DETERMINATION OF RELATIONSHIPS BETWEEN LOCAL GOVERNMENT SERVICES AND SOCIO-ECONOMIC STRUCTURES IN MICHIGAN: AN EXPLORATORY APPROACH By Leon Berton Perkinson A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Resource Development 1971 PLEASE NOTE: Some pages may have indistinct print. Filmed as received. University Microfilms, A Xerox Education Company ABSTRACT DETERMINANTS OF RELATIONSHIPS BETWEEN LOCAL GOVERNMENT SERVICES AND SOCIO-ECONOMIC STRUCTURES IN MICHIGAN: AN EXPLORATORY APPROACH By Leon Berton Perkinson Relationships between economic development and lo­ cal governmental services are not well defined. If one assumes industrial expansion is synonomous with economic d e ­ velopment, most "location" studies indicate local community factors have relatively little importance in economic devel­ opment. However, many studies contain observations implying that community factors may be important in final industrial location decisions. Descriptive studies of depressed areas frequently cite the absence or inadequacy of local govern­ mental services. Investigators of developed areas frequently stress the availability and general adequacy of such serv­ ices. The objectives of this study were to determine re­ lationships between local governmental services and socio­ economic measures of local areas and to examine those Leon Berton Perkinson relationships for consistency among different levels of government. No single measure adequately accounts for d i f ­ ferences in economic growth because of interrelated socio-economic occurrences. Also, governmental revenues- expenditures m a y be highly interrelated. Therefore, factor analysis was used to account for the linkages of interrela­ tionships in two ways: 1) To develop separate regional configurations from socio-economic characteristics, county area government characteristics, characteristics. tors) aggregated and county government 2) To develop conceptual variables (fac­ from the separate interactions of socio-economic and governmental data. Results of the two were then compared. Regression techniques were utilized to examine the degree and strength of association between socio-economic and governmental variables. First, selected governmental characteristics were examined as dependent variables with selected socio-economic characteristics as independent v ari­ ables in order to establish a benchmark. identical governmental characteristics, Second, using socio-economic con­ ceptual variables were used as independent variables both alone and in conjunction with socio-economic regions. Lastly, selected governmental conceptual variables were analyzed with socio-economic conceptual variables and regions. Results were contradictory. For example, socio­ economic regions, county area government regions, and county governmental regions developed from factor analysis had few Leon Berton Perkinson similarities. Since regional patterns differed for the two types of governmental regions, it was concluded that inter­ actions between governmental revenue-expenditure character­ istics also differ. Inconsistency between socio-economic and governmental regions indicated that socio-economic struc­ ture and governmental services were not associated. Factor analysis was also used to develop conceptual variables (factors) characteristics. from socio-economic and governmental Comparison of the two types of governmental conceptual variables (CV) indicated that the interactions of revenues-expenditures are not similar. This analysis sup­ ported the conclusion of the regional analysis. A benchmark was established by selecting governmental characteristics as dependent variables for analysis with socio-economic characteristics selected as independent v a r i ­ ables. The coefficient of determination adjusted for degrees of freedom varied from a high of 70 per cent to a low of 7 per cent. Substitution of socio-economic CVs as independent variables generally improved the adjusted coefficient of determination with explanatory powers more than doubled in several cases. Socio-economic regions were then added as discrete independent variables. In general, insertion of these re­ gions increased the adjusted coefficient of determination. Using socio-economic regions in regression analysis indicated per capita governmental revenues-expenditures vary by geo­ graphic location. Leon Berton Perkinson Significant relationships between socio-economic CVs and governmental variables also were examined. It was assumed that accounting for linkages of the socio-economic structural system would assist the location of ubiquitous associations. Instead, governmental characteristics were related to the total socio-economic system. governmental characteristics, present. For specific only unique relationships were This conclusion was not altered when governmental conceptual variables were used instead of individual gove r n ­ mental characteristics. ACKNOWLEDGMENTS Major gratitude is extended to: my graduate advisor, Professor Milton Steinmueller, for his suggestions and coun­ sel throughout the period of graduate study and the prepara­ tion of this report; Professor Daniel Chappelle for his counsel on the various aspects of methodology utilized in this study; and Professors Raleigh Barlowe and Lawrence Libby for their constructive criticisms of the study. Special acknowledgment is due to the Economic D e ­ velopment Division, Economic Research Service, U.S. Department of Agriculture for the interest and financial support that made this study possible. Also, appreciation is extended to Dr. Melvin Janssen and Dr. Thomas Hady, EDD - E R S - U S D A , for their suggestions during various stages of the study. Special thanks go to Miss Laura Robinson, Programming Supervisor, Department of Agricultural Economics, and her staff for their ability in debuging original data tapes and successfully overcoming the many computational problems that developed during the study. Many people provided direct and indirect influence on the thought processes developed before and during this study. To all of these, Thank you. ii Last, but definitely not least, my sincere appre­ ciation is extended to my wife, Susan, for her constant encouragement and understanding during the "highs" and "lows" of this graduate study. iii TABLE OF CONTENTS CHAPTER I. PAGE INTRODUCTION ....................................... 1 The Problem S e t t i n g ...................... i A Suggested Solution - Community Facilities .................................... 6 The Objectives and H y p o t h e s e s ................... 1 3 O b j e c t i v e s .................................... . 1 3 H y p o t h e s e s ....................................... 1 4 P r o c e d u r e ......................................... 1 4 Assumptions and Limitations .................. 16 II. THE M E T H O D O L O G Y ..................................... 20 The "Determinants" Approach .................. 20 The "Interactions" Approach .................. . 23 The M o d e l ......................................... 30 III. SOCIO-ECONOMIC STRUCTURE ......................... 33 The R e g i o n a l i z a t i o n .............................. 3 3 The Conceptual Variables ....................... 46 IV. GOVERNMENTAL REVENUE-EXPENDITURE STRUCTURE ... 58 The R e g i o n a l i z a t i o n .............................. 5 8 Coianty Area G o v e r n m e n t ......................... 61 County Government ........................... 69 Governmental Regions Compared .............. 78 S u m m a r y ......................................... 80 The Conceptual Variables ....................... 81 County Area G o v e r n m e n t ....................... 81 ......................... 9 1 County Government . Governmental Conceptual Variables C o m p a r e d ....................................... 9 8 S u m m a r y ................. -■....................... 9 9 V. LINKAGES BETWEEN SOCIO-ECONOMIC AND GOVERNMENTAL STRUCTURE ......................... 101 iv 5 PAGE Regionalization Linkages ....................... 102 Socio-Economic and County ............................... 102 Area Regions Socio-Economic and County Government Regions ....................... . 1 0 7 Regionalization and Type of G o v e r n m e n t ....................................108 Conceptual Variable Relationships ........... 109 Basic L i n k a g e s ........................... Ill Governmental Characteristics— Socio-Economic C V s ...........................117 Governmental and Socio-Economic CVs . . . .127 VI. SUMMARY, CONCLUSIONS, RECOMMENDATIONS F O R FURTHER RESEARCH ........................... 135 S u m m a r y .......................................... 135 C o n c l u s i o n s ...................................... 137 Regionalizations ............................. 137 Conceptual Variables ......................... 139 ......................... 140 Regression Analysis Hypotheses Examined ........... 141 Recommendations For Further Research ......... 141 BIBLIOGRAPHY ............................................. 144 A P P E N D I C E S ................................................. 154 * A. SOCIO-ECONOMIC CHARACTERISTICS B. "IMPORTANT" VARIABLES ASSOCIATED WITH SOCIO-ECONOMIC REGIONS ......................... 158 C. GOVERNMENTAL CHARACTERISTICS ..................... 159 D. "IMPORTANT" VARIABLES ASSOCIATED WITH COUNTY A R E A GOVERNMENT REGIONS ................ 163 "IMPORTANT" VARIABLES ASSOCIATED WITH COUNTY GOVERNMENT REGIONS ....................... 164 E. V . .................. 154 LIST OF TABLES TABLE 1. PAGE Socio-economic regions formed from factor analysis .................................. 37 2. Means and standard deviations of selected variables for the basic socio-economic r e g i o n s .............................................. 40 3. Means and standard deviations -of selected variables for the bi-polar socio-economic regions ..................................... 43 4. The socio-economic conceptual variables and .................... associated factor loadings 49 County area governmental regions formed from factor analysis .................................. 62 5. 6. Means and standard deviations of selected per capita area governmental characteristics: single and bi-polar county area government r e g i o n s .............................................. 65 7. County governmental regions formed from factor a n a l y s i s ............................................ 71 8. Means and standard deviations of selected per capita county governmental characteristics: single and bi-polar county government r e g i o n s .............................................. 74 9. County area government conceptual variables and associated factor loadings . . . . ........... 83 County government conceptual variables and associated factor loadings .................... 92 Overlap between socio-economic regions and governmental regions ........................... 105 10. 11. vi \ PAGE 12. 13. 14. Regression relationships between selected governmental and socio-economic charac­ teristics: county area governments and county governments ....................... 113 Regression relationships between governmental characteristics and socio-economic conceptual variables and r e g i o n s : county area governments and county governments . . . . 119 Regression relationships of selected govern­ mental conceptual variables and socio­ economic conceptual variables and r e g i o n s : county area governments and county g o v e r n m e n t s ........................................ 129 LIST OF FIGURES FIGURE 1. PAGE Numerical System For Identification of Regions viii . . 38 CHAPTER I INTRODUCTION The Problem Setting It has become quite obvious that certain segments of our society have not shared in the economic prosperity of the rest of the U.S. Much of the concern for those economically bypassed has been concentrated in urban centers because there the poverty is visibly apparent and is r e l a ­ tively concentrated, thereby making assistance programs relatively easy to administer. tively limited. But success has been r e l a ­ Regional Economic Development Commissions were established to stimulate economic development in rural areas and thereby alleviate poverty but have faced unique 1 difficulties. Although it is known that the poor exist in rural areas, it is frequently difficult to identify exactly where the poor are located. located, Once pockets of poverty are they may be distributed over hundreds of square miles making tentative programs difficult to administer. 1The Coastal Plains, Four Corners, New England, Ozarks, and Upper Great Lakes Regional Commissions were es ­ tablished under Title V of the Public Works and Economic Development Act of 1965. The Appalachian Regional Comm i s ­ sion was established under the Appalachian Regional Develop­ ment Act of 1965. 1 2 For both urban and rural areas, more remains to be done than has already been accomplished. This has been caused, in part, by an inability to visualize many of the problems of rural and urban areas as interacting problems. In man y respects, poverty is a relative concept. If everyone has identical income, no one necessarily feels im­ poverished. With a disparity of incomes, can indeed be felt by the i n d i v i d u a l . however, poverty Individual poverty, as important as it is, m a y be secondary to the total effects of an impoverished area. The comparative aspects of p overty may make it easier to bear for the individual, but it makes substantial progress or corrective measures enormously greater for the community. A poor community is likely to lack lead­ ership, personal drive among its inhabitants, and eco­ nomic resources for local betterment. In such a community, man y people retreat from the outside world, become indrawn, develop strong personal ties to the community, and do n o t exert efforts to better their economic situation. Education and other services nearly always suffer. A vicious circle is begun and becomes self-prepetuating. Moreover, in a country such as the United States, where communication is highly developed, it is harder for any locality to take comfort in mutual poverty; the example of higher income areas is too inesca p a b l e .1 Ready examples of prosperous areas illustrated by mass media coupled with prosperity "surrounding" central city dwellers may also create internal strife and turmoil. noted in discussing such problems in India: As Mukherjee "It cannot be ^Marion Clawson, "Rural Poverty in the United States," Journal of Farm Economics, Vol. 49 (December 1967), p. 1228. 3 denied that the co-existence of affluence and poverty is not only the basic cause of tensions, but a potential danger to the unity of the country."1 Perhaps more developed countries should pay heed to the "potential danger to the unity of the country" resulting from the co-existence of affluence and poverty. Many of the present depressed rural areas existed on a mining or agrarian economic base. Technological advances and resulting increased efficiencies in both agriculture and mining paradoxically contributed greatly to the depression of rural areas. 2 The substitution of capital for labor re ­ leased a sizable labor force from the production of primary products. As m a n y rural areas lacked adequate alternative job opportunities, many of those displaced migrated to urban areas where there was mor e hope for employment. W i t h migra­ tion, however, there was frequently no longer a sufficient population base for the existing retail and service sectors of rural areas resulting in a second stage reduction in eco­ nomic activity. In addition, migration caused considerable strain on the cities. 1B. K. Mukherjee, "Regional Dispersal of Industries," Eastern E c o n o m i s t , Vol. 47 (September 9, 1966), p. 477. 2 Some of the governmental support programs also c o n ­ tribute to the problem, albeit that was not the intention. The reduction in acreage under the Cotton Program wit h the immediate impact of putting many thousands out of w o r k is one example. 4 Continual movement of people from rural to urban areas, for example, often causes unfilled public needs in both farm and city communities. Urban centers frequently experience difficulties in public service programs that ex­ pand too slowly to adequately serve a growing population. At the same time, rural areas are often subject to an erosion of their economic tax base in the form of loss of population and of taxable incomes needed to support desired levels of public services.^ The environment into which many migrants moved may be less than what modern standards would dictate. observed: "The reason for an immense migration of rural poor is easily seen. is, As Bonnen As bad as life in the central city ghetto it is still more attractive, holds more opportunities for the poor than does the rural life." 2 Migration is not just a shift in population however. Migration can negate local efforts to alleviate social and economic problems, hinder potential development of the area of migrant origin, and create unfulfilled service needs at both point of origin and point of arrival. Detroit's "^John E. Thompson, "Meeting Unfilled Public Service Needs in Rural Areas," Journal of Farm E c o n o m i c s , Vol. 45 (December 1963), p. 1140. 2 James T. Bonnen, "Progress and Poverty: The People Left Behind," paper presented at the Minneapolis Farm Forum, Minneapolis, Minnesota, March 6, 1968, p. 7. For a similar observation, see Clawson, "Rural Poverty," p. 1232. 5 experience in attempting to solve a social and economic di ­ lemma is a prime example of migration negating local initia­ tive. After the Detroit riot of 1967, a group of citizens worked hard to create 55,000 new jobs of which at least 15,000 went to hard core poor. A t the same time, an influx of migrants caused unemployment to rise by 1,000.’'’ Schachter has adequately pointed out the adverse impact on development efforts resulting from the migration of the labor force from depressed regions in Greece, Spain, Portugal, and Italy. 2 And as noted above, services in both rural and urban areas may suffer from migration. 3 In the U . S . , the age of massive migration may be behind us. Reports from the 1970 Census of Population indi­ cate that fewer areas had an absolute decline from migration during the 1960's than during the 1950's. Many areas still suffered a loss of population from migration, but the loss was not as severe as during the 1 9 5 0 's. Whether a similar slowing or a reversal will be prevalent in the 1970's will depend upon our ability to strengthen the rural hinterland. As agricultural operations continue to be consolidated, the rural economy m a y actually go into a third stage reduction ■'’Bonnen, 2 "Progress and Poverty," p. 7. Gustav Schachter, "Regional Development in the Italian Dual Economy," Economic Development and Cultural C h a n g e , Vol. 15 (July 1967), p. 410. 3 John E. Thompson, "Public Service Needs," p. 1140. 6 in economic activity. That is, absolute population may not diminish greatly, but they m a y pursue their economic activi­ ties outside of the local area. in some cases. This has already occurred As stated by Clawson: Business and social services of all kinds are de ­ clining in the small rural towns; the small rural com­ munity has been by-passed, both by farmers, who no longer support it, and by the public programs, which are generally inapplicable to it. A large proportion of small rural communities are no longer viable and many will vanish in time.l A Suggested Solution— Community Facilities One possible way to alleviate problems associated w it h the emigration from rural areas and immigration into urban areas is to increase the attractiveness of rural areas. Attractiveness m ust include development of economic oppor­ tunity if poverty and migration is to be reduced. doubtful, however, It is that modern man will respond to economic opportunities alone. He is also concerned with adequate housing, quality educational opportunities for his children, modern and convenient health and hospital services, good roads, adequate police and fire protection for his family and property, etc. In addition, he m a y want cultural and recreational facilities nearby. Although muc h has been said about importance of com­ m unity facilities in economic development, relatively little "''Clawson, "Rural Poverty," p. 1233. 7 empirical work has been done. Most empirical work has been generated through industrial location studies and is there­ fore not necessarily directly relevant to the present study. If one assumes that industrial development in an area is i synonomous with economic development, the studies become i slightly more relevant. Almost without fail, the four mos t important consid­ erations found for industrial location are markets, portation, labor, and r a w materials. trans­ Responses to the r e l a ­ tive importance of community attitudes, community facilities such as hospitals, education, housing, police and fire protection, cultural aspects of the community, etc. invari­ ably indicate that these factors are of only secondary or minor importance in location decisions.'*' The primary reason that these studies do not show the importance of community Mirze Amjad Ali Beg, Regional Growth Points in E c o ­ nomic Development (with special"reference to West V i r g i n i a ), Economic Development Series, No. 8, Bureau of Business Re­ search, Wes t Virginia University (December 1965); Thomas P. Bergin and William F. Eagan, "Economic Growth and Community Facilities," Municipal F i n a n c e , Vol. 33 (May 1961); Melvin L. Greenhut, "An Explanation of Industrial Development in Underdeveloped Areas of the United States," Land E c o n o m i c s , XXXVI (November 1960) , Melvin L. Greenhut, Plant Location~in Theory and in P r a c t i c e , (Chapel Hill, North Carolina: The University of North Carolina Press, 1956); Louis K. Loewenstein and David Bradwell, "What Makes Desirable Industrial Property," Appraisal J o u r n a l , XXXIV (April 1966); T. E. Me Millan, Jr., "Why Manufacturers Choose Plant Locations vs. Determinants of Plant Location," Land E c o n o m i c s , XLI (August 1965); and V. W. Ruttan and L. T. Wallace, "The Effectiveness of Location Incentives on Local Economic Development," Journal of Farm Economics, XLIV (November 1962) . 8 facilities may be that they are based on either national or regional types of surveys. Unfortunately studies dealing with decisions for more local areas indicate a similar hierarchy of characteristics. 1 For example, Ruttan's and Wallace's study of industrial location in southern Indiana found community facilities ranked from medium to low in im­ portance. They noted however: There was an indication that noneconomic or amenity factors were also influential in the local decision. The evidence is of two types. F i r s t , the relative im­ portance placed on noneconomic factors rose as the num­ ber of skilled workers or managerial personnel transferred from other locations rose. The unwillingness of personnel to live in communities that do not possess a minimum of civic facilities and amenities is a factor considered by the firm in the location process. Salary increases and/or promotions were cited as ways of over­ coming this unwillingness. S e c o n d , comments from firm officials indicated that they did not consider noneco­ nomic factors an issue about which they could bargain with community leaders.1 If the minimum level of c o m ­ munity facilities and amenities was not met, there was a tendency to simply omit the community from further consideration.2 Although m o s t locational analyses conclude that com­ munity facilities are not important in attracting industry (and thereby stimulate economic development), responses as noted above and frequently contained within such studies The difference in importance for certain consider­ ations vary by whether one has a regional, or site perspec­ tive. For example, see U.S. Department of Commerce, Industrial Location As A Factor in Regional Economic Devel­ o p m e n t , Economic Development Administration, (Washington, D . C . : U.S. Government Printing Office, n.d.p. 14). 2 Ruttan and Wallace, 77. "Location Incentives," p. 976- 9 leave room for broader interpretation. Attributes noted for industrial parks or considered important by industrial re­ searchers also seem to favor a broader interpretation than usually found in location studies.'*' The reason location studies generally fail to substantiate the relative impor­ tance of local facilities is that perhaps the correct questions are not asked. As stated by Smith: Locational-economics models of the conventional varieties beg the primary development issues. They analyze optimum locations, assuming as given those cost and productivity facts, such as forest-land yields under gross mismanagement, and ill-adapted property institu­ tions, which are (or should be) the primary objects of development policy. Finally, they assume away important instrumentalities of control, such as subsidies in various disguises, restrictions on property rights of various k i n d s , calculated discrimination in the taxation of foreign corporations, and the like, by w h i c h u n d e r ­ developed countries, with varying degrees of skill,, manipulated these f a c t o r s . 2 "Conflicting" views of whether or not local community facilities play a role in attracting n e w industry persists into the discussion of depressed areas. Beg, for example, believes that such facilities are quite important. The development of an appropriate social infrastruc­ ture mu s t always precede further economic development. Moreover, social overhead capital should be distributed equitably over the area, rather than concentrated in Advisory Commission on Intergovernmental Relations, State-Local Taxation and Industrial L o c ation, A-30, (Washington, D.C., April, 19671 pp. 71-72, and 74-75; and McMillan, Jr., "Why Manufacturers Choose," p. 245. 2 Eldon D. Smith, "Restrictions on Policy Alterna­ tives Relating to Underdeveloped Regions of Developed Countries," Journal of F a r m Economics, Vol. 48 (December 1966) p. 1231. 10 islands of relative prosperity. Education, for example, brings awareness of the rights and responsibilities of citizenship, whereas illiteracy or lack of adequate education and training excludes the human resources from full participation in the process of growth. If such exclusion is due to the lack of regionally diffused social overhead, the backward area may constitute a drag on the national economy and may even cause stagnation in an otherwise accelerated pace of economic growth. Similarly, adequate means of transportation insure access to— and mobility o f — resources. The economy b e ­ comes more flexible, viable, and resilient when changes occur. If communication media and transportation facil­ ities are concentrated in the primate cities, the im­ pulses of growth are restricted to the already developed a reas.1 Priedmann, on the other hand, tant as (community facilities) states that "as impor­ are for enhancing the quality of life, man-made amenities play a very subordinate role in. guiding the location of productive facilities." 2 The con­ flict is not even resolved at a national or international level. In developing countries, for example, differences in opinion on unbalanced growth versus balanced growth is an 3 issue of considerable importance. ■^Beg, Regional Growth P o i n t s , p. 11. 2 John Friedmann, "Regional Planning in Post-Indus­ trial Society: Some Policy Considerations," Journal of Farm E c o n o m i c s , Vol. 45 (December 1963), p. 1077. 3 For a discussion of Hirschman's unbalanced growth theory, see W. F. Ilchman and R. C. Bhargava, "Balanced Thought and Economic Growth," Economic Development and Cultural C h a n g e , Vol. 14 (July 1966) p. 390. For balanced growth, see W. Arthur Lewis, Development P l a n n i n g : The Essentials of Economic P o l i c y , (New York: Harper & Row, Publishers, 1966) pp. 97-1(1)2. i 11 The issue of whether or n o t community facilities play a role in the economic development of an area is more than a moot point. It has been shown that m a n y public services or community facilities in depressed regions of the U.S. are either inadequate or lacking. In studying Appa­ lachia, Grossman and Levin cited obsolescent community fa­ cilities as one of six obstacles to the economic development of the region. In m any of the r e g i o n 's farming and mining areas important public facilities and services are either substandard or non-existent, residential and commercial structures are dilapidated and community planning efforts have not been in evidence. Education and health levels are low, particularly in the agricultural areas. Lac k ­ ing the basic preparation necessary for technical train­ ing, many of the region's residents have been relegated to marginal, low-paying jobs in factories and service establishments.1 The noted absence of local facilities may make future development of such areas more difficult. A t the same time, the advantages of already developed areas are: In short, the developed areas can offer immediately certain external economies to n e w or expanding industry that the less developed cannot offer. These advantages David A. Grossman and Melvin R. Levin, "The A ppa­ lachian Region: A National Problem Area," Land E c o n o m i c s , XXXVII (May 1961), p. 136. For similar comments on the Ozark Region, see Max F. Jordan and Lloyd D. Bender, An Economic Survey of the Ozark R e g i o n , Agricultural Economics Report No. 97, U.S. Department of Agriculture, Economic R e ­ search Service, Washington, D . C . , 1966, pp. 67-68. This situation is not unique to the U.S. Similar discussions related to rural areas in Israel are in Raanan Weitz, "Rural Development Through Regional Planning In Israel," Journal of Farm Economics, Vol. 47 (August 1965), p. 644. 12 are a result of past development and facilitate future development. The developed areas currently possessing certain external economies will attract new or expanding industry more readily than will less developed areas currently possessing few of these man-made advantages. Obviously, the existing geographical distribution of external economies is in part attributable to historical accident and in part to public p o licies.1 The ability of less developed areas to offset the external economies available in more developed areas may be quite limited as it would take a m u c h larger infusion of money than has been made available.^ In general, relationships between economic develop­ me n t and local governmental services are not well defined in the sense that there are considerable contradictory opinions available. If one is willing to assume that industrial e x ­ pansion in an area is synonomous with economic development, most of the empirical "location" studies indicate that local community factors are of relatively minor importance. At the same t i m e , m any of these studies contain internal o b ­ servations implying that the role of community factors may be relatively important in final industrial location d e ­ cisions. Descriptive studies of depressed areas frequently William E. Laird and James R. Rinehart, "Neglected Aspects of Industrial Subsidy," Land E c o n o m i c s , XLIII (February 1967), p. 28. 2 Grossman and Levin, "The Appalachian Region," p. 140; and Niles M. Hansen, "Some Neglected Factors in A m e r i ­ can Regional Development Policy: The Case of Appalachia." Land Economics, XLII (February 1966) , pp. 5-6. 13 cite the absence or inadequacy of local governmental services whereas comments on developed areas stress availability and general adequacy of such services. However, there are those that discount the significance of local governmental services in the developmental processes. Although there is hardly a concensus on either side of the issue, it appears that the availability of local governmental facilities does have a role in the process of local economic development. The Objectives and Hypotheses Objectives Relationship between economic development and local governmental services have not been well established. The literature is divided as to the importance of governmental services for rural economic development. And yet, develop­ ment of rural areas may be essential to alleviate the con­ gestion and possible collapse of large urban centers. It is therefore essential to quantify the relationships b e ­ tween services and development in a more complete form than has previously been found. The objectives of this study are: 1. To determine the relationships between local govern­ mental services and socio-economic measures of local areas. 2. To examine the relationships found in objective one for consistency between different levels of govern­ ment and for influence of geographic location. Hypotheses The working hypotheses to accomplish the objectives set forth for this study are: 1. There is a positive relationship between socio­ economic measures of local areas and local govern­ mental services. 2. The relationships developed from diverse character­ istics will establish similar geographic patterns (regions). 3. The relationships found in hypothesis one will vary by type of governmental unit studied. Procedure References were made in prior sections to "socio­ economic measures" and "local governmental services" without defining the terms. The socio-economic measures include population characteristics, income characteristics, business characteristics, agricultural characteristics, labor charac­ teristics, e t c . 1 several ways. These characteristics were included in The first was level of performance such as 1These characteristics are explicitly identified in Appendix A. 15 wholesale sales per capita for a given period. The second was distribution of performance such as percentage of the population with less than $3,000 income for a given period. The third was a change in level and distribution of perform­ ance as measured by relative changes in income, sales, etc. over t i m e . "Local governmental services" includes revenues and expenditures by function on a per capita basis.'*' Again, characteristics were examined on the basis of level, distri­ bution, and changes in level and distribution of performance. The purpose of this paper is to examine the relation­ ship between local socio-economic structure and local gov­ ernmental revenue-expenditure patterns. For the most part, the socio-economic structure will be a proxy for past eco­ nomic development. Further, it is assumed that governmental revenue-expenditure patterns will be adequate measures of available local governmental facilities and services. The relationships between the two will then be examined by two methods. First, "regions" and "conceptual variables" de­ veloped from the socio-economic characteristics and from the governmental characteristics are examined. Second, "concep­ tual variables formed from both the socio-economic and ^These characteristics are explicitly identified in Appendix C, p. 159. 16 governmental characteristics are examined by ordinary least square regression analysis.^" Assumptions and Limitations Several limiting assumptions are necessary to make the project feasible. The first is that availability of local governmental facilities can be measured by revenues and functional expenditures of a governmental unit. If a governmental unit has expenditures for a given function, it can be assumed that the service is provided. no expenditures for a given function, that the service is not provided. If there are it will be assumed The latter assumption m a y not always be true, but almost all services require e x ­ penditures before the service can be delivered. Ideally, one would have first hand knowledge of the existence of a service, the number of people it serves, the product being provided, etc. Unfortunately, it would be necessary to have a complete inventory for several broad areas to obtain the crucial information and was therefore considered to be im­ practical for this study. The second assumption, related to the first, is that a dollar of expenditure will buy an equal quantity and quality of service anywhere in the State. Variations in ^The procedure for forming the "regions" and the "conceptual variables" will be discussed in Chapter II. 17 expenditure are therefore assumed to be proxies of variations in adequacy or quality of service. limiting, This is perhaps the most and potentially least realistic, assumption made. A natural conclusion arising from the assumption is that if one government is spending more than another, the larger spender is providing a higher level of service. While this is undoubtedly partly true, there are characteristics which can negate this conclusion. scale are present, If substantial economies of a given dollar expenditure will not buy an equal amount and quality of service. In addition to and coexistent with the problem as ­ sociated with scale, in product available. scale m a y be associated with a change For example, the "product" available from a small town police force m a y be considerably different from that produced b y a metropolitan police force. Although the small town "product" may be completely adequate for most problems, it is unlikely to be sufficiently sophisticated for all possible situations. The same can be said for a small community hospital versus a large metropolitan hospi­ tal. The small hospital m a y be quite adequate for routine surgery and care but specialized treatment and care m a y have to be obtained in a large hospital that can afford the e x ­ pensive modern equipment and related personnel. large hospitals m a y also be involved in teaching. "products" available are therefore not the same. In addition, The An a ddi­ tional complicating factor is that the cost of delivering 18 the service may not be the same. has an associated cost. Distance, for example, Two school districts may have identical total expenditures per pupil and equal enrollments. But if one school district has a sizable transportation ex ­ pense and the other has none at all, there is an immediate difference in the cost of delivering the service. Despite the disadvantages, the assumption that e x ­ penditures are a proxy for service levels is practical. It would be necessary to inventory size and itemize the dif­ ferent services available and costs associated w i t h each to have a reasonable approximation of the mix of production functions for a given service. Although efforts expended in this area m a y be quite fruitful, it was considered to be beyond the scope of this project. A third assumption is that socio-economic structures found will approximate various levels of local economic d e ­ velopment. Instead of assuming that economic development areas could be designated on the basis of a few key charac­ teristics such as per capita income, unemployment rates, change in retail sales, etc., the present study delineates areas on the basis of over tics.'*' 100 socio-economic characteris­ The rationale for this approach is that development, or lack of it, hinges on more than pure economics. Areas ^"The statistical method for delineating the areas is discussed in Chapter II. 19 that are depressed or are developed m a y well have distinct differences in their socio-demographic attributes. If so, these characteristics should also be considered. i CHAPTER II THE METHODOLOGY The attempt to unite the relationships between e c o ­ nomic development and local governmental services .is, in part, unique. As noted previously, several studies of d e ­ pressed areas indicated the relative lack of governmental services, but there appear to be no studies that attempt to systematize the relationships. A t the same time, however, there have been studies of governmental expenditures and revenues which include certain variables that would usually be expected to be associated with economic development. The "Determinants" Approach The "determinants" studies examined relationships between governmental expenditures for a particular function (dependent variable) characteristics and various selected socio-economic (independent v a r i ables). A least squares regression model was used to "determine" the percentage of variance of the dependent variable explained by the inde­ pendent variables.'*' ■*"The results of m a n y of these studies tractors) were published in the early 1970's. 20 (and their d e ­ Some examples 21 "Determinants" (or total) studies examined relationships for per capita spending patterns for cities, counties, districts, etc. school Generally these studies cut across state boundaries but some used the state as the aggregative obser­ vation unit and others examined only a local area. and functional expenditures such as education, Total highways, p o ­ lice, and fire were usually included as separate items for analysis with m o s t studies modifying the data to exclude capital expenditures. The mos t popular independent variables utilized in "determinants" studies included total population, population density, a measure of personal income, and a measure of e d u ­ cational attainment. Not all studies included the same set are: Glenn W. Fisher, "Determinants of State and Local Government Expenditures, A Preliminary Analysis," National Tax J o u r n a l , XIV (December 1961) , pp. 349-355; Glenn W. Fisher, Interstate Variations in State and Local Government Expenditure," National Tax J o u r n a l , XVII (March 1964), pp. 57-74; Werner ¥~. Hirsch, "Determinants of Public Education Expenditures," National Tax J o u r n a l , XIII (March 1960), pp. 29-40; Ernest Kurnow, "Determinants of State and Local E x ­ penditures Reexamined," National Tax J o u r n a l , XVI (Septem­ ber 1963), pp. 252-255; Seymour Sacks and Robert Harris, "The Determinants of State and Local Government Expenditures and Intergovernmental Flows of Funds," National Tax J o u r n a l , XVII (March 1964), pp. 75-85; Henry J. Schmandt and G. Ross Stephens, "Local Government Expenditure Patterns in the United States," Land E c o n o m i c s , XXXIX (November 1963), pp. 397-406; Elliott R. Morss, wSome Thoughts on the Determinants of State and Local Expenditures," National Tax J o u r n a l , XIX (March 1966), pp. 95-103; George B. Pidot, J r . , "A Principal Components Analysis of the Determinants of Local Government Fiscal Patterns," The Review of Economics and St a t i s t i c s , LI (May 1969), pp. 176-188; Roy W. Bahl, Metropolitan City E x ­ penditures: A Comparative A n a l y s i s , (Lexington: University of Kentucky Press, 1969). ! 22 of independent variables but even accounting for these dif­ ferent studies frequently had different independent variables as the most important explanatory variables. Masten and Quindry examined this problem by studying Wisconsin cities of different sizes and concluded "City size...is an extremely important factor in determining the relative explanatory ability of other socio-economic factors."''' Using the same set of variables, different "most important" variables were obtained for different city sizes. The major focus of man y determinants studies ap­ peared to be on the improvement of the coefficient of determination. That is, how m u c h greater is the percentage of a prticular functional expenditure explained by one model versus another model. (and slope) The second focus was on direction of different relationships as indicated by p a r ­ tial regression coefficients. It is not the purpose of the present study to question the implied objectives of these studies. However, one aspect that apparently was n o t ade­ quately considered was the possibility that some socio­ economic (independent) variables would work together into an interlocking system. 2 The same can be said for ^■John T. Masten, Jr., and Kenneth E. Quindry, "A Note on City Expenditure Det e r m i n a n t s ," Land E c o n o m i c s , XLVI (February 1970), p. 81. 2 Two exceptions to thi are: Pidot, "Principal Components Analysis," and Bahl, Metropolitan City E xpenditures. \ 23 classification of expenditures by function ables) (dependent v a r i ­ since various functional expenditures may well be mutually interdependent. Therefore, instead of examining relationships between single variables or partial relation­ ships between a small group of variables, this study concen­ trates on the identification of linkages between socio-eco­ nomic characteristics and between governmental characteristics to develop conceptual variables. One method of doing this is through the use of factor analysis. The "Interactions" A p p r o a c h 1 The view held in this study is an attempt to relate economic development with local governmental expenditure patterns requires the use of a multivariate technique. single measure No (such as change in per capita income) can adequately account for differences in economic growth across a state or nation since economic development is a complex This section is at best rudimentary. There are several good references available from which the majority of this discussion is based. In order of increasing sophis­ tication, these are: Benjamin Fruchter, Introduction to Factor Analysis (Princeton, N.J.: D. Van Nostrand Company, Inc., 1954); Raymond B. Cattell, Factor Analysis: An Intro­ duction and Manual for the Psychologist and Social Scientist (New York: Harper & Brothers, 1952); Raymond B. Cattell, "Factor Analysis: An Introduction to Essentials. I. The Purpose and Underlying Models," B i o m e t r i c s , Vol. 21 (March 1965), pp. 190-215; Raymond B. Cattell, "Factor Analysis: An Introduction to E s s e n t i a l s . I I . The Role of Factor A n a l y ­ sis in Research," Bi o m e t r i c s , Vol. 21 (June 1965), pp. 405435; and Harry H. Harman, Modern Factor A n a l y s i s , (Second Edition, Revised; Chicago: University of Chicago Press, 1967) . 24 of interrelated occurrences or manifestations. Also, r eve­ nues and functional governmental expenditures may also be strongly interrelated. Therefore, a technique that utilizes a complex linkage of interrelated manifestations would be extremely useful. Factor analysis has the unique capability to consider a large number of interrelated characteristics tions) (manifesta­ and reduce these into a smaller number of factors (conceptual v a r i a b l e s ) . As stated by Fruchter: More and more it is being realized that the end product of science is in the form of statements concern­ ing the interrelationships between things. It is also realized that the most fruitful type of concept of the things related is in the form of variables or dimensions. The major problems are therefore correlation problems. Where the appropriate variables are readily observable or easily inferred from objective data, one may proceed to the discovery of the interrelationships. ...where the number of potential variables is very large and the useful variables that we need for economical and d e pend­ able descriptive purposes are overlaid with multiplex manifestations, the demand for some method that will facilitate the discovery of those underlying variables is very great. It is in the fulfillment of this ob j e c ­ tive that we find factor-analysis methods to be of greatest v a l u e . 1 In addition, factor analysis provides more freedom in initial stages of analysis since it does not force premature assump­ tions on possible interrelationships of characteristics and their relative importance in determining areal environments. Factor analysis provides...a method far more free than most methods from the necessity to elaborate rigid ■^Fruchter, Introduction to Factor A n a l y s i s , p. vii. 25 hypotheses. It is the ideal method of open exploration in regions unstructured by present knowledge. In em­ barking upon a factor analysis one need have no more definite idea than Columbus had of America in regard to wha t may be found. It is sufficient to hypothesize that some structure lies t h e r e . 1 Although the development of factor analysis began almost 70 years ago, its primary usage has been in fields related to psychology and aptitude testing. As factor analysis is not a common technique employed in other fields, a brief description is in order. Basically, factor analysis begins with a square simple correlation matrix. (The diagonal of this matrix is a crucial issue which will be discussed under communalities.) The simple correlation matrix is factored giving the princi­ pal factor solution. But, this solution does not yield a framework which is easily interpreted. As stated by Cattell, "...factors may be, indeed, almost certainly are, quite remote from correspondence w i t h the patterns of any of the real influences behind the data. Indeed, the arrangement of factors as they come fresh from the com­ puter is affected by such accidental matters as the raw score scaling (in the case of the principal axis solu­ tion) ... " 2 Therefore, a rotation of the principal factor solution may be performed. Rotation of factors does not affect the percentage of total variance explained by the factors but redistributes ^Cattell, Factor Analysis; M a n u a l , p. 14. 2 Cattell, A n Introduction and "Factor Analysis Purpose," p. 205. \ 26 their explanatory functions among a corresponding number of new factors. The rotation does affect the magnitude of factor loadings as they tend toward unity and zero, thus lending toward more accurate interpretation of the factors. The varimax rotation "...is a precisely defined method which indeed approximates orthogonal simple structure."'*' With the introduction of several terms in the d i s ­ cussion above, these terms will n o w be discussed in detail. The terminology is used for both principal factors and ro ­ tated factors. Factor loadings are crucial as their r e spec­ tive values for each variable in combination w ith the variables which cluster together are what the analysis hinges upon. Each variable has a factor loading on each factor, and the loading can be considered similar to a simple cor­ relation between a variable and a factor. Like a simple correlation coefficient, the value of a factor loading varies from - 1 . 0 to + 1 . 0 with the sign indicating whether the variable varies directly or inversely wit h the factor. The classical factor analytic model is designed to maximally reproduce correlations and has the general form: 1. Z. = a . nF. +a.«F 0 + ...+ a . F + a .U . l ll 1 i2 2 ip p xi Harman, Modern Factor A n a l y s i s , p. 310. There is considerable argument for usxng an oblique rotation solution as this would permit the development of correlated factors, a situation which would be m ost likely to exist in the real world. A t the time this study was conducted however, oblique rotations were not available as part of the factor analysis program package. 27 where there are i = 1 ,2 , . ..n variables, p = common factors, with a ^ 1 ,2 , .. .p being the factor loading for the ith variable on factor p, w ith F being common factors and a^ and U being the unique factor loading and unique factor, respectively. The basic problem of factor analysis is to determine common factor loadings 2 The a ^ (i.e. the a. 's). xp is the proportion of variance of variable i explained by factor p. The proportion of total variance explained by a factor is 2 . ~ a ^ . r (trace of the matrix) u lp i=l r The form to determine the percentage of the variable's var i ­ ance accounted for by the total solution communality) (i.e. the variable's is 2 2 2 2 3. h^ = a * , + a z .9 + ... +a . ll iz ip Note that only loadings of the common factors are utilized in computing communality. Two different types of factors were introduced above. 1. These are distinguished by: Common factors involve more than one variable. a. b. 2. General factor— almost all variables load highly on one factor. Group factor— more than one variable but not all variables loaded on the factor. Unique factors involve a single variable. 28 Common factors account for the variables intercorrelations whereas unique factors represent that portion of a variable not accounted for by its correlations with other variables in the set. An issue deferred earlier was that of c o mmunality. previously mentioned, As factor analysis begins with a square, simple correlation matrix. Each diagonal element equals unity because of perfect correlation of a variable with itself. The issue of communality is whether or not a diag­ onal element should be left equal to unity or should be estimated. Harman devotes an entire chapter to selection of communality so this discussion will cover only the point as to whether one should use unities or some other estimate of communality.^" The use of unities as the communality estimate is frequent but Cattell feels this "closed" model should be called only "component analysis." 2 He also believes that the use of the closed model is misleading. ...it is wildly unlikely that the small sample of variables employed will actually represent within them­ selves all the real common influences required to ac­ count for all the variance of all the performances. The trick of putting ones in the diagonals, though com­ forting in accounting fully for the variance of v a r i a b l e s , ^Ha r m a n , Modern Factor A n a l y s i s , C h p . 5. ^Cattell, "Factor Analysis Role," p. 411. 29 perpetrates a hoax, for actually it really drags in all the specific factor and error v a r i a n c e . . .to inflate specious, incorrect common factors . 1 Cattell prefers to reserve the use of the term "factor analy­ sis" to the "open" model where estimates of communality are something less than one. The estimate is only for the initial matrix as from there on, the communalities are re ­ iterated at each step to the final solution. Communality selection can be crucial as noted by different results found from different estimates by Harman. 2 Most researchers, how­ ever, will find that the greater constraint on selection of "appropriate" communality estimates lies more on options •available in standard computer p r o g r a m s 1than on theoretical soundness of different estimates. 3 One last point remains for clarification. In normal correlation analysis, two characteristics are correlated over a series of individuals. When the pair-wise correlates are drawn over a number of characteristics, the correlation matrix for the usual R-technique of factor analysis Cattell, 2 (or "Factor Analysis Purpose," p. 201. Harman, Modern Factor A n a l y s i s , pp. 88-90. 3 The program used in this study was: Anthony V. Williams (revised by James Peterson and Robert P a u l ) , Factor A A , Technical Report No. 34.1, Computer Institute for Social Science Research, Michigan State University, East Lansing, Michigan (May 1969). The communality estimates available were ones, highest correlations, and Guttman communalities. Highest correlations were used as estimates in early stages of the study. However, the resulting factors violated the "orthogonality" constraints of the Varimax rotation. There­ fore, one's were used as communality estimates throught this study despite the difficulties noted by Cattell. 30 simple correlation matrix for regression analysis) results. "The transpose of the R-technique is that in which people are correlated in pairs, instead of performances, the c o r ­ relation being over performances..." and is called the Qt echnique.^ One particular use of the Q-technique is that it "...is mos t useful if one wishes immediately to see how many types there are in a population and to divide it up into types. This usually has merely descriptive value." 2 The Model As noted previously, the usual "determinants" of governmental revenue expenditures studies use ordinary least squares regression models as the analytic tool. Wit h regression analysis, one is faced w i t h selecting independent variables which may only be proxies for a whole set of inter­ related variables. Regression analysis can account, in part, for interrelationships of variables through partial correla­ tion coefficients but assumes that "independent" variables are in fact independent. Adelman and Morris indicate concisely the difference between regression analysis and factor analysis. Cattell> "Factor Analysis Role," 415. Also see Cattell, Factor Analysis; An Introduction and M a n u a l , C h p . 7. 2 Cattell, Factor Analysis; Manual, p. 101. An Introduction and 31 Technique of factor analysis shares certain charac­ teristics with both nonquantitative comparative studies and statistical regression analyses. In essence, it is equivalent to a systematic application of comparative studies which simultaneously tests a large number of ceteris paribus propositions. As in regression analysis, factor analysis breaks down the original variance of a variable into variance components associated with the variation of a set of other quantities. In regression analysis, the variable whose variations are decomposed in this manner is known as the dependent variable, and the variables that ac­ count for different portions of its variation are the independent variables. In factor analysis, all vari­ ables are dependent and independent in turn. Thus, by contrast w ith regression analysis, wh i c h is a study of dependence, factor analysis is a study of mutual inter­ dependence . (Emphasis m i n e .) -L This study uses the technique of factor analysis as its basic model to utilize the information available in m u ­ tual interdependence. used, Although the same basic model is it is used in two separate ways. The first is the analysis of county correlates drawn over a number of socio­ economic characteristics and is called the R-technique. The result will be a clustering of individual variables into a component which is called a conceptual variable. The second use of the factor model will be the correlation of counties in pairs with the correlation being over charac­ teristics and is called the Q - technique. The result will be a conceptual regionalization or typology of counties based on socio-economic characteristics. To avoid difficulty of scale of the data when the data Irma Adelman and Cynthia Taft Morris, Society, Politics, and Economic Development; A Quantitiative A p p r o a c h , (Baltimore: The John Hopkins Press, 1967) , p. 131. ~ 32 matrix is transposed (a problem directly parallel to having extreme observations in simple correlation analysis), all data for the Q-technique will be normalized.'*' Governmental data will be analyzed in the same two ways as the socio-eco­ nomic data. Although results from the factor analysis could be considered as final output, the conceptual variables will be utilized as inputs into the final analysis. This will entail usage of conceptual socio-economic variables as in ­ dependent variables and conceptual governmental variables as dependent variables in an ordinary least squares regres­ sion model. Conceptual socio-economic and governmental regions found from the Q-technique will be compared for similarities and differences. If similar area configura­ tions are found for each, differences between areal groups will be examined. ■*"The formula for normalizing the data was: zi = /0 1 CHAPTER III SOCIO-ECONOMIC STRUCTURE The primary objective of this study is to determine relationships between public community facilities and socio­ economic measures of local economic development. To accomplish this objective.and to account for the mutual interdependence likely to be found, conceptual socio-economic variables and conceptual socio-economic regions are developed. Regions are developed to illustrate the homogeneity— hete r o ­ geneity of socio-economic characteristics between and among geographic areas. Conceptual variables are developed to illustrate the homogeneity— heterogeneity between and among selected socio-economic characteristics and to provide inte­ grated "variables" for final analysis. The Regionalization Linkages of and mutual interdependence of socio­ economic characteristics do not necessarily stand alone. Different geographical areas m a y be affected differently by similar characteristics. Therefore, this study divided Michigan's counties into different types on the basis of selected socio-economic characteristics. 33 One approach 34 frequently followed for regionalizing any given area is to select a few key characteristics and then match areal con­ figurations on the basis of those selected characteristics. The approach followed here is quite similar but considers many characteristics simultaneously. The mutual interde­ pendence of the selected characteristics is analyzed instead of making the implicit assumption that such characteristics are basically independent. In addition, straint on configurations found. there is no con­ For example, there is no assumption that like areas must be conterminous, although this modification could be made. To develop regions, 110 socio-economic characteris­ tics were selected for analysis. ■*" These characteristics can be classified into three basic types: (1 ) level of performance such as per capita wholesale and retail sales, per capita income, education, etc., for a base year, (2 ) These variables are explicitly identified in A p ­ pendix A. Originally, 203 characteristics were analyzed. Ninety-three variables indicating employment and relative change in employment by SIC code and occupations were in­ cluded with the final 110. These employment variables were excluded from this analysis because their inclusion resulted in finding two non-discriminating regions for the state. Specific employment data are apparently enough different from general socio-economic data to cause general distor­ tions. This implies, however, that if one's objective was to unite type of employment with socio-economic structure, employment would have to be considered separately. For the objectives established for this study, specific types of employment were not considered to be strategic characteris­ tics and the 93 related variables were therefore dropped. 35 distribution of performance as indicated by percentage of population wit h less than $3,000 income, four years of high school education or more, urban, etc., for a base year, and (3) change in level and distribution of performance as measured by relative changes in income, sales, per cent urban, etc. over time. Therefore, level of, distribution of, and change in performance are analyzed simultaneously. The socio-economic characteristics were normalized and transposed so that counties were correlated in pairs with correlations generated being over per f o r m a n c e s .^ The resulting linkages from the factor analysis gave the areal configuration. Thirteen basic socio-economic regions (factors) were found for Michigan with each region containing from one to 25 counties. 2 Each region is technically orthogonal ■^See Chapter II, pp. (i.e. 29-32 for the details. The following assumptions and constraints were made in determining the final regionalized solution: (1 ) c o m ­ munality estimates were made equal to unity, (2 ) rotation of factors was constrained to all factors with an eigenvalue equal to or greater than 1.5. It would have been equally as possible to set the number of factors to be rotated or to adjust the eigenvalue. For example, given assumption 1, and factoring with an eigenvalue equal to 0 , there would be as many factors as variables but many factors would be nonsig­ nificant. Therefore, to keep the number of factors m a nage­ able and to avoid the necessity of predetermining the number of factors by specifying the number to be rotated, the eigen­ value of 1.5 was selected. (3) a county was assumed to be in any given region only if it had a factor loading equal to or greater than + .40. (i.e. at least 16 per cent of the county's variance was assignable to the given region.) 36 not correlated with the other regions) unique attributes. and therefore contains Since bi-polar factors were found in most factors, eac h pole may represent a unique sub-region. The complete set of counties within a given region were af­ fected by i d e n t i c a l 'characteristics and were therefore clas­ sified together; but they were affected by those character­ istics in directly opposite ways. Region (factor) one was made up of 25 counties lo­ cated in the Lower Peninsula and was bi-polar (Table 1). ^ As indicated by Table 1, the poles had approximately equal representation wit h 12 counties in one sub-region and 13 counties in the other. Although 110 variables entered into consideration of the region, only mary importance. non-farm, 2 These were: 8 variables were of pri- per cent of the population fertility rate, per cent of the employed labor force working outside of the county, relative change in the per cent of families with less than $3,000 income, percentage change in the number with $3,000 income, per cent of the ^The counties included in a region are listed numer­ ically in Table 1. The numerals correspond to the county numbers in Figure 1. 2 Primary importance was determined from the respec­ tive factor score associated with each variable. The factor score for a variable is computed by: FSk = a kiX ki where i=l...n is the number of counties (83) and k stands for the variable. To be considered "important" for this discus­ sion, F S ^ + 2 . 0 meaning that the variable FS was more than 2 standard deviations from the mean FS since factor scores are normalized values. i 37 Table 1. Socio-economic regions formed from factor analysis. Region 3 Counties included *3 Region 3 Counties Included la 35,36,43,46,47,50, 52,56,59,60,61,63 lb 1,3,9,10,11,12,17 21,22,25,26,29,32 2a 69,70,72,73,75,76, 79,80,82 2b 3,55 3a 19,20,39 3b 31,33,34,41,50,52 4a 2,5,15,16,18,23,24 25,27,38 4b 13,69,70,74,77,83 5a 1,7,8,14,35 5b 66,73 6a 5 6b 53,64,82 7a 43,44,49 7b 33 8 54,61,65,67,74,81 9a 27 9b 40,48,71 12b 25,26,28,32 10 30,42,55 11 45,58,63 12a 37 13 62 NAC 4,6,51,57,68 The numeral corresponds with the factor number from the analysis. The letter classification is followed for b i ­ polar factors. County numbers are keyed to the numerals associated with the counties on Figure 1. Counties not allocated to any region by the factor a nal y s i s . i 38 ISLE ROYALE EWEENAW ALGER SCHOOLCRAFT DELTA .CHEBOYGAN> | PRESQUE 6 6 X ! 67 >ISLr~ ( r- — — ^ jOTSEGO MONTMOR. ALPENA ANTRIM f 61 62 64 \ 63 'KALKASKA CRAWFORO OSCODA 1 57 J 56 ^SISSAUKEEl ROSCOM. 'O S C E O L A I C L A R E 1 1 ' I _44 t '45 43 58 59 OGEMAW IOSCO 52 53 51 ,'50 S0NT l AKE ALCONA 'G L A D wT n "^ A R E N A C 1 '| 46 1 A7 ! < -/ r HURON I BAY / 41 NEWAYGO W c O S T A | i ®A 0 E L L * ,M IO LAN d ' ! 1 ' ! ;I--------------37 L i _ 38 39 _ _ I _ _p 36 MUSKE. | M O N T C A LM ;yK-ENJf - ] 30 G R A T IO T I S A G IN A W 1 c l in t o n i 22 TUSCOLA | ! 34 33 I ; 31 1 21 40' ^ - . - - TUT 1 23 i 26 i 27 OAKLAND ALLEGAN | BAHRT | EATON 15 i 16 i_ WAN BUREN K A L A N A Z .' C ALHO UN I 11 I BER RIEN ! | CASS |ST.«K)SEPH BRANCH >2 I :3 4 | i 18 i x ___ [ JACKSON_ T M SHT r N A * | » * Y " E 10 8 ! 17 ! 1 12 HILLSDALE LEN AN EE | 5 Figure 1. Numerical System For Identification of Regions. 6 II 13 ;»ON«OE i 7 1ST. C L A IR ' 1 28 'UACONB| ! 20 39 population urban, bank deposits per capita and effective buying income per capita. The last three characteristics operated inversely to the other "important" variables. For Region la, the counties were directly related to the first five characteristics above and indirectly related to the final three. For Region lb, just the opposite is the case.'*' Regions formed from factor analysis are "unique" so far as their relationships with the "important" variables are concerned. To test the uniqueness of the regions for more general characteristics, means and standard deviations of ten variables were computed for each region. All v a r i ­ ables selected, except 1970 population, were included in the factor analysis. 2 of some variables Although Sharp differences between means (especially population) were found for different regions, variances around the different means were quite large (Table 2). account the variance, In most cases, if one takes into the differences found between the dif­ ferent basic regions are negligible. The counties within each region are identified in Table 1. Although individual characteristics affecting each of the 13 major regions could be reported in depth here, they are listed instead in Appendix B. Individual items going into each region are not of primary importance for the p u r ­ poses of this study but are presented in the Appendix for those interested in the key relationships. 2 Of the nine variables included in the factor analy­ sis, the 1960 population, and the two variables for income distribution were the only ones that were not included as an "important variable" for any region. Changes in income distribution were included as an "important variable" for several regions however. Table 2. Means and standard deviation of selected variables for the basic socio­ economic regions. Population _____________ Region c^ t? f s no. 1 25 2 11 3 4 5 6 9 16 7 4 1970 1960 no. no. 110,137 . 94,594 (127,802)1 (109,056) Family Income Income 3 G^ h b Urban 0 Youth 3 Ageae Lowf High* E3 o o 3 J dol. pet. pet. pet. 1,618 (419) 15.0 (13.1) 33.3 (33.3) 32.7 14.9 (2 .0 ) (3.9) pet. pet. pet. no. 24.4 10.9 (1 1 .8 ) (5.6) 10.3 (1 .0 ) 26,778 (18,848) 25,330 (15,883) 1,475 (181) (11.3) 37.9 (22.5) 32.7 14.7 24.8 (1 .8 ) (2 .0 ) (4.6) 7.1 (2.5) 10.1 (0 .8 196,978 (331,017) 145,624 (239,403) 1,597 (402) 29.3 (42.5) 32.7 (36.6) 35.0 (2.7) 13.1 (4.4) 24.7 (11.9) 12.8 10.1 (7.8) (1.4) 202,951 (657,293) 197,376 (658,520) 1,579 (266) 11.6 34.0 (24.1) 31.9 (3.5) 14.6 (2 .6 ) 22.8 (16.5) (5.1) 9.4 (4.6) 10.3 (0 .8 ) 68,192 (54,588) 60,551 (48,986) 1,567 (215) 15.9 (14.7) 31.5 33.9 (2 0 .0 ) (1.4) 14.7 (2.4) 21.7 (4.9) 9.4 (3.2) 10.0 (0 .6 31,299 (5,064) 28,114 (8,155) 1,518 (105) 25.2 (19.5) 32.7 (26.7) 33.8 (1.9) 13.1 (2 .2 ) 23.7 (3.9) 8.2 10.8 (1 .2 ) (0.9) 1,333 (136) 3.1 (6.9) 17.3 (25.9) 18.6 31.9 (2 .2 ) (5.0) 31.9 (11.3) 6.5 (2.5) 9.4 (0 .6 ) 1,212 -1 . 6 (4.1) 28.2 (22.9) 17.3 32.1 (2 .2 ) (1.4) 30.0 (3.7) 6.3 (2.7) 9.8 (0.7) 7 4 22,205 (18,538) 20,176 (16,343) 8 6 11,556 (4,330) 10,493 (2,884) (95) 6.1 ) O ) 9 4 3 10 3 11 12 5 13 1 53,375 (45,010) 47,982 (40,585) 1,526 (196) 9.3 (11.7) 49.9 (26.8) 32.3 (2.4) 15.1 (3.5) 21.3 (4.8) 9.4 (3.1) 33,816 (9,706) 30,405 (7,430) 1,524 (49) 13.2 (5.4) 39.6 (17.4) 16.7 31.5 (1 .2 ) (1 .0 ) 24.0 (3.1) (2 .1 ) 8,889 (6,765) 6,506 (4,479) 1,260 (2 0 1 ) 10.3 (3.2) 31.6 (1.4) 18.0 (1 .8 ) 36.9 (5.2) 5.0 (0 .8 ) 10.1 175,065 (167,101) 149,353 (141,207) 1,830 (332) 21.3 (10.5) 56.2 (16.4) 33.4 (3.1) 12.5 (2 .2 ) 19.4 (8.5) 11.9 (3.3) 10.5 (0.3) 1,186 17.2 34.0 33.0 14.6 27.2 6.1 9.6 10,422 7,545 0 0 8.6 Effective buying income per capita, 1960. b Q 1960. cl Percentage of population under 15 years old, 1960. 0 Percentage of population age 60 or more, 1960. fPercentage of families with less than $3,000 income, 1960. ^Percentage of families with $10,000 income or more, 1960. Median educational attainment of population 25 years old or more, 1960. 1Standard deviations are in parentheses ( ). (0.4) 10.5 (0 .2 ) (0.3) js. Relative net population growth, 1950-1960. Percentage of population living in urban places, 10.0 42 As indicated previously, however, most basic regions (factors) consisted of two basic sub-groups since the factors were bi-polar. Therefore, means and standard deviations for the same variables were computed for each sub-region to ex­ amine whether or not sub-regions were in fact different (Table 3). For Region 1, for example, one sub-region had an average population of less than 10,000 people in 1960 and a per capita buying income of less than $1,230. The other sub-region of Region 1 had an average population of over 170,000 and an effective buying income of over $1,975. At the same time, standard deviations for these two variables were smaller than those found for the basic region. The differences in means between these sub-regions for the r e ­ maining variables, except youth, were also quite large. Likewise, standard deviations for sub-regions were consist­ ently smaller than those associated with basic regions. Differences between sub-regions are not unique to Region 1. Sharp contrasts were found between sub-regions of each bi-polar region although not for all variables. It was not expected that differences would exist for all se­ lected variables for all sub-regions since the variables selected for comparisons were only a small part of the total number of variables entering into the regionalization frame­ work. Therefore, even though basic regions were formed on the basis of counties being affected by the same variables, Table 3. Means and standard deviations of selected variables for the bi-polar socio-economic regions. Population Regionac^ t? f s no. la lb 2a 12 13 9 1970 1960 no. no. Inoomeb dol. Drband Youthe A g e d f W pet. pet. pet. 32.2 (1.9) pet. pet. I pet. no. 5.9 (1 .8 ) 9.5 (0.5) 9,833 (5,660) 1,229 (128) 3.6 (6 .8 ) (4.0) 201,310 (117,757) 172,835 (99,704) 1,977 (213) 25.7 (6.9) 62.9 (14.8) 33.2 11.9 15.5 14.0 (2 .1 ) (1 .6 ) (2 .0 ) (3.3) 23,109 (18,883) 22,535 (16,189) 1,442 (167) 3.3 (10.4) 36.0 (24.3) 33.1 (1.7) 14.4 26.0 (2 .0 ) (4.3) (1.3) 9.9 (0.7) 16.3 19.6 (1 .0 ) (1.3) 11.5 (0.7) 10.9 (0 .2 ) 11,367 . (6,536)3 1.2 35.7 18.1 (2 .8 ) (5.6) Highh E^ 6.1 11.0 (0.7) 2b 2 43,284 (5,814) 37,911 (6,252) 1,625 (219) 18.9 (2.5) 46.4 (1 2 .2 ) 30.8 (0.7) 3a 3 532,316 (429,666) 382,504 (320,041) 2,086 (252) 79.3 (37.8) 76.5 (19.6) 38.0 7.5 (1 .8 ) (0.9) 10.7 (2.5) 22.3 (5.7) 11.7 (0 .6 ) 3b 6 29,308 (16,248) 27,183 (15,178) 1,352 (134) 4.3 (7.8) 33.5 (1.4) 15.9 (1.4) 31.7 (7.1) 8.1 (16.2) (1.5) 9.3 (0 .6 ) 50,834 (10,602) 40,315 (6,842) 1,587 (139) 21.8 (9.7) 27.0 (11.5) 33.7 (1.3) 20.7 13.3 (1 .6 ) (3.0) 457,730 459,145 (1,082,252)(1 ,081,341) 1,566 (421) -5.5 (9.2) 45.8 (35.1) 28.8 (4.0) 16.8 (2.5) 26.4 6.8 (6 .0 ) (6 .8 ) 9.8 (0 .8 ) 22.2 24.3 (17.8) 34.0 (1.5) 14.2 (2.7) 20.5 (5.3) 9.8 (0.4) 49.4 (15.3) 33.7 (1.3) 15.8 (0.9) 24.7 6.2 (2 .8 ) (1 .0 ) 4a 10 4b 6 5a 5 5b 2 84,617 (57,015) 74,731 (51,743) 1,623 (236) (11.9) 27,128 (12,440) 25,101 (13,006) 1,427 (14) (5.7) 0.2 10.8 11.0 (1.4) 10.6 (2.9) 10.7 (0.5) 10.6 (0 .6 ) 6a 1 37,171 37,742 6b 3 29,342 (3,936) 25,905 1,526 (8,395) (127) 30.5 (19.8) 36.3 (31.6) 7a 3 13,405 (7,137) 12,466 1,289 (6,636) (127) -0.3 (1.6) 18.3 (31.6) 7b 1 48,603 43,305 1,464 13.2 14.4 33.7 14.7 9a 1 52,317 41,926 1,366 17.1 14.7 33.6 12.9 9b 3 53,729 (55,119) 6.7 (12.8) 61.7 (15.8) 12a 1 27,992 1,383 11.0 41.3 12b 4 211,833 (167,981) 1,942 (252) 23.9 (10.1) 59.9 (16.3) 1,496 50,000 1,579 (49,459) (202) 21,051 181,429 (140,452) 8.9 21.9 9.8 11.4 34.4 12.2 22.7 (1.7) (1.7) (4.1) 7.6 (0.7) 10.5 (1.0) 31.4 20.0 34.7 (2.4) (5.3)(11.9) 5.7 (2.4) 9.5 (0.7) 23.3 8.9 9.1 22.4 10.9 9.7 31.9 15.7 26.6 31.9 15.9 20.9 (2.7) (3.9) (5.8) 28.1 15.1 33.5 8.9 (3.6) 10.1 (0.4) 7.1 10.6 34.7 11.9 15.9 13.2 (0.9) (2.0) (3.6) (2.1) 10.5 (0.3) Regions 8, 10, 11, and 13 did not consist of two discrete groups of counties. Therefore, the appropriate data are found in Table 1. ^Effective buying income per capita, 1960. cRelative net population growth, 1950-1960. P e r c e n t a g e of population living in urban places, 1960. ePercentage of population under 15 years old, 1960. P ercentage of population age 60 or more, 1960. ^Percentage of families with less than $3,000 income, 1960. P e r c e n t a g e of families with $10,000 income or more, 1960. M e d i a n educational attainment of population 25 years old or more, 1960. 9Standard deviations are in parentheses ( ). 45 the nine bi-polar regions had distinct and separate sub­ groups indicating that variables affected those sub-groups in opposite ways. Also, the existence of sub-regions may also explain the relative absence of sharp contrasts among the basic regions. Comparisons across sub-regions also indicated sub­ stantial differences in averages found for the selected variables. For example, sub-regions 2a, 3b, 5b, 6 b, and 12a each had an average 1960-70 population between 20,000 and 30,000 (Table 3). The number of counties contained w i t h ­ in each varied from one in 12a to nine in 2a. Even though average county population for these five regions was ap­ proximately the same, the ranges of values for other v a r i ­ ables were in some cases quite large. Per capita income for example ranged from $1,352 for Region 3b to $1,526 for Region 6 b. Change in population between 1950-60 varied from 0 in Region 5b to 30 per cent in Region 6 b. The average percentage of the population classified as urban varied from 11 per cent in Region 3b to 49 per cent in Region 5b. Similar ranges were found for the remaining selected v a r i ­ ables. Each sub-region had at least one extreme value. The r e f o r e , even though average populations were approxi­ mately the same, the average performance recorded for other characteristics indicated that the regions were uniquely different in at least one of the selected attributes. Even if this were not found to be the case it would not mean that 46 the sub-regions were totally alike since the variables examined represent less than ten per cent of all variables that went into the regionalization. In summary, this study developed 22 socio-economic regions given the assumptions stated at the beginning of this chapter. polar factors These 22 regions were the outgrowth of 9 bi ­ (regions) and 4 single pole factors (regions). As indicated by means and standard deviations computed for ten selected variables, it is necessary to consider each pole of a bi-polar region as two separate regions. The socio-economic regions described above will be compared with governmental revenue-expenditure regions developed in the next chapter. If similar regionalizations are found between the two types of regions it can be stated that, 110 so far as socio-economic variables are representative of the social and economic structure, there is a relationship be ­ tween socio-economic structure and governmental structure. Actual quantification of the structure is still to be^ d e ­ veloped. The first input into that quantification is discussed in the next section. The Conceptual Variables The mutual interdependence of attributes of counties and the resulting regionalization was discussed above. The primary objective of this section is to develop the mutual interdependence of socio-economic variables into a smaller 47 set of conceptual variables factor analysis (factors). The R-technique of (as discussed in Chapter II) was used to develop the factors. The 110 socio-economic characteristics previously discussed formed the base of the analysis for conceptual variables. The number of variables which could be utilized in the R-technique was constrainted to less than 100 by computer program capacity. The necessary reduction in variables was accomplished by examining the simple correla­ tion matrix. One of each pair of variables wit h a simple correlation coefficient equal to or greater than + .90 was dropped from further consideration. The decision as to which of two variables should be dropped was somewhat a r ­ bitrary but followed the general rule of retaining the variable which would be easiest to interpret. The selection process reduced the number of variables to 93 and are the first 93 variables of Appendix A. The variables deleted are those from 94 to 110 of Appendix A. An alternative selection process for reducing the number of variables would be eliminating those character­ istics which one assumed to be of limited value. The p r o ­ cess actually completed however was preferred since it eliminated only redundant information. The information eliminated is considered redundant because both variables from a given high correlation would be loaded onto the same 5 48 factor (conceptual variable.) If it had not been necessary to reduce the number of variables, the redundancy would not be of special interest. Factor analysis and rotation reduced the original 93 variables to 16 "conceptual variables" (factors) counting for 78 per cent of the total variance. 1 ac- By re­ ducing the original variables to "conceptual variables", the number of "variables" is reduced by more than 80 per cent. And yet, 78 per cent of the variance explained by the orig­ inal variables is retained. Conceptual variables and individual variables are shown in Table 4. ceptual variable The first con­ (CV) will be called General Socio-Economic Structure 1 for two reasons. First, 20 of the original 93 variables were associated with this CV. Secondly, those associated variables included characteristics such as income (agricultural and g e n e r a l ) , education and age composition, population distribution, fertility-birth rates, employment, The assumptions and constraints made in determining the final conceptual variable solution were identical to those made for regionalization except for item 3 (See foot­ note 2 page 35.) The third item is modified here by assum­ ing that a variable enters into the naming process only if it had a factor loading equal to or greater than + .50. (i.e. at least 25 per cent of the variable's variance was assignable to a given conceptual variable.) It should be emphasized that selection of critical factor loadings affects the visual representation of the factor pattern and the "naming" process only since all factor loadings (weights) are taken into consideration in determining factor scores. Factor scores are the primary objective of the analysis as they will be the inputs into the final analysis. Table 4. The socio-economic conceptual variables and associated factor loadings. Conceptual Variables Itema 2 9 11 14 18 22 24 27 35 38 80 87 4 6e 21 33 34 39 88 93 29 30 67 69 81 83 1 2 .63 .73 .85 .67 .53 .54 .55 .64 .81 .54 .82 .81 -.73 -.55 -.50 -.86 -.95 -.56 -.69 -.82 3 4 5 6 7 8 9 10 11 12 13 -.56 14 15 16 h2 .95 .67 .94 .83 .91 .79 .89 -76 .86 .81 .78 .87 .77 £ .77 .77 .85 .94 .74 .85 .84 .92 .90 .85 .61 .96 .87 -.70 .54 .75 .87 .71 .66 .89 .88 &$SSBS§XS55SS3&5BB&S&£B£S! 50 c\iir>ot'~''3, cNOi' '( NC'i <- l ^t , c N r ~ o ' i ' £ > o i H t ~ - o ( v) k o m o oooooovor~-r~oooor^oocDoooot^oooor>t^>r^vo oo • • • i i I C M O ’ J P ) id r* id in «> • • • I rH O in • N i l I ' N in in i « « I in loiono r- oo in • i • i • l l l l VO "4*00 00 in r" in in r» in • • • • • • I I I I o i-H ro in oo oo vo i vo r-~ in in I I I I I I m Cn xi (n 'jin N O M S f'im fn n io ^ H O N 'jric o o N in c o o o o io M o o io io iD in id id id r> oi in t» H n 1oo n in in to H 'tH c N O iin m in io H o iH o o o o 5 Table 4. Continued, Conceptual Variables Itema 1 2 3 4 5 6 7 8 9 68 82 12 23 7 40 41 61 62 78 91 71 77 70 74 10 11 12 13 14 15 16 .90 .89 .59 .61 -.72 -.85 -.89 .58 .71 .73 .68 .59 -.60 -.61 -.70 Prop. Var.1 15 .11 .06 .07 .04 .03 .04 .04 .04 .03 .03 .03 .02 .03 .02 .03 Cum. Prop, Var. 3 .15 .26 .32 .39 .43 .46 .50 .54 .58 .61 .65 .68 .70 .73 .75 .78 h 2C .92 .93 .73 .78 .80 .86 .86 .59 .58 .85 .85 .55 .71 .76 .67 aThe item numbers identify the individual variables as listed in Appendix 1. The names of the conceptual variables are as follows: Economic Structure 1. (2) Agricultural Business Composition 1. (4) Agricultural Business Composition II. (5) Agglomeration. (1) General Socio(3) Absence of Growth. (6) Economic Well-Being. (7) Recent Agricultural Adjustments. (8) Non-agricultural Employment Opportunities. (9) Population Characteristics. (10) Dairy. (11) General Socio-Economic Structure II. (12) Unemployment. (13) Recent Business Activity. (14) Rural Non-Farm Population. (15) Agricultural Business Composition III. (16) Agricultural Business Composition IV. cThe final communality estimate represents the percentage of the variance of a variable explained by the 16 conceptual variables. Variable associated with Conceptual Variable 1 and Conceptual Variable 3. eVariable associated with Conceptual Variable 1 and Conceptual Variable 9. ^Variable associated with Conceptual Variable 2 and Conceptual Variable 4. ^Variable associated with Conceptual Variable 3 and Conceptual Variable 5. h Variable associated with Conceptual Variable 4 and Conceptual Variable 16. 1The percentage of the variance of the total matrix explained by a particular Conceptual Variable. -*The cummulative percentage of the variance of the total matrix explained by successive Conceptual Variables. The total does not necessarily equal the sum of its parts at each stage due to round off error. 53 etc. It would be possible to name this conceptual variable INCOME since m o s t of those characteristics not directly associated w i t h income tend to be related to income. For example, percentage of the population employed, migration, and percentage of the population aged 25 or over with 4 years of high school or more would all be expected to be directly related to income levels. Fertility and birth rates, per cent of the population aged 60 or over and p e r ­ centage of the population classified as rural non-farm would be expected to be inversely related to income levels. These expected relationships were found in their association with the CV. Although it would be legitimate to call CV 1 Income, the General Socio-Economic Structure title is r e ­ tained to indicate the more general nature of the factor. The second CV will be called Agricultural Business Composition 1 because 10 of the 12 "important" character­ istics deal directly with agricultural enterprise composition or change in number of farms. The remaining two variables are measures of rural farm population. The third CV will be considered a measure of Absence of Growth since measures of migration, change in buying income, change in dwellings and change in families w i t h less than $3,000 income over­ power the two cropland variables. If the signs of the individual characteristics were changed, called Growth. the CV could be 54 Conceptual variable four is called Agricultural Business Composition II since its component parts are quite similar to those found in CV 2. by total population, The fifth CV is primarily affected level of wholesale sales and bank d e ­ posits per capita, per cent of the population employed, and per cent of the 1960 population who lived in a different county five years earlier. As these relationships would be expected to be associated with urbanization this var i ­ able will be called Agglomeration. Conceptual variable 6 is called Economic Well-Being since it is primarily affected by per cent of residential dwellings that are dilapidated, percentage change in families with more than $10,000 family income and is inversely re ­ lated to non-farm business income per farm. The negative relationship found for non-farm business income could indi­ cate that farmers earn enough income from the farm and therefore do not pursue off-farm employment. On the other hand, this variable could be interpreted as indicating that there are few off-farm employment opportunities available. If the latter alternative was found to be the actual case, it would indicate that instead of measuring well-being, CV is actually a measure of ill-health. the The variable for crop value as a per cent of farm product sold does not d i ­ rectly enter into the process of selecting a name. The seventh conceptual variable is a measure of Recent Agricultural Adjustments since two of its major 55 components are related to change in farm products sold be ­ tween 1959-64. Change in wholesale sales per capita between 1948-58 is also highly associated with CV 7 but its presence is not indicated by the name. CV 8 is made up of the per cent of farm operators working off of the farm 100 or more days and the per cent of farm operators with income from agriculture being less than other income earned by the family. Non^agricultural Employment Opportunities is there­ fore the title of CV 8. Conceptual variable 9 is called Population Charac­ teristics with the relative change in educational attainment level, percentage of labor force that is male, and fertility rates being the major components of the variable. Major components of CV 10 are measures of dairy enterprises so this CV will be called Dairy. The eleventh CV is called General Socio-Economic Structure II because characteristics entering into CV 11 are similar to those in CV 1. Conceptual variable 12 will be called Unemployment since percentage change in total unemployment and change in per cent unemployed were the only variables that were as­ sociated with CV 12. CV 13 measures Recent Business Activity with its highest associations being wit h percentage change in wholesale sales per capita and value added by m anufac­ turing per capita between 1958-64. CV 14 consists of two measures of the rural non-farm population and is therefore called Rural Non-farm Population. 56 CV 15 and CV 16 will be called Agricultural Business Composition III and Agricultural Business Composition IV, respectively, because of the similarity of their component parts with CV 2 and CV 4. Naming these 16 conceptual variables is a subjective process. Others could examine the component parts of these variables and decide that perhaps another name would be better and be justified in that belief. however, It should be noted that variables associated with the conceptual v a r i ­ ables but at a value lower than that selected for tabular representation were examined to assist in assignment of a name. The purpose of naming conceptual variables is only to provide a means of identification and to provide a feel for the types of variables that go into the CV. Factor scores computed for each CV are developed from all 93 socio-economic variables and not just those selected for assisting in de t e r ­ mination of the name. In summary, the original 93 socio-economic variables analyzed were reduced by over 80 per cent to 16 conceptual variables. These conceptual variables retained 78 per cent of the original information found in the total matrix. Con­ ceptual variables 2, 4, 7, 10, 15 and 16 were related to agricultural activities. The remaining 10 conceptual v a r i ­ ables were related to different aspects of socio-economic structure. Conceptual variables found in this analysis and 57 factor scores generated for each provide one of the basic inputs into the analysis of relationships between socio­ economic structure and governmental revenue-expenditure structure. CHAPTER IV GOVERNMENTAL REVENUE-EXPENDITURE STRUCTURE As indicated previously, the primary objective of this study was to determine the relationships between public community facilities and socio-economic measures of local economic development. The first step towards the satisfac­ tion of that objective was completed in Chapter III with the development of socio-economic regions and socio-economic conceptual variables. In this chapter, governmental regions will be developed to examine the homogeneity— heterogeneity of governmental revenue and expenditure patterns between and among geographic areas. Conceptual governmental variables will be developed to examine the homogeneity— heterogeneity between and among the selected revenue and expenditure char­ acteristics and to provide integrated "variables" for final analysis. The Regionalization The mutual interdependence of the governmental revenue-expenditure characteristics m a y vary b y geographic areas. That is, different geographical areas m a y have dif­ ferent interrelationships for similar characteristics. 58 59 Therefore, Michigan's counties were divided into different regions on the basis of selected governmental characteris­ tics. To develop governmental regions, 90 governmental revenue-expenditure characteristics were selected for analysis.^ These characteristics can be classified into three basic types: (1) level of activity such as specific per capita revenue or per capita expenditure for a base year, (2) mix of activity such as per capita expenditure for a specific function relative to total per capita expendi­ tures, and (3) change in level of activity as measured by relative changes in per capita revenues or expenditures over time. Therefore, level of, mix of, and change in activity are analyzed simultaneously. Governmental characteristics used in this study were stated as percentages or on a per capita basis. To obtain relevant population estimates for the governmental data, 1962 and 1967 populations were estimated by a straight line ^The variables are explicitly identified in Appendix C. The data were collected by the U.S. Bureau of the Census during the 1962 and 1967 Censuses of Governments. The data identified in Appendix C are sometimes more detailed than those available in U.S. Bureau of the Census, Census of Governments, (Washington, D . C . : U.S. Government Printing Office, various v o l u m e s .) This is because original data tapes were.used instead of available published reports. Data used for this study can be aggregated into classes similar to those published in the Census of Governments. 60 extrapolation from the 1960 and 1970 population censuses. Therefore, the per capita values are based on estimated p o p ­ ulations for 1962 and 1967. Capital expenditures are not included in this analy­ sis with the exception of "total capital expenditures per capita" and "relative change in total capital expenditures per capita." Capital expenditures data were available by specific function but were not used since capital expendi­ tures tend to be lumpy. The use of total capital expendi­ tures should reduce the unevenness. Individual expenditure items used in this study are therefore direct, current expenditures since capital expenditures are excluded. Governmental revenue-expenditure regions were formu­ lated on the same basis as the socio-economic regions. That is, governmental characteristics were normalized and transposed so that counties were correlated in pairs with the correlations being over performances.^ The resulting linkages from the factor analysis gave the areal configur ration. Two types of governmental regionalizations were accomplished. The first was for county area government which is synonymous with the "county area" classification reported in the Census of Governments. ^See Chapter II, pp. County area 30-32 for the details. 61 government is the simple summation of all revenues and direct expenditures of all local governments ties, townships, school districts, located within a county. (county, municipali­ and special districts) The second type of regionalization was for county government and was based on its revenues and direct general expenditures. County Area Government Factor analysis of 90 county area governmental char­ acteristics identified 17 "regions" for Michigan. The number of counties contained within the non-contiguous regions varied from 2 to 17.^ Bi-polar factors were found for 11 regions with each pole possibly representing a unique subregion. The complete set of counties within any given region were affected by identical characteristics and were therefore classified together; but the poles were affected by those characteristics in directly opposite ways. County area governmental region 1 contained 17 2 counties and was bi-polar. One pole (sub-region) contained 11 counties and the other contained 6 counties (Table 5.) Although 90 variables were considered for the region, only ^Assumptions and constraints made in determining the final regionalized solution were identical to those made in establishing the socio-economic regions. See footnote 2, p. 35. 2 Counties included in a region are listed numerically in Table 5. The numerals correspond to the county numbers of Figure 1, p. 38. 62 Table 5. County area governmental regions formed from factor a n a l y s i s . Regiona Counties Included*5 la 15, 43, 51 , 52, 56, 59, 60, 63 , 67, 75, 83 lb 3, 6, 26, 30, 71, 82 2a 2, 7 2b 66 3a 9, 12, 13, 19, 22 3b 44, 54, 61, 63, 81 4a 27, 37 4b 53, 76 5 50, 68 6 1, 11, 19, 20, 29 7a 30, 36, 80 7b 25, 28, 48 8 23, 54, 60 , 65 9a 4, 17 9b 46 Region3 Counties Included*5 10 16, 72 11 10, 12, 28 , 29, 40, 55 12a 3 12b 43, 45 13a 49, 73, 77 , 79 13b 64 14a 39 14b 35 15a 5, 15 15b 57, 62 16 6, 31, 33, 34, 38, 41 17a 7, 18, 24, 38 17b 69, 70, 74, 78 NAC 8, 14, 21, 32, 42, 47, 58 The numeral corresponds with the factor number from the analysis. The letter classification is followed for b i ­ polar factors. County numbers are keyed to the numerals associated with the counties on Figure 1. Counties not allocated to any region by the factor a nalysis. 63 6 were of primary importance.^ These were: percentage of total expenditures spent for welfare, general control, and general public buildings; per capita expenditures for edu­ cation and general control; and relative change in per capita educational expenditures. 2 The educational characteristics operated inversely to the other "important" characteristics. Counties of Region la were inversely related to educational characteristics and directly related to the remaining im­ portant characteristics. 3 be the case. For Region lb, the opposite would County area governmental regions formed from factor analysis are "unique" so far as their relationships with "important" variables are concerned. To examine the u n i q u e ­ ness of the regions for more general characteristics, means To be considered "important" for this discussion, the factor score for the variable was more than two standard deviations from the mean factor score. For more detail on this particular point, see footnote 2, p. 36. 2 General control expenditures are for governing bodies, courts, chief executive office, central staff services and agencies concerned w i t h personnel administration, law, planning and zoning, etc. General public buildings expendi­ tures are for the maintenance of public buildings not allo­ cated to particular functions. 3 The counties within each region— sub-region are listed in Table 5. Although individual characteristics affecting each of the 17 major regions could be reported in depth here, they are itemized instead in Appendix D. Ap­ pendix D contains more detail than contained in the discus­ sion since the "critical" factor score value is reduced from 2 to 1.5. Individual items going into each region are not of primary importance for the purposes of this study but are presented in the Appendix for those interested in the key relationships. 64 and standard deviations of eight governmental variables were computed. All variables selected were included in the factor analysis, and all were included as an "important" variable for at least one region. It was shown in Chapter III that poles of a given factor are basically unique sub-regions and should therefore be considered separately. This was also found to be the case for governmental regions. Therefore, only unique sub- regions and single pole regions are presented Differences in means between sub-regions region were found to be substantial. (Table 6.) (poles) of a basic For example, charges and miscellaneous revenues were $20 for sub-region la and $66 for sub-region lb. education, hospital, wages paid. Similar differences occurred for total expenditures, and salaries and Capital expenditures for the two sub-regions were identical. The differences noted for these sub-regions also generally occurred for sub-regions of the remaining bi-polar regions although not necessarily for identical characteristics. Absence of differences between sub-regions, such as 7a and 7b, does not alter the general conclusion of uniqueness of sub-regions. The reason is that the charac­ teristics selected represent a small proportion of the total characteristics considered in the regionalization analysis. Therefore, it should not be expected that dif­ ferences would be found for one selected characteristic for every sub-region. Table 6. -ona Means and standard deviations of selected per capita area governmental characteristics: single and bi-polar county area government regions. No. of Counties Prop.. Tax Charges0 Tot. G e n 11 Rev.^ Ed.e Hosp.^ dol. dol. dol. Tot. Exp. 9 dol. Salaries11 Capital dol. dol. no. dol. dol. la 11 82.33 . (14.19) 19.87 (9.60) 253.73 (29.65) 94.76 (21.88) 2.92 (6.83) 188.63 (27.88) 121.97 (17.76) 52.15 (24.36) lb 6 106.40 (22.92) 65.84 (22.90) 312.65 (33.70) 141.12 (12.76) 39.12 (24.32) 251.76 (25.40) 175.13 (20.62) 52.13 (12.08) 2a 2 80.31 (11.91) 19.15 (5.64) 214.49 (4.02) 112.03 (12.37) 4.57 (5.87) 173.57 (11.48) 125.80 (10.53) 51.38 (3.81) 2b 1 97.21 29.77 275.71 126.40 0.45 221.27 142.76 61.06 3a 5 136.33 (15.34) 47.82 (11.38) 301.25 (48.93) 131.73 (17.46) 9.95 (9.29) 224.29 (35.43) 175.22 (36.88) 64.78 (9.72) 3b 5 90.27 (17.45) 30.46 (12.34) 306.35 (20.45) 135.96 (8.62) 8.68 (12.63) 228.79 (13.65) 150.04 (11.70) 58.23 (25.77) 4a 2 62.08 (1.88) 81.74 (32.72) 255.32 (36.62) 95.63 (2.03) 54.69 (37.60) 211.51 (34.87) 130.24 (30.21) 35.57 (15.94) 4b 2 107.47 (43.56) 22.30 (0.73) 301.03 (39.58) 167.54 (2.11) 0.14 (0.20) 237.26 (25.18) 159.29 (7.03) 69.69 (45.29) 5 2 87.53 (2.97) 18.67 (6.61) 296.06 (46.92) 139.89 (42.55) 6 5 121.58 (14.11) 43.26 (5.53) 294.18 (15.64) 143.80 (8.66) 7a 3 95.23 (4.69) 25.24 (5.24) 288.30 (16.42) 7b 3 102.32 (15.75) 26.73 (12.38) 8 4 93.43 (18.40) 9a 2 9b 0 0 232.43 (42.42) 154.31 (32.26) 28.72 (6.58) 7.09 (4.05) 228.25 (17.17) 166.25 (12.21) 64.86 (22.08) 137.15 (16.40) 3.52 (6.10) 218.27 (8.53) 147.18 (4.32) 37.26 (10.89) 264.91 (13.63) 117.93 (13.81) 13.70 (5.06) 205.73 (5.52) 145.32 (9.24) 55.86 (14.76) 25.24 (14.62) 266.02 (43.23) 117.89 (16.71) 12.59 (6.48) 199.11 (33.15) 139.06 (26.27) 43.17 (10.06) 123.52 (36.43) 74.43 (4.43) 326.40 (35.95) 135.04 (17.78) 34.54 (25.32) 248.79 (9.03) 185.69 (33.24) 72.19 (29.24) 1 84.05 14.65 237.63 113.71 1.75 189.41 128.60 30.68 10 2 81.53 (7.78) 27.99 (11.71) 254.36 (11.95) 121.17 (6.07) 18.49 (7.77) 202.02 (10.54) 139.80 (15.24) 71.90 (3.21) 11 6 115.97 (16.79) 42.46 (6.65) 279.04 (16.83) 121.86 (12.79) 9.06 (5.55) 216.58 (16.14) 159.19 (8.59) 60.37 (12.39) 12a 1 100.65 72.90 307.64 141.78 45.40 248.64 173.78 56.88 12b 2 80.19 (21.53) 18.03 (2.86) 257.84 (15.61) 99.31 (38.17) 0.51 (0.73) 193.60 (10.44) 125.69 (8.50) 53.58 (29.11) 13a 4 80.20 (16.73) 27.38 (6.43) 250.03 (20.50) 111.50 (15.93) 5.13 (5.87) 205.76 (11.76) 137.16 (23.73) 38.00 (13.53) Table 6. a Region Continued. No. of Counties no. Prop. Taxb Charges0 Tot. Gen'l Rev.d Ed.e Hosp.^ dol. dol. dol. dol. dol. Tot. Exp. ^ Salaries Capital1 dol. dol. dol. 13b 1 162.20 107.24 390.81 134.90 74.40 291.91 198.47 148.92 14a 1 179.23 36.75 324.13 158.84 0 236.48 188.31 102.19 14b 1 66.36 18.11 260.49 101.60 11.17 172.30 106.47 59.37 15 a 2 71.55 (4.36) 36.84 (22.82) 234.92 (39.08) 104.19 (19.74) 16.39 (21.19) 185.05 (41.44) 122.58 (43.67) 30.18 (13.31) 15b 2 79.48 (12.67) 20.37 (5.50) 258.73 (14.41) 109.92 (2.20) 0 0 199.89 (3.66) 139.04 (6.78) 42.48 (11.28) 16 6 91.30 (12.08) 31.05 (9.40) 252.54 (25.37) 127.99 (20.62) .8.02 (9.65) 197.65 (26.11) 133.30 (17.54) 41.72 (9.77) 17a 4 76.37 (8.32) 21.75 (5.62) 204.42 (9.37) 105.06 (14.12) 3.22 (6.16) 160.97 (15.62) 113.41 (13.68) 57.92 (5.35) 17b 4 108.20 (33.40) 69.75 (31.69) 365.16 (21.46) 131.36 (14.87) 64.64 (10.46) 305.26 (25.01) 199.01 (10.47) 37.81 (17.97) a The counties contained within each region are identified in Table 5. Property taxes per capita, 1967. cCharges and miscellaneous revenue, 1967. Total general revenue per capita, 1967. eEducational expenditures per capita, 1967. ^Hospital expenditures per capita, 1967. gTotal direct expenditures per capita, 1967 ll Salaries and wages paid per capita, 1967. ^■Capital expenditures per capita, 1967. •^Standard deviations are in parentheses ( ) o\ 00 69 In summary, 28 county area governmental regions were developed given the assumptions stated at the beginning of this section. polar regions (factors.) These regions were the result of 11 bi ­ (factors) and six single pole regions It is necessary to consider each pole of a bi­ polar region as two separate regions as indicated by means and standard deviations computed for selected governmental variables. County Government Regionalization analysis of aggregated county area governments utilized 90 governmental revenue-expenditure characteristics. The analysis for county governments used 82 characteristics since none of the county governments had any activity for eight characteristics.^ Factor analysis created 18 county governmental regions for Michigan. The humber of counties contained within each region varied from nine to one. 2 All but six regions were found to be bi-polar ^The characteristics, and their respective numbers as identified in Appendix C, are: sales taxes and income taxes per capita (3 and 4), measures of per capita expendi­ tures on higher education (27, 45, and 65), and measures of per capita expenditures on housing and urban renewal (34, 52, and 72). ? "The assumptions and constraints made in determining the final regionalized solution were identical to those made in establishing the socio-economic and county area govern­ mental regions. See footnote 2, p. 35. 70 with each pole possibly representing a unique sub-region. The complete set of counties within a given region were affected by the same characteristics and were therefore classified together; but poles were affected by those char­ acteristics in directly opposite ways. County governmental region one was bi-polar and con­ tained 9 counties.'*' region) 2 (sub- contained seven counties and the other contained two counties. tance. As indicated in Table 7, one pole Only nine characteristics were of primary imporThese were: per capita charges and miscellaneous revenues, total own revenue per capita, and total general revenue per capita; current hospital expenditures as a p e r ­ centage of total current expenditures; current hospital expenditures and salaries and wages paid per capita; property taxes as a percentage of total own r e v e n u e , relative change in current total expenditures per capita, and current public building expenditures as a percentage of total current expenditures. 3 The last three characteristics The counties included in a region are listed numer­ ically in Table 7. The numerals correspond to the county numbers of Figure 1, p. 38. 2 To be considered "important" for this discussion, the factor score for the variable was more than two standard deviations from the m e a n factor score. For more detail on this particular point, see footnote 2, p. 36. 3 "Charges and miscellaneous revenues" include all current charges, special assessments, and all other general revenue except taxes and intergovernmental revenue. "Total own revenue" includes all of total own revenue plus inter­ governmental revenue. Public building expenditures are for the maintenance of public buildings not allocated to partic­ ular functions. 71 Table 7. County governmental regions formed from factor a n a lysis. Region3 Counties Included |_ Region3 Counties Included la 4, 27, 69, 71, 74, 78, 82 lb 39, 64 2a 8, 72 2b 60, 75, 76 3a 1, 21 3b 7, 45, 47, 53, 59, 62, 63 4a 43, 58, 83 4b 13, 19, 20, 24 5a 61 5b 46,48, 50, 65 6 6, 21, 31, 33, 34, 41, 66 7 67, 80 8a 2, 3, 7, 16, 17, 18 32 8b 44 9a 62 9b 5, 15, 30 10a 28, 29 10b 36, 49, 52 11a 35,56 lib 8, 68 12 10, 26 13a 9, 11, 22, 26 13b 43 14a 51, 58 14b 16, 54, 81 15a 13, 29, 66, 75, 77, 79 15b 16 22, 42, 55 17 23 18 25, 37 NAC 12, 14, 38, 40, 57, 70, 73 59 aThe numeral corresponds with the factor number from the analysis. The letter classification is followed for bi­ polar factors. County numbers are keyed to the numerals associated with the counties of Figure 1, p. 38. c cCounties not allocated to any region by the factor analysis. 72 operated inversely to the other important variables. There­ fore, for Region la, the counties wer e affected directly by the first six governmental characteristics and inversely by the last three characteristics. For counties in Region lb, the opposite would be the case.'*' County governmental regions formed from factor analy­ sis (as wit h socio-economic and county area regions) are "unique" so far as their relationships with "important" variables are concerned. Means and standard deviations of seven county governmental variables were computed to illustrate uniqueness of sub-regions. 2 All variables selected were included in the factor analysis and all were included as an "important" variable for at least one region. It was previously shown that differences in mean values for selected characteristics of basic regions, while generally different for each region, are not particularly The counties within each region— sub-region are listed in Table 7. The individual characteristics affecting each of the 18 major regions could be reported in depth here but are itemized instead in Appendix E. Appendix E contains more detail than contained in the discussion since the "critical" factor score value is reduced from 2 to 1.5. The individual items going into each region are not of primary importance for the purposes of this study but are presented in the Appendix for those interested in the key relation­ ships. 2 The characteristics are identical to those examined for county area regions except that education is not included. Educational expenditures were deleted since education is not generally an important expenditure item for county govern­ ments and relatively few county governments have any direct educational expenditures. significant when standard deviations were considered. was also true for county governmental regions. This Therefore, only means and standard deviations of unique regions are presented (Table 8.) The means and standard deviations of the selected county governmental characteristics for sub-regions had patterns paralleling those of socio-economic and county area government sub-regions. That is, in general, sub-regions had distinct means and relatively small standard deviations. In sub-regions la and lb for example, the means were very distinct and standard deviations quite small for all var i ­ ables except per capita property tax revenues and capital expenditures. Absence of differences for respective sub- regions would not necessarily indicate lack of uniqueness since the selected variables represent a small percentage of the total variables considered for the regionalization. The regionalization process developed 30 county government regions given the assumptions stated at the b e ­ ginning of this section. 12 bi-polar factors (regions.) These regions were the result of (regions) and six single pole factors It was previously indicated that it is necessary to consider each pole of a bi-polar region as two separate entities. This is supported by the values determined for sub-regions of county governments. Table 8. Means and standard deviations of selected per capita county governmental characteristics: single and bi-polar county government regions. No. of [iona Counties No. Prop. Tax" dol. la 7 21.95 . (6.50)1 lb 2 2a Charges Q dol. Tot. G e n '1 Rev.d Hosp.0 Tot. Exp.f dol. dol. dol. dol. 101.37 (20.89) 53.83 (13.76) 19.19 (16.18) 57.95 (11.54) Salaries^ Capital dol. 55.10 (17.49) 131.54 (35.54) 24.46 (5.84) 1.68 (0.02) 53.75 (2.25) 0 0 35.75 (1.31) 16.36 (4.31) 13.73 (0.55) 2 15.57 (1.14) 6.04 (0.56) 59.56 (22.60) 11.99 (16.96) 51.62 (17.69) 27.54 (20.80) 12.68 (5.17) 2b 3 36.42 (10.39) 5.52 (3.26) 102.08 (19.27) 1.17 (2.03) 65.15 (20.61) 29.66 (6.96) 21.79 (12.54) 3a 2 15.52 (3.29) 7.97 (7.15) 48.02 (11.79) 4.61 (6.52) 42.39 (15.68) 15.68 (4.40) 7.66 (2.43) 3b 7 18.31 (4.12) 3.19 (1.43) 76.93 (22.06) 0.21 (0.40) 48.66 (13.42) 25.34 (10.90) 26.96 (9.50) 4a 3 29.80 (11.35) 5.92 (1.86) 147.99 (30.74) 0.23 (0.39) 111.05 (12.97) 58.02 (30.54) 38.21 (36.43) 4b 4 17.04 (5.50) 6.19 (3.86) 47.68 (11.33) 3.17 (3.56) 27.59 (6.43) 14.98 (3.98) 16.00 (5.02) 5a 1 22.99 29.34 5b 4 20.78 (3.85) 4.22 (2.78) 91.00 (15.16) 6 7 17.93 (2.60) 4.60 (1.23) 59.75 (15.13) 7 2 24.93 (0.72) 4.62 (1.74) 93.81 (16.11) 8a 7 16.82 (1.75) 7.52 (5.99) 49.94 (4.66) 7.69 (6.01) 8b 1 21.10 6.57 74.07 9a 1 19.50 1.64 85.68 9b 3 17.35 (1.37) 5.22 (2.06) 63.84 (5.75) 10a 2 22.74 (2.07) 4.52 (1.85) 10b 3 18.00 (1.88) 11a 2 lib 2 98.86 27.71 78.74 37.69 18.41 8.46 (9.21) 64.96 (10.02) 39.57 (8.38) 21.94 (2.99) 2.05 (2.50) 39.90 (7.42) 17.22 (3.16) 17.05 (11.98) 61.80 (8.01) 24.35 (8.77) 20.55 (3.70) 35.18 (4.84) 16.64 (3.92) 10.57 (3.99) 0 56.86 39.46 4.38 0 61.09 36.62 29.44 0.47 (0.81) 40.99 (2.49) 15.10 (7.90) 17.42 (3.31) 55.36 (0.52) 6.37 (2.50) 39.93 (2.67) 15.28 (4.69) 8.61 (2.96) 3.46 (1.09) 83.27 (8.66) 3.56 (6.07) 54.33 (2.15) 28.59 (3.34) 18.92 (1.63) 19.52 (2.68) 2.25 (1.29) 96.02 (26.52) 7.29 (5.50) 63.66 (24.83) 31.02 (19.70) 27.66 (3.43) 16.65 (2.67) 3.70 (2.76) 71.09 (24.76) 0 0 50.92 (16.71) 22.47 (13.63) 18.08 (12.81) 0 0 Table 8. C o n t i n u e d . sgiona No. of Counties Prop. Taxb Tot. Charges0 Gen' Rev. Hosp.0 Tot i _ Exp. Salaries^ Capital^ 12 no. 2 dol. 19.68 (5.42) dol. 5.33 (1.92) dol. 50.51 (0.34) dol. 8.32 (0.19) dol. 37.52 (4.12) dol... 16.79 (1.33) dol. 7.98 (2.04) 13a 4 20.87 (2.46) 6.01 (1.99) 49.31(6.51) 5.40 (2.51) 34.49 (1.41) 16.99 (1.99) 10.14 (5.34) 13b 1 27.44 5.14 132.66 0 96.14 53.06 72.54 14a 2 24.58 (6.74) 6.50 (2.71) 113.58 (20.29) 0 0 99.52 (28.60) 46.54 (23.03) 4.50 (6*37) 14b 3 22.52 (4.39) 7.20 (7.18) 113.60 (49.50) 9.39 (8.28) 66.48 (21.51) 34.57 (13.57) 56.79 (39.77) 15a 6 23.08 (5.39) 8.03 (3.74) 76.03 (20.29) 6.17 (4.77) 54.96 (18.45) 21.12 (7.02) 14.57 (12.35) 15b 1 11.60 5.99 85.80 0 56.61 38.07 27.89 16 3 20.09 (1.92) 4.00 (3.18) 62.45 (5.32) 6.72 (6.58) 38.09 (6.16) 17.74 (0.30) 20.98 (6.72) 17 1 13.03 3.99 47.91 0 38.37 16.90 3.83 18 2 17.92 (1.29) 40.71 (0.37) 21.61 (1.46) 3.07 (0.18) 56.86 (3.25) 5.32 (7.52) 16.16 (9.45) aThe counties contained within each region are identified in Table 7. ^Property taxes per capita, 1967. c Charges and miscellaneous revenue, 1967. ^Total general revenue per capita, 1967. eHospital expenditures per capita, 1967. fTotal direct expenditures per capita, 1967. ^Salaries and wages paid per capita, 1967. Capital expenditures per capita, 1967. 1Standard deviations are in parentheses ( ). 78 Governmental Regions' Compared The regionalization process of county area govern— „ » ments and county governments was conducted independently. Although the same types of data were used for each, levels of expenditures and revenues were different since county area data are totals including county government data as well as all other local governmental units within the county. If county government revenue-expenditure structure is an accurate reflection of the structure of total (county area) revenues-expenditures, the regions developed would be similar. It has been shown that poles of bi-polar regions are unique sub-regions and must therefore be treated as separate entities. Therefore, any regional comparisons must use polar sub-regions in conjunction with single pole basic re­ gions. In addition, to compare regions, it is not necessary for counties in county government Region la to be represented in county area government Region la since the regional num­ bering system is arbitrary. What is important is that groups or clusters of counties "stick" together and therefore move to a "new" region more or less intact. Or, if the cluster does not "stick" together, splinter counties should predominate in new regions. Examination of the regional patterns found in Table 5 and Table 8 indicates that, in general, there are no con­ sistencies between county area governmental and county gov­ ernmental regions. For example, counties in county area 79 Region la are contained in 10 different county governmental regions. Only three of those regions were "dominated" by the splintered counties."^ That is, county government regions 2b and 4a each had two counties from county area government Region lb. Only three counties were in county government Regions 2b and 4a. In general, regional patterns are scattered between the two types of governmental regions. And it makes r ela­ tively little difference whether one attempts to follow the governmental pattern for county areas or for county govern­ ment. The types of county transfers discussed above are considered to be relatively marginal. Although it is some­ wh a t assuring to know that some of the splits are going to ­ gether to dominate a different region, there are relatively few of these. In only one case does a major group of coun­ ties show any cohesiveness. Counties 6, 31, 33, 34, and 41 of county area Region 16 all transfer to county government Region 6. 2 Only county 38 of Region 16 does not mak e the transfer, and it is "not allocated" among county government ^Domination of a region by transferred counties was determined to exist only if more than half of the counties within the region came from the original region. A t the same time, a region was considered to transfer in a "cluster" so long as more than one-half of the counties transferred together. 2 The counties discussed here and their identification numbers are: 6) Lenawee, 21) Ottawa, 31) Gratiot, 33) Tus>cola, 34) Sanilac, 38) Isabella, 41) Huron, and 66) Emmet. 80 regions. Only two counties in county government Region 6 did not exist in Region 16. These are county 21 which was "not allocated" among county area government regions and county 66 which was the only county in county area Region 2b. Therefore, w ith few exceptions, the regions developed from revenue^expenditure patterns of county area governments and county governments are basically different. Summary In summary, 28 county area governmental regions and 30 county government regions were developed from revenueexpenditure patterns by factor analysis. It has been shown that for all practical purposes the regionalizations of the two types of governmental regions are basically different. The primary objective of the regionalization analysis was to develop governmental regions that would be used for compari­ sons with the socio-economic regions developed in Chapter III. If similar regional patterns are found for either of the governmental regional patterns and the socio-economic regions, the assumption is that socio-economic structure is related to governmental revenue-expenditure patterns. regionalization patterns are found to be different, If the reverse assumption would be that socio-economic structure is not related to governmental revenue-expenditure patterns. Governmental and socio-economic regional comparisons will be made in Chapter V. 81 The Conceptual, Variables Mutual interdependence of local governmental revenue and expenditure data and resulting regionalizations were discussed above. The primary objective of this section is to develop mutual interdependence of revenue-expenditure variables for both county government and all local govern­ mental units (area governments) within a county into a smaller set of conceptual variables. The R-technique of factor analysis was used to develop the conceptual v a r i ­ ables. ^ County Are a Government As indicated previously, revenue and expenditure items for area governments is the summation of items for all local governmental units located within a county. was true for the regionalization process, And as the 90 variables identified in Appendix C were used. Factor analysis and varimax rotation reduced the 90 variables to 17 "conceptual variables" accounting for 77 per cent of the total variance. 2 By reducing the original variables to "conceptual variables", the number of variables ■*"See pages 29-32 for a description of this tech­ nique. 2 Assumptions and constraints made in determining the final conceptual variable solution were the same as those made for the socio-economic conceptual variables. (See footnote 1, p. 48.) 82 are reduced by more, than 80 per cent. At the same time, 77 per cent of the variance explained by the original variables is retained. Area government conceptual variables and the individual variables contained within each are shown in Table 9. The naming process for the first 6 conceptual v a r i ­ ables (CV) is somewhat mor e difficult than was true for the socio-economic conceptual variables. The reason is that the predominant variables are a mixture of items. For example, CV 1 primarily measures distribution of and level of police, fire, sanitation, higher education, and parks and recreation expenditures. Conceptual variable 2 measures distribution of and level of highways, financial administration, and general public buildings expenditures w ith the distribution of education expenditures operating inversely to the other variables in CV2. Therefore, both CV 1 and CV 2, can be given the general name of Selected Governmental Expenditures with a numeral 1 or 2 to designate the respective CV. Conceptual variable 3 has six of nine important variables dealing with change in different aspects of r e v e ­ nue. The remaining three variables represent change in level of educational expenditures, level of total expenditures, and level of salaries and wages paid. CV 3 will therefore be called Revenue-Expenditure Change. The idea behind e s ­ tablishing conceptual variables is that it pulls similar variables together. With that in mind, it is interesting Table 9. County area government conceptual variables and associated factor loadings. Conceptual Variables Itema 21 22 25 27 31 39 40 43 45 49 36 76 20 23 32 35 41 50 31 53 84 2 16 .17 18 19 58 77 81 87 1 2 3 .74 .80 .71 .65 .68 .70 .76 .67 .66 .66 .70 .68 -.55 .61 .76 .75 .69 .82 .61 .82 .61 .65 .62 .64 .83 .93 .70 .85 .70 .59 4 5 6 7 8 9 10 11 12 13 14 15 16 17 h 2 .84 .85 .69 .85 .82 .88 .89 .75 .85 .85 .71 .74 .87 .89 .79 .80 .90 .82 .84 .81 .84 .86 .71 .88 .87 .92 .77 .89 .75 .74 84 M'3l>vDOr-ncfo^c\iaino(rir'r"a,a ) i n H n ^ ' i ,r^'fLnc\oooooiTioH aiaiaiaicioicTiaicTicoaioor^'Dt^-r^ooiD'Dr^Lnr^r^t'-r'OococnooooiD aicTi ................................................................ O CM <71<71 OOOh 00 co in • • • 1 l l CM CM <71 in cm o 00 00 <71 in r- r' t" • • I l o in h in m id CM VD <71rH in O in m in in in r~ • • • • • • I I I I I I ID ID • • I I in Is* r - rH ** io in VO oo ID oo io • • • • • ■ I I I I i I iDoonmcocO'S'H cooominoicTiioin # • • • • • • • l •d T3 *0 TJ l o r M ^ m o i r ' r ' O H r ' t n o o ooai 0 0 ^ v o o o i o j o o i o v o c - c o o c o i o ^ ' i f c - i n H h h cm in co rin H H in c o io o o o ic M 'jio io v o n ^ c M 'ii'io fo in Table 9. Continued. Conceptual Variables^ Itema 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 0c h2 .68 .54 .80 .74 .62 .86 .85 .61 .82 .84 .68 .67 .53 .84 .81 82 83 79 4 5 72 34 52 61 71 88 .75 -.83 .84 -.63 .55 .56 .55 frr°P4 10-2 7.8 8.0 7.0 7.0 4.1 2.8 3.0 3.3 2.9 3.8 2.8 3.1 2.7 3.6 2.8 2.3 Var.c Cum. Prop. 10.2 18.0 26.0 33.0 39.9 44.0 46.8 49.8 53.1 56.0 59.8 62.5 65.7 68.4 71.9 74.7 77.0 Var.f aThe item numbers identify the individual variables as listed in Appendix C. j. The names of the conceptual variables are as follows: (1) Selected Governmental E x ­ penditures 1. (2) Selected Governmental Expenditures 2. (3) Revenue Expenditure Change. (4) Revenue-Expenditure Level 1. (5) Revenue-Expenditure Level 2. (6) Intergovernmental Revenue. (7) Capital Expenditures. (8) Library Expenditures. (9) Change in Miscellaneous Expenditures. (10) Health Expenditures. (11) Welfare Expenditures. (12) Natural Resources Expenditures. (13) Asset Position. (14) Change in Interest.Payments on Debt. (15) Revenue Items. (16) Housing and Urban Renewal E x p e n d i t u r e s . (17) Miscellaneous Items. cThe final communality estimate represents the percentage of the variance of a variable explained by the 17 conceptual variables. ^Variables associated with Conceptual Variable 4 and Conceptual Variable 5. © . The percentage of the variance of the total matrix explained by a particular Conceptual V a r i a b l e . ^The cumulative percentage of the variance of the total matrix explained by successive Conceptual Variables. The total does not necessarily equal the sum of its parts at each stage due to round off error. 87 that change in per capita educational expenditures, per capita total expenditures, and per capita salaries and wages paid are considered to be "like" the changes in the revenue variables. The implication is that any given change in revenues is accompanied by a directly related and approxi­ mately equal change in those three expenditure items. Conceptual variable 4 is somewhat like CV 3 except l e v e l , instead of c h a n g e s , of revenue and expenditures are the crucial variables. And instead of a variable for change in educational expenditures, variables for level and distribution of hospital expenditures are present. Also, the percentage of the total locally generated revenue that is represented by property taxes is inversely related to the other characteristics. The implications found for the interrelationships of revenues and expenditures in CV 3 carry over to this conceptual variable. In addition, the inverse relationship of the property tax variable implies that as the percentage of locally produced revenue origin­ ating from the property tax decreases, the level of the other characteristics increases. CV 4 will be called Revenue-Expenditure Level 1. The factor creating the fifth CV is more like CV 3 in the types of variables it contains than CV 4 but, like CV 4, deals with level of revenues and expenditures and is therefore called Revenue-Expenditure Level 2. Four vari­ ables are held in common between CV 4 and CV 5 and account 88 for at least 50 per cent of the variables entering into the naming process for each conceptual variable. CV 3 in that expenditure items are in common. CV 5 is like It should be emphasized that with the common elements, CV 4 and CV 5 are approximately the same. And wit h the exception that CV 3 measures change and CV 5 measures le v e l , CV 3 and CV 5 are approximately the same. The sixth CV will be called Intergovernmental Rev e ­ nue since it contains four variables that measure intergov­ ernmental revenue by functional designations and one variable that measures per capita revenue from other local g o vern­ ments. The variable for current correction expenditures per capita is also present but was not included in the naming process since intergovernmental characteristics represented five of the six variables included in the CV. The only other conceptual variable related to rev e ­ nue characteristics was CV 15. In this case, per capita income taxes were inversely related to property taxes as a per cent of total taxes. The only other "important" charac­ teristic was the relative change in housing and urban renewal expenditures. Therefore, the name of this CV could reflect either the level or the proportionality variable. It was therefore decided to call this variable Revenue Item. The remaining conceptual variables expenditure patterns. Fortunately, itures tend to cluster together. (factors) reflect similar types of expend­ Therefore, a relatively 89 simple discussion of these CV's will be sufficient except where internal variables are considerably different. CV 7 is primarily Capital Expenditures, will be known as Library Expenditures. and CV 8 CV 9 contains three variables measuring relative change in current per capita expenditures for libraries, hospitals, and health. There­ fore, CV 9 will be named Change in Medical and Library Ex­ penditures . Health Expenditures will be the name of CV 10, and Welfare Expenditures will be the name of CV 11. a measure of Natural Resources Expenditures. CV 12 is Asset Position is the name of CV 13 whereas Change in Interest Payments on Debt is the name of CV 14. Housing and Urban Renewal E x ­ penditures are represented in CV 16. Only two variables enter into the naming process associated wit h CV 17. These are relative change in current general control expenditures per capita and relative change in per capita revenue from the Federal Government. As one is a revenue characteristic and the other is an expenditure characteristic, this CV will be called Miscellaneous Items. Naming these 17 conceptual variables is a subjective process for the most part. Others could examine the com­ ponent parts of these variables and decide that perhaps another name would be better and be justified in that belief. The names for CV 7 to CV 17 are relatively dictated by the types of variables contained within the factor. For the 90 others, more subjectivity is present. The purpose of naming conceptual variables is only to provide a means of identi­ fication and to provide a feeling for the types of variables contained in the CV. Factor scores computed for each CV are developed from all 90 county area governmental revenueexpenditure characteristics and not just those selected for assisting in determination of the name. In summary, the original 90 county area governmental variables analyzed were reduced by approximately 80 per cent to 17 conceptual v a r i a b l e s . These conceptual variables re­ tained 77 per cent of the information found in the original variables. Conceptual Variables 3, 4, 5, 6, and 15 were primarily related to revenue characteristics. CVs except 17 were related to expenditures. only as miscellaneous items. All remaining CV 17 was noted It should be emphasized that while the naming process could classify a conceptual vari­ able as either a revenue or an expenditure characteristic, both types of characteristics were frequently contained within any given CV. Conceptual variables found in this analysis and factor scores generated for each provide one of the basic inputs into the analysis of the relationships between socio­ economic structure and governmental revenue-expenditure structure. 91 County Government The factor analysis process of reducing the 82 county government revenue-expenditure characteristics into concep­ tual variables is the same process used for both socio­ economic and county area governmental characteristics. The county government characteristics were reduced by 8 0 per cent into 16 conceptual variables accounting for 78 per cent of the total variance. The county governmental conceptual variables and the individual variables contained within each are shown in Table 10. The process of assigning names to each of the county conceptual variables was somewhat more subjective than the naming process for county area conceptual variables. The reason for this is that the split between revenues and ex ­ penditures is less clear for the first few CVs than it was for county area CVs. For example, CV 1 (Table 10) contains four variables measuring level of revenue, eight measures of level of expenditures, and one measure of distribution of expenditures. Therefore, since four important revenue vari­ ables are contained within CV 1, it will be called Govern­ mental Revenue-Expenditure Levels. CV 2 contains two measures of revenue l e v e l s , one of change in r e v e n u e , and a measure of level and change for per capita capital expenditures and per capita asset p osi­ tion. In addition, change in interest payments on general i Table 10. County government conceptual variables and associated factor loadings. Conceptual Variables Itema 1 13 15x 23d 39 41 50e 51 53 57d 80 84 89f 9 igg 89 82 83 85 90 6 12 14 29 47 11 24 42 1 2 3 4 .67 .68 .84 .55 .55 .50 .81 .74 .79 .57 -.50 .77 .80 .88 .51 .56 .66 .55 .53 .90 .68 .86 .50 .89 -.92 -.86 -.92 -.94 5 6 7 8 9 .51 .55 -.88 -.94 -.92 10 11 12 13 14 15 16 h2 .87 .87 .98 .91 .73 .91 .86 .80 .85 .95 .90 .93 .78 .69 .91 .48 .88 .79 .88 .68 .90 .90 .91 .94 .95 .82 .94 .91 93 Lniri(NCNr't^ncri(ND,^rc7i'X)o cr»[''-CTir^ o i oo o vr m o i m r * vo oo in vo l l t in o > vo in Cl t"-CTl