. a . . , .. - - - * ■ *• — — - I 72-4595. '
NWALA, Eze Ogbueri Ajoku, 1940- SPATIAL PATTERNS OF INSTITUTIONAL INNOVATIONS WITHIN A MODERNIZING SOCIETY.
The Ohio State University, Ph.D., 1971 Geography
University Microfilms, A XEROX Company, Ann Arbor, Michigan
<0, Copyr i ght by
Eze Ogbueri Ajoku Nwala
1971
THIS DISSERTATION HAS BEEN MICROFILMED EXACTLY AS RECEIVED SPATIAL PATTERNS OF INSTITUTIONAL INNOVATIONS WITHIN A MODERNIZING SOCIETY
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
Eise Ogbueri Ajoku Nwala, B.A. (Hons), M.A«
The Ohio State University
1971
Approved by
Advj. sor Department of Geography PLEASE NOTE:
Some Pages have indistinct print. Filmed as received.
UNIVERSITY MICROFILMS ACKNOWLEDGMENTS
Many persons have helped me by their interest, encour agement, criticisms and comments during the preparation of this study. They certainly merit my warm appreciation and thanks.
I wish to thank Dr. L. A. Brown, my adviser, for the stimulus, comments and direction that made the logical presentation of this research possible.
My thanks are extended to Dr. G. J. Demko for his keen interest, help and comments. Dr. H. L. Gauthier, Dr. K. R.
Cox and Dr. R. K. Serople are thanked for encouragement and comments that helped elucidate some ideas that were hazy to the author during the study. Dr. E. J. Taaffe, Chairman,
Department of Geography and the Graduate School, The Ohio
State University, are deeply thanked for providing the con ducive working time and an award of a teaching assistant- ship that made this task a reality. The staff of the
Inter-Library Loan of The Ohio State University Library are remembered. My thanks also are for Eastern Nigerians at
Ohio State for their encouragement. In particular, I wish to thank Dr. E. Okechukwu Odita, Division of the History of To Late Chief Nwokogba Amadi Nwala c
VITA
August 5, 1940 • . . . Born - Egwi-Etche, Port Harcourt, Nigeria
1967 ...... B.A. (Hons), University of Durham, England
1967-1968...... Research Assistant, Department of Geography, University of Ottawa, Canada
1969 ...... M.A., University of Ottawa, Canada
1968-197 1 ...... Teaching Associate, Department of Geography, The Ohio State Univer sity, Columbus, Ohio.
FIELDS OF STUDY
Major Field: Geography
Urban Geography. Professors L. A. Brown, R. G. Golledge and L. J. King
Social/Population Geography. Professors L. A. Brown, G. J. Demko and K. R. Cox
Development Geography, professors H. L. Gauthier, G. J. DemRo and R. K. Semple
( v TABLE OF CONTENTS
Page
ACKNOWLEDGMENTS...... i:Li
VITA ...... v
LIST OF TABLES ...... viii
LIST OF FIGURES...... xi
Chapter
I. INTRODUCTION...... 1
II. DIFFUSION STUDIES IN GEOGRAPHY...... 7
Introduction ...... 7 Spatial Diffusion Patterns ...... 7 Spatial Diffusion Processes...... Innovation Types ...... ■j’® Methodological Notes ...... ** Overview of the Research Design..... 24
III. SETTING, DATA, VARIABLES AND HYPOTHESES . . 26
Introduction ...... 26 Setting...... 26 Data Sources ...... 34 Nature of Variables...... 38 Urban Place Variables. 39 Administrative District Variables. .. . 40 Hypotheses ...... 41
IV. EMPIRICAL TESTS - ORIGIN TIMES OF ADOPTION...... 45
Introduction ...... 45 Origin Times Adoption Test Results - Individual Central Places...... 48 vi CONTENTS (continued)
Chapter Page
IV. (continued)
Origin Time Adoption Test Results - Administrative Districts...... 57 Summary...... 67
V. EMPIRICAL TESTS - RATES OF ADOPTION TEST. 71
Introduction ...... 71 Rates of Adoption Results - Individual Central Places. «...... 71 Rates of Adoption Test Results - Administrative Districts. • • • • • 79
VI. SPATIAL DIMENSIONS OF INSTITUTIONAL INNOVATION PATTERNS IN EASTERN NIGERIA. . 88
Introduction ...... ••••• 88 Principal Components Results - Summary ...... 90 Secondary School Diffusion Patterns - Basic Structural Dimensions. . • ... 91 Hospital Diffusion Patterns - Basic Structural Dimensions .....•• 101 Summary...... 107
VII. SUMMARY, CONCLUSIONS AND IMPLICATIONS . . H I
Summary and Conclusions...... Ill Implications for Future Research . . . 114
APPENDIX
A. DATA SOURCES...... 118
B. SECONDARY SCHOOL DIFFUSION IN EASTERN NIGERIA ...... H 8
BIBLIOGRAPHY 123 LIST OF TABLES
TABLE Page
1. CITY SIZE DISTRIBUTIONS IN EASTERN NIGERIA. . 35
2. SIMPLE CORRELATION COEFFICIENTS BET I VEEN ADOPTION TIMES OF SECONDARY SCHOOLS AND EIGHT INDEPENDENT VARIABLES (INDIVIDUAL CENTER LEVEL) ......
3. SIMPLE CORRELATION COEFFICIENTS BETWEEN ADOPTION TIMES OF HOSPITALS AND EIGHT INDEPENDENT VARIABLES ...... 50
4. MULTIPLE STEPWISE REGRESSION RESULTS - ADOPTION TIMES OF SECONDARY SCHOOLS (INDIVIDUAL CENTER LEVEL) ...... 52
5. •MULTIPLE STEPWISE REGRESSION RESULTS - ADOPTION TIMES OF HOSPITALS (INDIVIDUAL CENTER LEVEL) ...... 54
6. SIMPLE CORRELATION COEFFICIENTS BETWEEN ADOPTION TIMES OF SECONDARY SCHOOLS AND 17 INDEPENDENT VARIABLES (DISTRICT LEVEL) . . 58
7. MULTIPLE STEPWISE REGRESSION RESULTS - ADOPTION TIMES OF SECONDARY SCHOOLS (ADMINISTRATIVE DISTRICT LEVEL) ...... 6*
8. COMPUTED REGRESSION RESULTS AT THE SEVENTH ORDER OF VARIABLE ENTRY ...... 63
9. COMPUTED REGRESSION RESULTS AT THE FIFTEENTH ORDER OF VARIABLE ENTRY 65
10. SIMPLE CORRELATION COEFFICIENTS BETWEEN RATES OF DIFFUSION OF SECONDARY SCHOOLS AND EIGHT INDEPENDENT VARIABLES (INDIVIDUAL CENTER LEVEL) ......
v• m • » TABLES (continued)
TABLE Page
11. MULTIPLE STEPWISE REGRESSION RESULTS - RATES OF ADOPTION OF SECONDARY SCHOOLS (INDIVIDUAL CENTERS) ...... 74
12. SIMPLE CORRELATION COEFFICIENTS OF RATES OF ADOPTION OF HOSPITALS AND EIGHT INDEPENDENT VARIABLES (INDIVIDUAL CENTER LEVEL)...... 76
13. MULTIPLE STEPWISE REGRESSION RESULTS - RATES OF ADOPTION OF HOSPITALS (INDIVIDUAL CENTER LEVEL)...... 77
14. SIMPLE CORRELATION COEFFICIENTS BETWEEN RATES OF ADOPTION OF SECONDARY SCHOOLS AND SIXTEEN INDEPENDENT VARIABLES (ADMINIS TRATIVE DISTRICT LEVEL) ...... 80
15. SIMPLE CORRELATION COEFFICIENTS BETWEEN RATES OF ADOPTION OF HOSPITALS AND SIXTEEN INDEPENDENT VARIABLES (ADMINISTRATIVE DISTRICT LEVEL)...... 81
16. MULTIPLE STEPWISE REGRESSION RESULTS - RATES OF ADOPTION OF SECONDARY SCHOOLS (ADMINISTRATIVE DISTRICT LEVEL)...... 82
17. MULTIPLE STEPWISE REGRESSION RESULTS - RATES OF ADOPTION OF HOSPITALS (ADMINIS TRATIVE DISTRICT LEVEL)...... 84
18. LIST OF HIGH INTERCORRELATION COEFFICIENTS AMONGST INDEPENDENT VARIABLES. , ...... 86
19. COMPONENTS OF SECONDARY SCHOOL ADOPTIONS (INDIVIDUAL CENTER LEVEL) - POPULATION- WEALTH DIMENSION, ROTATED FACTOR ONE .... 92
20. COMPONENTS OF SECONDARY SCHOOL ADOPTIONS (INDIVIDUAL CENTER LEVEL) - HIERARCHICAL DIMENSION, ROTATED FACTOR TWO ...... 96
21. COMPONENTS OF SECONDARY SCHOOL ADOPTIONS (INDIVIDUAL CENTER LEVEL) - NEIGHBORKOOD- ACCESSIBILITY DIMENSION, ROTATED FACTOR THREE...... * 98 ix TABLES (continued)
TABLE Page
22. COMPONENTS OF HOSPITAL ADOPTIONS (INDIVIDUAL CENTER LEVEL) - POPULATION- WEALTH DIMENSION, ROTATED FACTOR ONE . . . . 102
23. COMPONENTS OF HOSPITAL ADOPTIONS (INDIVIDUAL CENTER LEVEL - NEIGHBORHOOD EFFECT DIMENSION, ROTATED FACTOR TWO . . . 103
24. COMPONENTS OF HOSPITAL ADOPTIONS (INDIVIDUAL CENTER LEVEL) - HIERARCHICAL DIMENSION, ROTATED FACTOR THREE...... 104
x LIST OF FIGURES
FIGURE Page
1. Eastern Nigeria Traditional Marketing Network...... 27
2. Eastern Nigeria - Administrative Organisa tion 1900-1954 ...... * ...... 30
3. Eastern Nigeria - Administrative Provinces and Divisions 1955-1965...... 31
4. Eastern Nigeria - Highway Transportation System 1914-1965 ...... 33
5. Eastern Nigeria - Urban Places 1953 . • • • 36
6. Eastern Nigeria - Urban Places 1963 • • • • 37
7. Eastern Nigeria - Temporal Phases of Secondary School Adoptions ...•••• 46
8. Location of Study Centers • •••#.#•• 47
9* Residuals from Regression ...... 69
10# Rotated Factor One Scores - Economic Wealth Development Dimension ...... ®3
11# Rotated Factor Two Scores - Hierarchical Effect Dimension ......
12# Rotated Factor Three Scores - Accessibility Effect Dimension •••*•• ...... *00
13# Rotated Factor One Scores - Hospitals Economic Wealth Development Dimension • 105
xi ('
CHAPTER I
INTRODUCTION
Diffusion studies focusing on processes and patterns
have long been recognized as a major concern for research
amongst the social sciences, /nthropologists, rural
sociologists, economists and geographers have shown deep-
seated interest in diffusion research. The result of this
interdisciplinary interest has been an accumulation of an
extensive literature relating to diffusion problems.
Although such a massive build up of literature in
diffusion studies abounds, it is, nevertheless, true to
note that majority of these papers have concentrated heavily
on diffusion of innovations within the highly urbanized and
developed societies rather than modernizing societies. This > observation is particularly true with respect to diffusion
research in Africa. The lack of research in this area
becomes particularly apparent when reference is made to
three contemporary publications. Amongst four hundred and
sixty-eight bibliographic items compiled by Jones only two
such items relate to Africa.* The other two reviews and
*G E. Jones, 1967, "The Adoption and Diffusion of Agricu1tura1 pra o t i ce s, ’1 World Agricultural Economics and Rural Sociology Abstracts. Vol. ~IX,' 2333-3427.
1 bibliographic publications by Brown and Gould also indi cate that diffusion studies have largely neglected
Africa.2 However, it is noted that within the last decade some notable efforts have been made to alter this state of affairs. Case examples, illustrating a changing trend in
African diffusion research, include such excellent studies by Bohannan, Taaffe, Morrill and Gould, ,-Soga, WLtthuhn,
Ascroft et_ al., and Riddell. Nonetheless it is observed that these studies are gust a trickle and therefore point up a need for continuing investigation within a develop mental context where the diffusion of innovations, especially institutional innovations, are currently making their greatest impact upon society.
2 L. A. Brown, 1968, Diffusion Processes and Location: A Conceptual Framework and Bibliography, Philadelphia: Regional Science Research Institute, Bibliography Series, No. 4, P. R. Gould, 1969, Spatial Diffusion, Washington, D.C., Annals, Association of American Geographers, Resource Paper No. 4. Q P. Bohannan, 1959, "The Impact of Money in an African Subsistence Economy," Journal of Economic History, Vol. XIX, 491-503. E. J. Taaffe, R. L. Morrill and p. R. Gould, 1963, "Transport Expansion in Underdeveloped Countries: A Compar ative Analysis," The Geographic Review, Vol. LIII, No. 4, 503-529. E. Soga, 1966, The Spatial Dimension of Moderniza tion j.n Kenya, Bloomington, Indiana. B. 0. Witthuhn, 1968, "The" Spatial Integration of Uganda as a Process of Modernisa tion," Unpublished Doctor Dissertation, University Park: Pennsylvania State University. J. R. Ascroft et al., 1969, Patterns of Diffusion in Rural Eastern Nigeria, East Lansing, Michigan: Michigan State University, Diffusion of Innova tions Research Report II. J. B. Riddell, 1970, Structure, 3
The purpose of this research derives from the above observation* First, the study is another attempt at filling the gap in African diffusion research. Secondly, the study provides an alternate testing ground for spatial diffusion theories developed for the highly dynamic and complex European and North American landscapes.
The present study investigates then the nature of spatial patterns of institutional innovations (secondary schools and hospitals) within Eastern Nigeria. Concepts consistent with spatial diffusion of innovations within the industrialized and highly urbanized nations are applied and tested for innovation diffusion patterns within a developmental framework. Two such concepts are considered.
They are hierarchical and neighborhood effect theories.
Their effectiveness in explaining diffusion patterns within a modernizing society is assessed via the statis tical techniques of regression and principal components analyses.
A final objective of the current research is to outline further lines for continuing investigation. In addition, it is intended the study will be of some utility in provid ing a better understanding of the spatial patterning of agents of modernization witliin transitional economies.
Diffusion and Response; The Spatial Dynamics of Moderni zation in Sierra LeoneT Evanston: Northwestern University, Studies in Geography, Number 27. 4
The aims of the study are executed by the following tasks. First, a discussion of literature related to geographic diffusion studies is presented. It forms the theme of Chapter II which provides a conceptual background for the study. In particular, literature related to hierarchical, neighborhood and spatio-temporal diffusion patterns is reviewed. Two critical factors for the current research are pin-pointed, namely, origin and rate of dif fusion. Next, a methodological review of the analytical models enployed in previous studies is presented. The analytical model for the present study is then derived.
Chapter II finally ends with a presentation'of the research design of the current dissertation. It is noted that the statistical analyses are conducted at two levels of inves tigations, namely, the individual central place and admin istrative district levels. Altogether 1S7 central places and 27 administrative districts are used. However it is noted that the principal components analysis is done at only one level for reasons discussed later in the study.
Chapter III presents a setting for the study. Three aspects of the spatial organization of the study area, considered relevant to an understanding of the spatial patterns of innovations being investigated, are presented. 5
These aspects include the spatial nature of the indigeneous marketing network, modern political organization, modern road transportation system and the pattern of urbanization in the study area. Next the chapter discusses data
sources and variables employed in the study. Two sets of variables are identified. The first set of variables relate to individual centers whereas the second set relate to administrative districts. Finally, a presentation of the study hypotheses is given. Two sets of hypotheses relating particularly to origin-time and rate of adoption are established.
Interpretation of the analytical results obtained from tests concerning the hypothesized postulates provides the subject-matter of the next two chapters. Chapter IV takes on the results relating origin adoption time patterns, on the one hand. Chapter V deals with results of rates of adoption, on the other hand. Conclusions regarding the predictive power of variables indexing hierarchical and neighborhood effect theories are reached and suggestions for further analysis are outlined.
Chapter VI gives the results of the principal com ponents analysis performed on institutional innovation patterns in Eastern Nigeria. Three component dimensions accounting for over two-thirds of the total explained variance are identified. Their factor scores are then 6 mapped. Interpretations of both the factor dimensions and scores are utilized in collaborating the evidence regard ing the effectiveness of the application of hierarchical and distance-decay theories of diffusion within a develop mental setting. The final chapter gives an overview of the study and its implications for further research. CHAPTER II
DIFFUSION STUDIES IN GEOGRAPHY
Introduction
Most diffusion studies in geography to date . emphasize two orientations. The first concentration is pattern oriented. The second thrust is process inclined.
Both research emphases have received such considerable attention in the literature so that they are only given a brief review here.
Spatial Diffusion Patterns
Diffusion pattern research in geography has resulted in the recognition of some fairly consistent regularities.
These patterns repeatedly form the focus of a number of papers.-*-
Three patterns usually dominate research thinking whenever diffusion patterns are discussed. These are neighborhood effect, hierarchical diffusion, and spatio- temporal logistic patterns.
The latest paper in which these patterns are exten sively reviewed is that by L. A. Brown and I(. R. Cox, 1970 "Empirical Regularities in the Diffusion of Innovation," Xerox Paper, Columbus, Department of Geography, The Ohio State University, 27 pp. 7 8
L. Neighborhood Effect
One of the commonly identifiable patterns in innova
tions diffusion studies is contagion exhibited by diffusing
objects# It is regularly observed that adoption of new
ideas follows a spatial order in which initial adoption for
a group of individuals or centers is inversely related to
the distance between the past adopter and subsequent
adopters. Hagerstrand calls this spatial clustering of new
adoptions around the vicinities of past adoptions the
"neighborhood effect•"2
Following Hagerstrand's study, many other completed in
vestigations present evidence which affirm the presence of
this spatial regularity# Griliches, Brown and Gould,
among others, have identified similar patterns which are
referred to as "distance bias" or "distance decay#" In
addition Coleman, Katz and Menzel, in the sociological
2 Torsten Hagerstrand, 1952# "On the Monte Carlo Simu lation of Diffusion," in William L. Garrison, 1967. Quan titative Geography, Evanston, Illinois# Department of Geography, Northwestern University. Also by the same author, 1966. "Aspects of the Spatial Structure of Social Communication and the Diffusion of Information," Papers, Regional Science Association, European Congress, Cracow, 27-42. 3 Zvx Griliches, 1957. "Hybrid Corn: An Exploration in the Economics of Technological Change," Econometricaa Vol. 29, 741-766. L. A. Brown, 1968. Diffusion Dynamics: A Review and Revision of the Quantitative Theory of the Spatial Diffusion of Innovation. Lund, Lund studies in Geography, No. B-29, 94 pp. P. R. Gould, 1969. Spatial Diffusion, Washington, D.C. Annals, Association of American Geographers, Resource Paper No. 4, 72 pp. 9 literature, draw attention to the same empirical regularity among social structures where the links between individuals in their acquaintance nets are important in the channeling of information flows and influence,4 '
Although contagious pattern has become firmly en trenched in diffusion research, nonetheless, a few re searchers still express some doubt about its validity.
Xornqvist and Cliff, for example, do not find it in their investigations,"^ Cliff, in particular, using the same data as Hagerstrand, registers his failure in proving the presence of ''neighborhood effect."^ Despite the contro versy over the question of the existence of contagion in innovation diffusion adoption patterns, the idea has con tinuously persisted in nearly all subsequent diffusion studies. Thus one of the aims of this study is to probe the degree to which new adoptions of institutional in novations are influenced by neighborhood effect.
F. S. Coleman, E. Katz and H. Menzel, 1957. "Diffusion of an Innovation among Physicians," Sociometry, Vol. 20, 253-270.
5 See Gunnar Tornqvist, 1967. Growth of T. V. Owner ship in Sweden, 1956-1965; An Empirical-Theoretical' Stuciy. StockholmT Almqvist and Wikesells. Andrew Cliff, 1968, "The Neighborhood Effect in the Diffusion of Innovations," Transactions, Institute of British Geographers, Vol. 24, 75-84.
6Ibid. 10 ( Hierarchical Diffusion Pattern
Another commonplace pattern, first noted by Hager-
strand and later confirmed by others, is that innovations
diffuse in a hierarchical fashion. It is demonstrated
that large urban centers usually acquire new ideas or
technologies before smaller towns. These innovations then
diffuse from these high order centers down the hierarchy to
low order centers regardless of their relative locations.
Thus urban size is considered to exert a considerable
influence on the date when a center adopts an innovation.
There are many studies which demonstrate urban popula-
tion-size effect on the temporal incidence of innovations.
Studies such as those by Bowers, Boon, Pyle and Pedersen g are good examples illustrating this point.
Critics also raise questions about the concept of
innovations cascading dowmvards through the central place
hierarchy. Brown, for instance, maintains that it is
7 Torsten Hagerstrand, 1953. Innovationsforloppet ur Korologisk Synpunkt, Lund, Sweden: Gleerup. Translated by Alan Pred as Innovation Diffusion as a Spatial Process. Chicago, Illinois: University of Chicago Press. g R. V. Bowers, 1937. "The Direction of Intra-Societal Diffusion," American Sociological Review, Vol. 2, 826-36. F. Boon, 1967. "A Simple Model for the Diffusion of an Innovation in an Urban System," Regional Economic Develop ment Study, Background Paper No. 1, Center of Urban Studies, University of Chicago. G. F. Pyle, 1969. "The Diffusion of Cholera in the U.S. in Nineteenth Century," Geographical Analysis, Vol. 1, No. 1, 59-76. P. 0. Pedersen, 1970, "Innovation Diffusion within and between National Urban Systems," Geographical Analysis, Vol. 2, Mo. 3, 203-54. 11 important to define exactly the stage of development in- g volved when referring to hierarchical diffusion pattern#
He adds that hierarchical spread pattern of new ideas can only be suitably discussed within an advanced economy while it is inappropriate to talk about the same phenomenon within an economy at a low level of development# Brown suggests that a haphazard distribution pattern is more typical of diffusion within a low stage economy.
Hudson, similarly, criticises the authenticity of hierarchical spread diffusion#'1'® He suggests that the sudden appearance of innovations at widely separated large urban centers without their prior occurrence at the smaller intervening centers is suspect. Given that the probability of interaction between a large town and lesser centers in its hinterland is greater than that between it and another town of similar rank located at a father distance, Hudson contends that such a possibility may very likely operate against hierarchical diffusion pattern#
This observation implies that the locational arrangement of urban centers within a central place system may be a strong force determining the time when a center adopts an innovation. Therefore, one of the interests of the current
g. L. A# Brown, 1968, op. cit. See footnote 3.
*°J. C. Hudson, 1969. ’’Diffusion in a Central Place System," Geographical Analysis, Vol. 1, No. 1, 45-58. 12 study is to assess how important the geometry of central places affects adoption time*
Diffusion Time Patterns
A third empirical regularity about innovatinns- diffusion concerns time. It is observed that the propor tion of adopters of an innovation over time when plotted on a cumulative basis yields an S-shaped curve. The S- * curve or the logistic function is given by:
-(a + bt) -1 ,,x y ~ k(l + e ) (1) where y equals the proportion of adopters; k represents the upper level of adopters; t the time periods since initial adoption while a and b indicate estimated parameters of the fitted function.
The use of the logistic for summarizing the temporal path of diffusion is attributed to two principal reasons.
First, the parameters of the equation are readily estimated through the least squares method. Secondly, its popularity is due to the fact that various parts of the curve neatly fit the different phases of diffusion. Griliches breaks these phases into origin, diffusion and saturation. In addition he provides an evaluation of the factors, associated with each of these stages of diffusion.Rural sociolo-
■^Zvi Griliches, 1957. op ._ cit. Footnote 3. 13
gists also link adopter categories to various sections of
the curve. Rogers, for example, suggests a typology of
innovators and early adopters that agrees with the origin phase of the curve; early and late majority adopters with
the diffusion stage and laggards with the saturation phase.12 Although the logistic function has constantly been used in describing the temporal paths of innovations- diffusion, nevertheless it has come under constant criti cisms. Coleman remarks that other functions under entirely different set of assumptions can similarly produce the observed S-curve.-*-2 Indeed Rapoport and Brown also suc
cessfully model the time curve of diffusion of information and innovation respectively by the use of second degree functions.14 The implication is that further consideration
should be given to the form of diffusion time graph.
Spatial Diffusion Processes
Diffusion processes constitute the second major focus in innovation diffusion research. These processes are the
12E. M. Rogers, 1962. Diffusion of Innovations, New York: Free Press, 148-92. 13 Refer to J. S. Coleman, 1964. Introduction to Mathe matical Sociology. New York: Free Press, 492-515. "*
14 Anatol Rapoport, 1953. "Spread of Information through a Population with Socio-Structural Bias," Bulletin of Mathematical Biophysics, Vol. 15, 523-47. L. A. Brown, op. cit., footnote 3• 14
underlying mechanisms that generate the different patterns
discussed in the preceding section. By comparison, these processes have received far more attention in sociological
and geographic diffusion studies than any other field of
study. Therefore in this section only a cursory summary of these processes is presented paying greater attention
to how they are conceptualized in geography:
Initial conceptualization of diffusion processes in geography is the work of Hagerstrand who visualizes them as a learning process,'16 Adoption, in this respect, is thought to proceed only when a non-adopter has gained sufficient information about an idea. This learning sequence is controlled chiefly through an efficient flow of informa tion that, in turn, requires a thorough understanding of information flow and resistance factors, Hagerstrand,
Yuill and Brown each elaborates on these factors so that 17 they do not need any further discussion. However, it may
15For a detailed discussion of these processes consult E, M. Rogers, 1962, op. cit., footnote 12, See also Torsten Hagerstrand, 1953, oj>. cit., footnote 7,
■^Torsten Hagerstrand, 1952, op. cit., footnote 2. 17 Torsten Hagerstrand, 1952. The Propagation of Inno vation Waves, Lund, Lund Studies in Geography, Series B, No. 4. R. S. Yui^-l, 1964 . A Simulation Study of Barrier Effects in Spatial Diffusion Problems, Evanston, Illinois, Technical Report No. 1, Department of Geography, North western University. L. A. Brown, op. cit., footnote 3. 15 be noted that the flow of information is effected through an interpersonal communication network consisting of three dominant tiers or levels.3-0
A second conceptualization of diffusion processes, also advanced by Hagerstrand, is that innovation adoption processes operate in a series of waves from an origin point.^ The major concern of the wave process model of diffusion is a society under the influence of innovation expansion. Fundamentally, an innovation is assumed to originate from a point. It then spreads outwards from that location to all areas under its influence. Frictional distance on social communication affects the pattern of spread and so it proceeds in an orderly fashion.
Diffusion processes besides are visualized as occurring in a series of steps rather than waves. The commonest step-like conceptualization is the two-step flow model which originates from mass communication research.20 How ever the idea has now permeated geographic thinking through
Wolpert's study.23. In essence the two-step flow process
18 Torsten Hagerstrand, op. cit., footnote 7.
19 Torsten Hagerstrand, op. cit., footnote 17.
20E. Katz, 1957. "The Two-Step Flow Communications: An Up-to-date Report on the Hypothesis," Public Opinion Quarterly, vol. 21, 61-78. 21 J. Wolpert, 1965. "A regional simulation model," mimeographed paper, Philadelphia, Department of Regional Science, University of Pennsylvania. model divides a given population into two groups, namely,
"leaders" and "followers" Usually "leaders" are the first persons to be aware of an information or innovation,
"Followers," on the contrary, receive an information from
"leaders" or from other "accepters" who nonetheless acquired the knowledge from "leaders."
The two-step flov; model assumes one leader to be present at a given situation. Clearly, in reality, there may just be more than one leader disseminating information at one time. Harvey points out the inadequacy of this model on the grounds of evidence presented to date in dif- 22 fusion research. Hudson, likewise, remarks that the two-step flow hypothesis breaks down particularly when it
O Q is applied to innovation diffusion at a macro-level.
He puts forward a multiple-step flow process model which seems very attractive when diffusion is considered on a regional scale where there may be several "leaders" or
"centers" knowledgable of an innovation simultaneously.
Furthermore, multiple step flow model is intuitively attractive to empirical research especially to location- oriented innovation studies. The model becomes very useful
22 D. W. Harvey, 1966. "Geographical process and the analysis of point patterns: testing models of diffusion by quadrat sampling." Transactions. Institute of British Geographers, Vol. 40, 81-95.= 23 J. C. Hudson, 1970. "Spatial Diffusion Processes and Models of Society," Paper presented for NSF . Seminar on Form and Process in Geographic Theory, Ann Arbor, Michigan• 17 because it gives the researcher the flexibility to modify his model to suit the item being studied as it allows more than one origin point of adoption. Brown and Moore make this point very clear when they call for construction of diffusion models that replicate the item being studied and its locational setting.24
Given that several conceptual processes of innovation are feasible, it becomes extremely difficult to place diffusion processes into types. Gould, appreciating this problem, produces a complementary cross-classification 2 *5 which reinforces Brown's earlier typology. However, a major underlying problem brought out clearly in this section is that models employed in any diffusion study should be such that they closely replicate the item studied and the conditions of the locality in which the diffusing item is found. Thus one of the tasks of this
study is to employ a model adapted to the investigation of location-oriented innovations such as institutions.
24 L, A. Brown and E. G. Moore, 1969. "Diffusion research in geography: a perspective," in Christopher Board e£ a^L., eds.. Progress in Geography, New York, St. Martin's Press, Vol. 1. 25 P. R. Gould, op. cit., footnote 3. 18
Innovation Types
An outgrowth of the disparate efforts at categorizing
diffusion patterns and processes is the related task of placing the diffusing items themselves into distinct
and recognizable groups. Although Hagerstrand, the pro
genitor of modern quantitative diffusion research,
implicitly identified innovation types, explicit classifi cation of these items belongs to latter researchers.
Formal classification of innovations into groups is
the result of Pedersen's work where three distinctive
classes are recognized.^ The first class is termed
resource-oriented, regional specific innovations. This
class of innovations do not have any widespread occurrence.
Rather they are mainly point or areally bound. Common ex amples include innovations specifically designed for extraction of mineral ores or for conversion of less pro ductive agricultural areas into productive ones by irri gation. Household and entrepreneurial innovations form the second and third types respectively. No effort is expended here to point out their characteristics as
Pedersen already provides an excellent list of them. The interested reader is urged, therefore, to consult him.
P. O. Pedersen, ojd. cit., footnote 8. 19
A fourth class of innovations, the topic of this inves
tigation, is recognizable. This class comprises institu
tional innovations. At first sight, the distinction between
entrepreneurial and institutional innovations is blurred biit
subtle differences exist between both types.
Many of the attributes associated with entrepreneurial
innovations, such as a high degree of economic, social and
political risk, competitiveness, non-adoption in rural en
vironments, are true for institutional innovations. Contrar-
ily, institutional innovations occur in rural and urban en
vironments alikej in other words, they are "space competi- 27 tive" and embrace all orders of central places. Institu
tional innovations are, therefore, rightly called "central place innovations" and form the topic of the current research.
Methodological Notes
So far the major diffusion patterns and innovation types have been recognized. There remains a discussion of the strategies employed in analyzing these patterns. Some of these methodologies are subsequently considered.
It has been noted, a priori, that there is a shift of emphasis in diffusion methodology currently in vogue. The
27 The term "space-competitive" in diffusion research belongs to P. R. Gould, 1969. "Geography, Spatial Planning and Africa: The Responsibilities of the Next Twenty YearsJ1 in Cater and Paden, eds. Expanding Horizons in African Studies, Evanston: Northwestern University Press. 20 shifting trend is towards the adoption of other analytical framework, besides the celebrated distance-decay techniques, involving actual statistical testing rather than the visual comparison test associated with simulation techniques.^®
The plea for the use of other analytical models besides
Hagerstrand1s is perhaps first noticeable in Griliches' 29 study. He used a regression model to study the diffusion of hybrid corn among the cornbelt states of the United
States of America. Hudson's negative binomial model is 30 another case in point. Casetti and Derriko, as well as
Casetti and Semple studies, though an "expansion" method, are also indicative of the shifting emphasis in
Some of the more popularly cited diffusion studies^^e^ the Mean-Information Field dominates research methodology^ include R. L. Morrill, 1965. "The Negro Ghetto: Problems and Alternatives." Geographical Review. Vol. 55, 339-62. R. L. Morrill and F. R. Pitts, 1967. "Marriage, Migration and the Mean Information Field." Annals, Association of American Geographers, Vol. 57, 402-22. An excellent recent use of ^he simulation technique is the study by H. M. Rose, 1970. "The Development of an Urban Subsystem: The Case of Negro Ghetto." Annalsf Association of American Geographers, Vol. 60, 1-17.
29 Z. Griliches, op. cit.
30 J. C. Hudson, op. cit. 21
qi methodology. A In both studies Casetti and associates
employed various polynomial functions to test diffusion patterns from given diffusion poles and within given regions.
In particular, Casetti and Demko in their diffusion study of
Soviet Fertility Decline employed a model which assumes the
existence of a linear relationship between the dependent 32 * variable and the independent variable. Their model is
of the form:
y = a +bs (2) where y, fertility rate, is the dependent variable, s, distance, the independent variable and a and b estimated coefficients. Equation (2) can be written as a first-order model as:
y - aQ + blX + e (3) which can still be expanded when more than one predictor variable is involved to take the form:
qi E. Casetti and G. J. Demko, 1969. "A Diffusion Model of Fertility Decline: An Application to Selected Soviet Data: 1940-1965,11 Discussion Paper No. 5 , Columbus, Ohio, Department of Geography, The Ohio State University. See also G. J. Demko and E. Casetti, 1970, "A Diffusion Model for Selected Demographic Variables," Annals. Association of American Geographers, Vol. 60. E. Casetti and R. K. Semple, 1969, "Concerning the Testing of Spatial Diffusion Hypoth eses," Geographical Analysis., Vol. 1, 254-591 32 E. Oisetti and G. J.Demko, o p . c i t . 22
y = ao +blXl + b12X2 + • • • • * blnXn + e (4) which reduces to a multiple regression problem.
Mansfield, in his studies of technical change and the
rate of imitation and intrafirm rates of diffusion of an
innovation, shows how a logistic function can be modified
and used in investigating a diffusion problem.33 He measures the rate of imitation by the model:
(S) where
nij = total number of firms using jth innovation in the
1th industry
mij(t) = number of firms that introduced the innovation
at time t
ij = profitability of installation in terms of alternate
investment
lij = investment needed to install the innovation
Equation (5) is transformed from its logistic form into a linear frame by taking its natural logarithms to assume:
33 E. Mansfield, 1961. "Technical Change and the Rate of Imitation," Econometrica, Vol. 29, 741-66. See also by the same author, 1963. "Intrafirm Rates of Diffusion of an Innovation," Review of Economics and Statistics, Vol. 45, 348-59. 23
mij ft)______In = lij + 0ijt (6) nij - mij(t) which reduces to a regression equation. In essence, by a regression process using the least squares method the estimates of lij and 0ij are obtainable. Mansfield further 34 observes how model (6) can be applied to various problems. The current study is an adaptation of Griliches' approach following the examples of Casetti and Demko and
Mansfield to a study of innovation diffusion patterns 35 within a developing economy. ^ The study model can thus be stated as:
~ a1 + b11X1 + b12X2+ . . . . + blnXn + e]L
y = a + b X + b X + . . • ,+b X + e 2 2 21 1 22 2 2n n 2
ymm = a m + bm1XT ml 1 + bmoXo+ m2 2 • • • ■ + b ran xn + e„ n (?) where y^ • . . • y^ - Origin times or rates of adoption
X^ * . • . Xn = Predictor variables associated with
geographic centers
enx , . , , eH n = Other variables associated with geographic centers not directly
quantifiable
34 E. Mansfield, op. cit.
35E. Casetti and G. J. Demko, op. cit. E. Mansfield, ibid. 24
a, b = Estimated parameters of the model
Equation (7) is operationalized using a multiple stepwise
regression procedure.
Overview of the Research Design
In this study diffusion relationships are examined in
three stages. The first stage involves the use of correla tion and multiple stepwise regression procedures indicated
by equation (7). Origin times or rates of adoptions for geographic centers separately treated as dependent variables are regressed against independent variables such as popula tion size, population density; rank-order of an adoption center within the central place hierarchy; per capita annual gross revenue of centers; airline distance to initial point of adoption, nearest past adoption center, nearest highest order center within the urban hierarchy; and degree of accessibility of adoption centers.
The second phase of the study considers interpretation of the regression analysis in order to identify the prin cipal determinants of innovation patterns. The third stage of the analysis handles the problem of high inter relatedness among independent variables that makes it difficult to assess the individual contribution of each predictor variable in accounting for explained variance.
Principal components analysis is utilized in decomposing 25 the collinearity among independent variables and aids towards isolating clusters of variables underlying observed patterns. The factor scores are graphed and employed in assessing the applicability of hierarchical and neighbor hood effect concepts to a diffusion problem within a modernising context• CHAPTER XIX
SETTING, DATA, VARIABLES AND HYPOTHESES
Introduction
The following sections present discussions of the setting, data sources, variables and hypotheses for the study.
Setting
Eastern Nigeria, the test region, covers an area of about 30,000 square miles. By 1963 its total population amounted to slightly over twelve million persons. It has functioned as a separate region since the division of 1 Southern Nigeria into two parts in 1939. However, the region has recently been balkanized into three states by military decree.2 This study is concerned with the period
■^An excellent summary account of the political and con stitutional developments in Nigeria is presented in J. S. Coleman, 1958. Nigeria: Background to Nationalism, Berk eley, California, University of California press.
^Eastern Nigeria operated as a single political region until 1966 when the present military government of Nigeria split it up, following a national crisis, into Rivers, East Central and South Eastern States.
26 EASTERN NIGERIA Traditional Marketing Network
Q . Principal Market Centers
© Coostcj Ports end Markets
• Secondary Market Centers
Principal Road*
llburu1
-N-
Opobo &
20 4 0 6 0 ___ VEN (cdootod from UkwLp
Fig. 1 28
'( f prior to the formation of the present states*
Eastern Nigeria, as a single political entity, has
experienced many phases of spatial organization. A brief
reference to these phases is deemed necessary for a full
appreciation of some of the forces responsible in the
spatial patterning of modernization of the area.
Before the introduction of western ideas of government
and spatial integration, the study area has already
attained a high level of functional organization based on
an excellent marketing network linking a great number of
coastal ports of Bonny, Opobo, Calabar and Brass with
hinterland centers of Arochuku, Bende, Uzuakoli, Ndizuogu,
Nkwerre, Awka, Oguta and Onitsha, to mention but a few
(Figure 1). Trade brought the missionaries, the progeni
tors of change, and finally, foreign form of administration.
Ukwu, in his studies, provides a highly descriptive and
quantitative geographical analysis of the various aspects
of this indigeneous organization of marketing system.^
U. I. Ukwu, 1965. Markets in Iboland, Eastern Nigeria. Unpublished doctoral dissertation, Departrnent of Geography, University of Cambridge, England. See also B. W. Ilodder and U. I. Ukwu, 1969. Markets in West Africa, Ibadan, Ibadan University Press. 29
Historically Dike, Achebe, Anene and Talbot present a description of the traditional organization of space and the gradual disruption of this space organization by the introduction of western administration.^ There is no intent here to catalogue the sequential developments re sulting in the break-up of the traditional pattern since these sources already fulfil this function. Nevertheless, it may be noted that indigeneous system of space organiza tion created a conducive environment that made for the easy implantation of western spatial organization. Soja makes reference to this point in his monograph although stated slightly in a different vein.^ The eventual outcome of the invitation to British industrialists by indigeneous authorities to teach them the art of making sugar resulted rather into setting up a new pattern of administrative areas.^ These spatial organizational units today are indelibly printed upon the Eastern Nigeria landscape {Figs*2,3)*
4 K. 0. Dike, 1956. Trade and Politics in the Niger Delta, 1830-1685, Oxford, Clarendon Press. C. Achebe, 1958. Things Fall Apart, London and Toronto, Heinemann Educational Books Ltd., Publishers. J. C. Anene, 3-966. Southern Nigeria in Transition 1885-1906, Cambridge, Cam bridge University Press. P. A. Talbot, 1926. The Peoples of Southern Nigeria, Four Volumes. London, Frank Cass and Co., Ltd.
^Edward W. Soga, 1971. The Political Organization of Space, Commission on College Geography. Resource Paper No. 8, Washington, D.C Association of American Geographers. £ P. A. Talbot, o£. cit., footnote 4. 30 I
EASTERN NIGERIA
OGOJA
©Owerrl
OWERRI CALABAR
Calabar
-N-
(g) REGIONAL CAPITAL
© PROVINCIAL CAPITALS
ONITSHA E N U G U ® Onitsha OGOJA
OWERRI
CALABAR iRt V E R S C alqbar Pori Harcourt ©
EVOLUTION OF MODERN SPATIAL ORGANIZATION
Fig. 2 EASTERN NIGERIA ADMINISTRATIVE PROVINCES a DIVISIONS
ENUGU ©
OGOJA © ABAKALIKI! fONITSHA'i
1 9 5 5 - 1965
OWERRI JMUAHir f® © fCALABAR i ® /PORT HARC^J- H ¥ ® © YENAGOA UYO DEGEM
©REGIONAL CAPITAL
© PROVINCIAL CAPITALS (5 © DIVISIONAL CAPITALS^^SUKKA
UDI OGOJ ABAKALI'l 1 9 0 0 - 1 9 6 6 © Kl AwkW u> PH P O R T A / \ /AFIKPC IK0M> HARCOURT /oK LG V fTX iS b u b r a ‘BENDE ENYC, (CWERR1/ KCrri NG/ CAT-AB/ ® I ® Iek p j AHOADA /A B PH/ /BAKi UYO, BRASi^"^®^>SGtON7 1 V'1 |/ORpB/-i EKETJ
e v o l u t i o n o f m o d e r n VEN s p a t i a l ORGANIZATION.
Fig. 3 c 32 A second wave of spatial integration experienced by
the study region, a concomitant of the new form of admin
istration, ensued with the introduction of a road trans
portation system. The traditional transport network did
not suit the new mode of transport, the automobile.
Road development, perhaps one of the greatest changes
experienced in Eastern Nigeria, began in the early twenties.
By 1926 a recognizable highway network became apparent
and by the end of the Second World War the present highway
network has been completed (Figure 4). This singularly
revolutionary transportation development provided the
arteries through which innovations permeated successively
the Eastern Nigeria surface. Thus it is considered that
in an analysis of the spatial forms of modernization in
the study area, the role of roads in aiding change cannot
be neglected.
Urbanization constitutes a third potential force
underlying spatial organization in Eastern Nigeria, Urban
ization, as Mabogunje rightly points out, is a recent
feature of the Eastern Nigeria environment. The rapid
growth of cities in this area can be appreciated by
^See for instance A. L. Mabogunje, I960. Urbanization in Nigeria, London, University of London Press Limited. Also A. L Mobogunje, 1965. "Urbanization in Nigeria: A Con straint on Economic Development," Economic Development and I Cultural Change, Vol. 13, 413-38. I 33
EASTERN NIGERIA
J 914 - 1926 lafter talbot)
1 9 2 6 - 1965
HIGHWAY TRANSPORTATION SYSTEM P Fig. 4 34
reference to Table 1. More vividly the spatial patterns
of urbanization in the study area are portrayed in
Figures 5 and 6. In this study it is being pointed out
that the spatial form of innovations diffusion is closely
controlled by the spatial distribution of towns. Thus an
understanding of the spatial patterns of modernization
requires an appreciation of the way the population is
distributed. Many other generative forces of modernization
in the study region are recognized, but the ones considered
are thought to have considerable spatial implications for
the study.
Data Sources
The data for this research are drawn from a number of
sources. One of the sources utilized comprised intensive
reading of several published documents that were written
before the compilation of any systematic statistics for the
study area. For instance, the ascertaining of the years of adoption of the innovations, namely, secondary schools
and hospitals, by central places and administrative dis
tricts, involved going over several pages of published works
as the following example illustrates. Ojike, in his nar
rative of the impact of modern medicine in Eastern Nigeria
writes:
. . . there was an institution called Iyienu Hospital where all manner of diseased were cured. . . . This hospital was located in a 35
TABLE 1
CITY SIZE DISTRIBUTIONS IN EASTERN NIGERIA
Sizes 1921* 1953a 1963a 5,000-9,999 (No data) 78 228**
10,000-29,999 10 49 138**
30,000-49,999 4 2 12
50,000-99,999 - 4 3
100,000-180,000 - - 5
Total 14 133 486
lTable is compiled by the author from information contained in Eastern r^^gria Census of Populations, 1952-53 and^T962~1963. Urban centers are localities having 5,000 persons and more (A* L. Mabogunje, 1962* Yoruba Towns, Ibadan. Ibadan University Press, 22 pp.). * P. A. Talbot, 1926. Linguistics and Statistics, Vol. IV, London, Frank Cass and Co *, L t d •
Many of the centers within these size classes are not indicated on Figure VI be cause the base-map employed, the latest available on the study region, was compiled in 1962* EASTERN NIGERIA
URBAN PLACES
5,000 - 9,99 9 • 10,000- 29,999 ® 30,000- 49,999 ® 50,000- 80,000 '9
20 4,0 60
VEN source - eo stern nlcerlo census 1953 Ea s t e r n N i g e r i a 0 URBAN PLACES
. - 5 , 0 0 0 - 9,9 9 9
• 10,000- £9,999
• 30,000- 49,999 0 50,000- 9 9,99 9
I 00,000-180,000
8 ••
10 0 4i 0 miles IP VEN. source-eastern niQcrio census 1963 • Fig., 6
u> 38
town called Ogidi about thirty miles away. It was conducted by a strange people called the Church Missionary Society, who had come from 3ritain and established this hospital at the dawn of the century. The King wanted very much to send his blind brother to Iyienu for optical treatment. . . . He arrived at this hospital in 1910 and in 1912 he opened his eyes.8
These written sources are complemented by oral interviews
with Eastern Nigerians in Columbus, Dayton and Cincinnati,
The reader, however, is referred to Appendix A for the
list of the major statistical sources consulted in the preparation of this study.
Nature of Variables
The choice of variables included in this research is made in such a way to reflect two aspects of the study.
First, the variables are selected so that traditional distance and recent locational and behavioral considera
tions are both adequately represented. Second, the variables are organized into two parts, namely, variables associated with individual urban places and those asso ciated with administrative districts.
Mbonu Oj'ike, 1946. My Africa, New York: The John Day Company. 39
Urban Place Variables
(a) Origin time calculated as the number of years that
have elapsed since initial adoption.
(b) Rate of adoption measured as the speed over time at
which diffusion proceeds within and among centers
after the first adoption.
(c) Population size of an adoption center as of 1963.
(d) Population density of an adoption center as of 1963*
(e) Airline distance to initial point of adoption.
(f) Airline distance to the nearest past adoption center.
(g) Airline distance to the nearest highest order center
within the central place hierarchy. The first order
centers in the study area are Aba, Calabar, Enugu,
Onitsha and Port Harcourt.
(h) Per capita annual gross revenue by urban places. It
is measured as the mean annual gross revenue of a
center from 1952 through 1960.
(i) Rank order of an adoption center within the central
place system. This variable indicates the functional
rank-order, i.e., the rank order of services or
functions provided by a center within the central place
hierarchy. Types of government and parish status of
centers are employed in this ranking.
(j) Degree of accessibility of an adoption center. It is
a weighted measure of the number of links incident to 40
that center including consideration of other trans
portation links such as airline or railway or seaport
linkages*
Administrative District Variables
(a) Level of road transportation development within an
administrative district. This is measured as the
trunk road mileage per 100 square miles by an admin
istrative area as of 1962.
(b) Cumulative increase in gross annual revenue by admin
istrative district as of 1952 through 1960.
(c) Literacy Rate by administrative district as of 1952.
(d) Percent Christians by administrative district as of g 1963.
(e) Percent of labor force in population 15 years and over
by administrative district.
(f) Percent of persons engaged in professional, technical,
and related employment by administrative district.
(g) Percent of persons engaged in administrative,
executive and managerial employment by administrative
district•
(h) Percent of persons employed in clerical occupations by
administrative district..,
g Note all subsequent labor force figures relate to this data except otherwise stated. 41
(i) Percent of persons engaged in agricultural and related
employment by administrative district.
(g) Percent of persons engaged in transport and communica
tion employment by administrative district.
(k) Percent of persons engaged in manufacturing employment
by administrative district.
(1) Percent of persons engaged in service, sports and
recreation employment by administrative district.
As can be noticed the variables listed here consist of two parts. One part related to variables associated to individual centers whereas the other half relates to ad ministrative districts. These variables, therefore, reflect the two geographic scales at which the empirical tests of the study will be conducted. The hypotheses for the study can now be appropriately considered.
Hypotheses
Drawing from the conceptual background reviewed earlier and from the description of the variables in this chapter, the study hypothesis is now addressed. The con cern of the research is to determine to what extent the spatial adoption patterns of institutional innovations within a modernizing society* are the result of the combined operation of hierarchical and neighborhood factors. It is convenient to view the patterns of adoption of innovations 42 over time in two parts. The first part consists of the origin time pattern of innovation adoption* The second part comprises the pattern of rate of diffusion. In this con text, it is postulated that the hierarchical and neighbor hood factors operating to affect both the origin time and rate of adoption of innovation for a ’’geographical center" can be related to its locational, political, cultural, social and economic attributes.^ The hypothesis can be broken down into:
(1) Origin Time of Adoption
The date of origin of adoption by a geographical
center
(a) will vary directly with distance to the innovation
center
(b) will vary directly with distance to nearest pre
viously adopting center
(c) will vary directly with distance to nearest highest
urban center within the central place hierarchy
(d) will be inversely related to a center's socio
economic rank in the urban hierarchy
The term "geographical center" is loosely employed here. It can refer to a geographic region or a point of human concentration such as a village, town or city. It is used mainly in this study in terms of the latter defini tion. 43
(e) vd.ll vary inversely with the population size of a
center
(£) will vary inversely with the center's population
density
(g) will vary inversely with center's rank order in the
central place system
(h) will vary inversely with the center's overall accessi
bility.
(2) Rate of Adoption
H2 the rate of adoption for a geographical center
(a) will vary directly with variables (a) through (c)
as listed in
(b) will vary inversely with variables (d) through (h)
as listed in H^.
The testing of the postulates presented here depends to a large degree on data availability. As the hypotheses indicate the individual center or urban place constitutes the geographic unit at which the tests should be performed.
But as is sometimes encountered in research relating to the developing world, this is the geographic scale at which data are difficult to assemble particularly on some theoretically derived variables. More often, ample data, on the contrary, do exist at some larger geographic units such as administrative counties or districts. This is the case with the present study area. 44
In recognition of this problem, the empirical tests will be conducted at two geographic scales. First, the tests will be carried out for individual central places and will involve fewer variables. Secondly, the tests will be extended to the administrative district level where more variables can be included. The sections following imme diately will consider the results from the tests of the hypothesized relationships presented here at both geographic scales. CHAPTER IV
EMPIRICAL TESTS - ORIGIN TIMES OF ADOPTION
Introduction
This section examines the hypotheses developed with respect to origin times of adoption of secondary schools and hospitals at both the individual central place and administrative district levels within Eastern Nigeria.
Origin time, more precisely defined at this point of the analysis, refers to the time lag between initial date
(1895 for secondary schools and 1881 for hospitals) when both institutions first appeared in Calabar and Itu, respectively, and the dates at which they are subsequently first observed in the test centers or administrative districts included in this study. Figure 7 illustrates the temporal phases of all first adoptions of secondary schools in the study area. Altogether a sample of 157 secondary school and 57 hospital locations are used as test points for the origin time adoption test. Many of these centers, with respect to both institutions, are exactly the same except for very few cases. Their locations are shown in Figure 8•
45 o L
*»Hl|| (til h »»»»« »ii« *i»/n ll^k * OOC —
X\o m^ . v )j! , © © ” " ‘ --- . * T— v.‘V /• /t •• •« •« »
^ i _____
l!kW&Cf[| iiimrnmn »fl * l««l OCII - U H I f • * • *.V otti - rod E5SSSSZ3 ooci * ttti
Mm •it*l|N utai} • MIMK 47
1
CO
01 a. L ' - n
n v A \ l * i" • S51',! I- ■/(IM* t • o*
0 48
Perhaps a much more crucial exercise undertaken in
this section is an evaluation of the extent to which con
cepts developed in diffusion research for urbanized and
industrialized societies are applicable to an identical problem within a modernizing situation* In this regard,
the relative importance of variables indexing hierarchical
and distance decay effects are considered* The pertinence
of these concepts, with their associated variables to the problem, is judged using the multiple stepwise regression
model.
Origin Times Adoption Test Results - Individual Central Places
Given the theoretical postulates developed earlier in
the study, this section tests the hypothesized relationships concerning adoption times of secondary schools and
hospitals and a select number of independent variables.
The list of the sample of towns serving as observation centers is provided in Appendix B.
The dates at which these centers adopted the respec
tive institutions under study are regressed against eight explanatory variables listed as (c) through (j) under urban variables earlier in the study. Tables 2 and 3 represent
the simple correlation coefficients obtained and serve as preliminary indications of the relationships between the dependent and independent variables. Table 2 indicates v/>.
49 u TABLE 2
SIMPLE CORRELATION COEFFICIENTS BETWEEN ADOPTION TIMES OF SECONDARY SCHOOLS AND EIGHT INDEPENDENT VARIABLES (INDIVIDUAL CENTER LEVEL)
Abbreviations Coefficients Variable Full Name#
Popsiz -0.5222 Population Size of Center
Distor 0.2332 Distance to Origin Point of Adoption*
Distpa -0.3937 Distance to Nearest Past Adopter Center*
Dislcp -0.0173 Distance to Nearest Largest Functional Center*
Popden -0.2068 Population Density around Adoption Center**
Aaninc -0.3961 Average Gross Annual Revenue of Center*
Fctran -0.5247 Functional Rank of Adoption Center*
Nolink -0.5268 Nodal Link of Adoption Center*
^Calculated by the author.
Obtained from Eastern Nigeria Census of Population, 1953-1963.
#No further definitions are given with respect to these variables in subsequent tables with the exception of where more variables are used. TABLE 3
SIMPLE CORRELATION COEFFICIENTS BETWEEN ADOPTION TIMES OF HOSPITALS AND EIGHT INDEPENDENT VARIABLES
Variable Coefficients Popsiz -0.3811
Popden -0.1335
Distor 0.3075
Distpa -0.3756
Dislcp -0.1762
Aanine -0*2818
Fetran -0.4214
Nolink -0.3920 51
secondary school adoption times by central places are positively correlated with distance to initial point of origin. A negative relationship is established between
the dependent variable and other explanatory variables.
Based on these preliminary results the study hypotheses are upheld.
As regards the results from the hospital data, there is also a positive relationship established for the dependent variable and distance to original point of adoption. The other remaining predictor variables, as in the secondary school data, are inversely related to origin tiroes of adoption. Both tables of correlation coefficients illustrate that some variables, such as population size, rank order and overall connectivity of a center, compara tively have the same degree of association with the dependent variable, thus posing a problem of interpretation in assessing their importance.
The above problem is handled by shifting the analysis to the multiple stepwise regression results. Table 4 presents results obtained for secondary schools. Accessi bility is indicated as the first important variable entering the regression equation. It accounts for 27 percent of variance in the predicted variable. Its associated regression coefficient differs significantly from zero at the 99 per cent confidence limit and has the expected negative sign, thus validating the hypothesized relationship. TABLE 4 MULTIPLE STEPWISE REGRESSION RESULTS— ADOPTION TIMES OF SEOONDARY SCHOOLS (INDIVIDUAL CENTER LEVEL)
Variable Order Standard Partial Regr. Degrees of R R2 F-Value of Entry Error R Coefficient Freedom Nolink 0.5268 0.2775** 0.1091 59.9402** •0.5269 -0.8449** 156
Popsiz 0.6070 0.3684** 0.0260 45.2127** -0.3547 -0.1229** 155
Distor 0.6264 0.3924** 0.0198 33.1607** 0.1950 0.0489** 154
Fctran 0.6377 0.4067** 0.1724 26.2262** -0.1534 -0.3309** 153
Dislcp 0.6522 0.4253** 0.0389 22.5056** 0.1772 0.0865** 152
Distpa 0.6642 0.4411* 0.0555 19.8697** -0.1658 -0.1147* 151
Popden 0.6652 0.4425 0.0003 17.0095 0.0486 0.0002 150
Aaninc 0.6668 0.4446 0.0000 14.9122 -0.0618 -0.0000 149
* Significant at the 95 per cent level*
Significant at the 99 per cent level* 53
Population size is entered second. In combination with accessibility, the coefficient of determination is
increased to 36 percent. The result is tested for an analysis of variance using the F-test. It proved highly significant at the 99 percent level of confidence. Dis
tance to original point of adoption, rank order of a center within the central place hierarchy and distance to nearest highest first urban place occupy the third, fourth and fifth places, respectively. Altogether their com bined influence, while the remaining three variables are held statistically constant, increased the R to 42 percent.
Distance to the nearest previously adopting center occupies the sixth position and only raises the level of explained variation by 2 percent. Population density and per capita gross annual revenue, which are closely associated them selves and also with population size, do not raise the level of explained variance. Consequently they are considered redundant in the predictive equation.
Regression results from hospital adoption times are presented in Table 5. As the table indicates changes in the positions of variables occurred. Rank order of a center within the central place hierarchy is the first variable entering the regression. It accounts for a corre lation of 42 percent with the dependent variable and its regression coefficient has the expected sign. From this o
TABLE 5
MULTIPLE STEPWISE REGRESSION RESULTS— ADOPTION TIMES OF HOSPITALS (INDIVIDUAL CENTER LEVEL)
Variable Order Standard Partial Regression Degrees of R R2 F-Value of Entry Error R Coefficient Freedom Fortran 0.4214 0.1776* 0.4194 11.8796** -0.4215 -0.1445** 55
Distpa 0.5408 0.2924* 0.1004 11.1608** -0.3737 -0.2973** 54
Distfir 0.6955 0.4838** 0.2861 16.5604** -0.5313 -0.4127** 53
Popsiz 0.7148 0.5110** 0.0615 13.5867** -0.2295 -0.1046** 52
Popden 0.7230 0.5227** 0.0719 11.1717** 0.1547 0.0008** 51
Nolink 0.7305 0.5337** 0.4085 9.5386** 0.1517 0.4434** 50
Aaninc 0.7327 0.5369**' 0.0002 8.1182** -0.0836 -0.0002** 49
Dislcp 0.7335 0.5381** 0.1138 6.9904** -0.0495 -0.0391** 48
^Significant at the 95 per cent level. ^ y Significant at the 99 per cent level.
Ui 55 result it is concluded that hospitals diffuse in a hierar chical manner.
Distance to the nearest previous adopting center is entered second and has a negative regression coefficient that differs significantly from zero at the 99 percent con fidence limit. It thus indicates that this variable con trary to hypothesis has an inverse relationship with dependent variable. It raises the level of explained variance markedly by 12 percent, thus with rank order variable increases the to 30 percent. Distance to original point of adoption is the third variable to enter next. Similarly, contrary to the hypothesis, a negative relationship is noted, population density and degree of accessibility, on the other hand, are positively associated with adoption time. Though this result invalidates the study hypothesis, it is still very reasonable xn that it demonstrates as the population density and accessibility of a center increases with time, there is greater proba bility of increased adoption for that center. This result can also be interpreted as indexing increased information and interaction factors that Brown identifies as being *1 important in understanding diffusion processes. All the eight explanatory variables used in the test proved to
A. Brown, o p . cit. S6 contribute significantly in explaining the observed vari ational pattern in hospital diffusion. An overall of
53 percent is accounted. The result is satisfactory.
Xn general the results of adoption time tests reveal some important findings about the diffusion of similar institutions. Normally it is to be expected that their patterns of diffusion will be the same. On the contrary, the results obtained indicate some apparent differences.
For secondary schools the degree of connectivity and population size of centers, having partial correlation coefficients of 52 and 35 percents, respectively, by far are the most important variables generating the observed patterns. Hospital adoption time patterns, conversely, are attributed largely to distance to origin point of adoption {partial r -.53), rank order of centers within central place system (partial r-.52) and distance to nearest previous adopting center (partial r - .37). Thus it is demonstrated that hospital adoption pattern is marked by a strong distance-decay component.
For both institutional patterns investigated, the predictor variables are far more effective in accounting for observed variations of hospital diffusion than they do for secondary schools. However the overall explained variance for secondary schools (44 percent) and hospitals
(53 percent) is poor since a large proportion of the variance remains to be explained. This situation does not 57 impair the conclusions reached here as it can be explained by the crude nature of the variables employed and also by the complexity of the problem. It is contended, therefore, the results obtained here can considerably be reinforced with the inclusion of more sophisticated variables. This task is done by turning the analysis to a higher level of geographic aggregation, the administrative district.
Origin Time Adoption Test Results - Administrative Districts
Seventeen independent variables measuring the socio economic characteristics of twenty-seven administrative districts (Figure 3) are used in the test. Since the idea is to ascertain how far the inclusion of more variables in the model can lead to a higher degree of predictability, the test is carried out only for secondary school adoption time patterns. Hospital adoption time patterns are not investigated further because a much more satisfactory result is indicated for it at the central place level.
The initial results of secondary school adoption time patterns are presented in Table 6. From the table, it is observed that nearly all the socio-economic variables do maintain a negative relationship with adoption times as' hypothesized except for three cases. Percent labor force in administrative employment, distance from initial point of origin and percent labor force in transport and communica- 58
TABLE 6
SIMPLE CORRELATION COEFFICIENTS BETWEEN ADOPTION TIMES OF SECONDARY SCHOOLS AND 17 INDEPENDENT VARIABLES (DISTRICT LEVEL)
Abbreviations Coefficients Variable Full Name*
Popsiz -0.6945 Population Size
Fctran -0.3269 Function Rank**
Diggre -0.3419 Different Gross Annual Revenue***
Popden -0.1384 Population Density
Pctlit -0.5055 Per cent of Literate Population
Pctxti -0.3897 Per cent of Population Christians
Pctlfp -0.2533 Per cent of Labour Force (15 years plus)
Pctptr -0.0435 Per cent Labour Force in Professional, Technical, and Related Employment
Pctadm 0.0699 Per cent Labour Force in Administrative Employ ment
Fctlsw -0.2131 Per cent Labour Force in Sales Employment
Petite 0.1170 Per cent Labour Force in Transport and Commun ication Employment
Pctagr •0.1120 Per cent Labour Force in Agricultural Employment
Pctlcp -0.2415 Per cent Labour Force in Crafts and production Employment TABLE 6 (continued)
Abbreviations Coefficients Variable Full Name*
Pctlsr -0.2824 Per cent Labour Force in Sports or Recreation Employment
Pctcln -0.0852 Per cent Labour Force in Clerical Employment
Trmphs -0.2856 Trunk Road Mileage Per Hundred Square Miles***
Distor 0.1844 Distance to Origin Point of Adoption
■* Variables, relating to Administrative District Data, are obtained from Eastern Nigeria Population Census, 1963, Volume II, Federal Census Office, Lagos, Nigeria, except otherwise stated.
Calculated by the author.
Obtained from Statistical Digest of Eastern Nigeria, Official Document No. 22 of 1963, Statistics Division, Edition 1, Ministry of Economic Planning, Eastern Nigeria, Enugu. 60 tions employment are positively related to origin times of adoption. Population size and percent of literate popula tion have the highest absolute correlation with the depen dent variable. All the remaining variables maintain nearly the same degree of association with the predicted variable thereby making it rather difficult to assess their relative contributions. However, it is noted too at this level that the study hypotheses are valid.
To assess the relative contribution of each of the independent variables, the regression results are analyzed.
From Table 7 it is demonstrated that population size is the most singular variable influencing adoption times. It 2 alone accounts for an R of 48 percent and has an expected negative regression coefficient that is statistically dif ferent from zero at 99 percent confidence limit. Alto gether seven explanatory variables proved to be contributing significantly to explained variance at the 99 percent level. The computed result of the seventh step in the regression analysis is presented in Table 8. Their com bined effects raised the coefficient of determination to
69 percent. Beyond this step the remaining variables made a slight contribution to explained variance but only at the 95 percent level of confidence. Two variables are deleted from the regression as their F-ratios fell below the specified minimum level. They are percent labour force in administration and agricultural employment. Fifteen TABLE 7
MULTIPLE STEPWISE REGRESSION RESULTS - ADOPTION TIMES OF SECONDARY SCHOOLS (ADMINISTRATIVE DISTRICT LEVEL)
Variable Order 2 Standard Partial Regr. Degrees of of Entry Error F-Vaiue r _____ Coefficient Freedom
Popsiz 0.6945 0.4824** 0.0487 23.29** -0.6945 -0.2352 26
Pctlcp 0.7375 0.5439** 0.0128 14.31** 0.3447 0.0231 24
Pctlit 0.7775 0.6046** 0.0407 11.72** -0.3649 -0.0765 23
Trmphs 0.7887 0.6220** 0.4486 9.05** -0.2099 -0.4516 22
Pctptr 0.8019 0.6430** 0.0845 7.56** 0.2358 0.0939 21
Diggre 0.8223 0.6762** 0.0009 6.96** 0.3048 0.0012 20
Pctlsr 0.8353 0.6977** 0.0992 6.26** -0.2579 -0.1154 19
Petite 0.8404 0.7064* 0.0079 5.41* -0.1693 -0.0057 18
Popden 0.8433 0.7112* 0.0166 4.65* 0.4693 0.3356 17
Pctxti 0.8516 0.7253* 0.0242 4.22* 0.2208 0.2197 16
Fctran 0.8534 0.7282* 0.2896 3.68* -0.1041 -0.1174 15
Pctlsw 0.8699 0.7567* 0.0122 3.62* -0.2454 -0.0095 14 TABLE 7 (continued)
Variable Order Standard Partial Regr. Degrees of R R2 F-Value of Entrv Error R Coefficient Freedom Pctlfp 0.8735 0.7630* 0.0407 3.22* -0.1609 -0.0239 13
Pctcln 0.8746 0.7649* 0.1166 2.78* -0.0896 -0.0363 12
Distor 0.8756 0.7668* 0.0745 2.41* -0.0891 -0.0221 11
Pctadm 0.0074 -0.0272***
Pctagr 0.0039 -0.0197***
* Significant at 95 per cent level. ** Significant at 99 per cent level.
Redundant Variables, F-ratios below the specified minimum level of 95 per cent confidence and consequently deleted from the regression equation. TABLE 8
COMPUTED REGRESSION RESULTS AT THE SEVENTH ORDER OF VARIABLE ENTRY
Computed Y (Ortime) = 74.6350 -*0.2717X-j + 0.0011X4 (10.68) (4.15) (1.20) -0.1110X6 + O.2890X7 + 0.0273X (2.14) (2.14) (1.50) 14 - 0.1154X,- - O,7141X,— o (1.16) 15 (1.55)* R = 0•69#*
Beta Weight s** * of the IridepCTde'nt Var iables Variable______Weight PopsizX2 -0.8022
DiggreX4 0.3895
PctlitX. -0.4767 6 PctptrXg 0.4642
PctlcpX-^4 0.3764
PctlsrX^g -0.2606
TrmphsX^7 —0.2460
* . . Values within the brackets are T-values
•• • • - Significant at the 99 per cent level of confidence. *** -- . Beta weights are \ standardized regression coefficients with zero means and unit variances. 64
variables consequently are significant to explaining adop
tion time patterns of secondary school diffusion in the
study region. They explain 76 percent of the observed
variance, a more satisfactory result than that obtained
for individual central places.
Beta weights of the variables included at the fifteenth
step of the regression are presented in Table 9. They in
dicate the relative standing of each of the significant
fifteen variables entering the final regression computation.
Population size, having the highest beta value, clearly is
the most important variable influencing secondary school
adoption patterns at the administrative district level.
Next in importance in accounting for variation in secondary
school adoption times at the administrative district units
are literacy level of the population, percentage of labour
force in professional, technical and related employment,
and gross annual income.
These results are quite satisfactory. As the adoption phases of secondary schools in the study area indicate,
the large urban centers are the first adopters. These
higher order urban places also are found within the more
economically developed administrative districts having high
levels of literacy, more professionally skilled personnel
and therefore more capital. Thus the results presented
here are in agreement with reality. It also suggests that TABLE 9
COMPUTED REGRESSION RESULTS AT THE FIFTEENTH ORDER OF VARIABLE ENTRY
Computed Y (Ortime) = 95.90 - 0.2676X2 - 0.9791X3 (3.09) (2.63) (1.32) + 0.0018X. + 0.0218X,- - 0.1770X. (1*21) (0.83) (1.44) + 0.0223X7 - 0.0267Xft + 0.4366X (0.71) (0.59) (2.21) + 0.0181X1;L - 0.0092X12 + 0.0256X14 (1.28) - 0.2332X1S - 0.0414X16 - 1.1424X17 - 0.0221X18* R2 = 76**
Beta Weights*** of the Computed Independent Variables Variable Weight PopsizX2 -0.7901
FctranX3 -0.5444
DiggreX4 0.6468 PopdenX^ 0.3018 PctlitX^ -0.7603 PctxtiX^ 0.2958
PctlxpXg -0.1210
PctptrXg 0.7011
PctltcX^g -0.2195
PctlcpX14 0.3522
P c tlsrX ^ -0.5265 TABLE 9 (continued)
Beta Weights*** of the Computed Independent Variables Variable Weight
PctclnX16 -0.0639
TrraphsX17 -0.3936
DistorX^g -0.0559
■# Values within the brackets are T-values.
Significant at the 99 per cent level of confidence.
Beta weights are standardised regres sion coefficients with zero means and unit variances. with the addition of more precise and refined variables
that a great degree of variance can be explained.
Summary
Some conclusions regarding origin time patterns of institutions in Eastern Nigeria can now be drawn based on the evidence presented so far. For both institutional patterns, it is shown that hierarchical and neighborhood factors can adequately with increased number of variables explain observed variation. The degree of connectivity of a center is shown to be a prime variable accounting for secondary school adoption time patterns. Hospital origin time pattern is demonstrated to be generated chiefly by the rank order of a center within the central place system. In addition, hospital diffusion time patterns are shown to display strong distance-decay biases. At both levels of investigations the explanatory variables, in spite of their crude nature, offer adequate explanation to observed vari ation. However it is noted the individual contributions of variables tend to be highly masked indicating strong cluster effect. This is illustrated by the fact that the first variable entering the regression equation largely accounted for a greater prop.ortion of the total explained variance. Other significant variables entering the regres sion later do not markedly raise the level of explanation even though an indication of their relative importance is 68
w given by their partial and beta coefficients. Thus it is
concluded that the independence and additivity assumptions
of the regression model are not adequately satisfied. This
in turn suggests the desirability of decomposing the matrix
of intercorrelations of the independent variables in order
to identify their structural similarity groups. A prin
cipal components analysis is to be employed in this connec
tion. The results of this task axe considered should
provide a more parsimonious description of innovation
adoption patterns of institutions in Eastern Nigeria.
Although the empirical results so far indicate a satis
factory level of explained variance is achieved, a still
considerable amount of the total variance remains to be
explained. This fact is illustrated by examining the
residuals from the regressions. It is discernible imme
diately from mapping of the residuals that there is evidence
of strongly marked regional variations. These residual
maps consistently exhibited the same regional patterns that
it is deemed unnecessary to reproduce^here individually.
However only one of such maps is shown here for illustration.
Figure 9 represents the residuals from the regression
regarding origin times of adoption of secondary schools by
individual centers. Several regional patterns are recog
nizable and these suggest that some observation centers
, clearly have some common underlying spatial similarities. I ‘ . ■ rjiou *ks*cssioii
Ogajt
E5HE3'o - W pTtTtTTtTTTl e - - *a | . j-S • *1-0
§ © 1 . . £ © ) ^
ioo.ooo- 110,0 oo - 50,006- 9 7 .9 9 9 - 3 0 . 0 0 0 - 49,999
“ t O . O C O - 29,9 9 9 "“ * ,0 0 0* 9,999 - - 5 0 0 - 4,999 Ctrclft »U« b*M< 1 1 4 9 m M* m * l i g w m
VEH
Fig. 9 I 70
Apparently, observation centers occupying southern portion
of the study srea in general belong to one large regional
group. Conversely, a second large regional pattern is dis cernible. This area unifies centers belonging to Owerri,
Okigwi, Bende and Afikpo districts. North of this zone is
another distinct region consisting of towns within Awka,
Orlu and Onitsha districts. Each of these zones seems to indicate among it a common unifying spatial dimension*
The towns within the peripheral districts of Nsukka, Ogoja and Yenagoa fall within another regional group and are in dexing relative inaccessibility.
Apart from these distinct localized patterns, one can also discern a strongly marked regional trend pattern of residuals running from the northwest to the southeast of the study region. This diagonal pattern suggests the existence of some common underlying spatial dimensions. It is difficult at this point in the study to define precisely these spatial components. However their presence supports the idea of carrying out further analysis on the intercorre lations observed among the independent variables. Such a task is undertaken later in the study. CHAPTER V
EMPIRICAL TESTS - RATES OF ADOPTION TEST
Introduction
Similar to the origin time test, rates of diffusion
tests for secondary schools and hospitals are conducted
also at two levels of investigation. Rate of diffusion,
that is, the speed at which a center adopts an innovation
over time after the first adoption, is regressed against the explanatory variables. Quite contrasting results are obtained. Tables 10 through 18 present the results ob tained with respect to the hypothesized relationships.
Origin Times Adoption Test Results - Individual Central Places
The preliminary results relating to rates of secondary school diffusion by individual central places are presented in Table 10. As hypothesized distances to innovation center, nearest previously adopting center and nearest highest urban center are positively correlated with rate of diffusion. On the contrary, the inverse relationships postulated for the dependent variable and population size, population density, rank order, per capita gross annual revenue and overall connectivity variables for centers
7/ TABLE 10
SIMPLE CORRELATION COEFFICIENTS BETWEEN RATES OF DIFFUSION OF SECONDARY SCHOOLS AND EIGHT INDEPENDENT VARIABLES (INDIVIDUAL CENTER LEVEL)
Abbreviations Coefficients Popsiz 0*8096
Distor 0.0562
Distpa 0.7087
Dislcp 0.1778
Popden 0.4177
Aanine 0.6880
Fctran 0.5822
Nolink 0.5435 73 are disproved. The simple correlation results are suspect and, therefore, cannot be regarded as completely valid.
The validity of the above results can be established by an examination of the regression results. From Table 11 it becomes clear that all the explanatory variables with the lone exception of population density are directly related to rate of diffusion thus confirming the contrasting results. However it is noted that rate of diffusion of secondary schools increases fox centers located farther from innovation center. Secondly, as the distance to the nearest highest order urban place increases the rate of adoption of secondary schools increases for centers. In creased overall connectivity of centers is also seen to be directly related to the speed of adoption of secondary schools for individual centers. Socio-economic factors, such as represented by the functional rank order of a center within central place system and per capita annual gross revenue of center, are positively related to speed of secondary school diffusion. These results can be summarized in two ways. It is demonstrated that increasing distances from innovation center and highest order central places considerably increase the rate of secondary diffusion in l':; the first stage. Secondly, it^illustrated as improvements overall accessibility of centers and their socio-economic characteristics increase with time, there is an accompanying increase in the rate of diffusion. The latter can be seen TABLE 11
MULTIPLE STEPWISE REGRESSION RESULTS - RATES OF ADOPTION OF SECONDARY SCHOOLS (INDIVIDUAL CENTERS)
Variable Order Standard Partial Regr. Degrees of R R2 F-Value of Entry Error R Coefficient Freedom
Distor 0.8094 0.6559** 0.0034 294.83** 0.8096 0.0590 156
Dislcp 0.8590 0.7378** 0.0062 216.77** 0.4892 0.0438 154
Nolink 0.8707 0.7581** 0.0151 159.87** 0.2780 0.0.416 153
Popden 0.8802 0.7747** 0.0053 130.70** -0.2620 -0.0180 152
Fctran 0.8853 0.7853** 0.0000 109.56** 0.2017 0.0000 151
Aaninc 0.8882 0.7888** 0.0024 93.42** 0.1517 0.0046 150
Popsiz***
Distpa***
** Significant at 99 per cent confidence limit.
Deleted from the regression because their F-ratios fell below the specified minimum level of 95 per cent. 75
to be a function of increased level of information and awareness and profitability factors. Generally the
explanatory variables effectively explain a large propor tion of the variation in the rate of secondary school diffusion (R2 = 78 percent).
Hospital diffusion rate test by centers are presented in Tables 12 and 13. The rate at which diffusion of hospitals proceeds through time for individual centers is positively related to distance variables (Table 12) but more strongly and directly related to population size, per capita revenue, rank order of center and general accessibility variables. The latter result is contrary to the hypotheses. Again this result can hardly be regarded as final.
More valid assessments can be made from Table 13 re garding the postulates of rate of hospital diffusion.
It is apparent then that population size (partial r .92), distance to nearest previous adopting center (partial r ,18) and per capita revenue (partial r .47) variables, with their positive regression coefficients, are directly related to hospital rate of diffusion. However it is observed that population size, contrary to the hypothesis, is positively related to rate of diffusion, thus corrobor ating evidence obtained from the sample correlation results. TABLE 12
SIMPLE CORRELATION COEFFICIENTS OF RATES OF ADOPTION OF HOSPITALS AND EIGHT INDEPENDENT VARIABLES (INDIVIDUAL CENTER LEVEL)
Abbreviations Coeffici Popsiz 0.9229
Distor 0.0357
Distpa 0.2113
Dislcp 0.2368
Popden 0.4419
Aaninc 0.7546
Fctran 0.6061
Nolink 0.5484 TABLE 13
MULTIPLE STEPWISE REGRESSION RESULTS - RATES OF ADOPTION OF HOSPITALS (INDIVIDUAL CENTER LEVEL)
Variable Order Standard Partial Degrees of of Entry R R2 Error F-Value R Coefficient Freeora
Popsiz 0.9229 0.8518** 0.0017 316.16** 0.9229 0.0311 56
Dislcp 0.9304 0.8657** 0.0030 174.10** 0.3065 0.0072 54
Distor 0.9331 0.8708** 0.0020 119.10** -0.1947 -0,0030 53
Nolink 0.9358 0.8757** 0.0114 91.62** -0.0164 -0.0164 52
Fctran 0.9393 0.8822*’* 0.0176 76.45** -0.2295 0.0296 51
Distpa 0.9414 0.8862** 0.0029 64.94** 0.1839 0.0039 50
Popden 0.9428 0.8889** O .0000 56.02** -0.1529 -0.0000 49
Aaninc 0.9562 0.9143** 0.0000 64.07** 0.4786 0.0000 48
Significant at the 99 per cent level of confidence. 78
On the contrary, the results obtained with respect to overall accessibility, rank order and population density variables are not confirmed. As the regression results indicate (Table 13) these variables truly maintain an in verse relationship with the dependent variable as hypoth esized* Thus the preliminary results from the simple corre lation are not validated. The certainty of these results can further be affirmed in that the regression and partial correlation coefficients of these variables are all negative (Table 13).
In making an overall summary of rates of diffusion for both institutions by central places, the following points can be made. The explanatory variables employed in the study are more effective in accounting for observed variances in diffusion rates than for origin times of adoption. It is shown that hierarchical and neighborhood variables do not display any dominance in the way they enter the regression steps. A shadow effect is apparent ft\ ' ■! 'V 1. / .! in that it is demonstrated that.variables are highly inter- related with one another. Furthermore it is illustrated that in both tests the initial variable entering the regres sion accounted for a large proportion of the total ex plained variance. Variables entered in succeeding steps only make a small increment to the previous levels of pre dicted variance. This is to be expected since the variables are characterized by great intercollinearity. Thus it is 79
shown that the contribution of the initial variables in
the regression is in fact masking the indirect effects of
the other variables. It becomes, therefore, desirable to
decompose the correlation matrix of the explanatory vari
ables to search out their cluster groupings underlying diffusion rates. This task is considered later in the
study.
Rates of Adoption Test Results - Administrative Districts
The results of diffusion rates for administrative dis
tricts are similar'. to the ones obtained for individual
centers* These results are presented in Tables 14 through
18, The simple correlation coefficients of the tests for
both institutions are shown in Table 14 and 15, As these preliminary results indicate, positive correlations are
established between the independent variables and predicted variable ivith exception of origin time which is now entered
as an explanatory variable. Thus the hypothesized rela
tionship for most of these socio-economic variables is not
established.
The last problem is looked at more closely from the regression results. As can be seen from Tables 16 and 17 a greater majority of the variables have positive partial and regression coefficients, thus upholding the preliminary findings. Only a few predictor variables indicate otherwise, thus agreeing with the hypotheses established for socio- TABLE 34
SIMPLE COUPELATION COEFFICIENTS BETWEEN RATES OF ADOPTION OF SECONDARY SCHOOLS AND SIXTEEN INDEPENDENT VARIABLES (ADMINTSTRATIVE DISTRICT LEVEL)
Abbreviations Coefficients Distor 0.3335
Or time -0.2131
Diggre 0.0110
Popden 0.4401
Pctlit 0.2206
Pctxti 0.2211
Pctlfp 0.0293
Pctptr 0.6648
Pctadm 0.2638
Pctlsn 0.3719
Pctltr 0.4166
Petite 0.3134
Pctlcp 0.3446
Pctlsr 0.5709 pctcln 0.1844
Trniphs 0.2981 TABLE 15
SIMPLE CORRELATION COEFFICIENTS BETWEEN RATES OF ADOPTION OF HOSPITALS AND SIXTEEN INDEPENDENT VARIABLES (ADMINISTRATIVE DISTRICT LEVEL)
Abbreviat ions Coefficients Distor 0.1762
Or time -0.5441
Diggre 0.2607
popden 0.2976
Pctlit 0.4165
Pctxti 0.2893
Pctlfp 0.2200
Pctptr 0.4357
Pctadm 0.0936
Pctlsw 0.4924
Pctltr 0.1432
Petite 0.3884
Petlep 0.4397
Pctlsr 0.5678
Pctcln 0.3670
Trmphs 0.1353' TABLE 16
MULTIPLE STEPWISE REGRESSION RESULTS - RATES OF ADOPTION OF SECONDARY SCHOOLS (ADMINISTRATIVE DISTRICT LEVEL)
Variable Order Standard Par tial Regr. Degrees of of Entry R R2 Error F-Value R Coefficient Freedom
Pctptr 0.6648 0.4419** 0.0553 19.80 0.6648 0.2462 25
Petite 0.7096 0.5035** 0.0084 12.17 0.3322 0.0145 24
Trmphs 0.7301 0.5330** 0.2505 8.75 0.2439 0.3021 23
Distor 0.7449 0.5548** 0.0378 6.85 0.2160 0.0393 22
Pctxti 0 .7581 0.5747* 0.0074 5.67 0.2112 0.0073 21
Popden 0.7766 0.6031* 0.0090 5.06 0.2585 0.0108 20
Fctlit 0.7987 0.6380* 0.0331 4.78 -0.2965 -0.2965 19
Pctcln 0.8126 0.6603* 0.0657 4.37 0.2482 0.0714 18
Pctlsw 0.8189 0.6706* 0.0146 3.84 -0.1736 -0.0106 17
Qrtiine 0.8249 0.6805* 0.1153 3.40 -0.1735 -0.0813 16
Pctlsr 0.8347 0.6967* 0.0987 3.13 -0.2254 -0.0884 15 00 00 Pctadm 0.8438 0.7120* 0.0996 . 0.2244 0.0858 14 r
TABLE 16 (continued)
Variable Order _ „2 Standard Partial Regr. Degrees of of Entry______Error_____ ”______R______Coefficient Freedom
Pctlcp 0.8550 0.7311* 0.0140 2.71 0.2578 0.0135 13
Pctlfp 0.8626 0.7441* 0.0251 2.49 -0.2195 -0.0196 12
Pctltr 0.8827* 0.7793* 0.0133 2.58* 0.3709 0.0176 11
Diggre 0.0176 0.0419 ***
Significant at 95 per cent level.
Significant at 99 per cent level.
Redundant Variable, F-ratio below the specified minimum level of 95 per cent confidence and thus excluded from the regression equation.
03 03 TABLE 17
MULTIPLE STEPWISE REGRESSION .RESULTS - RATES OP ADOPTION OF HOSPITALS (ADMINISTRATIVE DISTRICT LEVEL)
Variable Order Standard Partial Degrees of F-Value of Entrv R R2 Error R Coefficient Freedom
Pctlsr 0.5677 0.3223** 0.0102 11.89** 0.5678 0.0354 26
Ortime 0.6429 0.4133* 0.0262 8.45** -0.3665 -0.0506 24
Pctlcp 0.6852 0.4695** 0.0016 6.78** 0.3095 0.0026 23
Pctcln 0.7078 0.5011** 0.0150 5.52** 0.2439 0.0177 22
Pctxti 0.7387 0.5458** 0.0017 5.04** 0.0257 0.0025 21
Pctlit 0.7552 0.5703** 0.0110 4.42** -0.2326 -0.0118 20
Petite 0.7702 0.5933** 0.0036 3.95** 0.2311 0.0038 19
Pctltr 0.7958 0.6331** 0.0015 3.88** 0.3136 0.0021 18
Distor 0.8044 0.6471* 0.0119 3.46* 0.1942 0.0097 17
Popden 0.8066 0.6507* 0.0030 2.98* 0.1008 0.0012 16
Pctlsw 0.8115 0.6585* 0.0042' 2.63* -0.1498 -0.0025 15
Pctadm 0.8152 0.6646* 0.0247 2.31* 0.1335 0.0124 14 r *
TABLE 17 (continued)
Variable Order Standard ‘Partial Regr. Degrees of R R2 F-Value of Entry Error R Coefficient Freedom
Trraphs 0 08235 0.6781* 0.1173 2.10* -0.2009 -0.0867 13
Diggre 0.8276 0.6850* 0.0002 1.86* 0.1456 0.0001 12
Pctlfp 0.8330 0.6939* 0.0096 1.66* -0.1687 -0.0054 11
Pctptr 0.8386 0.7033* 0.0446 1.48* -0.1750 -0.0250 10
* . Significant at the 95 per cent level of confidence.
Significant at the 99 per cent level of confidence.
oo Ul TABLfe *18
LIST OF HIGH INTERCORRELATION COEFFICIENTS AMONGST INDEPENDENT VARIABLES
Abbreviations Coefficients Diggre and Popsiz 0,6020
Pctlit " Popsiz 0.5771
Pctlit " Diggre 0.7575
Pctxti ,f PctTit 0.6524
Pctptr " Popden 0.5267
Pctadm »• pctptr 0.4952
Pctslw ” Fctran 0.8715
Petite ” Popden 0.6180
Pctagr ** Diggre 0.5179
Petlep " Fctran 0.6262
Petlep ” ‘Diggre 0.7792
Petlep " Pctlit 0.6133
Petlep 11 Pctagr 0.5786
Pctlsr " Popden 0.6307
Pctlsr " Pctptr 0.7330 87 variables* It is, therefore, known that as the socio economic levels of administrative districts increase with time, the rate of institutional diffusion in Eastern
Nigeria is similarly accelerated.
A similar finding confirmed from testing diffusion rates at administrative district levels is the high collinearity noted for independent variables (Table 18).
This problem thus confirms that indeed the contribution of each variable in a regression step, by and large, can be attributed to just more than that variable. Again it is demonstrated, as in the case of origin times analysis, that the addition of more variables to the regression enhances the predictive power of the study model* This fact, notwithstanding, the residual variance to be explained remains sizeable. Lastly, the interrelatedness of the independent variables makes it impossible to stratify the variables into their common structural groups. This element complicates the problem of finding a more economic way to describe the major variables underlying the observed patterns. The solution to this problem is sought in the next chapter via the principal components analysis. CHAPTER VI
SPATIAL DIMENSIONS OF INSTITUTIONAL INNOVATION PATTERNS IN EASTERN NIGERIA
Introduction
A common finding of the last two chapters concerning
institutional diffusion patterns in Eastern Nigeria is
that its predictor variables exhibit a high degree of
multicollinearity# It is also observed that the contribu
tions of some variables in the regression, because of the
above reason, are masked by the influence of the first
variable entering the regression at each occasion# Thus
it is explicitly concluded that the independence and addi
tivity among explanatory variables in a regression framework
are not completely satisfied. This finding, in addition,
perhaps can be attributed to the non-inclusion of more
important and pertinent variables in the analysis# The
last point is affirmed by the examination of the residuals
from the regressions# It is noted that even though a
reasonable level of explained variance is achieved, the
residuals from the regression still exhibited distinct regional patterns. Given these observations some questions
88 89 are posed. Can the decomposition of the cluster effect noted for the explanatory variables provide a more parsi monious description of institutional patterns under study?
Will the identification of the resultant common dimensions among the predictors shed some light on the applicability of hierarchical and neighborhood concepts to diffusion problems within a developing society?
The current chapter is an attempt to provide some answers to the questions raised in the preceding paragraphs.
But then the question arises, What is the most suitable technique that can deal with the problem of high intercor relations noted among the independent variables and simul taneously yield clues to common structural dimensions?
Principal components analysis is chosen to handle this problem.
The use of the principal components technique in cur rent study recognizes that no inferential questions are posed. It is chosen purely as a descriptive device aimed at facilitating an understanding of the generative elements of diffusion patterns within a developing society. The model, moreover, is selected because of its suitability in handling traits of individuals or observation centers that form the test points of the present study. Finally, the model is chosen for this research because its utility has been proved in similar studies such as the one being 90 currently undertaken here.1 The results of the principal components analysis follow immediately.
Principal Components Results - Summary
The principal components analysis is performed for both institutions but only at one level of geographic scale, the individual central places. Administrative districts are dropped in this exercise because of the reason already made for individual centers in using a principal components technique. The results demonstrate that, for both insti tutions, three rotated factor dimensions having eigenvalues greater than unity are obtained. These three factor com ponents explained more than two-thirds of the variance among the explanatory variables. For secondary school diffusion patterns, the three factor components accounted for 77 percent of the entire variance. In regard to hos pital diffusion patterns, they yielded 74 percent of the variance attributable to the entire set of original vari-. ables. The results are now analyzed in detail^ with respect to each type of institution.
Similar studies where this technique is used include: E. W. Soja, 1963. The Geography of Modernization in Kenya, Syracuse University Press, Syracuse, New York. John Thomp son, et al., 1962. "Toward a Geography of Economic Health: The Case of New York State," Annals, Association of American Geographers, Vol. 50-1-20. L. SchnoY'a, 1961. "The Statis tical Measurement of Urbanization and Economic Development," Land Economics. Vol. 37, 224-45. 91
Secondary School Diffusion Patterns - Basic Structural Dimensions
The first common factor dimension underlying secondary
school diffusion patterns accounted for 45 percent of the
variance (Table 19), The variables with the highest load
ings on this factor are per capita annual revenue, popula
tion density and population size of an adoption center.
Clearly this factor is a Population-Wealth component and is
consequently labelled as such. On the contrary, it can be
termed an Economic-lfealth Development Dimension, especially
if due regard i^,given to its factor scores.
From Table 19 it is clear that the centers that scored
the highest positive values are those that are experiencing
the greatest level of economic development. These centers
are Port Harcourt (11.39), Onitsha (2.01), Aba (1.83) and
Enugu (1,79). Apparently these four cities constitute the
industrial, demographic and economic life of Eastern Nigeria.
On the other hand, the lowest negative scores on this
factor relate to the least developed agricultural and pastoral centers of Obudu (-1.37), Ogoja (-1.07), Obubra
(-1.03) and Ikom (-0.92). Clearly this factor dimension is measuring economic growth.
Spatially the mapping of factor scores from this dimen
sion reveals some distinct regional patterns (Figure 10).
At the individual city level Port Harcourt, Onitsha, Aba 92
TABLE 19
COMPONENTS OP SECONDARY SCHOOL'ADOPTIONS (INDIVIDUAL CENTER LEVEL)
Population - Wealth Dimension - Rotated Factor 1 Variable Loading Variable Full Name Popsiz 0.7349 Population Size of Adoption Center
Distor 0.0946 Distance to Origin Point of Adoption
Distpa 0.3184 Distance to Nearest Past Adopter
Dislcp -0.0662 Distance to Largest Nearest Functional Urban Center
Popden 0.9103 Population Density Around Adoption Center
Aaninc 0.9628 Per Capita Average Gross Annual Revenue of Adoption Center
Fctran 0.3027 Functional Rank of Adoption Center
Nolink 0.3212 Nodal Link of Adoption Center
Some Remarkable Individual Factor Scores on R .F. 1
Highest Lowest Centers Positive Centers Negative Scores* Scores**
Aba 1.83 Ikom -0.92
Enugu 1.79 Obubra -1.03
Onitsha 2.01 Obudu -1.37
Port Harcourt 11.39 Ogoja -1.07
Level of Explained Variance: 45 per cent, Eigenvalue 3.62 *
Positive Scores - Highly developed industrial urban centers. ** Negative Scores - Least developed service centers. CCOHOWIC WCAL7H OCVCUO^UCur OlMtNJtON V 82 ' r f t t 93 y* fit 0* 94
and Enugu belong to a class, highest order centers in
Eastern Nigeria central place hierarchy. Calabar, BuguyJa,
Awka and Nnewi fall into a group of second order centers.
On the regional scale the more economically developed area
of Onitsha, Awka and Orlu districts group within one dis
tinctive pattern. The same is true for the relatively
advanced region of the industrial and petroleum district
of Port Harcourt. Otherwise, one can also isolate a
regional pattern extending through the relatively resource- kp poor but heavily populated districts of Abak, Ikot E^pene, r Bende, Owerjfrx and okigwi. The swampy, deltaic and sparsely populated divisions of Brass and Degema and the rural and 3 agricultural districts of Ikom, Oyoja and Obudu define
another regional group. From the evidence presented so far, it is concluded that secondary school diffusion pat
tern is controlled chiefly by economic and demographic factors. This result suggests that perhaps profitability factors are the major motives behind secondary school
adoptions in the study area. Such a conclusion agrees with
the entrepreneurial objectives of the innovators of these institutions since education in this region is seen by most 2 persons as a profit-making device.
This point is in agreement with a previous observation by R. IC. Udo, 1970. Geographical Regions of Nigeriaj London, Heinemann's. 95
Factor two (Table 20) shotvs that distance to higher order urban places, distance to nearest previously adopting center and rank order of center within the central place system have highest positive coefficients. The first two variables load highest while functional rank order of a center within the urban hierarchy has the highest third loading. At first sight this factor may be termed a
’’neighborhood effect dimension but with reference to its factor scores it becomes apparent that the centers that score highest are all provincial or district capitals.
Therefore this factor is interpreted as a Hierarchical
Dimension. As can be seen from the table and from Figure 11, all the provincial and administrative towns scored highly on this factor, on the one hand. Rural service centers and villages are negatively scored on the other hand. This factor accounted for 17 percent of the residual variance.
It in effect suggests that after the demographic influence of adoption centers is removed, innovation diffusion patterns seem to be dominated next by the functional position of a center within the central place network.
The third factor designated Accessibility Dimension
(Table 21) indicates that distance to origin point of adoption, degree of connectedness of an adoption center and functional rank of adoption center have absolute high load ings. It accounted for 15 percent of the residual variance.
As the factor scores on this dimension indicate, all the 96 TABLE 20
COMPONENTS OF SECONDARY SCHOOL ADOPTIONS (INDIVIDUAL CENTER LEVEL) HIERARCHICAL DIMENSION - ROTATED FACTOR 2
Variable Loading Variable Full Name Popsiz 0.4133 Population Size of Adoption Center
Distor 0.1991 Distance to Origin Point of Adoption
Distpa 0.8181 Distance to Nearest Past Adopter
Dislcp 0,8217 Distance to Largest Nearest Functional Urban Center
Popden -0,0542 Population density around Adoption Center
Aaninc 0.1859 Per Capita Average Gross Annual Revenue of Adoption Center
Fctran 0.6561 Functional Rank of Adoption Center
Nolink 0.3434 Nodal Link of Adoption Center
Some Outstanding Individual Factor Scores on R.F. 2
High Lowest Centers Positive Scores Centers Negative Scores
Aba 1.47 Aletu-Alesa -0.78 Abakaliki 1.64 Bakana -0.92 Enugu 3.26 Elelenv/a -0.91 Ikora 2.14 Ibiaku -0.72 Obubra 2.17 Mbiakong -0.86 Obudu 4.18 Nsu -0.87 Ogoja 4.71 Nvosi -0.80 Onitsha 5.74 Odot -0.91 Owerri 1.93 Umuola-Obia —0,99 Yenagoa 2.26 Uya-Oron -1.17 Level of Explained Variance: 17 percent. Eigenvalue, 1.38. Positive Scores - Provincial and Administrative District Capitals. Negative Scores - Rural service centers and villages. HIERARCHICAL EFFECT OlUCNStOft *•!«!•* F«tl«r Tw* S l i m
,*JHN Clf(l< Y ■tco.ooo•H£O0 - * it*£&co Alt** A lfw •iO.OGO* «*fr.03T *10,000- 29*t* •*0.000- fi.fr fr* CTttf* %ijf bf»«l •o •-COO* 4^99 110 R|tfti Fig. 11 98 TABLE 21 COMPONENTS OF SECONDARY SCHOOL ADOPTIONS (INDIVIDUAL CENTER LEVEL) NEIGHBORHOOD-ACCESSIBILITY DIMENSION - R.F. 3 Varlable Loading Variable Full Name Fopsiz 0.1952 Population Size of Adoption Center Distor -0.8289 Distance to Origin Point of Adoption Distpa 0.0494 Distance to Nearest Past Adopter Dislcp -0.0886 Distance to Largest Nearest Functional Urban Center Popden 0.0039 Population Density Around Adoption Center Aaninc 0.0834 Per capita Average Gross Annual Revenue of Adoption Center Fctran 0.5281 Functional Rank of Adoption Center Nolink 0.7029 Nodal Link of Adoption Center Some Extreme Individual Scores on R.F. 3 High Low Centers Positive Scores Centers Negative Scores Aba 2.97 Achina -1.18 Abak 2.13 Amarata -1.78 Calabar. 4.03 Imiringi -1.34 Ikot Ekpene 2.79 Nembe -1.54 Or on 2.63 Obolo Eke -1.51 Owerri 2.47 Obudu -1.32 Umuahia 2.57 Oporoma -2.25 Uyo 3.05 Umunya -1.06 Level of Explained Variance: 15 per cent. Eigenvalue, 1.24 Positive Scores - More Accessible Centers to Origin Point of Adoption. \ ) Negative Scores - Very Remote Centers to Origin Point of Adoption. 99 observation centers located very near the initial point of adoption. Calabar, have positive values. On the contrary, the very remote centers to Calabar have very low negative scores. Briefly, this factor indexes a "neighborhood effect" in operation. The spatial pattern of the factor scores (Figure 12) demonstrates that centers like Oron, Eket, Etinan, Abak, Uyo, Ikot Ekpene and Aba distinctly fall within one regional area. Centers around Port Harcourt form another regional cluster while centers lying around Onitsha, Awka and Orlu districts group within another distinct region. Towns of Owerri, Okigwi, Bende and Aki^po divisions form a transitional zone. The frontier remote districts of Nsukka, Ogoja and Yernagoa belong to another regional class*. Factor three interpreted in another form reveals some interesting elements of the pattern of secondary school dif fusion in Eastern Nigeria. All the Cross River towns of the Southeast, including Arochuku, Afikpo, Abakaliki and ■ Ugep define a general regional pattern. Culturally this area represents the pioneering activity space of the Scottish Christian Missions such as the Presbyterian and Methodist denominations. These denominations operated ■ ■ • „ . chiefly along the Cross River and thus it is not a surprise that all centers where they established schools cluster in one regional pattern. Acccssiaur irncT oiuttision / Rmiil fMftr tkfii Sim * GO jv 11' 100 101 The cluster around Port Harcourt indicates the influ ence of the indigeneous enterprise of the coastal cities and the indigeneous Niger Delta Pastorate Church around Bonny and its hinterland. The Onitsha group represents the work of the Church Missionary Society which came through the natural highway provided by River Niger. The transition zone of Owerri, Okigwi and Bende indicates the meeting area of the various denominations operating from Calabar, Bonny and Onitsha simultaneously. Generally the three distinct localized regions generated by this factor indicate a mul tiple step flow diffusion process in operation. Hospital Diffusion Patterns - Basic Structural Dimensions Three factor dimensions are similarly displayed for hospitals. They are population-wealth, hierarchical and accessibility components. The results and contributions of each factor component are presented in Tables 22, 23 and 24, There are no marked differences in the regional pat terns obtained when compared to those patterns identified with respect to secondary school diffusion data. This implies that similar processes are at work as regards the spread of both types of institutions. This conclusion is strengthened when Figures 10 and 13 are compared, it will be noted that higher order urban centers such as Port Harcourt, Onitsha, Knugu and Aba are within the same 102 TABLE’22 COMPONENTS OF HOSPITAL ADOPTIONS (INDIVIDUAL CENTER LEVEL) POPULATION - WEALTH DEVELOPMENT DIMENSION R.F. 1 Variable Loading Popsiz 0.7016 Distor 0.0177 Distpa 0.1447 Dislcp -0.1822 Popden - 0.9303 Aaninc 0.9413 Fctran 0.2179 Nolink 0.3171 - Some High Positive and Low Negative Scores on R. F. 1 High Low Centers Postive Centers Negative Values Values Aba 0.96 Ikora -0.78 Enugu 0.77 Obudu -1.40 Onitsha 0.82 Obubra -0.84 Port Harcourt 6.83 Ogidi -0.99 Level of Explained Variance: 40 per cent • Eigenvalue 3.34 Positive Scores - Highly Urbanized Centers Negative Scores - Rural service centers. 103 TABLE 23 COMPONENTS OF HOSPITAL ADOPTIONS (INDIVIDUAL CENTER LEVEL) NEIGHBORHOOD EFFECT DIMENSION R. F. 2 Variable Loading Popsiz 0.0628 Distor 0.8305 Distpa 0.6610 Dislcp 0.5304 Popden 0.0389 Aaninc 0.1057 Fctran 0.0799 Nolink -0.2085 Some High Positive and Low Negative Scores on R.F. 2 High Low Centers jPositive Values Centers Negative Values Enugu 1.01 Abak -1.28 Joinkrama 2.83 Anua -1.14 Obudu 2.71 Arochulcu -1.07 Ogidi 2.56 Ikut Ekpene -1.35 Ogoja 2.46 Itu -1.61 Yenagoa 1.19 Uyo -1.65 Level of Explained Variance: 19 per cent. Eigenvalue, 1.47. Positive Scores - Very distant centers to Origin Point of Adoption* Negative Scores - Centers in close proximity to Origin Point of Adoption. ( r 104 TABLE 24 COMPONENTS OF HOSPITAL ADOPTIONS (INDIVIDUAL CENTER LEVEL) HIERARCHICAL-EFFECT DIMENSION R. F. 3 Variable Loading Popsiz 0.5479 'Distor -0.0846 Distpa 0.0406 Dislcp 0.5735 Popden 0.0194 Aaninc 0.2790 Fetran 0.8896 Nolink 0.7893 Some High Positive and Low Negative Scores on R. F. 3 High Low Center s Positive Values Centers Negative Values Aba 2.20 Enugu Ukwu -1.03 Calabar • 2 o40 Mbano -1.01 Enugu 2.27 Ogidi -1.53 Ikot Ekpene 1.30 . Okomoko -1.00 Nsukka 1.50 Orumba -1.04 Onitsha 1.90 Umuleri -1.19 Umuahia 1.56 Umunnato -1.09 Level of Explained Variance: 15 per cent. Eigenvalue 1.23. Positive Scores - Urbanized Provincial Centers. Negative Scores - Rural Service Centers. 105 to « O’ iZ ( > 106 structural pattern.■ Centers within Onitsha, Awka and Orlu districts are seen to have similar scores on both maps. Hospital diffusion factor dimension maps on the remaining two components are not reproduced because they resemble closely those shown in Figures 11 and 12. In spite of the marked similarities noted in the spatial dimensions of both institutions, it is remarked that hier archical and accessibility dimensions change positions in regard to hospital diffusion. Population-wealth component, as in the case of secondary school data, occupies the first position. Accessibility component displaces hierarchical dimension to the third position. The change in position i of these two factors supports the earlier finding in which hospital origin time adoption patterns are characterized by a marked distance-decay or interaction bias. Furthermore this finding is in accord with the historical development of hospitals in the study area. Many of these institutions developed through imitation of the community development programs originating from Awgu. Thus it is seen that this community action oriented program readily cascaded into neighboring districts of Awka, Onitsha, Okigwi, Orlu and Owerri with marked distance-decay bias. Elsewhere hospitals are seen as characteristic features of administrative district capitals• c Summary Firmer conclusions relating to institutional innova tion patterns in Eastern Nigeria can now be reached. Based on the findings of this chapter, it is demonstrated that population size-wealth dimension is the most important element underlying the diffusion of both institutions in the study area. It thus suggests that the spatial distri bution of population has considerable lead-effect on the spread of institutions in the area. Therefore it is con cluded that the times at which institutions are available to centers is chiefly controlled by the way the population is distributed over time. This can be regarded as a function of the propagator1s perception of a marketing surface (in this case, the missionaries) and his perception of resis tance and density-potential adopter surfaces (in this regard, the presence of established populous traditional 3 marketing and coastal port centers). This conclusion is consistent with the manner in which the initial spread of both institutions occurred. The missionaries, the primary propagators of the institutions, in order to win converts The influences of these factors on diffusion of innovations are exhaustively discussed in L. A. Brown and K. R. Cox, op. cit. 107 108 to their ideologies located first at important traditional centers, such as Calabar (Duke Town), Onitsha, Uzuakoli, and later, as urbanization gathered impetus, in centers like Port Harcourt, Aba, Enugu and Owerri. The result is consequently in agreement with the general theory of dif fusion studies relating to persuasion and acquisition which in turn are related to information and market factors.4 With the removal of the effect of population-wealth dimension, two additional components are seen to underlie the spread-pattern of both institutions. These are hierarchical and neighborhood effect components. For both institutions their influences are demonstrated to be equal. However, it is to be noted that, while hierarchical compon ent is the second component explaining the variational pattern of secondary schools, distance-decay dimension is the next dominant element accounting for the distribution of hospitals. Both component dimensions are seen to have lag-effects upon the diffusion of these institutions. This observation agrees with temporal spread pattern of both innovations in the study area. The secondary role of the hierarchical dimension is accepted given that the latter See, for instance, L. A. Brown's Diffusion Dynamics, and also L. A. Brown, 1969, "Diffusion of Innovation: A Macro View," Economic Development and Cultural Change, 17, 189-211. 109 spread of secondary schools and hospitals is explainable in terms of the importance attached to administrative or religious capitals such as provincial, county and parish capitals and in respect to centers located closely to highest order urban places. Examples of this pattern of occurrence include the establishment of secondary schools and hospitals at such places as Oba, Ihiala, Orlu, Okigwi, Okrika, Nsukka, Awka, Abak and Uyo, to mention a few. This finding is consistent with conceptualization of diffusion processes by which the dissemination of innova tions is regarded as a function of information availability. Thus, logically, the propagators of these innovations having already occupied the traditional centers, next located them at administrative capitals indicating the impact of the introduction of new functional organization of space upon the study area. Neighborhood effect dimension has a similar effect as the hierarchical effect. The establishment of political capitals and the development of road transportation network in the study area occurred simultaneously. This situation partly explains the equal influence of both factor dimen sions. As many administrative and parish headquarters already acquired these innovations, the next points for * locations became centers located on important highways and near to previously adopting centers. Centers such as Asa, 110 0 Awomamma, Nbawsi, Ogidi, Obosi, Aletu-Alesa, Umuola, and M' Ihioroa cite a few examples a m are indicative of this pattern. Numerous examples abound throughout Eastern Nigeria. This trend is consistent with diffusion theory that particularly emphasizes interaction effects. Thus the finding gives support to the theory which stresses the importance of interaction in the diffusion of innovations. The findings of this chapter are summarized by the following remarks. Population wealth and hierarchical dimen sions extracted by the factor analysis both indicate that hierarchical diffusion is an adequate process-explanation to diffusion patterns in Eastern Nigeria. Therefore it is of great applicability to diffusion problems within a developing society and indicates that the haphazard innova tions diffusion pattern hitherto observed for these societies is non-existent. Secondly, neighborhood effect concept is shown to be an effective explanation process to diffusion within developing nations even though it has a lag-effect rather than lead-effect as it does with the diffusion material innovations. CHAPTER VII SUMMARY, CONCLUSIONS AND IMPLICATIONS Summary and Conclusions Diffusion theories stemming from research carried out within highly urbanized and industrialized societies to a large extent remain untested as regards identical problems within modernizing societies. The result is the apparent gap in diffusion literature particularly with respect to African topics. This gap in recent years has begun to be bridged . by some well-known publications. Yet the situation continues to emphasize a need for continuing research. The research reported here is undertaken in recogni tion of this need. More specifically, the dissertation has been carried out with a view of providing an alternate testing ground for hitherto developed diffusion concepts within advanced economies. Given this objective, two major diffusion theories, namely, hierarchical and neighbor hood effects, are reviewed and tested for postulates re lating to origin times and rates of adoptions of institu tions in Eastern Nigeria. The study concentrated throughout on the determination of the nature of relationships 111 112 existing between observed diffusion patterns and hypothet ical process variables. The relationships are examined via regression and principal components models using data assembled for 157 and 57 secondary school and hospital locations, respectively, in Eastern Nigeria. Several findings concerning the effective application of these concepts to the study of innovations diffusion patterns in Eastern Nigeria are obtained. Adoption times pattern of secondary schools are shown to be largely influenced by population size and the overall connectivity of center within the central place network. Hospital dif fusion times pattern, on the contrary, are affected prin cipally by the rank order of centers within the urban hierarchy. Furthermore, in contrast to secondary schools, the adoption times pattern of hospitals are marked by a strong distance bias. The hypotheses relating to adoption times pattern are proved to be consistent. Relationships between rates of diffusion of both institutions and some explanatory variables are demonstrated to be inconsistent. However it is noted that a more satisfactory level of explained variance is obtained for rates of diffusion than for origin adoption times. Despite these general results, the study notes that no economic description concerning the hypothesized relation ships emerged because of the high degree of collinearity observed among the predictor variables. Moreover, the 113 regression model employed did not completely satisfy its independence and additivity assumptions. Thus the contri butions of most variables are masked by that of the first variable entering the regression equation. As a result the first variable generally accounted for a substantial part of the explained variance. The increments due to succeeding variables are shown to be minimal even though their overall contributions are demonstrated to be significant. The situation, therefore, resulted in the subjection of the data to a principal components analysis. The results of the principal components analysis indi cate the institutional diffusion patterns in Eastern Nigeria are describable more parsimoniously by three factor dimensions, namely, population-wealth, hierarchical and neighborhood components. The trio accounted for more than two-thirds of the total explained variance. Population- wealth dimension is shown to have a lead-effect in the spatial patterning of innovations whereas hierarchical and neighborhood effect components are seen to produce lag- effects. All three factors are demonstrated to be con sistent with the general theory'underlying most diffusion studies in that they are mainly a function of information and interaction factors. Thus the concepts under investi gation by and large are applicable to a diffusion problem within a modernizing context. 1X4 Implications for Future Research Given the exploratory nature of this study and, therefore, its associated limitations, the current re search still has a lot of problems that remain to be investigated. These problems indicate some directions for continuing research. It has been noted that informa tion and interaction factors are the prime elements under lying diffusion patterns in Eastern Nigeria. Information factors have been shown to be related to persuasion and acquisition which, in turn, are a function of inter personal or acquaintance communication networks. Yet as far as this study goes, nothing is said about the role of innovators or propagators nor of their interpersonal communication links in the diffusion of schools or hos pitals. Neither is there any treatment given to their distribution policies which underlie the hierarchy effect identified in the study. These elements, left untouched in the current study, evidently constitute problems demanding further investigation. Thus for a thorough assessment of the effectiveness of the concepts applied •^The importance of these elements in diffusion process is emphasized by: L. A. Brown, ojp. cit., L. A. Brown and K, R . Cox, op. cit., and K. R. Cox, 1970, "The Genesis of Acquaintance Field Spatial Structures. A Con ceptual Model and Empirical Tests" in Cox and Golledge, op. cit. 115 f and tested here to be meaningful, and exhaustive examin ation of these factors is needed. Therefore, subsequent research will be better directed along these lines and can considerably strengthen the findings of the current study. ( c APPENDIX A DATA SOURCES Government Publications 1. Eastern Nigeria, 1962, Committee for the Review of the Educational System in Eastern Nigeria, Officialn Document No. 19, Ministry of Education, Enugu. 2. Eastern Nigeria, 1963, Distribution of Amenities in Eastern Nigeria, Data and statistics, The Government Printer, Enugu. 3. Eastern Nigeria, 1963, Statistical Digest of Eastern Nigeria, Official Document No. 22, Statistics Division, Ministry of Economic Planning, Enugu. 4. Federal Government of Nigeria, 1953, Population Census, Eastern Nigeria, Vol. 1, Federal Census Office, Lagos. 5. Federal Government of Nigeria, 1963, Population Census, Eastern Nigeria, Vol. 1, Federal Census Office, Lagos. 6. Federal Government of Nigeria, 1963, Population Census, Eastern Nigeria, Vol. 2, Federal Census Office, Lagos. 7. Federal Government of Nigeria, 1965, Statistics of Education in Nigeria, Series No. 1, Vol. V, Federal Ministry of Education, Lagos. 8. Federal Government of Nigeria, 1967, Statistics of Education in Nigeria, Series No. 1, Vol. VII, Federal Ministry of Education, Lagos, Books 1. Anene, J. C., 1966, Southern Nigeria in Transition 1885- 1906, Cambridge, Cambridge University Press. 2. Dike, K. 0., 1956, Trade and Politics in the Niger Delta, 1830-1885, Oxford, Clarendon Press. 3. Floyd, B., 1969, Eastern Nigeria— A Geographical Review, New York, Frederick A. Praeger, Publishers. 116 Ojike, M , 1946, My Africa, New York, The John Day Company. Talbot, P. A., 1926, The Peoples of Southern Nigeria, Vol. 1, Historical Notes, A Sketch of Theirhistory, Ethnology and Languages with An Abstract of 1921 Census, London, Frank Cass and Co., Ltd. ______, 1926, The Peoples of Southern Nigeria, Vol. IV, Linguistics and Statistics, London, Frank Cass and Co., Ltd. APPENDIX B TCA 460, 'NWALA, V. 05/11/71 VF(14X, F2.0,2F5.0.2F4.0.F8.2.1X.F3.0.F6.0.F7.) MP 157 9 333 VN 010RTIME 02P0P3I2 03DIST0R 04DISTPA 05DISLCP 06P0PDEN 07AANINC VN 08FCTRAN 09FCTRAN NOLINK DATA SECONDARY SCHOOL DIFFUSION IN EASTERN NIGERIA ABA 52 131.00 068 030 030 00565 085150 14 37 001 ABAGANA 63 022.17 132 006 Oil 01035 007760 3 17 002 ABAK 46 018.74 036 016 030 01016 007496 6 21 003 A3AKALIKX 65 031.17 072 032 044 00342 001559 13 12 004 ABATETA 66 006.27 124 009 010 00693 003135 3 6 005 ABIRIBA 59 033.18 064 014 020 00516 006336 3 6 006 AG3AJA 69 004.58 120 002 010 00693 002290 3 6 007 ABOH 62 009.35 084 016 018 00830 003273 3 15 008 ABONNEMA 66 053.26 110 008 017 00321 010652 5 6 009 ACHI 63 028.46 H O 010 024 00502 011384 1 6 OlO ACHINA 66 015.83 128 004 028 01035 005541 1 6 Oil ADAZI 65 010.88 120 007 016 01035 003808 1 6 012 AFIKPO 58 036.09 069 032 040 00519 018045 9 10 013 AG30GUGU 67 014.50 074 006 013 00502 005800 1 6 014 AGULERI 63 013.34 131 012 014 00693 006670 3 6 015 AGULU 63 013.34 113 006 020 01035 004669 1 6 016 AGUIGBU-OWA 66 009.48 123 020 020 00417 006162 1 6 017 AHOADA 62 006.29 117 032 032 00253 031450 7 9 018 AKAI 66 007.56 036 025 038 01048 001512 1 6 019 AKOKWA 62 023.29 107 004 028 01632 005823 1 9 020 AKWETE 68 002.64 068 010 016 00516 001716 1 6 021 ALETU-ALESA 66 008.20 085 004 008 00573 003690 3 12 022 ALOR 63 004.38 124 005 021 00693 002190 1 6 023 AMAIMO 66 012.45 090 014 021 00830 004358 1 6 024 AMAKOHIA 69 012.10 084 002 016 00830 004235 1 6 025 AMARAKA 66 006.53 095 003 025 01267 001633 1 11 026 AMARATA 68 001.41 138 008 047 00093 000282 1 3 027 o APPENDIX B (continued) AMAUMARA 63 007.95 077 003 014 00830 002783 1 6 028 AMUZI 67 016.25 076 004 018 00830 005688 1 6 029 ANGARA 67 005.37 096 006 025 01267 001611 1 16 030 a r o c h u k w u 38 008.80 041 032 031 00516 003080 10 031 ASA 66 002.43 074 014 015 00565 015795 1 14 032 ATTA 69 010.99 095 006 026 00830 003847 1 14 033 AWGU 62 018.50 096 026 026 00502 007400 7 14 034 AWKA 64 048.72 122 018 019 01035 017052 7 18 035 AWKA-ETITI 69 008.95 120 002 014 00693 003133 1 6 036 AWKUNANAW 66 007.72 108 016 016 00417 002702 1 6 037 AWOIDEMIRI 63 017.43 113 010 026 01632 004358 1 8 038 AWOMANMA 63 020.01 104 012 036 01632 005003 1 11 039 AZIA 67 004.13 059 012 022 00565 002685 11 040 AZUMINI 67 013.53 113 008 022 00693 006765 1 6 041 BAEN 69 002.57 065 008 034 00573 001028 1 6 042 BAXANA 68 027.32 096 004 004 00321 005464 1 6 043 BODD 61 014.25 076 008 021 00573 005700 1 6 044 BONNY 69 007.41 088 014 026 00321 001482 6 045 BOR I 61 009.16 070 024 026 00573 003664 11 11 046 BUGUMA 53 100.62 104 Oil Oil 00321 020124 7 6 047 CALABAR 01 076.41 001 001 001 00094 045846 20 21 048 EGBU 66 004.48 094 003 030 00830 001568 8 049 EiCE 66 004.54 122 012 009 00417 002951 1 6 050 EKET 68 010.22 036 026 039 01048 002044 11 14 051 EXWEREAZU 68 015.31 084 005 037 00830 005359 1 6 052 ELELENWA 46 003.61 088 006 006 00253 001625 1 6 053 EMEKUKU 68 008.65 092 006 036 C0830 003460 1 8 054 EMENA 66 002.90 116 004 008 00417 001885 1 13 055 ENUGU 50 138.45 118 052 052 00417 089993 18 26 056 ESSENE 66 006.29 058 001 040 00913 001258 1 12 057 ETE-OPOBO 68 008.43 060 001 040 00913 001686 1 8 058 EXINAN 45 021.35 034 027 034 01171 007473 15 059 EZEOKE‘ 69 009.47 092 OlO 024 01267 002841 1 16 060 EZIAMA OBIAIO 69 004.34 102 006 035 00830 001519 1 8 061 APPENDIX B (continued) IBIAKU 57 000.64 028 004 027 01171 000224 1 14 062 IFE 62 009.41 076 014 015 00830 003294 1 6 063 IFITE-UKPO 66 . 008.26 128 002 Oil 01035 002891 1 6 064 JGBOUKWU 69 019.46 112 008 025 01035 006811 1 6 065 N5UKKA 56 026.20 144 030 029 00525 013100 3 14 093 120 NID NQANG 66 024.12 052 016 018 01016 000965 1 12 094 NVOSI 69 001.01 064 006 015 00565 000657 1 8 095 OBA 52 010.98 126 009 009 00693 005490 1 14 096 APPENDIX B (continued) OBIZI 66 006.89 096 006 018 00830 002412 1 6 097 OBOLO EKE 65 021.22 139 018 032 00417 009549 1 6 098 CEO SI 68 007.01 130 004 005 00693 003505 1 8 099 OBOWO 69 012.44 083 005 012 01267 003732 1 6 100 OBUBRA 69 002.16 074 031 064 00198 000540 13 11 101 OBUDU 66 012.63 128 024 124 00103 005684 13 8 102 ODOT 69 003.88 022 03.4 014 01171 000776 1 11 103 OGHE 69 003.86 124 006 014 00417 002509 1 6 104 CGIDI 63 010.78 130 006 004 00693 005390 3 8 105 OGOJA 58 013.69 120 080 093 00103 005476 13 12 106 OGUTA 56 015.31 118 022 032 00830 005359 7 13 107 OGWA 69 020.83 098 004 028 00830 007291 1 9 108 OHAPIA 63 013.61 057 008 024 00516 004764 1 11 109 OKIGWI 65 007.26 090 018 023 01267 002178 15 14 110 OKIJA 68 034.06 120 004 019 00693 017030 1 8 111 OKPALA 68 016.25 078 008 016 00830 005688 1 17 112 OKPOSI 66 026.25 082 014 036 00519 011813 1 12 113 OKRIKA 46 024.13 083 007 006 00321 004826 7 11 114 OLD OMUAHIA 66 002.46 070 002 002 00516 000861 7 13 115 OMOKU 67 020.32 120 020 046 00253 010115 3 9 116 OMOR 68 017.33 138 016 027 00525 008665 1 6 117 ONICHA 67 020.23 084 007 016 01267 006069 1 6 118 ONITSHA 30 163.03 142 142 052 00693 081515 18 19 119 OPOSO 67 035.45 062 016 040 00913 007090 13 13 120 OPOROMA 69 003.36 158 016 064 00093 000672 1 5 121 ORAIFITE 67 013.53 126 004 010 00693 ' 006765 1 9 122 ORAUKWU 68 004.12 122 002 013 00693 002060 1 6 123 ORLU 58 007.23 106 006 030 01632 001808 15 25 124 ORON 65 034.16 013 013 012 01048 006832 15 12 125 OVIM 65 005.91 077 008 014 00516 001773 1 13 126 OWERRI ■ 53 025.01 097 032 032 00830 009103 20 33 127 121 OWERRINZ1 67 007.38 073 004 014 00565 002583 1 14 128 OZUABAM 69 008.32 059 006 018 00516 002912 1 6 129 OZUBULU 60 020.00 123 007 015 00693 010000 1 8 130 APPENDIX B (continued) OZUOBA 68 001.02 097 007 008 00253 000510 1 6 131 PORT HAR- COURT 37 179.56 092 062 - 042 23942 359120 20 26 132 TAABAA 69 001.90 066 005 028 00573 000760 1 6 133 TOMBIA 69 016.46 101 004 008 00321 003292 1 5 134 UBULU IHE- JIOFOR 65 014.52 112 004 028 01632 003630 1 6 135 UDI 66 003.10 112 008 010 00417 002015 7 14 136 UGEP 69 044.94 060 010 045 00197 011235 7 9 137 UGIRI 69 005.11 096 003 024 01267 001533 1 8 138 ULI 67 021.66 116 004 026 01632 005415 1 8 139 ULAKWO 68 009.47 089 008 026 00830 003315 1 8 140 UMUAHIA 34 028.84 070 008 030 00516 010094 14 29 141 UMUAKA 66 017.18 104 006 034 01632 004295 1 8 142 UMUAKPU 68 004.31 098 020 032 00830 001509 1 8 143 UMUATURU 68 001.19 087 016 019 00253 000595 1 6 144 UMUAWULU 62 003.57 126 005 020 01035 000893 1 8 145 UMUEZELA 69 019.04 086 004 016 01267 005712 1 11 146 UMUOBI- AKANI 69 004.41 091 003 005 00253 002205 1 8 147 UMUNYA 67 007.31 132 003 009 00693 003655 1 6 148 UMUNZE 68 023.02 102 008 034 01035 005755 1 6 149 UMUOJI 68 008.46 126 002 015 00693 004230 1 6 150 UMUOLA-OBIA 63 000.72 094 004 004 00253 000360 1 6 151 UTUTU 69 011.65 043 002 030 00516 004078 1 8 152 UZOAGBA 68 010.79 092 008 025 00830 003777 1 8 153 tJZUAKOLI 26 011.84 070 008 016 00516 004144 1 16 154 UYA-ORON 69 003.57 016 004 016 01048 000714 1 8 155 UYO 52 014.47 028 Oil 027 01048 005065 14 24 156 YENAGOA 66 001.60 144 029 053 00093 000320 14 9 157 w N> to 123 BIBLIOGRAPHY Articles Bohannan, P. 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