milieu.

The research question outlined here and the subsequent

research arises out of Casetti's Expansion Method. The

Expansion Method represents, firstly, a research paradigm

for directing questions about the differential operation of

relatively simple, theoretically grounded relationships in

differing contexts. It is also a methodological tool which

provides a set of orderly routines for testing hypotheses

concerning model stability/drift, and obtaining functional

portraits of the same. Here, the expansion method is

applied to the examination of the temporal and spatial v a r ­

iation in the relationship between sectoral labor alloca­

tion and economic development. 6

1.2 LABOR FORCE SHIFTS AND ECONOMIC DEVELOPMENT

Theoretical arguments advanced by Fisher (1939) and Clark

(1940, 1957) posited that as a country's GNP per capita grows, the share of the labor force in the primary sector declines, that in the secondary sector first increases and then declines, and that in the tertiary sector increases monotonically.2 They attributed these shifts to changes in the structure of domestic demand and inter-sectoral differ­ ential in labor productivity growth.

The sequence of sectoral shifts postulated by Fisher and

Clark has been empirically supported by the historical

experience of the More Developed Countries (MDCs).

Table 2, by way of example, presents the labor force struc­

ture since the late 1800's for the United States and the

United Kingdom. The table shows a steady decline in prima­

ry labor allocation and increase in tertiary labor shares.

Manufacturing labor shares show an initially rising tenden­

cy, peaking at about 50 percent in 1956 in the UK, and at

about 35 percent in 1965 in the USA. A large number of

studies have confirmed these tendencies in the past devel­

opment of the now industrialized countries (Clark, 1957;

Kuznets, 1966b).

* The terms primary, secondary, and tertiary will be used interchangeably with agriculture, manufacturing and ser­ vices in this study. An extended discussion of the defi­ nition of these sectors is provided in Section 2.1. Table 2

Labor Force Structure in the U.K. and the USAi 1900-1980.

COUNTRY YEAR LABOR ALLOCATION

PRIMARY SECONDARY TERTIARY

U.K. 1901 13.4 45.4 41.2 1911 12.1 45.4 42.5 1921 9.3 53.9 36.8 1926 8.3 45.2 46.5 1931 7.6 49.8 42.6 1936 6.5 44.0 49.5 1949 3.6 46.9 49.5 1956 2.7 48.8 48 5 1961 2.9 45.4 51, 7 1966 2.3 44.2 53, 5 1971 1.8 41.2 57, 0 1976 1.6 37.3 61, 1 1981 1.4 32.5 66 1

U.S.A. 1900 44.2 24.3 31.5 1905 39. 0 26.6 34.4 1910 35. 7 28.2 36.1 1915 33.4 28.0 38.6 1920 28.2 32.3 39.5 1925 28.2 29.0 42.8 1930 26.9 27.6 45.5 1935 27.9 27.5 44.6 1940 23.4 29.4 47.2 1945 18.0 34.2 47.8 1950 13.0 33.8 53.2 1955 11.8 32.6 55.6 1960 9.7 30.8 59.5 1965 8.2 34.7 57.1 1970 4.5 25.7 69.8 1975 4.4 24.8 70.8 1980 4.4 27.7 67.9

SOURCEt Lleaner (1985) The recent experience of the Less Developed Countries

{LDCs}, while not qualitatively inconsistent with the his­ torically observed labor shifts, has been different in two respects. Firstly, labor allocation in the manufacturing sector has been significantly lower, and increasing much more slowly, than what was observed historically in the

MDC*s. Table 3 reveals that as late as 1980, only about 17 percent of the LDC labor force was engaged in secondary sector activities, in contrast to the MDC's in 1900 where well over a fourth of the labor force was in industry.

Secondly, service labor shares in the LDC's have been con­ siderably larger than industry shares, even in relatively early stages of development, a phenomenon referred to as tertiary sector hypertrophy. As Table 3 indicates, in 1980 the LDCs had as much as 27 percent of their labor in the tertiary sector, an allocation that was not achieved in the

M D C s until 1920.

There is also a considerable contrast between the nature of service activities in the MDCs and the LDCs. In the more developed countries, service sector occupations are characterized by high skilled, high income professions such

as health care, financial services, and real estate. Ter­

tiary sector activities in the less developed countries are

largely made up of small scale, mainly unskilled, individu­

al and family enterprises such as street vending and domes­

tic service. 9

Table 3

Labor Force Structurei MDCs and LDC'a 1900-1980.

REGION/SECTOR 1900 1920 1930 1950 I960 1970 1980

MDCS

Agriculture 48.1 39.9 36.2 30.7 22.9 21.7 15.2 Manufacturing 28.7 31.3 30.5 32.9 36.0 36.8 37.9 Services 23.3 28.8 33.3 36.5 41.1 41.5 46.9

LDCS

Agriculture 77.9 77.6 76.6 73.3 70.7 62.0 55.2 Manufacturing 9.8 9.9 10.0 9.9 11.5 14,4 17.3 Services 12.3 12.5 13.4 16.7 17.8 23.6 27.5

SOURCESt Squire (197912) for 1900-1960 figures. (1983) for 1970-1980 figures.

The preceding discussion suggests the possibility of temporal and/or spatial variation in the relationship between labor shares in the primary, secondary, and terti­ ary sector and economic development. It also directs attention to the service sector, whose rapid expansion in the contemporary LDCs has preceded manufacturing growth, and constitutes a departure from the Fisher-Clark hypoth­ esized dynamics of post-industrial service sector expansion that has characterized MDC development. The study outlined in the following section, consequently analyzes the drift of the sectoral labor allocation relation keeping a a par­ ticular focus upon the behavior of the service sector. 10

1.3 ORGANIZATION OF THE DISSERTATION

The rest of the dissertation is organized as follows.

Chapter 2 provides a'discussion of the relationship between sectoral labor allocation and economic development, with an emphasis upon established theory. The chapter initially clarifies definitional issues, and goes on to describe the traditionally hypothesized patterns of labor movement across the sectors. The bulk of the chapter is devoted to a discussion of the theoretical determinants of sectoral shifts as put forth in the literature.

Chapter 3 presents an overview of the sectoral labor allocation patterns currently underway in the LDCs, empha­ sizing upon the observed tertiary sector hypertrophy and the traditional-modern dualism within the non-agricultural sector. It discusses at length the various themes advanced in the literature to explain these phenomena.

A descriptive summary of the labor structure since 1960

is provided at various levels of spatial resolution in

Chapter 4. The discussion highlights the temporal change and the spatial variation in labor shifts.

The empirical analyses of labor allocation during devel­ opment are presented in Chapter 5. A model of sectoral

labor allocation during development is first defined and estimated. Then, the temporal and spatial variation in

this model Is investigated. 1 The paper ends with a summary of the important findings and conclusions. CHAPTER II

SECTORAL LABOR ALLOCATION DURING DEVELOPMENT

This chapter provides a detailed discussion of the estab­ lished theory of sectoral shifts of labor during economic development. It is divided into three sections. The first part briefly discusses the definition of each of the sec­ tors, and their composition. The historical changes in sectoral employment are detailed next. The third section discusses the theoretical determinants of these changes.

2.1 DEFINITION OF THE SECTORS

In his pioneering work of sectoral shifts, Allen Fisher

(1935a, 1935b) defined the sectors as followst3

Primary Sectors all activities related to the extraction

of raw materials from the environment, i.e. agricul­

tural and pastoral activities, fishing, forestry, and

mining.

* The terms 'primary' and 'secondary' had previously been used in the official statistical publications of Austra­ lia and . However, their definitions were much more restrictive than those proposed by Fisher. See Fisher (1939).

- 12 - 13

Secondary Sectori all activities related to the treat­

ment of raw materials, i.e. manufacturing, construc­

tion, and utilities.

Tertiary Sector* all activities not included in the pri­

mary and secondary sectors, namely "facilities for

travel, amusements of various kinds, governmental and

other personal and intangible services, flowers,

music, art, literature, education, science, philosophy

and the like" (1935b* 28).

This definition of the three sectors has, over time, been modified and made more explicit. Fisher's definition of the primary sector has undergone the least change; it

is still defined to include agriculture, forestry, fishing, and mining. The term 'agricultural sector1, often used in the literature, comprises of all primary activities except mining. Given that the labor allocation in mining is gen­

erally low at a few percent points, the terms primary sec­

tor and agricultural sector will be used synonymously in

this study.

The division of activities between the secondary and

tertiary sectors has been less uniform. Clearly, there is

a unanimity in the classification of certain activities,

viz. manufacturing and construction in the secondary sec­

tor, and wholesale and retail trade, finance and profes­

sional services, public administration, and personal servi­ 14 ces in the tertiary sector. The main area of confusion has been the classification of transportation and communica­

tion, and utilities. The problem arises out of two differ­

ent classification criteria, i.e. whether the classifica­

tion is based on the nature of production activity or the

type of output created.

Production-based classifications generally include

transport and communication, and utilities in the secondary

sector, the argument being that they resemble industry in

their "dependence upon heavy capital equipment and complex

technology" (Fuchs, 1968* 16). This classification is used

by Fisher, Stigler (1957), Kuznets (1966a), Fuchs, and

Sabolo (1975b).

Classifications based upon the type of output categorize

all activities that produce an 'intangible good* into the

tertiary sector. Thus, transportation, communications and

utilities, by this are considered a part of the tertiary

sector (Clark, 1940; ECLA, 1957; Ofer, 1967).

The secondary and tertiary sectors are often referred to

as manufacturing and service sectors respectively. While

the tertiary and service sectors are identical in their

definitions, the manufacturing sector differs from the sec­

ondary sector in that the latter includes mining activi­

ties. However, for reasons stated earlier, these terms

will be used interchangeably in this study. 15

2.2 CHANGES IN SECTORAL LABOR ALLOCATION

Much of the theoretical work related to changes in sectoral

employment during development grew out of the observation of patterns in industrialized economies. These economies,

in most cases, saw a fairly orderly transition of the labor

force from primary occupations to secondary occupations,

and, in later stages of development, to tertiary occupa­

tions. These shifts were initially documented by Fisher

(1939) and Clark (1940), Others, since then, have con­

firmed the tendency in more developed countries for the

agricultural labor allocation to fall with development,

that in manufacturing to initially rise and then decline,

and that in services to steadily increase (Kuznets, 1966a,

1966b, 1971; Sorrentino, 1969; Szabadi, 1975; Blades et

al,, 1975; Bacon and Eltis, 1976; UN-ECE, 1977; Blackaby,

1978).

The commonest interpretations of the sectoral shifts

observed in the MDC's are provided by Fourastie (1939) and

Lengelle (1966). According to Fourastie, all countries

pass through three stagest "civilization primarie" when

the bulk of the labor force is engaged in agriculture;

"periode transitoire" characterized by a declining share in

the primary sector, an increasing share in the secondary

sector that peaks during this period, and a gradually

increasing share in the tertiary sector; and "civilization 16 tertiarie" when the bulk of the labor force Is in the ser­ vices.

Lengelle provided a more detailed, four-stage descrip­ tion of the sequential shifts in the labor force.*

Stage 1* The bulk of the labor force is employed in the

primary sector. Economic development at this time

is characterized by a decline in primary employ­

ment share, an increase in secondary employment

share, and little change in the tertiary labor

share.

Stage 2i The society is not fully industrialized. Economic

growth is characterized by a continued decline in

the primary labor allocation, and a relative

increase in the secondary and tertiary labor

force.

Stage 3i The maximum level of industrialization is reached,

and the secondary labor allocation is large and

stable. Primary labor allocation contiues to

decline and tertiary labor allocation increases.

Stage 4i Countries have now entered a "post-industrial"

phase. Economic growth is now characterized by a

relative decline in secondary labor force, the

tertiary sector continuing to grow in relative

size.

* This section is based largely upon the summary provided by Ramos (1970i 134-35). 17

2.3 THEORETICAL DETERMINANTS OF SECTORAL LABOR SHIFTS

The transformation of a nation's labor force from a predom­

inantly agrarian occupations to predominantly service- oriented occupations has been the subject of much theoreti­

cal investigation. It is generally attributed to two

important structural changes occurring in the course of

economic developmenti

(1) changes in the demand for sectoral output, and

{2) changes in the sectoral labor productivity.

2.3.1 Demand Determinants

Demand centered theories maintain that sectoral employ­

ment is determined by sectoral output, which, in turn, is

determined by the demand for sectoral goods. It is further

argued that, since this demand changes systematically with

economic development, the labor force shares will do like­

wise. Three types of demand are generally considered*

(1) Household demand for sectoral final products;

(2) Industry demand for sectoral intermediate products;

and

(3) Foreign demand for sectoral intermediate and final

products.

2.3.1.1 Household Demand

In 1857, Ernst Engel suggested that household consump­

tion patterns that influence demand change with income per 18 capita (Zimmerman, 1932; Houthakker, 1957). He Is said to have maintained!

As the income of a family increases, a smaller percentage.... is spent on food, the percentage on clothing remains approximately the same, the per­ centage for rent, fuel and light is exactly the same, while a constantly increasing percentage is expended on education, health, recreation, amuse­ ment and other miscellaneous items. (Fisher, 1935bi 18)

The hypothesis advanced by Engel, now referred to as

Engel's Law, provided the theoretical basis for the pio­ neering work on sectoral shifts of labor by Fisher (1935b,

1939} and Clark (1940, 1957).

In his empirical investigation of sixty-one countries over a maximum time span ranging from 1920 to 1950, Clark observed a progression of labor force allocation from the primary to the secondary and then to the tertiary sector.

Clark attributed this transformation to changes in domestic demand. According to him, as per capita income increases,

"the relative demand for agriculture falls all the time,

the relative demand for manufacture first rises and then falls in favour of services" (Clark, 1957* 493).

A systematic investigation of the effect of income on demand patterns was made by Kuznets (1966b), who calculated the income elasticities of sectoral demand for thirteen industrialized countries. He found that the elasticity of demand for manufactured goods and services exceeded that for agricultural products. A percent increase in per cap­ ita income, therefore, would induce a decline in the rela­ tive demand for agricultural goods and a corresponding increase in the demand for non-agricultural products. It has been further suggested that there is a tendency for the elasticity of demand for services tc rise relative to that for goods as Incomes rise, i.e. at lower levels of develop­ ment, the income elasticity of demand for tertiary products is less that that for goods, while at higher levels of development it exceeds that for manufactures (Ramos, 1970;

Gemmell, 1985). Thus, once a certain level of goods con­ sumption has been reached, further increments in income tend to be spent increasingly on services, a trend that has been noted by Bell (1973), Shelp (1981) and Stanback et al

(1981) .

Stigler (1956) and Kuznets (1966a) suggest that the ris­ ing demand for service output relative to manufacturing output over time may be partly explained by urbanization.

According to Kuznets, the agglomeration of population that has occured due to the rise of modern industry has created the demand for a range of new consumer services such as public transit, sanitation and public health services, and government administration. Services that once were provid­ ed within households are being increasingly provided by specialized establishments (e.g. home cooking being

replaced by restaurants). The relation between urbanize- 20 tion and household demand for services was also brought out by Tulpule (1968) in a study of London.

The link between changes in household demand and the sectoral allocation of the labor force has been investigat­ ed by numerous authors (Maizels, 1963; Fuchs, 1968; Ramos,

1970; Thirwall, 1978). Ramos noted that while the decline in primary labor allocation was strongly related to the low income elasticity of demand for farm products, the rela­ tionships for the secondary and tertiary sectors was not as clear. In an examination of the U.S. service sector during the post-World War II decade, Fuchs observed that the growth in tertiary sector employment far exceded that which would have been predicted by the income elasticity of demand alone.

Bauer and Yamey (1951) question the validity of the income-demand relationship as applied at a macro-economic level. They argue that since income and output shares are average figures for the population as a whole, there is no basis for assuming a unique relationship between them. The actual effect of an increase in national income on sectoral output would depend upon income distribution patterns, and who were the principal beneficiaries of the growth. 21

2.3.1.2 Intermediate Demand

A number of professions attribute their emergence and growth not due to the final demand exerted by households, but due to the intermediate demand made by, in particular, the goods-producing sector. The most important beneficiar­ ies of this growth have been service sector activities such as goods transportation, wholesale trading, advertising, business finance services and research and development

(sometimes collectively referred to as producer services).

Unlike the final, or household demand for sectoral products which is linked to per capita income through Engel's Law, the changes in the structure of intermediate demand during development occur due to the trend towards increased spe­ cialization.

The relationship between economic development and spe­ cialization was investigated in detail by Stigler (1951,

1956). According to himi

Progressive specialization characterizes a grow­ ing economy. When goods are few and production processes simple, when technical knowledge is largely empirical and the pace of technology slow - then there is little need for specialization. But as goods multiply, processes of production become complex, technological knowledge becomes abstract and formal, and the rate of obsolescence of knowledge rises, specialization must become even finer. (Stigler 1956* 159)

Stigler called attention to the fact that as the market expands and firms increase in size, production costs become subject to growing economies of scale. This makes it pos­ 22 sible for a specialist firm to enter the marketplace to provide that good or service. Stanback et al (1981) in their study of the U.S. service sector, observed that the size of firms grew as the market expanded, allowing for greater specialization of functions. They noted that while economies of scale have been traditionally associated only with goods-producing technology, today the same economies are available in the services.

In a study of business service linkages, Marshall (1983) found that a substantial part of the total demand for intermediate services derives from within the service sec­ tor itself, particularly the government sector. This find­ ing is supported by Damesick (1986) who highlighted the importance of specialization and the increased division of labor within services in the development of producer servi­ ces.

Leibenstein (1962) is responsible for one of the most detailed analyses of the income-specialization relation and

its effect on sectoral employment. According to him, not only did the degree of specialization increase with devel­ opment, but for every level of income per capita there existed an optimal level of specialization. Leibenstein argued that the income-specialization relation alone could provide the theoretical basis for the observed shift of the

labor force from the primary to the secondary sector, and then from the secondary to the tertiary sector. In the former case, an increased division of labor in agriculture creates activities that can no longer be classified as strictly primary - the processing and refining of produce or the transportation of crops. In the latter case, spe­ cialization in the secondary sector leads to a separation of clerical, marketing and financial functions from activi­ ties more directly related to the transformation of raw materials. Leibenstein's hypothesis implies that the ter­ tiary sector cannot lose employment to the other two sec­ tors (nor the secondary lose to the primary sector) as a result of further specialization, but only as a consequence of greater integration of activities.

Studies by Stigler (1956) and Greenfield (1966) for the

United States confirm the dramatic growth producer services with increased specialization of manufacturing. Greenfield found that during the 1939-1963 period, the non-production workers demanded by industry grew by 127 percent in con­ trast to a mere 51 percent increase in the demand for production-related workers. Galenson (1963) examined the growth in service sector employment for twenty-five coun­ tries between 1952 and 1962. He concluded that, in the majority of cases, growth in manufacturing output was the key to the generation of new employment in services. 24

The effect of Intermediate demand on service sector employment growth was also investigated by Fuchs (1968).

Selecting five service industries in the U.S., he compared

input-output tables for 1947 with those for 1958. Although

Fuchs confirmed the hypothesis that some of the growth of

service employment is attributable to an increase in inter­ mediate demand, he concluded that this source accounts for only a small part of the total shift.

2,3.1.3 Foreign Demand

Foreign demand, as expressed through the volume of a

country's exports,® is yet another factor affecting domes­

tic production, and therefore employment. Whereas in a

closed system, there is an ideal correspondence between

domestic demand {both household and intermediate) and pro­

duction, in today’s open economies this relation is offset

by trade; exports generate production and employment in

response to demands that are not local. Not surprisingly,

it has been found that foreign demand is a less important

determinant of employment in larger countries, i.e. those

with a larger domestic demand than in smaller countries

(Chenery and Taylor, 1968; Chenery and Syrquin, 1975).

8 The term exports as used here refers to net exports, or exports minus imports. Imports influence sectoral demand in an opposite manner, and their counter-effect must be controlled for. 25

Hilgerdt (1945) suggested that trade levels grow with development due to two main reasonsi the demand for Indus­ trial raw materials that are domestically absent stimulates primary trade, and the growth in personal Income results in a diversification of the demand for manufactured articles leading to a greater international exchange of industrial goods. The impacts of rising export levels upon domestic employment has been the subject of a voluminous body of

literature (e.g. Kindleberger, 1962; Michaely, 1977;

Balassa, 1978; Krueger, 1978; Tyler, 1976, 1981; Kavoussi,

1984; Ram, 1985).

This study, however, is not concerned with total employ­ ment changes, but with relative changes in the sectoral

employment. What needs to be established, then, is the

compositon of and changes in exports by sector8 during

development, rather than the total exports. If a country

is a major exporter of manufactures, for example, its

industrial output and employment will be higher than that

anticipated by domestic demand alone.

6 Whereas trade in agriculture and manufacturing is rela­ tively easy to measure, that in services is beset by problems of definition. In many cases, tertiary trade is not accompanied by any clear activity at international borders. For example, a service is often delivered through the mail or a telephone conversation, not normal­ ly monitored for trade. See Riddle (1986) for an extend­ ed discussion. Linder (1961) postulated that changes in the nature of exports closely changes in the sectoral composition of domestic demand. This implies that the effects of foreign demand on sectoral employment would be the same as those suggested by Fisher and Clark. Oberai's study of fifty-one countries in 1960 and 1970 lends support to this hypothesis

(Oberai, 1979). He found that the addition of a variable

representing the sectoral composition of exports did not

improve the explanatory power of his regression model of

sectoral allocation of labor with development. However,

another recent study of the effects of trade size and

structure on sectoral labor allocation found that rising

shares of agricultural goods in exports tended to boost the

rates of manufacturing labor allocation (Pandit, 1986).

These and other such findings have highlighted the fact

that the income-demand-output relationship is not entirely

straightforward. Factors such as prices and the substitut­

ability of consumer preferences arising from relative price

changes may cause a differential growth of sectoral output

with changes in demand. For example, Gershuny and Miles

(1983) showed that the demand and employment in some servi­

ces declined due to the growth to the "self-service" econo­

my; households tended to substitute goods with which they

could, using their own labor, provide service functions for 27

themselves, for externally supplied services. Thus cars, washing-machines, vaccum cleaners and television sets

replaced public transport, laundries, domestic servants arid

theatre performances respectively.

2.3.2 Production Determinants

Changes in the structure of demand and output that

accompany economic development provide only a partial

explanation for sectoral labor allocation patterns. Char­

acteristics of production also influence labor allocation

patterns through the critical linkage between sectoral out­

put and sectoral employment. Sectors which are highly

efficient in their use of labor, i.e. have high labor pro­

ductivities,7 will employ less additional laborers per unit

growth in output than sectors with lower labor productivi­

ties, Further, the sector with the fastest growth rate of

labor productivity will, ceritus paribus, account for a

declining relative share of the labor force.

Colin Clark was amongst the first to note sectoral dif­

ferentials in the growth of labor productivity. He

observed that as per capita national income rises, "real

product per man-hour in manufacture.... nearly always

7 Productivity is traditionally defined as output per work­ er. In agriculture and industry, it is relatively easy to measure the quantity of output, and therefore the pro­ ductivity. The measurement of output in services, how­ ever, raises complex conceptual and statistical questions due to its intangibility. Detailed discussions of the problems of measuring service sector productivity can be found in OECD (1966), Heaton (1977) and Riddle (1986). 28 advances at a greater rate than the real product per man- hour in other sectors of the economy'’ (Clark, 1957i 493).

However, he found that the labor productivity in some types of services exceeded that in manufacturing. The implica­ tion of Clark’s findings, paradoxically, was that if rela­ tive demand was held constant, industrial employment would decline relative to that in agriculture and services, and that the percent employed in certain services would decline even more. This led Clark to conclude that demand determi­ nants dominate in the sectoral shifts pattern.

A study by the Bank of Montreal (1956) of Canada's ser­ vices sector, assigned greater importance to inter-sectoral variations in labor productivity in explaining the growth of service employment. The study found that tertiary sec­ tor productivity growth consistently lagged that of indus­ try. Thus, the maintenance of service sector product share had to be accomplished by increasing employment shares.

In his analysis of the industrial structure of thirteen countries between 1801 and 1958, Kuznets (1966a) found sys­

tematic differences in sectoral productivity growth. He

noted that while labor productivities in agriculture and

manufacturing grew at rates equal to and greater than the

national rate, respectively, service sector productivity

grew at a slower rate than that of the economy as a whole.

In a later study (Kuznets, 1971), he confirmed that there 29 was an international trend towards a rising ratio of prod­ uct per worker in industry to that in services.

Fuchs (1968) examined labor productivity trends in the

U.S. between 1929 and 1965. He found that of the three sectors, agriculture had the highest rate of productivity growth and services the slowest. These findings were con­ sistent with the observed trends in sectoral allocation,

i.e. the decline in primary labor allocation and the rise

in service labor allocation over time.

Numerous authors have attempted to explain the sectoral variations in labor productivity and labor productivity growth. The differences are most often attributed to four factorst

(1) inter-sectoral differentials in factor intensity;

(2) inter-sectoral differentials in the elasticities of

factor substitution;

(3) inter-sectoral differentials in technological growth

rates; and

(4) inter-sectoral differentials in economies of scale.

2.3.2.1 Differentials in Factor Intensity

It is often contended that tertiary production is more

labor intensive than secondary production (Oberai, 1979;

Squire, 1979). This implies that an increase in tertiary

output will generate more additional employment than a sim­

ilar increase in manufacturing output. However, this 30

observation is inadequate in explaining why there are changes in the relative allocation of labor during develop­ ment. In other words, if the factor-intensities of the

sectors were constant over time, changes in output would

not alter the sectoral share in employment. What needs to

be demonstrated is that tertiary production is becoming

increasingly labor-intensive relative to manufacturing, or,

conversely, that capital intensity in the manufacturing

sector is advancing more rapidly than that in the services

sector.

Changes in factor intensity over time are primarily due

to the changes in the prices of capital and labor during

economic development (Herrick and Kindleberger, 1983: 227).

In order to examine how a change in relative factor prices

differentially impacts each factor intensity,• we need to

examine the differentials in the sectoral elasticities of

factor substitution.

• It is interesting to note that if we assume that factor intensity is constant in the short run, changes in the relative cost of capital and labor can influence the sec­ toral allocation of labor through their effect on the relative prices of sectoral output. For example, if the interest-wage ratio declines, there will be a relative decline in the- cost of the output of the more capital- intensive sector. Changes in sectoral output prices, in turn, affect sectoral demand as noted in Section 2.2.1. 31

2.3.2.2 Differentials In Elasticities of Factor

Substitution

The elasticity of factor substitution measures how the proportion of capital and labor used in production changes as their relative availability changes. Formally, it is defined as the percentage change in the capital-labor ratio brought about by a percentage change in the interest-wage

(or factor price) ratio (Mansfield, 1979» 385).

Economic theory suggests that development is accompanied by a decline in the factor price ratio as capital becomes

increasingly available relative to labor (Harrod, 1939;

Domar, 1946). All else being equal, then, the greater a sector’s elasticity of factor substitution, greater will be the substitution of capital for labor in that sector. As a country modernizes, consequently, we would expect an

increase in the relative labor force share of the sector with the lowest elasticity of factor substitution, and a decline in the relative employment in the sector with the highest elasticity of factor substitution.

Arrow, Chenery, Minhas and Solow (1961) calculated the

elasticities of factor substitution for several industries

in each of the three sectors using U.S. and Japanese data.

They found that the elasticity of substitution between cap­

ital and labor was the highest in the primary sector and

the lowest in the tertiary sector. Their findings implied 32 that a rise in capital per worker (the capital-labor ratio) would induce, per unit output, the greatest decline in pri­ mary sector employment and the least decline in tertiary sector employment. The relative allocation of labor in the primary sector, as a result, would increase and that in the tertiary sector decline with economic development.

Guadagni (1964) applied the methodology of Arrow et al to calculate sectoral elasticities of factor substitution in Argentinian industries during the 19S0-61 period. His results were qualitatively similar, i.e. he found that the agricultural sector had the highest elasticity and the ser­ vices the lowest.

2.3.2.3 Differentials in Technological Growth Rates

Technological growth implies an increase in technical efficiency, i.e. the ability to produce the same amount of output with fewer inputs of capital or labor or some combi­

nation of the two (Herrick and Kindleberger, 1963i 222).

Assume, initially that technological progress is factor

neutral, i.e. its benefits accrue equally to capital and

labor such that their marginal productivities rise by the

same percent. In such a case, the greater a sector's tech­

nological growth rate, the lesser will be its increase in

relative employment per unit growth of output. It is fre­

quently contended that technological growth is faster in

agriculture and industry than in services (Clark, 1957; 33

Kuznets, 1966a). All else being equal then, technological progress will result in an increasing labor allocation in the tertiary sector.

The findings of Fuchs (1964) support this contention.

He found that in the U.S., the rate of technological growth

in industry exceeded that in services by as much as O.S percent per year during the 1929-61 period. According to him, this differential was one of the main reasons for the superior labor productivity in manufacturing. Similar con­ clusions were drawn by Ramos (1970) for Latin America and

Berry (1978) for Colombia. Berry statesi

In factory manufacturing, improved technology has made factory productivity relatively high by his­ torical standards ..... Technological change in services...has been relatively limited. In com­ merce, apart form supermarkets and refrigera­ tion. ..little has happened. (p. 218)

The slower rate of technological growth in services is

generally thought to be the result of the lower incentive

it provides to innovators. Ramos suggests that this may be

due to two reasons. Firstly, since tertiary production is

generally small-scale (a fact supported by others such as

Stanback et al), "the benefits of innovation accrue only

fractionally to the innovator since his share of the over­

all market for the product is apt to be small" (Ramos,

1970t 167). Secondly, since trade in services is much low­

er than that in manufactures, it has a lesser stimulus of

competition for innovation. 34

So far we have assumed that technological growth is fac­ tor neutral. In reality, technological change usually saves labor and capital in different proportions, i.e. it is factor biased (Herrick and Kindleberger, 1983t 224).

Modern economic growth has, for the most part, been accom­ panied by capital-biased (or labor saving) technological growth. In terms of sectoral employment, this implies that the more capital biased a sector's technological growth, the greater will be its employment decline per unit output

(as usual, all else being equal.) The implications of inter-sectoral variations in factor savings due to techno­ logical progress upon relative employment in services are discussed by Ofer (1967)i

We have no clear evidence of the nature of tech­ nological change in services, but what there is suggests that even if improvements in services are in themselves labour-saving and capital­ consuming, the improvements in other branches are even more so; in effect, therefore, technologic­ al change in services is relatively labour­ consuming and capital saving. (p. 21)

2.3.2.4 Differentials in Economies of Scale

¥ High labor productivity in the manufacturing sector rela­ tive to the tertiary sector is also attributed to the greater availability of economies of scale in the former.

Economies of scale refer to the decline in cost per output that occurs within a production unit accompanying increases in the volume of output. These economies result from the 35 increasing division of labor, the use of specialized equip­ ment, lowered costs associated with massing inventories, and the opportunity for new design (Herrick and Kindleber­ ger, 1983*240-241). They can be realized at the plant, firm, and urban/regional level.

The nature of manufacturing activities allows the secon­ dary sector to take the greatest advantage of scale econo­ mies. At the plant level, goods production can be broken up into a series of standardized processes that lend them­ selves to the division of labor and the use of specialized machinery. Economies at the firm level are also observed with the specialization of functions such as product devel­ opment, financing, advertising and marketing. Agglomera­ tion economies are seen in the lowered costs of specialized labor associated with an expanding urban labor market, and lower costs of infrastructure provision.

Tertiary production, in contrast, is less able to ben­ efit from scale economies in a similar manner. Economies of scale at the "plant level" are difficult to realize, as noted by an OECD report*

Services are highly personal and depend very much for their adequate performance and quality on the provider, but the consumer may also have a role to play in this. A doctor often depends on the co-operation of his patient and is greatly assisted if he is given a clear outline of the symptoms and medical history. In view of these personalized variables it is very difficult to standardise or even control quality... (OECD, 1966*22-23). 36

Agglomeration economies, although present, are not as extensive in tertiary production as in the secondary. This

is due to the nature of service activity which requires it to be close to the consumer. Thus, an overconcentration of persons/establishments rendering a particular service can significantly reduce the market size of each.

It is mainly at the plant level that tertiary production has enjoyed economies of scale. Numerous authors have reported the increasing replacement of small-scale, local businesses by multi-establishment enterprises such as fast food, supermarket, real-estate brokerage, and hotel chains

(Stigler, 1956; Kuznets, 1966b; Chandler, 1977; Stanback et al., 1981). CHAPTER III

SECTORAL SHIFTS AND THE SERVICE SECTOR IN THE

THIRD WORLD

The sequence of sectoral shifts of the labor force observed by Fisher and Clark, and later formalized into a 'stage* model by Fourastie and Lengelle, was based upon the obser­ vation of industrialized country economies. The pattern, however, has been generalized to the point that all econo­ mies, regardless of their historic circumstances, are seen to be evolving from an agrarian orientation to a service orientation, almost as a natural or an inevitable conse­ quence of economic growth.

Recent evidence from the LDC's suggests that the pattern unfolding there deviates from the one observed historically in the MDC's. The differences have been observed both in the overall sequence of sectoral shifts as well as in the manner of growth of the service sub-sectors. The first part of this chapter will discuss these deviant patterns.

The observation of alternative patterns of sectoral shifts has prompted a re-examination of traditional theory, and its application to less developed economies. The sec­

- 37 - 38 ond part of this chapter will discuss several of the major themes that have emerged in an attempt to account for these unprecedented trends.

3.1 DIFFERENTIAL PATTERNS OF LABOR ALLOCATION IN THE

LDC* S

The body of literature describing sectoral labor force shifts in the LDC's has been growing rapidly since the

1950's when relatively reliable and uniform data began to become available. These data indicate that the Third World employment patterns differ from those observed historically

in Europe and North America in two main ways.

1. The non-agricultural sector of the LDC's has been dom­

inated by services, even in relatively early stages of

development. The historical development of the MDC's,

in contrast, exhibited a much more robust industrial

sector growth in comparative stages, with service sec­

tor domination of the labor force occurring only after

industrial maturity had been achieved.

2. Within the tertiary sector, there has been an unprece­

dented and sometimes dramatic increase in the employ­

ment in the low productivity, old services. This

expansion is occurring at the same time as the new

services in the modern sector are undergoing a consis­

tent albeit a considerably more modest growth. 39

3.1,1 Sequence of Sectoral Shifts and Tertiary

Hypertrophy

Empirical evidence of the 'overgrowth' of services in early stages of development has been extensively documented since the 1950's. A cross-sectional study of the shares of the three sectors in national employment was undertaken by Kuz- nets {1957). The study indicated that the dispersion of the share of services between the more developed and the developing countries is less than that for agriculture and manufacturing, i.e. there was an unusually high percent of labor engaged in the Third World service sector. Kuznets considered such a pattern to be deviant from 'normal' structural development where service expansion is preceded by industrial expansion.

In a study of the world labor force between 1880 and

1960, Bairoch and Limbor (1968) confirmed that, "on the whole, the structure of the labour force in the developed countries fit fairly well into the pattern emphasised by

Colin Clark, which Jean Fourastie has adopted and developed in his studies, namely a gradual shift of the labour force from agriculture, first to industry and then to services"

(p. 320). They noted, however, certain distortions in the

Third World labor allocations. Although the 1960 Third

World economy had an agricultural labor share equivalent to that of Western Europe and North America circa 1810, Bair- 40 och and Limbor found that its tertiary labor share was equal to that observed in the advanced regions in 1890, for trade and banking, and 1840-50 for transportation and ser­ vices. In contrast, the secondary sector in the Third

World lagged behind, possessing the same proportion that the MDC's did in 1800. This they termed as the "hypertro­ phy'’ of services. In a more extended study, Bairoch (1975) reiterated these findings.

Kuznets (1971) compared long term (1800-1960) changes in the industrial structure of the labor force in twenty-five countries, eight of which are LDC's. He observed that the initial share of labor in agriculture in the presently developed countries, prior to entry into modern economic growth, was generally lower than that in many LDC's today.

He also found that the employment share in services rose before that in industry in most of the Third World coun­ tries examined.

A similar study by Turnham (1971) found that the decline in the share of labor in the agricultural sector of most

LDC's between 1947 and 1967 has been accompanied by a con­ siderably larger increase in commerce and 'other services' than in industry. Turnham contrasted this pattern with the sectoral shifts in several MDC's between 1801 and 1911, where the share of labor in industry grew more rapidly, and consistently accounted for a greater percent of the labor force than the share of labor in services. 41

Sabolo (1975a) examined sectoral labor force shifts in

the major world regions between 1960 and 1973. He noted

that although the labor share in agriculture declined in

the less developed regions, the absolute numbers in that

sector showed an increase. This was in contrast to the pattern observed in the more developed regions, where both

the agricultural labor share and the numbers engaged in

agriculture declined.

As regards the non-agricultural sectors, Sabolo found

that the MDC patterns conformed to the Fisher-Clark model,

i.e. services growing to outrank industry only after the

peaking of the secondary sector. In contrast, the LDC's

showed a considerable expansion in services long before

their industrial sectors had reached maturity.

In a more extended study for the International Labour

Office, Sabolo (1975b) traced back service sector trends in

selected developed and developing countries to the begin­

ning of this century. He noted that in the 1900-1910 peri­

od, the developed countries had less than half their popu­

lation in the primary sector. Thus, he observed, "even

before entering the phase of modern industrialization that

followed the First World War, all these countries had

already advanced a long way along the road towards absorp­

tion of the agricultural labour force" (p. 10). The labor

force in the non-agricultural sectors of these same coun­ 42

tries grew at comparable rates until the 1930's, when, in most cases, the tertiary labor allocation began to outpace secondary labor allocation. Sabolo concluded that these

trends confirmed the Clark hypothsis.

In the less developed regions, Sabolo noted that in 1900

the tertiary labor share was already larger than the secon­

dary labor share. He found a high negative correlation

between the shares of employment in the primary and the

tertiary sectors, suggesting direct transfers of labor from

agriculture to services. Given that the growth of the ser­ vice sector occurred prior to the appearance of a strong,

labor-absorbing secondary sector, Sabolo concluded that the

Fisher-Clark hypothesis was weak in its application to the

contemporary development of Third World countries.

The findings of Kuznets, Bairoch and Limbor, Turnham and

Sabolo, have been supported by a number of similar interna­

tional comparative studies, such as those by Blades et al.

(1974) for the OECD, and by Squire (1979) for the World

Bank. In addition to these, there have been several

regional studies of sectoral shifts that have highlighted

the divergent patterns characterizing recent Third World

development.

The most extensive documentation of labor force alloca­

tion at the regional level is that for Latin America. One

of the first comprehensive studies was done by the Economic 43

Commmission for Latin America (ECLA, 1957). The study examined the employment structure in Latin America between

1945 and 1955 and compared it to that of other world regions. It found that the Latin American economy was characterized by < i > a predominance of primary labor, and

(ii) higher numbers in services than in industry. As a result, the service to industry ratio in Latin America was close to the level found in many European countries, a fact not consistent with the Fisher-Clark hypothesis.

A subsequent report by the ECLA (ECLA, 1965) extended the earlier findings. It found that during the 1955-1962 period, the labor force absorption in services grew even more rapidly than during the 1945-55 decade. Whereas before 1955, manufacturing and services had absorbed the displaced agricultural labor in roughly equal proportions, after 1955 services absorbed the bulk of the labor outflow from agriculture. Once again, a comparison with the advanced countries indicated that at comparable levels in their development, the goods-producing sector and not the services sector had been the main provider of employment in the urban areas. Similar findings were reported by Jones

(1968).

Jaffee (1959), examined the labor force patterns in Mex­ ico. He found that between 1900 and 1950, the share of service employment rose by 16 percent points to double its starting level. In contrast, the manufacturing employment share remained practically unchanged.

Ramos (1970) is credited with one of the most thorough investigations of the Latin American labor force. He exam­ ined structural changes in fifteen Latin American countries between 1950 and 1960, and noted a strong movement of labor from agriculture to services, bypassing the secondary sec­ tor. Thus, whereas the share of employment in services increased in all countries, that in industry changed unevenly. Furthermore, almost all of the decline in prima­

ry labor allocation was taken up by growth in the tertiary sector.

Studies of other developing regions show that the emerg­

ing patterns are qualitatively similar to those observed in

Latin America. Mehta (1961) compared the structure of the urban labor force in Burma in 1953 to that of the United

States in 1950. He found that the tertiary labor alloca­

tion in Burma exceeded that in the USA even though the U.S.

had a substantially higher GNP per capita. Hypertrophy in

Israel's services sector was reported by Ofer (1967).

Finally, Oshima (1971) reported a direct movement of work­

ers from the primary to the tertiary sector in some Asian

countries.

It is interesting to note that although tertiary sector

hypertrophy is the most evident in the contemporary devel­ 45 oping countries, it has been observed, albeit to a lesser extent, in the historical development of some advanced countries, Clark (1957) found that during the 1900-1930 period in Denmark, the share of agriculture declined and industry stagnated, resulting in a growth of service labor share from 30 to 39 percent. This growth in services was arrested only when industrialization was renewed after

1930. Evidence of service sector 'bulging' was also found in Australia and New Zealand. Clark noted that between

1900 and 1930, the employment share in services rose sharp­ ly in both countries. At the same time there was little or no growth in manufacturing, while the share in agriculture declined. It was only after policies of deliberate indus­ trialization were undertaken in the mid 1930's, that ser­ vice sector growth levelled off, and the industrial employ­ ment rose. Brown (1962) reported similar findings in

Japan. Bairoch (1975) contended that hypertrophy in servi­ ces is a phenomenon that "is present to a greater or lesser extent in all countries, but that it is present in the

Third World countries as a whole is most evident" (p. 161).

3.1.2 Service Sector Dualism

Evidence of tertiary sector hypertrophy in the Third World has directed greater attention to the service sector itself. It has been observed that the major growth has occured in tertiary activities variously referred to as 46

"traditional'*, "old", or "informal". These are in contrast

to the "modern", "new", or "formal" occupations that domi­

nate the MDC tertiary sectors, but have shown only a modest growth in the Third World.’ Such a division of the terti­

ary sector emerges from a tradition of dualistlc models of

Third World economies dating back to the works of Boeke

(1953), Lewis (1954) and Fei and Ranis (1964).

Numerous studies have attempted to define the nature of

the dual sectors, and their employment characteristics.

Early studies by the Economic Commission for Latin America

(ECLA, 1957; ECLA, 1965; Jones, 1968) characterized the

sectors based upon productivity differentials. They drew a

distinction between the "dynamic" sectors of the economy --

those that influence real rather than simply monetary eco­

nomic growth -- and the "slow growth" sectors, consisting

primarily of services and artisan industry.

Turnham (1971), in contrast, distinguished between mod­

ern and traditional occupations based upon characteristics

of employment. He defined modern employment as that repre­

sentative of a developed labor market and characterized by

institutionalized working conditions, predominantly

employer/employee relationships based upon contractual

agreements with respect to hours of work and remuneration.

* It should be mentioned that dualism is not restricted to the tertiary sector; the primary and secondary sectors exhibit the dichotomy between traditional and modern com­ ponents as well, albeit to a lesser extent (Mazumdar 1976)666; Tokman 1978)1066). 47

Traditional employment consisted of various activities organized around the household as a working unit, in which the employer/employee relationships are substituted by self employment or family labor.

A classification similar to that of Turnham was adopted by Keith Hart. In an influential paper on urban employment

in Ghana, Hart (1973) drew the distinction between the

"formal", or wage-earning sector and the "informal" or self-employed sector. According to him, the key difference was whether or not labor was recruited on a "permanent and regular basis for fixed rewards", or what he termed the degree of rationalization of work. His study indicated that tertiary sector activities provided the greatest

informal income earning opportunities to the incoming mig­

rants to Accra.

Another type of classification proposed to distinguish

between the modern and the traditional sectors is based upon the type of service activity. Bauer end Yamey (1951) pointed out that while some services may positively associ­

ated with economic development, others show a negative

relationship. In this regard, Katouzian's classificatory

scheme of tertiary sector activities is very useful (Katou-

zian, 1970). He propsed a tripartite division of the ter­

tiary sector which Mouley and Costa (1974) and Sabolo

(1975a) adapted to the dualistic framework. Services labelled as "old" by Katouzian consist of

"those activities which flourished before industrialization and whose importance and contribution has almost continu­ ously declined since then" (p. 367). The major components of this category are domestic and personal services. These services were contrasted with the "new" and "complementary" services which expand as a result of modernization. Katou­ zian 's definition of the new services coincides most close­ ly with Fisher's definition of tertiary products; it includes educational, medical, tourist, and entertainment occupations, services whose demand is highly sensitive to and an increasing function of income per capita. Services labelled complementary were those that are closely linked to the process of industrialization such as banking, finance, goods transportation, and wholesale and retail trade. Katouzian suggests that these services expand sharply after the onset of industrialization, but later slow down due to the relative decline in manufacturing demand and increased productivity. Mouley and Costa noted that these two broad categories closely approximate the distinction Prebisch (1970) makes between 'labor expelling activities' and 'labor absorbing activities.'

Weeks (1973, 1975) contends that the difference between the informal and formal activities hinges about their rela­ tionship with the state. According to him, the state plays 49 an active role in the formal sector by restricting competi­ tion and providing the formal sector firms privileged access to resources. In contrast, the informal sector is characterized by an absence of state intervention.

Mazumdar (1976) agrees that the formal sector is pro­ tected through government policies and trade unionism. He notes that the wages and conditions in the formal sector are not available to all urban job-seekers unless they cross certain barriers. These barriers are absent in the

informal sector, which is characterized by ease of entry, and a lack of long term contractual arrangements. Mazumdar

feels that the differences derive from a dichotomy within

the urban labor market rather than between enterprises or workers. Squire (1979) adopts a similar labor market

approach in his analysis of urban dualism.

The biased growth of the traditional/informal/old seg­

ment of the tertiary sector in many LDC's has been docu­

mented in a number of studies. ECLA (1965) found that dur­

ing the 1925-1950 period in Latin America, the slow growth

sectors absorbed a larger percent of the labor force incre­

ment than did the dynamic sectors. The difference

increased even more in 1955-1962 when over three-fourths of

the additional labor entering the urban labor market was

absorbed in the slow growth sectors. 50

In his examination of eighteen less developed countries since 1960# Turnham found a strong positive relationship between shares of modern, wage employment and the level of' economic development. Similar conclusions were reached by

Emi (1969), Bhalla (1970), and Hopkins (1983). Emi exam­

ined the occupational structure in the service sectors of thirty-six countries circa 1960, and found that there was a systematic shift from self-employment to wage/salaried employment with rising GNP per capita. Bhalla found the same to be true in studies of of Taiwan between 1961 and

1966, and the Philippines between 1958 and 1964. In a study of the major less developed regions between 1960 and

1980, Hopkins noted the trend, over time, of self employ­ ment giving way to wage employment.

3.2 EXPLANATIONS OF TERTIARY SECTOR HYPERTROPHY AND

DUALISM

The explanations for the deviant patterns of sectoral

shifts in the LDC’s, and for the overdevelopment of servi­

ces fall into two major categoriest

1) those that view the Third World service sector as a

predominantly * residual * sector that has grown because

there is a expanding urban labor supply but a shortage

of industrial job opportunities; and 51

2} those suggesting that the tertiary sector labor force

has grown rapidly because the Third World countries

are presently experiencing a greater demand for servi­

ces than did the MDC's at comparable levels in their

development.

3.2.1 Service Sector Growth as Residual

The majority of explanations view the creation and perpetu­ ation of the informal services sector as a result of the imbalance between the urban labor supply and the industrial sector labor demand. According to Mouley and Costa (1974), the old services "act the role of a sponge" and absorb labor unable to find work elsewhere. The general assump­ tion is that these services do not make a positive contri­ bution to development in themselves, but merely spread a fixed amount of work and income amongst a growing number of workers. This view is supported by Bhalla (1970), Friedman and Sullivan (1974), and Fan (1978) among others. Bhalla, for example, contends!

a small proportion of tertiary employment in the less developed labor surplus economies it; a func­ tion of the income elasticity of demand for ser­ vices. The bulk is to be found in such tradi­ tional and unorganised services as shoe-shining and petty retail trades bearing no observable relationship to effective labour demand. Here the supply of labour creates its own opportuni­ ties by sharing out a given total amount of work. (p. 520) 52

The literature based on this theme generally falls into two categories depending upon its exact focus:

a) that which focuses upon problems related to formal

sector labor demand, and

b) that which stresses upon factors related to urban

labor supply.

3.2.1.1 Demand-Oriented Explanations

These explanations hinge about the 'failure of manufactur­ ing* to provide adequate job opportunities to the urban labor force. This failure is attributed by some to the high capital intensity of the secondary sector, and by oth­ ers to the imperfections in the labor market that make entry into the formal industrial sector difficult. These alternative themes are discussed below.

Capital Intensive Industrialization

It is frequently contended that the low labor absorption

rates in industry are a consequence of the adoption of highly capital intensive manufacturing techinques by the

LDC’s. In Latin America, for example, ECLA (1957) found that industrial production between 1950 and 1955 grew much

faster than industrial employment as a result of high capi­ tal intensity in industry. The secondary sector, they

therefore noted, "has been unable to absorb an adequate proportion of total incoming labor, the majority of which

is forced into the services sector” {p. 25). Baer and Herve (1966) examined the industrial sectors of

India, Egypt, and a number of Latin American countries in the 1950's. Their study found a lag between industrial product and industrial employment. It further showed that not only had there been an across the board increase in mechanization, but that industries with higher capital intensities had grown faster. This led Baer and Herve to conclude that the low labor absorption rates in manufactur­ ing were primarily the consequence of the increasing capi­ tal intensity in that sector. Similar observations were made by Ramos (1970).

Bairoch and Limbor (1968) attributed hypertrophy of the service sector to the high productivity and capital inten­ sity of Third World industry and the generally low consump­ tion levels. These conditions reduce the labor absorption in manufacturing and cause the swelling of the residual service sector.

The ILO Kenya mission report (ILO, 1972) found that Ken­ ya's import substitution policies favored the growth of capital intensive, foreign owned manufacturing plants.

These were able to absorb only a small percent of the rural labor influx, forcing migrants to seek work in the informal services sector of the economy. Similar findings are reported in a number of city studies conducted by the ILO

World Employment Program (see ILO 1974, 1975a, 1975b, 54

1975c, 1976b, 1977, 1978). A comprehensive discussion of the ILO reports is found in Moser (1978),

The adoption of capital intensive techniques in the face of an abundant, albeit unskilled labor ^supply highlights a unique dilemma in the Third World. Whereas the presently developed countries began their early industrialization along fairly labor intensive lines (Baer and Herve, 1966), industrialization efforts by the LDC's have to make use of current technology developed in the MDC's.10 This technol­ ogy is based upon an underlying assumption of an abundance of capital and the scarcity of labor. Singer (1964) notes that unlike countries such as the USA and Germany that ben­ efited as 'latecomers' of the industrialization process by avoiding costs of technological experimentation, today's

LDC's find themselves at a disadvantage because technology has advanced so far as to be remote from their factor endowments.

A very similar thesis is put forward by Nugent and Yoto- poulos (1979), who noted the positive and negative effects of the latecomer status of the contemporary LDC's. On the one hand, the LDC’s have been able to rely on the Import market rather than the slow, expensive process of research and development to bridge the technological gap and begin a

10 The use of older, labor-intensive technologies is not an economically feasible alternative for Third World coun­ tries; see Eckaus (1955), Singer (1964), Baer and Herve (1966), Baer and Samuelson (1981). 55 highly efficient industrial development. On the other hand, they have suffered because the imported technology is not always appropriate to their conditions and cannot be easily adapted to their local requirements. This has led to disequilibrium and dualism in the urban economy.

Dore (1974) argued that "late developer industrializa­ tion starts dualistic in organizational form." He noted that while European industrialization began with little else than small, labor intensive enterprises, the LDC's have, in addition, large, capital intensive multinationals already in place. The latter totally dwarf the small-scale enterprises. In a similar vein, Berry (1978) declared that, "contemporary urbanization occurs in advance of

industrialization than hand-in-hand with it" (p. 213).

Labor Market Imperfections

Some authors contend that the low absorption of labor in

the formal sectors and the consequent burgeoning of infor­ mal services is due to imperfections in the labor market, whereby certain 'barriers' restrict the free entry of labor

into the formal manufacturing sector. This approach was

first proposed in order to explain the continued rural to

urban migration in the LDC's in the face of open urban

unemployment, a phenomenon that cast doubts on the empiri­

cal plausibility of earlier economic models that were based

on the idea of equilibrium at market-clearing wages. Given 56 that a large part of the labor generally regarded as 'unem­ ployed' is usually engaged in informal sector occupa­ tions/ 11 the thesis can shed light on the reasons behind tertiary sector hypertrophy in the Third World.

Harris and Todaro (1970) noted that many LDC's have a set minimum wage for industrial workers. This wage is invariably above the market clearing wage, and cannot be bid down by competing workers. Given that employers hire only upto the point where the marginal productivity of the last worker is equal to the wage rate, there is an excess of labor available at the minimum wage. Those workers that are not absorbed by industry constitute the unemployed or the informal sector labor force.

According to Harris and Todaro, any increase in the state minimum wage would lead to the growth of the 'holding pool' of labor, since (i) industrialists would reduce the number they employ in order to equate the wage rate with marginal labor productivity, and (ii) there would be a greater influx of labor into the urban areas as the differ­ ential between the rural and urban wages increased. Equi­ librium in the system, i.e. full employment (in formal enterprises) at a single, economy-wide wage rate, according

11 It is often suggested that the concept of unemployment, while easy to define in countries where conditions of employment are specified in contractual agreements, is inappropriate in most LDC's. This is because a large percent of the labor force is engaged in informal enter­ prises where the distinction between work and leisure is not quite clear. (See Turnham, 1971 and Squire, 1979). 57

to this model can be restored only by removing the minimum

wage and allowing the free play of market forces.

The minimum wage, however, represents only one type of

market imperfection. Weeks {1973, 1975) cited government protectionism of formal sector enterprises as another cause

of urban market segmentation. He observed that the govern­

ment nurtures the formal sector by giving it privileged

access to resources and reducing the competition therein.

The resultant benefits to labor in terms of wages and work

conditions are fiercely guarded by trade unions, creating

another artificial 'barrier* to the entry into the formal

sector.

3.2.1.2 Supply-Oriented Explanations

A second group of authors stress the role of the labor sup­

ply in the creation of the residual services sector. The

factors most often cited relate to (i) the rapid growth of

the urban labor force, and (ii) the poor skill endowment of

the LDC labor force. Each one of these themes will be dis­

cussed in turn.

Size and Growth of the Urban Labor Force

A number of authors attribute tertiary sector bulging and

the emergence of the informal services sector in the LDC's

to the accelerated growth of the urban labor force. Sabolo

(1975b) explored the effect of population growth and rural 58 to urban migration on tertiary sector labor allocation. He found that while both factors were influential, the growth of employment in the tertiary sector depended primarily on the labor provided through rural to urban migration. Sabo-

lo's findings were supported by Bairoch (1973).

Beier et al.(1975) in a detailed report for the World

Bank found that in the contemporary LDC's, population growth in the urban areas was twice that in the rural areas due to rural to urban migration. Berry and Sabot (1984)

feel that it is this rise in the urban labor supply that

has been the main cause of rising unemployment and informal

sector activities in the cities.

Perhaps the most convincing evidence in support of the

excess labor supply explanation of tertiary sector hyper­

trophy is presented by Bairoch (1975) and Squire (1979),

Bairoch compared the rates of labor force growth and manu­

facturing job increase between 1920 and 1950 in the LDC's

with the same rates for Europe between 1840 and 1870.

Bairoch found that whereas the European industrial sector

had been able to annually absorb 30 to 40 percent of the

incremental urban labor, the industrial sectors of the

LDC's absorbed only 10 to 12 percent. According to Bair­

och, the difference between these rates was

not, however, the result of faster expansion in manufacturing industry in the developed countries (on the contrary the growth of industrial produc­ tion in under-developed countries is higher at present than it was in the developed countries 59

during the nineteenth century), but is due essen­ tially to differences in population growth {Bair­ och, 1975*164).

Squire makes similar comparisons between the LDC's in the

1960-1970 period and the MDC's in the 1900-1920 period. He states, "The major lesson of the historical comparison, therefore, is that the relatively slow transformation of the sectoral composition of the labor force in the LDC's arises from their rapid rate of population growth and not from their low rate of labor transformation, at least by historical standards, in the industrial sector." (p. 13)

An interesting hypothesis is put forward by Singleman

(1978). He suggests that the reason why European industri­ alization was not accompanied by an overgrowth of services was the region's ability to 'export' people to other conti­ nents, especially the Americas. If this 'safety-valve' had not been available, the large numbers of workers released

from agricultural activities would have been channelled

into services. He notes that few LDC's have seen this type and scale of outmigration.

Characteristics of the Labor Force

It has sometimes been suggested that the capital-intensive

industry, labor market imperfections, and excess labor sup­ ply explanations of informal service sector growth make the unwarranted assumption of labor homogeneity. In reality,

the labor force is differentiated along a number of dimen­ 60 sions such as education, training, age and physical abili­ ty. Given that there is a different market demand for each type of labor, it is possible that unemployment can exist for some types of occupations and not others (Squire,

1979). The basic thesis, therefore, is that the absorption of labor into manufacturing has been low because there is an inadequate supply of skilled workers. Hirschman (1958) and Myint (1964), for example, contend that LDC's adopted capital-intensive industrialization only due to this short­ age of skilled labor. According to this approach, there­ fore, the informal service sector has grown only because it has fewer 'human capital1 requirements for its entrants.

A large number of descriptive studies have documented the characteristics of the informal sector labor. Merrick

(1976) provided detailed information on the characteristics of informal sector workers in Belo Horizonte, Brazil. His survey indicated that the sector was characterized by a disproportionate number of workers outside the prime age groups, and persons who have not completed primary educa­

tion. Similar findings were reported by Webb (1977) for

Peru. The ILO city studies for Abidjan (ILO, 1975a) and

Calcutta (ILO, 1974) also highlighted the low educational

and formal skill levels of the migrants engaging in infor­ mal activities. (See Moser, 1978 for a detailed discussion

of these studies.) The Sixty-ninth Session of the Interna­ 61 tional Labour Conference also provided a description of the informal sector workers. They described the labor as being relatively young, with less than six years of schooling

(ILO, 1983).

Mouley and Costa discussed the relationship between edu­ cation and employment. Low levels of education, according to their analysis, contribute to underemployment, and therefore, informal sector growth, in three ways:

1. persons enter the labor pool at a younger age, swell­

ing the labor supply;

2. there is lower demand for formal sector workers such

as teachers and auxilary staff in educational and

training institutes; and

3. there are fewer job opportunities in sectors requiring

skilled workers.

Becker (1964, 1967) and Mincer (1970) Investigated the effect of skill levels upon earnings. Their studies showed

that a large part of the earnings differentials between the

formal and informal sectors were in fact due to differen­

tials in labor skills and education. Gregory (1979) also contends that the wage differentials are due to human capi­

tal differences rather than market imperfections. 62

3.2.2 Service Sector Growth as Demand Determined

In recent years, a number of authors have come out in open criticism of the 'residual sector' hypothesis of tertiary growth in the LDC's. The disillusionment with the 'excess labor' explanations grew along with the mounting evidence that informal sector workers usually do as well, if not better, than industrial sector workers. Much of this evi­ dence was provided by the ILO World Employment Program city studies referred to earlier, as well as the country studies

(ILO 1970, 1972, 1973, 1976a). Surveys of rural to urban migrants have also shown that many persons move to the city with the expectation of finding long-term employment in the traditional sector. These findings run contrary to hypoth­ eses such as that of Harris and Todaro, maintaining that migrants move in hope of finding modern sector jobs, and it

is only those denied these jobs who join the informal sec­ tor (Cole and Sanders, 1985).

Udall (1976) attempted to dispel the 'excess supply* explanations of informal services growth in his study of

Bogota, Colombia. He noted that during the decade

1948-1958, Bogota's urban labor force grew rapidly due to a civil war in the rural areas, while the labor demand

remained virtually unchanged. Rather than there being a

dramatic increase in the share of labor engaged in servi­

ces, manufacturing employment remaining fairly static (as 63 the 'residual sector* hypothesis would have predicted),

Udall found that the sectoral labor allocation showed very little change.

Udall also cited research done by Kamerschen (1969) to support his contention that service sector growth in the

LDC's was not residual, or a result of excess labor supply.

Kamerschen identified all countries considered to be 'over­ urbanized1, i.e. had high shares of labor in the tertiary sector at relatively low per capita incomes. Examining these countries, he was unable to find a systematic rela­ tion between the overurbanization measures and labor supply measures such as rural population densities.

As the residual sector hypothesis of tertiary sector growth became increasingly discredited, researchers began

looking more and more towards demand-centered explanations.

The effect of demand, intermediate, final, or internation­

al, on service sector employment has already been discussed

in Chapter 2. What needs to be established here, however,

is that the present day LDC's have experienced a higher

overall demand for services than did the MDC's at compara­

ble levels in their development. Studies that have

attempted to do this, attribute the higher demand to two

main mechanismst

1. long term economic changes in demand and productivity;

and 64

2. demand generated by the government and government pol­

icies.

3.2.2.1 Long-term Economic Changes

The hypothesis of Fisher and Clark regarding the effects of demand and productivity changes that accompany economic development on sectoral labor allocation have been dis­ cussed at length in Chapter II. It has often been suggest­ ed that the deviant patterns of tertiary growth in the

Third World point not so much to the inappropriateness of these mechanisms as to their differential application in the contemporary LDC's.

On the demand side, Bauer and Yamey (1951) noted that

large income inequalities in a society can distort the

ideally hypothesized patterns of sectoral demand. They contended that in any society, it is unlikely that all per­ sons spend the same share of their income on services, and

that it is the average for the population as a whole that determines the national demand for services. The degree to which this average is pulled up with rising incomes depends greatly upon which sub-group of the population is the main

beneficiary of the income increase. In the Third World

countries, it is observed, the bulk of the demand for ser­

vices is exerted by the rich classes who desire both tradi­

tional services (e.g. domestic servants), and modern servi­

ces (e.g. fancy restaurants, clubs), many of the latter 65 being non-existent when the MDC's underwent their industri­ alization. Furthermore# this demand is growing along with the rising degree of income inequality in the LDC's (Katou- zian, 1970).

Besides a higher household demand for final services,

Third World countries today also experience a greater demand for intermediate services by the industrial sector.

Berry (1978) noted the greater specialization in present day manufacturing technologies as opposed to those that existed during the industrial revolution. According to him, "all large scale technologies imply a number of work­ ers who specialize in the production of a given good or service" (p. 220). In a study of the informal service sec­ tor in Pakistan, Guisinger and Irfan (1980) found that the high wages and employment levels in the sector were partly due to the rapid growth of the formal sector, which created a demand for supporting services. Several other authors have commented upon the close linkages that exist between the formal industrial enterprises and the informal service enterprises {Mazumdar, 1976; Squire, 1979).

Regarding sectoral labor productivities, Galenson (1963) found that there were substantial differentials between manufacturing and tertiary sector productivities in the

LDC's due to the contrast between modern industrial plants and traditional services. In Nigeria, for example, he not­ 66 ed that output per worker in the secondary sector was six­ teen times that of the economy as a whole, while in a developed country like Canada, manufacturing productivity was only ten percent higher that the economy-wide level.

He felt that it was this characteristic of the LDC's that contributed to the unprecedented growth of services. His explanation was two-foldi

1) In general, given the higher productivity of manufac­

turing, the addition of another worker in the secon­

dary sector would increase GNP and overall demand for

the products of all sectors more than would ax addi­

tional worker in the tertiary sector. However, in the

LDC's the effect would be much larger than in the

MDC's, i.e. a unit increase in manufacturing output

would generate a greater absoulute increase in service

demand.

2) Given their much lower relative productivity of servi­

ces, a unit increase in service demand in the LDC's

fosters a greater relative increase in tertiary

employment that an equivalent increase in service

demand does in the MDC's.

Galenson noted, as a result, that while Ghana and the

Federal Republic of Germany both had substantial growth in

manufacturing employment, in the former case there were

seven times as many new jobs In services while in the lat­

ter the ratio was only about one to one. 67

Galenson's arguments were supported by Berry. Comparing contemporary LDC's with MDC's in the 1800's, he suggested that the only difference in the two was that LDC's have a superior technology in agriculture and industry. He went on to show that if (i) technological growth is assumed to be neutral, and (ii) the two economies produced bundles with the same share of agricultural, manufacturing and ser­ vice sector products, then today's LDC's would employ a higher share of the labor force in services.

3.2.2.2 Government Demand

It has been frequently observed that the number of govern­ ment employees in the LDC's is much higher than that observed during the historical development of the MDC's

(Katouzian). Although this does not contribute to informal service growth, it does add to tertiary sector hypertrophy.

Mouley and Costa, for example, noted that in Africa, gov­ ernment employees made up between 38 and 52 percent of the non-agricultural employment. Sabolo reported high percent­ ages in the government sector in Latin America. In Colom­ bia, Berry found that the labor share in government was five to seven percent points above the historical norm set by the MDC's.

Several reasons are advanced for the higher direct hir­ ing by governments in the LDC's. Frequently mentioned is the absence of employment opportunities for high school and 68 college graduates in the other economic sectors. Given the government's 'anxiety to please' (Katouzian) this component of the middle and upper classes, the educated unemployed are absorbed in the . This has resulted in the inflation of the government establishment, causing a form of disguised unemployment (Katouzian, Sabolo).

Other explanations for larger government sectors in the

LDC's hinge about the prestige and security provided by this sector (Sabolo), and the patronage system. The size of the armed forces in the developing countries is often larger that that observed historically in the west (Berry).

According to Katouzian:

nearly all of the underdeveloped countries of today, the army plays an important role, but mainly as an instrument of internal rather than external security. As such, it fulfils a func­ tion that has hardly ever been assumed by the armies of the West....It therefore takes a large share of the national income and the labor force, not comparable in size to those of the Western countries on the whole, at the dawn of their industrialization (pp. 374-375). CHAPTER IV

SECTORAL LABOR ALLOCATION - RECENT PATTERNS

This chapter examines international patterns in sectoral labor shifts since I960, focussing upon the temporal chang­ es and the regional variation in the patterns. The compar­ isons are based upon data provided by the World Bank, and cover 122 countries. A complete listing of the data is provided in Appendix A.

4.1 WORLD PATTERNS

Table 4 shows the distribution of the world's economically active population by the three economic sectors, agricul­ ture, manufacturing and services, for the years 1960, 1965,

1970, 1975 and 1980. In 1960, over half of the world's labor force (57.6 percent) was engaged in agriculture, for­ estry and fishing, almost one fourth in service-related activities, with manufacturing accounting for the remain­ der. By 1980, the agricultural sector, although still the largest , had less than 45 percent of the labor force, and the shares of services and manufacturing had grown to almost 33 and 23 percent respectively.

- 69 - 70

Table 4

Sectoral Distribution of the World*s Labor Force. 1960-1980

Year Percent of Labor Force

Agriculture Manufacturing Services

1960 57.61 18.48 23.91

1965 54.52 19.46 26.02

1970 51.44 20.26 28.31

1975 48.08 21.42 30.50

1980 44.61 22.73 32.66

Table 5 presents the annual growth rates, in percentage points, of the sectoral labor allocation during the five- year periods 1960-65, 1965-70, 1970-75 and 1975-80. These growth rates were calculated simply by computing the abso­ lute increase in the sectoral shares over the period and dividing it by five, and were used instead of, for example, exponential or logarithmic growth rates for two reasons.

Firstly, it can be argued that the 5-year periods over which these rates are averaged are short enough for the sectoral allocation trends to be represented in a linear manner. Secondly, these growth rates have a desirable additive property over the three sectors, such that the net growth rate is zero. This is intuitively appealing when 71 dealing with labor force shares which always add up to 100 percent. However, it should be kept in mind that percent­ age point growth rates do not account for the size of the sector, i.e. a sector growing from 5 to 10 percent would

report the same growth rate as one whose share grows from say 55 to 60 percent. Thus, it is important to examine sectoral labor shares along with sectoral growth rates.

The most dramatic changes were evidenced in the agricul­

tural sector, which declined at a rate of over 0,6 percent points per year in every period. The decline in agricul­

tural labor allocation was balanced by non-agricultural

labor growth, with services growing faster at an average

rate of over 0.4 percent points per annum. The service

sector therefore absorbed about two-thirds of of the dis­

placed primary sector labor, with manufacturing employing

the balance. While the growth rate of the service sector

remained fairly steady during the period examined, the

growth rates of the manufacturing sector increased from

0.20 percent points per year in 1960-65 to 0.24 percent

points per year in 1975-80. 72

Table 5

Sectoral Growth Rates of the World's Labor Force

Period Growth Rate (percent points p.a)

Agriculture Manufacturing Services

1960-65 -0.62 0.20 0.42

1965-70 -0.65 0.18 0.47

1965-70 -0.67 0.23 0.44

1975-80 -0.65 0. 24 0.41

4.2 MDC VS. LDC PATTERNS

The sectoral distribution of labor varies considerably between MDC's and LDC's,13 as shown in Table 6.

In 1960, over 65 percent of the LDC labor force was engaged in agricultural activities. The second largest share of the labor force, 20 percent, was in services. In contrast, for the same year, agriculture accounted for the smallest share (less than 30 percent) of the economically active population in the MDC's, Manufacturing and services shared the remainder of the labor force in an approximately equal manner (35 percent each).

13 See Appendix B for countries included in each category. 73 Table 6

Sectoral Labor Allocation, 1960 -1980i MDCs vs. LDCs

Region Year Percent of Labor Force

Agriculture Manu f ac tu r i ng Servicef

MDCs 1960 29.83 34.92 35.25

1965 25.82 36.10 38.08

1970 21.68 36.83 41.48

1975 18.18 37.54 44.28

1980 15.18 37.90 46.92

LDCs 1960 67.60 12.57 19.83

1965 64.84 13.47 21.69

1970 62.01 14.36 23.62

1975 58.70 15.69 25.60

1980 55.20 17.27 27.53

The pattern in 1980 remained qualitatively the same in the LDC's, although the agricultural labor share declined to about 55 percent, and services grew to almost 28 per­ cent. In the MDC's, however, services out-grew manufactur­

ing to become the leading sector in terms of labor force

(47 percent). Agriculture further declined to about 15 percent of the labor force. 74

The annual growth rates per sector for the four 5-year periods are given in Table 7, and show a similarly differ­ entiated pattern between the MDC's and LDC’s. While both groups of countries had negative rates of growth for the agricultural sector, in the LDC's the rate of decline

increased from 0.55 percent points per year in the 1960-65 period to 0.67 in 1975-80. In the MDC’s, however, the declines in agriculture apperared to be slowing down (0.80 percent points per year in the first period to 0.60 in the

last}.

Table 7

Growth Rates of Sectoral Labor Shares! MDCs vs. LDCs

Region Period Growth Rates (percent points p.a.)

Agriculture Manufacturing Services O CD o MDCs 1960-65 1 0.24 0.56

1965-70 -0.83 0,15 0.68

1970-75 -0.71 0.15 0.56

1975-80 -0.60 0.07 0.53

LDCs 1960-65 -0.55 0.18 0.37

1965-70 -0.58 0.19 0.39

1970-75 -0.66 0.27 0.40

1975-80 -0.67 0.30 0.37 75

In the case of manufacturing, the sectoral growth rate in the LDC's increased from 0.18 to 0.30 percent points per annum. For the MDC’s, the growth of the manufacturing sec­ tor appeared to be levelling off, with the sectoral growth rate declining from 0.24 percent points per year in 1960-65 to 0.07 in 1975-80. The service sector in both groups of countries showed relatively stable growth - around 0.38 percent points per year in the LDCs and 0.56 in the MDC’s.

4.3 SECTORAL SHARES BY LDC REGION

Within the LDC’s, the distribution of labor force by sector varied considerably from region to region. Table 8 pres­ ents the statistics for labor force allocation between 1960 and 1980 in the developing countries of Latin America,

Africa, and Asia.11

The highest shares of primary labor force are observed in Africa, which, in 1980, had almost 68 percent engaged in that sector. The percentages of the economically active population in the non-agricultural sectors were 13 and 19 percent in industry and services respectively, the lowest allocations in these sectors of the three regions.

The pattern is qualitatively similar in Asia, where too the agricultural sector is the largest employer followed by services and industry. However the percentage of the eco­ nomically active population in agriculture, at 50 percent

13 See Appendix C for a list of countries in each region. 76

Table 8

Sectoral Labor Allocation 1960-1980, by LDC Region

Region Year Percent of Labor Force

Agriculture Manufacturing Services

Latin 1960 49.95 19.73 30.32 America 1965 46.69 20.76 32.55

1970 43.42 21.74 34.84

1975 39.71 22.53 37.76

1980 36.26 23.19 40.55

Africa 1960 78.88 7.98 13.14

1965 76.43 8.95 14.63

1970 73.74 10.00 16.26

1975 70.70 11.49 17.81

1980 67.62 13.20 19.18

Asia 1960 63.55 14.24 22.21

1965 60.71 14.90 24.39

1970 58.20 15.38 26.42

1975 54.78 16.92 28.30

1980 50.98 18.92 30.11 77

in 1980, was significantly lower than that in Africa, Ser­ vices accounted for 30 percent and manufacturing the

remaining 19 percent.

Latin America is the only region where, presently, agri­ culture is not the dominant sector. In 1980, the services sector of Latin America was the largest employer, account­

ing for over 40 percent of the labor force, and agriculture was second at 36 percent. Although the industrial sector was the smallest of the three as in the other regions, Lat­

in America in 1980 was by far the most industrialized of

the developing regions, with the secondary sector engaging

23 percent of the economically active population.

The sectoral labor force growth rates by region are pre­

sented in Table 9. All regions exhibit an acceleration in

the rates of agricultural labor decline, with Asia report­

ing the highest decline -- 0.76 percent points per annum in

the 1975-80 period. Although Latin America was the region

with the highest secondary labor allocation, the most rapid

increase in the manufacturing labor share was seen in Asia.

Here, the labor absorption rates into manufacturing grew

from 0.13 percent points per year in 1960-65 to 0.40 per­ cent points per annum in 1975-80. Latin America, in con­

trast, showed an actual slow down in the rates of secondary

labor absorption from 0.21 percent points per year in the

first period to 0.13 percent points per year in the last

period examined. 78

Table 9

Growth Rates of Sectoral Labor Shares by LDC Region

Region Period Growth Rates (percent points p.a.)

Agriculture Manufacturing Services

Latin 1960-65 -0.65 0.21 0.45 America 1965-70 -0.65 0.20 0.46

1970-75 -0.74 0.16 0.59

1975-80 -0.69 0.13 0.56

Africa 1960-65 -0.49 0.19 0.30

1965-70 -0.54 0.21 0. 33

1970-75 -0.61 0.30 0. 31

1975-80 -0.60 0.32 0.28

Asia 1960-65 -0.57 0.13 0.44

1965-70 -0.60 0.16 0.44

1970-75 -0.68 0.31 0.38

1975-80 -0.76 0.40 0.36

The service sector growth rates also showed regional differentiation. Services grew the fastest in Latin Ameri­ ca, where, in the 1970-80 decade their growth was almost

0.6 percent points per annum. The corresponding figures

for Asia and Africa were 0.37 and 0.29 respectively. It is 79 interesting to note that for both Asia and Africa, the ser­ vice sector growth between 1970 and 1980 was generally low­ er than in the 1960-70 decade.

4.4 INTRA-REGIONAL VARIATION IN SECTORAL SHARES

Despite the distinctive regional patterns observed in the previous section, there is considerable variation within regions if country-level data are examined. The figures for the sectoral labor allocation between 1960 and 1900 for selected countries are presented in Table 10.

Although Latin America, as a region, had only 40 percent of the labor force engaged in agriculture in 1980, in Haiti and Honduras agriculture accounted for the bulk of the eco­ nomically active population (74 and 63 percent respective­ ly). At the other end of the spectrum, were Uruguay and

Argentina, the former boasting the highest percentage

(32.4) of labor force in manufacturing in 1980 for the Lat­

in American region. In Argentina, the percent manufactur­

ing labor actually declined from 36 percent in 1960 to 28 percent in 1980, with a corresponding growth in service employment which had reached 59 percent of the labor force

in 1980.

Within Africa, most countries had a predominantly agrar­

ian labor force. Extreme examples of this are provided by

Chad and Tanzania which, in 1980, had respectively 95 and 80 Table 10

Sectoral Labor Allocation 1960-1980. for Selected Countries

Country Year Percent of Labor Force

Agriculture Manufacturing Services

Argentina 1960 20.0 35,9 44.1 1980 13.1 28.0 56.9

Bangladesh 1960 87.0 3.0 10.0 1980 74.0 11.0 15.0

Belgium 1960 8.0 47.7 44.3 1980 2.9 41.1 56.0

Chad 1960 95.0 2.0 3.0 1980 85.0 7.0 8.0

Haiti 1960 80.0 6.4 13.6 1980 73.5 7.2 19.3

Honduras 1960 70.2 10.6 19.2 1980 62.6 14.6 22.8

Nepal 1960 95.0 2.0 3.0 1980 93.0 2.0 5.0

South Africa 1960 32.0 30.0 38.0 1980 30.0 29.0 41.0

Syria 1960 54.0 19.0 27.0 1980 33.0 31.0 36.0

Tanzania 1960 89.0 4.0 7.0 1980 83.0 6.0 11.0

United States 1960 6.6 36.4 57.0 1980 2.0 32.0 66.0

Uruguay 1960 20.6 29.5 49.9 1980 10.8 32.4 56.8

Yugoslavia 1960 63,0 18.0 19.0 1980 29.0 35.0 36.0 81

89 percent of their economically active populations in the primary sector. South Africa and Tunisia were amongst the most industrialized of the African nations with, respec­ tively, 29 and 32 percent engaged in the manufacturing sec­

tor. It is interesting to note that even in these two countries, about a third of the labor force was agrarian.

The developing countries of Asia show a similar varia­

tion, ranging from Nepal with 95 percent of its labor in

agriculture and 2 percent in manufacturing to Syria with

corresponding figures of 33 and 31 percent.

Inter-country differences are also evident in the more developed countries. The United States had only 2 percent

of its labor force in agriculture in 1980, and the services

labor force at 66 percent was over twice that in manufac­

turing. In contrast, countries of Eastern Europe such as

Yugoslavia still had as much as 30 percent of their labor

force in agriculture, the rest divided about equally

between the non-agricultural sectors. CHAPTER V

EMPIRICAL ANALYSIS OF SECTORAL SHIFTS

The previous discussions has suggested that there are theo­

retical and empirical rationales for expecting the rela­

tionship between sectoral labor allocation and economic

development to drift over time and geographical region.

This chapter, consequently, presents an empirical analysis

that searches for the existence of such drifts. The analy­

sis presented here:

(1) seeks to define a model relating sectoral labor shares

to the level of economic development, and

(2) searches for the existence of temporal and spatial

variation in the relationship.

5.1 SECTORAL LABOR ALLOCATION AND DEVELOPMENT

The specification of a model of sectoral labor allocation

during development has been approached in a number of ways.

The literature includes analyses based upon complex models

in which several economic relationships and many variables

define the subsystems producing the observed sectoral labor

allocation patterns. Examples include subsystems made up

- 82 - 83 of, among others, production, consumption, and population growth functions, with inclusion of variables such as per capita GNP, urbanization, trade levels, population size, educational attainment levels, labor force participation rates, etc. When accurately specified, these models can provide an insight into the determinants of labor alloca­ tion during development, and their parameters may possess smaller variances. Studies by Moir (1977), Oberai (1979), and Thompson and Stollar (1983) Casetti and Pandit (1987) exemplify this approach.

Simpler models, on the other hand, such as those relat­ ing shares of the labor force in each sector to per capita

GNP, can be conceptualized as reduced forms of unspecified, multi-equation, multivariable development models. Analyses based on these models emanated from Chenery's influential article on the patterns of industrial development (Chenery,

1960), and are seen in the works of Taylor (1969), Chenery and Syrquin (1975), Gemmell (1982) and Jameson (1982).

The use of simpler models involves advantages and disad­ vantages. One disadvantage lies in the fact that the parameter estimates of these models are likely to be less efficient than those that could be obtained from the corre­ sponding, explicitly specified, structural model. This is pointed out by Turner (1969) who wrotet

One possible source of inefficiency..(in simpli­ fied models)..rests with the fact that the equa­ tions actually estimated can be interpreted as 84

reduced forms of some general, dynamic model of economic development. Presumably, proper speci­ fication of the model would lead to more effi­ cient estimation of its reduced forms. (p. 223)

Another shortcoming of the simple models is that by not

explicitly specifying the underlying structural equations,

the causal mechanisms through which economic transforma­

tions occur are not revealed. However, it should be point­

ed out the detailed specification of the models can involve

a number of arbitrary decisions by the researcher.

The advantages of the simpler models lie in the fact

that they can be estimated using a much larger data base.

Furthermore, problems of data comparability, inherent in

all international cross-sectional analyses, are kept to a

minimum when simplified models are used. These underlying

strengths in simple models are particularly important in

studies such as this one where the focus is on model varia­

tion, and provide the rationale for their use in this

study.

The analysis outlined in the following sections is based

upon the theoretical elaborations of Clark and Fisher, as

operationalized by Chenery and others. It is presented in

three parts. The first part is devoted to the definition

of a theoretical model of sectoral labor shifts. The sec­

ond part elaborates the data and the analytical technique

used. Results of the analysis are presented in the third

section. 65

5.1.1 The Sectoral Shlfta Model

Denote the percentages of labor in the agricultural, manu­ facturing and service sectors of a country by A, M, and S respectively. Per capita GNP is used as the principal indicator of development, and is symbolized by Y.

The labor allocation in the agricultural sector is given by i

A = exp (a + b.Y) b<0 (1)

Equation (1) replicates the decline in 'A* with rising val­ ues of ' Y'. The exponential formulation eliminates the possibility of the occurence of non-positive values.

For the manufacturing sector, labor allocation is given by*

M * exp (p + q.Y + r.Ya> q>0 r<0 (2)

Equation (2) incorporates a quadratic term in 'Y' to incor­ porate the initial increase and later decline in manufac­ turing labor share. The exponential form is used once again to ensure that all values of 'M' are positive.

Given that the sum of the labor allocation in the three

sectors must add up to 100 percent, the service labor share

is given by*

S - 100 - A - M (3) 86

Since the service sector experience has differed qualita­

tively between the MDC's and the LDC's, the designation of services as a 'residual' amount has a distinct advanntage

in that no restrictive, a-priori assumption regarding ser­ vice sector behaviour needs to be made.

Equations (1) and (2) are easily linearized by taking

the natural logarithms of each side to givei

In A = a + b.Y (4>

In M = p + q.Y + r.Ya (5)

Given that Equations (4) and (5) are intrinsically linear,

they can be easily estimated using ordinary least squares

regression.

One of the interesting insights provided by the model

relates to the peak level of industrialization. The value,

Y', of Y at which the labor share in the manufacturing sec­

tor is maximum can be readily calculated by computing the

derivative dM/dY and setting it equal to zero. We geti

Y' * -q/2r (6 )

Since the second derivative of M with respect to Y, given

by '2r', must be negative in accordance with the condition

indicated in Equation (2), the second order conditions for

a maximum are satisfied. The value M 1 of M at Y' is given

by i

M' * exp (p - q a/ 4r) (7) 87

5.1.2 Data and Methodology

The analysis was conducted using a cross-sectional sample of 122 countries for 1980-81. Of these countries, thirty-

two were classified as MDC's by the World Bank, giving a proportion that roughly approximates the proportion of the world population living in these countries. Countries with

especially small populations, i.e. less than half million, were eliminated in order to eliminate distortions in the

sectoral shares data.

In order to maintain consistency in data classification

and measurement across countries, all data were taken from

a single source -- the World Bank’s World Tables. The

original sources of the income and labor force statistics

reported by the World Bank, were the International Labour

Office's Yearbook of Labour Statistics and the World Bank

data files.

Per capita GNP figures are measured in constant (1981)

U.S. dollars. The sectoral labor allocation figures use

the following definition of the sectors:

- Agriculture: agricultural activities, including farm­

ing, forestry, hunting, and fishing*

- Industry: industrial activities, including mining and

quarrying, manufacturing, construction, and public

utilities (electricity, gas, water, and sanitary ser­

vices ); and 68

- Services) all economic activities not classified

above.

Since service sector is defined as a 'residual' sector, and the labor engaged in services as a residual labor force, it is important to examine how the total labor force is defined. The World Tables define the total labor force as,

'all economically active persons, including those in the armed forces and unemployed, but excluding housewives, stu­ dents, and other inactive groups.'

All statistical operations were performed using the Sta­ tistical Analysis System (SAS). The regression technique used was forward selection (PROC REG procedure in SAS).

5.1.3 Results

Table 11 presents the regression statistics for the model represented in Equations (4) and (5). All estimated coef­ ficients were significant at the 1 percent level. As expected, the coefficient of 'Y' is negative in the case of the agricultural sector, indicating an exponential decline in the primary labor share. In the case of the manufactur­ ing sector, the coefficient of 'Y' is positive while that of 'Y ' is negative, confirming the tendency of manufactur­ ing labor share to first increase and then decline with rising income.

The substitution of the estimated parameter values into

Equations (4) and (5) givesi 89

Table 11

Rearesslon Statistics for the Sectoral Shifts Model

Independent Regression Coefficients Variables In A In M

Intercept 4.105 2.299 ( 65.399) ( 30.143)

Y -0.0002 0.0003 (-17.328) { 7.687)

Y a -1.7 E-8 ( -5.483)

R a 0.739 0.504

F 300.255 53.408

(Numbers in parentheses are T-values)

A = exp (4.105 - 0.002Y) (8)

M = exp (2.299 + 0.0003Y - 1.7E-8Ya > (9)

The peak labor absorption in manufacturing is equal to 37.4 percent and is achieved at a per capita GNP of $8824. Fig­ ure 1 graphs the sectoral allocation of labor in agricul­

ture and manufacturing, as given by the estimated func­

tions, against per capita GNP.

The relative employment in the service sector at differ­

ent levels of per capita GNP can be readily calculated by

substituting Equations (8) and (9) into Equation (3). Fig- PERCENT LABOR FORCE 40 40 - 60 - 0 2 80 “I 0 0 - - Figure 1,* Labor Allocation in Agriculture and Manufacturing and Agriculture in 1,* Allocation Figure Labor 006000 4000 GNP PER CAPITA 12000 M a n u f a c t u r i n g A g r l c u l t u r a 16000 o VO 91 ure 2 shows the changes in service labor allocation with rising GNP per capita. Rather than a monotonic increase, the service labor share showst

- an initial increase, which peaks in the early stages

of development, at Y equal to approximately $3800;

- an intermediate decline, that bottoms out at Y equal

to approximately $9000; and

- a later, steady increase corresponding to values of Y

greater than $9000.

The early increase in the service labor share is consis­ tent with the expansion in informal activities observed in many of the contemporary Third World countries. From Fig­ ure 1, it is clear that the rise in the manufacturing labor share in the early stages of economic development is much less than the rate of decline in the agricultural labor share. Consequently, there is a growth in the 'residual* service sector at relatively low levels of per capita GNP.

The peaking of the tertiary labor share associated with informal activities is followed by a temporary decline in service labor allocation. This levelling off is coincident with the peaking of the manufacturing labor share, and a reduction in agricultural labor displacement rates. This suggests that a gradual absorption of the informal service labor into manufacturing activities is occurring, and the pace of industrialization is picking up. PERCENT LABOR FORCE 40 20 60- 60 0 0 - “ 4000 iuG2 LbrAlcto i Services in Allocation Labor FigurG_2i ! 1 GNP PER CAPITA 00 12000 8000 --- -- 16000 1 to to 93

The exponential increase in service labor share seen at high per capita GNP's is consistent with the growth of the formal type of services. The figures indicate that this expansion follows the peaking of the manufacturing labor share. This suggests a dynamics described by the Fisher-

Clark hypothesis -- as the manufacturing sector becomes increasingly productive/ there is a movement from the sec­ ondary to the tertiary sector.

5.2 VARIATION IN THE SECTORAL SHIFTS MODEL

The second part of the analysis involves the examination of the stability/drift of the sectoral shifts model with respect to time and region. The investigation of how mean­ ingful functional relationships vary with context has been addressed in a number of substantive areas including the study of demographic trends (Demko and Casetti, 1970; Han­ ham, 1974; Zdorkowski and Hanham/ 1983; Casetti, 1982b), social service delivery (Jones, 1984; Kodras, 1986), and urban dynamics (Casetti, 1973; Ying, 1982; Krakover 1983,

1984; Danta, 1984; Selwood, 1984). Contextual variation of economic development patterns, although addressed in a few studies such as those related to migration (Brown and

Jones, 1985) and diffusion (Casetti and King, 1975; Hanham and Brown, 1976), has been generally neglected in the study of structural changes such as those enumerated in Table 1. 94

The analysis outlined in this section seeks to fill this void by examining parameter drift in the previously articu­ lated relationship between sectoral labor shares and eco­ nomic development. This examination is facilitated by the set of orderly routines provided by the expansion method technique. A short discussion of the Expansion Method fol­ lows .

The Expansion Method

Casetti*s Expansion Method consists of a set of orderly routines for creating and modifying models. Its ability to sequentialize model generation makes it potentially useful for testing hypotheses concerning the drift/stability of a model's parameters, and obtaining functional portraits of the same (Casetti, 1966: 30).

The general procedure first calls for the identification of a relatively simple 'initial model* such ast

y = a + b.x (10)

One or all of the parameters of this model are then rede­

fined as functions of other, selected variables as follows:

a = aO + al.V (1 1 )

b = bO + bl.V (12)

The substitution of these 'expanded* parameters into the

initial model yields the ’terminal' model* 95

y - aO + al.V + bO.x + bl.V.x (13)

As long as the Initial model and the parameter expansions are linear, the terminal model will be intrinsically lin­ ear, its parameters capable of being estimated by an ordi­ nary multiple regression program. Assuming that the param­ eters aO, al, bO, bl are significantly different from zero, they can be substituted back into Equation (13). The resultant equation will provide a varying-parameter por­ trait of the initial model within the domain defined by the selected variable 'V'.

5.2.1 Expansion of the Sectoral Shifts Model

5.2.1.1 The Initial Model

The initial model defined in this study corresponds to the sectoral shifts model outlined in Equations (1) and (2),

the linearized versions of which are given byt

In A * a + b.Y (14)

In M = p + q.Y + r.Y3 (15)

5.2.1.2 The Expansion Equations

The parameters of the initial model are then expanded as

linear functions of temporal and spatial variables.

(i) Temporal Drifti

In order to investigate the drift of the initial model over

time, the parameters of the model were expanded as follows! 96

x - xO + xl.t (16)

wherei x - parameters a,b,p,q,r in Equations (14) and (15)

t *> time in years

The expansion variable ' t' takes on a minimum value of 0 corresponding to the first time period (the earliest cross- section) of the data. Its maximum value, 'N*, is given by the number of years that span between the first and last time period in the data set used.

(ii) Spatial Drifti

To examine the spatial drift in the sectoral shifts rela­ tion, three dummy variables Dl, D2, and D3 were created.

These variables took on values of either 0 or 1 depending upon the regional affiliation of the country or observa­ tion. Table 12 summarizes the values of the three dummy variables in each of the regions defined here.

The expansion equations are given byi

x = xO + xl.Dl + x2.D2 + x3.D3 (17)

where: x = parameters a,b,p,q,r in Equations (14) and (15)

Dl, D2, D3 * regional dummy variables 97

Table 12

Values of Variables Dl, D2. and D3 bv Region

Region Variable

Dl D2 D3

Latin America 1 0 0

Africa 0 10

Asia (excl. Japan) 0 0 1

All others 0 0 0

5.2.1.3 The Terminal Model

The substitution of the expansion equations into the ini­ tial model results in the terminal model. Two sets of equations were derived here, corresponding to each of the two expansions.

(i) Temporal Drift:

The substitution of Expansion Equation (16) into the ini­ tial model gives:

In A = aO + al.t + bO.Y + bl.t.Y (18)

In M - pO + pl.t + qO.Y + ql.t.Y + r0.Ya

+ rl.t.Y* (19) 98

(ii) Spatial Drifti

The substitution of Expansion Equation (17) into the ini­ tial model givesi

In A - aO + al.Dl + a2.D2 + a3.D3 + bO.Y

+ bl.Dl.Y + b2.D2.Y + b3.D3.Y (20)

In M - pO + pi.Dl + p2.D2 + p3.D3 + qO.Y

+ ql.Dl.Y + q2,D2.Y + q3.D3.Y + rO.Ya

+ rl.Dl.Y* + r2.D2.Ya + r3.D3.Ya (21)

Although Equations (18)-(21) refer only to the agricul­ tural and the manufacturing sector, it should be mentioned here that they carry clear implications for the services sector. Each set of temporally or spatially variant equa­ tions automatically determines the behavior of the residual service sector in accordance with Equation (3).

5.2.2 Data and Methodology

For the temporal drift analysis, the cross-sectional data set described earlier was supplemented by time-series sta­ tistics made available, once again, by the World Bank.

This expanded data base included, at a maximum, figures for

1960, 1965, 1970, and 1975 in addition to the original 1980 statistics. The per capita GNP in each of these years was given in constant (1981) dollars. 99

The analysis of spatial variation was done using the original, 1980 cross-sectional figures. Based on the val­ ues of variables Dl, D2, and D3, the observations were

automatically classified by region (see Appendicles B and

C). The number of countries falling into each region was

as followst

Latin America - 22

Africa - 43

Asia - 25

Others (MDC's) - 32

Two types of regressions were carried out for each on

the equations constituting the terminal modelsi

- regression with all independent variables entering,

and

- stepwise regression with only those variables signifi­

cant at the 95 percent or greater level entering.

5.2.3 Results

5.2.3.1 Temporal Variation

Table 13 and Table 14 present the regression statistics for

the temporally expanded Equations (18) and (19) respective­

ly. Whereas the time variable 't' was significant by

itself and as a cross-product in the case of the manufac­

turing sector, it did not enter into the stepwise regres­

sion in case of the agricultural sector. This suggests 100 that the labor allocation during development In the agri­ cultural sector has been remarkably stable over time, but has shown a temporal variation in the manufacturing sector.

Table 13

Regression Statistics - Temporal Drift in Agriculture

Independent Regression Coefficients Variables Full Stepwise

Intercept 4.273 4.105 ( 93.974) ( 65.399)

t -0.008 ( -2.336)

Y -0.0002 -0.0002 (-16.794) (-17.328)

t . Y 0.000001 _ ( 1.307)

R3 0.696 0.739

F 408.180 300.255

(Numbers in parentheses are T-values)

The substitution of the parameters that entered into the stepwise regression into the terminal model , represented by

Equations (18) and (19) yields the following equations*

In A - 4.105 - 0.0002.Y (22) 101

Table 14

Rearession Statistics - Temporal Drift in Manufacturina

Independent Regression Coefficients Variables Full Stepwise

Intercept 1.834 1.834 ( 28.275) ( 28.275)

t 0.023 0.023 ( 4.456) ( 4.456)

Y 0.0006 0.0006 ( 12.169) ( 12.169)

t.Y -0.00001 -0.00001 ( -3.667) ( 3.667)

Y* -3.886E-08 -3.886E-08 ( -8.077) ( -8.077)

t. Ya 1.080E-09 1.080E-09 ( 3.467) ( 3.467)

R* 0.519 0.519

F 114.149 114.149

{Numbers in parentheses are T-values)

In M = 1.834 + 0.023.t + 0.0006.Y

- 0.00001.t.Y - 3.886E-8,Y*

+ 1.08E-09. t. Ya (23)

The graphical representation of Equation (23) is presented in Figure 3 for three arbitrarily selected time periods

1960, 1970, and 1980 (corresponding, respectively, to t=0. 102 t=*10, and t“20). The labor allocation in the agricultural sector as given by Equation (22) is not graphed; since the relation is temporally stable, it can b6 represented by a single curve such as that shown in Figure 1. The curves for manufacturing show two interesting tendencies!

- as compared to 1960, the curve for 1980 shows a dis­

tinctively lower peak level of manufacturing labor

absorption. Whereas in 1960, the maximum labor

absorption was approximately 59 percent of the total

labor force, in 1980 it had declined to about 51 per­

cent.

- with time, the occurence of the maximum or peak level

in manufacturing labor share occurs later on along the

development continuum. In 1960, the maximum occured

at a GNP per capita of about $7600 while in 1980, the

peak level was observed at a GNP per capita of $9730.

The findings correspond to the observations made in other studies that indicate that contemporary Third World coun­ tries have an unlikely chance of achieving the high levels of secondary labor force allocation seen in the past devel­ opment of the MDC's.

The implications of the temporally varying pattern in the manufacturing sector upon service labor allocation is explored by substituting Equations (22) and (23) into Equa­ tion (3). Figure 4 shows graphically the temporal varia- PERCENT LABOR FORCE - 0 4 60 60 - 0 2 80-1 - 0 - 0 Figure 3 i Temporal Drift in Manufacturing Labor Allocation Labor Manufacturing in Drift Temporal i 3 Figure 4000 GNP PER GNP CAPITA 12000 1060 1070 1080

160008000 103 104 tion in the relationship between the share of labor in ser­ vices and development. Three prominent differences between the 1960 and 1960 patterns can be commented upont

- the early increase in service labor allocation, i.e.

the 'hump' in the diagram, reached a higher peak level

over time. Whereas in 1960, the initial surge in ser­

vices peaked at about 42 percent, in 1980 it increased

to approximately 48 percent. At the same time this

maximum was attained at a later stage in the develop­

ment process than it was in 1960.

- the intermediate decline in service labor allocation

becomes less prominent over time, and occurs at higher

levels of GNP per capita. Thus, in 1960, there was a

decline of over 10 percent points in service labor

allocation before it began increasing again at a GNP

per capita of $7000. In contrast, the 1980 decline in

service labor share was only about S percent points,

bottoming out at a higher per capita GNP of $9000.

- the final, monotonic increase in service labor alloca­

tion becomes less dramatic with time. Thus, while in

1960 countries with incomes of $16000 had theoretical­

ly attained service labor shares over 90 percent, in

1980 the figure was only 75 percent. 106

5. 2.3.2 Spatial Variation

The first step in the examination of spatial variation of the initial model is to test for the occurrence of regional differentiation. This was done by testing for the signifi­ cance of the regional dummy variables Dl, D2, and D3, taken as a group. The results, presented in Table 15 (for agri­ culture), and Table 16 {for manufacturing) indicate that the regional dummies are statistically significant. On this basis, further analyses are justified.

Table 15

ANOVA of Models with Dummy Variablesi Agriculture

MODEL SOURCE DF SUM OF SQUARES F

Initial Model 1 83.04 Error 106 29.31

With Dl, Model 7 98.50 D2 & D3 Error 100 13.86 18.59**

With Dl Model 3 86.01 Error 104 26.35 5.84**

With D2 Model 3 87.14 Error 104 25.23 8.41**

With D3 Model 3 92.06 Error 104 20.30 23.08**

** Significant at the .001 level 107

Table 16

ANOVA of Models with Dummy Variables* Manufacturing

MODEL SOURCE DF SUM OF SQUARES F

Initial Model 2 29.19 Error 105 29.69

With Dl, Model 11 37.07 D2 & D3 Error 96 20.81 4.53**

With Dl Model 5 31.73 Error 102 26.15 3.30*

With D2 Model 5 33.62 Error 102 24.26 6,21**

With D3 Model 5 29.81 Error 102 28.07 0.75

* Significant; at the .005 level ** Significant; at the .001 level

Table 17 and Table 18 present the regression statistics

for the terminal model given by Equations (20) and (21). A cursory look at the tables indicates that the sectoral shifts model did exhibit spatial variation/ as many of the

regional dummy variables were found to be significant in

the stepwise regression.

The substitution of the parameters that entered the

stepwise regression into Equations (20) and (21) yields a

spatially varying portrait of the relationship between sec­

toral labor allocation and per capita GNP. 108

Table 17

Regression Statistics - Spatial Drift in Agriculture

Independent Regression Coefficients Variables Full Stepwise

Intercept 3.473 3.561 ( 19.426) ( 21.051)

Dl 0.612 0.524 ( 2,719) ( 2.405)

D2 0.852 0.716 ( 4.458) ( 4.273)

D3 1.083 0.995 ( 5.079) ( 4.841)

Y -0.0001 -0.0002 ( -8.126) ( -9.286)

Dl. Y -0.0002 -0.0002 ( -3.299) ( -3.153)

D2.Y -0.00007 - ( -1.450)

D3.Y -0.0005 -0.0005 ( -8.550) ( -8.398)

R* 0.877 0.874

F 101.510 116.800

(Numbers in parentheses are T-values) Table 18

Rearession Statistics - Spatial Drift in Manufacturina

Independent Regression Coefficients Variables Full Stepwise

Intercept 3.582 2.760 ( 8.925) ( 19.855)

Dl -0.819 - ( -1.656)

D2 -1.693 -0.539 ( -4.033) ( -3.931)

D3 -1.513 -0.524 ( -3.339) ( -2.568)

Y -0.000007 0.0002 ( -0.072) ( 4.112)

Dl.Y 0.0002 - ( 0.795)

D2.Y 0.0008 - ( 3.959)

D3.Y 0.0007 0.0002 { 2.409) ( 2.133)

Y* 8.847 E-10 -1.086 E-08 ( 0.163) ( -3.121)

Dl.Y* -1.450 E-08 - ( -0.308)

D2.Y* -7.806 E-08 - ( -3.426)

D3.Y* -6.522 E-08 - ( -1.335)

R* 0.641 0.576

F 15.546 27.670

(Numbers in parentheses are T-values) 110

In A - 3.561 + 0.524.D1 + 0.716.D2 + 0.995.D3

- 0.0002.Y - 0.0002.D1.Y

- 0.0005.D3.Y (24)

In M * 2.760 - 0.539.D2 - 0.524.D3 + 0.0002.Y

+ 0.0002.D3.Y - 1.086E-08.Y* (25)

The regionally specific patterns of labor allocation in agriculture and manufacturing can be easily specifed by substituting the appropriate values of dummy variables Dl,

D2, and D3 (as given in Table 12), into Equations (24) and

(25). The results are presented in Figure 5 (Latin Ameri­ ca), Figure 6 (Africa), Figure 7 (Asia), and Figure 8 (More

Developed Countries), which graph the regional labor allo­

cation by sector against GNP per capita. Note that the

range of per capita GNP values for each graph corresponds,

generally, to the income range of countries in that region.

While the trends exhibit qualitatively similar patterns,

there are definite regional differences in the sectoral

labor allocation with development.

As compared to Asia and Africa, Latin America had over­

all lower shares of labor in agriculture in the early stag­

es of development. At national incomes of $500, the agri­

cultural labor allocation was about 48 percent in Latin

America, versus 65 percent and 67 percent in Africa and

Asia respectively. The most rapid rates of decline in the

agricultural labor share, however, was observed in Asia, 76 AGRICULTURE

0 4 00 0 GNP PER CAPITA

7 5-| MANUFACTURING

4000 0 GNP PER CAPITA

75 SERVICES

0 4000 GNP PER CAPITA

LEGEND: Regional Trend - ■ Overall Trend

Figure 5t Sectoral Labor Allocation - Latin America AGRICULTURE

0 2000 GNP PER CAPITA

7 5 1 MANUFACTURING

0 2000 QNP PER CAPITA

76-1 SERVICES

0 2000 GNP PER CAPITA

LEGEND: Regional Trend ---- Overall Trend

Figure 6 * Sectoral Labor Allocation - Africa 7*n 113 AGRICULTURE

o ?COQ GNP PER CAPITA

MANUFACTURING

0 2000 GNP PER CAPITA

SERVICES

0 2000 GNP PER CAPITA

LEGEND: Regional Trend Overall Trend

Figure 7i Sectoral Labor Allocation - Asia T 5 1 114 AGRICULTURE

6000 160 0 0 GNP PER CAPITA

76 MANUFACTURING

cc o a < _i

6000 GNP PER CAPITA

SERVICES

s o a <

6000 16000 GNP PER CAPITA

LEGEND: Regional Trend --- Overall Trend

Figure 6i Sectoral Labor Allocation - MDC's 115 where the share declined from the 67 percent level to as low as 24 percent at Incomes of $2000. Africa reported the slowest decline in the relative labor force in agriculture

-- at the $2000 income level the labor shares were still as high as 48 percent. Latin America exhibited moderate rates of agricultural decline; at the $2000 per capita GNP level 27 percent of the labor force was in the agricultural sector. Finally, both the lowest shares of agricultural labor and the smallest declines in these shares are found in the contemporary MDC's, where, over the $6000 to $16,000

GNP per capita range examined, the shares fell from 11 per­ cent to 2 percent.

Regional differences are also found in the manufacturing sector. Of the four regions, only the one made up of the

MDC’s have achieved incomes high enough to observe the peaking of the manufacturing labor share. The peak level of 40 percent is attained at a GNP per capita of $9200.

The lowest overall levels of industrial labor allocation

are found in Africa. Here, at income levels of $2000, only

13.2 percent of the labor force was engaged in industrial

activities. Latin America exhibited the highest shares,

with almost a quarter of its work force absorbed in the

industrial sector at the same income level. The fastest

(or least sluggish) growth in manufacturing labor alloca­

tion is found in Asia, where the share grew by 9 percent 116 points between incomes of $500 and $2000. The overall lev­ el, however, was low - only 20 percent of the labor was in the manufacturing sector at the 32000 income level.

The regional variation in service sector evolution reflects the regional differentials in agriculture and man­ ufacturing. Since the less developed regions are still in the early stages of development, the "hump” in service labor allocation is not apparent. Rather, only the initial increase that shows a levelling off tendency is observed.

In all cases, however, service labor shares outrank those in manufacturing. Asia exhibits the most dramatic growth in services, with over half of its labor engaged in that sector at incomes of $2000. This is not surprising, given that the region showed the most rapid declines in agricul­ tural labor allocation, as well as low overall absorption in manufacturing. Service sector growth in Africa, although outranking that in manufacturing, was moderate; agriculture still dominated the economy. Only in the case of the MDC's is the second, exponential growth in tertiary labor allocation apparent, with almost 75 percent of the labor force engaged in services at Incomes of $16000. CHAPTER VI

SUMMARY AND CONCLUSIONS

The changes in a country's economic and social structure that accompany economic development constitute a set of relatively simple, theoretically justified 'laws' that often are not exactly replicated over time, region, or con­ text. This dissertation was aimed at assessing the tempo­ ral and spatial variation of the relationship between sec­ toral labor shares and economic development, and emanated

from the Expansion Method Paradigm. This paradigm directs

the researcher to question whether theoretically grounded

relationships hold differently in differing contexts, and

then to investigate the theoretical and empirical basis of

this contextual variation. The Expansion Method Paradigm

provides a compelling alternative to the conventional

approach that is based upon the assumption of parametric

stability without testing for it. There is a strong

rationale for such an emphasis in the study of the changes

in sectoral labor allocation during development.

Much of the theoretical work related to the relationship

between sectoral labor shares and per capita GNP was based

- 117 - 118 upon the experience of the presently industrialized coun­

tries. Their experience showed that with economic develop­ ment , the relative employment in agriculture declines, that

in manufacturing first rises and then falls, and that in

services rises monotonically. A review of pertinent liter­

ature in Chapter 2 revealed that the explanation for this phenomenon was based upon changing patterns of sectoral

demand (household, intermediate, and foreign), and the

inter-sectoral differentials in labor productivity.

Recent evidence from the Third World countries has

revealed that the sectoral employment patterns being

observed there do not exactly follow the traditionally pos­

tulated ones. The deviations observed in the LDC's, dis­

cussed at length in Chapter 3, were twofold. Firstly, the

non-agricultural sector of the developing countries has

been consistently dominated by services, whereas the now

developed countries exhibited a more robust growth of

industrial employment at comparable levels of development.

Secondly, the services that have experienced the most dra­

matic growth in the LDC's have been the low productivity,

individual and family enterpries variously referred to as

'old*, 'traditional', or 'informal'. Services in the

MDC's, by contrast, have been characterized by high produc­

tivity, wage earning opportunities called the 'new', 'mod­

ern', or 'formal' services. Numerous explanations have 119 been advanced to account for these deviant trends. Expla­ nations have viewed the service sector overgrowth in the

Third World as a result of, alternatively, an overabundance of urban labor, or a heightened demand for that sector's products/ labor.

The literature review, along with the examination of recent labor allocation patterns (Chapter 4) highlighted the possible existence of spatio-temporal drift. The anal­ yses undertaken, therefore, first articulated a model of sectoral labor shifts during development, and then examined it for temporal and spatial variation.

6.1 THE SECTORAL SHIFTS MODEL

The first part of the analysis outlined in Chapter 5 con­ sisted of the formulation of the sectoral labor share - per capita GNP relationship and estimating it using a cross- sectional data set for 1980. Unlike past empirical studies of sectoral shifts, where the behavior of each of the three sectors with rising income was defined based upon conven­ tional theory, and then estimated, this analysis specified a model only for the agricultural and the manufacturing sectors. The service sector, instead of being defined a-priori in accordance with the partially validated tradi­ tional model, was considered as a residual. 120

The results showed that the agricultural and manufactur­ ing sectors were well defined by, respectively, negative exponential and quadratic exponential models. The service labor share, deduced from the other two, showed an early rise and peaking ($0 to $4000 income range), an intermedi­ ate levelling off/small decline ($4000 to $9000 range), and a later monotonic increase ($9000 and higher). This pat­ tern was found to be consistent with the transformation from informal to formal service dynamics during develop­ ment.

6.2 VARIATION IN THE SECTORAL SHIFTS MODEL

The exploration of the drift in the model defined in the first part of Chapter 5 was carried out using the Expansion

Method. The Expansion Method provides a methodological tool by way of systematic procedures for testing hypotheses concerning model drift/stability, and for obtaining func­ tional portraits of the same. It first calls for the iden­ tification of a relatively simple ’initial model', in this case given by the sectoral shifts model. A series of 'ex­ pansion equations', which include variables that may be

responsible for the drift of the initial model, are then

specified. Here, the expansion variables used were time

(the data ranging over a period 1960-1980), and region (the

classifications used being Latin America, Africa, Asia, and 121 the MDC's). The substitution of the expansion equations into the initial model gave the 'terminal model’ which then was estimated using OLS regression. The significance of the estimated parameters of the terminal model shed light on the nature of the temporal and spatial variation of the

initial model.

6.2.1 Temporal Drift

Results indicated that the relationship between agricultur­ al labor share and GNP per capita was remarkably stable.

However, this was not true in the case of the secondary sector, where two pronounced trends were observed. First­

ly, over time, it was found, there was a lower and lower

absorption of labor into the manufacturing sector at a giv­

en level of per capita GNP. Secondly, the peak level of

manufacturing labor absorption occured later along the

development continuum. Given the stability of the agricul­

tural patterns, and the temporally declining labor alloca­

tion levels in the manufacturing sector, the tertiary sec­

tor was observed to have, over time, generally rising labor

shares at all levels of per capita GNP. Furthermore, the

analysis indicated that the early peaking of services or

the "hump" became less pronounced over time, i.e. the dif­

ference between the early peak level and the intermediate

low level in tertiary labor allocation became less over

time. 122

6.2.2 Regional Drift

The relationship between labor allocation and per capita income exhibited a drift with respect to region as well.

Amongst the LDC regions, Africa was the only one in which the agricultural labor allocation was consistently higher, and that for the manufacturing and service sector consis­ tently lower, than the levels for the pooled data. Terti­ ary sector hypertrophy was the most advanced in Asia, where service labor allocation was as high as 55 percent at GNP per capitas of only $2000. The region also reported the most rapid declines in agricultural labor shares. Trends in Latin America did not show any major departures from the overall trends. However, the growth of manufacturing labor shares with rising income here was the fastest of the less developed regions. Of the four regions, only the one made up of the MDC's had achieved Incomes high enough to observe the peaking of the manufacturing labor share, and the lat­ er, exponential growth of the service sector.

The substantive findings of this research can potentially impact policy formulation. For example, much of the policy debates related to service sector overgrowth in the Third

World, have hinged about whether this observed hypertrophy is a temporary, short-term aberration, or whether it is a permanent, long-term phenomenon. The former viewpoint sug­ gests policies aimed at facilitating the smooth transition of labor from the informal to the formal sectors. These policies include, among others, the removal of discriminat­ ing practices in local public hiring, improving methods of information dissemination about formal sector job avail­ ability, investing in job training and educational programs to prepare informal sector workers for formal sector employment. The second viewpoint, in contrast, focuses upon policy alternatives aimed towards improving the eco­ nomic viability of the informal service sector, such as providing this sector with greater access to capital, infrastructure and land, reducing restrictions in licens­ ing, and aiding the marketing of the sector's output. This study identifes temporal and region specific patterns of sectoral labor force allocation that can serve as a guide to policy makers. Given the dearth of accurate data in a number of Third World countries, as well as the paucity of theoretical bases for determining feasible policies, the research outlined here provides an important source of information to planners.

Finally, this dissertation has broad methodological implications for the study of development processes. The environment in which today's developing countries are embarking upon their industrialization differs significant­ ly from the conditions that existed during the past devel­ opment of the now industrialized countries. Furthermore, 124 there are sharp regional contrasts between the LDC's based upon specific historical and cultural conditions. Given this, it seems not only unadvisable, but even incorrect to build development models that are expected to be universal­

ly applicable, no matter how persuasive their theoretical

rationale may be. Studies of the type outlined here pro­ vide a compelling alternative approach whereby the contex­

tual stability of development processes rather than the

formulation of universal models becomes the prime focus of

research. Appendix A

LIST OF COUNTRIES AND DATA USED IN THE ANALYSIS

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $) AGRI. MANU. SERV

AFGHANISTAN 1960 85.0 6.0 9.0 1965 83.6 6.5 9.9 1970 82.0 7.0 11.0 1977 79.9 7.7 12.4 1980 79.0 8.0 13.0

ALGERIA 1960 691 67.0 12.0 21.0 1965 719 58.8 13.6 27.6 1970 1027 50.0 15.0 35.0 1977 1196 32.0 22.1 45.9 1980 2140 25.4 25.0 49.6

ANGOLA 1960 1659 69.0 12.0 19.0 1965 1990 66.5 13.0 20.5 1970 2102 64.0 14.0 22.0 1977 894 60.5 15.4 24.1 1980 840 59.0 16.0 25.0

ARGENTINA 1960 1886 20.0 35.9 44.1 1965 2093 18.2 34.0 47.8 1970 2450 16.4 32.1 51.5 1977 2719 14.0 29.2 56.8 1980 2560 13.1 28.0 58.9

BANGLADESH 1960 102 87.0 3.0 10.0 1965 114 86.5 3.0 10.5 1970 119 86.0 3.0 11.0 1977 118 78.6 7.6 13.8 1980 140 74.0 11.0 15.0 126

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $) AGRI. MANU. SERi

BENIN 1960 292 54.0 9.0 37.0 1965 300 52.0 10.4 37.6 1970 300 50.0 12.0 38.0 1977 298 47.2 14.7 38.1 1980 320 46.0 16.0 38.0

BOLIVIA 1960 391 61.0 18.1 20.9 1965 447 58.2 19.6 22.2 1970 498 55.4 21.1 23.5 1977 617 51.5 23.3 25.2 1980 600 49.7 24.2 26,1

BOTSWANA 1960 256 92.0 3.0 5.0 1965 284 89.8 3.5 6.7 1970 408 87.0 4.0 9.0 1977 989 81.3 6.4 12.3 1980 1010 78.3 7.7 14.0

BRAZIL 1960 901 51.9 14.8 33.3 1965 947 48.8 16.5 34.7 1970 1232 45.6 18.3 36.1 1977 1952 34.3 22.6 43.1 1980 2220 29.9 24.4 45.7

BURMA 1960 126 • « •

1965 143 * • « 1970 143 69.9 8.0 22.1 1977 158 68.0 9.2 22.6 1980 190 67.1 9.8 23.1

BURUNDI 1960 155 90.0 3.0 7.0 1965 165 88.6 3.5 7.9 1970 200 87.0 4.0 9.0 1977 205 85.0 4.7 10.3 1980 230 84.0 5.0 11.0

CAMEROON 1960 474 87.0 5.0 8.0 1965 503 86.0 5.5 8.5 1970 572 85.0 6.0 9.0 1977 621 83.6 6.7 9.7 1980 880 83.0 7.0 10.0

CENTRAL 1960 344 94.0 2.0 4.0 AFRICAN REP. 1965 321 92.6 2.5 4.9 1970 346 91.0 3.0 6.0 1977 368 89.0 3.7 7.3 1980 320 88.0 4.0 8.0 COUNTRY YEAR GNP PC PERCENT LABOR IN

(US S) AGRI. MANU. SERV

CHAD 1960 198 95.0 2.0 3.0 1965 186 92.9 2.8 4.3 1970 183 90.0 4.0 6.0 1977 171 06.7 5.9 7.4 1980 110 85.0 7.0 8.0

CHILE 1960 2032 30.5 20.0 49.5 1965 2200 26.4 20.6 53.0 1970 2477 22.6 21.0 56.4 1977 2213 20.2 19.9 59.9 1980 2560 19.2 19. 4 61.4

COLOMBIA 1960 756 51.4 19. 2 29.4 1965 807 44.6 20. 3 35.1 1970 935 37.9 21.0 41.1 1977 1198 29.2 21.3 49.5 1980 1380 25.8 21.2 53.0

CONGO 1960 722 52.0 17.0 31.0 PEOPLE'S REP. 1965 733 47.0 19.0 34.0 1970 846 42.0 21.0 37.0 1977 932 36.3 24.5 39.2 1960 1110 34.0 26.0 40.0

COSTA RICA 1960 857 51.2 18.5 30.3 1965 923 46.6 19.5 33.9 1970 1102 42.1 20.4 37.5 1977 1383 32.7 22.4 44.9 1980 1430 29.0 23.0 48.0

CYPRUS 1960 1240 42.0 27.1 30.9 1965 1459 40.3 27.4 32.3 1970 2075 36.6 27.7 35.5 1977 2119 32.2 27.1 40.7 1980 3740 29.5 26.8 43.7

DOMINICAN 1960 683 66.5 12.2 21.3 REPUBLIC 1965 739 63.9 13.1 23.0 1970 891 61.2 14.0 24.8 1977 1245 52.7 16.8 30.5 1980 1260 49.0 18.0 33.0

ECUADOR 1960 • 57.4 19.4 23.2 1965 653 54.2 20.8 25.0 1970 690 50.9 22.2 26.9 1977 1037 51.5 18.5 30.0 1980 1180 51.6 17.1 31.3 128

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US S) AGRI. MANU. SER1

EGYPT 1960 268 58.0 12.0 30.0 1965 367 56.2 15. 2 28.6 1970 364 54.0 19.0 27.0 1977 512 51.6 26. 4 22.0 1980 650 50.0 30. 0 20.0

EL SALVADOR 1960 499 61.7 17.1 21.2 1965 596 58.9 18. 4 22.7 1970 636 56.1 19.7 24.2 1977 735 52.2 21.6 26. 2 1980 650 50.5 22.4 27.1

ETHIOPIA 1960 108 88.0 5.0 7.0 1965 123 86.1 5.5 8.4 1970 131 84.0 6.0 10.0 1977 132 81.3 6.7 12.0 1980 140 80.0 7.0 13.0

FIJI 1960 1164 55.7 15.0 29.3 1965 1182 51.6 16.8 31.6 1970 1508 47.5 18.6 33.9 1977 1867 41.8 21.2 37.0 1980 2000 39.4 22.4 38.2

GAMBIA 1960 266 85.0 7.0 8.0 1965 292 83.6 7.5 8.9 1970 307 82.0 8.0 10.0 1977 430 79.9 8.7 11.4 1980 370 79.0 9.0 12.0

GHANA 1960 534 64.0 14.0 22.0 1965 550 61.0 15.5 23.5 1970 572 58.0 17.0 25.0 1977 479 54.5 19.1 26.4 1980 400 53.0 20.0 27.0

GREECE 1960 1587 55.8 19.8 24.4 1965 2236 50.9 21.9 27.2 1970 3093 46.0 24.0 30.0 1977 4042 39.3 26.8 33.9 1960 4420 36.6 28.0 35.4

GUATEMALA 1960 687 66.7 14.4 16.9 1965 773 63.9 15.8 20.3 1970 886 61.0 17.3 21.7 1977 1092 56.8 19.5 23.7 1980 1140 55.5 20.5 24.0 129

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US S) AGRI. MANU. SER1

GUINEA 1960 282 88.0 6.0 6.0 1965 293 86.6 6.9 6.5 1970 293 85.0 8.0 7.0 1977 346 83.0 10.0 7.0 1980 300 82.0 11.0 7.0

GUYANA 1960 585 37.0 28.7 34 .3 1965 556 35.3 35.1 29.6 1970 611 33.0 42.0 25.0 1977 582 33.7 44.1 22.2 1980 720 34.0 45.0 21. 0

HAITI 1960 284 80.0 6.4 13.6 1965 268 77.3 6.8 15.9 1970 252 74.2 7.1 18,7 1977 291 73.7 7.2 19.1 1980 300 73.5 7.2 19.3

HONDURAS 1960 462 70.2 10.6 19.2 1965 495 66.4 11.5 20.1 1970 544 66.5 12.5 21.0 1977 540 63.8 13.9 22.3 1980 600 62.6 14.6 22.8

HONGKONG 1960 1177 8.0 52.0 40.0 1965 17 j 8 5.7 53.7 40.6 1970 2326 4.0 55.0 41.0 1977 3474 3.3 26.4 70.3 1980 5100 3.0 57.0 40.0

INDIA 1960 178 74.0 11.0 15.0 1965 194 74.0 11.0 15.0 1970 221 74.0 11.0 15.0 1977 237 70.8 12.5 16. 7 1980 260 69.3 13.2 17.5

INDONESIA 1960 184 75.0 8.0 17.0 1965 180 70.7 9.0 20. 3 1970 232 66.0 10.0 24.0 1977 351 58.4 13.4 28.2 1980 530 55.0 15.0 30.0

IRAN 1960 • 54.0 23.0 23.0 1965 * 50.0 25.5 24.5 1970 » 46.0 28.0 26.0 1977 • 41.1 32.2 26.7 1980 ♦ 39.0 34.0 27.0 COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $) AGRI. MANU. SER1

IRAQ 1960 • 53.0 18.0 29.0 1965 50.0 19.9 30.1 1970 • 47.0 22.0 31.0 1977 • 43.5 24.8 31.7 1980 t 42.0 26.0 32.0

ISRAEL 1960 2608 14.0 35.0 51.0 1965 3425 11.9 35.1 53.0 1970 4477 10.0 35.0 55.0 1977 5213 7.8 35.7 56.5 1980 5160 7.0 36.0 57.0

IVORY COAST 1960 666 89.0 2.0 9.0 1965 896 86. 7 2.5 10.8 1970 1064 84.0 3.0 13.0 1977 1102 80.6 3.7 15.7 1980 1200 79.0 4.0 17.0

JAMAICA 1960 1196 39.0 24.9 36.1 1965 1340 34.1 25.4 40.5 1970 1577 29.5 25.6 44.9 1977 1398 23.7 25.4 50.9 1980 1180 21.4 25.2 53.4

JORDAN 1960 • 44.0 26.0 30.0 1965 * 40.6 16.0 43.4 1970 1110 34.0 9.0 57.0 1977 1421 26.0 14.0 60.0 1980 1620 20.0 20.0 60.0

KENYA 1960 274 56.0 5.0 9.0 1965 295 84.1 5.9 10.0 1970 334 82.0 7.0 11.0 1977 358 79.3 9.0 11.7 1980 420 78.0 10.0 12.0

KOREA REP. OF 1960 451 66.0 9.0 25.0 1965 544 58.3 12.6 29.1 1970 791 50.0 17.0 33.0 1977 1339 38.7 25.1 36.2 1980 1700 34.0 29.0 37.0

LAO PEOPLE'S 1960 « 83.0 4.0 13.0 REPUBLIC OF 1965 • 81.1 4.5 14.4 1970 * 79.0 5.0 16.0 1977 ■ 76.3 5.7 18.0 1980 • 75.0 6.0 19.0 131

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $) AGRI. MANU. SER1

LEBANON 1960 38.0 23.0 39.0 1965 • 28.2 24.5 47.3 1970 * 20.0 25.0 55.0 1977 • 13.2 26.6 60.2 1980 • 11.0 27.0 62.0

LESOTHO 1960 198 93.0 2.0 5.0 1965 272 91.6 2.5 5.9 1970 282 90.0 3.0 7.0 1977 410 88.0 3.7 8.3 1980 540 87.0 4.0 9.0

LIBERIA 1960 * 80.0 10.0 10.0 1965 545 77.6 11.0 11.4 1970 635 75.0 12.0 13.0 1977 609 71.6 13.4 15.0 1980 520 70.0 14.0 16.0

MADAGASCAR 1960 384 93.0 2.0 5.0 1965 371 91.6 2.5 5.9 1970 421 90.0 3.0 7.0 1977 336 88.0 3.7 8.3 1980 330 87.0 4.0 9.0

MALAWI 1960 139 92.0 3.0 5.0 1965 142 90.6 3.5 5.9 1970 152 89.0 4.0 7.0 1977 189 87.0 4.7 8.3 1980 200 86.0 5,0 9.0

MALAYSIA 1960 754 63.0 12.0 25.0 1965 900 59.5 13.0 27.5 1970 1054 56.0 14.0 30.0 1977 1473 51.8 15.4 32.8 1980 1840 50.0 16.0 34.0

MALI 1960 146 94.0 3.0 3.0 1965 151 92.7 3.5 3.8 1970 154 91.0 4.0 5.0 1977 179 79.8 8.' 11.5 1980 190 72.6 11. 7 15.7

MAURITANIA 1960 336 91.0 3.0 6.0 1965 447 69.6 3.5 6.9 1970 491 68.0 4.0 8.0 1977 475 69.0 8.0 23.0 1960 460 • • • 132

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $) AGRI. MANU. SER

MAURITIUS 1960 766 40.0 26.0 34.0 1965 875 37.0 25.6 37.4 1970 783 34.0 25.0 41.0 1977 1240 30. 5 24.3 45. 2 1980 1270 29.0 24.0 47.0

MEXICO 1960 1032 55.1 19.5 25.4 1965 1256 50. 2 21.2 28.6 1970 1558 45.2 22.9 31.9 1977 1729 28.4 25.0 46.6 1980 2250 35.6 25.8 38.6

MOROCCO 1960 527 62.0 14.0 24.0 1965 571 59.5 15.4 25.1 1970 684 57.0 17.0 26.0 1977 876 53.5 19.7 26.8 1980 860 52.0 21.0 27.0

MOZAMBIQUE 1960 524 81.0 8.0 11.0 1965 529 77.3 10.2 12.5 1970 711 73.0 13.0 14.0 1977 458 68.2 16.4 15.4 1980 360 66.0 18.0 16.0

NEPAL 1960 150 95.0 2.0 3.0 1965 156 94.5 2.0 3.5 1970 159 94.0 2.0 4.0 1977 163 93.3 2.0 4.7 1980 150 93.0 2.0 5.0

NICARAGUA 1960 788 62.3 16.0 21,7 1965 1116 57.0 15.9 27.1 1970 1202 51.3 15.5 33.2 1977 1419 45.2 18.5 36.3 1980 860 42.6 19.9 37.5

NIGER 1960 343 95.0 1.0 4.0 1965 387 94.1 1.4 4.5 1970 334 93.0 2.0 5.0 1977 300 91.7 2.7 5.6 1980 330 91.0 3.0 6.0

NIGERIA 1960 563 71.0 10.0 19.0 1965 644 66.7 11.9 21.4 1970 707 62.0 14.0 24.0 1977 888 56.5 17.4 26.1 1980 870 54.0 19.0 27.0 COUNTRY YEAR GNP PC PERCENT LABOR IN

(US 9) AGRI. MANU. SERI

PAKISTAN 1960 190 61.0 18.0 21.0 1965 236 60.0 18. 5 21.5 1970 285 59.0 19.0 22.0 1977 299 57.6 19.7 22.7 1980 350 57.0 20.0 23.0

PANAMA 1960 945 50.9 13.7 35.4 1965 1195 46.2 14.9 38.9 1970 1503 41.6 16.0 42.4 1977 1546 31.3 17.6 51.1 1980 1910 27.3 18.1 54.6

PAPUA NEW 1960 566 89.0 4.3 6.7 GUINEA 1965 708 87.6 5.0 7.4 1970 849 86.0 5.8 8.2 1977 1010 83.4 7.1 9.5 1980 840 82.1 7.7 10.2

PARAGUAY 1960 733 56.3 19.0 24.7 1965 782 54.5 19.1 26.4 1970 859 52.6 19.2 28.2 1977 1130 50.2 19.4 30.4 1980 1630 49.1 19.4 31.5

PERU 1960 923 52.5 19.6 27.9 1965 1080 50.3 19.0 30.7 1970 1108 48.0 18.4 33.6 1977 1246 42.2 18.5 39.3 1980 1170 39.8 18.5 41.7

PHILIPPINES 1960 444 61.0 15.0 24.0 1965 493 57.1 15.5 27.4 1970 547 53.0 16.0 31.0 1977 696 48.1 16.7 35.2 1980 790 46.0 17.0 37.0

PORTUGAL 1960 965 44.1 29.0 26.9 1965 1261 38.6 31.2 30.2 1970 1745 33.3 33.2 33.5 1977 2250 29.7 34.6 35.7 1980 2520 28.2 35.1 36.7

RWANDA 1960 204 95.0 1.0 4.0 1965 156 94.1 1.4 4.5 1970 205 93.0 2.0 5.0 1977 239 91.7 2.0 6.3 1980 250 91.0 2.0 7.0 134

COUNTRY YEAR GNP PC PERCENT LABOR IN

{US $) AGRI. MANU. SERI

SENEGAL 1960 458 84.0 5.0 11.0 1965 488 82.1 5.9 12.0 1970 465 80.9 7.0 12.1 1977 471 77.9 9.0 13.1 1980 430 76.9 10.0 13.1

SIERRA LEONE 1960 284 78.0 12.0 10.0 1965 318 74.7 13.5 11.8 1970 350 71.0 15.0 14.0 1977 326 66.9 17.7 15,4 1980 320 65.0 19.0 16.0

SINGAPORE 1960 1325 8.0 23.0 69.0 1965 1506 5.7 26. 4 67.9 1970 2515 4.0 30.0 66.0 1977 4013 2.5 36.2 13.1 1980 5240 2.0 39.0 59.0

SOMALIA 1960 288 88.0 4.0 8.0 1965 245 86.6 4.9 8.5 1970 251 85.0 6.0 9.0 1977 232 83.0 7.3 9.7 1980 280 82.0 8.0 10.0

SOUTH AFRICA 1960 1737 32.0 30.0 38.0 1965 2124 31.5 29.5 39.0 1970 2510 31.0 29.0 40.0 1977 2729 30.3 29.0 40.7 1980 2770 30.0 29.0 41.0

SRI LANKA 1960 170 56.0 14.0 30.0 1965 183 55.5 14.0 30.5 1970 217 55.0 14.0 31.0 1977 234 54.3 14.0 31.7 1980 300 54.0 14.0 32.0

SUDAN 1960 272 86.0 6.0 8.0 1965 299 84.1 6.9 9.0 1970 275 82.0 8.0 10.0 1977 331 75.4 9.4 15.2 1980 380 72.0 10.0 18.0

SWAZILAND 1960 285 89.0 4.0 7.0 1965 472 85.5 4.9 9.6 1970 561 81.0 6.0 13.0 1977 723 76.3 8.0 15.7 I960 760 74.0 9.0 17.0 COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $) AGRI. MANU. SERV

SYRIA 1960 636 54.0 19.0 27.0 1965 785 52.5 20.0 27.5 1970 871 51.0 21.0 28.0 1977 1327 38.1 28.0 33.9 1980 1570 33.0 31.0 36.0

TANZANIA 1960 194 89.0 4.0 7.0 1965 220 87.6 4.5 7.9 1970 257 86.0 5.0 9.0 1977 297 84.0 5.7 10.3 1980 280 83.0 6.0 11.0

THAILAND 1960 291 84.0 4.0 12.0 1965 358 82.1 4.9 13.0 1970 470 80.0 6.0 14.0 1977 618 77.3 8.0 14.7 1980 770 76.0 9.0 15.0

TOGO 1960 219 80.0 8.0 12.0 1965 302 76.7 9.4 13.9 1970 352 73.0 11.0 16.0 1977 388 68.9 13.7 17.4 1980 380 67.0 15.0 18.0

TRINIDAD AND 1960 3122 21.7 34.4 43.9 TOBAGO 1965 3363 22.5 35.2 42.3 1970 3843 23.4 35.9 40.7 1977 4120 12.9 38.6 48.5 1980 5670 9.8 39.0 51.2

TUNISIA 1960 612 56.0 18.0 26.0 1965 716 53.0 19.5 27.5 1970 830 50.0 21.0 29.0 1977 1282 39.4 28.5 32.1 1980 1420 35.0 32.0 33.0

TURKEY I960 826 78.5 10.5 11.0 1965 948 73.6 11.4 15.0 1970 1137 67.7 12.1 20.2 1977 1557 58.1 12.7 29.2 1980 1540 53.5 12.8 33.7

UGANDA 1960 274 89.0 4.0 7.0 1965 312 87.9 4.5 7.6 1970 353 86.0 5.0 9.0 1977 298 84.0 5.7 10.3 1960 220 83.0 6.0 11.0 136

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $> AGRI. MANU. SERV

UPPER VOLTA 1960 182 92.0 5.0 3.0 1965 189 89.8 6.3 3.9 1970 202 87.0 8.0 5.0 1977 188 83.7 11.3 5.0 1980 240 B2.0 13.0 5.0

URUGUAY 1960 1976 20.6 29.5 49.9 1965 1841 17.7 30.4 51.9 1970 2104 15.2 31.2 53.6 1977 2303 12.0 32.1 55.9 1980 2820 10.8 32.4 56.8

VENEZUELA 1960 2854 34.9 22. 2 42.9 1965 3422 30.0 23.6 46.4 1970 3742 25.6 24.9 49.5 1977 4357 20.1 26.3 53.6 1980 4220 18.0 26.8 55.2

YEMEN 1960 • 83.0 7.0 10.0 REPUBLIC OF 1965 • 81.1 7.9 11.0 1970 250 79.0 9.0 12.0 1977 386 76.3 10.4 13.3 1980 460 75.0 11.0 14.0

YEMEN PEOPLE'S 1960 » 70.0 15.0 15.0 DEMOCRATIC REP. 1965 • 67.6 16.0 16.4 1970 t 65.0 17.0 18.0 1977 « 51.6 16.0 32.4 1980 • 45.0 15.0 40.0

YUGOSLAVIA 1960 984 63.0 18.0 19.0 1965 1279 57.1 20.5 22.4 1970 1648 51.0 23.0 26.0 1977 2351 35.1 31.5 33.4 1980 2790 29.0 35.0 36.0

ZAIRE 1960 248 83.0 9.0 8.0 1965 272 81.1 10.0 8.9 1970 297 79.0 11.0 10.0 1977 263 76.3 12.4 11.3 1980 210 75.0 13.0 12.0

ZAMBIA 1960 695 79.0 7.0 14.0 1965 798 76.1 8.0 15.9 1970 802 73.0 9.0 18.0 1977 791 68.9 10.4 20.7 1980 600 67.0 11.0 22.0 137

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $) AGRI. MANU. SERI

ZIMBABWE 1960 903 69.0 11.0 20.0 1965 894 66.5 12.0 21.5 1970 1016 64.0 13.0 23.0 1977 1009 61.2 14.4 24.4 1980 870 60.0 15.0 25.0

LIBYA 1960 1749 53.0 17.0 30.0 1965 5599 42.2 19. 8 38.0 1970 10090 32.0 22. 0 46.0 1977 • 22.4 26.3 51.3 1980 8450 19.0 28.0 53.0

AUSTRALIA 1960 6549 11.4 40.0 48.6 1965 7704 9.6 38.4 52.0 1970 9509 8.1 36.6 55.3 1977 10626 6.3 34.0 59.7 1980 11080 5.6 32.8 61.6

AUSTRIA 1960 4757 24.0 46.0 30.0 1965 5705 19.1 45.0 35.9 1970 7144 14.8 43.0 42.2 1977 9275 10.1 38.8 51.1 1980 10210 8.5 36. 7 54.8

BELGIUM 1960 5692 8.0 47. 7 44.3 1965 7059 6.3 46.4 47.3 1970 8797 4.9 44.8 50.3 1977 11117 3.4 42.3 54.3 1980 11920 2.9 41.1 56.0

CANADA 1960 6094 13.3 34.5 52.2 1965 7379 10.5 33.4 56.1 1970 8596 8.2 32.1 59.7 1977 10862 5.7 29.9 64.4 1980 11400 4.9 28.9 66.2

DENMARK 1960 7463 18.0 37.1 44.9 1965 9211 14.2 37.2 48.6 1970 10782 11.1 36.9 52.0 1977 12729 7.7 36.0 56.3 1980 13120 6.6 35.4 58.0

FINLAND 1960 5280 36.1 31.4 32.5 1965 6393 28.1 33.4 38.5 1970 7967 21.3 34.6 44.1 1977 9798 13.9 34.8 51.3 1980 10680 11.4 34.5 54.1 COUNTRY YEAR GNP PC PERCENT LABOR IN

(US $) AGRI. MANU. SER1

FRANCE 1960 5821 22.1 38.7 39.2 1965 7219 17. 5 39.5 43.0 1970 8997 13.7 39.7 46.6 1977 11140 9.5 39.3 51.2 1980 12190 8.1 38.9 53.0

FEDERAL REP. 1960 7052 14. 2 47.7 38.1 1965 8376 10. 4 48.2 41. 4 1970 10141 7.5 48.1 44. 4 1977 11810 4.7 47.1 48. 2 1980 13450 3.8 46.4 49.8

IRELAND 1960 2586 36.4 24.7 38.9 1965 3086 31. 2 27.9 40.9 1970 3901 26. 5 31.1 42.4 1977 4575 20.6 35.5 43.9 1980 5230 18.4 37.3 44.3

ITALY 1960 3242 30.8 39.5 29.7 1965 4020 34.3 42.0 23.7 1970 5305 18.8 43.8 37,4 1977 6177 12.7 45.1 42.2 1980 6960 10. 7 45.3 44.0

JAPAN 1960 2778 33.0 30.0 37.0 1965 4274 26.0 32.3 41.7 1970 6978 20.0 34.0 46.0 1977 8999 14.1 37.6 48.3 1980 10080 12.0 39.0 49.0

NETHERLANDS 1960 6508 11.0 42.0 47.0 1965 7655 9.5 42.9 47.6 1970 9585 8.1 43.8 48.1 1977 11239 6.6 44.5 48.9 1980 11790 6.1 44.8 49.1

NEW ZEALAND 1960 6039 14.7 36.7 48.6 1965 6733 13.2 36.3 50.5 1970 7289 11.9 35.9 52.2 1977 7924 9.8 35.3 54.9 1980 7700 9.0 35.0 56.0

NORWAY 1960 6601 19.8 36.6 43.6 1965 7878 15.4 37.2 47.4 1970 9133 11.9 37.4 50.7 1977 12100 8.2 37.0 54.8 1960 14060 6.9 36.6 56.5 139

COUNTRY YEAR GNP PC PERCENT LABOR IN

(US 3) AGRI. MANU. SER

SPAIN 1960 2396 42.1 31.4 26.5 1965 3409 33.6 34.8 31.6 1970 4392 26.0 35.7 38. 3 1977 5626 17.4 39.8 42.8 1980 5640 14.4 40.3 45.3

SWEDEN 1960 8595 14.1 45.2 40. 7 1965 10710 10.9 43.1 46.0 1970 12598 8.3 40.4 51.3 1977 13790 5.5 36.1 58.4 1900 14840 4.6 34.2 61. 2

SWITZERLAND 1960 12012 11.4 50.3 38.3 1965 14061 9.5 49.5 41.0 1970 16143 7,8 48.5 43. 7 1977 16143 5.9 46.8 47. 3 1980 17430 5.2 45.9 48.9

UNITED 1960 6094 4.0 47.7 48.3 KINGDOM 1965 6862 3.4 46.4 50. 2 1970 7650 2.8 45.0 52. 2 1977 8609 2.1 43.0 54.9 1980 9110 1.9 42.1 56.0

UNITED STATES 1960 8064 6.6 36.4 57.0 OF AMERICA 1965 9440 5.0 35.5 59.5 1970 10423 3.7 34.4 61.9 1977 11890 2.4 32.0 64.8 1980 12820 2.0 32.0 66.0

ALBANIA 1960 • 71.0 18.0 11.0 1965 • 68.7 19.6 11. 7 1970 • 66.3 21.2 12.5 1977 • 62.4 24.0 13.6 1980 * 60.7 25.3 14.0

BULGARIA 1960 • 56.5 24.7 18.8 1965 • 51.6 28.1 20.3 1970 • 46.6 31.6 21.8 1977 • 39.7 36.6 23. 7 1980 • 36.9 38.8 24.3

CZECHOSLOVAKIA 1960 ■ 25.7 46.1 28.2 1965 ft 21.0 47.5 31.5 1970 • 16.9 48.3 34.8 1977 • 12.2 48.6 39.2 1980 • 10.6 48.4 41.0 COUNTRY YEAR GNP PC PERCENT LABOR IN

[US $) AGRI. MANU. SERV.

GERMAN 1960 • 17.6 48.1 34.3 DEMOCRATIC REP. 1965 • 15.2 48.9 35.9 1970 • 13.0 49.5 37.5 1977 * 10.5 50.1 39.4 I960 * 9.5 50.2 40.3

HUNGARY 1960 775 37.0 35. 3 27.7 1965 961 28.3 41.8 29.9 1970 1280 24.8 44.4 30.8 1977 1900 20.8 44.0 35.2 1980 2100 20.5 42.6 36.9

POLAND 1960 48.2 28.8 23.0 1965 43.5 31. 5 25.0 1970 39.0 34.2 26.8 1977 33.0 37.8 29.2 1980 30.6 39.3 30.1

ROMANIA 1960 66.7 15.4 17.9 1965 58.2 19.1 22. 7 1970 49.1 23.0 27.9 1977 34.9 31. 7 33.4 1980 29.4 35. 5 35.1

USSR 1960 41.9 28.6 29.5 1965 33.3 33.3 33.4 1970 25.7 37.7 36.6 1977 17.1 42.8 40.1 I960 14.2 44.7 41.1 Appendix B

LIST OF COUNTRIES BY DEVELOPMENT CLASSIFICATION

MORE DEVELOPED COUNTRIES (MDC'S)

1. Albania 17. Ireland 2. Austria 18. Italy 3. Australia 19. Japan 4. Belgium 20. Netherlands 5. Bulgaria 21. New Zealand 6. Canada 22. Norway 7. Cyprus 23. Poland 8. Czechoslovakia 24. Portugal 9. Denmark 25. Romania 10. Finland 26. Spain 11. France 27. Sweden 12. German Democratic Rep. 28. Switzerland 13. Germany, Federal Rep. 29. United Kingdom 14. Greece 30. United States 15. Hungary 31. USSR 16. Israel 32. Yugoslavia

- 141 - 142

LESS DEVELOPED COUNTRIES (MD C 'S )

1. Afghanistan 46. Libya 2. Algeria 47. Madagascar 3. Angola 48. Malawi 4. Argentina 49. Malaysia 5. Bangladesh 50. Mali 6. Benin 51. Mauritania 7. Bolivia 52. Mauritius 8. Botswana 53. Mexico 9. Brazil 54. Morocco 10. Burma 55. Mozambique 11. Burundi 56. Nepal 12. Cameroon 57. Nicaragua 13. Central African Republic 58. Niger 14. Chad 59. Nigeria 15. Chile 60. Pakistan 16. Colombia 61. Panama 17. Congo, Democratic Rep. 62. Papua New Guinea 18. Costa Rica 63. Paraguay 19. Dominican Republic 64. Peru 20. Ecuador 65. Philippines 21. Egypt 66. Rwanda 22. El Salvador 67. Senegal 23. Ethiopia 68. Sierra Leone 24. Fiji 69. Singapore 25. Gambia 70. Somalia 26. Ghana 71. South Africa, Rep. of 27. Guatemala 72. Sri Lanka 28. Guinea 7 3. Sudan 29. Guyana 74. Swaziland 30. Haiti 75. Syria 31. Honduras 76. Tanzania 32. Hongkong 77. Thailand 33. India 78. Togo 34. Indonesia 79. Trinidad and Tobago 35. Iran 80. Tunisia 36. Iraq 01. Turkey 37. Ivory Coast 8 2. Uganda 36. Jamaica 83. Upper Volta 39. Jordan 84. Uruguay 40. Kenya 85. Venezuela 41. Korea, Republic of 86. Yemen, P.D.R. 42. Lao, P.D.R. 87. Yemen, Republic of 43. Lebanon 88. Zaire 44. Lesotho 89. Zambia 45. Liberia 90. Zimbabwe Appendix C

LIST OP LESS DEVELOPED COUNTRIES BY REGION

LATIN AMERICA

1. Argentina 12. Haiti 2. Bolivia 13. Honduras 3. Brazil 14. Jamaica 4. Chile 15. Mexico 5. Colombia 16. Nicaragua 6. Costa Rica 17. Panama 7. Dominican Republic 18. Paraguay 8. Ecuador 19. Peru 9. El Salvador 20. Trinidad and Tobago 10. Guatemala 21. Uruguay 11, Guyana 22. Venezuela

AFRICA

1. Algeria 23. Mauritania 2. Angola 24. Mauritius 3. Benin 25. Morocco 4. Botswana 26. Mozambique 5. Burundi 27. Niger 6. Cameroon 28. Nigeria 7. Central African Republic 29. Rwanda 8. Chad 30. Senegal 9. Congo, Democratic Rep. 31. Sierra Leone 10. Egypt 32. Somalia 11. Ethiopia 33. South Africa, Rep. of 12. Gambia 34. Sudan 13. Ghana 35. Swaziland 14. Guinea 36. Tanzania 15. Ivory Coast 3 7, Togo 16. Kenya 38. Tunisia 17. Lesotho 39. Uganda 18. Liberia 40. Upper Volta 19. Libya 41. Zaire 20. Madagascar 42. Zambia 21. Malawi 43. Zimbabwe 22. Mali

- 143 - 144

ASIA

1. Afghanistan 14. Malaysia 2. Bangladesh 15. Nepal 3. Burma 16. Pakistan 4. Fiji 17. Papua New Guinea 5. Hongkong 18. Philippines 6. India 19. Singapore 7. Indonesia 20. Sri Lanka 8. Iran 21. Syria 9. Iraq 22. Thailand 10. Jordan 23. Turkey 11. Korea, Republic of 24. Yemen, P.D.R. 12. Lao, P.D.R. 25. Yemen, Republic of 13. Lebanon BIBLIOGRAPHY

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