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Xerox University Microfilms 300 North Zeeb Road Ann Arbor, Michigan 48106 SOCIO-ECONOMIC CHARACTERISTICS AND ATTITUDES TOWARD SELECTED LAND USE CONTROL MEASURES IN THE THUMB AREA OF MICHIGAN By Gordon L. Szlachetka A DISSERTATION Submitted to Michigan S tate U niversity in p a r tia l f u lf illm e n t o f the requirements fo r the degree o f DOCTOR OF PHILOSOPHY Department o f Resource Development 1976 ABSTRACT SOCIO-ECONOMIC CHARACTERISTICS AND ATTITUDES TOWARD SELECTED LAND USE CONTROL MEASURES IN THE THUMB AREA OF MICHIGAN By Gordon L. Szlachetka This study sought to in ve s tig ate the re la tio n s h ip of s p e c ific socio-economic c h a ra c te ris tic s and a ttitu d e s toward selected land use control measures. I t attempted to estab lish whether in divid ual a ttitu d e s toward land use control measures were r e fle c tiv e of tra d itio n a l socio-economic in dicators such as age, educational attainm ent, income, e t c ., or whether such a ttitu d e s were conditioned by other facto rs which were more d i f f i ­ c u lt to recognize and measure such as upbringing and c u ltu ra l h e r it ­ age. Data fo r the study were co lle cte d through the use o f a questionnaire mailed to a randomly selected sample o f the popu­ la tio n in three counties in Michigan. The questionnaire contained questions which provided inform ation about an in d iv id u a l's socio­ economic c h a ra c te ris tic s and his opinion on three aspects o f land use c o n tro l: land use planning, ordinances to enforce land use planning, and zoning. The three counties, Huron, S an ilac, and Gordon L . ' Szlachetka Tuscola, were selected because a t the time o f the study they were p rim a rily ru ra l counties which were undergoing varying degrees of developmental pressure. I t was thought th a t such an environment would be an appropriate context w ith in which to conduct the study, because i t seemed th a t these impending land use changes had created an atmosphere of in te re s t in land use control measures. The co llected data were subjected to weighted regression analysis. The three questions re la tin g to aspects of land use control being the dependent variab les and the socio-economic in fo r ­ mation being u t iliz e d as the independent v a ria b le s . Weighted regression analysis was u t iliz e d because the dependent variables were dichotomous and the m ajo rity o f the independent variables were not continuous in form. The problem o f non-continuous independent variab les was p a r t ia lly overcome through the use of complex dummy v a riab le systems based on inter-comparison m atrices. The re su lts o f the analysis o f the data were three equations which indicated the conditional p ro b a b ility o f an in divid ual favoring each of the three s p e c ific land use control measures. The independent variables in the in d ivid u al equations were the socio-economic c h a ra c te ris tic s which were shown to be s t a t i s t ic a l ly s ig n ific a n t in the context o f the respective questions re la tin g to land use. Thirteen basic hypotheses were tested during the conduct of the study. These were a l l re la te d to in d iv id u a l socio-economic c h a ra c te ris tic s and were d ire c tio n a l in th a t they a n tic ip a te d the Gordon L. Szlachetka e ffe c t a c h a ra c te ris tic would have on an in divid ual approving or re je c tin g an issue re la te d to land use control measures. The study showed th a t an in d iv id u a l's age, whether or not he owned a home, or i f he perceived land use c o n flic ts , had no bearing on a ttitu d e s toward land use control measures. On the other hand, an in d iv id u a l's perception o f how w ell his local government was serving his needs was indicated as being the single most important aspect in p redictin g an in d iv id u a l's response to land use control measures. This v ariab le was found to be the most s ig n ific a n t v a riab le in a l l three equations. There were other variables th a t also appeared to be s ig n if i­ cant across the three equations. I t was found th a t the p ro b a b ility of an in divid ual favoring land use control measures was increased i f he liv e d in a high population density area, voted re g u la rly , had high income, and controlled e ith e r large or small amounts o f property. S ig n ific a n t relatio n sh ip s between variab les associated with other hypotheses and approval o f land use control measures were more d i f f i c u l t to in te rp re t. Some dimension o f the occupation va riab le and the group p a rtic ip a tio n v a ria b le appeared as being s ig n ific a n t in each of the three p re d ic tiv e equations. The primary occupation v a riab le appeared as s ig n ific a n t in only the zoning equation. The action o f th is v a riab le fa ile d to support the contention th a t "white c o lla r" occupations would increase the p ro b a b ility o f favoring land use control measures. In contrast to th is , second occupation appeared as being a s ig n ific a n t v a ria b le Gordon L. Szlachetka in a ll three equations and generally supported the hypothesis o f "white c o lla r" occupations increasing the p ro b a b ility o f favoring land use control measures. The v a riab le re la te d to fa th e r's occu­ pation also appeared as being s ig n ific a n t in a l l three equations. Variables re la te d to education and p o lit ic a l party i d e n t i f i ­ cation appeared as being s ig n ific a n t in both the ordinance and zoning equations. The action o f the education v a riab le generally supported the basic hypothesis th a t increased education would increase the p ro b a b ility o f favoring land use control measures. In terms o f p o lit ic a l party id e n tific a tio n , i t was hypothe­ sized th a t in divid uals who considered themselves as being Democrats would be more lik e ly to favor land use control measures than would e ith e r Republicans or American Independent Party members. basic hypothesis was not supported. This I t was found th a t Republican party a f f i l i a t i o n increased the conditional p ro b a b ility o f favoring land use control measures to a greater degree than did Democratic party a f f i l i a t i o n . The variab le re la te d to sex appeared as being s ig n ific a n t in only the zoning equation. In th is context, the basic hypothesis o f males being more lik e ly to favor land use control measures than females was supported. O v e ra ll, the research re su lts indicated th a t the r e la tio n ­ ships between socio-economic c h a ra c te ris tic s and a ttitu d e s toward land use control measures were extremely complex. This research did not bear out the tr a d itio n a l relatio n sh ip s between socio-economic variables and partisan voting behavior id e n tifie d in most previous Gordon L. Szlachetka research. I t seems th a t the usual socio-economic variables cannot be used to predict a ttitu d e s or voting behavior in such nonpartisan environmental issues as land use planning and c o n tro l. ACKNOWLEDGMENTS Numerous persons, both fa c u lty and fe llo w students, c o n tri­ buted to the completion o f th is study. several. Special thanks are due to I wish to thank Dr. Manfred Thullen fo r his advice and numerous c r it ic a l readings o f the document. Dr. W illiam Kimball also deserves g ratitu d e fo r both his advice and d ire c tio n in the development o f the study. The s t a t is t ic a l techniques u t iliz e d were a d ire c t re s u lt o f the in te re s t and help provided by Dr. Daniel Chappelle. Dr. Stanley Brunn offered a great deal of assistance in respect to the conceptual organization o f the study and evaluation o f appropriate s ta tis t ic a l techniques. to Dr. Richard Rodefeld. Special thanks are also due Dr. Rodefeld not only provided astute insights as to the nature o f the study, but also provided encourage­ ment a t the times when i t was most needed. Two fe llo w graduate students also were instrumental in the conduct of the study. Both Alan Kirk and F r it z Saver were o f inestim able help in the design and conduct o f the survey which was the basis o f the study. Also, special thanks are due to Jo Ann Ohn fo r patience and the typing o f countless pages o f d r a ft copy. TABLE OF CONTENTS Page LIST OF FIGURES.....................................................................................................v ii LIST OF T A B L E S ...................................................................................................v i i i Chapter I. II. THE PROBLEM .. . 1 Introduction ................................................................................. Statement o f the Problem .................................................. Significance o f the Problem ............................................ Objectives o f the S t u d y .................................................. 1 7 8 10 EFFECTS OF SOCIO-ECONOMIC CHARACTERISTICS ON VOTING BEHAVIOR....................................................................................... 12 L ite ra tu re Review ..................................................................... "Class" and Voting Behavior ............................................ Age and Voting B e h a v i o r .................................................. Sex and Voting B e h a v i o r .................................................. Education and Voting Behavior ...................................... Income and Voting Behavior ............................................ Occupation and Voting Behavior ...................................... Property Ownership and Voting Behavior . . . . Population Density and Voting Behavior . . . . Perceived C o n flic ts and Voting Behavior . . . . Perception o f Local Government Service and Voting Behavior ............................................................... P o litic a l Party Id e n tific a tio n and Voting B e h a v io r................................................................................. Group P a rtic ip a tio n and Voting Behavior . . . . The Io n ia P r o j e c t ............................................................... H y p o t h e s e s ................................................................................. III. RESEARCH PROCEDURES .................................................................... The Study A r e a ........................................................................... Description o f the Study A r e a ...................................... Models ........................................................................... Choice o f the Appropriate Model fo r the Study . . iii 12 15 17 19 21 22 23 25 27 28 29 30 31 32 34 37 37 39 58 58 Chapter Page Regression Analysis .............................................................. 60 Simple and M u ltip le Regressions......... ............................... 60 Reasons fo r Using M u ltip le Regression ......................... 61 Design o f the Method o f Analysis . . . . . . . 63 Weighted Regression .............................................................. 64 The Io nia Study in Which the Weighted Regression Procedure was U tiliz e d .................................................. 66 Ionia M o d e l........................................................................... 66 V a r ia b le s ........................................................................... 66 Stepwise Least Squares Regression Routine ( L S S T E P ) ..................................................................... 71 I n i t i a l Regression Model ............................................ 72 "RESID" Routine ........................................................ 75 "CONVERT" Routine .................................................. 75 "SWITCH" Routine ........................................................ 76 Weighted Regression .................................................. 76 Examples o f the Weighted Regression Output . 78 The Dummy V ariable Technique ............................................ 84 Thumb Area Data C ollectio n ............................................ 94 Instrument Design .............................................................. 94 Questionnaire P retest .................................................. 97 Sampling Procedures ........................................................ 103 Questionnaire M ailing .................................................. 107 Response Rates ..................................................................... 108 Non-Respondent Check ........................................................ 109 Data P re p a ra tio n ............................................................................ 114 Data T r a n s fo r m a tio n ................................................................115 IV . ANALYSIS AND RESULTS............................................................................ 122 The I n i t i a l Model--Land Use Planning ............................... 122 "LSSTEP" Routine ..................................................................... 124 "RESID" Routine ..................................................................... 124 "CONVERT" Routine .............................................................. 124 "SWITCH" Routine ..................................................................... 128 Weighted Regression .............................................................. 128 Hypotheses V a lid a tio n ........................................................ 131 Occupation is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures ......................... 135 P a rtic ip a tio n in Various Types o f Groups is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control M e a s u r e s .............................................138 P a rtic ip a tio n in Elections is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures ........................................................ 139 An In d iv id u a l's Perception o f How Well His Local Government is Serving Him is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures ........................................................ 140 iv I If I .’ i | 1 I I | 1 1 I 1 I f | I j j ; Page Chapter The Amount o f Property a Person Owns or Controls is S ig n ific a n tly Related to A ttitudes Toward ..................................... Land Use Control Measures IncomeLevel is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures . . Population Density is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures . . Calculation o f the Conditional P ro b a b ility of Favoring Land Use Planning ........................................... The Second Model--Ordinances to Enforce A Land Use Plan • * • ■ > * * • • • • « « • Results o f the "LSSTEP" Routine ..................................... Weighted Regression ................................................................ HypothesesV a lid a tio n . .......................................... Occupation is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures ......................... P a rtic ip a tio n in Various Types of Groups is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures ..................................... Id e n tific a tio n with a S p e c ific P o litic a l Party is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures .......................... P a rtic ip a tio n in Elections is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures .................................................. An In d iv id u a l's Perception o f How Well His Local Government is Serving Him is S ig n ific a n tly Related to A ttitu d es Toward Land Use Control Measures ..................................... The Amount o f Property a Person Owns or Controls is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures ..................................... Educational Attainment is S ig n ific a n tly Related to A ttitu d es Toward Land Use Control Measures . Income Level is S ig n ific a n tly Related to A ttitudes Toward Land Use ControlMeasures . . Population Density is S ig n ific a n tly Related to A ttitudes Toward Land Use ControlMeasures . C alculation o f the Conditional P ro b a b ility of Favoring Ordinances to Enforce a Land Use Plan . The Third Model--Zoning ........................................................ Results o f the "LSSTEP" Routine ..................................... Weighted Regression .............................................................. Hypothesis V a lid a tio n ........................................................ Sex is S ig n ific a n tly Related to A ttitu d es Toward Land Use Control Measures ..................................... Occupation is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures ......................... v 141 143 144 144 147 148 148 152 156 158 159 160 161 161 163 164 164 165 168 169 169 173 173 178 Chapter Page P a rtic ip a tio n in Various Types o f Groups is S ig n ific a n tly Related to A ttitudes Toward Land Use Control M e a s u r e s .............................................180 Id e n tific a tio n With a S p e c ific P o litic a l Party is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control M e a s u r e s .............................................181 P a rtic ip a tio n in Elections is S ig n ific a n tly Related to A ttitudes Toward Land Use Control M e a s u re s ....................................................................... 181 An In d iv id u a l's Perception o f How Well His Local Government is Serving Him is S ig n ific a n tly Related to A ttitudes Toward Land Use Control M e a s u r e s .......................................................................182 The Amount o f Property a Person Owns or Controls is S ig n ific a n tly -R e la te d to A ttitu d e s Toward Land Use Control M e a s u r e s .................................. 183 Educational Attainment is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures . 184 Income Level is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures ......................... 184 Population Density is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures . . 185 C alculation o f the Conditional P ro b a b ility of Favoring Zoning .............................................................. 186 V. SUMMARY, CONCLUSIONS, LIMITATIONS, AND RECOMMENDATIONS S u m m a r y ....................................................................................190 C o n c lu s io n s .............................................................. L i m i t a t i o n s .................................................................... ...... . Recommendations.............................. 190 214 218 224 BIBLIOGRAPHY .................................................................................................... 232 APPENDIX 237 LIST OF FIGURES Figure Page 1. Location o f the Study A r e a ........................................................ 40 2. Regression Slopes Illu s t r a t in g the E ffe c t o f the Deletion o f a Single Class in a Dichotomous Dumniy V ariable S y s t e m ..................................... 86 Regression Slopes Illu s t r a tin g the E ffe c t o f the Deletion o f a Single Class o f a Dichotomous Dumny Variable System when used in Conjunction with a Continuous V ariable ............................................ 87 Regression Slopes Illu s t r a t in g the E ffe c t of the Deletion o f a Single Class o f Dummy V ariable from a M ulti-Classed Dummy V ariable System . . 89 3. 4. 5. 6. . . Example o f a M ulti-Classed Dummy V ariable System M a t r i x ............................................................................................. 91 Regression Slopes Resulting from U t iliz a t io n of M ulti-Classed Dummy V ariable System Matrices 93 v ii . . . LIST OF TABLES Table 1. 2. Page Approximate Acreage o f Land in P rincipal NonA g ric u ltu ra l Uses fo r Selected Years, 1920-1964 . . 2 Land Use C haracteristics o f Huron, Sanilac and Tuscola Counties 1970 42 A g ric u ltu ra l C h a ra c te ris tic s w ith in Huron, Sanilac and Tuscola Counties 1969 .............................................................. 43 Total Earnings by Major Source Within Huron, Sanilac and Tuscola Counties 1969...... .................................................. 45 Population Growth Within Huron, Sanilac and Tuscola Counties 1940-1970 .................................................................... 47 Urban and Rural Population D is trib u tio n w ithin Huron, Sanilac and Tuscola Counties 1970 ..................................... 48 Population Growth in the Standard M etropolitan S ta tis tic a l Areas Peripheral to the Study ........................................................ Area 1960-1970 50 Total and Seasonal Housing Units w ith in the Study Area 1970 ....................................................................................... 51 Population Age Composition w ith in Huron, Sanilac and Tuscola Counties 1970 .............................................................. 52 Years of School Completed by Persons 25 Years Old and Older in Huron, Sanilac and Tuscola Counties 1970 . 53 Occupations o f Employed Persons w ith in Huron, Sanilac and Tuscola Counties 1970 .................................................. 55 Family Income Levels w ith in Huron, Sanilac and Tuscola Counties 1970...... ........................................................................... 56 13. Ionia Model O rig inal Regression C o efficien ts . . . . 72 14. Ionia Model Weighted Regression C o e fficien ts . . . . 77 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. v iii Table 15. Page Theoretical Responses and Resulting V ariable Values used in the Ionia Weighted Regression Model to Generate the Greatest Conditional P ro b a b ility o f Saying "No R estrictions" on the Location o f Additional Housing ..................................................................... 80 16. Theoretical Responses and Resulting V ariable Values used in the Ionia Weighted Regression Model to Generate the Smallest Conditional P ro b a b ility o f Saying "No R estrictions" on the Location o f Additional Housing ..................................................................... 81 17. P (y |x ) The Conditional P ro b a b ility o f Saying "No R estrictions" on the Location o f A dditional Housing in Ionia C o u n ty ........................................................................... 82 18. Return Rates Associated with the Bunker H ill P retest . 19. Mailed Questionnaire Survey Sample Size fo r Huron, S anilac, and Tuscola Counties ............................................ 101 104 20. Sample Size o f Elected and Appointed O ffic ia ls and Opinion Leaders fo r Huron, Sanilac and Tuscola C o u n t i e s ....................................................................................... 106 21. Total Sample U tiliz e d in the Thumb Area Study Mailed S u rv e y .................................................................... ...... 22. Returned Thumb Area Questionnaires by Source . 23. Results of the I n i t i a l Thumb Area Study Non-Respondent Telephone Survey ..................................................................... 110 Results o f the Second Thumb Area Study Non-Respondent Telephone Survey ..................................................................... Ill 24. 25. I n i t i a l Data Sort of the Thumb Area Study Data 26. . . 107 . . . 109 . 116 New Variable Numbers Assigned to the Thumb Area Study D a t a ..............................................................................................118 27. New Variables Added to the Thumb Area Study Data Set 28. Variables Deleted from the Land Use Planning Model 29. Variables Retained in the Land Use Planning Model 30. Weighted Regression C o e ffic ie n ts fo r the Land Use Planning Model ........................................................................... ix . 120 . . 125 . . 127 129 Table Page 31. Grouped Variables fo r the Land Use Planning Model 32. An Example Illu s t r a tin g the Calculated E ffe c t o f an Individual V ariable on the Conditional P ro b a b ility o f Approving Land Use P la n n in g ...................................................132 33. E ffe c t o f In divid ual Variables on the Conditional P ro b a b ility o f Approving Land Use Planning . . . . 133 Second Occupations which Increased the Conditional P ro b a b ility o f Approving Land Use Planning . . . . 136 34. 35. 36. . . 130 Father's Occupations which Increased the Conditional P ro b a b ility o f Approving Land UsePlanning . . . . 137 Group P a rtic ip a tio n which Increased the Conditional P ro b a b ility of Approving Land UsePlanning . . . . 139 37. The E ffe c t of Voting P a rtic ip a tio n Rates on the Conditional P ro b a b ility o f Approving Land Use P la n n in g ...........................................................................140 38. Variables Retained in the LandUse Ordinance Model 39. Weighted Regression C o e ffic ie ’ s fo r the Land Use .........................................150 Ordinance Model . . . . 40. Grouped Variables fo r the Land Use Ordinance Model 41. E ffe c t o f In divid ual Variables on the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use P l a n ...............................................................153 42. Variable Groupings Appearing in the Land Use Planning and Ordinance Equations ........................................................ 43. Second Occupations which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use P l a n ............................................................... 157 44. Father's Occupations which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use P l a n ............................................................... 158 45. Group P a rtic ip a tio n which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use P l a n ............................................................... 159 x . . . . 149 152 156 Table Page 46. P o litic a l Party A f f ili a t i o n which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use P l a n ......................................................... 160 47. Voting Behavior which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use P l a n ..................................................................................160 48. Amounts of Property Owned which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use P l a n ......................................................... 162 49. Amounts of Property Leased which Increased the Conditional P ro b a b ility of Approving Ordinances to Enforce A Land Use P l a n ......................................................... 162 50. Educational Levels which Increased the Conditional P ro b a b ility of Approving Ordinances to Enforce A Land Use P l a n ..................................................................................163 51. Income Levels which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use P l a n .................................................................................. 164 52. Variables Retained in the Zoning Ordinance Model 53. Weighted Regression C o e ffic ie n ts fo r the Zoning Ordinance M o d e l.......................................................................... . 54. Grouped Variables fo r the Zoning Ordinance Model . 55. E ffe c t o f Individual Variables on the Conditional P ro b a b ility of Approving Zoning Ordinances . . . . 56. 57. 58. 59. 60. . 170 171 . 172 174 Variable Groupings Appearing in the Land Use Planning, Ordinance and Zoning Equations ............................................ 177 Primary Occupations which Increased the Conditional P ro b a b ility o f Approving Zoning ...................................... 178 Second Occupations which Decreased the Conditional P ro b a b ility o f Approving Zoning ..................................... 179 Father's Occupations which Increased the Conditional P ro b a b ility of Approving Zoning .................................... 180 Group P a rtic ip a tio n which Increased the Conditional P ro b a b ility o f Approving Zoning .................................... 180 xi Page Table 61. 62. 63. 64. 65. P o litic a l Party A f f ilia t io n which Increased the Conditional P ro b a b ility of Approving Zoning . . . 182 Voting Behavior which Increased the Conditional P ro b a b ility o f ApprovingZoning ....................................... 182 Amounts of Property Leased which Increased the Conditional P ro b a b ility of Approving Zoning 183 . . . Educational Levels which Increased the Conditional P ro b a b ility o f ApprovingZoning . 184 Income Levels which Increased the Conditional ....................................... P ro b a b ility of ApprovingZoning 185 66. V ariable Groupings Appearing as Being S ig n ific a n t in the Land Use Planning, Ordinance and Zoning E q u a tio n s .............................................................................................. 191 67. E ffe c t o f the Educational Attainment V ariab le in Respect to the.Ordinance and Zoning Equations . . . 194 68. E ffe c t o f the Income V ariable in Respect to the Land Use Planning, Ordinance and Zoning Equations ............................................................................195 69. E ffe c t o f the Primary Occupation V ariable in Respect to the Zoning E q u a t i o n ..................................................196 70. E ffe c t of the Second Occupation V ariable in Respect to the Land Use Planning, Ordinance and Zoning E q u a tio n s ................................................................................ 197 71. E ffe c t of the Father's Occupation V ariable in Respect to the Land Use Planning, Ordinance and Zoning E q u a tio n s .................................................................................199 72. E ffe c t of the Property Owned Variable in Respect to the Land Use Planning and Ordinance Equations . . . 73. E ffe c t o f the Property Leased V ariable in Respect to the Land Use Planning, Ordinance and Zoning E q u a tio n s ................................................................................. 201 74. E ffe c t o f the Perception of Governmental Service V ariable in Respect to the Land Use Planning, Ordinance and Zoning Equations . 200 203 Table 75. 76. 77. Page E ffe c t of the P o litic a l Party A f f ili a t i o n V ariable in Respect to the Ordinance and Zoning Equations . E ffe c t o f the Group P a rtic ip a tio n Variable in Respect to the Land Use Planning, Ordinance and Zoning E q u a tio n s .......................................................................... . 204 205 E ffe c t o f the Voting Behavior V ariable in Respect to the Land Use Planning, Ordinance and Zoning E q u a tio n s .............................................................................................. 207 xi i i CHAPTER I THE PROBLEM Introduction Less than a century ago the economy o f the United States was p rim a rily agrarian. The m ajority o f the population was com­ prised o f independent farmers with r e la tiv e ly small holdings. During th is period o f one hundred years ago pressures fo r the development o f rural lands e x is ted , but the forces were concentrated p rim a rily toward a sing le end; the bringing o f more land in to a g r i­ c u ltu ra l production. While pressures existed with respect to rural land, they were mainly focused toward a single use. Today, developmental pressures being exerted on ru ral land no longer focus upon a single use. Rural lands are presently being subject to developmental pressures fo r a v a rie ty o f uses. In ­ creasing population has subjected land to numerous pressures fo r both increasing food production and increasing liv in g space. Technological advances have s h ifte d the economy o f th is country from an agrarian one to one p rim a rily concerned w ith manufacturing and service in d u s trie s . Along w ith the technological changes there has been a dramatic change in the l i f e s ty le o f the American population. 1 2 TABLE 1 . —Approximate Acreage o f Land in P rin cip al Non-Agricultural Uses fo r Selected Years, 1920-1964.a (Land in M illio n s o f Acres) Year 1920 1930 1945 1950 1959 1964 Urban Areas 10.0 12.0 15.0 18.3 27.2 29.3 Highways and Roads 15.0 19.0 19.1 19.4 20.5 21.2 4.0 _b 4.0 _b 3.4 3.4 3.4 3.3 1.3 1.3 1.4 1.5 8 .0 __b 12.0 17.9 18.7 29.7 31.9 1.0 4.7 8 .9 17.2 29.0 2.0 2 .0 24.8 21.4 24.4 23.6 39.0 39.0 86.2 91.4 123.8 139.8 Type o f Land Use Railroad Rightsof-Ways Ai rports S tate and National Parks W ild life Areas National Defense Areas Total N .J .: aRaleigh Bariowe, Land Resource Economics (Englewood C lif f s , P re n tic e -H a ll, In c ., 1958), p. 47. ^Not separately reported. The v a rie ty of pressures being exerted on land, outside the a g ric u ltu ra l secto r, in the United States could be documented through examination o f some o f the increases in n o n -ag ricu ltu ral land uses during the past f i f t y years. The ta b le illu s tr a te s th a t over 100 m illio n addition al acres o f land were consumed by n o n -ag ricu ltu ral uses in the period between 1920 and 1964. The to ta l population o f the United States has increased g re a tly over the past years. I f ju s t the one hundred year time span between 1870 and 1970 is considered, the population o f th is country has increased from 38,558,371 to 2 0 3,211,926.1 While i t is true th a t the United States must be considered an urbanized country, the ru ra l population has also continued to increase in terms o f absolute numbers. Between 1870 and 1970 the ru ra l popu­ la tio n increased from 28,656,010 to 5 3 ,8 8 6 ,9 9 6 .3 The ru ra l popu­ la tio n o f the 1970's is very d iffe r e n t than th a t o f the 1870's. No longer is the ty p ic a l ru ra l resident cast in the mold o f the Jeffersonian id e a l. The independent American farmer with small holdings has la rg e ly become a r e fle c tio n o f the past. 1916 marked the farm population highwater mark in the United S tates. In th is year over 32 m illio n o f the natio n 's inhabitants were residents o f farms. This number dropped to less than 13 m illio n in the I9 6 0 's . 3 The new ru ra l resident is an amalgamation o f both rural and urban c h a ra c te ris tic s and desires. Suburban or ru ra l liv in g has become the American dream or norm. Technological advances have made the American population increasingly mobile, and workers are no longer forced to liv e in the immediate proxim ity o f employ­ ment. The new ru ra l resident has escaped the pressures o f the ^Michigan S tate U n iv e rs ity , D ivision o f Research, Graduate School o f Business A dm inistration , Michigan S t a t is t ic a l A b s tra c t, comp. David I . Verway (9th e d .; East Lansing: Michigan State U n iv e rs ity , 1972), p. 4. ^ Ib id ., p. 4. 3 Calvin L. Beale, "Rural Depopulation in the United States Some Demographic Consequences o f A g ric u ltu ra l Adjustments," Demography, Vol. 1, No. 1 (1 9 64 ), 264-72. urban s e ttin g by moving to rural areas. However, he has s a tis fie d his gregarious yearnings by locating in r e la t iv e ly close proxim ity to other residents. Great q u a n titie s o f what was once prime ru ra l a g ric u ltu ra l land has been covered by land consumptive sing le fam ily residences. Improved tran sportation and other technological advances have made lo cation o f in dustries less r e s tric te d . New in d u s tria l site s are being in creasingly found in what were once considered rural areas. Increased le is u re time and affluence has created demands fo r recreation and second home s ite s . These fa c to rs , and many more, such as land being u t iliz e d fo r highway construction and population support f a c i l i t i e s , have been responsible fo r in ­ creasing developmental pressures being exerted upon rural land. With increasing population in ru ra l areas, there have been in ­ creasingly diverse pressures placed upon ru ra l land. Developmental pressures are no longer being exerted toward a single end, ra th e r developmental pressures are directed toward a m ultitude o f uses which are often c o n flic tin g in nature. In many cases, development o f ru ral areas has been e ith e r i l l conceived or unplanned. Haphazard development has often wasted the true p o te n tia l or ru ra l areas. C o n flic tin g and in ­ compatible land uses were and are often located in close proxim ity. Farms being surrounded by re s id e n tia l areas, fa c to rie s w ith in re s id e n tia l zones, and s ite s o f la n d f ills have, and w ill continue to be points of contention. In many instances, the "growth syndrome" 5 has superceded planned, orderly development. Without some type of control mechanisms there is no reason to think th a t th is w ill not continue in the fu tu re . Land must be considered a f i n i t e resource which should not be wasted or misused. The importance o f the land resource was illu s tr a te d by the statement "we may a c tu a lly again be returning to the point where land, as a lim ite d productive resource, w ill play a s tra te g ic ro le in determining human progress."^ In t e llig e n t and long range policy decisions d ic ta te th a t land u t iliz a t io n should be geared toward providing the g reatest benefits possible to a population. How the maximization o f benefits is to be re a liz e d is dependent on the establishment o f c r it e r ia re la tin g to the con­ ce p tu a lizatio n o f b e n e fits . A problem which arises is one o f what constitutes the greatest b e n e fit, in terms o f land use and it s derived b e n e fits , to the greatest proportion o f the population. contains many facets and ra m ific a tio n s . This stated problem The issue o f public vs. p riv a te land in te re s ts must be recognized in such a statement. These two in tere s ts are often not compatible in a short run s itu ­ a tio n . Id e a lly , the two should be compatible in a long term s itu a tio n . However, since most people's planning horizons are short term, the issue o f public vs. p riv a te land use in te re s ts are frequently brought in to sharp focus. The desire o f the ^Arthur Mauch, "Land Use in a Changing World," Land Use in Michigan, Extension B u lle tin 610, Natural Resources S eries, Cooperative Extension S ervice, Michigan S tate U n iv e rs ity , 1969, F ir s t Revision, pp. 5-6. 6 in divid ual to obtain short term benefits or p ro fits is often d ia m e tric a lly opposed to long term benefits o f the society w ith in which he liv e s . The geographic d is trib u tio n o f land use benefits must also be considered. Depending on what geographic area is being con­ sidered, the concept o f the greatest b e n e fit to the g reatest pro­ portion o f the population takes on d iffe r e n t meanings. In terms o f broad in te re s ts , such as national defense or energy production, in divid ual p riv a te in tere s ts are often superceded fo r the b e n e fit o f the m a jo rity o f the population. This also holds true in the consideration o f lesser geographic areas. In divid ual property in te re s ts often must give way to public in te re s t when s ite s and routes are considered fo r the location o f public u t i l i t i e s , tran sportation routes, public services, recreation areas and other land consumptive uses which are considered public in nature. However, regardless o f what level is being considered, i t must be re a liz e d th a t in divid ual property rig h ts and in te re s ts are being a ffe c te d . Because o f the frequent c o n flic ts between public and p riv a te land use in te re s ts , two primary schools o f thought have evolved in regards to land use po licy and it s re s u lta n t controls. One school o f thought advocates the employment o f systematic controls to d ire c t and lim it development. Through th is process i t is believed th a t controls can be used to fo s te r o rd erly develop­ ment. Public controls can be used to regulate land use in a manner which is perceived to b e n e fit the g reatest proportion o f the 7 population. The lim ita tio n o f p riv a te in te re s ts and options is viewed as a mechanism to insure the c o lle c tiv e best use o f land resources. Opposed to th is philosophy are those who fe e l a property owner has the r ig h t to do as he wishes with his property and are opposed to any governmental controls with respect to land uses. P rivate property rig h ts are viewed as being almost sacred and should therefo re not be subjected to governmental controls which lim it or r e s t r ic t options or a lte rn a tiv e s . This group believes th a t c o lle c tiv e good is created exclusively through in divid ual e f f o r t and in divid ual in te re s ts . Rather than imposing govern­ mental controls and re s tric tio n s to determine land use develop­ ment and d ire c tio n , the a n ti-c o n tro l fa c tio n believes th at the market stru ctu re w ill determine the appropriate d ire c tio n fo r land use. Between these two extremes there is a middle ground made up o f both philosophies. P rivate in te re s ts are viewed as being extremely im portant, but there is a re a liz a tio n th a t c o lle c tiv e public rig h ts are also im portant. I t is w ith in th is middle ground th a t the American system o f government was designed to function. In divid ual rig h ts and in te re s ts are to be both fostered and protected. At the same tim e, public rig h ts are also encouraged and protected. Statement o f the Problem Land use control measures and issues a ffe c t both society and the in d iv id u a l. Since society is the c o lle c tiv e expression 8 o f the in divid ual i t is important to understand the in d iv id u a l's a ttitu d e s toward land use control measures. Obviously, many factors w ill condition a person's feeling s toward land use control measures, but an important question is whether p a rtic u la r a t t i ­ tudes are re fle c tio n s o f s p e c ific socio-economic c r it e r i a . Are a ttitu d e s toward land use controls r e fle c tiv e o f age, educational attainm ent, income, amount o f property owned, and physical location? Or, are such a ttitu d e s conditioned by other factors which are more d i f f i c u l t to recognize and measure such as up­ bringing and c u ltu ra l heritage? The problem is to is o la te and id e n tify s p e c ific in dicators or variables which are s ig n ific a n t in understanding the in d iv id u a l's a ttitu d e s toward land use measures. S ig nificance o f the Problem The power o f local governments to regulate land use is delegated by sta te government. The inherent power of s ta te govern­ ment to re g u la te , promote or li m i t the a c t iv it ie s of c itiz e n s in th e ir use o f land is usually divided in to fiv e categories: the police power, the power o f eminent domain, the taxing power, the spending power, and the power o f public ownership.1 The po lice power, exercised to insure the h e a lth , s a fe ty , morals, and general w elfare o f the population is o f p a rtic u la r in te re s t. Land use c o n tro ls, e s p e cia lly zoning, are often fostered by local governments through the exercise of th is power. ^ b i d . , pp. 31-32. 9 Zoning, a method o f dividin g land in to zones or areas fo r s p e c ific types of development, is a method o f attempting to assure ordered and ra tio n a l development. In Michigan Zoning must be proposed by government, but i t may be re jected by the a ffected populace. In terim zoning may be in s titu te d by f i a t during the period o f time required fo r the preparation o f a permanent zoning ordinance. In terim zoning remains v a lid fo r one year and may be renewed fo r two more years. Permanent zoning ordinances, once accepted by the le g is la tiv e body, may be defeated by the c itiz e n ry through referendum. Thus, a permanent zoning ordinance is a r e fle c tio n o f the consensus o f the populace. How the residents o f a region fe e l about land use controls is a cru cial issue. In a democratic system, government is intended to be responsive to the needs and desires o f it s constituents. Knowing how residents fe e l toward s p e c ific land uses and controls w ill enable governmental o f f ic ia ls to more e ffe c tiv e ly serve the resident population and formulate proposals which w ill b e tte r r e fle c t the residents' needs and desires. Knowledge o f the r e s i­ dents' desires w ill reduce the number o f i l l conceived zoning ordinances which are ty p ic a lly defeated by popular vote. The a b il it y to a n tic ip a te residents' feelin g s about land uses and control measures w ill fo s te r closer cooperation between area residents and governmental o f f ic i a l s . Such cooperation and understanding o f local residents' a ttitu d e s w i ll give c itiz e n s a g reater voice in shaping fu tu re land uses in t h e ir lo c a lit y . 10 P reviously, attempts to ascertain how residents f e l t toward land use issues and controls u t iliz e d some type o f survey. These surveys have been both co stly and time consuming. I f the various socio-economic c h a ra c te ris tic s re la tin g to a ttitu d e s p ertainin g to land use control measures could be id e n tifie d , secondary sources could be used as a basis fo r the p redictio n o f residents' a ttitu d e s . This would g re a tly reduce the cost of ascertaining a population's a ttitu d e s toward land use control measures as compared to tr a d itio n a l techniques such as question­ naires mailed to the resident population. I f the factors a ffe c t­ ing a ttitu d e s toward land use controls could be understood and measured, p re d ic tiv e techniques could be developed which would f o r e t e ll residents' a ttitu d e s toward land use control measures. Id e n tific a tio n o f variables which re la te to a ttitu d e s concerning land use control measures would also add to general knowledge in the realm of understanding in d iv id u a l actions. Addition of such inform ation would enable decision makers to function with more complete data than presently e x is ts . Objectives o f the Study The objectives of th is study were th re e -fo ld in nature. F ir s t , to try to id e n tify some socio-economic and ph ysical/ lo catio nal facto rs which were s ig n ific a n tly re la te d to a ru ral re s id e n t's a ttitu d e s toward land use control measures. Id e n tifi­ cation and is o la tio n o f these s ig n ific a n t variab les would g re a tly strengthen the knowledge and understanding o f re la tio n s h ip s between 11 population c h a ra c te ris tic s and a ttitu d e s . Illu s tr a tio n s o f such relationsh ips would serve as important inputs in both public and p riv a te decision making processes. Administrators and p o licy makers would be able to u t i l i z e such relationships to b e tte r serve th e ir c lie n te le . Second, u t iliz in g population c h a ra c te ris tic s id e n tifie d as being s ig n ific a n t in explaining or understanding a ttitu d e s , an attempt was made to develop p re d ic tiv e models which could a n tic ip a te a ttitu d e s o f ru ral populations toward a lte rn a tiv e land use control measures. Meeting these two o b jectives would have accomplished a great deal w ith respect to id e n tify in g whether relationsh ips and linkages between s p e c ific population c h a ra c te ris tic s and a ttitu d e s toward land use control measures existed . Development o f models would have provided a mechanism, hopefully based on secondary data sources, by which to a n tic ip a te ru ral residents' a ttitu d e s toward land use control measures. A th ird o b je ctiv e o f th is study was to add to the know­ ledge gained during a p relim inary in ve s tig atio n conducted in Ionia County in 1972. In the conduct o f th is study i n i t i a l observations were made pertainin g to id e n tific a tio n of s p e c ific variables re la te d to a ttitu d e s concerning land use control measures. A lso, p relim inary e ffo r ts were in it ia t e d in regard to developing p re d ic tiv e models which would a n tic ip a te in divid uals reactions to land use control measures. This study was purpose­ f u lly designed to elaborate upon and attempt to p e rfe c t research techniques developed in the i n i t i a l exploratory study. CHAPTER I I EFFECTS OF SOCIO-ECONOMIC CHARACTERISTICS ON VOTING BEHAVIOR L ite ra tu re Review In many cases the decision o f whether or not to adopt various land use control measures has been made by c itiz e n s through the process o f voting. For th is reason, the process o f voting has been viewed as the u ltim ate in d ic a tio n of a c itiz e n 's a ttitu d e toward land use control issues. The lit e r a t u r e review indicated th a t the decision to vote "yes" or "no" in respect to any issue was found to be based upon a m ultitude o f a ttitu d e s and perceptions which the in divid ual possessed. As a general comment, Pattannaik stated th a t voting is the most common device used fo r recording people's preferences.^ There­ fo re , i t was reasonable to assume th a t a ttitu d e s held by the popu­ lace would be re fle c te d in th e ir voting behavior. There are many references p e rta in in g to in divid ual voting behavior and a number o f the works have attempted to re la te s p e c ific socio-economic c h a ra c te ris tic s to voting behavior. The m ajority o f the lit e r a t u r e supported the contention th a t the socio-economic V ra s a n ta K. Pattannaik, Voting and C o lle c tiv e Choice (Cambridge: Cambridge U n iversity Press, 1971), pp. 5-6. 12 13 c h a ra c te ris tic s o f an in divid ual g re a tly influenced his in te re s t in voting and played an important ro le in determining how he would cast his b a llo t. However, i t was important to r e a liz e th a t socio­ economic c h a ra c te ris tic s alone did not determine an in d iv id u a l's voting behavior. As Burdick found: Apparently the voter is caught in an in tr ic a te and in v is ib le web o f r e lig io n , desire fo r secu rity w ith in s ta tu s , social a s p ira tio n , economic class, and fam ily background. But how these elements work on him, why one argument is persuasive a t one time and in e ffe c tiv e a t another, the way these a t t i ­ tudes are transm itted is s t i l l unknown. U n til these elements are is o la te d and explicated i t is impossible to conscientiously draw up a theory o f concord based on contemporary em pirical data from the voting s tu d ie s .1 Burdick found th a t voting behavior was determined by a host o f conditioning and causal fa c to rs . Many o f these factors were apparently emotional and hence d i f f i c u l t or impossible to q u an tify or measure. However, he did admit th a t c e rta in quanti­ fia b le variables such as r e lig io n , economic c lass, and fam ily background existed . While i t was obvious th a t many factors which help decide how a person w ill vote were d i f f i c u l t to measure and in te r p r e t, there were s p e c ific in dication s which could be useful in p redictin g a person's voting behavior. Key in d ic a to rs , in the form o f socio­ economic c h a ra c te ris tic s , have been recognized by many researchers as g re a tly influencing a person's voting behavior. However, s p e c ific socio-economic c h a ra c te ris tic s apparently were not ^Eugene Burdick, " P o litic a l Theory and Voting Studies," American Voting Behavior, ed. by Eugene Burdick and Arthur J. Brodbeck (Glencoe, I l l i n o i s : The Free Press, 1959), p. 147. 14 equally s ig n ific a n t in a ll cases in vestig ated. I t was apparent th a t the degree to which a s p e c ific v a riab le re la te d to a person's voting behavior depended on context. Thus, d iffe r e n t variables were important in d iffe r e n t s itu a tio n s . In a given s itu a tio n a va riab le showed a p o sitiv e c o rre la tio n with a s p e c ific voting response w hile in other situ atio n s the same v a riab le showed a negative c o rre la tio n or fa ile d to be s ig n ific a n t in e ith e r a p o sitiv e or negative manner. This dichotomous s itu a tio n existed even a ft e r compensation was made fo r chance v a ria tio n s . Therefore, e x is tin g studies were useful in illu s t r a tin g socio-economic c h a ra c te ris tic s which were generally considered to be linked to voting behavior. However, linkages between popu­ la tio n socio-economic c h a ra c te ris tic s and is s u e -s p e c ific voting behavior were not c le a rly defined. I t was obvious th a t linkages between socio-economic c h a ra c te ris tic s and voting response to land use control measures would have to be valid ated by em pirical research. The lit e r a t u r e suggested a number o f socio-economic charac­ te r is tic s which seemed to be more or less u n iv ers a lly recognized as in fluencing voting behavior. The s p e c ific c h a ra c te ris tic s included age, sex, education, income, occupation, property owner­ ship, p o lit ic a l a ttitu d e s , and group p a rtic ip a tio n . When these in divid ual c h a ra c te ris tic s were combined they formed a ra th e r nebulous composite known as "socio-economic status" or "class." 15 "Class" was widely recognized as p o te n tia lly influencing voting behavior.^ "Class" and Voting Behavior However, even though "class" was recognized by most researchers as being re la ted to voting behavior, the d ire c tio n in which s p e c ific c h a ra c te ris tic s included in the concept of class were re la ted to voting behavior was not u n iv ers a lly agreed upon. Unless a s p e c ific context or s itu a tio n was id e n tifie d , i t was repeatedly indicated th a t i t was v ir t u a lly impossible to generalize the contribution a c h a ra c te ris tic would make in respect to voting behavior. Key and Munger were w ell aware of th is when they observed: Much fu rth e r refinement o f our knowledge o f the place of social c h a rac te ris tic s in e le c to ra l decision, fo r example, Paul F. L azarsfeld , Bernard Berelson, and Hazel Gaudet, The People's Choice (New York: Columbia U n iv e rsity Press, 1949); and Robert R. A lfo rd , Party and Society (Chicago: Rand McNally and Company, 1963). The follow ing references are contained in American Voting Behavior, ed. by Eugene Burdick and Arthur J. Brodbeck (Glencoe, Il l i n o i s : The Free Press, 1959): Eugene Burdick, Chapter 6, " P o litic a l Theory and Voting S tudies," pp. 136149; L e s lie A. F ie d le r, Chapter 9, "Voting and Voting S tudies," pp. 184-196; R. S. M ilne, Chapter 11, "Second Thoughts on 'S tra ig h t F lig h t ,'" pp. 209-216; Kurt and Gladys Engle Lang, Chapter 12, "The Mass Media and Voting," pp. 217-235; It h ie l De Sola Pool, Chapter 13, "TV a New Dimension in P o lit ic s ," pp. 237-261; R. Duncan Luce, Chapter 18, "Analyzing the Social Process Underlying Group Voting P attern s," pp. 330-52; and Angus Campbell and Donald E. Stokes, Chapter 19, "Partisan A ttitu d es and the P re sid e n tia l Vote," pp. 353-71. See also: Seymour M artin L ip s e t, P o lit ic a l Man (Garden C ity , New York: Double-Day and Company, 1960); Angus Campbell, Gerald Gurin, and Warren E. M il le r , The Voter Decides (Evanston, I l l i n o i s : Row, Peterson, and Company, 1954); Harold F. G osnell, Grass Roots P o litic s (New York: Russell and R ussell, 1942); and G ille s Picard and A lb e rt Juneau, A S ociological Study o f A gri­ c u ltu ra l Change in the P ilo t Region ( BAEQ) , ARDA Condensed Report 16 would probably quickly follow once the s e ttin g o f p o lit ic a l a lte rn a tiv e s and the m atrix o f ob jective conditions w ith in which these determinants operate were brought more s p e c ifi­ c a lly in to the f ie ld o f observation. I t seems apparent th at social c h a ra c te ris tic s move in to and out o f the zone o f p o lit ic a l relevance, th a t they 'e x p la in ' the actions o f some people and not those o f others, and th a t in s o fa r as social c h a ra c te ris tic s determine p o lit ic a l preference they encounter considerable preference. What are the consequences, fo r example, of the subjection of d iffe r in g proportions o f the vote to determination by sp ecified social c h a rac te ris tic s ? There can be no doubt th a t there is a t times a high degree o f association between re a d ily id e n tifia b le social c h a ra c te ris tic s and p o lit ic a l preference. At the extreme po sition i t might be argued th a t p o lit ic a l preference is a hitch hiker on social c h a ra c te ris tic s . Yet there seems to be always a very considerable p art o f the e le c to ra te fo r which no re a d ily is o la b le social c h a ra c te ris tic 'e x p la in s ' p o lit ic a l preference. Some o f the considerable variance un­ accounted fo r by social determination might be removed by attempts to analyze the nature of the in d iv id u a l's i d e n t i f i ­ cation w ith the community and the natio n, the character o f his id e n tific a tio n with p o lit ic a l p a rty , his perception o f the p o lit ic a l w orld, his general o rie n ta tio n toward the complexes o f po licy questions, his conceptions o f his ro le as a voter and as a c itiz e n . There may w ell be, fo r a p a rt o f the e le c to ra te a t le a s t, ro le s , id e n tific a tio n s and preferences o f a purely p o lit ic a l nature with q u ite as much r e a lit y as his 's o c ia l character­ is t ic s . ' ' In the conduct o f th is study, the composite of in d ivid u al c h a ra c te ris tic s known as class was found to be d i f f i c u l t to q u an tify and deal w ith . examined. Instead the in divid ual key components o f class were I t was thought th a t examination o f the in divid ual CR-NO. 15 (Ottawa: Canada Department of Forestry and Rural Develop­ ment, Queen's P rin te r and C o n tro lle r o f S ta tio n a ry , 1968). ^V. 0. Key, J r. and Frank Munger, "Social Determinism and E lecto ral Decisions: The Case o f In d ia n ," American Voting Behavior, ed. by Eugene Burdick and A rthur J. Brodbeck (Glencoe, I l l i n o i s : The Free Press, 1959), pp. 297-99. 17 c h a ra c te ris tic s which contributed to the concept o f "class" would be manageable and re s u lt in the c la r if ic a t io n o f in divid ual c h a ra c te ris tic roles in regard to voting behavior re la te d to land use control measures. The id e n tific a tio n o f the s p e c ific c h a ra c te ris tic s made i t possible to concentrate on linkages between a r e la tiv e ly few socio-economic c h a ra c te ris tic s and voting behavior. The follow ing represents in v e s tig a tio n o f the selected c h a ra c te ris tic s . Age and Voting Behavior I t has been widely held th a t age tended to cause a person to be more conservative in both his a ttitu d e s and voting behavior. Lipset found th a t "an older population w ill probably slow down p o lit ic a l change."^ He fu rth e r stated th a t d iffe r e n t ages affected l e f t and rig h t p o lit ic a l behavior in th a t younger persons were more lib e r a l than older persons. Campbell discovered th a t age was a causal fa c to r leading to the perception o f p a rtie s and stronger party id e n tific a tio n . Lazarsfeld stated th a t " tra d itio n has i t th a t youth shuns the conservative, in p o litic s as w ell as in cloth es, music and manners."^ He went on to say th a t "legend ^Seymour M artin Lipset, P o litic a l Man (Garden C ity , N .Y .: Double-Day and Company, 1960), p. 269. 2Ib i d . , p. 264. 3 Angus Campbell, P h ilip E. Converse, and Warren E. M ille r , The American Voter (New York: John Wiley and Sons, In c ., 1960), pp. 496-97. ^Paul F. L azarsfeld , The People's Choice (New York: Columbia U n iv e rsity Press, 1949), p. 16. 18 has i t th a t older people are more conservative in most things, including p o litic s . . . younger people are more li b e r a l , more receptive to change."1 Thus, the works o f several researchers reinforced the corranonly held stereotype th a t older persons are more conservative and less amenable to change. In a voting context th is apparently meant th a t the older an in d iv id u a l was the more lik e ly he was to re je c t new concepts or issues. This seemed to in dicate th a t older persons would r e je c t land use control issues because they would represent a change in the status quo. In the opposite vein Brunn, e t a l . , found, in a s p e c ific instance, th a t age did not re la te to a voting pattern which could be in te rp re te d as being conservative. In the course o f analyzing voting behavior re la te d to a school m ilage, i t was shown th a t there was a weak p o s itiv e re la tio n s h ip between age and a p o sitiv e vote. 2 This appeared to c o n tra d ic t the notion th a t older persons are more conservative and less amenable to change. An addition al study also concluded th a t "a progressive a ttitu d e is not neces­ s a r ily dependent on age" and "younger people are not necessarily more progressive than th e ir e ld e rs , although they are less t r a d i3 tio n al in t h e ir a ttitu d e s ." h b i d . , pp. 23-24. o Stanley D. Brunn, Wayne L. Hoffman, and Gerald H. Romsa, "The Youngstown School Levies: A Geographical Analysis in Voting Behavior," Urban Education, Vol. V, No. 1. 3 G ille s Picard and A lb e rt Juneau, A Sociological Study of A g ric u ltu ra l Change in the P ilo t Region (BAEQ), ARDA Condensed 19 Thus, one v a ria b le , age, which was thought to be linked with voting behavior had been shown to c o rre la te d iffe r e n tly depending on the s p e c ific s itu a tio n . I t became c le a r th a t be­ havior o f th is v ariab le was dependent on context or s itu a tio n . Unless a s p e c ific context or s itu a tio n was id e n tifie d , i t did not seem possible to dogm atically g eneralize the s p e c ific d ire c tio n in which the v a riab le would influence voting behavior. The prelim inary study which was conducted in Ionia County presented an opportunity to see how the age v a ria b le was re la ted to decisions in a s p e c ific context. I t was shown th a t older persons were s lig h tly more receptive to land use control measures than were younger persons. e x is t. The d iffe re n c e was minimal but i t did These re s u lts , in conjunction with some o f the lit e r a t u r e , seemed to in d ic a te th a t increased age would favor the acceptance o f land use control measures. Sex and Voting Behavior The ro le th a t a person's sex played in determining voting behavior did not appear to be c le a r cut. "The p o s s ib ility o f sex d ifferen ce in p o lit ic a l behavior remains a subject o f in te re s t in p art because female suffrage is s t i l l disputed in some modern western democracies and in p art because o f our own acceptance of female a c t iv it y in p o litic s is o f ra th e r recent vintage."^ Campbell Report CR-NO. 15 (Ottawa: Canada Department o f Forestry and Rural Development, Queens P rin te r and C o n tro lle r o f S ta tio n a ry , 1968), p. 13. ^Campbell, e t a l . , The American V o te r, p. 483. 20 went on to say th a t past c le a rly defined p o lit ic a l sex roles were apparently breaking down. However, the degree to which th is was happening was dependent upon both social and geographic c o n te x tJ One researcher f e l t th a t womens' voting patterns were 2 merely re fle c tio n s o f th e ir husbands'. Another f e l t th a t sex was r e la tiv e ly important in influencing voting behavior. Lipset indicated th a t women tended to be more conservative than men. 3 The Langs stated th a t w hile women tended to more or less follow th e ir husbands' voting decisions, the "'woman's vote' is less 4 c le a rly linked to social class than is the male vote." Once again i t was indicated th a t there was a d iffe re n c e in opinion as to both the importance and the d ire c tio n o f influence a s p e c ific socio-economic c h a ra c te ris tic would e x ert on voting behavior. The issue o f context appeared to play a major ro le as to the v a ria b le 's influence. In the context o f land use control measures, the sex v ariab le was not found to be s ig n ific a n t in the Ionia Study. The v a riab le was elim inated and not included in the regression equations. How­ ever, other researchers had shown a great deal o f in te re s t in th is 1 I b i d . , pp. 483-89. 2 Lazarsfeld , The People's Choice, p. 141; H. H. Remmers, "Early S o c ia liz a tio n o f A ttitu d e s ," American Voting Behavior, p. 57. 2 Seymour M artin L ip s et, P o litic a l Man (Garden C ity , N .Y.: Double-Day and Company, 1960), p. 221. \ u r t and Gladys Engle Lang, "The Mass Media and Voting," American Voting Behavior, p. 57. 21 v a riab le in respect to voting behavior. This previous in te re s t d ictated th a t the sex v ariab le be included in th is study. Based p a rtly on the lit e r a t u r e and p a rtly on in t u it io n , i t was suspected th a t women would be less amenable to land use control measures than men would. Education and Voting Behavior The influence o f formal education upon voting behavior has been widely recognized. "Formal education, nevertheless, has many s trik in g consequences fo r p o lit ic a l behavior th a t are independent o f status im plications and th a t undoubtedly remain constant in strength even in times when class differences lose most o f th e ir partisan importance."^ I t was fu rth e r indicated th a t the b e tte r educated person's view o f p o lit ic a l objects and events would be more s p e c ific and more highly d iffe r e n tia te d . ? A lford contended th a t b e tte r educated persons were more lik e ly to vote in a manner which would protect t h e ir business in te re s ts and vested in te re s ts . At the same time these b e tte r educated persons were less lik e ly to favor general w elfare pro­ p o sals.3 In contrast to A lfo rd 's p o s itio n , Adrain stated th a t the b e tte r educated person could see the "la rg e r p ictu re" and understand ^Campbell, e t a l . , The American V o te r, p. 475. 2I b i d . , p. 476. 3 Robert R. A lfo rd , "Class Voting in the Anglo-American P o litic a l Systems," Party Systems and Voter Alignment, ed. by Seymor M. Lipset and Stein Rokkan (New York: The Free Press, 1967), p. 6. 22 th a t the productive u n it in which they had a stake was not lim ite d or bounded by t h e ir vested in te r e s t. The b e tte r educated segment o f society tended to favor measures which would b e n e fit a ll levels o f society.^ Once again, an apparent dichotomy existed . The Io nia Stutty did not contain data which indicated educational attainm ent so no comment on the action o f th is v a riab le in the issue s p e c ific context was possible. I t was decided to take an o p tim is tic position and accept A drian's contention. I f land use control measures could be viewed as a b e n e fit to s o ciety, then the b e tte r educated segments o f the population would support them and vote fo r them. Income and Voting Behavior Income, lik e the previously mentioned c h a ra c te ris tic s , had been recognized by many researchers as influencing voting behavior. Gosnell indicated th a t income was an important in d ic a to r 2 in respect to national e le c tio n s . Campbell also indicated th a t people o f s im ila r economic status tended to unite and form s im ila r perceptions in regard to p o lit ic a l issues. 3 This same view was re fle c te d by Lazarsfeld when he stated th a t persons o f the same 4 economic level "have about the same p o lit ic a l a ttitu d e s ." A lford ^Charles R. A drian, "A Typology fo r Nonpartisan E le c tio n s ," Western P o litic a l Q u a rte rly , Vol. 12 (1 9 5 9 ), p. 203. p Harold F. Gosnell, Grass Roots P o litic s (New York: Russell and R ussell, 1942), p. 2. 3 Campbell, e t a l . , The American V o ter, p. 385. ^Lazarsfeld, The People's Choice, p. 20. 23 also supported the contention of s im ila r economic groups holding s im ila r p o lit ic a l views e s p e cia lly in the case o f the higher income segment o f the population.^ There seemed to be a w idely held consensus th a t income was one o f the key indicators o f voting behavior. Income was also linked to the development o f a ttitu d e s which were considered the precursors o f voting behavior. In respect to a ttitu d e formation in the adoption of new a g ric u ltu ra l p ra c tic e s , Picard and Juneau made the statement th a t "income, o f course, is also re la te d to progress, although those liv in g on small incomes do not necessarily have unprogressive a ttitu d e s . We fin d , too, th a t those with small 2 incomes are g e n e ra lly , but not always, the most in d iv id u a lis tic ." I f lower income persons were indeed the most in d iv id u a lis tic , i t seemed reasonable to assume th a t they would most lik e ly oppose land use control measures and vote against them. Individualism would d ic ta te th a t lower income persons oppose any aspect of c o lle c tiv iz a tio n . Occupation and Voting Behavior Campbell indicated th a t occupation was possibly the single most important in d ic a to r o f p o litic a l behavior. "Occupation tends 3 to p re d ic t p o lit ic a l a ttitu d e s and voting most e f f ic ie n t ly . " He 1A lfo rd , "Class Voting in the Anglo-American P o lit ic a l Systems," Party Systems and Voter Alignment, ed. Lipset and Rokkan, p. 68. O Picard and Juneau, A Sociological Study o f A g ric u ltu ra l Change in the P ilo t Region, p. 14. 3 Campbell, e t a l . , The American V o te r, p. 344. 24 supported th is contention by sta tin g th a t an occupation defined the group o f people w ith whom the in d ivid u al worked and thereby delim its spheres o f primary group in flu en ce. Occupations lead to the development o f perspectives and some occupations created unusually d ire c t relatio n sh ip s w ith government and th is w ill influence p o lit ic a l response. A lfo rd also found th a t " fo r a comparative study o f voting behavior, occupation is probably the best single in d ic a to r."^ However, Lazarsfeld f e l t th a t once a person's general socio-economic status was determined fu rth e r c la s s ific a tio n by occupation did not re fin e the groups very g re a tly . "In other words, people o f the same general socio-economic status have about the same p o lit ic a l a ttitu d e s regardless o f th e ir occupation." 2 The apparent in te n t o f th is statement is to in d icate th a t a per­ son's "class," the composite o f many socio-economic character­ is tic s , influences his vote more than his actual occupation. However, i t seemed reasonable to assume th a t there were p o sitiv e correlations between occupation and education and in ­ come. Generally b e tte r educated persons have more prestigious occupations and usually higher incomes. Since i t was suspected th a t the b e tte r educated and higher income segments o f the popu­ la tio n would support and vote fo r land use control measures, i t 1 Robert R. A lfo rd , Party and Society (Chicago: and Company, 1963), p. 74. 2Lazarsfeld , The People's Choice, p. 20. Rand McNally 25 was assumed th a t persons in more prestegious "white c o lla r" occupations would also. Property Ownership and Voting Behavior A lford found th a t a landed in te re s t is placed in to a d iffe r e n t social class than unlanded persons, actuated by d i f f e r ­ ent sentiments and view sJ Vested in te re s ts and fu tu re oppor­ tu n ity costs would surely enter in to the formation and the deter­ mination o f voting behavior. However, simply the amount o f property a person possessed was not thought to be the sole fa c to r conditioning the in d ivid u al response to land use control measures. I f an in divid ual held a great deal o f land fo r purely speculative purposes, and was hoping to reap b enefits which would accrue from a change in use, he would oppose any re s tric tio n s on his use o f the property. Conversely, an in d ivid u al possessing a great deal o f property who wished to continue u t iliz in g i t in the same manner would support any mechanism which would assure continuation o f the e x is tin g usage w ithout p e n a liza tio n . The reaction o f persons possessing small amounts o f property towards land use control measures was also a m atter o f pure specu­ la tio n . Since w in d fa lls are less lik e ly to occur to small property owners in ru ral areas, i t was speculated th a t they would most lik e ly support re s tric tio n s which would perpetuate the status quo. Because 1Robert R. A lfo rd , "Class Voting in the Anglo-American P o lit ic a l Systems," Party Systems and Voter Alignment, ed. by Lipset and Rokkan, p. 69. 26 o f t h is , any change in the status quo would lik e ly be to the disadvantage o f the small property owner because o f the re su ltin g change in l i f e s ty le . The small property owner was thought lik e ly to support land use control measures which would guarantee con­ tinuous u t iliz a t io n o f his property. In the context o f the Thumb Area, with the great deal o f a g ric u ltu ra l a c t iv it y , i t was suspected th a t the m ajo rity o f large property owners would be a g r ic u ltu r a lis ts . Therefore, i t seemed lo g ic a l to assume they would p re fe r th a t t h e ir property be protected from developmental pressures. The influence o f home ownership on a ttitu d e s and voting decisions had been recognized by several researchers. Gosnell recognized home ownership as being s ig n ific a n t in determining voting behaviorJ Lee also recognized the importance of th is aspect when he stated: "The major d iffe r e n tia tin g fa c to r reported in lo cal p o litic s was the occasionally contrasting in tere s ts and 2 views o f the homeowner versus the occupant o f re n ta l property." The homeowner has ty p ic a lly been considered one o f the tr a d itio n a l p illa r s o f society. Home ownership re fle c te d a degree o f s t a b ilit y and permanence not thought of as being associated with non-homeowners. Adm ittedly, in the face o f a decreasing percentage o f homeowners, th is widely held contention may be under­ going m o d ificatio n . However, i t was thought th a t there would s t i l l ^Harold F. Gosnell, Machine P o litic s Chicago Model, 2nd ed. (Chicago: U n iversity o f Chicago Press, 1968), p. 111. ^Eugene C. Lee, The P o litic s o f Nonpartisanship (Berkeley: U niversity o f C a lifo rn ia Press, I9 6 0 ), p. 144. 27 be a d iffe re n c e between homeowners and renters in respect to land use control measures. ment in his home. The homeowner has a large c a p ita l in vest­ Anything which'would adversely a ffe c t his property values or his amenity level would be opposed by the home­ owner. C o n flic tin g or non-compatible land uses adjacent to r e s i­ d en tial areas would have a greater e ffe c t on the homeowner than they would on the re n te r. The re n te r would have g reater freedom to move to a new lo catio n than would the homeowner w ith his invested capi t a l . Therefore i t was suspected th a t homeowners would more strongly favor land use control measures to protect t h e ir vested in te re s ts than would non-homeowners. Population Density and Voting Behavior I t seemed reasonable to expect th a t d iffe r in g population d e n s itie s , and the pressures exerted, would condition in divid u als to view land use controls d if fe r e n t ly . I t was expected th a t persons being subjected to higher densities would view land use controls d iffe r e n tly than persons liv in g in lower density areas. The d iffe r in g population densities would not only create condi­ tions in themselves which influenced people b u t, d iffe r in g densities would allow fo r d iffe r in g group in tera c tio n s and s o c ia liz a tio n processes. The impact o f growth and increasing population densities were recognized by Lee. "Growth brings with i t the problems and pressures w ith which local p o litic s are concerned--the c o n flic t 28 of p e rs o n a litie s , the creation o f new in te re s t groups, and the change in the character o f new in te re s t groups, and the change in the character o f e x is tin g ones."^ A lfo rd also pointed out a suspected re la tio n s h ip between population density and the level o f urbanization and voting be­ havior. He noted th a t in the more densely populated areas th a t the correlations between income, education, and occupation and voting behavior tended to break down. 2 Based on the concept of the breakdown o f some o f the primary co rrelate s ; income, education, and occupation, and voting behavior i t was suspected th a t d iffe r in g population densities would assume a s ig n ific a n t ro le . I t was thought th a t increased crowding would c a ll fo r an attempt to achieve increased structure and order. With th is as a conceptual base i t was expected th a t in ­ creasing population densities would re s u lt in greater approval o f land use control measures which would represent an abstraction o f structure and order. Perceived C on flicts and Voting Behavior Perceived c o n flic ts in respect to land use was an issue s p e c ific s itu a tio n which was not re a d ily found in the lit e r a t u r e reviewed. In t u it iv e ly i t was f e l t th a t whether or not a person perceived c o n flic ts between various types o f land uses was sure 1Ib id . , p. 150. O Robert R. A lfo rd , "Class Voting in the Anglo-American P o litic a l Systems," Party Systems and Voter Alignment, ed. by Lipset and Rokkan, p. 24. 29 to create a ttitu d e s re la tin g to various land use controls. If c o n flic ts were perceived, the a ffected person was lik e ly to develop opinions regarding land use control measures. I t was thought th a t the perception o f c o n flic t would draw persons to­ gether, forming groups or co lle ctio n s of persons w ith common in te re s ts . The desire to elim inate perceived c o n flic ts was sure to a ffe c t the voting on s p e c ific land use control issues. In d i­ viduals who perceived c o n flic ts in terms o f land use and were concerned about them were lik e ly to favor land use controls more than persons who did not perceive c o n flic ts . Perception o f Local Government Service and Voting Behavior Several researchers indicated th a t a sense o f a lie n a tio n between the voter and the governmental powers represented in the e le c tio n would have s ig n ific a n t impact. I f the voter f e l t alien ated from the government, no m atter what the cause, his vote would take the form of a p ro te s t, an expression o f p o lit ic a l discontent. This protest vote was often "independent of economic s e lf - in t e r e s t and re la te d v a ria b le s .1,1 The degree and amount o f a lie n a tio n was suspected to follow "class" lines when s p e c ific issues were being considered. ? ^John E. Horton and Wayne E. Thompson, "Powerlessness and P o litic a l Negativism: A Study of Defeated Local Referendums," The American Journal o f Sociology, Vol. 67 (1968), p. 485. 2 Gerald Pomper, "Ethnic and Group Voting in Non-Partisan Municipal E lectio n s," The Public Opinion Q u a rte rly , Vol. 30 (1 9 66 ), p. 260. 30 I t was also indicated th at a person's perception o f his a b ilit y to influence governmental action or response would also contribute to his propensity to p a rtic ip a te in e le c tio n s. If a c itiz e n f e l t th a t a governing body or mechanism was capable of being influenced in the decision making process he had a sense of e le c to ra l potency which would stim ulate more ac tiv e p a r t ic i­ pation in the e le c tio n process.1 The lit e r a t u r e review made i t obvious th a t an in d iv id u a l's perception o f his local government would g re a tly a ffe c t his voting actions in respect to land use control measures. I f he distrusted or d is lik e d the governing body he would not be lik e ly to approve land use control measures. The disenchanted c itiz e n would view governmental proposals w ith misgivings and d is tru s t. On the other hand i f the in divid ual f e l t his in tere s ts were being well served by the governing body he would be more lik e ly to support the measures put fo rth by the governmental s tru c tu re . The greater fa ith an in d ivid u al had in the governmental system and it s com­ ponent parts the more lik e ly he would be to approve land use control measures. P o lit ic a l Party Id e n tific a tio n and Voting Behavior Id e n tific a tio n w ith a s p e c ific p o lit ic a l party was obviously caused by many o f the variables which were mentioned in the previous R o b e rt E. Agger, Daniel Goldrich, and Bert E. Swanson, "C lassifying Power Structures and P o lit ic a l Regimes," The Search fo r Community Power, ed. by W illiam D. Hawley and Frederick M. W irt (Englewood C lif f s , New Jersey: P re n tic e -H a ll, In c ., 1968), p. 322-42. 31 portion o f the lit e r a t u r e review. So, i t may have been a case o f "double counting" to suggest th a t id e n tific a tio n w ith a c e rta in p o lit ic a l party would influence voting behavior. However, both Lazarsfeld and Adrain recognized the r e la ­ tionship between p o lit ic a l party association and voting behavior. Lazarsfeld indicates th a t in the case o f p re s id e n tia l election s the notion o f Republicans being more "conservative" and Democrats being more " lib e r a l" holds true in respect to voting behaviorJ Adrain stated th a t, in the case o f nonpartisan e le c tio n s , party a f f i l i a t i o n influenced voting patterns in much the same way. 2 In respect to land use control measures i t was suspected th a t more "conservative" people would be less lik e ly to favor addition al re s tr ic tio n s . Therefore, u t iliz in g commonly held stereotypes of Democrats being more lib e r a l than e ith e r Republicans or members o f the American Independent P arty, i t was thought th a t in d ivid u als who considered themselves as being Democrats would be the most lik e ly to favor land use control measures. Group P a rtic ip a tio n and Voting Behavior Many references existed re la tin g group p a rtic ip a tio n to voting behavior. Perhaps the most concise explanation of the influence o f group p a rtic ip a tio n on voting behavior was presented by Riecken. Riecken pressed the case th a t group or organization membership would influence voting behavior. He found th a t "people ^Lazarsfeld, The People's Choice, p. 24. 2Adrain, "A Typol P o litic a l Q u a rte rly , p. 2 fo r Nonpartisan E le c tio n s ," Western 32 who are closely associated tend to vote a lik e " and members o f groups " w ill endeavor to bring t h e ir opinions in to lin e with the norms of each group. Thus, by ascertaining a person's group a f f i l i a t i o n s , i t would be possible to determine whether or not associations with s p e c ific groups would influence voting behavior and opinion form ation. The manner in which s p e c ific groups would influence voting behavior was mostly conjecture. However, i t was f e l t th a t member­ ship in some s p e c ific groups would r e fle c t s im ila r ity o f various socio-economic c h a ra c te ris tic s or "classes." The groups would therefore possibly act as surrogates fo r s p e c ific socio-economic c h a ra c te ris tic s . For example, farmer organizations would consist p rim a rily o f farmers and r e fle c t th e ir views. said fo r various professional organizations. The same could be Membership in these groups would r e fle c t a person's "class" or status and should contribute to voting behavior in a s im ila r manner as would the in divid ual c h a ra c te ris tic s . As a g e n e ra liz a tio n , the more con­ servative the group, the less lik e ly the member would be to favor land use control measures. The Io nia P roject A dditional supportive inform ation fo r th is study was obtained from a survey which was conducted in Io nia County during ^Henry W. Riecken, "Primary Groups and P o litic a l Party Choice," American Voting Behavior, p. 163. 33 1972. Prelim inary research was c arried out in respect to c itiz e n s ' a ttitu d e s concerning various developmental questions and land use control measures in Ionia County. A to ta l o f more than 5,000 questionnaires were mailed to the County's ru ra l box holders. 1,336 of the County's ru ra l residents took the time to complete and return the questionnaire. The re su lts o f th is questionnaire were used to add to the information obtained from the lit e r a t u r e review and to support some in t u it iv e feelin g s about voting behavior and land use control measures. The c o lle c tio n o f data related to c e rta in socio-economic c h a rac te ris tic s was hampered by re s tra in ts imposed by the County Board of Commissioners. The Commissioners sp ecified th a t questions pertaining to economic or educational c h a ra c te ris tic s could not be asked. This lack o f variables l e f t a void in what must be con­ sidered as being germane to th is type o f research. However, using the a v a ila b le data from the questionnaires, three p re d ic tiv e models were constructed. A major portion o f the thinking re la te d to the design of these models was directed a t id e n tify in g the socio­ economic c h a ra c te ris tic s which influence a ttitu d e s concerning land use control measures. Through id e n tific a tio n of such in d i­ cators i t was possible to construct p re d ic tiv e models which provided indications as to c itiz e n s ' p o ten tial votes re la tin g to land use control issues. The only socio-economic c h a rac te ris tic s which proved to be s ig n ific a n t were age, township population density, and township population density change. The same variables did not prove to be 34 s ig n ific a n t in every model. With the lim ite d number o f social c h a rac te ris tic s which were included in the models, i t is not surprising th a t the variables retained were so few in number. Even though the resu lts o f the Ionia Project were somewhat disappointing, a great deal was gained in the course of the research. Such aspects as questionnaire refinem ent, model b u ild in g , and goal s p e c ific a tio n were c la r if ie d and developed. The knowledge gained from the Ionia P roject proved invaluable in the conduct of th is stu d yJ Hypotheses The lit e r a t u r e review and the re su lts of the Ionia project created a basis fo r the generation o f the hypotheses which were formulated fo r th is study. The follow ing hypotheses were generated in an attempt to r e la te population c h a rac te ris tic s to an issue s p e c ific v o te --la n d use control measures. The hypotheses were created to serve as a conceptual framework fo r the analysis phase of the study. The basic assumption fo r th is study was th a t rural r e s i­ dents' a ttitu d e s toward land use control measures were a re s u lt o f th e ir perception of t h e ir po sition and ro le in both society and th e ir immediate environment. Land use controls would have d iffe r e n t meanings and impacts on an in d iv id u a l, conditioned by both real and imagined pressures and ro le s . An in d iv id u a l's ^Results o f the Io nia P roject w i ll be contained in a forthcoming A g ric u ltu ra l Experiment S tatio n B u lle tin . 35 a ttitu d e s toward land use control measures would be conditioned by the classic socio-economic indicators as well as his perception o f land use c o n flic ts and the service he was being provided by government. Thirteen s p e c ific hypotheses were developed and tested. These hypotheses were: 1. Increased age w ill increase the p ro b a b ility o f favoring land use control measures. 2. Males w ill be more lik e ly to favor land use control measures than w ill females. 3. Increased educational attainm ent w ill increase an in d iv id u a l's p ro b a b ility o f favoring land use control measures. 4. Increased income level w ill increase an in d iv id u a l's p ro b a b ility of favoring land use control measures. 5. In divid uals with more prestigious "white c o lla r" occupations w ill be more lik e ly to favor land use control measures than w ill in d ivid u als with less prestigious "blue c o lla r" occupations. 6. Possession o f e ith e r small or very large amounts o f property w ill increase the p ro b a b ility of an in d i­ vidual favoring land use control measures. 7. Being a homeowner w ill increase the p ro b a b ility o f an in d ivid u al favoring land use control measures. 8. In divid uals residing in higher population density areas w ill be more lik e ly to favor land use control measures than w ill in d ivid u als residing in lower population density areas. 9. In divid uals perceiving c o n flic ts in land usage w ill be more lik e ly to favor land use control measures than w ill in d ivid u als who perceived no such c o n flic ts . 10. In divid uals who fe e l t h e ir in te re s ts are being w ell served by th e ir local government w ill be more lik e ly to favor land use control measures than w ill in divid uals who fe e l local government is not serving th e ir in te re s ts . 36 11. In divid uals who consider themselves Democrats w ill be more lik e ly to favor land use control measures than w ill in divid uals who consider themselves Republi­ cans or American Independents. 12. In divid uals belonging to groups which are considered "conservative" w ill be less lik e ly to favor land use control measures than w i ll in divid u als belonging to " lib e r a l" groups. ■ 13. In divid uals with high voting p a rtic ip a tio n rates in local election s w ill be more lik e ly to favor land use control measures than w ill in divid uals with low voting p a rtic ip a tio n ra te s . The preceeding hypotheses were generated as a re s u lt of the lite r a tu r e review , the Ionia P ro je c t, and through personal judgment and speculation. These hypotheses were aimed a t id e n ti­ fying variables which might il lu s t r a t e relationsh ips between various physical and socio-economic c h a ra c te ris tic s and a ttitu d e s pertaining to land use control measures. The v a lid it y of these hypotheses were tested in the analysis phase of the study. CHAPTER I I I RESEARCH PROCEDURES The Study Area Reasons fo r Selecting the Study Area In order to conduct the research required fo r th is study, i t was desirable to fin d an area where land use changes were ju s t beginning to occur. The ideal type o f area would be one s t i l l predominately ru ral but beginning to experience e ffe c ts o f urban pressures. Within such an area the resident population would be ju s t beginning to experience land use pressures and should be form ulating a ttitu d e s toward land use control measures. Such an area provides an ideal sampling frame w ith in which to gather data pertainin g to residents' a ttitu d e s toward land use control measures. The Ionia Study had shown th a t residents o f areas undergoing s h ifts in land usage had w ell defined a ttitu d e s concerning land use control measures. I t was decided th a t the sampling units fo r the study would consist o f in divid ual counties. The question o f how people f e l t about land use issues could best be d e a lt w ith in a lim ite d geographic region. I f too large a geographic area were encompassed, responses to questions would most lik e ly have been couched in general terms. However, i f land use control questions were made 37 38 community s p e c ific , the responses would most lik e ly r e fle c t the respondents' views o f issues which have local and personal importance. The county as a geographic e n tity is a fa m ilia r concept to most people. The county o ffe rs an e a s ily recognizable region upon which to base questions dealing with land use control measures and issues. A county is geographically small enough to enable people to r e a liz e th a t land use decisions which they favor or r e je c t w i ll have a personal impact on them. The county is also a recognized and functioning a d m in istrative u n it which provides a sense o f realism fo r respondents discussing land use issues. A d d itio n a lly , the county provides a mechanism through which land use p o lic ie s could be formulated and implemented. There has been increased in te re s t expressed by the federal govern­ ment in returning c e rta in decision-making and po licy implementation functions to the local le v e l. S p ecific examples o f th is include water q u a lity planning and development and urban systems tran s ­ po rtatio n fund monies. This increased in te re s t in local level adm in istrative decisions makes a county a p o lit ic a l u n it with an increasing p o te n tia l fo r ad m in is tra tiv e power. Also, the county is a geographic u n it which is recognized by the United States census in a d e fin itio n a l sense. a t th is le v e l o f aggregation. Data are gathered and displayed The county is fu rth e r divided geographically in to townships and minor c iv il d iv is io n s . Some data are also a v a ila b le a t th is lesser geographic le v e l, which could provide a greater degree o f local s p e c ific a tio n . 39 During the time th a t the in vestig atio n was underway to se le c t a research area fo r th is study, a separate study was being in itia t e d by the Department of Resource Development a t Michigan State U n iv e rsity. This l a t t e r study was being conducted in conjunction with the O ffic e of Economic Opportunity and was also to be concerned w ith c itiz e n 's a ttitu d e s re la te d to land use change. The area selected fo r the MSU-OEO study was a three county region in the Michigan Thumb Area. In vestig atio n showed th a t the three county region would also be applicable fo r the purposes o f th is study. The area was predominately rural and experiencing land use changes due to increasing pressures from both population increase and urbanizing forces being exerted from surrounding areas. Also, data fo r the study were being co llected on a county basis. Since the area was appropriate fo r the purposes o f the study and data to be co llected were to be gathered on a county basis, the p o s s ib ility o f a jo in t questionnaire was presented. A fte r i t had been determined th a t a jo in t questionnaire was r e a l is t ic , i t was decided to u t i l i z e the three county area in the Michigan Thumb as the s ite o f th is study. Description o f the Study Area The study area was comprised of a three county region in the Michigan Thumb Area (r e fe r to Figure 1 ). The thumb area counties o f Huron, S a n ila c , and Tuscola, provided an appropriate region to ascertain local residents' fe e lin g and opinions toward land use control measures and issues. 40 NORTH Figure 1 .--L o c a tio n o f the Study Area. Huron 41 A ll three counties had a portion o f t h e ir boundaries made up o f coast lin e , e ith e r Lake Huron or Saginaw Bay. They were comparable in terms o f c lim a tic conditions because of t h e ir s im ila r la titu d in a l and longitudinal lo ca tio n . In terms o f land use c h a ra c te ris tic s , Huron and Sanilac Counties were more comparable to each other than they were to Tuscola County. Data presented in Table 2 illu s tr a te s th a t Tuscola County contained la rg e r amounts o f forested and recre­ atio n al lands and s ig n ific a n tly less a g ric u ltu ra l land than did Huron or Sanilac Counties, The importance o f a g ric u ltu re to the three counties should not be minimized. Each o f the in d ivid u al counties had between approximately 70 to 80 percent o f t h e ir land area devoted to a g ric u ltu re . A ll three o f the counties had roughly comparable amounts o f land devoted to tran sp o rtatio n and urbani­ zatio n . In lig h t o f the importance placed upon a g ric u ltu ra l pro­ duction in recent years, an examination o f a g ric u ltu ra l trends w ith in the counties was thought to be d e s ira b le . A ll three counties were considered to contain some o f the best and most productive a g ric u ltu ra l lands in the s ta te . The market value o f farm products sold to ta le d $94,022,700 fo r the three counties in 1969.^ In ­ creasing population and developmental pressures being exerted on the land resource w ill u ltim a te ly re s u lt in c o n flic ts between a g ric u ltu ra l and n o n-agricultu ral land uses. ^County and Regional Facts, State Planning and Development Region 7 , W illiam J. Kimball Coordinator, Michigan State U n iv e rsity Cooperative Extension Service, 1974, Section IV , Table 24, Market Value o f Farm Products sold Selected C h a ra c te ris tic s , pp. 77-78. TABLE 2 . — Land Use C h a ra c te ris tic s o f Huron, S a n ilac and Tuscola Counties 1970.a Huron County Type of Land Use Acres Percentage Inland Water 3,328 0.6 Land Surface 524,032 99.4 Forested 61,600 Sanilac County Acres Percentage Tuscola County Acres Percentage 0.0 3,264 0.6 615,040 1 00 . 0 521,536 99.4 11.8 70,200 11.4 105,500 20.2 426,244 81.3 461,108 75.0 359,139 68.9 17.030 3.2 19,529 3.2 19,027 3.6 Recreation 3,543 0.7 8,535 1.4 27,462 5.3 Urbanization 3,104 0.6 2,570 0 .4 3,944 0.7 12,511 2.4 53,098 8.6 6,464 1.2 527,360 100.0 615,040 100.0 524,800 100.0 A g ric u ltu ra l Transportation Otherb Total 0 aCounty and Regional Facts, State Planning and Development Region 7, W illiam J. Kim ball, Coordinator, Michigan State U niversity Cooperative Extension Service, 1974, pp. 82-85. b0ther land includes a ll lands not previously categorized. P rivate recreational land and unproductive fo re s t land, such as c o n ife r swamps and bogs, is included in th is d e fin itio n . 43 Some s h ifts were evident in respect to a g ric u ltu ra l land uses in the past few years. Table 3 illu s tr a te s th a t between 1964 and 1969 a g ric u ltu ra l acreage decreased by fiv e percent in Huron County. During the same period, a g ric u ltu ra l acreage increased by approximately eleven and e ig h t percent resp ectively in Sanilac and Tuscola Counties. Also, during th is time period, a ll three counties evidenced a decrease in the absolute number of farms. TABLE 3 . --A g ric u ltu ra l C haracteristics w ith in Huron, Sanilac and Tuscola Counties 1969.a County A g ric u ltu ra l C haracteristics Huron Total A g ric u ltu ra l Acreage 426,244 461.108 359,139 -5 .0 11.1 8.1 Acres per Farm - A ll Farms 170.0 165.4 162.6 Acres per Farm - Commercial*3 205.4 206.4 206.8 Number o f Farms 1964 2,656 3,321 2,664 Number o f Farms 1969 2,507 2,787 2,208 -5 .6 -16.1 -17.1 $52,633 $44,031 $68,271 $34,845,900 $32,910,300 $26,266,500 Change in Total Acreage 1964-1969 (35) Change in number o f Farms 1964-1969 (35) Value o f Land and Buildings per Farm 1969 Total Market Value o f Farm Products Sold 1969 Sanilac Tuscola County and Regional Facts, S tate Planning and Development Region 7 , W illiam J. Kimball Coordinator, Michigan State U n iv e rsity Cooperative Extension S ervice, 1974, pp. 71-74, 77-78. ^Farms w ith sales o f $2,500 or more. 44 Each o f the counties had a much higher percentage o f to ta l earnings from the a g ric u ltu ra l sector than did the s ta te . Table 4 illu s tr a te s th a t as a s ta te average, a g ric u ltu ra l earnings repre­ sented only approximately one percent o f to ta l earnings. Agri^- c u ltu ra l earnings accounted fo r approximately 20 percent o f both Huron and Sanilac Counties' to ta l earnings and 11 percent o f Tuscola County's to ta l earnings. While a g ric u ltu ra l earnings were more important in the economies o f both Huron and Sanilac Counties than they were in Tuscola County, a l l three counties' a g ric u ltu ra l earnings g re a tly exceeded the s ta te average. Differences in major sources o f earnings were also evident in respect to other sectors of the economy. Manufacturing was more important in Sanilac County than in e ith e r Huron or Tuscola Counties. Manufacturing accounted fo r nearly 40 percent of to ta l earnings in Sanilac County while i t accounted fo r only approxi­ mately 25 percent in both Huron and Tuscola Counties. Also, the governmental sector o f the economy was more important in Tuscola County than in e ith e r o f the other two counties. Nearly 25 percent of Tuscola County's to ta l earnings were from the governmental sector while both Huron and Sanilac Counties derived approximately 15 percent o f th e ir to ta l earnings from th is sector. While differences e x is te d , the three counties did e x h ib it a degree o f s im ila r ity . They were p rim a rily ru ra l w ith very important a g ric u ltu ra l sectors o f t h e ir economies. S h ifts were evident in respect to a g ric u ltu ra l land uses in th a t both Sanilac and Tuscola counties evidenced absolute increases in a g ric u ltu ra l acreage while Huron County exhibited reduced a g ric u ltu ra l acreage. TABLE 4 . — Total Earnings by M ajor Source W ithin Huron, S a n ilac and Tuscola Counties 1969.a Region Michigan Percentage Huron County Percentage Sanilac County Percentage Tuscola County Percentage 1.1 2 1. 1 20.3 11.2 Government 12.0 16.9 12.5 24.7 Manufacturing 45.4 23.1 39.1 26.2 Mining 0.1 — — — Contract Construction 5.7 3 .8 3.2 3.8 Transportation, Communi­ cations and Public U tilitie s 4.9 6.6 1.2 4.5 13.9 15.9 12.4 18.4 D is trib u tio n of Earnings Farm Wholesale and R eta il Trade Finance, Insurance and Real Estate Services Other Total Earnings __ 1.4 12.2 9.0 8.8 8.8 0.2 0.9 1.1 0.7 $29,607,631 $71,165 $75,585 $80,998 3.2 aCount.y and Regional Facts, State Planning and Development Region 7 , W illiam J. Kimball Coordinator, Michigan S tate U n iversity Cooperative Extension S ervice, 1974, pp. 48-51. 46 Evidence indicated th a t the Thumb Area Counties may be on the verge o f experiencing major s h ifts in land uses due to develop­ mental pressures. While the population o f these counties has been r e la tiv e ly stable in the past, events have been taking place which w ill lik e ly a lt e r both population and the d ire c tio n and magnitude o f developmental pressures. Table 5 illu s t r a te s th a t the resident population o f the area has exhibited a general moderate increase since 1940. During the decade o f 1940-1950, a ll three counties were r e la tiv e ly s ta tic with growth rates o f less than ten percent. However, a fte r 1950 a d iffe r e n tia l in population growth became evident. The population of Huron County exhibited the slowest ra te o f the increase, less than three percent, w hile Tuscola county's population exhibited the greatest rate o f increase, over 13 percent. During the decade of 1960-1970 the population of both Sanilac and Tuscola Counties were growing a t a s ig n ific a n t ra te of approximately nine and twelve percent re s p e c tiv e ly , w hile the population o f Huron County was r e la tiv e ly s t a tic . The g reater growth rates in both Sanilac and Tuscola counties indicated th a t land use changes and adjustments due to population pressures were occurring a t a d iffe r e n t ra te than in Huron County. I f a c la s s ific a tio n scheme fo r ranking the counties in terms o f absolute population increase were developed, Tuscola County would have ranked f i r s t , followed by Sanilac and then Huron. Table 6 illu s t r a te s th a t there was also a s ig n ific a n t d ifferen ce between the counties in terms o f the percentage of TABLE 5 . — Population Growth W ithin Huron, S a n ilac and Tuscola Counties 1940-1970.a Population % Change % Change 1940 1950 1940-1950 1960 Huron 32,584 33,149 1.7 34,006 2.6 34,083 0.2 Sanilac 30,114 30,837 2.4 32,314 4.8 35,181 8 .9 Tuscola 35,694 38,258 7.2 43,305 13.2 48,603 12.2 98,392 102,244 3.9 109,625 7.2 117,867 7.5 Total 1950-1960 1970 $ Change 1960-1970 County aMichigan S ta tis tic a l A bstract, Compiled under the D irection o f David I . Verway, D ivision o f Research, Graduate School o f Business A dm inistration, Michigan State U n iv e rs ity , Ninth E d itio n , 1972, pp. 34-36. TABLE 6 . — Urban and Rural Population D is tr ib u tio n W ith in Huron, S an ilac and Tuscola Counties 197 0.a County Sanilac Huron Population D is trib u tio n Urbanb Rural Total Number Percentage Number Tuscola Number Percentage 0.0 6.503 13.4 Percentage 2,999 8.8 0 31,084 91.2 35,181 1 00 . 0 42,100 86.6 34,083 100.0 35,181 100 .0 48,605 100.0 aMichiqan S ta tis tic a l A b stract, Compiled under the D irection of David I . Verway, D ivision o f Research, Graduate School of Business A dm inistration, Michigan State U n iv e rs ity , Ninth E d itio n , 1972, pp. 38-41. bUrban being defined as places 2,500 or la rg e r. 49 population which was c la s s ifie d as being e ith e r urban or r u r a l. Sanilac County had no population which was c la s s ifie d as being urban, w hile Tuscola County had the greatest percentage o f urban population, approximately 13 percent. Huron County's urban popu­ la tio n accounted fo r approximately nine percent o f the to ta l popu­ la tio n . As w ell as there being population growth w ith in the study area, there has been extensive population growth in surrounding areas. The study area is peripheral to densely populated South­ eastern Michigan and the populated regions containing Bay C ity , F lin t , and Saginaw. Table 7 documents the population growth in the standard m etropolitan areas which are peripheral to the study area. During the decade o f 1960-1970 the in divid ual S.M.S.A.s o f Bay C ity , D e tro it, F l in t , and Saginaw have increased in population somewhere between ten and twenty percent. The combined population increase in these four S.M.S.A.s to ta le d nearly 600,000 persons. The increasing population in the areas surrounding the study area added an addition al dimension to the developmental pressures being exerted. The location of the study area counties has h is t o r i­ c a lly made them vacation, recreation and retirem ent areas. In ­ creasing pressure upon the land use in these counties has resulted because of both the a c q u isitio n o f vacation and retirem ent home s ite s and the in flu x o f vacationers. The m ajo rity o f land consumption fo r second home s ite s has occurred along the shore lin e allow ing the in te r io r o f the counties to escape th is pressure. However, as the 50 TABLE 7 .— Population Growth in the Standard M etropolitan S ta tis tic a l Areas Peripheral to the Study Area 1960-1970. Population % Population Standard M etropolitan S ta tis tic a l Area Change 1960-1970 I9603 1970b 107,042 117,339 + 9 .6 3,762,360 4,199,931 +1 1 . 6 F lin t 374,313 486,658 +19.3 Saginaw 190,752 219,743 +15.2 4,434,467 5,033,671 +13.5 Bay C ity D e tro it Total Michigan State U n iv e rs ity , D ivision o f Research, Graduate School o f Business A dm inistration, Michigan S ta tis tic a l A b stract, Comp. David I . Verway (9th E d .); East Lansing: Michigan State U n iv e rsity, 1972, pp. 33-36. bU.S. Department o f Commerce, Bureau o f the Census, 1970 Census o f Population, General Population C h aracteristics Michigan, PC())-B24 M ich., pp. 24-59. a v a ila b ilit y o f shore property decreases, increasing pressure w ill be brought to bear on in t e r io r lands. Vacation v is it s are no longer seasonal in the Thumb, but are year round p rim a rily because o f heavy snowmobile usage. The in flu x o f non-residents fo r both vacations and retirem ent has added yet another dimension to the land use control issue. A cquisition of vacation and retirem ent property has put addition al s tra in s on land uses. Table 8 shows th a t a sizeable number o f housing units w ithin the three county area were seasonal in nature. Over 12 percent o f the three county to ta l housing units were seasonal. 51 TABLE 8 . —Total and Seasonal Housing Units w ith in the Study Area 1970.a Housing Units Total County Seasonal and M igrational % o f Housing Units which were Seasonal and M igrational Huron 14.647 2,736 18.7 Sanilac 14,841 2,425 16.3 Tuscola 15,523 326 2.1 45,011 5,487 1 2. 2 Total County and Regional Facts, S tate Planning and Development Region 7 , W illiam J. Kimball Coordinator, Michigan S tate U n iversity Cooperative Extension Service, 1974, pp. 23-28. Huron and Sanilac Counties both contained over 15 percent seasonal housing u n its , w h ile , Tuscola County had the fewest seasonal housing u n its , approximately two percent o f the t o t a l. The growth o f these surrounding regions has, and w ill continue to , e x ert both influence and pressure on land uses in the Thumb Area counties. C h a racteristics o f the three county populations were also examined in respect to age, education, occupation and income. In terms o f age composition, Huron and Sanilac counties were very s im ila r w hile Tuscola County d iffe re d s lig h tly from e ith e r o f the two. Table 9 illu s t r a te s th at differences between Tuscola County and the other two counties were evident in both the 20-44 years o f age group and the 65+ years age group. Tuscola County had more residents in the younger group, nearly 30 percent as compared to approximately 25 percent, and fewer in the o ld e r, nine percent as compared to approximately twelve percent. 52 TABLE 9 .--P opu lation Age Composition Within Huron, Sanilac and Tuscola Counties 1970.a Percent o f Population W ithin Cohort Huron County Sanilac County Tuscola County Percentage Percentage Percentage Under 5 years 8.8 9 .0 9 .5 5 to 19 years 10.2 10.8 1 1. 6 10 to 19 years 21.4 21.3 21.4 20 to 44 years 25.2 26.3 29.7 45 to 64 years 21.6 20.7 18.8 65 years and Older 12.8 11.9 9 .0 100.0 100 .0 1 00 . 0 Age Cohort Total County and Regional Facts, State Planning and Development Region 7 , W illiam J. Kimball Coordinator, Michigan S tate U niversity Cooperative Extension S ervice, 1974, p. 14. Table 10 shows th a t in respect to educational attainm ent there were very s lig h t differences between the counties. Huron County exh ib ited a s lig h tly lower percentage o f persons with high school education, but in the main, d ifferences a t a l l le v e ls were minimal. The median years o f school completed fo r persons 25 years and over was s im ila r in a ll three counties, approximately 11 years. A much g reater d iffe re n c e in population c h a ra c te ris tic s between the counties became evident when the occupations o f employed persons were examined. TABLE 1 0 .— Years o f School Completed by Persons 25 Years Old and O lder in Huron, S a n ila c and Tuscola Counties 197 0.a Percent o f Population 25 Years Old and Older Years o f School Completed Huron County Sanilac County Tuscola County Percentage Percentage Percentage 0.9 1.1 1.0 14.2 11.1 10.4 27.3 22.2 21.0 1-3 years 15.7 19.0 20.6 4 years (completed high school) 30.4 35.5 34.5 1-3 years 6.7 7.0 7.5 4 years or more (completed college) 4 .8 4.1 5.0 10.5 11.4 11.6 None Elementary School 1-7 years 8 years (completed elementary school) High School College Median Years of School Completed aCount.y and Regional Facts, S tate Planning and Development Region 7 , W illiam J. Kimball Coordinator, Michigan State U niversity Cooperative Extension Service, 1974, pp. 21-22. 54 Table 11 illu s tr a te s th a t, once again, c h a ra c te ris tic s o f Huron and Sanilac counties were s im ila r while those o f Tuscola County d iffe re d . The major differences occurred in respect to the o p era tiv e /la b o re r and farmer categories. Tuscola County had a higher percentage o f op eratives/ laborers, approximately 34 percent, than did e ith e r Huron or Sanilac Counties, 26 and 29 percent re s p e c tiv e ly . Conversely, Tuscola County possessed a much lower percentage o f farm ers, approximately fiv e percent, when compared to e ith e r Huron or Sanilac Counties where farmers comprised approximately 15 percent o f the to ta l labor force. Family income was also u t iliz e d as a comparison between the three counties. Table 12 illu s t r a te s th a t Tuscola County had a greater proportion o f it s fa m ilie s in income classes above $6 , 0 0 0 than did e ith e r Huron or Tuscola Counties. Nearly 80 percent of the fa m ilie s in Tuscola County had income levels above $6,000. Huron County had s lig h tly more than 60 percent o f it s fa m ilie s above the $6,000 per year level w hile approximately 70 percent o f Sanilac County fa m ilie s were a t th is income le v e l. Huron County had a greater proportion of it s fa m ilie s in the lower income groups than did e ith e r of the other counties. Nearly 37 percent o f Huron County fa m ilie s were below the $6,000 per year level while the percentages were approximately 30 percent and 20 percent resp ectively fo r Sanilac and Tuscola Counties. The d ifferen ces between fam ily income levels in the three counties were not g re a t, but they showed th a t Tuscola County fa m ilie s had s lig h t ly higher income levels TABLE 11.--O ccupations o f Employed Persons W ithin Huron, S a n ila c and Tuscola Counties 1970.a County Huron Occupation Professional, Technical and Kindred Workers Number Sanilac Percentage Number Tuscola Percentage Number Percentage 1,010 9.3 856 7.3 1,590 10.0 854 7.9 659 5.6 769 4 .9 Sales and C le ric a l Workers 1,546 14.3 1,781 15.2 2,410 15.2 Craftsmen and Foremen 1,623 15.0 1,940 16.6 2,760 17.4 Operatives and Laborers 2,807 25.9 3,419 29.3 5,333 33.6 Farmers and Farm Workers 1,652 15.3 1,839 15.7 765 4 .8 Service Workers 1,329 12.3 1,205 10.3 2,228 14.1 10,821 100.0 11,699 100.0 15,855 100.0 Managers, A dm inistrators, Self-employed and S alaried Total Employed Persons 16 Years Old and Over aU.S. Department o f Commerce, Bureau o f the Census, United States Census o f Population: 1970, General Social and Economic C haracteristics Michigan, PC(1)-C24, pp. 24-560 and 24-564. 56 TABLE 1 2 .— Family Income Levels Within Huron, Sanilac and Tuscola Counties 1970.a Percentage o f Families by Income Level Huron County Sanilac County Tuscola County Income Group Percentage Percentage Percentage Less than $3,000 16.6 12.8 9.2 $3,000 - $5,999 20.3 17.8 12.3 $6,000 - $8,999 22.2 22.9 23.7 $9,000 - $11,999 17.7 21.6 22.5 $12,000 - $14,999 11.3 12.4 15.0 $15,000 - $24,999 9 .8 10.8 14.4 $25,000 - $50,000 2.0 1.7 2.7 Above $50,000 0.2 0.3 0.3 1 00 . 0 1 0 0 .0 100.0 Total County and Regional Facts, State Planning and Development Region 7, W illiam J. Kimball Coordinator, Michigan State U niversity Cooperative Extension S ervice, 1974, pp. 41-43. than did the fa m ilie s of e ith e r Huron or Sanilac Counties (r e fe r to Table 12). . In summary, there were differences between the three counties in the selected study area. Tuscola County exhibited differences in demographic c h a ra c te ris tic s when compared with the other two counties. A ll in dication s seemed to point to the conclusion th a t Tuscola County was more urban in nature than were Huron or Sanilac Counties. Tuscola County had a greater absolute population as w ell as a greater population growth ra te than e ith e r o f the other counties. A greater proportion of Tuscola County's population was considered urban. The population o f Tuscola County was also s lig h t ly younger 57 than th a t o f the other two counties. There were fewer farmers in Tuscola County and a g reater number o f persons employed in the occupations which were considered urban. F in a lly , the fam ily income levels were higher in Tuscola County than they were in e ith e r Huron or Sanilac Counties. When these features were considered in t o t a l, Tuscola County emerged as the most urbanized o f the three counties. For the purposes o f th is study, th is degree of urbanization was con­ sidered as a surrogate fo r the degree o f developmental pressures which were being exerted on the counties. From an examination of the u t iliz e d c h a ra c te ris tic s , Tuscola County was considered the area subjected to the most developmental pressures followed by Sanilac County and then Huron County. However, examination o f data also showed th a t the counties exhibited great s im ila r itie s in respect to population c h a ra c te ris tic s . The demographic and land use data showed th a t a l l three counties were experiencing developmental pressures to some degree. A ll these aspects combined to produce a region which was about to undergo s h ifts in respect to land use. The pressures which were being exerted on the land resource in the three county study area would necessitate reevaluation o f both the goals and objectives o f land use p o lic y . The study area was considered s im ila r to many portions of the United States which were undergoing compa­ rable changes in respect to developmental pressures. An area such as the one in which the study was conducted provided an e x c e lle n t 58 opportunity to determine how local residents f e l t about land use issues and controls in the face o f impending change. Models There were three major objectives to th is study: (1 ) Id e n tify variables re la te d to land use control measures, (2 ) Develop p re d ic tiv e models, and (3) Elaborate prelim inary findings gained from the Io nia P ro je c t. The accomplishments o f these stated objectives depended heavily upon form ulation and u t iliz a t io n o f models. Because o f th is , i t was important to s e le c t the appropriate type o f model fo r the studyChoice of the Appropriate Model fo r the Study Because r e a lit y was being represented sym bolically, the appropriate model to s e le c t was the symbolic model. Because symbols were used to represent q u a n titie s , the type of model selected was a c tu a lly a mathematical model. The mathematical model was selected because i t perm itted manipulation and precision to a greater degree than do models expressed in other forms. Mathematical models are required i f the tools o f modern technology, p a r tic u la r ly the e le c tro n ic computer, are to be f u l ly u t iliz e d in the conduct o f the research. The form o f the model which was u t iliz e d in th is research was a s t a t is t ic a l model: I t was a symbolic model in equation form u t iliz in g m u ltip le regression. 59 Since a model could be viewed as a set o f hypotheses, i t was possible to in teg ra te the hypotheses under in ve s tig atio n in to an equation form. Measurements o f the variables re la tin g to the hypotheses could be assigned q u a n tita tiv e values and would lend themselves to being inputs in to the s t a t is t ic a l model. For example, the lit e r a t u r e review indicated th a t many variables conditioned or influenced voting behavior. I f voting behavior, the decision to vote in a s p e c ific manner, were con­ sidered the dependent v a ria b le , then c e rta in socio-economic charac­ te r is tic s could be considered dependent v a ria b le s . The socio­ economic c h a rac te ris tic s would have a bearing on an in d iv id u a l's voting behavior. In d ivid u al c h a ra c te ris tic s , expressed as inde­ pendent variables such as age, sex, educational attainm ent, income, e t c ., would influence a s p e c ific vote. Based on th is premise, a s im p lis tic description o f the research e ffo rts could be illu s t r a te d by the follow ing equation in im p lic it form: Dependent V ariable Y In d ivid u al A ttitu d e s P ertaining to Land Use Control Measures (Surro­ gate fo r voting behavior) Independent Variables X = f [ ( a g e ) , (s e x ), (educational a t t a in ­ m ent), (income l e v e l) , (occupation), (land c o n tro lle d ), (home ownership), (population d e n s ity ), (perceived land use c o n f lic t s ) , (perception of governmental s e rv ic e ), ( p o lit ic a l p arty id e n t if ic a t io n ) , (group p a r t ic i­ p a tio n ), (p a rtic ip a tio n in e le c tio n s )] 60 This, in a very general and basic f o r m, was the model which was conceptualized fo r the conduct o f the research. Regression Analysis Simple and M u ltip le Regressions A basic ob jective o f the study was to be able to p re d ic t an in d iv id u a l's response to various questions about land use control measures i f c e rta in c h a ra c te ris tic s pertaining to the in d iv id u a l were known. Simple regression o ffe rs a method o f examining the r e la ­ tionship between two v a ria b le s , one which may be c a lle d x (the independent v a ria b le ) and another y (the dependent v a ria b le ). How­ ever, i f x and y are s t a t is t ic a lly independent i t is impossible to p re d ic t y from x. In the case of s t a tis t ic a l independence, know­ ledge o f x w i ll not improve the predictio n of y J When the two variables being considered are not s t a t i s t ic a l ly independent, knowledge of x assists in the pred ictio n o f y . The stronger the dependence between x and y the more accurate the predictions w i ll be. 2 Simple regression examines the re la tio n s h ip between the two variables x and y . is measured. For each change in x the re s u lta n t change in y The strength o f the re la tio n s h ip between x and y is indicated by the c o rre la tio n c o e ffic ie n t. I f there is p e rfe c t ^Hubert M. B lalock, J r . , Social S ta tis tic s (New York: McGraw-Hill Book Company, 1960), p. 363. 2 I b i d . , p. 363. 61 c o rre la tio n between x and y exact prediction is possible, fo r each change in x would re s u lt in a consistent change in y . A c o rre la tio n between the two which is less than p e rfe c t ( 1 . 0 ) w ill re s u lt in predictions of y which are not exact. Basic regression th erefo re measures the degree o f re la tio n ­ ship between two variables and enables a p redictio n o f y (the dependent v a ria b le ) based on knowledge o f it s re la tio n s h ip to x (the independent v a ria b le ). M u ltip le regression is s im ila r to simple regression except th a t the re la tio n s h ip between a number o f independent variables and the dependent v a riab le is in vestig ated . An attempt is made to p red ict a single dependent v a riab le from a number o f independent variab les.^ Predictions o f y are no longer based on the r e la tio n ­ ship between the dependent v a riab le and a single independent v a ria b le . As in the case o f simple regression, c o rre la tio n co­ e ffic ie n ts in dicate the degree o f re la tio n s h ip between each inde­ pendent v a riab le and the dependent v a ria b le . A d d itio n a lly , m u ltip le regression o ffe rs the advantage o f providing p a r tia l c o rre la tio n c o e ffic ie n ts . P a rtia l c o rre la tio n c o e ffic ie n ts may be u t iliz e d to summarize the degree o f re la tio n s h ip between two v a ria b le s , con­ tr o llin g fo r a ll other v a riab le s . Reasons fo r Using M u ltip le Regression As mentioned previously, a basic o b jective o f the study was to be able to p re d ic t an in d iv id u a l's response to various land use 1 I b i d . , p. 429. 62 control questions i f knowledge pertaining to his c h a rac te ris tic s were known. M u ltip le regression provided an ideal mechanism by which to generate predictions using answers to s p e c ific land use control measure questions as the dependent v a riab le and measures o f individual c h a ra c te ris tic s as independent variab les. Another o b jective o f the study was to id e n tify s p e c ific a lly which socio-economic c h a ra c te ris tic s could be used to p re d ic t an in d iv id u a l's a ttitu d e s and opinions regarding land use control measures. In addition to providing estimates o f population parameters ( i . e . , regression c o e ffic ie n ts ), m u ltip le regression o ffe rs a method o f id e n tify in g which independent variables are re la te d to the dependent v a ria b le . By s e ttin g a sign ificance le v e l, i t is possible to have independent variab les included or deleted in the regression equation based on th e ir sign ificance in predicting the values o f the dependent v a ria b le . Since the variables specified fo r the fin a l equation were based on hypotheses, inclusion or d eletion of a v a riab le amounts to a te s t o f the hypothesis. Deletion of an independent v a ria b le a t a given sign ificance level constitutes re je c tio n of a given hypothesis a t th a t p a rtic u la r sig n ifican ce le v e lJ Inclusion of an independent v a riab le in d i­ cates th a t the p a rtic u la r independent v a riab le is re la ted to the dependent v a ria b le . A given hypothesis is valid ated to the extent th a t the associated independent v a riab le is shown to be s t a t i s t i ­ c a lly re la te d to the dependent v a ria b le . ^Ronald J . Wonnacott and Thomas H. Wonnacot, Econometrics (New York: John Wiley and Sons, In c ., 1970), pp. 64-67 and 256-257. 63 M u ltip le regression was id e a lly suited to the major objectives of th is study since i t offered a method fo r predictio n w hile con­ cu rren tly providing a mechanism o f te s tin g hypotheses. Design o f the Method o f Analysis The method o f analysis employed in th is study was a v a ria tio n of m u ltip le lin e a r regression. This technique allowed fo r the id e n tific a tio n o f those in divid ual c h a ra c te ris tic s (independent v a riab le s ) which exerted a s ig n ific a n t influence upon in divid ual responses to questions pertaining to land use control measures (dependent v a riab les) and an estim ation o f the extent o f the in fluence. For use in regression equations, dependent variables were coded in a dichotomous, binary form. This meant th a t the dependent va riab le in the regression equation only had two possible values ra th e r than an in f in i t e number o f values th a t the dependent v a riab le is normally assumed to take. That is , values which the dependent va riab le "y" could assume are the follow ing: 1 I f the respondent approved o f the land use control measure. 0 I f the respondent did not approve o f the land use control measure. _ The use o f dichotomous, "dummy," dependent variab les presented problems in respect to variances in m u ltip le regression. Use o f a dichotomous "y" v io la te d the assumption o f homogeneous variance which is considered c r it ic a l in respect to the general lin e a r model in s t a tis t ic a l theory. 64 Weighted Regression To c o rrect fo r the problems created through the use of a dummy dependent v a ria b le , i t was possible to u t i l i z e weighted regression. Weighted regression d iffe r s from general lin e a r regression in the respect th at i t compensates fo r the e rro r term e, as discussed below: The form o f a general lin e a r regression model is : y = 3q + 3i xi + $2 x2 * • • 3i xi + e where y represents the dependent v a riab le X-], Xg • • . x.j are the specified independent variables B , 3r e2 • • • 3 .j represents unknown population parameters th a t measure the e ffe c t o f the independent variables in the p red ictio n o f the random response y e is an e rro r term which explains the random flu c tu a tio n in y fo r fix e d settings o f x-j, x2 > . • . x^. The random component e creates problems when the dependent v a riab le (y) is o f a dichotomous form. When the dependent v a ria b le is not dichotomous i t is assumed th at e "is a normally d is trib u te d random 2 i v a ria b le , with mean zero and variance a F u rther, repeated values 2 o f e are "not only uncorrelated but necessarily independent." York: ^N. R.Draper and H. Smith, Applied Regression Analysis (New John Wiley and Sons, In c ., 1966), p. 177 ^ Ib id ., p. 17. 65 Goldberger has shown th a t when y is dichotomous the assump­ tion o f homogenous variance is untenable. With a dichotomous dependent v a riab le i t has been found th at e values are heteroscedastic that they vary system atically with estimated values of y and hence, with p a rtic u la r values o f the independent variab les.^ Goldberger suggests a process to compensate fo r the problems created by a dichotomous dependent v a ria b le . To obtain the best lin e a r unbiased estimates w ith a dichotomous dependent v a ria b le , a two stage le a s t squares procedure is recommended. F ir s t , the calculated values of $ are obtained fo r each observation from an ordinary le a s t squares s o lu tio n . The y values are then used to c a lc u la te the term y ( l - y ) which is an approximation of the variance of e fo r th a t p a rtic u la r observation. Then, values of the dependent and independent variables fo r each observation are transformed by d ividin g them by the corresponding y ( l - y ) term. F in a lly , analysis of the transformed values by ordinary le a s t squares is conducted to derive parameter estim ates. Through this procedure, b e tte r unbiased estimators are obtained, and v a lid it y of the sign ificance determination is increased. 2 Also, because o f the dichotomous nature o f the dependent v a riab le : . . . the calculated value o f y fo r any given x is in terp re te d as an estim ate of the conditional p ro b a b ility o f y , given x. That is , i f x changes by one u n it then the p ro b a b ility o f y ^Arthur S. Goldberger, Econometric Theory (New York: Wiley and Sons, In c ., 1964), p. 249. 2 Ib i d . , pp. 250-255. John 66 correspondingly changes by the estimated parameter value associated w ith th a t x J The Ionia Study in Which the Weighted Regression Procedure was U tiliz e d Ionia Model Mention has been made o f the Ionia P ro je c t which was p re v i­ ously conducted. The concepts o f weighted regression and condi­ tio n a l p ro b a b ility are best illu s t r a te d by an example from the Ionia P ro je c t. A model which was created in the course o f the Io nia P ro je c t d e a lt w ith the issue o f where addition al housing should be located w ithin the county. A question in the mailed questionnaire asked " I f more single fa m ily , non-farm residences are added, where would you p re fe r they be located?" 2 The response options provided were: No re s tric tio n s on location (anywhere) Large ru ra l lo ts Rural subdivisions Subdivisions adjacent or w ith in v illa g e s and c itie s Don11 know Variables The response to the lo ca tio n a l question was used as the dependent v a riab le in a regression equation. was coded in a dichotomous, binary form. The dependent v a riab le The no r e s tr ic tio n response was coded as, a 1 . Douglas Melvin Crapo, "Recreational A c tiv ity Choice and Weather: The S ig nificance of various weather preceptions in i n f l u ­ encing preference fo r selected recreatio n al a c t iv it ie s in Michigan State Parks" (unpublished Ph.D. d is s e rta tio n , Michigan S tate Uni­ v e rs ity , 1970), p. 53. O A copy o f the Io nia Questionnaire is found on pages 238-241 o f the Appendix. 67 Responses in d ic a tin g a desire fo r control over location (la rg e ru ra l lo t s , ru ral subdivisions, and subdivisions adjacent or w ith in v illa g e s and c it ie s ) were coded as a 0. and unusable responses were deleted. "Don't knows" This process yield ed a dependent v a riab le which was dichotomous in nature, in d ic a tin g a desire f o r , or a re je c tio n o f, controls over location o f addition al housing. The dependent v a riab le was designated x^. Because o f the dichotomous nature o f the dependent v a ria b le , the regression equation was in the form o f a conditional p ro b a b ility equation. The general form o f a conditional p ro b a b ility lin e a r regression equation is P (y |x) = 8Q + 3-j + 82 x2 . . . + 3^ + e. Ten independent v a ria b le s , thought to be useful in p re d ic t­ ing response to the location o f a d d itio n al housing were selected fo r inclusion in the equation. The ten independent variables were: Occupation (X2) The occupation c la s s ific a tio n s were: 1 = unemployed or handicapped and students 2 = re tire e s 3 = housewife 4 = semi or un skilled blue c o lla r (fa c to ry )— s k ille d 5 = c le r ic a l and sales workers 6 = farmers 7 = proprietors or self-employed 8 = o f f ic ia ls = government, business and in d u s tria l supervisors 9 = teaching or white c o lla r There was an in te re s t in the re la tio n s h ip between farmers and lo catio nal issues. For th is reason, the occupation v a riab le was coded in a dichotomous form. Farmers were coded as 1 while a ll other occupations were coded as 0. This was done s p e c ific a lly to in dicate 68 the re la tio n s h ip between farmers and the location o f a d d itio n al housing. Age (X3 ) actual ages were recorded and coded. Mobile Home Location (Xg) The question re la tin g to the location o f mobile homes was, " I f more mobile homes are added, which location would you prefer?" The response options were: No re s tric tio n s on location (anywhere) Rural mobile home parks Mobile home parks adjacent to or w ith in v illa g e s and c itie s Don't know This v a riab le was also coded in a binary, dichotomous form. "No re s tric tio n s " was coded as a 1 while re s tric tio n s on location (ru ral mobile home parks and mobile home parks adjacent to or w ith in v illa g e s and c it ie s ) were coded as a 0. The "Don't know" responses were deleted. Shopping Location (Xg) The s p e c ific question was " I f more shopping and service f a c ili t i e s were added, where would you p re fe r they be located?" The response options were: No re s tric tio n s on lo catio n (anywhere) Downtown areas Shopping centers Don't know This v a ria b le was also coded in a 0-1 form at. "No re­ s tric tio n s " was coded as 1 and responses in d ic a tin g re s tric tio n s 69 were desired (downtown areas and shopping centers) were coded as 0 . The "Don't know" responses were deleted. Industry Location (Xy) The question asked was, " I f more industry were added, where would you prefer i t be located?" The possible responses were: No re s tric tio n s on lo cation (anywhere) Within incorporated c it ie s and v illa g e s Only in c o n tro lle d , s p e c ifie d , in d u s tria l parks D on't know Once again a 0-1 coding format was used. "No re s tric tio n s " was coded as 1 and responses desiring re s tric tio n s (w ith in incorpo­ rated c itie s and v illa g e s and only in c o n tro lle d , s p e c ifie d , indus­ t r i a l parks) were coded as 0. The "Don't know" responses were deleted. Zoning (Xg) A question designed to ascertain respondents' a ttitu d e s toward zoning was also contained in the questionnaire. The question asked, "What are your feeling s as to the timing fo r use o f each o f the land control measures?" cited was land use zoning. One o f the control measures s p e c ific a lly The responses provided were: Now Later Never Don't know "Now" and " la te r" were thought to in d ic a te a desire fo r zoning a t some tim e. They were combined and coded as a 1. responses were coded as a 0. The responses were thus divided in to those favoring zoning and those opposed to i t . know" responses were deleted. The "never" Again, the "Don't 70 1970 Township Density (Xg) In divid ual responses were id e n tifie d as to township o f residence. Thus, i t was possible to c a lc u la te population density associated with each respondent. The 1970 population density per square m ile o f the respondent's township was calculated and coded to the nearest thousandth. 1960-1970 Township Density Change (X-jq) The 1960-1970 township density change was calcu lated fo r each township. These values, rounded to the nearest thousandth, were coded and assigned to the in divid ual data sets. Zoning-Age In te ra c tio n ( X - j - j ) I t was hypothesized th a t an in te ra c tio n between a respond­ e n t's age ( X g ) and his opinion on zoning ( X g ) might be o f s ig n if i­ cance in p redictin g the p ro b a b ility of a given response to location of a d d itio n al housing u n its . Therefore, an in te ra c tio n v a ria b le , (X3 Xg), was created and designated X ^ . 0ccupation--Age In te ra c tio n (X12) I t was also hypothesized th a t an in te ra c tio n between occu­ pation ( X 2 ) and age ( X g ) might be s ig n ific a n t in p red ictio n responses. The in te ra c tio n v a ria b le X^2 was created (X2 Xg) to explore th is possible re la tio n s h ip . The above were the 10 independent variables which were hypothesized to be o f value in pred ictin g the p ro b a b ility o f a response in d ic a tin g a desire fo r e ith e r "no re s tric tio n s " or " re s tric tio n " on location o f addition al homes in Io nia County. 71 In respect to each v a ria b le , a ll responses which did not indicate e x p lic it preference by a sing le answer were not included. In order to be included in the f in a l data s e t, a l l questions had to be answered. The fin a l re su ltin g data s e t, because o f the above re s tr ic tio n s , consisted o f 734 in d iv id u a l observations from the to ta l o f 1336 completed questionnaires which were returned. With the responses coded in the form o f a dependent and ten independent v a ria b le s , the data set was subjected to analysis through a stepwise le a s t squares (LSSTEP) regression ro u tin e. Stepwise Least Squares Regression Routine (LSSTEP) The LSSTEP routine was used to estim ate the "best" r e la ­ tio n sh ip , based on the "goodness o f f i t " c r it e r io n , between a dependent v a riab le and a set of independent variab les.^ The LSSTEP routine included in the f in a l equation only those variables which were most s ig n ific a n t. The LSSTEP routine calculated the sig n ifican ce p ro b a b ility o f the F s t a t i s t ic fo r the p a r tia l regression c o e ffic ie n t associated w ith an independent v a ria b le . Through th is c a lc u la tio n i t was possible to determine whether or not the v ariab le should be deleted from, or l e f t in , the equation. Deletion is accomplished by a user specified "sigout" value. In the Ionia regression model a s ig n ific a n c e level o f .1 0 , commonly used in sociological research, was s p e c ifie d . This resulted in ^Prelim inary documentation, MSU STAT System (6500) May 17, 1972, p a rt 12, LSSTEP Program. 72 variables which had a sig n ifican ce greater than . 1 0 being retained fo r the f in a l regression equation. In the Io n ia Study* fiv e variables were retained in the fin a l regression equation: TABLE 1 3 .— Ionia Model O rig inal Regression C o e ffic ie n ts . V ariable Regression C o e ffic ie n t (X3 ) Age CM O O 1 (X5 ) Mobile Home location +.40 Age O riginal Regression C o e ffic ie n ts -.0007 -.002 (x5) A dditional Mobile Home Location +.39 + .40 X X97 X99 O o X X101 1970 Minor C iv il D ivision Population 1960 Minor C iv il D ivision Population 1970 Minor C iv il D ivision Population Density 1960 Minor C iv il D ivision Population Density 1960-1970 Minor C iv il D ivision Population Density Change Minor C iv il D ivision Area— Square Miles 1970 County Population X 1960 County Population CO X o X102 Variable o X105 X o X106 X108 1970 County Population Density 1960 County Population Density 1960-1970 County Population Density Change County Area— Square Miles As a re s u lt o f the transform ation routine and the addition of new v a ria b le s , 107 v a ria b le s , ^ through X-jQg, were created. variables were used in the analysis o f the data. questionnaire id e n tific a tio n number. Variables 107 X^ was simply the (Land Use Planning Response), Xg (Ordinances to Enforce A Land Use Plan Response), and 121 X. (Zoning Response) were used as in divid ual dependent variables in separate regression equations. Variables Xg through X1Q8 were the independent variab les fo r the regression equations. CHAPTER IV ANALYSIS AND RESULTS The I n i t i a l Model--Land Use Planning The i n i t i a l model which was constructed u t iliz e d the response to the land use planning question (Xg) as the dependent v a ria b le . The dependent v a riab le was coded in a dichotomous fashion w ith 1 indicating approval o f land use planning and 0 in d ic a tin g opposition to land use planning. The independent variables were a l l the 106 variables which were lis te d previously. The Stepwise Least Squares Regression Routine (LSSTEP) was u tiliz e d to estim ate a "best" re la tio n s h ip between the dependent variable and the set o f independent v a ria b le s . A sign ificance level o f .10 was s p e c ifie d which resu lted in the variables having a significance o f less than .10 being deleted from the fin a l equation. The LSSTEP routine y ie ld ed a m u ltip le regression equation which retained 19 o f the o rig in a l 106 independent v a ria b le s . The m ultiple c o rre la tio n c o e ffic ie n t (R) was .6437 while the m u ltip le c o e ffic ie n t o f determ ination (R ) was .4144. Examination o f the output of the LSSTEP program resu lted in a distu rb ing discovery. When the p a r tia l c o rre la tio n c o e ffic ie n ts were examined i t was found th a t the v a ria b le Xg (response to the ordinances q u estio n ), 122 when correlated with X2 , (response to the planning q u estio n), had a p a rtia l c o rre la tio n c o e ffic ie n t o f .59402. This indicated a very high c o rre la tio n between th is independent v a ria b le and the dependent v a ria b le (X2) . Upon re fle c tio n i t became c le a r th a t the two v a ria b le s , response to planning and response to the ordinances question, were re la te d in a very obvious manner. In e ffe c t, they were more or less measuring the same general concept. This resulted in these two variables being highly c o llin e a r with one another. I t was obvious th a t fu tu re dependent variables were not to be included as independent variab les in the various equations. A d d itio n a lly , inspection of the simple c o rre la tio n m atrix revealed th a t many o f the independent variables were e ith e r very highly correlated or had v ir t u a lly no c o rre la tio n w ith one another. To reduce the problems associated w ith s in g u la rity i t was decided to delete one o f each p a ir o f variables which, when correlated with one another, exhibited a c o rre la tio n o f .80 or g re a te r. The variable which was deleted was the one which exh ib ited the lower c o rrelatio n w ith the dependent v a ria b le . Also, i t was a r b it r a r i ly decided to e lim in a te a l l variables which exhibited a c o rre la tio n c o e ffic ie n t o f less than .01 with any of the three dependent v a riab le s ; X2 (response to the land use planning q u estio n ), X^ (response to the ordinance q u es tio n ), and X^ (response to the zoning question). This elim inated a l l independ­ ent variables which were v ir t u a lly uncorrelated w ith the dependent variables. 124 This d e letio n process elim inated 31 o f the independent variables. Thus, the number o f independent variab les which would be used in the succeeding equations was 75 instead o f the o rig in a l 107. The variab les which were deleted were as shown in Table 28. "LSSTEP" Routine The reduced data set was subjected to the LSSTEP routine using Xg (response to the land use planning question) as the dependent v a ria b le . A sign ificance le v e l of .10 was specified and the follow ing independent variables were retained as shown in Table 29. The LSSTEP routine id e n tifie d the variab les which were s ig n ific a n t a t the .10 level and would be included in the fin a l weighted regression equation. "RESID" Routine Using the "RESID" Routine, the residuals (y -y ) fo r each observation was ca lc u la te d . These data were compiled in a new Data Deck and used to c a lc u la te the weights necessary fo r the weighted regression. "CONVERT" Routine Using the re s id u a ls , a weight fo r each observation was calculated, through the use of the formula: 1 weight = -------------y ^ i-y ^ 125 TABLE 2 8 .—Variables Deleted from the Land Use Planning Model. V ariable X16 X24 X CO ro X37 CO X X55 X59 X CO X61 X64 -.9 3 Occupation + .86 Occupation +.00003 Occupation Second occupation + .004 Father's occupation Father's occupation -.005 Father's occupation -.001 P o litic a l party id e n tific a tio n -.001 General voting behavior -.91 County of residence County of residence Residence location -.8 7 Years liv e d in local community +.00008 Total fa m ily size +.009 *3" X58 Occupation 40 X31 -.8 6 o •“s! X14 Occupation 1 X13 Dependent V ariable o o 1 xn Zoning o o i X9 Dependent V ariable O X4 Ordinances to enforce land use plan 1 o o i£> X3 C orrelatio n C o e ffic ie n ts o f Deleted Variables 126 TABLE 2 8 .— Continued. X70 C orrelation C o e fficien ts o f Deleted Variables LO X CO X Property leased or rented - .9 0 Property leased or rented -.9 7 X o XJ X106 o o o ro -.8 4 1970 county population +.005 1960 county population -.0 0 5 o XI X105 Minor c iv il d iv is io n area— square miles 1970 county population density 1960 county population density -.8 1 1960-1970 county population density change -.0 0 5 O CO o 4* X o X X102 1960-1970 minor c iv il d iv is io n population density change 1 00 cr> X101 O -.0 0 9 1 1960 minor c iv il d iv is io n population CM -.0 0 0 8 CO Education X X Education 1 - .9 0 X 00 XI Property owned or buying CO V ariable TABLE 2 9 .--V a ria b le s Retained in the Land Use Planning Model. V ariable X18 X27 X28 X30 X34 X35 X36 o X X44 X53 X56 X66 X74 X90 X100 Second occupation Second occupation Father's occupation Father's occupation Father's occupation Father's occupation Father's occupation Farm organization p a rtic ip a tio n P o litic a l organization p a rtic ip a tio n General voting behavior Response o f county government o f f ic ia ls Property owned Property leased Income 1960 minor c iv il d iv is io n population density A new Data Deck was created containing weights fo r each observation. "SWITCH" Routine The "SWITCH" Routine was used to merge o rig in a l data fo r each observation and created weights onto a single data card. The data deck re s u ltin g from the "SWITCH" Routine was the input to the weighted regression analysis. Weighted Regression The weighted regression routine weighted the v a ria b le s , which were shown to be s ig n ific a n t through the use of the "LSSTEP" routine, by the calculated weights. These transform ations were p a r tia lly corrected fo r h etro s e d a stic ity and scaling problems. Regression c o e ffic ie n ts generated by the weighted re ­ gression analysis were as shown in Table 30. The fin a l mathematical equation, derived by u t iliz in g the values o f the weighted regression c o e ffic ie n ts was: P(Y.[X) = .43 + .09 - .04 X35 + X]8 - .05 X2y - 07 X28 + .06 X3Q - .04 X34 .02 X36 +.08 X40 + .11 X44 - .06 X53 + .097 X56 + .05 X66 - The weighted .099 Xy4 - .07 XgQ + .00008 X1Q() regression equation y ie ld ed a m u ltip le corre­ la tio n c o e ffic ie n t (R) o f .3549 and a m u ltip le c o e ffic ie n t of o determination (R ) o f .1259. The value o f the m u ltip le c o e ffic ie n t of determination indicated th a t s lig h tly more than 12 % o f the TABLE 3 0 .—Weighted Regression C o e fficien ts fo r the Land Use Planning Model. Second occupation +.09 X no *■*-4 Second occupation -.0 5 roX Father's occupation - .0 7 Father's occupation +.06 Father's occupation -.0 4 Father's occupation -.0 4 Father's occupation + .0 2 Farm organization p a rtic ip a tio n +.08 P o litic a l organization p a rtic ip a tio n +.11 CO LO X General voting behavior -.0 6 cnX Regression C o e ffic ie n t Response to county government o f f ic ia ls +.097 Property owned +.05 Property leased -.0 9 9 XO) Income -.0 7 X o o V ariable 1960 minor c iv il d iv is io n population density +.00008 00 X18 X30 X34 X35 X36 O X X44 X66 X74 o 130 variatio n in the dependent v a ria b le , response to the land use planning question, was associated w ith the influence exerted by the 15 s ig n ific a n t independent v a riab les. The 15 independent variables which were retained f e l l into eight major groupings. These groupings were as shown in Table 31. TABLE 3 1 .— Grouped Variables fo r the Land Use Planning Model. Group X18* X ro Father's occupation CO C\J X X30J X34* X35’ X36 Organization p a rtic ip a tio n X o Second occupation V ariable Number X44 Voting behavior X53 Response to governmental o f f ic ia ls Property contro lled X6 6 * X74 X lO o Income X56 o o x~ Population density By u t iliz in g the matrices which were constructed fo r u t i l i z ­ ing the dummy variab les i t was possible to c a lc u la te the influence that variables exerted on the conditional p ro b a b ility o f approving land use planning. This was accomplished by m u ltip ly in g the m atrix value of the v a ria b le by the regression c o e ffic ie n t fo r th a t v a riab le . I f two or more variables were contained w ith in the grouping the net e ffe c t o f the variables were u t iliz e d to show 131 the e ffe c t on the conditional p ro b a b ility o f approving land use planning. As an example the second occupation c la s s ific a tio n of farmer could be considered. Variables retained from the second occupation m atrix were X-jg and Xgy. The values from the m atrix were -1 and -3 re s p e c tiv e ly , while the regression c o e ffic ie n ts were +.09 and - .0 5 . The net e ffe c t o f these two variab les was: -1 (.0 9 ) -3 (.0 5 ) = -.0 9 + .15 = +.06 Thus, i f a respondent's second occupation was farm ing, the condi­ tio n a l p ro b a b ility o f approving land use planning was increased by s lig h tly more than 6 %. U t iliz in g th is process i t was possible to c a lc u la te the e ffe c t th a t various second occupations had upon the conditional p ro b a b ility o f approving land use planning. This basic process was used to c a lc u la te the influence th a t a ll the general groupings o f variab les exerted on the condi­ tio n a l p ro b a b ility o f approving land use planning. The follow ing ta b le (Table 33) illu s tr a te s the values generated by m ultip lyin g a s ig n ific a n t v a ria b le 's regression c o e ffic ie n t by it s m atrix value. Hypotheses V a lid a tio n The inclusion o f s p e c ific variab les in the weighted re ­ gression equation served as a method to e ith e r support or r e je c t the various hypotheses which were previously presented. A number of the hypotheses which were generated were re je c te d in th a t the 132 TABLE 3 2 .—An Example Illu s t r a t in g the Calculated E ffe c t o f an Individual V ariable on the Conditional P ro b a b ility o f Approving Land Use Planning. Second Occupation E ffe c t on the Conditional P ro b a b ility o f Approving Land Use Planning +.14 Professional, te c h n ic a l, and kindred workers +.14 O ffice holder +.15 1 Craftsmen and foremen Operatives and laborers +.04 Farmers +.06 Service workers -.15 Reti red -.1 5 Unemployed or handicapped -.19 Housewife -.04 O Sales and c le r ic a l workers o o i o cn No Response Lf) 133 TABLE 3 3 .— E ffe c t o f In d ivid u al Variables on the Conditional P ro b a b ility o f Approving Land Use Planning. V ariab le E ffe c t on the Conditional P ro b a b ility o f Approving Land Use Planning Second Occupation No Response Professional, Technical and Kindred Workers O ffice Holder Sales and C le ric a l Workers Craftsmen and Foremen Operatives and Laborers Farmers Service Workers Reti red Unemployed or Handicapped Housewife +.14 +.14 +.15 -.0 0 5 -.0 0 5 +.04 +.06 -.1 5 -.1 5 -.1 9 -.0 4 Father's Occupation No Response or Deceased Professional, Technical and Kindred Workers Managers, A dm inistrators, Self-employed or S alaried Sales and C le ric a l Workers Craftsmen and Foremen Operatives and Laborers Farmers Service Workers Reti red Unemployed or Handicapped Housewife +.096 +.006 -.0 4 +.10 -.1 2 - .1 4 - .0 4 +.21 -.1 7 +.17 +.04 Organization P a rtic ip a tio n Farm Organization P a rtic ip a tio n P o litic a l Organization P a rtic ip a tio n +.08 +.11 Voting Behavior General voting behavior Do not vote in e lectio n s ( 0 % o f e le c tio n s ) Vote in some election s (1% - 50% o f e le c tio n s ) Vote in most election s (51% - 99% o f e le c tio n s ) Vote in a l l e lectio n s ( 100 % of e le c tio n s ) -.0 6 -.0 6 +.06 +.06 TABLE 3 3 .--Continued. V ariable E ffe c t on the Conditional P ro b a b ility o f Approving Land Use Planning Response to Governmental O ffic ia ls Response o f county governmental o f f ic ia ls County o f f ic ia ls responsive County o f f ic ia ls not responsive +.097 0 Property Ownership Property owned None Less than 1 Acre 1-10 Acres 11-40 Acres 41-80 Acres 81-160 Acres 161-320 Acres 321-640 Acres More than 640 Acres Property leased None Less than 1 Acre 1-10 Acres 11-40 Acres 41-80 Acres 81-160 Acres 161-320 Acres 321-640 Acres More than 640 Acres +.05 +.05 + .05 0 -.0 5 -.0 5 -.0 5 0 0 -.0 9 9 -.0 9 9 -.0 9 9 0 +.099 +.099 +.099 0 0 Income Less than $3,000 $3,00l-$ 6 ,0 0 0 $6,001-$9,000 $9,001-$12,000 $12,001-$15,000 $15,001-$25,000 $25,001-$50,000 $50,000 + -.0 7 -.0 7 -.0 7 0 +.07 +.07 +.07 0 Population Density 1960 Minor C iv il D ivisio n Population Density The regression c o e ffic ie n t o f +.00008 meant th a t g reater d ensities would increase the p ro b a b ility o f approving land use planning. 135 variables associated with them were not included in the fin a l regression equation. The variables o f age, sex and education attainment were deleted as were homeownership, perceived c o n flic ts regarding land usage, and p o lit ic a l party id e n tific a tio n . V aria­ bles were retained which were associated w ith the hypotheses deal­ ing w ith occupation, group p a rtic ip a tio n , voting behavior, perception of adequacy of governmental s e rv ic e , property ownership, income, and population density. I t was possible to assess, through the regression c o e ffic ie n ts , i f the fin a l regression equation valid ated the proposed hypotheses. Occupation is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures I t was hypothesized th a t in divid u als with more prestigious "white c o lla r" occupations would be more lik e ly to favor land use control measures than in d iv id u a ls with less prestigious "blue c o lla r" occupations. The variab les retained did not deal with primary occupation. Rather, they were an extension o f the con­ cept of occupation. Variables were retained which indicated a respondent's second occupation and fa th e r's occupation. The reason fo r the primary occupation v a ria b le being elim inated and these two being retained was unclear. However, when the v a riab le pertaining to second occupation was compared to the hypothesis, the hypothesis was generally supported. The second occupations which would increase the conditional p ro b a b ility o f approving land use planning were as shown in Table 34. 136 TABLE 3 4 .— Second Occupations Which Increased the Conditional P ro b a b ility o f Approving Land Use Planning. E ffe c t on the Conditional P ro b a b ility of Approving Land Use Planning Second Occupation Professional, te c h n ic a l, and kindred workers +.14 O ffice holder +.15 Operatives and laborers +.04 Farmers +.06 The two second occupations which could c le a rly be defined as "white c o lla r" occupations exerted a strong influence toward increasing the conditional p ro b a b ility o f approving land use plann­ ing. Although the classes o f operatives and laborers and fanners also increased the conditional p ro b a b ility o f approving land use planning, t h e ir influence was much less s ig n ific a n t. I t was possible th a t farmers approved o f planning in order to protect vested in te re s ts . A ll other second occupation c la s s ific a tio n s decreased the conditional p ro b a b ility o f approving land use planning. These c la s s ific a tio n s were g enerally associated with "blue c o lla r" occupations. In respect to the fa th e r's occupation v a ria b le , the resu lts were b a s ic a lly d iffe r e n t. The occupation c la s s ific a tio n s which would increase the conditional p ro b a b ility o f approving land use planning were mostly "blue c o lla r" occupations which apparently presented a basis fo r re je c tio n o f the occupation hypothesis. The 137 only occupation which would be defined as "white c o lla r" was pro­ fessio n al, te c h n ic a l, and kindred workers. The occupation c la s s if i­ cations which increased the conditional p ro b a b ility were as shown in Table 35. TABLE 3 5 .— Father's Occupations which Increased the Conditional P ro b a b ility o f Approving Land Use Planning. Father's Occupation E ffe c t on the Conditional P ro b a b ility of Approving Land Use Planning Professional, te c h n ic a l, and kindred workers +.006 Sales and c le r ic a l workers +.1 0 Service workers +.21 Unemployed or handicapped +.17 The e ffe c ts of a respondent's second occupation on a ttitu d e s pertaining to land use planning was very d iffe r e n t than the e ffe c ts exerted by the respondent's fa th e r's occupation. In terms o f second occupation, the "white c o lla r" occu­ pations o f p rofessional, te c h n ic a l, and kindred workers and o ffic e holder, g re a tly increased the conditional p ro b a b ility o f approving land use planning. This was consistant with the basic hypothesis th at persons with more prestigious "white c o lla r" occupations would be more lik e ly to approve land use control measures than would persons with less prestigious "blue c o lla r" occupations. The p o sitiv e e ffe c t o f a person's second occupation being farming has 138 previously been ra tio n a liz e d in th a t farmers may approve o f planning to protect vested in te re s t. Why a second occupation c la s s ific a tio n of operatives and laborers would increase the conditional p ro b a b ility of approving land use planning is r e la t iv e ly unclear. Perhaps, these in divid uals had a primary occupation such as farming which would condition th e ir a ttitu d e s . The fa th e r's occupation v a riab le e ffe c t was inconsistent with the basic hypothesis. Only one "white c o lla r" occupation c la s s ific a tio n appeared as being s ig n ific a n t in increasing the conditional p ro b a b ility o f approving land use planning. The fa th e r's occupation c la s s ific a tio n o f p ro fessio n al, technical and kindred workers increased the conditional p ro b a b ility o f ap­ proving land use planning by a minor amount (+ .0 0 6 ). The other occupation c la s s ific a tio n s which increased the conditional p ro b a b ility of approval, sales and c le r ic a l workers, service workers, and unemployed and handicapped, were d e fin ite ly not "white c o lla r" occupations. Why these fa th e r 's occupation c la s s ific a tio n s increased the conditional p ro b a b ility o f a respondent favoring land use planning is not obvious. The re ­ search necessary to discover the nature o f such relatio n sh ip s is obviously beyond the scope and in te n t o f th is study. P a rtic ip a tio n in Various Types o f Groups is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures I t was hypothesized th a t various groups would r e f le c t common in terests and membership in s p e c ific groups would predicate a 139 person's response to various land use control measures. Member­ ship in farm organizations and p o lit ic a l organizations both in ­ creased the conditional p ro b a b ility o f approving land use planning while p a rtic ip a tio n in other types o f groups had a zero e ffe c t on the p ro b a b ility o f approving land use planning. TABLE 3 6 .— Group P a rtic ip a tio n which Increased the Conditional P ro b a b ility o f Approving Land Use Planning. Group P a rtic ip a tio n E ffe c t on the Conditional P ro b a b ility o f Approving Land Use Planning Farm Organization + .08 P o litic a l organization +.11 Farm organization p a rtic ip a tio n in fluence was possibly re la te d to the concept th a t farmers were concerned w ith a g r i­ c u ltu ra l land preservation and viewed planning as a mechanism to accomplish th is end. Any explanation o f the influence o f p o lit ic a l organization p a rtic ip a tio n would be pure conjecture. P a rtic ip a tio n in Elections is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures I t was hypothesized th a t in d iv id u a ls w ith high voting p a rtic ip a tio n rates would be more lik e ly to favor land use control measures than in divid u als with low voting p a rtic ip a tio n ra te s . The regression c o e ffic ie n ts supported th is general hypothesis. In divid uals w ith lower voting p a rtic ip a tio n s ra te s , 0% to 50%, 140 were more lik e ly to oppose land use planning than were in d ivid u als with higher voting p a rtic ip a tio n ra te s . Those w ith the lower rates a c tu a lly decreased the conditional p ro b a b ility o f approving land use planning, w hile those with the higher rates increased the conditional p ro b a b ility o f approval. TABLE 3 7 .— The E ffe c t o f Voting P a rtic ip a tio n Rates on the Conditional P ro b a b ility of Approving Land Use Planning. E ffe c t on the Conditional P ro b a b ility o f Approving Land Use Planning General Voting Behavior Do not vote in election s (0% o f e le c tio n s ) -.0 6 Vote in some elections (l%-50% o f e le c tio n s ) -.0 6 Vote in most election s (51%-99% o f e le c tio n s ) +.06 Vote in a ll election s (100% o f e le c tio n s ) +.06 An In d iv id u a l's Perception o f How Well His Local Government is Serving Him is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures I t was hypothesized th a t in d iv id u a ls who f e l t they were being well served by governmental o f f ic ia ls would be more lik e ly to favor land use control measures. This hypothesis was supported in th a t the response o f those in divid u als who f e l t th a t county government o f f ic ia ls were responsive increased the conditional p ro b a b ility of favoring land use planning (+ .0 9 7 ). The responses o f those in d i­ viduals who f e l t th a t the county o f f ic i a l s were not responsive had a zero e ffe c t on the conditional p r o b a b ility . 141 Since conditional p ro b a b ilitie s id e a lly range between 0 and 1 , th is meant th a t in d ivid u als who f e l t they were being well served by governmental o f f ic ia ls were approximately 10 % more lik e ly to approve land use planning than were in d ivid u als who f e l t they were not being w ell served by governmental o f f ic i a l s . This strongly supported the concept th a t persons who f e l t they were being adequately served by governmental o f f ic ia ls would support measures which the government proposed. The Amount of Property a Person Owns or Controls is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures I t was hypothesized th a t the possession o f e ith e r small or large amounts o f property would increase the conditional proba­ b i l i t y o f an in divid ual favoring land use control measures. Property owned and property leased were treated as two separate dimensions. Persons owning small amounts o f property contributed to the conditional p ro b a b ility o f favoring land use planning, while individuals owning large amounts o f property detracted from i t . I f an in divid ual owned no property, or between less than 1 acre to 10 acres, the conditional p ro b a b ility o f favoring land use planning was increased by + .0 5 . However, i f an in d ivid u al owned between 41 and 320 acres the conditional p ro b a b ility o f favoring land use planning was decreased by -.0 5 . The s itu a tio n was completely reversed with respect to the amount of property leased. In d ivid u als leasing no prop erty, or between less than 1 acre to 10 acres, decreased the conditional - --- ' 142 p ro b a b ility o f favoring land use planning by -.0 9 9 . Individuals leasing between 41 and 320 acres increased the conditional proba­ b i l i t y by +.099. I t is possible to speculate as to the reason fo r the reversal o f favoring land use planning which occurred between property owned and property leased. In divid uals owning between 1 and 10 acres were most lik e ly concerned about the preservation of home s ite s and would therefo re favor land use control measures which would preserve th e ir home s ite s and t h e ir surroundings. Individuals owning la rg e r amounts o f land, 41 to 320 acres would possibly oppose planning fo r two reasons. F ir s t , i f such land was being held fo r speculation, land use planning could possibly lead to r e s tr ic tio n in use and reduced p r o f it margins. Secondly, i f an in divid ual was a farm er, there could have been fears th a t land use planning could lead to the e lim in a tio n o f a g ric u ltu ra l areas. Also, many farmers, although they are farming f u l l tim e, are also "speculating." They are hoping to s e ll th e ir property fo r a substantial p r o f it when they r e t i r e , so as to assure them­ selves an adequate retirem ent income. Such in d ivid u als could possibly view land use planning as an imposition which would r e s t r ic t the p r o f it margin they could r e a liz e through the sale o f th e ir property. With respect to property leased, speculation as to meaning of the increases and decreases in the conditional p ro b a b ility of favoring land use planning and the amount of property leased, was 143 mere conjecture. The reasons fo r persons leasing between 1 and 10 acres opposing land use planning were not evident. The only specu­ la tio n fo r persons leasing between 41 and 320 acres was one th a t planning and re s u lta n t land use controls would enable them to continue th e ir leasing p ractices. In t o t a l, the relationsh ips between property controlled and e ith e r favoring or opposing land use planning were not c le a r. Apparently, the proposed hypothesis was fa r from s u ffic ie n t. analysis o f the property controlled va riab le s did not The r e a lly sup­ port or disprove the hypothesis. In retro sp ect, an attempt to f i t a d ire c tio n a l hypothesis to relatio n sh ip s which were not i n i t i a l l y c le a r, was a f u t i l e exercise. A much more appropriate approach would have been to u t i liz e a non-directional hypothesis which would have simply illu s tr a te d relatio n sh ip s ra th e r than try in g to ju s t if y them. Income Level is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures I t was hypothesized th a t in d iv id u a ls w ith higher incomes would be more lik e ly to favor land use control measures than lower income in d iv id u a ls . The resu lts o f the regression equation sup­ ported th is hypothesis. Individuals w ith incomes from less than $3,000 to $9,000 decreased the conditional p ro b a b ility o f favoring land use planning by - .0 7 . In d ivid u als with higher incomes, between $12,001 and $50,000 increased the conditional p ro b a b ility of favor­ ing land use planning by +.07. 144 Population Density is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures I t was hypothesized th a t higher population densities would increase the conditional p ro b a b ility o f an in divid ual favoring land use c o n tro l. For the regression equation, population densities were u tiliz e d fo r both 1960 and 1970. The hypothesis was supported in respect to 1960 population d e n s itie s. Population density, persons per square m ile , were m u ltip lie d by a fa c to r o f +.00008. This meant th a t higher 1960 population densities would m inim ally increase the conditional p ro b a b ility o f favoring land use planning. Why 1960 population densities were s ig n ific a n t and 1970 densities were not, was not completely c le a r. However, i t should be recognized th a t the 1960 and 1970 population densities were highly correlated and the weaker one was "washed" out. For pre­ d ictio n purposes, i t probably did not m atter which o f the two variables was retained. C alculation of the Conditional P ro b a b ility o f Favoring Land Use Planning Using the variab les which were retained in the weighted regression equation and the associated regression c o e ffic ie n ts i t was possible to c a lc u la te the conditional p ro b a b ility o f an individual favoring land use planning. For purposes o f i l l u s t r a ­ tio n , the maximum and minimum values were u t iliz e d in two separate equations. This provided an in d ic a tio n o f the in d iv id u a l charac­ te r is tic s which were associated with the extremes o f favoring or 145 opposing land use planning. A ll other cases f e l l between these two extremes. U tiliz in g the general form o f the equation: P( Y|X) = 80 + B1 X1 . . . 8 .X. I t was possible to generate, u t iliz in g the retained v a ria b le s , the greatest conditional p ro b a b ility o f favoring land use planning. P(Y|X) = + .43 + [Net e ffe c t o f (second occupation)] + [Net e ffe c t of (F a th e r's occupation)] + .08 (Farm organization p a rtic ip a tio n ) + .11 ( P o lit ic a l organization p a rtic ip a tio n ) + .06 (General voting behavior) + .097 (Response o f county government o f f ic i a l s ) + .05 (Property owned) + .099 (Property leased) + .07 (Income) + .00008 (1960 minor c iv il d ivis io n population density) P( Y|X) = + .43 + .15 + .21 (second occupation: (Fath er's occupation: o ffic e holder) service worker) + .08 (Organization P a rtic ip a tio n : Farm O rganization) + .06 (Voting Behavior: Vote in most e le c tio n s , Vote in a l l e le c tio n s ) + .05 (Property owned: None, less than one acre, 1 -1 0 acres) + . 11 (Organization P a rtic ip a tio n : P o litic a l Organization) + .097 (Response to governmental o f f ic ia ls : County o f f ic ia ls responsive) + .099 (Property leased: 41-80 acres 81-160 acres, 161-320 acres) 146 + .07 + .17 (Income: $12,000-$15,000 $15,001-$25,000 $25,001-$50,000) (1960 minor c i v i l d iv is io n population density: Garo 2208.75/square m ile ) P( Y|X) = 1.53 Thus u t iliz in g the greatest values associated w ith the retained v a ria b le s , i t was shown th a t the maximum level o f the conditional p ro b a b ility o f approving land use planning was +1.53. An in divid ual who re fle c te d the c h a ra c te ris tic s indicated in the preceeding equation would be s t a t i s t ic a l ly the most lik e ly to approve land use planning. The lowest conditional p ro b a b ility o f approving land use planning was generated through use o f the follow ing equation: P(Y|X) = + .43 - .19 (Second occupation: unemployed or handicapped) + 0 (Organization p a rtic ip a tio n : Do not belong to farm organi­ za tio n ) - .06 (Voting behavior: Do not vote in any e le c tio n s , vote in some e le c tio n s ) - .05 (Property owned: 41-80 acres, 81-160 acres, 161-320 acres) .17 (Fath er's occupation: r e tir e d ) + 0 (Organization p a rtic ip a tio n : Do not belong to a p o lit ic a l organization) + 0 (Response to governmental o f f ic ia ls : county o f f ic ia ls not responsive) - .099 (Property leased: None, Less than 1 acre 1 -1 0 acres) 147 - .07 + .0009 (Income: Less than $3,000 $3,001-$6,000 $6,001-$9,000) (1960 minor c i v i l d ivis io n population density: Minden Township 12.5/square m ile ) P(Y | X) = - .017 The sm allest conditional p ro b a b ility o f approving land use planning was - .017. This resulted from u t iliz a t io n o f the variables with the lowest values associated with them. Thus, an in d ivid u al re fle c tin g the c h a ra c te ris tic s indicated in the preceeding equation, would be s t a t is t ic a lly the le a s t lik e ly to approve land use planning. In theory, values associated with conditional p ro b a b ility are only supposed to range between 0 and +1. In th is case, the extreme values generated through the use o f p re d ic tiv e equation vio lated th is assumption. The extreme values were purposely generated to dramatize the differences which in divid ual responses played in the c a lc u la tio n o f conditional p r o b a b ilitie s . The extreme values set bounds between which a l l other conditional p ro b a b ilitie s would f a l l , determined by in d ivid u al responses. However, the r e la tiv e values were o f importance in th a t they gave an in d ic a tio n o f both the d ire c tio n and magnitude o f the e ffe c t that in divid ual variab les exerted upon the conditional p ro b a b ility of approving land use planning. The Second Model--Ordinances to Enforce A Land Use Plan The second model was constructed in the same basic manner as was the f i r s t model. The variab les which were co rrelate d greater 148 than .80 or less than .10 were again deleted. Also, variables which were dependent variables in the other two equations were deleted. The dependent v a riab le in the second model was X3 (response to the ordinances question). This dependent v a riab le was run against the 74 independent variab les in the LSSTEP Routine. Results o f the "LSSTEP" Routine Using Xg (response to the ordinances question) as the dependent v a riab le and a sp ecified s ig n ifican ce level o f . 1 0 , the data was subjected to the "LSSTEP" ro u tin e . The follow ing independ­ ent variables were retained (see Table 3 8 ). The "LSSTEP" routine id e n tifie d the variab les which were s ig n ific a n t a t the .1 0 level and would be included in the fin a l weighted regression equation. The steps which were indicated fo r the previous regression equation were u t iliz e d to prepare the data fo r the weighted regres­ sion. The data was subjected to the "RESID," "CONVERT" AND "SWITCH" routines and used as input in to the weighted regression. Weighted Regression The regression c o e ffic ie n ts generated by the weighted regression were as shown in Table 39. The fin a l mathematical equation, derived by u t iliz in g the values o f the weighted regression c o e ffic ie n ts was: P(Y)X) = .48 + .06 (X^g second occupation) - .04 second occupation) - .06 (Xgg Father's occupation) + .02 149 TABLE 3 8 .— Variables Retained in the Land Use Ordinance Model. V ariable X-jg Second occupation Xgy Second occupation X28 Father's occupation Xgg Father's occupation Xgy Father's occupation X^q Farm organization p a rtic ip a tio n X^5 Other group p a rtic ip a tio n X^ 7 P o litic a l party id e n tific a tio n X^g P o lit ic a l party id e n tific a tio n X5g P o lit ic a l party id e n tific a tio n X52 Voting in local elections Xgg Response o t county government o f f ic ia ls Xg-| Residence location Xgg Property owned Xg7 Property owned Xyg Property leased Xg2 Education Xg3 Education Xgg Education Xgg Education Xgg Education Xgg Income Xg-j Income Xg5 Income X ,Q, 1960-1970 minor c iv il d iv is io n population density change TABLE 3 9 .—Weighted Regression C o e ffic ie n ts fo r the Land Use Ordinance Model. V ariable X18 X27 X28 X36 X37 O •5^X X45 X47 X49 X50 X52 *56 X61 X66 X67 X76 X82 *83 *86 X88 X89 X90 X91 X95 X96 X101 Regression C o e ffic ie n t Second occupation +.06 Second occupation -.0 4 Father's occupation -.0 6 Father's occupation +.02 Father's occupation + .02 Farm organization p a rtic ip a tio n + .09 Other group p a rtic ip a tio n +.05 P o litic a l party id e n tific a tio n +.06 P o litic a l party id e n tific a tio n - .0 4 P o litic a l party id e n tific a tio n +.05 Voting in local election s +.07 Response to county government o f f ic ia ls + .13 Residence location -.0 2 Property owned +.04 Property owned -.0 4 Property leased +.098 Education -.0 6 Education +.05 Education +.06 Education -.0 4 Education -.0 5 Income -.1 4 Income +.06 Income +.05 Income +.08 1960-1970 minor c iv il d iv is io n population density change +.23 151 (Xgg Father's occupation + .02 (Xg7 Father's occupation) + .09 (X^g Farm organization p a rtic ip a tio n ) + .05 (X^g other group p a rtic ip a tio n ) + .06 (X^7 p o lit ic a l party id e n tific a tio n ) - .04 (XflQ p o lit ic a l party id e n tific a tio n ) + .05 (Xgg p o litic a l party id e n tific a tio n ) + .07 (Xgg voting in lo cal e le c tio n s) + .13 (Xgg response to county government o f f ic ia ls ) - .02 (Xg-j residence lo catio n + .04 (Xgg property owned) - .04 (Xg7 property owned) + .098 (Xyg property leased) - .06 (Xgg education) + .05 (Xgg education) + .06 (Xgg education) - .04 (Xgg education) - .05 (Xgg education) - .14 (Xgg income) + .06 (Xg^ income) + .05 (Xgg income) + .08 (Xgg income) + .23 ( X ^ i 1960-1970 minor c iv il d ivis io n population density change.) The weighted regression equation y ie ld e d a m u ltip le corre­ la tio n c o e ffic ie n t (R) o f .3974 and a m u ltip le c o e ffic ie n t o f p determination (R ) o f .1579. The value o f the m u ltip le c o e ffic ie n t of determination indicated th a t nearly 16% o f the v a ria tio n around the mean o f the dependent v a ria b le , response to the ordinance question, was associated with the 26 s ig n ific a n t independent variables. The independent variab les which were retained f e l l in to eleven major groupings. These groupings were as shown in Table 40. By u t iliz in g the matrices which were constructed fo r using the dummy v a ria b le s , i t was possible to c a lc u la te the net e ffe c t of the variab les upon the conditional p ro b a b ility o f approving ordinances to enforce a land use plan (See Table 4 1 ). TABLE 4 0 .— Grouped Variables fo r the Land Use Ordinance Model. Group V ariable Number Second Occupation X18* X27 Father's occupation X28* X36* X37 Organization p a rtic ip a tio n X40* X45 P o litic a l party id e n tific a tio n Voting behavior X47* X49* X50 X52 Response to governmental o f f ic ia ls Residence location X56 X61 Property controlled X6 6 * X67* X76 Education X82* X83* X8 8 ’ X89 Income X90* X91 * X95* X96 Population density X101 Hypotheses V alid atio n Once again, the inclusion o f s p e c ific variab les in the weighted regression equation served as a method to e ith e r support or r e je c t the various hypotheses. In terms o f grouping o f v a ria b le s , there was a great s im ila r ity in the variables retained in both equations. A dditional variables were retained in the response to the ordinance question which could be grouped in to general headings o f p o lit ic a l party id e n tific a tio n , residence lo c a tio n , and educational attainm ent 153 TABLE 4 1 .— E ffe c t o f In divid ual Variables on the Conditional P ro b a b ility o f Approving Ordinances to Enforce A Land Use Plan. V ariable E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Second Occupation No Response Professional, Technical and Kindred Workers O ffice Holder Sales and C le ric a l Workers Craftsmen and Foremen Operatives and Laborers Farmers Service Workers Reti red Unemployed or Handicapped Housewife +.10 +.10 +.12 -.0 1 -.1 4 +.03 +.05 -.1 0 -.1 0 -.1 4 -.0 3 Father's Occupation No Response or Deceased Professional, Technical and Kindred Workers Managers, A dm inistrators, Self-Employed or S alaried Sales and C le ric a l Workers Craftsmen and Foremen Operatives and Laborers Farmers Service Workers R etired Unemployed or Handicapped Housewife - .0 3 - .0 3 -.0 2 -.1 5 -.0 7 -.0 9 5 -.0 6 +.13 +.05 +.15 +.002 Organization P a rtic ip a tio n Farm Organization P a rtic ip a tio n Other Group P a rtic ip a tio n +.09 +.05 P o litic a l Party Id e n tific a tio n Democrat Republican American Independent Other None +.07 +.11 - .0 9 -.1 2 -.0 2 154 TABLE 4 1 .— Continued. Variable E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Voting Behavior Vote in Local Elections Do Not Vote in LocalElections +.07 0 Response to Government O ffic ia ls Response o f County Government O ffic ia ls County O ffic ia ls County O ffic ia ls Responsive NotResponsive +.13 0 Residence Location Open Country Side B uilt-up Area City or V illa g e -.0 2 -.0 2 +.05 Property Ownership Property Owned None Less than 1 Acre I-1 0 Acres II - 4 0 Acres 41-80 Acres 81-160 Acres 161-320 Acres 321-640 Acres More Than 640 Acres +.004 +.08 +.004 +.04 - .0 4 -.0 0 4 -.0 8 +.04 - .0 4 Property Leased None Less Than 1 Acre I-1 0 Acres I I - 4 0 Acres 41-80 Acres 81-160 Acres 161-320 Acres 321-640 Acres More Than 640 Acres -.0 9 8 0 +.098 0 -.0 9 8 0 +.098 +.098 -.0 9 8 155 TABLE 41.--C ontinued. Variable E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Educational Attainment Less Than 6 Years o f Elementary School Completed Elementary School (6 Years) Some Junior High School (Less Than 8 th Grade) Completed Junior High School( 8 th Grade) Some High School (1 -3 Years) Completed High School (4 Years) Vocational School or other Training College (1 -3 Years) College (4 Years or More) +.08 - .2 2 +.06 - .0 5 - .0 8 -.0 1 - .0 9 +.08 +.06 Income Less Than $3,000 $3,001-$6,000 $6,001-$9,000 $9,001-$12,000 $12,001-$15,000 $15,001-$25,000 $25,001-$50,000 $50,000 + - .1 5 - .0 9 - .0 4 +.008 +.03 +.03 +.04 +.17 Population Density The regression c o e ffic ie n t o f +.23 meant th a t increases in population densities would increase the p ro b a b ility o f approving ordinances to enforce a land use plan. 156 TABLE 4 2 .--V a ria b le Groupings Appearing in the Land Use Planning and Ordinance Equations. Land Use Planning Equation Ordinance Equation Variable Groupings V ariable Groupings Second occupation Second occupation Father's occupation Father's occupation Organization p a rtic ip a tio n Organization p a rtic ip a tio n P o litic a l party id e n tific a tio n Voting behavior Voting behavior Response to Governmental O ffic ia ls Response to Government O ffic ia ls Property controlled Property controlled Education Income Income Population density Population density Residence Location Occupation is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures As with the land use planning equation, primary occupation was not retained as a s ig n ific a n t v a ria b le . Both second occupation and Father's occupation were once again retained. In terms o f second occupation, the resu lts were id e n tic a l with those o f the land use planning equation. The second occu­ pations which would increase the conditional p ro b a b ility o f approving ordinances to enforce a land use plan were id e n tic a l to those which increases the c o n d itio n a l p r o b a b ility o f approving land use planning (See Table 4 3 ). TABLE 4 3 .— Second Occupations which Increased the Conditional P ro b a b ility of Approving Ordinances to Enforce a Land Use Plan. Second Occupation E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Professional, Technical, and Kindred Workers - O ffice Holder + .1 2 Operatives and Laborers +.03 Farmers + .05 + .1 0 Once again, the hypothesis th a t in d ivid u als w ith more prestigious "white c o lla r" occupations would be more lik e ly to favor land use control measures was, to some degree, supported. The two occupations which are g en erally recognized as being "white c o lla r" exerted a strong influence towards increasing the condi­ tional p ro b a b ility o f approving ordinance to enforce a land use plan. The v a riab le re la te d to Father's occupation exhibited a very d iffe r e n t re s u lt when compared to the second occupation variab le. The re su lts d iscredited the hypothesis o f more prestigious "white c o lla r" occupations favoring land use control measures. Father's occupations which increased the conditional p ro b a b ility The 158 of approving ordinances to enforce a land use plan were d e fin ite ly not "white c o lla r ." The only le g itim a te occupation was th a t o f service worker which is generally considered a "blue c o lla r" occu­ pation. The other "occupations" which increased the conditional p ro b a b ility o f approval were r e tir e d and unemployed or handicapped (See Table 4 4 ). TABLE 4 4 .— Father's Occupations which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan. Father's Occupation E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Service Worker +.13 Reti red +.05 Unemployed or Handicapped +.15 Again, the e ffe c t o f Father's occupation upon a respondent was nearly opposite to the e ffe c t exerted by second occupation. P a rtic ip a tio n in Various Types o f Groups is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures Farm organization p a rtic ip a tio n again increased the condi­ tional p ro b a b ility o f approving a land use control measure. The only other type o f group p a rtic ip a tio n which increased the condi­ tional p ro b a b ility was the catch a ll category o f "other group p a rtic ip a tio n " (See Table 4 5 ). 159 TABLE 4 5 .--G roup P a r tic ip a tio n which Increased the C onditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan. Group P a rtic ip a tio n E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Farm Organization +.09 Other Group P a rtic ip a tio n +.05 P a rtic ip a tio n in the other types o f s p e c ifie d groups had a zero e ffe c t on the conditional p ro b a b ility o f approving ordinances to enforce a land use plan. Id e n tific a tio n w ith a S p e c ific P o lit ic a l Party is S ig n ific a n tly Related to A ttitu d e s Towards Land Use Control Measures Respondents who considered themselves e ith e r Republicans or Democrats increased the conditional p ro b a b ility o f approving ordinances. Responses o f in d iv id u a ls who thought of themselves as being American Independents, "o th e r," or having no p o lit ic a l party a f f i l i a t i o n decreased the conditional p ro b a b ility o f approving ordinances. I t was hypothesized th a t in d iv id u a ls who considered them­ selves Democrats would be more li k e l y to favor land use control measures than e ith e r Republicans or American Independent Party members. This hypothesis was re je cte d in th a t respondents who id e n tifie d themselves as being Republicans exh ib ited the strongest influence upon increasing the p ro b a b ility of approving ordinances to enforce a land use plan (See Table 4 6 ). 160 TABLE 4 6 .- - P o l i t i c a l P a rty A f f i l i a t i o n which Increased the C onditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use P lan . P o litic a l Party A f f ili a t i o n E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Democrat +.07 Republican + .1 1 P a rtic ip a tio n in Elections is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures The hypothesis th a t in d iv id u a ls w ith high voting p a rtic ip a tio n rates would be more lik e ly to favor land use control measures was again supported. In th is case in d ivid u als who voted in local elections increased the conditional p ro b a b ility o f approving ordinances when compared to in d iv id u a ls who did not vote in local e le c tio n s. In divid u als who did not vote in local electio n s had a zero e ffe c t on the conditional p ro b a b ility o f approving ordinances. TABLE 4 7 .— Voting Behavior which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan. Voting Behavior E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Vote in Local Elections +.07 161 An In d iv id u a l's Perception o f How Mel! His Local Government is Serving Him is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures The hypothesis th a t in d iv id u a ls who f e l t they were being well served by governmental o f f ic ia ls would be more lik e ly to favor land use control measures was again supported. In divid uals who believed th a t county governmental o f f ic ia ls were responsive increased the conditional p ro b a b ility o f approving ordinances (+ .1 3 ). The responses o f in d iv id u a ls who f e l t county governmental o f fic ia ls were not responsive had a zero e ffe c t on the conditional p ro b a b ility . The Amount of Property a Person Owns or Controls is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures I t was hypothesized th a t possession o f e ith e r small or large amounts o f property would increase the conditional p ro b a b ility o f an individual favoring land use control measures. In terms o f approving ordinances, the ownership o f r e la t iv e ly small amounts of property increased the p ro b a b ility o f favoring ordinances to enforce a land use plan. In d iv id u a ls owning between 0 and 40 acres increased the conditional p ro b a b ility o f favoring ordinances. Also, ownership of between 321 and 640 acres also increased the p ro b a b ility o f favoring ordinances. A ll other size grouping o f property owned reduced the conditional p ro b a b ility o f approval. These resu lts more or less supported the hypothesis (See Table 4 8 ). In respect to property leased, a pattern emerged which was not as c le a r. Only in d iv id u a l leasing 1 to 10 acres, 161 to 320 TABLE 4 8 .— Amounts o f P rop erty Owned which Increased th e C o nditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan. E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Property Owned None +.004 Less than 1 acre +.08 1 -1 0 acres +.004 11-40 acres +.04 321-640 acres +.04 acres, or 321 to 640 acres increased the conditional p ro b a b ility of approving ordinances. A ll other responses e ith e r reduced the p ro b a b ility of approval or had a zero e ffe c t. These re su lts did not provide a firm basis upon which to e ith e r r e je c t or accept the hypothesis (See Table 4 9 ). TABLE 4 9 .--Amounts of Property Leased which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan. Property Leased E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan 1 -1 0 acres + .0 9 8 161-320 acres + .098 321-640 acres + .0 98 Educational Attainment is S ig n ific a n tly Related to A ttitudes Toward Land.Use Control Measures I t was hypothesized th a t more educated in d ivid u als would be more lik e ly to favor land use control measures than in d ivid u als with less education. The re su lts fa ile d to e ith e r support or d is ­ c re d it the hypothesis. Educational le vels which added to the conditional p ro b a b ility o f approving ordinances were scattered throughout the range o f le v e ls . However, there was a s lig h t tendency fo r in divid u als w ith more education to favor approval than those in divid u als w ith less education. Responses o f in d i­ viduals w ith less than s ix years o f elementary school, some ju n io r high school, vocational or other tr a in in g , and 1 to 4 or more years o f college a ll increased the conditional p ro b a b ility o f approving ordinances. Responses fo r a l l other educational a t t a in ­ ment le v e ls reduced the conditional p ro b a b ility o f approval (see Table 5 0 ). TABLE 5 0 .— Educational Levels which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan. Educational Attainment E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan Less than s ix years o f elementary school +.08 Some ju n io r high school (less than 8 th grade) +.06 Vocational School or other tra in in g +.09 College (1 -3 years) +.08 College (4 years or more) +.06 164 Income Level is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures The hypothesis th a t in d iv id u a ls with higher incomes would more lik e ly favor land use control measures was strongly supported. The responses o f a ll in d ivid u als w ith in income groupings o f $9,000 or more increased the conditional p ro b a b ility o f favoring o rd i­ nances. There was a general pattern o f increasing p ro b a b ility of acceptance with increasing income levels (See Table 5 1 ). TABLE 5 1 .— Income Levels which Increased the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan. Income E ffe c t on the Conditional P ro b a b ility o f Approving Ordinances to Enforce a Land Use Plan $9,001-$12,000 +.008 $12,001-$15,000 +.03 $15,001-$25,000 +.03 $25,001-$50,000 +.04 $50,000 + +.17 Responses from in d iv id u a ls w ith incomes less than $9,000 reduced the conditional p ro b a b ility o f approving ordinances to enforce a land use plan. Population Density is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures The two variab les dealing w ith population den sity, which were re ta in e d , supported the hypothesis th a t higher population 165 d e n s itie s would increase the c o n d itio n a l p r o b a b ility o f fa v o rin g land use co n tro l measures. The regression c o e ffic ie n t fo r the 1960-1970 Minor C iv il Division population density change was + .2 3 . This meant th a t the greater the increase in population density during th is time period the greater the conditional p ro b a b ility o f favoring ordinances. The second v a ria b le retain ed d e a lt w ith population densities in an abstract manner. Residence lo catio n gave a rough in d ic a tio n of population density. The three response choices were open country s id e , b u ilt up area (semi-developed ru ra l a rea s ), or w ith in a c ity or v illa g e . This was viewed as a progression from low population densities to higher population d e n s itie s. Only the response associated w ith the highest population d ensity, w ithin a c ity or v illa g e , increased the conditional p ro b a b ility o f ap­ proving ordinances (+ .0 5 ). The other two responses detracted from the p ro b a b ility o f approval. C alculation o f the Conditional P ro b a b ility o f Favoring Ordinances to Enforce a Land Use Plan Again, by using the variab les which were retained in the weighted regression equation and the associated regression c o e ffi­ cien ts, i t was possible to c a lc u la te the conditional p ro b a b ility of an in d ivid u al favoring implementation o f ordinances to enforce a land use plan. The minimum and maximum values were u t iliz e d in two separate equations to provide an in d ic a tio n o f the in d ivid u al ch a rac te ris tic s which were associated w ith the extremes of favoring 166 or opposing ordinances to enforce a land use plan. A ll other cases would f a l l between these two extremes. The greatest conditional p ro b a b ility o f favoring ordinances was as follow s: P(Y[X) = + .48 + [n e t e ffe c t o f (second occupation)] + [n e t e ffe c t o f (fa th e r's occupation)] + .09 (farm organization p a r t ic i­ pation) + .05 (o th er group p a rtic ip a tio n ) + [n e t e ffe c t o f ( p o lit ic a l party id e n t if ic a t io n ) ] + .07 (voting in local e le c tio n s) + .1 3 (response to county governmental o f f ic ia ls ) - .0 2 (residence lo catio n ) + [n e t e f f e c t o f (property owned)] + .098 (property leased) + [n e t e f f e c t o f (education )] + [n et e ffe c t o f (income)] + .23 (1960-1970 Minor C iv il D ivision population density change) P(Y|X) = + .48 + .12 (Second occupation: o ffic e holder) + .15 (F ath er's occupation: unemployed or handicapped) + .09 (Organization p a r t ic i­ pation: farm organization) + .05 (Organization p a r t ic i­ pation: other groups) + .11 (P o litic a l party id e n ti­ fic a tio n : Republican) + .07 (Voting behavior: vote in lo cal e le c tio n s ) + .13 (Response to county govern­ mental o f f ic ia ls : county o f f ic ia ls responsive) + .05 (Residence lo catio n: c ity or v illa g e ) + .08 (Property owned: than one acre) + .098 (Property leased: 1-10 acres, 161-320 acres, 320-640 acres) less 167 + .09 (Educational attainm ent: vocational school or other tra in in g ) + .17 (Income: $50,000 +) + .12 (1960-1970 Minor C iv il D ivision population Density change: C aseville Township, Huron County +53%) P(Y1X) = + 1.81 Through u t iliz a t io n o f the g re atest values associated with the retained variab les the maximum value o f the conditional proba­ b i l i t y o f approving ordinances to enforce a land use plan was calculated to be + 1.81. The lowest conditional p ro b a b ility o f approving ordinances to enforce a land use plan was calculated through the use o f the follow ing equation: P( YJX) = + .48 - .14 - .095 (Second occupation: (F a th e r's occupation: unemployed or handi- operative or la b o re r) capped) + 0 (Organization p a r t ic ipation: did not belong to a farm o rg an izatio n ) - . 12 +0 (Organization p a r t ic i­ pation: did not belong to other groups) + 0 ( P o lit ic a l party id e n tific a tio n : oth er) (Voting behavior: not vote in local e le c tio n s ) +0 . 02 (Residence lo catio n : open country s id e , b u ilt-u p area) (Response to Governmental o f f ic ia ls : county o f f ic i a l s not responsive) - .08 (Property owned: acres) 161-320 did - .098 (Property leased: none, 41-80 acres, more than 640 acres) 168 - . 22 - .15 (Educational attainm ent: completed elementary school [ 6 y e a rs ]) - (Income: $3,000) less than .20 (1960-1970 minor c iv il d iv is io n population density change: Point Aux Barques Township, Huron county - 88 %) P( Y|X) = - .64 The lowest conditional p ro b a b ility o f approving ordinances was calculated to be - .64. This was c alcu lated by u t iliz in g the variables w ith the lowest associated values. Again, the calculated values were beyond the range o f 0 and +1 which are normally associated with conditional p r o b a b ilitie s . However, the r e la tiv e values provided an in d ic a tio n o f both the magnitude and d ire c tio n o f the influence the in d iv id u a l variab les had upon the conditional p ro b a b ility o f approving ordinances to enforce a land use plan. The Third Model--Zoning The th ird model was constructed in the same basic manner as were the f i r s t two models. The variab les which were correlated greater than .80 or less than .10 were again deleted. Also, the variables which were dependent variables in the other two equations were deleted. The dependent v a ria b le in the th ir d model was (response to the zoning question— Do you favor land use zoning?). This dependent v a riab le was run against the 74 independent variables in the LSSTEP Routine. 169 Results o f the "LSSTEP" Routine Using X4 (response to the zoning question) as the dependent variable and a sp e c ifie d sign ificance level o f . 1 0 , the data were subjected to the LSSTEP Routine. The follow ing independent variables were retained as shown in Table 52. The LSSTEP Routine id e n tifie d the variab les which were s ig n ific a n t a t the .1 0 level and were to be included in the fin a l weighted regression. The same steps u t iliz e d in the preceeding regression equations were used to prepare the data fo r the weighted regression. Data were subjected to the "Resid," "Convert," and "Switch" Routines and f in a l ly used as input in to the weighted regression. Weighted Regression The regression c o e ffic ie n ts generated by the weighted regression were as shown in Table 53. The fin a l mathematical equation, derived by u t iliz in g the values o f the weighted regression c o e ffic ie n ts , was: P(Y|X) = .40 + .06Xg - .03X14 + .20X ] 8 - .11X2() - .05X23 - -06X25 + -04X26 - .09X27 + .01X 36 + .07X44 + .05X45 + .06X47 - .05X49 + .07X50 + .07X5] + .14X56 - .02Xg8 + . 03Xg] .02Xgl - .03X81 + .03X86 - + .04Xg2 + .04Xg4 + . 15X1q1 . The weighted regression equation y ie ld e d a m u ltip le corre­ la tio n c o e ffic ie n t (R) o f .3899 and a m u ltip le c o e ffic ie n t o f 2 determination (R ) o f .1520. The value o f the m u ltip le c o e ffic ie n t 170 TABLE 5 2 .--V a ria b le s Retained in the Zoning Ordinance Model. Variable X6 X14 *18 X19 X20 X23 X25 X ro X26 X36 X44 X '-j X45 X49 *50 *51 *56 *61 *81 > cc 1 CO X X86 X91 X92 *94 *101 Sex Occupation Second occupation Second occupation Second occupation Second occupation Second occupation Second occupation Second occupation Father's occupation P o litic a l organization p a rtic ip a tio n Other group p a rtic ip a tio n P o litic a l party id e n tific a tio n P o litic a l party id e n tific a tio n P o litic a l party id e n tific a tio n Voting in county election s Response to country governmental o f f ic ia ls Residence location Property leased Education Education Income Income Income 1960-1970 Minor c iv il d ivisio n population density change TABLE 5 3 .— Weighted Regression C o e ffic ie n ts f o r the Zoning Ordinance Model. V ariable X6 X14 X,8 X19 X20 *23 X25 X26 CM X X36 X44 X45 X47 X49 X50 X51 X56 X61 X81 X86 X88 X91 X92 X94 X101 Regression C o e ffic ie n t Sex +.06 Occupation - .0 3 Second occupation + .2 0 Second occupation + .11 Second occupation -.1 1 Second occupation -.0 5 Second occupation -.0 6 Second occupation +.04 Second occupation -.0 9 Father's occupation + .01 P o litic a l organization p a rtic ip a tio n +.07 Other group p a rtic ip a tio n +.05 P o litic a l p a rty id e n tific a tio n +.06 P o litic a l p a rty id e n tific a tio n - .0 5 P o litic a l p a rty id e n tific a tio n +.07 Voting in county election s + .07 Response to county governmental o f f ic ia ls +.14 Residence lo catio n -.0 2 Property leases -.0 3 Education +.03 Education -.0 2 Income +.03 Income +.04 Income + .0 4 1960-1970 Minor c i v i l d iv is io n population density change +.15 of determination indicated th a t s lig h tly more than 15% o f the variatio n in the dependent v a ria b le , response to the zoning question, was associated with the influence exerted by the 25 s ig n ific a n t independent v a riab les. The 25 independent v a riab les which were retained f e l l in to th irte e n major groupings. These groupings were as shown in Table 54. TABLE 5 4 .— Grouped Variables fo r the Zoning Ordinance Model. Group Sex Occupation Second occupation Father's occupation Voting behavior Response to governmental o f f ic i a l s Residence location Property controlled Education Income Population density X14 X18* X19’ X20* X23’ X25* X26’ X27 X36 X44* LT> P o litic a l party id e n tific a tio n X6 X Organization p a rtic ip a tio n V ariable Number X47* X49* X50 X51 X56 X61 X81 X8 6 ’ X88 X91» X92* X94 X101 173 Qy u t iliz in g the matrices which were constructed fo r using the dummy v a ria b le s , i t was possible to c a lc u la te the net e ffe c t o f the variables on the conditional p ro b a b ility o f approving zoning (see Table 55). Hypotheses V a lid a tio n As in the previous models, the inclusion o f s p e c ific variables in the weighted regression equation served as a method to support or r e je c t the various hypotheses. When the groupings o f variab les which were retained in the zoning equation were compared to the groupings o f variab les retained in the two previous equations, i t was obvious th a t great s im ila r i­ tie s existed . Two a d d itio n al v a ria b le s , sex and primary occupation, which were not s ig n ific a n t in the previous equations were retained in the zoning equation (See Table 5 6 ). Sex is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures I t was hypothesized th a t females would be less li k e l y to favor land use control measures than would males. The e f f e c t o f the sex v a ria b le on the conditional p ro b a b ility o f approving zoning supported th is hypothesis. Being a male contributed + .0 6 to the conditional p ro b a b ility o f approving zoning. Being female had a zero (0 ) e ffe c t on the p ro b a b ility o f approval. In respect to the zoning equation, i t was c le a r th a t males viewed zoning more favorably than females. TABLE 5 5 .— E ffe c t o f In d iv id u a l V a ria b le s on the C onditional P r o b a b ility o f Approving Zoning Ordinances. V a ria b le E ffe c t on the C onditional P r o b a b ility o f Approving Zoning Ordinances Sex Male Female +.06 0 Occupation Professional, technical and kindred workers Managers, Adm inistrators, self-em ployed, s a la rie d Sales and c le r ic a l workers Craftsmen and foremen Operatives and laborers Farmers Servi ce Workers Retired Unemployed or handicapped Housewife -.0 3 0 +.03 0 -.0 3 +.03 - . 03 +.03 - .0 3 +.03 Second Occupation No response P rofessional, te c h n ic a l, and kindred workers O ffice holder Sales and c le r ic a l workers Craftsmen and foremen Farmers Service workers R etired Unemployed or handicapped Housewife +.13 +.11 +.09 -.0 3 +.02 +.02 +.06 -.2 9 6 - .4 0 +.11 Father's Occupation No response or deceased P rofessional, technical and kindred workers Managers, A dm inistrators, self-em ployed, s a la rie d Sales and c le r ic a l workers Craftsmen and foremen Operatives and laborers Farmers Service workers Reti red Unemployed or handicapped Housewife +.03 +.03 +.03 0 -.0 3 -.0 3 - .0 3 +.02 - . 02 +.02 -.0 2 175 TABLE 5 5 .--C o n tin u e d . V ariable E ffe c t on the Conditional P ro b a b ility o f Approving Zoning Ordinances Organization P a rtic ip a tio n P o litic a l organization p a rtic ip a tio n Other group p a rtic ip a tio n +.07 +.05 P o litic a l Party Id e n tific a tio n Democrat Republican American Independent Other None +.08 +.11 - .1 4 -.1 3 +.08 Voting Behavior Vote in county election s Do not vote in county election s +.07 0 Response to Governmental O ffic ia ls Response to county governmental o f f ic ia ls County o f f ic ia ls responsive County o f f ic ia ls not responsive +.14 0 Residence Location Open country side B uilt-u p area C ity or v illa g e -.0 2 -.0 2 +.05 Property Ownership Property leased None Less then 1 acre I - 1 0 acres I I - 4 0 acres 41-80 acres 81-160 acres 161-320 acres 321-640 acres More than 640 acres 0 +.06 + .03 +.03 -.0 3 -.0 3 +.06 -.0 6 TABLE 5 5 .— Continued. V a ria b le E ffe c t on the C onditional P ro b a b ility o f Approving Zoning Ordinances Educational Attainment Less than 6 years o f elementary school Completed elementary school (6 years) Some ju n io r high school (less than 8 th grade) Completed ju n io r high school ( 8 th grade) Some high school (1 -3 years) Completed high school (4 years) Vocational school or other tra in in g College (1 -3 years) College (4 years or more) +.10 -.0 6 -.0 2 -.0 3 -.10 +.01 +.02 +.02 +.06 Income Less than $3,000 $3,001-$6,000 $6,001-$9,000 $9,001-$12,000 $12,001-$15,000 $15,001-$25,000 $25,001-$50,000 $50,000 + +.01 - .1 1 -.0 3 +.03 + .08 +.04 -.0 0 7 -.01 Population Density 1960-1970 Minor C iv il D ivision Population Density Change The regression c o e ffic ie n t o f +.15 meant th a t increases in population densities would increase the p ro b a b ility of approving zoning. TABLE 5 6 .— V a ria b le Groupings Appearing in the Land Use P lanning, Ordinance and Zoning Equations. Land Use Planning Equation Variable Grouping Ordinance Equation V ariable Grouping Zoning Equation V ariable Grouping Sex Primary Occupation Second Occupation Second Occupation Second Occupation Father's Occupation Father's Occupation Father's Occupation Organization P a rtic ip a tio n Organization P a rtic ip a tio n Organization P a r t ic i­ pation P o litic a l Party Id e n tific a tio n P o litic a l Party Id e n tific a tio n Voting Behavior Voting Behavior Voting Behavior Response to Governmental O ffic ia ls Response to Governmental O ffic ia ls Response to Govern­ mental O ffic ia ls Property Controlled Property Controlled Property Controlled Educational Attainment Educational Attainment Income Income Income Population Density Population Density Population Density Residence Location Residence Location 178 Occupation is S ig n ific a n tly Related to A ttitu d es Toward Land Use Control Measures Primary occupation appeared as a s ig n ific a n t v a ria b le fo r the f i r s t time in the zoning equation. This v ariab le had not been s ig n ific a n t in the two previous equations. I t was hypothesized that in d ivid u als with the more prestigious "white c o lla r" occu­ pations would be more lik e ly to favor land use control measures than in d ivid u als with less prestigious "blue c o lla r" occupations. This hypothesis was c le a rly rejected in view of the re s u lts . The primary occupations which contributed p o s itiv e ly to the approval of zoning could not be classed as "white c o lla r" occupations (See Table 5 7 ). TABLE 5 7 .— Primary Occupations which Increased the Conditional P ro b a b ility o f Approving Zoning. Primary Occupation E ffe c t on the Conditional P ro b a b ility o f Approving Zoning Sales and c le r ic a l workers +.03 Farmers +.03 Reti red +.03 Housewife +.03 A ll other occupations had e ith e r a zero (0 ) or negative e ffe c t on the conditional p ro b a b ility o f approving zoning. As with the previous equations, both second occupation and fa th e r's occupation were retained as s ig n ific a n t v a ria b le s . In respect to second occupation, the basic hypothesis concerning occupation was neith er supported or re je c te d . a blanket approval o f zoning. There was almost Very few second occupations detracted from the conditional p ro b a b ility o f approving zoning. These exceptions were as shown in Table 58. TABLE 5 8 .— Second Occupations which Decreased the Conditional P ro b a b ility o f Approving Zoning. Second Occupation E ffe c t on the Conditional P ro b a b ility o f Approving Zoning Sales and c le r ic a l workers -.0 3 Reti red -.2 9 6 Unemployed or handicapped -.4 0 A ll other second occupations contributed p o s itiv e ly to the conditional p ro b a b ility o f approving zoning. This was somewhat d iffe re n t than the resu lts o f the previous equations. In the other equations, "white c o lla r" second occupations were more or less associated with approving land use control measures. In this equation, there was no such d is tin c tio n . Father's occupation somewhat supported the basic hypothesis. The occupations which are usually considered white c o lla r c o n tri­ buted p o s itiv e ly to the p ro b a b ility o f approving zoning. The influence exerted by these "white c o lla r" occupations was greater than th a t o f the other occupations.(See Table 5 9 ). 180 TABLE 5 9 .— F a th e r's Occupations which Increased the C onditional P ro b a b ility o f Approving Zoning. Father's Occupation E ffe c t on the Conditional P ro b a b ility o f Approving Zoning Professional, te c h n ic a l, or kindred workers +.03 Managers, adm inistrators, s e lf employed or s a la rie d + .03 Service workers + .02 Unemployed or handicapped +.02 P a rtic ip a tio n in Various Types o f Groups is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures Again, membership in a p o lit ic a l organization and membership in "other groups" increased the conditional p ro b a b ility o f approving zoning. P a rtic ip a tio n in the other types o f groups lis te d had a zero ( 0 ) e ffe c t on the conditional p ro b a b ility o f approving zoning (See Table 60). TABLE 6 0 .— Group P a rtic ip a tio n which Increased the Conditional P ro b a b ility o f Approving Zoning. Group P a rtic ip a tio n E ffe c t on the Conditional P ro b a b ility o f Approving Zoning P o litic a l organization p a rtic ip a tio n + .0 7 Other group p a rtic ip a tio n + .0 5 181 Perhaps p o lit ic a l organization p a rtic ip a tio n lead to an increase in the le v e l o f awareness o f issues p ertain in g to zoning. This could possibly increase the p ro b a b ility o f approving zoning. Any speculation as to why p a rtic ip a tio n in "other groups" would increase the p ro b a b ility o f approving zoning would be mere conjecture because o f the vague nature o f the question. Id e n tific a tio n With a S p ecific P o litic a l Party is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures I t was hypothesized th a t in d iv id u a ls who considered them­ selves Democrats would be more lik e ly to favor land use control measures than would e ith e r Republican or American Independent party members. This hypothesis was re je cte d in th a t respondents who id e n tifie d themselves as being Republicans exh ib ited the strongest influence upon increasing the conditional p ro b a b ility of approving zoning. Respondents who considered themselves Democrats or had no p o lit ic a l party a f f i l i a t i o n also contributed p o s itiv e ly to the p ro b a b ility of approving zoning. American Independent Party membership and "other" p o lit ic a l party member­ ship both detracted from the p ro b a b ility o f approval (See Table 6 1 ). P a rtic ip a tio n in Elections is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures The hypothesis th a t in d iv id u a ls who p a rtic ip a te d in elections would be more li k e l y to favor land use control measures than in d iv id u a ls who did not was again supported. In d iv id u a ls who voted in county election s exhibited a p o s itiv e e f f e c t on the conditional p ro b a b ility o f approving zoning. In d iv id u a ls who did 182 TABLE 61.- - P o lit i c a l Party A f f ili a t i o n which Increased the Conditional P ro b a b ility o f Approving Zoning. P o litic a l Party A f f ili a t i o n E ffe c t on the Conditional P ro b a b ility o f Approving Zoning Democrat +.08 Republican +.11 None +.08 not p a rtic ip a te in county election s had a zero e ffe c t on the proba­ b i l i t y o f approval (See Table 62). TABLE 6 2 .— Voting Behavior which Increased the Conditional P ro b a b ility o f Approving Zoning. Voting Behavior E ffe c t on the Conditional P ro b a b ility o f Approving Zoning Vote in county election s +.07 An In d iv id u a l's Perception o f How Well His Local Government is Serving Him is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures The hypothesis th a t in divid u als who f e l t they were being well served by governmental o f f ic ia ls being more lik e ly to favor approval of land use control measures was again supported. In d i­ viduals who f e l t county o f f ic ia ls were responsive increased the conditional p ro b a b ility o f approving zoning (+ .1 4 ). The responses of in divid uals who f e l t county governmental o f f ic ia ls were not responsive had a zero (0 ) e f f e c t on the c o n d itio n a l p r o b a b ility of app ro val. The Amount of Property a Person Owns or Controls is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures In th is equation, property ownership fa ile d to appear as a s ig n ific a n t v a ria b le . cant v a ria b le . Property leased was retained as a s ig n if i­ The basic hypothesis was th a t control o f e ith e r small or large amounts o f property would increase the conditional p ro b ab ility of approving land use control measures. fa ile d to support the hypothesis. The resu lts The greatest p o sitiv e influence upon approval of zoning was concentrated in the responses o f persons who leased rath er moderate amounts o f property, 1 to 80 acres (See Table 63). TABLE 6 3 .--Amounts o f Property Leased which Increased the Conditional P ro b a b ility o f Approving Zoning. Property Leased E ffe c t on the Conditional P ro b a b ility o f Approving Zoning 1 -10 acres + .06 11-40 acres + .03 41-80 acres + .03 321-640 acres + .06 The responses from in divid uals who leased a ll other indicated amounts of property e ith e r detracted from the conditional p ro b a b ility of approval or had a zero ( 0 ) e ffe c t. 184 Educational Attainment is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures I t was hypothesized th a t more educated in divid uals would be more lik e ly to favor land use control measures than would in d i­ viduals with less education. In terms o f the zoning equation the hypothesis was strongly supported. As a g e n e ra lity , the more educated in divid uals were more lik e ly to favor zoning than less educated in divid uals (See Table 6 4 ). TABLE 6 4 .--Educational Levels which Increased the Conditional P ro b a b ility o f Approving Zoning. Educational Attainment E ffe c t on the Conditional P ro b a b ility o f Approving Zoning Less than 6 years o f elementary school + .10 Completed high school (4 years) + .01 Vocational school or other tra in in g + .0 2 College (1-3 years) + .0 2 College (4 years or more) + .06 Responses to a ll other educational attainm ent levels reduced the conditional p ro b a b ility o f approving zoning. Income Level is S ig n ific a n tly Related to A ttitu d e s Toward Land Use Control Measures The basic hypothesis th a t in d ivid u als w ith higher income levels would be more lik e ly to favor land use control measures than individuals with lower incomes, was not supported by the re s u lts . 185 The responses which increased the conditional p ro b a b ility of approving zoning were generally concentrated in the mid income range, $9,001 to $25,000. Responses from in d iv id u a ls with higher income levels reduced the p ro b a b ility o f approval (See Table 6 5 ). TABLE 6 5 .--Income Levels which Increased the Conditional P ro b a b ility o f Approving Zoning. Income E ffe c t on the Conditional P ro b a b ility o f Approving Zoning Less than $3,000 + .01 $9,001-$12,000 + .03 $12,001-$15,000 +.08 $15,001-$25,000 +.04 Population Density is S ig n ific a n tly Related to A ttitudes Toward Land Use Control Measures Two v a ria b le s , residence location and 1960-1970 minor c iv il division population density change, dealing with population density were re tain ed . The e ffe c t exhibited by both supported the basic hypothesis th a t greater population densities would increase the conditional p ro b a b ility o f approving land use control measures. In terms o f residence lo c a tio n , an abstractio n o f population density, the response o f an in divid ual liv in g in a c ity or v illa g e increase the conditional p ro b a b ility o f approving zoning (+ .0 5 ). Responses o f in divid uals liv in g in the open country side or b u ilt up areas decreased the p ro b a b ility o f approval ( - . 0 2 ) . 186 The regression c o e ffic ie n t fo r the 1960-1970 minor c iv il division population density change was + .1 5 . This meant th a t the greater the increase in population density during th is time period, the greater the conditional p ro b a b ility o f favoring ordinances. C alculation of the Conditional P ro b a b ility o f Favoring Zoning Through use of the variab les which were retained in the weighted regression equation and the associated regression c o e ffi­ c ie n t, i t was again possible to c a lc u la te the conditional p ro b a b ility of an in divid ual approving zoning. The minimum and maximum values were u tiliz e d in two separate equations to provide an in d ic a tio n of the in divid ual c h a ra c te ris tic s which were associated w ith the extremes o f favoring or opposing zoning. The greatest conditional p ro b a b ility o f favoring zoning was as follow s: P(Y|X) = + .40 + .06 (sex) - .03 (occupation) + [n et e ffe c t o f (second occupation)] + .01 (fa th e r's occupation) + .07 ( p o lit ic a l organization p a rtic ip a tio n ) + .05 (other group p a rtic ip a tio n ) + [n e t e ffe c t o f ( p o lit ic a l party id e n t if ic a t io n ) ] + .07 (voting in county e le c tio n s ) + .14 (response to county governmental o f f ic i a l s ) - .02 (residence lo ca tio n ) - .03 (property leased) + [net e ffe c t o f (education)] + [n e t e ffe c t o f (income)] + .15 (1960-1970 minor c iv il d iv is io n population density change). P(Y|X) = + .40 + .06 (sexrmale) + .11 (second occupation: pro­ fe s s io n a l, technical and kindred workers) + .03 (occupation: sales and c le r ic a l workers, farm ers, r e tir e s , housewife) + .03 (fa th e r's occupation: p ro fessio n al, te c h n ic a l, or kindred worker, managers, adm inistrators, self-employed or s a la rie d ) + .07 (organization p a rtic ip a tio n : p o lit ic a l organization) + .05 (organization p a rtic ip a tio n other groups) + .11 + .07 (voting behavior: county e le c tio n s ) ( p o lit ic a l party i d e n t i f i ­ cation: republican) vote in + .14 (response to county govern­ mental o f f ic ia ls : county o f f ic ia ls responsive) + .05 (residence lo ca tio n : or v illa g e ) + .06 (property leased: 1 -10 acres, 321-640 acres) + .10 (educational attainm ent: less than 6 years of elementary school) + .08 (income: + .08 (1960-1970 minor c iv il d iv is io n population density change: C aseville town­ sh ip , Huron county + 53%) $12,001-$15,000) c ity P(Y|X) = +1.44 Through u t iliz a t io n o f the greatest values associated with the retained v a ria b le s , the maximum value o f the conditional proba­ b i l i t y o f approving zoning was calculated to be +1.44. The lowest conditional p ro b a b ility o f approving zoning was calculated through use o f the follow ing equation: 188 P(Y|X) = +.40 + 0 (sex:fem ale) - .03 (occupation: profes­ s io n a l, te c h n ic a l, and kindred workers, operatives and la b o rers, service workers, unemployed or handicapped) - .40 (second occupation: unemployed or handi­ capped) - .03 (fa th e r's occupation: craftsmen and foremen, operatives and labo rers, farm ers) + 0 + 0 (organization p a r t ic i­ pation: do not belong to a p o lit ic a l organi­ zatio n ) (organization p a r t ic i­ pation: do not belong to other groups) - .14 ( p o lit ic a l party i d e n t i f i ­ cation: American independ ent p a rty ) + 0 + 0 - (response to county govern mental o f f ic i a l s : county governmental o f f ic ia ls not responsive) - .06 (property leased: none, more than 640 acres) - . 11 (income: (voting behavior: do not vote in county e le c tio n s) $3,Q01-$6,000) . 02 (residence lo catio n : open country s id e , b u ilt-u p area) - . 10 (educational attainm ent: some high school [1 -3 y e a rs ]) - .13 (1960-1970 minor c iv il d ivis io n population density change: Point Aux Barques township, Huron county - 88 %) P(Y|X) = -.6 2 The lowest conditional p ro b a b ility o f approving was calcu­ lated to be -.6 2 . 189 The calculated values were once again beyond the range o f 0 and +1 which are normally associated with conditional p ro b a b ilitie s . However, the r e la tiv e values were useful in providing an in d icatio n of both the magnitude and d ire c tio n o f the influence the in divid ual variables had upon the conditional p ro b a b ility of approving zoning. CHAPTER V SUMMARY, CONCLUSIONS, LIMITATIONS, AND RECOMMENDATIONS Summary The study had three basic o b jectives: 1. To attempt to id e n tify some o f the socio-economic and ph ysical/locatio nal factors which were s ig n ific a n tly re la te d to a rural re sid e n t's a ttitu d e s toward land use control measures. 2. To attempt to develop p re d ic tiv e models which could be used to a n tic ip a te the a ttitu d e s of rural population toward a lte rn a ­ tiv e land use control measures. 3. To add to the knowledge gained in the prelim inary Ionia County Study. The study b a s ic a lly met the three objectives which were set fo rth . However the degree o f success in meeting the in divid ual objectives varied g re a tly . A discussion o f the resu lts in terms o f the individual objectives seems appropriate. Objective 1. Id e n tific a tio n o f In d ivid u al C h aracteristics which were S ig n ific a n tly Related to A ttitu d es Toward Land Use Control Measures The study was r e la t iv e ly successful in id e n tify in g both socio-economic and p h y s ic a l/lo c a tio n a l facto rs which were s ig n if i­ cant in influencing a ttitu d e s toward land use control measures. A to ta l o f 13 hypotheses, re la tin g to in divid ual c h a ra c te ris tic s and 190 191 a ttitudes toward land use control measures were i n i t i a l l y generated. The follow ing simple m atrix summarizes the variab les s ig n ific a n tly associated with s p e c ific hypotheses (see Table 6 6 ) . TABLE 6 6 . — Variable Groupings Appearing as Being S ig n ific a n t in the Land Use Planning, Ordinance and Zoning Equations. Independent Variables Indicated as Being S ig n ific a n t in the . . . Types of Variables Planning Equation Ordi nance Equation Zoning Equation Age X Sex Education X X Income X X X Occupation X X X Property Controlled X X X X X X X X X X X Home Ownership Population Density Perceived Land Use C onflicts Governmental Service P o litic a l Party Id e n tific a tio n Group P articip an ts X X X Voting Behavior X X X 192 Examination o f the m atrix revealed th a t variables associated with three hypotheses, age, home ownership, and perceived land use c o n flic ts , were not s ig n ific a n t in any o f the equations. Variables associated with a l l other hypotheses were s ig n ific a n t in a t le a s t one o f the three equations. Why variables associated with the three hypotheses con­ cerning age, home ownership, and perceived land use c o n flic ts fa ile d to be s ig n ific a n t in any o f the equations is not c le a r. Two of these v a ria b le s , age and home ownership, had been c ite d by various authors as influencing a ttitu d e form ation. The th ird v a riab le , a perception o f land use c o n flic ts , seemed in t u it iv e ly to be a lo g ic a l fa c to r which would influence a ttitu d e s toward land use control measures. Apparently, the influence of these three variables was not of nearly the magnitude which was suspected. The ten remaining hypotheses, and the e ffe c t o f the associated variab les were best examined on an in divid ual basis. - Sex would influence a ttitu d e s toward land use control measures. Females would be less lik e ly to favor land use control measures. The sex v a ria b le appeared as being s ig n ific a n t only in the zoning equation. In th is equation, being male increased the condi­ tional p ro b a b ility o f favoring zoning by nearly 6 %. Being female had a zero ( 0 ) e ffe c t on the conditional p ro b a b ility . Since the sex v a riab le appeared as being s ig n ific a n t in only one equation, there is no firm basis to e ith e r accept or re je c t the hypothesis. However, the value o f Vhis v a riab le in 193 respect to p redictin g a ttitu d e s toward land use control measures is questionable. The fa c t th a t the v a ria b le was s ig n ific a n t in a single equation and supported the hypothesis is a tenuous basis upon which to make decisions. - Educational Attainment would influence a ttitu d e s toward land use control measures. More educated in d iv id u a ls would be more lik e ly to favor land use control measures. The variables associated w ith th is hypothesis appeared in both the ordinance and zoning equations. In both equations there was a general tendency fo r the more educated in d iv id u a ls to favor both ordinances and zoning more than the less educated in d iv id u a ls . However, in both equations, responses from the lowest le v e l o f educational attainm ent (less than 6 years o f elementary school) contributed p o s itiv e ly to the conditional p ro b a b ility o f approving land use control measures. Because of the influence o f the edu­ cation variables in both the ordinance and zoning equations, the hypothesis was generally supported (See Table 6 7 ). - Income Level would influence a ttitu d e s toward land use control measures. In divid uals with higher incomes would be more lik e ly to favor land use control measures. Variables re la te d to income levels appeared as being s ig n ific a n t in a ll three equations. validated. The hypothesis was generally In both the planning and ordinance equations, increased income le v e ls increased the conditional p ro b a b ility o f approving land use control measures. In the same equations, the lower income levels decreased the conditional p ro b a b ility o f approval. In the 194 TABLE 6 7 .— E ffe c t o f the Educational A ttainm ent V a ria b le in Respect to the Ordinance and Zoning Equations. E ffe c t of V ariable Ordinance Equation Educational Attainment Zoning Equation Less than 6 years o f elementary school +.08 +.10 Completed elementary school ( 6 years) -.22 -.0 5 Some ju n io r high school (less than 8 th grade) +.06 -.02 Completed ju n io r high school ( 8 th grade) -.0 5 -.0 3 Some high school (1 -3 years) - .0 8 -.10 Completed high school (4 years) -.01 + .01 Vocational school or other tra in in g +.09 + . 02 College (1 -3 years) +.08 +.02 College (4 years or more) + .06 +.06 zoning equation the pattern was not c le a r cut. Responses of in d i­ viduals in the lowest income level increased the p ro b a b ility of approval w hile responses of in d iv id u a ls in the two highest cate­ gories reduced the p ro b a b ility o f approval. The reason fo r the difference in the e ffe c t o f the income v a riab le in the zoning equa­ tion is not known (See Table 6 8 ). - Occupation would in fluence a ttitu d e s toward land use control measures. In d iv id u a ls with more prestigious "white c o lla r" occupations would be more lik e ly to favor land use control measures than in divid uals w ith less p restigio us "blue c o lla r" occupations. TABLE 6 8 .— E ffe c t o f the Income V a ria b le in Respect to the Land Use Planning, Ordinance and Zoning Equations. E ffe c t o f V ariable Ordinance Equation Income Planning Equation Zoning Equation Less than $3,000 -.0 7 - .1 5 +.01 $3 , 001 -$ 6 ,0 0 0 -.0 7 -.0 9 -.10 $6,001-$9,000 -.0 7 - .0 4 -.0 3 $9,001-$12,000 0 + .01 + .03 $12,001-$15,000 + .07 + .03 +.08 $15,001-$25,000 + .07 + .03 +.04 $25,001-$50,000 +.07 - .0 4 -.01 $50,000 + 0 +.17 -.01 The v a riab le re la tin g to primary occupation appeared as being s ig n ific a n t only in one equation, the zoning equation. Based on th is single equation, i t was impossible to c le a rly accept or re je c t the basic hypothesis of occupation in fluencin g a ttitu d e s toward land use control measures. Again, one example of a v a riab le being s ig n ific a n t is a tenuous basis upon which to form ulate judg­ ments. The primary occupations which increased the conditional p ro b ab ility were d e fin ite ly not "white c o lla r" occupations (See Table 6 9 ). In contrast to the appearance o f primary occupation in only one equation, second occupation appeared as being s ig n ific a n t in a ll three equations. The hypothesis was va lid ate d in th a t the two TABLE 6 9 .— E ffe c t o f the Primary Occupation V a ria b le in Respect to the Zoning Equation. E ffe c t o f V ariable Zoning Equation -.0 3 Managers, adm inistrators, self-employed, s a la rie d 0 Sales and c le r ic a l workers + .03 Craftsmen and foremen 0 Operatives and laborers -.0 3 Farmers +.03 Service Workers Unemployed or handicapped -.0 3 Housewife +.03 O CO Professional, te c h n ic a l, and kindred workers 1 Primary Occupation second occupations which could c le a rly be id e n tifie d as being "white c o lla r," professional, te c h n ic a l, and kindred workers and o ffic e holder, both strongly increased the conditional p ro b a b ility o f ap­ proving land use control measures. However, i f a respondent's second occupation was operative or laborer or farm er, the conditional p ro b a b ility of approval was also increased. G enerally, second occupations which were recognized as being "blue c o lla r" detracted from the conditional p ro b a b ility of approval. Why second occu­ pations valid ated the hypothesis and primary occupations fa ile d 197 TABLE 7 0 .— E ffe c t o f the Second Occupation V a ria b le in Respect to the Land Use P lanning, Ordinance and Zoning Equations. E ffe c t o f V ariable Planning Equation Ordinance Equation Zoning Equation +.10 +.13 Professional, te c h n ic a l, and kindred workers + .14 + . 10 +.10 Office holder +.15 +.12 +.09 Sales and c le r ic a l workers -.0 3 +. 11 o Craftsmen and foremen -.01 Operatives and laborers +.14 +.03 + . 02 Farmers +.05 +.05 +. 01 Service workers -.1 5 -.10 +.06 Retired -.1 5 -.10 - .3 0 Unemployed or handicapped -.1 9 -.1 4 - .4 0 Housewife -.0 4 -.0 3 +. 11 LO -.01 o o i cn +.14 O No response 1 Second Occupation Also, classed under the general heading o f occupation, was the variab le re la te d to fa th e r's occupation. While th is was not occupation per se, there was a suspected re la tio n s h ip between a respondents fa th e r's occupation and the respondents a ttitu d e s toward land use control measures. I t was f e l t th a t the environment in which the respondent was raised would be somewhat conditioned by the respondents fa th e r's occupation. I t was fu rth e r f e l t th a t a fa th e r's occupation and the influence i t exerted on the respondent 198 would have the same e ffe c t as was described in the hypothesis. The variab le pertaining to fa th e r's occupation appeared as being s ig n ific a n t in a l l three equations. There were only fiv e fa th e r's occupations which exhibited a consistant re la tio n s h ip across a ll three equations. Three fa th e r's occupations, craftsmen and foremen, operatives and labo rers, and farmers, detracted from the conditional p ro b a b ility o f approving zoning. This was consistent with the hypothesis occupation. However, pertain in g to i t was o f in te re s t to note th a t the farmer variable had the opposite e ffe c t in terms o f fa th e r's occupation than i t did in regard to e ith e r primary or second occupation. Only two fa th e r's occupations, service workers and unemployed or handi­ capped, were consistent in increasing the conditional p ro b a b ility of approving land use control measures. In t o t a l, the attempt to r e la te fa th e r's occupation to a respondent's a ttitu d e s toward land use control measures was not p a rtic u la rly successful. The basic lack o f consistency in the e ffe c t o f the v a riab le on the three equations, lead to th is con­ clusion (See Table 71). - The amount of property a person owns or controls would influence a ttitu d e s toward land use control measures. Possession of e ith e r small or very large amounts o f property w ill increase the p ro b a b ility of favoring land use control measures. Variables re la te d to th is hypothesis appeared as being s ig n ific a n t in a ll three equations. 199 TABLE 7 1 .— E ffe c t o f the F a th e r's Occupation V a ria b le in Respect to the Land Use P lanning, Ordinance and Zoning Equations. E ffe c t of V ariable Father's Occupation Planning Equation Ordinance Equation Zoning Equation No response or decreased + .10 -.0 3 +.03 Professional, te c h n ic a l, or kindred worker +.007 -.0 3 +.03 Managers, adm inistrators, s e lf employed or s a la rie d -.0 4 -.02 + .03 Sales and c le r ic a l workers + . 10 -.02 0 Craftsmen and foreman -.12 -.0 7 -.0 3 Operatives and laborers - .1 4 -.10 -.0 3 Farmers - .0 4 -.0 6 -.0 3 Service workers +. 21 + .13 +.02 Retired -.1 7 + .03 -.02 Unemployed or handicapped +.17 + .15 + .02 Housewife + .04 + .002 -.02 The v a riab le re la te d to property owned appeared in two equa­ tions, planning and ordinances. The e ffe c t o f th is v a riab le indicated that the hypothesis should be te n ta tiv e ly re je c te d . The conditional p ro b a b ility of approving land use control measures was mainly in ­ creased by responses o f persons who owned r e la t iv e ly small amounts of property, less than 40 acres. The responses o f persons owning larger amounts o f property reduced the conditional p ro b a b ility of approving land use control measures in nearly a ll cases (See Table 7 2 ). 200 TABLE 7 2 .— E ffe c t o f the P roperty Owned V a ria b le in Respect to the Land Use Planning and Ordinance Equations. E ffe c t of V a riab le Ordinance Equation None + .06 + .004 Less than 1 acre +.06 +.08 1 - 1 0 acres +.06 +.004 11-40 acres 0 + .04 41-80 acres -.0 6 -.0 4 81-160 acres -.0 6 1 161-320 acres -.0 6 -.0 8 321-640 acres 0 + .04 More than 640 acres 0 -.0 4 o o• Planning Equation Property Owned The property leased v a riab le appeared as being s ig n ific a n t in a ll three equations. However, the action o f th is v a ria b le lead to the te n ta tiv e re je c tio n o f the hypothesis. The re su lts did not support the contention th a t persons leasing small or large amounts of property would increase the conditional p ro b a b ility o f approving land use control measures. No real pattern emerged. R ather, the p o sitive influence o f the v a ria b le seemed to be scattered throughout the various categories (See Table 73). - Population density would influence a ttitu d e s toward land use control measures. In divid uals residing in higher population density areas would be more lik e ly to favor land use control measures than in d iv id u a ls residing in lower density areas. 201 TABLE 7 3 .— E ffe c t o f the Property Leased V a ria b le in Respect to the Land Use Planning, Ordinance and Zoning Equations. E ffe c t o f V ariable Property Leased Planning Equation Ordinance Equation Zoning Equation None -.10 -.10 -.0 6 Less than 1 acre -.10 0 0 1-10 acres -.10 + .10 +.06 11-40 acres 0 0 + .03 41-80 acres +.10 -.10 + .03 81-160 acres +.10 0 -.0 3 161-320 acres +.10 + .10 -.0 3 321-640 acres 0 + .10 + .06 More than 640 acres 0 -.10 - .0 6 Variables in dicating some aspect of population density appeared in a ll three equations. In every instance the e ffe c t of the variables supported the hypothesis. A to ta l of three separate variables appeared, a ll being s ig n ific a n t in one or more of the equations. These variables were: 1960 minor c iv il d iv is io n popu­ la tio n d ensity, 1960-1970 minor c iv il d iv is io n population density change, and residence lo c a tio n . The 1960 minor c i v i l d ivis io n population density v a riab le appeared as being s ig n ific a n t in a single equation, the planning equation,w hile both the 1960-1970 minor c iv il d ivis io n population density change v a ria b le and the 202 residence location v a riab le appeared in the other two equations, ordinances and zoning. The reason fo r the 1960 density v a riab le being s ig n ific a n t was to ta lly unknown. Both the 1960-1970 density change v a riab le and the residence location v a riab le acted in a manner which sup­ ported the hypothesis. The p o s itiv e e f f e c t o f the density change variable occurred as densities increased. The residence lo cation variable exhibited the same e f f e c t in both the ordinance and zoning equations. In both cases the re p lie s o f respondents liv in g in the open country side or b u ilt up areas (semi-developed ru ral areas) decreased the conditional p ro b a b ility o f approving land use control measures. Replies o f respondents liv in g in c it ie s or villag es increased the p ro b a b ility o f approval. The e ffe c t of a ll the population density variab les valid ated the suspected r e la ­ tionship between increasing densities and increasing lik e lih o o d o f approving land use control measures. - An in d iv id u a l's perception o f how w ell his local govern­ ment is serving him would influence a ttitu d e s toward land use control measures. In d ivid u als who f e l t they were being well served by th e ir local government would be more lik e ly to favor land use control measures than in d ivid u als who f e l t local government was not serving th e ir in te re s ts . The v a riab le re la te d to perception o f local government appeared in a ll three equations. In each case, the e ffe c t o f the variable strongly supported the hypothesis. respondents who The responses of f e l t county governmental o f f ic ia ls were responsive to th e ir needs increased the conditional p ro b a b ility o f approving land use control measures. The responses o f in divid uals who f e l t county governmental o f f ic ia ls were not responsive had a zero ( 0 ) e ffe c t on the conditional p ro b a b ility o f approval (See Table 74). TABLE 7 4 .— E ffe c t o f the Perception o f Governmental Service V ariable in Respect to the Land Use Planning, Ordinance and Zoning Equations. E ffe c t o f V ariable Response to Governmental O ffic ia ls Planning Equation Ordinance Equation Zoning Equation County o f f ic ia ls responsive +.10 +.13 +.14 County o f f ic ia ls not responsive 0 0 0 - An in d iv id u a l's id e n tific a tio n w ith a s p e c ific p o lit ic a l party would influence a ttitu d e s toward land use control measures. Individuals who considered themselves Democrats would be more lik e ly to favor land use control measures than in d ivid u als who considered themselves Republicans or American Independents. The v a ria b le re la te d to p o lit ic a l party id e n tific a tio n appeared as being s ig n ific a n t in both the ordinance and zoning equations. The e ffe c t o f the v a riab le was s im ila r in both equations. The responses o f persons who considered themselves as e ith e r Demo­ crats or Republicans increased the conditional p ro b a b ility o f ap­ proving land use control measures. The responses o f persons who considered themselves American Independents or "other" p o lit ic a l 204 party members decreased the conditional p ro b a b ility . The responses of persons who indicated they had no p o lit ic a l party a f f ilia t io n s decreased the p ro b a b ility o f approving ordinances but increased the p ro b a b ility o f approving zoning. The hypothesis o f Democrats being more lik e ly to approve land use control measures was refu ted . Instead Republicans were shown to be more receptive (See Table 75). TABLE 7 5 .--E ffe c t o f the P o litic a l Party A f f ili a t i o n Variable in Respect to the Ordinance and Zoning Equations. E ffe c t o f Variable P o litic a l Party A f f ilia t io n Ordinance Equation Zoning Equation Democrat +.07 +.08 Republican + .11 + . 11 American Independent -.0 9 -.1 4 Other -.12 -.1 3 None -.02 -.0 8 A basic re la tio n s h ip was e x h ib ite d . Persons considering themselves members o f the two la rg e r established p o lit ic a l p arties were in favor of land use control measures. Members o f lesser p o litic a l p arties opposed land use control measures, while people with no p o lit ic a l a f f i l i a t i o n were in co nsistent. - P a rtic ip a tio n in various types o f groups would influence attitu d e s toward land use control measures. In divid uals belonging to groups which were considered "conservative" would be less lik e ly 205 to favor land use control measures than in divid uals belonging to " lib e ra l" groups. These hypothesis was, a t best, i l l conceived. The attempt to ascribe " lib e r a l" or "conservative" labels to groups was t o t a lly without foundation. I t would have been s u ffic ie n t to say th a t group membership would influence a ttitu d e s toward land use control measures and then describe the re s u lts . Some dimension o f the organization p a rtic ip a tio n question appeared as being s ig n ific a n t in a ll three equations. However, membership in a single group did not appear as being s ig n ific a n t across a ll three equations. Membership in a farm organization, p o litic a l organization, and "other groups" a ll appeared as being s ig n ific a n t in two of the three equations (See Table 7 6 ). TABLE 7 6 .— E ffe c t o f the Group P a rtic ip a tio n V ariable in Respect to the Land Use Planning, Ordinance and Zoning Equations. E ffe c t o f Variable Group P a rtic ip a tio n Planning Equation Ordinance Equation Zoning Equation Farm Organization + .08 + .09 — P o litic a l Organization + .11 — +.07 "Other Group" — +.05 + .05 In a ll cases the e ffe c t o f the group p a rtic ip a tio n v a riab le was p o s itiv e and increased the conditional p ro b a b ility o f approving land use control measures. Speculation could be made about the 206 reasons fo r members o f farm organizations favoring land use control measures, but speculation as to why members o f p o lit ic a l and "other" organizations favored land use control measures would be absurd. The only conclusion which could be drawn, was th a t membership in various groups did have an e ffe c t on favoring land use control measures, but there was no basis fo r speculation as to the e ffe c t that membership in the various groups exerted. - P a rtic ip a tio n in local election s would influence a ttitu d e s toward land use control measures. In divid uals with high voting p a rtic ip a tio n rates in local election s would be more lik e ly to favor land use control measures than in divid uals w ith low voting p a rtic ip a tio n ra te s . Once again, some dimension o f th is hypothesis appeared in each of the three equations. in each o f the th re e . However, a d iffe r e n t v a ria b le appeared The resu lts generally supported the hypothesis in th at voting p a rtic ip a tio n increased the conditional p ro b a b ility of approving land use control measures (See Table 77). In summary, the study was successful in id e n tify in g a number of socio-economic and p h y s ic a l/lo c a tio n a l factors which were s ig n if i­ cantly re la te d to a ttitu d e s toward land use control measures. How­ ever, several in d iv id u a l c h a ra c te ris tic s which have tr a d itio n a lly been thought to be associated with voting behavior, i . e . age and homeownership, f a ile d to be s ig n ific a n t. Also, in respect to the variables which were s ig n ific a n t, some issues were raised as to th e ir in te rp re ta tio n . S p e c ific variables re la te d to in d ivid u al ch aracteristics often did not act in a consistent fashion across 207 TABLE 77.—Effect of the Voting Behavior Variable in Respect to the Land Use Planning, Ordinance and Zoning Equations. E ffe c t o f V ariable Planning Equation Voting Behavior Ordinance Equation Zoni ng Equation General Voting Behavior Did not vote in elections (0% of e le c tio n s ) -.0 6 — — Vote in some elections (1-50% o f e le c tio n s) -.0 6 — - - Vote in most elections (51-99% o f e le c tio n s) +.06 — — Vote in a ll elections (100% o f e le c tio n s ) +.06 — — Vote in local elections — +.07 — Vote in county elections — — +.07 a ll three equations. The same class w ith in a va riab le would have an opposite e ffe c t w ith in in divid ual equations. In most cases, the relationship between a s p e c ific in d iv id u a l c h a ra c te ris tic s and the approval o f land use control measures was complex ra th e r than simple. Perhaps the single statement which could be made was th a t the study did id e n tify in d ivid u al c h a ra c te ris tic s which were s t a t i s t ic a l ly related to a ttitu d e s toward land use control measures but the exact nature o f the re la tio n s h ip was unclear in many cases. 208 Objective 2. Development o f P red ictive Models which could A n tic ip ate the A ttitu d e s of Rural Population Toward A lte rn a tiv e Land Use Control Measures The study was somewhat successful in respect to developing predictive models which could a n tic ip a te the a ttitu d e s o f ru ral population toward a lte rn a tiv e land use control measures. I t was shown th a t the techniques employed could generate models which would provide the conditional p ro b a b ility o f approval o f s p e c ific land use control measures. However, the degree o f p r e d ic ta b ility provided by the models l e f t a great deal to be desired. A ll three models had low c o e ffic ie n ts o f determination 2 (R ). 2 The R f o r the land use planning model was only .1259. This meant th a t s lig h t ly more than 12% o f the v a ria tio n in the dependent v a ria b le , approval or disapproval o f land use planning, was associ2 ated with independent v a riab le s . The R 's fo r the ordinance and zoning models were also low. 2 The ordinance model's R was .1579 2 while the zoning model's R was .1520. Thus, in a l l three models the amount o f v a ria tio n in the dependent v a ria b le associated with the independent variab les was very low. The low c o e ffic ie n ts o f determination generated by the three models indicated th a t in a ll cases the degree of successful prediction was low. A degree of p r e d ic ta b ility in the neighborhood of 15% is a tenuous basis upon which to make assumptions or decisions. Combined with the low c o e ffic ie n ts o f determination were r e la tiv e ly large residuals ( y - y ) . This meant th a t the equations were not generating accurate estimates o f the dependent v a ria b le s . This was due in p art to the nature o f the dependent variab les in 209 each of the three models. In each case the dependent v a ria b le was discreet ra th e r than continuous and the dependant variab les were also dichotomus, 0-1 v a ria b le s . Given these r e s tr ic tio n s , i t was to be expected th a t the residuals generated by the p re d ic tiv e equations would be large. Also, the large residuals would account 2 for the low R s, the percentage o f the v a ria tio n in the dependent variable associated with the independent v a riab les. 2 Taken a t face value, these aspects o f low R 's and large residuals seem to in d ic a te p r a c tic a lly worthless p re d ic tiv e equations. However, another aspect o f the s t a t is t ic a l analysis illu m in ates a completely d iffe r e n t aspect. In each o f the three p re d ic tiv e equa­ tions the F te s t s t a t is t ic has a s ig n ifican ce o f < 0.0005. The F value represents the ra tio o f the explained variance to the unexplained variance adjusted fo r the degrees o f freedom lo s t. The F r a tio is used to describe c o e ffic ie n ts th a t may be expected to occur by chance alone among samples o f uncorrelated data. Very s im p lis tic a lly , the F ra tio provides an in d ic a tio n o f whether or not the r e la tio n ­ ship between the dependent variab les and the independent variables in the equations could have occurred by chance rath er than because of some basic underlying re la tio n s h ip . The F r a tio o f < 0.0005 indicated th a t such relatio n sh ip s should be expected to occur by chance less than 5 times out o f 10,000. The F te s t showed th a t while there was a r e la t iv e ly weak re la tio n s h ip between the v a ria tio n in the dependent v a riab le as associated w ith the independent variab les, the v a ria tio n associated w ith the independent variables did not occur by chance. The F te s t r a tio showed th a t there was a d e fin ite re la tio n s h ip between the dependent and independent v a ria b le s . 210 How then are the s t a t is t ic a l re su lts generated by the predictive equations to be in terp re te d and u tiliz e d ? The equations illu s tr a te d the r e la tiv e increase or decrease which each s ig n if i­ cant independent v a ria b le contributed to the conditional p ro b a b ility of approving land use planning, ordinances to enforce plans, or zoning ordinances. The regression c o e ffic ie n ts generated by the equations illu s tr a te d the r e la tiv e importance o f the s ig n ific a n t independent v a ria b le s . The absolute q u a n tita tiv e contribution of the s ig n ific a n t independent variables was suspect because o f the v io la tio n o f the 0-1 parameters established fo r conditional proba­ b ilitie s . Thus, the models proved capable o f generating predictions of approval o f various land use control measures. However, the predictions were v a lid in a r e la tiv e sense ra th e r than an absolute q u a n tita tiv e sense. I t was also hoped th a t the study would id e n tify variables which could be gathered from secondary data sources fo r use as inputs in to the p re d ic tiv e models. re a liz e d . This hope was not t o t a lly A number o f the variables which were id e n tifie d as being s ig n ific a n t are unobtainable from secondary sources and must be gathered by personal in terview or questionnaire. In divid ual c h a rac te ris tic s such as second occupation, fa th e r's occupation, perception o f governmental s e rv ic e , and group p a rtic ip a tio n a ll proved to be s ig n ific a n t, but they are unobtainable from secondary sources. This in a b ilit y to c o lle c t data re la tin g to many s ig n ific a n t variables from secondary sources g re a tly reduces the usefulness and a p p lic a b ility o f the p re d ic tiv e models. The e f f o r t required to gather much of the data re la te d to s ig n ific a n t variables could • ju s t as e a s ily be spent in an attempt to d ire c tly ascertain in d i­ vidual views on the s p e c ific issues in question. Objective 3. Add to the Knowledge Gained in the Prelim inary Ionia Study The study was very successful in th is respect. Not only were more in divid ual c h a ra c te ris tic s id e n tifie d as influencing attitudes toward land use control measures, but new techniques were developed with which to deal with the data. The prelim inary Ionia Study must be recognized fo r what i t was, an i n i t i a l e f f o r t toward id e n tify in g in divid ual character­ is tic s re la te d to influencing a ttitu d e s toward land use control measures. The Io n ia Study was, fo r a ll in ten ts and purposes, crude in nature. Very few in divid ual c h a ra c te ris tic s were id e n ti­ fie d and u t iliz e d in the preparation of the p re d ic tiv e equations. This study u tiliz e d the p relim inary knowledge gained in the Ionia Study as a point o f departure and am plified i t to a great degree. The p re d ic tiv e equation generated from the Ionia Study presented in Chapter I I I illu s t r a te s the rudimentary nature of the i n i t i a l research e ffo r ts . Very few independent variables were u tiliz e d in th is equation which sought to ascertain an in d iv id u a l's opinion on the location o f addition al housing w ith in the county. The small number of independent variab les was in p a rt due to the re strictio n s which had been placed upon data c o lle c tio n . But ju s t as im portantly, the small number o f independent variables was also dictated by the lack of a conceptual framework upon which to base rational or directed data c o lle c tio n . The independent variables- u tiliz e d in the Ionia Model were rudimentary re la tin g only to: Occupation Age Location o f addition al mobile homes Location o f addition al shopping Location o f addition al industry A ttitu d e toward the timing of the implementation of zoning ordinances Township population density These variables represented a curious mix o f c h a ra c te ris tic s related to the in divid ual and a ttitu d e s which were most lik e ly conditioned by in divid ual c h a ra c te ris tic s . Only two o f the variables u tiliz e d could be c la s s ifie d as in d ivid u al c h a ra c te ris tic s , age and population density. The other independent variables were r e a lly a ttitu d in a l measures which were most lik e ly strongly in te rre la te d . This curious mixture o f c h a ra c te ris tic s and a ttitu d e s illu s tr a te d the fa ilu r e to develop a lo g ic a l conceptual base fo r the study. As opposed to th is e rro r in conceptual lo g ic , th is study c a re fu lly selected independent variab les which were measures o f individual c h a ra c te ris tic s and perceptions. I t was hoped th a t in this manner, the mixing of c h a ra c te ris tic s and strongly re la te d attitu d es would be avoided. b asically successful. I t is thought th a t th is e f f o r t was While i t is true th a t some o f the independent 213 variables u t iliz e d were proxies fo r basic underlying values, there seemed to be no independent variab les u t iliz e d which were measure­ ments of a ttitu d e s . This represented a major step in terms of conceptual design over the previous study. Numerous relationsh ips between in divid ual c h a ra c te ris tic s and a ttitu d es pertain in g to land use control measures were id e n ti­ fied in the course of th is study. The illu m in a tio n o f these s ta tis t ic a lly s ig n ific a n t re la tio n sh ip s must be recognized as a contribution to the subject area of the re la tio n s h ip between in d i­ vidual c h a ra c te ris tic s and a ttitu d e form ation. The methods u t iliz e d in the p relim inary Io nia Study were somewhat unique in th is type o f research. Conditional p ro b a b ility models are ty p ic a lly not u t iliz e d in th is type o f research. Again, in respect to the Ionia Study, the methods u t iliz e d must be recognized as being prelim inary and rudimentary. The model developed, u t iliz in g a number o f dummy v a ria b le s , was an i n i t i a l step toward the more sophisticated modeling procedures used in this study. The dummy independent variab les u t iliz e d in the Ionia Study were in a simple dichotomous form. Because o f th is , in t e r ­ dependent comparisons between various levels or classes o f s p e c ific independent variables were b a s ic a lly impossible. The technique u tiliz e d in the course o f th is study involved the development of matrices re la tin g to the dummy variab les which were being used. These matrices allowed fo r inter-dependent comparisons w ith in the dummy v a riab le systems. This modeling technique represents a much greater level of s o p h isticatio n than was present in the prelim inary 214 Ionia Study. The net re s u lt o f the method u t iliz e d in th is study is the refinement of a technique which has not been t r a d itio n a lly u tiliz e d in th is type o f research, the development o f conditional pro b ab ility models incorporating inter-dependent comparisons w ith in dummy variab le systems. I t is hoped th a t th is study could stim ulate other studies which would re fin e both the knowledge gained and the techniques u tiliz e d . Conclusions A number o f basic conclusions were drawn from the study in respect to both the subject area and the methods area. In reference to the subject area, the study was successful in illu m in atin g linkages between an in d iv id u a l's socio-economic characteristics and a ttitu d e s p ertain in g to land use control measures. This alone made the study worthwhile because o f the basic knowledge which was gained in respect to the complex f ie ld of a ttitu d e form ation. However, a t the same time i t was found that the re la tio n s h ip between s ig n ific a n t socio-economic variab les and a ttitu d e s was f a r from simple. I t would not be an overstate­ ment to say th a t the nature o f the re la tio n s h ip s appears to be exceedingly complex. Many variables which were thought to explain a ttitu d e formation and re s u ltin g voting behavior f a ile d to appear as being s ig n ific a n t in the context o f th is study. Other variables which were thought to be r e la tiv e ly in s ig n ific a n t in respect to a ttitu d e formation proved to be extremely s ig n ific a n t as a re s u lt 215 of the s t a tis t ic a l an alysis. To compound th is s itu a tio n , id e n tic a l variables did not act the same, in both degree and d ire c tio n , across the three equations. The v a riab le which contributed the most to increasing the conditional p ro b a b ility o f approving land use control measures was the one which d e a lt with an in d iv id u a l's perception o f govern­ mental service. In a ll three equations, i f the respondent f e l t he was being well served by local government, the p ro b a b ility of approving land use control measures g re a tly increased. This single v a riab le was the greatest contrib uting fa c to r to approval in a ll three equations. To a lesser degree, the population density variable and the voting behavior v a riab le acted in the same manner. Increased population densities and increased voting p a rtic ip a tio n rates both increased the p ro b a b ility o f approving land use control measures in the three equations. These three variab les were the only ones which acted in a clear and consistent manner in a ll three equations. Other variab les which had tr a d itio n a lly been re la te d to voting behavior acted in a basically inconsistent manner. Variables such as education, income and occupation did not prove to be consistent in e ith e r d ire c tio n or magnitude in the three equations. A d d itio n a lly , the sex v a riab le appeared as being s ig n ific a n t in only a singe equation and the age variable fa ile d to be s ig n ific a n t in any o f the three equations. There are many possible explanations fo r the behavior of the socio-economic c h a ra c te ris tic s which were transformed to variables fo r the conduct o f th is study. However, two possible 216 explanations appear to be the most p lau sib le and ra tio n a l a t th is time. The f i r s t deals with the context o f the study while the second re la tes to what was attempted to be measured. In terms o f study context, i t appeared lo g ic a l to assume that voting behavior was a lo g ic a l surrogate fo r a ttitu d e s p e rta in ­ ing to land use control measures. assumption. This was perhaps an in v a lid I t was perhaps wrong to assume th a t variables would react id e n tic a lly in d iffe r in g contexts. In retrospect i t is obvious th a t voting behavior in general should not be equated d ire c tly with issue s p e c ific a ttitu d e formation which could or could not re s u lt in voting on th a t issue. In the basic study design, there was perhaps, an e rro r o f g e n e ra liza tio n . However, there is the p o s s ib ility th a t the previous research which was used as the basis fo r th is study has become dated. I t appears plausible th a t the re su lts o f research conducted even a short time ago could quickly become dated and obsolete. I t has y e t to be proven th a t there are "basic laws" in respect to human behavior. Human behavior must be viewed as a dynamic e n tity rather than a s ta tic one. Therefore, past studies may have re lected "truth" a t th a t po int in time but may now not r e fle c t r e a lit y . Changing conditions and the pressures, roles and perceptions influencing the in divid ual may qu ickly generate rad ical s h ifts in human behavior. Because o f t h is , there is a chance th a t the be­ havior o f socio-economic c h a ra c te ris tic s in the context o f th is study represents the "tru th " o f the moment. Perhaps the resu lts generated by th is study represent a tru e r r e fle c tio n o f the ro le 217 of socio-economic conditions than did studies conducted several years ago. Secondly, a problem existed in respect to what was actually being measured vis a vis what was being attempted to be' measured. In most cases the independent variables which appeared to be s ig n ific a n t were proxies fo r more basic values which could not, in the context o f th is study, be d ir e c tly measured. The basic conceptual issue, expressed in s im p lis tic terms, was the respondent's view o f government in our society. An attempt was made to ascertain an in d ivid u al perception of the governmental role in society pertain in g to land use control measures through a series of in d ire c t in d ic a to rs . I t was hoped th a t in d ire c t indicators in the form o f socio-economic c h a ra c te ris tic s would illum inate an in d iv id u a l's perception of the governmental ro le in respect to land use control measures. This could have been too simple an approach to an extremely complex problem. Encompassed in this generalized approach was an attempt to synthesize the entire value system o f an in d ivid u al which is the basic element in an in d iv id u a l's decision making process. was overly ambitious. Perhaps the study I t w i ll depend upon fu rth e r research to establish whether or not th is study was aimed conceptually in the proper d ire c tio n . The contribution made in the methods area must be con­ sidered important. The study elaborated on the prelim inary modeling e ffo r ts of the Ionia Study. Extensive knowledge was gained both in respect 218 to the u t iliz a t io n o f dummy variables and the construction o f inter-dependent comparisons w ith in dummy variables systems. The refinement of the u t iliz a t io n o f f a i r l y sophisticated dummy variable techniques w ill contribute g re a tly to the a n a ly tic a l c a p a b ilitie s in s im ila r types o f research. Techniques u t iliz e d in th is study illu s t r a t e the c a p a b ility o f quantifying what is essen tially nominal data fo r regression analysis. This c a p a b ility greatly expands the u t iliz a t io n o f q u a n tita tiv e analysis in to areas which were often devoid o f r e la tiv e ly high powered s ta ­ t is tic a l analysis. However, i t must also be noted th at the resu lts generated by the s t a tis t ic a l analysis were not always easy to in te r p r e t. The exact meaning of conditional p ro b a b ilitie s whose values were less than 0 and greater than +1 are not completely c le a r. While conditional p ro b a b ilitie s in excess of the normally sp e c ifie d ranges seem to be lo g ic a l in a r e la tiv e sense, they have l i t t l e meaning in an absolute sense. This property o f the conditional p ro b a b ili­ ties ranging beyond the normally accepted values may lim it the application of the technique u t iliz e d in the study. U n til the questions and problems raised by the excessive conditional proba­ b i lit y values are answered, th is technique should be used w ith caution. Lim itations I t was f u l ly recognized th a t the study contained several lim itatio n s which r e s t r ic t the general a p p lic a b ility o f the re s u lts . The recognized lim ita tio n s include: 219 1. The study was conducted in ju s t a three county area. The lim ited areal extent o f the data c o lle c tio n r e s tr ic ts the application o f the re s u lts . A three county sample cannot be con­ strued as being representative o f the e n tire ru ra l population o f Michigan, le t alone the e n tire ru ra l population o f the United States. The counties u tiliz e d as a data base d iffe re d g re a tly from many other ru ra l areas of Michigan and the United States. Obvious differences such as economic s tru c tu re , re la tio n s h ip to urban are.as, population density, and age stru ctu re o f the resident population are e a s ily observable and in many cases s e lf evident. However, more subtle differences also e x is t which are not nearly as evident and were not considered in the conceptual design o f the study. No consideration was given to aspects such as race, ethnic or national heritage of the resident population. There is no doubt that these facets o f the resident population would have a bearing on the formation o f an in d iv id u a l's a ttitu d e s and perceptions. The p o litic a l clim ate in an area would also influence a ttitu d e formation. A ru ra l area with pervasive p a te r n a lis tic p o lit ic a l and economic systems would condition very d iffe r e n t a ttitu d e s toward land use control measures than would a ru ral area with a strong tra d itio n o f in divid ual independence in respect to p o litic s and live lih o o d . An in d iv id u a l's perceptions and a ttitu d e s are obviously conditioned in p a rt by his h e rita g e , social p o s itio n , perceived ro le in society and the local p o lit ic a l and economic systems. Since these aspects have d iffe r e n t m anifestations and vary g re a tly , 220 dependent upon geographic lo c a tio n , there is no way the population of three counties in Michigan could be considered representative of the e n tire ru ral population of the United States. 2. questionnaire. Data were co lle cte d through use o f a mailed survey • Inherent weaknesses o f mailed surveys also lim ite d the v a lid ity o f the study. Questionnaire bias and ambiguous ques­ tions were two items which are evident when mailed questionnaires are being u tiliz e d . I t was hoped th a t careful development o f the questionnaire and a p re -te s t would elim inate the m ajority o f bias and ambiguity which would be in h e rie n t in the questionnaire. Also, the s tra ta o f the population answering the question­ naire was an issue to be considered. There was no c e rta in ty th at a ll segments o f the population would take the time and e f f o r t required to complete and return the questionnaire. The age, edu­ cational le v e l, income le v e l, e t c ., o f the surveyed population would have a bearing on the completed questionnaire return ra te . This concern was shown to be v a lid by the telephone non-respondent follow-up to the mailed survey. S ig n ific a n t socio-economic d i f f e r ­ ences between those who completed and those who f a ile d to complete the questionnaire were shown to e x is t. Differences in respect to age, sex, occupation, education, and income were evident. This meant th at only p a rtic u la r s tra ta o f the population w ithin the study area were sampled. In th is respect, the inform ation generated from the returned questionnaires was biased. Therefore, questions as to the o v e rall v a lid it y o f the research e f f o r t may be raised. 221 3. The completeness o f the sampling frame was questionable. The sampling frame fo r the study was a series of telephone lis tin g s fo r the study area. While i t had been indicated th a t 90-95% o f the area's households had telephone s e rv ic e , the correctness o f th is estimate o f level o f service was never v e r ifie d . Therefore, i t was possible th a t s u b s ta n tia lly fewer households had telephone service. I f th is was the case, the sampling frame could have conceivably provided a much less complete enumeration o f households than was thought. This problem is compounded by the p ro b a b ility that households without telephones most lik e ly have s im ila r socio­ economic c h a ra c te ris tic s . I f th is is the case, an e n tire subset of the population could be overlooked as a re s u lt o f using telephone d ire cto ries as a sampling frame. A d d itio n a lly , the telephone lis tin g s fo r the study area were seriously out o f date. two years o ld . I t was found th a t some lis tin g s were This would not create major problems in a r e la tiv e ly s ta tic area, but would re s u lt in major sampling errors in an area of dynamic growth. In a growth area, new lis tin g s would not be contained in outdated telephone lis tin g s . Therefore, the sampling frame would not contain a major subset of the resident population. However, even with these mentioned weaknesses, i t is f e l t that telephone lis t in g s , i f they are u t iliz e d with caution, provide a b e tter and less expensive enumeration o f resident population than other techniques which are c u rren tly u t iliz e d . 4. The study was conducted during a single point in time. Opinions expressed by residents were more than li k e l y a r e fle c tio n 222 of both local and national issues which were developing and e x is te n t at the time o f the study. In divid ual a ttitu d e s are developed, in p a rt, as a re s u lt o f an in d iv id u a l's perception o f the society around him. This is true in regard to s o c ie ta l conditions on the macro level (national society) and the micro level (lo c a l s o c ie ty ). When the survey fo r th is study was conducted, the national society was in a sta te of upheaval. The Vietnamese War was con­ cluding, p o lit ic a l scandel was rocking the White House, and the energy c r is is was being given a great deal o f p u b lic ity . What e ffe c t this had on an in d iv id u a l's perception o f society and the future of society is pure speculation. Perhaps these events conditioned in d ivid u als to view the fu tu re in a pessim istic rath er than o p tim is tic fashion. I f th is were the case, then an in d i­ vid u al's a ttitu d e s toward land use control measures could be markedly d iffe r e n t than i f the in divid ual had an o p tim is tic perception o f the fu tu re . The same concept could hold tru e on the micro or local le v e l. I f local p o litic s were in turm oil or i f c e rta in local issues had helped to create strong in d iv id u a l opinions and a ttitu d e s , an in d iv id u a l's a ttitu d e s toward land use control measures could be tem porarily colored to r e f le c t local conditions. Therefore, i t is recognized th a t studies conducted during a single po int in time may not tru e ly r e f le c t long run perceptions and a ttitu d e s . 5. A complete understanding o f the re la tio n s h ip between the selected socio-economic variables and the a ttitu d e s which were 223 being attempted to be measured was lacking . I t would not be worth­ while a t th is time to belabor the point th a t i t is very possible that the independent variab les which were being u tiliz e d were merely proxies fo r more basic underlying values which could not be d ire c tly measured. This is illu s tr a te d by the re te n tio n and behavior o f certain independent variables in the three models. As was mentioned before, the behavior o f c e rta in independent variables was d i f f i c u l t to explain. A much greater understanding o f the re latio n sh ip between a number of the independent variables and the a ttitu d e s which were being projected is necessary before the results o f th is study may be accepted a t face value. 6. The lite r a tu r e reviewed fo r th is study was mainly related to partisan voting behavior. The m a jo rity of the case studies investigated d e a lt with issues which could be in terp reted as being decided a-long party lin e s . In the past few years party distinction s are no longer c le a r c u t, party lin es have become blurred and many issues taken to the e le c to ra te often cannot be decided on a party basis. A case in po int is the series of issues, prim arily in western s ta te s , which concern environmental issues and problems. I t is lik e ly th a t p a rty , and the classic determinates of party a f f i l i a t i o n , have l i t t l e referendums. influence on the outcome of these This may w ell be the case with issues dealing with land use control measures. formation may be surfacing. New dimensions of voter a ttitu d e I f th is is f a c t , then the creation of hypotheses based on the previous notions o f the determinates of voting behavior may not have been the appropriate approach. & 224 Hypotheses based on the recent lit e r a t u r e re la te d to non-partisan voting behavior may be more appropriate to th is type o f study. Recommendations In recognition of the mentioned lim ita tio n s and other issues which were raised during the conduct o f the study, i t is hoped th a t addition al research could be conducted in th is general subject area. Additional research could do much to c la r if y many of the relationsh ips which were exposed during th is study. S pecific recommendations are: 1. Based on the knowledge gained during the course o f th is study, conduct s im ila r studies in a d iffe r e n t geographic a re a . By s h iftin g the study location i t would be possible to te s t the resu lts in a d iffe r e n t s e ttin g . A d iffe r e n t study location could c o n tri­ bute a great deal toward minimizing the regional c u ltu ra l impact upon the respondent's a ttitu d e s toward land use control measures. Selection o f a new study area would provide the opportunity to choose an area of the United States which exhibited a markedly d iffe re n t set o f c h a ra c te ris tic s than were exhibited in the Michigan study area. A ru ra l area could be selected which had a d iffe re n t type o f economic base, p o lit ic a l c lim a te , e tc . Through this process, i t would lik e ly be possible to gain some in d ic a tio n as to the impact th a t regional c u ltu ra l c h a ra c te ris tic s exerted upon and in d iv id u a l's a ttitu d e s toward land use control measures. A d d itio n a lly , a new study area could be selected w ith in which the re s id e n t's in d ivid u al c h a ra c te ris tic s d iffe re d g re a tly from those o f the o rig in a l study area. A resident population could 225 be selected which was e s s e n tia lly d iffe r e n t in terms o f income, age, race, educational le v e l, occupation, e tc . This would provide a mechanism through which to judge the e ffe c ts o f d iffe r in g popu­ latio n c h a ra c te ris tic s upon a ttitu d e s toward land use control ' measures. Also, a study could be conducted in an area which was e s se n tia lly outside the influence of a major urban area and hope­ fu lly not subject to developmental pressures. The selection o f such a r e la tiv e ly s ta tic area would provide a measurement of attitudes concerning land use control measures which was fre e of a sense o f immediacy. This would provide y e t another dimension toward the understanding o f what conditions an in d iv id u a l's a t t i ­ tudes toward land use control measures. E s s e n tia lly , through s h iftin g the study area to accomodate d iffe rin g regional c h a ra c te ris tic s and population c h a ra c te ris tic s , i t would be possible to a lt e r the constructs of the o rig in a l study. The resu lts o f the o rig in a l study could be u t iliz e d in a manner sim ilar to th a t o f a control group in psychological research. Deviations from the re su lts of the o rig in a l study could hopefully be p a r t ia lly explained by the d iffe r e n t s e ttin g , c u ltu ra l c lim a te , and d iffe r in g population c h a ra c te ris tic s which existed during the conduct o f subsequent studies. The present study could be used as a benchmark or point o f c a lib ra tio n from which to judge the suspected e ffe c t o f other aspects o f a ttitu d e form ation. H opefully, addition al research in to th is subject area w ill support the v a lid it y of th is study in terms of both subject area and methodology. 226 2. Conduct a study in a d iffe r e n t time frame. Sequential studies conducted in an area would p a r t ia lly answer the question of what e ffe c t pervasive national and local issues have upon a t t i ­ tude formation in respect to land use control issues. As mentioned previously, the national mood was one o f turmoil when the survey for th is study was conducted. Perhaps a d iffe r in g national clim ate would have resulted in very d iffe r e n t a ttitu d e s being re fle c te d by the respondents. local issues. This could also be true in terms o f pressing Perhaps the respondent's a ttitu d e s would be very d iffe re n t in a d iffe r e n t time frame. Sequential studies re la te d to d iffe r in g national and local moods would contribute g re a tly toward understanding how these cyclic phenomenon a ffe c t a ttitu d e form ation, both generally and s p e c ific a lly in the context o f land use control measures. Sequential studies would also r e fle c t how the tra n s itio n of an area affected an in d iv id u a l's a ttitu d e toward land use control measures. Through using the o rig in a l study as a benchmark, i t would be possible to observe the e ffe c t change produced in respect to a ttitu d e form ation. The o rig in a l study would serve as a point of departure from which to document and measure change. By c o n tro llin g both geographic-cultural and temporal effec ts in subsequent studies, i t would be possible to gain a great deal o f in sig h t in to the e ffe c t these two phenomenon have upon the process o f a ttitu d e form ation. 3. Conduct a study in an area which is about to experience a zoning or land use control referendum. One o f the major objectives 227 o f th is study was an attempt to develop p re d ic tiv e models which would a n tic ip a te an in d iv id u a l's a ttitu d e s toward land use control measures. The p re d ic tiv e models were developed but there is presently no linkage between models and r e a lit y . An in ve s tig atio n conducted in an area which was about to conduct a referendum in re la tio n to land use control measures would perhaps forge the lin k between model and r e a lit y . The resu lts generated by the models must be considered pure speculation before they are tested in real world conditions. The te s t re la te d to the v a lid it y of the models would, by necessity, be gross measures. would be selected. An areal u n it as small as possible This would be the sm allest areal u n it fo r which votes could be tabulated. This areal u n it in ru ra l areas would most lik e ly be an in d iv id u a l town or township. Data c o lle c tio n would be based conceptually upon the independent variab les which were shown to be s ig n ific a n t in the model which re la te d to the specific issue which was the subject of the referendum. The population would then be divided into generally s im ila r subsets based on c r it e r ia established by the independent v a ria b le s . Sub­ sets of population would th erefo re be defined by le vels established by the individual independent v a ria b le s . P ro b a b ility statements regarding the acceptance or re je c tio n o f the land use issue in question could be generated fo r each population subset. Once these were estab lished, an aggregate p ro b a b ility statement regarding the acceptance or re je c tio n o f the issue could be 228 established based on the r e la tiv e s ize of each population subset in re la tio n to the to ta l population. A fte r the referendum, the generated p ro b a b ility statement re la tin g to the acceptance or re je c tio n o f the issue could be compared with the actual resu lts o f the voting. The d ire c tio n and magnitude o f the p ro b a b ility statement when compared to the actual vote would provide a basis upon which to generally v a lid a te or re je c t the model and the e n tire process. The e n tire procedure as ou tlin ed would be f a r from simple to conduct. Data c o lle c tio n would be a major problem in th a t many of the s ig n ific a n t independent variables are only a v a ila b le from primary sources. I f data were collected from secondary sources, the re s u ltin g models would be truncated and much less e ffe c tiv e than the o rig in a l models. The generation o f conditional p ro b ab ility statements based on population subsets would be imprecise to say the le a s t. The same problem would e x is t in creating the aggregate p ro b a b ility statements. Basic problems a ris e from attempting to u t i l i z e a model geared to the in d ivid u al fo r an aggregate purpose. However, i f this process was successful, i t would provide another tool to enhance decision-makers' c a p a b ilitie s . 4. Promote fu rth e r research in to the suggested r e la tio n ­ ships between s p e c ific socio-economic c h a ra c te ris tic s and a ttitu d e s toward land use control measures. Many of the relatio n sh ip s between specific socio-economic c h a ra c te ris tic s and a ttitu d e s toward land use control measures were unclear or perhaps even spurious. I t is 229 obvious th a t addition al research pertainin g to the re la tio n s h ip between in d ivid u al c h a ra c te ris tic s and issue s p e c ific a ttitu d e s is required. Most variables used in th is study were re la te d to voting behavior which was a surrogate fo r what was a c tu a lly under in vestig atio n . This approach was predicted by the lack o f in fo r ­ mation re la tin g to a ttitu d e s and the issue under in v e s tig a tio n , land use control measures. Perhaps the use o f a surrogate resulted in the choosing o f some inappropriate independent v a ria b le s . ever, s ig n ific a n t relationsh ips were found. How­ In th is respect th is study may be viewed as a prelim inary step in the d ire c tio n of establishing proven relationsh ips between c e rta in socio-economic c h a rac te ris tic s and a ttitu d e s re la te d s p e c ific a lly to land use control measures. The tenuous nature o f the illu s t r a te d relatio n sh ip s should prompt fu rth e r in ve s tig atio n in th is subject area. Such research would hopefully c la r if y questions which have been raised or sug­ gest new c h a ra c te ris tic s which would generate addition al independ­ ent variables which could be u t iliz e d in successively more s o p h is ti­ cated and accurate models. 5. voting behavior. Create hypotheses based on in v e s tig a tio n o f nonpartisan L ite ra tu re is becoming a v a ila b le concerning the voting behavior o f in divid uals in respect to nonpartisan issues. recent lit e r a t u r e dealing with nonpartisan referendums, connected with p rim a rily ecological issues, should provide new insights in to in d iv id u a l's voting behavior. Research in to these types o f issues The 230 would perhaps open new avenues o f in v e s tig a tio n . In vestig atio n in the realm o f nonpartisan voting patterns could possibly lead to the generation o f new hypotheses which would b e tte r f i t the requirements of a study such as th is . Perhaps, a great deal of knowledge could be gained which would more c le a rly illu m in a te the relationship between an in d iv id u a l's socio-economic c h a ra c te ris tic s and a ttitu d e s toward land use control measures. 6. Promote fu rth e r research in to the techniques u t iliz e d in this study. Techniques u t iliz e d in th is study were r e la tiv e ly sophisticated and unique in th is type o f research. Because of th is , resu lts did not lend themselves re a d ily to in te rp re ta tio n . Additional research and refinement is necessary in order th a t these techniques, p a r tic u la r ly the complex dummy v a riab le tech­ nique, may be u t iliz e d to th e ir f u lle s t po ten tial in subsequent research. H opefully, th is prelim inary e f f o r t with respect to the techniques w ill prompt fu rth e r u t iliz a t io n and expanded a p p lic a tio n of s im ila r techniques in re la te d studies. The preceeding recommendations have been made p rim a rily in response to the recognized lim ita tio n s o f the study. The study must be recognized fo r what i t is , a prelim inary step in terms of both subject area in ve s tig atio n and a n a ly tic a l techniques. I t is hoped th a t the findings of th is study w ill stim ulate addition al research in regard to both subject and technique. I t is a d d itio n a lly hoped th at subsequent research w ill v a lid a te the findings and con­ clusions which resulted from th is study. 231 As a fin a l note, an assessment must be made as to the value of th is study to the p ra c titio n e r. What value has th is study to the person who is a c tu a lly dealing w ith land use control issues? I t has been c a re fu lly stated in the te x t th a t many o f the relationships between socio-economic c h a ra c te ris tic s and a ttitu d e s toward land use control measures, which were shown to be s ig n if i­ cant, were tenuous in nature. Therefore, the p re d ic tiv e models which were developed are not y e t ready fo r actual p ra c tic a l a p p li­ cation. The models must be viewed as prelim inary and th e ir value lie s in the p o te n tia l development o f new methods o f a n tic ip a tin g or understanding an in d iv id u a l's a ttitu d e s toward land use control measures. Through the knowledge gained in th is study the p r a c ti­ tioner has perhaps gained new insights in to the complex r e la tio n ­ ships between socio-economic c h a ra c te ris tic s and a ttitu d in a l formation. Perhaps some previously held misconceptions w ill be removed and e ffo rts may now be concentrated toward more productive and f r u it f u l ends. BIBLIOGRAPHY 232 BIBLIOGRAPHY Ackoff, Russell. S c ie n tific Method. In c ., 1962. New York: John Wiley & Sons, Adrain, Charles R. "A Typology fo r Nonpartisan E lectio n s." P o litic a l Q u a rte rly , Vol. 12 (1959). A lford, Robert R. Party and S ociety. Company, 1963. Chicago: Western Rand McNally & A lford, Robert R. and Scoble, Harry M. Bureaucracy and P a r t ic i­ pation: P o litic a l Cultures in Four Wisconsin C it ie s . Chicago: Rand McNally & Company, 1969. Bariowe, Raleigh. Land Resource Economics. Jersey: P re n tic e -H a ll, In c ., 1958. Englewood C l i f f s , New Beal, Calvin L. "Rural Depopulation in the United States: Some Demographic Consequences o f A g ric u ltu ra l Adjustments." Demography, V o l .l, No. 1 (1964). 264-72. Berry, Brian J. L. and Marbel, Duane F ., Ed. Spatial A nalysis. Englewood C l i f f s , New Jersey: P re n tic e -H a ll, In c ., 1968. Blalock, Hubert M ., J r . Social S t a t is t ic s . H ill Book Company, 1960. New York: McGraw- Bonjean, Charles M .; C lark, Terry N .; and L inberry, Robert L ., Ed. Community P o lit ic s . New York: The Free Press, 1971. Brogan, D. W. P o litic s in America. Publishers, 1954. New York: Harper & Brothers Brunn, Stanley D .; Hoffman, Wayne L .; and Romsa, Gerald H. "The Youngstown School Levies: A Geographical Analysis in Voting Behavior." Urban Education, Vol. V, No. 1 (A p r il, 1970), 20-52. Burdick, Eugene and Brodbeck, Arthur J . , Ed. American Voting Behavior. Glencoe, I l l i n o i s : The Free Press, 1959. Campbell, Angus; Converse, P h ilip E .; and M ille r , Warren E. The American V o ter. New York: John Wiley & Sons, In c ., 1960. 233 234 Campbell, Angus; Gurin, Gerald; and M ille r , Warren E. The Voter Decides. Evanston, I l l i n o i s : Row Peterson & Company, 1954. Chao, Lincoln L. S ta tis tic s : Methods and Analyses. McGraw-Hill Book Company, 1969. New York: Chappelle, Daniel E. Financial M aturity o f Eastern White Pine in New York S ta te . Syracuse, New York: Syracuse U n iv e rs ity , College o f Fo restry, 1966. Christenson, James A. "A Procedure fo r Conducting Mail Surveys with the General P u b lic ." Paper presented a t the annual Meeting o f the Community Development S ociety, Wilmington, North C arolina, August 7, 1974. Crapo, Douglas M. "Recreational A c tiv ity Choice and Weather: The Significance of Various Weather Perceptions in Influencing Preference fo r Selected Recreational A c tiv itie s in Michigan State Parks." Unpublished Ph.D. D is s e rta tio n , Michigan State U n iv e rs ity , 1970. Draper, N. R. and Smith, H. Applied Regression A nalysis. John Wiley & Sons, In c ., 1966. New York: Ezekiel, Mordecai and Fox, Karl A. Methods of C o rrelatio n and Regression A nalysis. New York: John Wiley & Sons, In c ., 1959. Flanigan, W illiam H. P o litic a l Behavior o f the American E le c to ra te . Boston: A llyn & Bacon, In c ., 1968. Florence, P. Sargant. The S ta tis tic a l Method in Economics and P o litic a l Science. New York: Harcourt Brace & Company, 1929. Goldberger, Arthur S. Econometric Theory. & Sons, In c ., 1964. Gosnell, Harold F. Grass Roots P o lit ic s . R ussell, 1942. New York: New York: ________ . Machine P o litic s Chicago Model. 2nd ed. The U niversity of Chicago Press, 1968. John Wiley Russell & Chicago: Hamilton, David A ., J r. . Event P ro b a b ilitie s Estimated by Regression. Ogden, Utah: United States Department o f A g ric u ltu re , Forest S ervice, 1974. Hawley, W illis D. and W irt, Frederick M ., Ed. The Search fo r Community Power. Englewood C l i f f s , New Jersey: P ren ticeH a ll, In c ., 1968. 235 Horton, J. and Thompson, F. R. "Powerlessness and P o litic a l Negativism: A Study o f Defeated Local Referendums." American Journal o f Sociology, 68 (March, 1962), 485-493. Kelley, Francis J .; Beggs, Donald L .; McNeil, Keith A .; Eichelberger, Tony; and Lyon, Judy. M u ltip le Regression Approach. Carbondale, I l l i n o i s : Southern I l l i n o i s U niversity Press, 1968. Key, V. 0 . , J r. P o litic s , P a rtie s , and Pressure Groups. Thomas Y. Crowell Company, 1958. New York: Kimball, Solon T. "A Case Study in Township Zoning." Michigan A g ric u ltu ra l Experiment S tation Q uarterly B u lle tin , 28, No. 4 (May, 1946). King, Leslie J. S ta tis tic a l Analysis in Geography. Englewood C lif f s , New Jersey: P re n tic e -H a ll, In c ., 1969. Kmenta, Jan. Elements o f Econometrics. Company, 1971. New York: The MacMillan Lang, Kurt and Lang, Gladys Engel. Voting and Non-Voting. Waltham,. Massachusetts: B la is d e ll Publishing Company, 1968. Lazarsfeld, Paul F . ; Berelson, Bernard; and Gaudet, Hazel. The People's Choice. New York: Columbia U niversity Press, 1948. Lee, Eugene C. The P o litic s of Nonpartisanship. v e rs ity o f C a lifo rn ia Press, 1960. Berkeley: Uni­ Lipset, Seymour M artin. P o lit ic a l Man. Garden C ity , New York: Double Day and Company, In c ., 1960. Lipset, Seymour M artin and Rokkan, S te in , Ed. Party Systems and Voter Alignment. New York: The Free Press, 1967. Lockard, Duane. The P o litic s o f State and Local Government. York: The MacMillan Company, 1963. New Mauch, A rthur. "Land Use in a Changing World." Land Use in Michigan. (Extension B u lle tin 610, Natural Resources S e rie s .) East Lansing, Michigan: Michigan State Uni­ v e rs ity Cooperative Extension S ervice, 1969. Olson, David M. Nonpartisan Elections: A Case A nalysis. Texas: The U niversity o f Texas, 1965. A ustin, Pattanaik, Prasanta K. Voting and C o lle c tiv e Choice: Some Aspects o f the Theory o f Group Decision Making. Cambridge: The Cambridge U n iv e rs ity , 1971. 236 Picard, Giles and Juneau, A lb e rt. A Sociological Study o f A g ric u ltu ra l Change in the P ilo t Region (BAEQ). Ottawa: Canada Department o f Forestry and Rural Development, Queens P rin te r and C o n tro lle r o f S ta tio n ary , 1968. ARDA Condensed report CR-No. 15. Pomper, Gerald. "Ethnic and Group Voting in Non-Partisan Municipal E lectio ns." The Public Opinion Q u arte rly , Vol. 30 (1966). Snedecor, George W. S ta tis tic a l Methods. State College Press, 1968. Ames, Iowa: The Iowa Spicer, Edward H ., Ed. Human Problems in Technological Change. New York: John Wiley & Sons, In c ., 1952. Sweeney, Robert E. and U lv e lin g , Edwin F. "A Transformation fo r Symplifying the In te rp re ta tio n o f C o e ffic ie n ts o f Binary Variables in Regression A nalysis." The American S ta tis ­ t ic ia n , Vol. 26, No. 5 (December, 1972). Wonnacott, Ronald J. and Wonnacott, Thomas H. York: John Wiley & Sons, In c ., 1970. Econometrics. New ________. Michigan State U n iv e rs ity , D ivision of Research, Graduate School o f Business A dm inistration, Michigan S ta tis tic a l A bstract, Comp. David I . Verway (9th Ed.) East Lansing: Michigan State U n iv e rs ity , 1972. ________. County and Regional Facts, State Planning and Development Region 7 , W illiam J. Kimball Coordinator, Michigan State U n iv e rs ity , Cooperative Extension S ervice, 1974. _______ . U.S. Department o f Commerce Bureau o f the Census, General Population C haracteristics Michigan, PC(1 ) -B24 M ich., 1970 Census of Population. _______ . U.S. Department o f Commerce Bureau o f the Census, General Social and Economic C haracteristics Michigan, PC(0-C24 Mich. Social and Economic S ta tis tic s Adm inistration 1970 Census o f Population. _______ . Prelim inary Documentation, MSU STAT System (6500) May 17, 1972, Part 12, LSSTEP Program. APPENDIX 237 COOPERATIVE EXTENSION SERVICE Michigan State University ___________________________• U. S. Department of Agriculture and Ionia County Board of Commissioners Cooperating Ionia County Extension Service Courthouse - Ionia, Michigan Phone 527-lhOO Zip:U88U6 January 13 > 1971 Dear Ionia County Resident: We need your responses to the following questions for a public opinion survey. This information will help local leaders know your feelings and thoughts on planning and development issues in Ionia County. Your personal opinions will be confidential, and your name is not necessary for this survey. Please return the completed questionnaire in the enclosed envelope by January 25th. (no postage is necessary). Your responses will be very useful in guiding our Extension educational programs. If you have any questions contact me at 527-1^00. Thank you for your cooperation. Sincerely, ( a j —i a i ,S # t '• ‘1 William S. Pryer County Extension Director WSPrdg P.S. Even if you previously filled out a single page survey, please complete the enclosed survey as it has been sig­ nificantly revised. 239 LAND USE GOALS SURVEY Ionia County (Read all questions before marking any. Answer in terms of your county.) I. General Information A. Your Township B. Male _____ Female_______ C. Your occupation(s) : ______ _________________ _ D. Years lived in county _______ E. Age _____ . Agricultural Goals A. B. II Do you feel that there are any conflicts between agricultural and other land uses? Yes _____ No don’t know . Do you feel that good agricultural lands should be protected? Yes No__________ don't know _ III. ' Residential Goals A. B. Do you feel that more housing would be desirable? Yes _____ No don’t know _____ If more housing were added which would you prefer? no preference mobile homes -single family homes apartments don't know C. If more single family, non-farm residences are added, where would you prefer they be located? no restrictions on location (anywhere) large rural lots '____ rural subdivisions subdivisions adjacent or within villages and cities don't know D. If more mobile homes are added, which location wouldyou prefer? no restrictions on location (anywhere) _ rural mobile homeparks mobile home parks adjacent to or within villages and cities don’t know 240 . Shopping and Service Goals A. B. Do you feel that more shopping and service facilities are desirable? Yes No _____ don't know_____ If more shopping and service facilities were added, where would you prefer they be located? ______ no restrictions on location (anywhere) _____ downtown areas _____ shopping centers _____ don't know . Industrial Goals A. B. Do you feel that more industrial development would be desirable? Yes _____ No _ _ _ _ don't know _____ If more industrial development occurs what kind would you prefer? _____ no preference ' light manufacturing heavy manufacturing don't know C. If more industry were added, where would you prefer it be located? no restrictions on location (anywhere) within incorporated cities and villages only in controlled, specified, industrial parks don't know Recreational Goals A. Do you feel that more recreational areas would be desirable? Yes _____ No______ dor. 't know _____ B. Do you feel that unioue lands (Iskeshores river and stream banks flood plains, etc.) should I e controlled for recreational use? Yes _____ Nc.______ d o n : t know- _____ Land Use Priorities Which land uses would you give highest priority? highest (?) for ?nd. highest, etc.) _ _ _ _ agriculture _____ residential _____ shopping and services ____ industrial recreational - 2- (Number them (1) for 241 What are your feelings as to the timing for use of each of the land control measures? Check one category below for each measure. now later never don't know Land use planning _____ _ ___ _____ _____ _____ Subdivision regulations _____ _____ ______ _____ Building and housing codes ____ _____ ______ _____ Land use zoning If land use control measures are agreed upon in your county now, at what level would they be most desirable7 Check one category fcelow for each measure. county-wide Township don't know Land use planning _____ _ Land use zoning Subdivision regulations _ _____ _ Building and housing codes _____ __ In general do you think the patternsof land uses in your county will have any effect on the cost ofproviding services--water, sewers, schools, highways, etc.? Yes No don't know ______ _ Your reasons for your answers to any of the above questions will help in assuring that every citizen view is included in the decisions. What do you feel the greatest development problem is in the county at this t i m e ? ___________ _________________________ __________ _ Please return to: -3- William S. Pryer County Extension Director Courthouse Ionia, Michigan Phone 527-1400 242 DO NOT WRITE IN THIS SPACE THUMB AREA COMMUNITY DEVELOPMENT SURVEY The purpose of this survey is to obtain your opinions about various possible kinds of development and land use planning and control in your area. The results of this survey will be made available to Thumb Area residents and leaders to help better plan for future community development. DIRECTIONS: For each question, please check (/) the blank next to the answer that most closely matches your feelings on the subject. Space is provided for your comments at the end of the questionnaire, so please feel free to give your views on any of the topics covered. This questionnaire was addressed to the person listed in the telephone directory. However any adult member of the household may complete the questionnaire. A. Future Population 1.a. What would you like to see happen to the population of your county over the next 5 years? I’d like tosee the population: decrease b. stay about the same ____ Increase don't know Do you think there should be any definite action taken to encourage or discourage population growth at the county level? No _____Yes_________ ____Don’t Know 2.a. What would you like to see happen to the population ofyour next 5 years? I'd like to see the population: decrease b. stay about the same township over the increase don't know Do you think there should be any definite action taken to encourage or discourage population growth at the township level? No ____ Yes___________________Don't Know B. Land Use 1. Do you feel there is any competition between different uses of land in your area? (For Example: Agricultural Land being sought for Residential Development; Industrial Development taking place in Residential Areas). No 2. 4. 5. ____ Yes___________________ Don't Know What do you think of the idea of having a general overall public plan for the future uses of land? (For Example: A plan which says what land should be used for different kinds of housing, what land should be used for farming, what land should be used for industry, etc.) I don't like the idea ____ I don't care one way or the other I like the idea ____ I don't know If such a plan were developed (even though you may not favor the idea), at which level of government would it be most acceptable to you? township or municipal ____ multi-county region ____ no preference county ____ state______________________ don't know Do you know of any such plan within this county? No 6. Know Do you feel you understand what land use planning is? No 3. ____ Yes___________________Don't Yes Do you feel you understand what zoning means? No Yes Don't Know 7. Do you support the general concept of having ordinances to enforce a land use plan? 8. In oVder to control and regulate land use and development, do you favor: No a. Yes Don't Know Zoning ordinances? No Yes Don't Know 243 - b. Yes Don't Know Yes Don't Know Building regulations? No 9. If such land use regulations were established (even though you may not favor the idea), at which level of government would they be most acceptable to you? (CHECK ONE BLANK IN EACH GROUP) Zoning I township or municipal township or municipal township or municipal county county county multi-county region multi-county region state state state no preference no preference no preference don't know don't know don't know Generally speaking, do you feel that the different levels of government in this area cooperate in matters of land use planning and control? Yes No b. 11. Subdivision Regulations Building Regulations _ _ multi-county region 10.a. DO NOT WRITE IN THIS SPACE - Subdivision regulations? _ _ _ Ko c. 2 Don't Know If no, between which levels of government does this lack of cooperationexist? (For Example: Between townships; between township and city). Should the different levels of government in this area (county, township, village) cooperate in: a. Land use planning? No b. city, Yes ____ Don't Know Land use control, such as zoning? No Yes Don't Know 12. Is there any need to have zoning for the protection of farmland from other kinds of development? 13. Should more shoreline areas in this county be acquired and reserved for public use? Yes No No ■ ___ Yes Don't Know Don't Know Industrial Development l.a. Should more efforts be made to increase industry within this county? _ _ _ No b. 2.a. ______ Yes Why? _____________________________________________________________ Should efforts be made to increase industry in your local area (within your township or city or village)? No b. ____ Don't Know Yes Don't Know Why? If more Industrial development took place in this county (even though you may not favor the idea), which type of location would be most acceptable to you? no restriction on location; anywhere within incorporated cities and villages only in controlled, specified industrial parks don't know other; please explain below: fYnm nprclal D evelopm ent l.a. Would you favor having more commercial shopping and service facilities In your county? No b. 2. ____ Yes Don't Know If yes, what kinds would you like to have? _________________________________ If more shopping and service facilities were established In this county, where should they be located? downtown areas of cities and villages shopping centers at the outskirts of cities and villages ____ no preference; anywhere don't know Residential Development 1. Do you feel that the addition of more housing would be desirable: a. in yourcounty? b. in yourtownship No_______________ __ Yes _ _ _ No 2. Don't Know (or local community)? __ Yes Don't Know If more housingwerebuilt, whichtype would you prefer built in your area? (PLEASE CHECK ONE BLANK). mobile homes ____condominiums (apartment to _ _ _ single family homes ____ a mix of various duplexes buy) typeof housing ____ no preference apartments 3. If more single family, non-farm homes were built (even though you may not favor the idea), which type of location would be most acceptable to you? large rural lots ____ no restrictions on location; anywhere rural subdivisions ____ subdivisions adjacent to or within villages or don't know 4. cities If more mobile homes were added (even though you may not favor the idea), which type of location would be best? rural mobile home parks _ _ no restrictions on location; anywhere don't know________________ ____ mobile home parks adjacent to or within villages or cities Recreational Development l.a. Generally speaking, are the majority of the recreation needs of your family being met at the present time? No Yes Don't Know IF "NO": b. What additional types of recreation facilities do you feel are needed for your family? (For Example: Swimming areas, playgrounds, winter sports area, trails, skating rinks, etc.) Within your COUNTY: Within your TOWNSHIP: Reasons Needed: Reasons Needed: 245 DO NOT WRITE IN THIS SPACE - 4 c. 2.a. What additional types of recreation activity programs do you feel are needed for your family? (For Example: Playground activities, senior citizen recreation programs, handicapped recreation programs, types of cultural entertainment programs, etc.) Within your COUNTY: Reasons Needed: Within your TOWNSHIP: Reasons Needed: Do you feel that the growth of tourism in your county would be beneficial? No b. G. Yes Don't Know Why? _________________________________________________________________ General Information One of the major purposes of this survey is to find out the opinions of different groups of people. For this reason, we are asking a few questions about you and your family. This information will enable us to better understand the background of the respondents. All information will be regarded as confidential, and individual responses will not be revealed. 1. What is your age? ___________________ 2. What is your sex? 3. Female What is your marital status? _ _ 4.a. Male single married separated,divorced,or widowed What is your major full-time occupation? _____________________________________ b. If you have a second job, please name it:_____________________________________ c. What was or is your father's primary occupation? ______________________________ Are you active in any of the following types of organizations or groups which are active within your county? Fraternal service organizations (such as Lions, Rotary, Kiwanis, Elks, Moose, Masons, VFW, etc.) No Yes Number of organizations:_______ b. Other community service organizations (such as PTA, church service organizations, Boy Scouts, 4-H, etc.) c. Farm organizations (such as Grange, Farm Bureau, NFO, etc.) d. Formal social or recreational organizations (such as sportsmen's clubs, country clubs, etc.) No No _ _ _ No e. ____Yes ____ Yes ____ Yes Number of organizations:______ Number of organizations:______ Number of organizations: _____ Unions (such as UAW, AFL-CIO, Teamsters, etc.) No ____ Yes f. Professional organizations (such as AMA, No ____ Yes Number of organizations:_______ MEA, AAUP, etc.) Number of organizations:______ ___ 246 DO NOT WRITE IN THIS SPACE - 5 g. Political organizations (such as the Republican Party, Democratic Party, etc.) h. Other social or service groups, formal or informal (such as card clubs, discussion groups, etc.) No Yes No 6.a. b. Number of organizations:_______ Yes _____ Number of organizations:______ Are you a registered voter? No ______ _______ Yes_____________________________________ Which political party do you feel that you most closely identify with? Democratic Party _ _ _ Republican Party American Independent Party ____ Other: None (feel no strong affiliation with any single party) ______ c. Did you vote in the last National Election (1972)?____ ____ No Yes ______ d. Did you vote in the last County Election?____________ ____ No Yes ______ Did you vote in the last Local Election (Village,City, or Township)? e. _ _ f. No Yes______________________________________________________ ______ In general, do you vote in NONE (0%) ALL (100%) 7.a. , SOME (1-50%) ,.M0ST (51-99%) _____ , elections?___________________________________________________________ ______ How responsive do you feel county governmental officials are to your needs and' desires? not responsive at all veryresponsive somewhat responsive don’t know responsive_____________________________________________________________________________ b. How responsive do you feel local governmental officials are to your needs and desires? not responsive at all somewhat responsive responsive 8.a. In what county do you live?. Huron b. 9. veryresponsive don’t know Sanilac Tuscola Other:__________ In what township or incorporated village or city do you live? Do you live: (CHECK ONE) in the open countryside? in a built up area not within the boundaries of a village or city (an unincorporated settlement)? within an incorporated village or city? 10. How many years have you lived: a. in this township or local community? _________ b. in the county? _________ c. in the Thumb Area (Huron, Sanilac, orTuscola County)?___________ ‘ 11.a. If you have lived in the Thumb Arealess than10 years,where did you live previously? ___________________________________________________________ b. 12. Why did you choose to live here? How many people are there living at home: a. b. c. 13. ______________________________________ less than school age (under 5 years old)? ____ school age children?_________ adults? _________ Which of the following applies to you? (CHECK ONE) own or are buying a home renting or leasing a home (or apartment) ______ 14. Please Indicate how much total Real Property you have In this 3-county Thumb Area (Huron, Sanilac, Tuscola). (BOTH "own/buying" AND "renting/leasing"): (PLEASE CHECK THE APPROPRIATE BLANK (S)). Own/Buying Renting/Leasing UP TO 1 ACRE.................... ....................................... over 1 but less than 10 acres ......... ............................. 11 - 40 acres................... ......... .............. ............... 41 - 80 acres................... ....................................... 81 - 160 acres.................. ....................................... 161 - 320 acres................. ....................................... 321 - 640 acres................. ....................................... over 640 acres.................. ....................................... 15. What Is the highest number of years you have completed in school? some elementary school (but did not complete: less than 6 years) completed elementary school (6 years) some junior high school (but did notcomplete: less than eighth grade) completed junior high school (eighth grade) some high school (but did not complete: 1 - 3 years) completed high school (4 years) vocational school or other training. college: 1 - 3 years college: 4 years or more 16. What is your approximate yearly total family income? less than $3,000 $3,000 - $6,000 $6,001 - $9,000 $9,001 $12,001 $15,001 $50,000 _______ $25,001- $12,000______ _______ $50,000 - $15,000______ _______ morethan - $25,000 General Outlook 1. What are your feelings about the changes you have seen in this area over the past 10 years? (Changes you feel are. important; whether they've been generally for the better or for the worse; reasons why you feel this way; etc.). 2, What do you feel are the important issues the people of this area are faced with, concerning the future betterment of the Thumb? Thank you for your cooperation! Please return this questionnaire as soon as possible in the enclosed postpaid envelope. Alan Kirk 323 Natural Resources Bldg. Michigan State University East Lansing, MI 48824 248 BUNKER hill township community development survey UNIVERSITY M ith ig tn C e u n tm »nd S. D e p t o f A p rK tfH w f*. 260 COOPERATIVE EXTENSION SERVICE MICHIGAN STATE UNIVERSITY AND U. S. DEPARTMENT OF AGRICULTURE COOPERATING INGHAM COUNTY Cooperative Extension BIdg. 127 E. Maple St. Mason. Michigan 48854 Telephone 677-9411 November 24, 1973 Dear Bunker Hill Township Property Owner; The Bunker Hill Township officials are currently evalu­ ating the present zoning ordinances and a need for other land use ordinances. They are interested in how Bunker Hill property owners feel about many issues relative to zoning ordinances, population growth, community services, and kinds of growth the community desires. Therefore; they have asked the Cooperative Extension Service to assist them in conducting a survey of property owners. In a few days you will receive a questionnaire in the mail. It v/ill take about 10 to 15 minutes of your time to complete. The information you volunteer on this questionnaire will be categorized and presented back to your elected township offic­ ials to consider in their task of studying land use planning. If you choose to participate you will also receive a summary of the survey findings as soon as it is available. The survey will be confidential as you will not be asked to identify yourself on the questionnaire. Mr. Bob Roller and Allen Kirk, Michigan State University graduate students, will be conducting the survey and summariz­ ing the results. Sincerely yours, lines E . Mulvany iunty Extension Director JE M ;kb 261 COOPERATIVE EXTENSION SERVICE M IC H IG A NSTA TEU NIVERSITY A N D U .S .D EPA R TM EN TO FAG RICULTUREC O O PER A TIN G INGHAM COUNTY Cooperative Extension BIdg. 127 E. Maple St. Mason. Michigan 48854 Telephone 677-9411 Novem ber 27, 1973 Dear Bunker Hill Township Property Owner; The Bunker Hill Township Officials are currently evaluating the present zoning ordinances and a need for other land use ordinances. They are interested in how Bunker Hill property owners feel about many issues relative to zoning ordinances, population growth, community services, and kinds of growth the community desires. Therefore, they have asked the Coop­ erative Extension Service to assist them in conducting a survey of property owners. Enclosed is a questionnaire which will take about 10 to 15 minutes of your time to complete: The information you volunteer on this questionnaire will be categorized and pre­ sented back to your elected township officials to consider in their task of studying land use planning. If you wish, you may also receive a summary of the survey findings. The survey will be confidential as you will not be asked to identify yourself on the questionnaire. Mr. Bob Roller and Alan Kirk, Michigan State University grad­ uate students, will be conducting the survey and summarizing the results. Sincerely yours, J - p / Us mnes E. Mulvany iounty Extension Director JEM;kb end 262 CALCULATION OF SAMPLE SIZE FOR THE THUMB AREA QUESTIONNAIRE DISTRIBUTION The process which led to the generation o f the sample sizes fo r each county is summarized as follow s: Formula fo r an unbiased estimate of the v a riab le p: N-n pq 1 v(p) = S + pq = ----- (------) p (n -l)N N n-1 9 Where: N-n N = population s ize , n = sample s ize . p = proportion o f one response in a two response choice (yes-no). q = the proportion of the other response in a two response choice. /PQ N Thus Sp - / / -Nj -pn (jp p) The confidence in t e r v a l, e , was calculated from the standard d eviatio n , Sp, and the value from the z d is trib u tio n corresponding with the chosen le v e l o f s ig n ific a n c e , a. * ■ z< y 2 xu / N - n ,pq \ Thus e = z / — (— ) ^William G. Cochran, Sampling Techniques (2nd e d itio n ) New York: John Wiley and Sons, In c ., 1963, p. 51. 2 I b id . , p. 75. 263 The confidence in te rv a l was expressed as a plus or minus quantity: p + e or /N -n .pq . The above formula fo r the confidence in te rv a l was solved fo r n, the sample s iz e . /N - n pq e = Zv --- (--- ) N e 2 = z n-1 2 ,N-n e2 a z2 (J H ,, ( h _ } e2 - 2 (1 - 1 ) (pq) 2 „ z 2 (pg) _ z 2 (pq) e “ n N z2.(P.q.). ~ e2 + ,z2 (p.q). n N z 2 (p q ) z 2 (pq) , 2 n - — + e 264 When a = .10; thus z = 1.65 e = .05 p = .5 q = .5 N = Total number of households in each county 3 Huron County = 10,325 Sanilac County = 10,551 Tuscola County = 13,709 3 County and Regional Facts, S tate Planning and Development Region 7 , Section I , Table 8 A, p. 30. 265 M IC H IG A N STATE U N IV E R S IT Y DEPARTMENT OF RESOURCE DEVELOPMENT EAST LANSINC; • MICHIGAN ■ ISSM NATURAL RESOURCES BUILDING April 15, 1974 Dear Thumb Area Re sident: In many p a r t s o f Michigan dr a m a ti c changes are underway, i n v o l v i n g p o p u l a t i o n growth, commercial and i n d u s t r i a l development, r e s i d e n t i a l development, and increased demand f o r land use p la n n in g and c o n t r o l . The Thumb Area i s a l s o faced w i t h t hes e issu es. Your help is needed in d e t e r m i n i n g how people in t h e Thumb Area f e e l on these subjects. The enclosed q u e s t i o n n a i r e is being s e n t t o a sample o f r e s i d e n t s randomly chosen from t el ep hon e l i s t i n g s in Huron, Tu sc o la , and S a n i l a c C o u n t ie s , and t o a s e l e c t i o n o f o f f i c i a l s in th ese c o u n t i e s . Thi s survey i s bei ng con­ ducted by Michigan S t a t e U n i v e r s i t y , w i t h the c o o p e r a t i o n o f yo ur coun ty Board o f Commissioners, your Co ope ra ti ve Ext ens ion S e rv ic e o f f i c e , and t h e Thumb Area Human Development Commission. The q u e s t i o n n a i r e should ta k e about 15 o r 20 minutes t o complete, based on p i l o t study fin d in g s . I f you are m a r r ie d , e i t h e r you o r y o u r spouse may f i l l out t h e q u e s t i o n n a i r e . A l l responses w i l l be c o n f i d e n t i a l ; no names w i l l be i d e n t i f i e d w i t h i n d i v i d u a l responses o r w i t h t a b u l a t e d r e s u l t s . With t h e f i n d i n g s o f t h i s s ur v ey , local leaders and community groups should be b e t t e r ab le t o r e p r e s e n t c i t i z e n i n t e r e s t s and d e s i r e s . The more people who r e p l y t o t h i s q u e s t i o n n a i r e , the more r e l i a b l e and useful t h e r e s u l t s w i l l be. Please t ak e tim e t o f i l l i t o u t and r e t u r n i t as soon as p o s s i b l e in th e en cl ose d business r e p l y envelope. Thank you v er y much f o r y ou r c o o p e r a t i o n . Sincerely Alan Ki r k Research C o o r d i n a t o r Thumb Area Community Development Survey AK /j o 266 The general findings of the Community Development Survey will be presented in local newspapers. If, however, you would like a summary of the survey findings, please fill out this form and return it with your completed questionnaire. NAME ______________________________________ ADDRESS______________________________________ (zip code) Dear Resident: A questionnaire concerning community develop­ ment was recently mailed to you from Michigan State University. Your response is needed in order to make accurate conclusions. If you have not yet responded, I hope you w ill please take a few minutes now to f ill out the ques­ tionnaire and return it in the prepaid envelope. If you have already completed and returned the ques­ tionnaire, thank you for your cooperation. Thank you, Alan Kirk Research Coordinator 267 Dear Thumb Area Resident, Several weeks ago a questionnaire concerning issues in community development was mailed to you from Michigan State University. If you have not had a chance to respond, I hope you will take a few minutes to fill it out and return it to us. A greater number of responses will make the results of the study much more useful. I am enclosing an extra copy of the questionnaire for your convenience. Thank you very much for your help. Sincerely, Alan Kirk Research Coordinator Thumb Area Community Development Survey 268 CALCULATION OF CHI SQUARE TEST STATISTICS FOR THE NON-RESPONDENT SURVEY Question A.I.a What would you like to see happen to the population of your county over the next 5 years? Decrease Non-respondents Mail Survey Hq : Stay the Same Increase No. No. % No. 8 7.0 76 66.7 20 59 4.8 814 66.6 300 % % Don't Know Total No. % 17.5 10 8.8 114 24.5 50 4.1 1,223 There is no difference between the non-respondent distribution and the mail survey distribution. with a = .10 Calculated x Tabled x 2 2 value with 3 degrees of freedom = 6.25 =8.29 Reject H q Question B .3 What do you think of the idea of having a general overall public plan for the future uses of land? No p. *CI No. Non-respondents Mail Survey g H : No Preference Yes No. p. 'o Don't Know No. 0o- No. 'o 29 25.4 65 57.0 5 4.4 15 13.2 114 478 39.2 617 50.6 43 3.5 81 6.6 1,219 There is no difference between the non-respondent distribution and I I| \ i the mail survey distribution. with Total a = .10 Tabled x 1 | Calculated x 2 = 12.46 | Reject H Q 2 value with 3 degrees of freedom = 6.25 269 Question B.7 Do you support the general concept of having ordinances to enforce a land use plan? No % No. Non-respondents Mail Survey Hq : Don't Know Yes % No. Total % No. 25 21.9 77 67.5 12 10.5 114 311 25.6 789 65.0 113 9.3 1,213 There is no difference between the non-respondent distribution and the mail survey distribution. with a= .10 Calculated x Tabled x 2 2 value with 2 degrees of freedom = 4.61. = 0.85 Accept H q Question C.l.a Should more efforts be made to increase industry within this county? D o n 't No Yes g, *o No. Non-respondents Mail Survey H : o No. Know % No. Total o„ *o 45 39.5 54 47.4 15 13.2 114 366 30.7 641 53.7 187 15.7 1,194 There is no difference between the non-respondent distribution and the mail survey distribution. with a = .10 Tabled x Calculated x2 = 3.80 Accept H q 2 value with 2 degrees of freedom = 4.61 270 Question E .1.a Do you feel that the addition of more housing would be desirable in your county? No % No. Non-respondents Mail Survey Hq : D o n 't Know Yes No. % Total % No. 45 39.5 54 47.4 15 13.2 114 366 30.7 641 53.7 187 15.7 1,194 There is no difference between the non-respondent distribution and the mail survey distribution. with .10 a= Calculated x Tabled x 2 2 value with 2 degrees of freedom = 4.61 =3.80 Accept H q Question F .2.a Do you feel that the growth of tourism in your county would be beneficial? No a -Q No. Non-respondents Mail Survey H : o D o n 't Know Yes No. % No. Total o. 36 31.9 62 34.9 15 13.3 113 453 39.0 463 39.8 247 21.2 1,163 There is no difference between the non-resnondent distribution and the mail survey distribution. with a= .10 Calculated x Reject H q Tabled x 2 =10.2 2 value with 2 degrees of freedom = 4.61 271 Question G. 1 What is your age? 18-29_______ 30-39_______ 40-49_______ 50-59_______ 60-69________ 70+______ Total NonRespondents Mail Survey No. % No. % No. % No. % No. % No. % 15 13.2 19 16.7 22 19.3 19 16.7 20 17.5 19 16.7 114 147 12.3 183 15.4 213 17.9 226 19.0 247 20.7 175 14.7 1,191 H : o There is no difference between the non-respondent distribution and the mail survey distribution. with' a = .10 Calculated x Tabled x 2 2 value with 5 degrees of freedom = 9.24 =10.2 Reject H q Question G.2. What is your sex? Male Female No. Non-respondents Mail Survey Hq : *6 No. Total o, "o 52 45.6 62 54.4 114 810 67.4 391 32.6 1,201 There is no difference between the non-respondent distribution and the mail survey distribution. with a = .10 Calculated x Reject H Tabled value of x 2 =21.91 2 with 1 degree of freedom = 2.71 272 Question G.4.a What is your major full-time occupation? Profess. & Manager Retired Labor & Service House­ wife & % % % No. 7 6.1 15 13.2 20 17.5 17 14.9 28 24.6 27 23.7 114 185 15.9 192 16.5 192 16.5 145 12.5 268 23.0 182 15.6 1,164 Hq : % % Total No'. Non^ Respondents Mail Survey Sales Clerical & Crafts No. % No. No. No. There is no difference between the non-respondent distribution and the mail survey distribution. with a = .10 Tabled x Calculated x =12.09 value with 5 degrees of freedom = 9.24 Reject H Question G.9. in the open countryside? Do you live: in a built up area? _in an incorporated village or city? Open Countryside % No. Non-respondents Mail Survey Hq : Built up Area No. % Incorpor. Vil. or City No. Total 62 24.4 21 18.4 31 % 27.2 114 551 46.6 205 17.3 427 36.1 1,183 There is no difference between the non-respondent distribution and the mail survey distribution. with a = Accept H ,10 Tabled x 2 value with 2 degrees of freedom = 4.61 Question G.15. What is the highest number of years you have completed in school? Non-resondent Main Survey Hq : Elementary & Junior High School High School No. % No. 31 27.2 47 223 18.6 534 % Vocat. Train.& College______ Total No. % 41.2 36 31.6 114 44.4 445 37.0 1,202 There is no difference between the non-respondent distribution and the mail survey distribution. with a = .10 Calculated x Reject H q Tabled x 2 =5.12 2 value with 2 degrees of freedom = 4.61 274 Question G.16. What is your approximate yearly total family income? $9,001<$9,000_______ $15,000 Non-respondents Mail survey Hq : >$15,000 Total No. % No. % No. % 51 49.0 39 37.5 14 13.5 104 450 40.3 376 33.7 291 26.1 1,117 There is no difference between the non-respondent distribution and the mail survey distribution. with a = .10 Calculated x Reject H o Tabled x 2 =8.24 2 with degrees of freedom = 4.61 275 Thumb Area Project Coding Key Column 1-6 7 Question Individual Response Number Original Variable Number X^ Mailing Wave 0 - Not Known 1 - First Wave X 2 - Second Wave 8 Card Number 1 - First Card 9 Blank 10 A-l-a County Population X^ 0 - No Response 1 - Decrease 2 - Stay About the Same 3 - Increase 9 - Don't Know 11 A-l-b County Population Growth Policy X^ 0 - No Response 1 - No 2 - Yes 9 - Don't Know 12 A-2-a Township Population 0 - No Response 1 - Decrease 2 - Stay About the Same 3 - Increase 9 - D o n 't Know X,. Original Variable Number 276 Question Column 13 A-2-b Township Population Growth Policy X^ 0 - No Response 1 - No 2 - Yes 9 - Don't Know 14 Blank 15 B-l Land Use Competition 0 - No Response 1 - No 2 - Yes 9 - Don't Know 16 B-2 Understand Land Use Planning Xg 0 - No Response 1 - No 2 - Yes 9 - Don't Know 17 B-3 Land Use Plan Acceptance X^ 0 - No Response 1 - Don't Like the Idea 2 - Like the Idea 8 - Don't Care 9 - Don't Know 18 B-4 Level of Land Use Plan 0 - No Response 1 - Township or Municipal 2 - County 3 - Multi-County Region 4 - State 8 - No Preference 9 - Don't Know X^Q 111 Question Column 19 B-5 Knowledge of Land Use Plan Within County Original Variable Number X^ 0 - No Response 1 - No 2 - Yes 20 B-6 Zoning Understanding X^ 0 - No Response 1 - No 2 - Yes 9 - Don't Know 21 B-7 Ordinances to Enforce Plan X^ 0 - No Response 1 - No 2 - Yes 9 - Don't Know 22 B-8-2 Zoning Ordinances X^ 0 - No Response 1 - No 2 - Yes 9 - Don't Know 23 B-8-b Subdivision Regulations X^^ 0 - No Response 1 - No 2 - Yes 9 - Don't Know 24 B-8-c Building Regulations 0 - No Response 1 - No 2 - Yes 0 - Don't Know X.^. 278 Coli'1™’1 25 Question B-9 Original Variable Number Zoning Level 0 - No Response 1 - Township or Municipal 2 - County 3 - Multi-County Region 4 - State 8 - No Preference 9 - Don't Know 26 B-9 Building Regulations X^g 0 - No Response 1 - Township or Municipal 2 - County 3 - Multi-County Region 4 - State 8 - No Preference 9 - Don't Know 27 B-9 Subdivision Regulations X^g 0 - No Response 1 - Township of Municipal 2 - County 3 - Multi-County Region 4 - State 8 - No Preference 9 - Don't Know 28 B-10-a Land Use Planning and Control Cooperation 0 - No Response 1 - No 2 - Yes 9 - Don't Know X^ q 279 Question Column 29 B-10-b Level Original Variable Number of Lack of Cooperation *21 0 - No Response 1 - City-Township 2 - Township-Township 3 - Township-County 4 - County-City 5 - County-County 6 - County-State 7 - State-Local (Township, City or Village) 8 - Other 9 - Don't Know 30 B-ll-a Should There Be Cooperation-Planning X 22 There be Cooperation-Control X^g 0 - No Response 1 - No 2 - Yes 9 - Don't Know 31 B-ll-b Should 0 - No Response 1 - No 2 - Yes 9 - Don't Know 32 B-12 Should Farm Land be Protected 0 - No Response 1 - No 2 - Yes 9 - Don't Know *24 280 Question B-13 Original Variable Number Should Shoreline Areas be Reserved ^25 0 - No Response 1 - No 2 - Yes 9 - Don't Know Blank C-l-a Increase Industry Within the County 0 - No Response 1 - No 2 - Yes 9 - Don't Know C-l-b Why Increase County Industry 00 - No Response 01 - Unsuitable environment: area should be kept in farming, residences, and resorts-raises land prices too high for agriculture-industry belongs in urban areas. 02 - Costs too much: causes increases in taxes, industry does not carry fair share of costs 03 - Insufficient facilities to support industry: water, sewage, manpower. 04 - Undesirable effects: noise, pollution, population growth, traffic problems, loss of land. 05 - Not needed: have enough industry now, growing too fast now. 06 - Other miscellaneous negative reasons. X„ Zo Original Variable Number 281 Question Column 36-37 (cont.) Yes 07 - Increased employment opportunities, decreased unemployment. 08 - Broadened tax base: revenues. increase tax 09 - Eliminate need for long distance commuting: keep people closer to home, use less gas, keep money in area. 10 - Keep young people from leaving the area: more jobs for young people. 11 - Diversify the economic base: dependence on farming. reduce 12 - Reduce welfare. 13 - Conditional upon cleanliness: non­ polluting, type, small size, location. 14 - Generally contribute to community development: the economy, more business, buying, people, progress, build up the community, increased standard of living, more services possible, add to the value of the area. 15 - Increase incomes, supplement farm incomes, seasonal work. 16 - Other miscellaneous positive reasons. 17 - Other 38 C-2-a Increase Industry Within Local Area X^g 0 - No Response 1 - No 2 - Yes 9 - Don't Know 39-40 C-2-b Why Increase Local Industry 00 - No Response No 01 - Unsuitable environment: area should be kept in farming, residences, and resorts-raises land prices too high for agriculture-industry belongs in urban areas. X^ q Original Variable Number 282 Question Column 02 - Costs too much: causes increases in taxes, industry does not carry fair share of costs 39-40 (cont.) 03 - Insufficient facilities to support industry: water, sewage, manpower. 04 - Undesirable effects: noise, pollution, population growth, traffic problems, loss of land. 05 - Not needed: have enough industry now, growing too fast now. 06 - Other miscellaneous negative reasons. Yes 07 - Increased employment opportunities, decreased unemployment. 08 - Broadened tax base: revenues. increase tax 09 - Eliminate need for long distance commuting: keep people closer to home, use less gas, keep money in area. 10 - Keep young people from leaving the area: more jobs for young people. 11 - Diversify the economic base: dependence on farming. reduce 12 - Reduce welfare. 13 - Conditional upon cleanliness: non­ polluting, type, small size, location. 14 - Generally contribute to community development: the eocnomy, more business, buying, people, progress, build up the community, increased standard of living, more services possible, add to the value of the area. 15 - Increase incomes, supplement farm incomes, seasonal work. 16 - Other miscellaneous positive reasons. 17 - Other. 41 C-3 Location of Additional Industry 0 - No Response 1 - No restrictions 2 - Within incorporated cities and villages 3 - Only in controlled, specified industrial parks 4 - Other Q — - ij. X^ q 283 Question Original Variable Number Blank D-l-a More Commercial Shopping and Services in the County X 31 0 - No response 1 - No 2 - Yes 9 - Don't know D-l-b What Type of Shopping and Services 1 - General unspecific answer: any kind, all kinds, specialty shops, general stores, retail, wholesale, shopping services, etc. 2 - Shopping center, mall, plaza. 3 - Department store, chain store, discount store (Yankee, K-mart, Sears, Wards, etc.) 4 - Supermarket, grocery, food store 5 - Clothing, shoes. 6 - Doctors, dentists, pharmacy, hospital. 7 - Recreation-rollerrink, theater, etc. 8 - Restaurants, drive-ins. 9 - Specialty and other stores- lumber, plumbing, hardware, farm supplies, appliance repair, sporting goods, automotive. D-l-b (Second response) 0 - No response 1 - Better selection; lower prices; more competition and comparison; more in small communities-independently owned businesses. X^ Original Variable Number 284 Question 2 - Shopping Center, mall, plaza. 3 - Department store, chain store, discount store (Yankee, K-Mart, Sears, Wards, etc.) 4 - Supermarket, grocery, food store. 5 - Clothing, shoes. 6 - Doctors, dentists, pharmacy, hospital 7 - Recreation-rollerrink, theater, etc. 8 - Restaurants, drive-ins. 9 - Specialty and other stores - lumber, plumbing, hardware, farm supplies, applicance repair, sporting goods, automotive. D-2 Location of Additional Shopping Facilities 0 - No response 1 - No preference; anywhere. 2 - Downtown areas of cities and villages 3 - Shopping centers at the outskirts of cities and villages. 6 - Mix of locations. 9 - Don't know. Blank E-l-a Additional Housing in County 0 - No response 1 - No 2 - Yes 9 - Don't know E-l-b Additional Housing in Township of Local Community 0 - No response 1 - No 2 - Yes 9 - Don't know X^ X 285 Question Column 50 E-2 Additional Housing Type Original Variable Number X 37 0 - No response 1 - Moble homes 2 - Single family homes 3 - Duplexes 4 - Apartments 5 - Condominiums 6 - A mix of various types of housing 8 - No preference 51 E-3 Additional Housing Location X 38 0 - No response 1 - No restrictions on location; anywhere 2 - Large rural lots 3 - Rural subdivisions 4 - Subdivisions adjacent to or within villages or cities 6 - Multiple or mixed answers 9 - Don't know 52 E -4 Mobile Home Location X 39 0 - No response 1 - No restrictions on location; anywhere 2 - Rural mobile home parks 3 - Mobile home parks adjacent to or within villages or cities 6 - Multiple or mixed answers 9 - Don't know 53 Blank 54 F-l-a Are Family Recreation Needs Being Met 0 - No response 1 - No 2 - Yes 9 - Don't know X 40 286 Question Column 55-56 F-l-b Original Variable Number Additional Recreation Facilities in County (First Answer) 00 - No response 01 - Archery range 02 - Athletic field 03 - Band Shell 04 - Ball diamond - softball 05 - Ball diamond - baseball 06 - Basketball courts 07 - Bathhouse 08 - Beaches 09 - Boat launching ramps, Harbor facilities 10 - Campground - trailer 11 - Campgound (general) 12 - Campground Day-camps 13 - General recreation center 14 - Senior citizen center - program 15 - Handicapped center - program 16 - Cultural programs - theaters, plays, etc. 17 - Other center 18 - Docks, piers, waterfronts 19 - Fencing 20 - Football fields 21 - Golf courses 22 - Horseshoe courts 23 - Ice rink - outdoor artificial 24 - Ice rink - indoor artificial 25 - Ice rink - outdoor natural 26 - Land acquisition 27 - Landscaping 28 - Lighting - baseball 29 - Lighting - softball 30 - Lighting - football 31 - Lighting - tennis - tennis courts 32 - Lighting - basketball 33 - Magic square 41 287 Column 55-56(cont.) Question Original Variable Number 34 - Marina's 35 - Parking 36 - Picnic areas (tables, grills) 37 - Playground 38 - Rest rooms 39 - Roads 40 - Shelters 41 - Shuffleboard 42 - Site preparations 43 - Skiing areas 44 - Sled and toboggan areas 45 - Swimming, pool expansion, renovation, improvements 46 - Swimming pool - indoor 47 - Swimming pool - outdoor 48 - Tennis courts 49 - Trails - bicycle 50 - Trails - hiking or unspecified 51 - Trails - nature 52 - Trails - snowmobile 53 - Trails - off road recreation vehicle 54 - Utility service 55 - Program development 60 - Political (establishment of departments) 61 - Social open land 62 - Economic 63 - Scenic drives and over looks 64 - Bridle, horse trails 65 - Winter sports 66 - Parks 67 - Youth recreation programs or center 68 - Hunting 69 - Fishing 70 - All examples given; anything 80 - Other Original Variable Number 288 Question column 57-58 F-l-b Additional Facilities in County X^ (Second Answer) (Same as First Answer) 59-60 F-l-b Additional Recreation Facilities In Township X^ (First Answer) (Same as Additional Recreation Facilities in County) 61-62 F-l-b Additional Recreation Facilities in Township X ^ (Second Answer) (Same as First Answer) 63-64 F-l-c Additional Recreation Programs in County X^j. (First Answer) (Same as F-l-b Additional Recreation Facilities in County) 65-66 F-l-c Additional Recreation Programs in County X^ (Second Answer) (Same as F-l-b Additional Recreation Facilities in County) 67-68 F-l-b Additional Recreation Township Programs in X^ (First Answer) (Same as F-l-b Additional Recreation Faci­ lities in County) 69-70 F-l-b Additional Recreation Programs in Township (Second Answer) (Same as F-l-b Additional Recreation Facilities in County) X^g Original 289 I Question column F-2-a Variable Number Tourism Growth x„„ 49 0 - No response 1 - No 2 - Yes 9 - Don't know P-2-b Why Tourism Growth X rr. 50 0 - No response No 1 - Enough now, too crowded already, too many tourists already, we have plenty. 2 - Conflict with community or lifestyle; keep our town small and pleasant, like peace and quiet, we live very well with­ out them, mostly residential, agricultural, conflicts with farming. 3 - Create problems; environmental degradation, overcrowding, drugs, litter, traffic, un­ desirable people, vandals, need for more police, etc. 4 - Nothing to attract tourists; nothing of interest here, not conducive to tourism, inadequate facilities to have tourism. 5 - Other negative reasons. Yes 6 - Good for business, economy, jobs, brings in money, income for local residents, yearround business. 7 - Generally contributes to growth of the community; more people, developers, more facilities, social benefits. 8 - Good location; lakeshore, hunting, camping, fishing, $ % resort homes, facilities available. ij % 9 - Other positive reasons. | 1 _ Second Cafe? Question Column 1-6 Original Variable _Number Individual Response Number Mailing Wave 0 - Not known 1 - First wave 2 - Second wave Card Number 2 - Second card 9-10 G-l Age X 51 (Actual age will be coded) 00 - No response 11 G-2 Sex X 52 0 - No response 1 - Male 2 - Female 12 G-3 Marital Status X 53 0 - No response 1 - Single 2 - Married 3 - Separated, divorced, or widowed 13-14 G-4-2 Occupation 00 - No response 01 - Professional, technical, and kindred workers (Engineers, physicians, dentists, nurses, pharmacists, veterinarians, teachers (except administrators), technicians, accountants, librarians, reporters, lawyers, clergymen, social workers) X 54 291 Question Column 13-14 (cont.) Original Variable Number 02 - Managers, administrators, selfemployed, salaried (assessors, bankers, wholesale and retail buyers, railroad conductors, school administrators, public administration inspectors, "business men", "contractors," "merchants") 03 - Sales and Clerical Workers (real estate agents, brokers, sales clerks, bookkeepers, secretaries, bank tellers, cashiers, library attendents, mail carriers, mail handlers, mail clerks, teacher aids, telephone operators) 04 - Craftsmen and Foremen (builders-, mechanics, repairmen, machinists, carpenters, masons, electricians, painters, road machine operators, plumbers) 05 - Operative (Manufacturing, transporta­ tion, etc.) and Laborers (Gas station attendants, meat cutters, welders, bus drivers) 06 - Farmers 07 - Service workers (Military, janitors, maids, bartenders, cooks, waiters, health aides, orderlies, LPN's, barbers, housekeepers, welfare aides, firemen, policemen, guards) 08 - Retired 09 - Unemployed or handicapped 10 - Housewife 15-16 G-4-b Second Occupation X (Same as G-4-2 Occupation except, 02 office-holder with some other primary occupation.) 17-18 G-4-c Father's Occupation (Same as G-4-a Occupation except, 00 indicates both No response or Deceased) 19 Blank X 55 292 Question G-5-a Fraternal Organizations Original Variable Number X 57 0 - No response 1 - No 2 - Yes G-5-a Number of Fraternal Organizations X 58 (Actual number will be coded.) G-5-b Community Service Organizations 59 (Same as G-5-a Fraternal Organizations.) G-5-b Number of Community Service Organizations X 60 (Actual number will be coded.) G-5-c Farm Organizations 61 (Same as G-5-a) G-5-c Number of Farm Organizations X 62 (Actual number will be coded.) G-5-d Formal Social or Recreational Organizations 63 (Same as G-5-2.) G-5-d Number of Formal Social or Recrea­ tional Organizations X 64 (Actual number will be coded.) G-5-e Unions (Same as G-5-a.) G-5-e Number of Unions (Actual number will be coded.) X 65 293 Question G-5-f Original , Variable Number Professional Organizations X^ (Same as G-5-a) G-5-f Number of Professional Organizations X^g (Same as G-5-a.) G-5-g Political Organizations (Same as G-5-a.) X^g G-5-g X^^ Number of Political Organizations (Actual number will be coded.) G-5-h Other Groups X^^ (Same as G-5-a.) G-5-h Number of Other Groups ^72 (Actual Number will be coded.) Total Number of Organizations X^g (The total number of all types of organizations belonged to will be coded.) Blank G-6-a Registered Voter X^^ 0 - No response 1 - No 2 - Yes G-6-b Political Party Identification 0 - No response 1 - Democratic 2 - Republican 3 - American Independent Party 4 - Other 5 - None X^,- 294 Question Column 41 G-6-c Original Variable Number Voting in National Election 76 0 - No response 1 - No 1 - Yes 42 G-6-d Voting in County Election 77 0 - No response 1 - No 2 - Yes 43 G-6-e Voting in Local Election X 78 0 - No response 1 - No 2 - Yes 44 G-6-f Voting Rate 79 0 - No response 1 - None (0%) 2 - Some (1-50%) 3 - Most (51-99%) 4 - All (100%) 45 G-7-a Response of County Governmental Officials X 80 0 - No response 1 - Not responsive at all 2 - Somewhat responsive 3 - Responsive 4 - Very responsive 9 - Don't know 46 G-7-b Response of Local Governmental Officials (Same as G-7-a.) 47 Blank 81 295 Question Column 48 G-8-a County of Residence Original Variable Number X 82 1 - Huron 2 - Sanilac 3 - Tuscola 4 - Other 49-50 G-8-b Township or Incorporated Place of Residence Huron County - Incorporated Places 01- Bad Axe 06 - Owendale 02 - Caseville 07 - Pigeon 03 - Elkton ■ 08 - Port Austin 04 - Harbor Beach 09 - Port Hope 05 - Kinde 10 - Sebewaing 11 - Ubly Huron County - Townships 20 - Bingham 34 - McKinley 21 - Bloomfield 35 - Meade 22 - Brookfield 36 - Oliver 23 - Caseville 37 - Paris 24 - Chandler 38 - Pointe Aux Barques 25 - Colfax 39 - Port Austin (Grindstone City) 26 - Dwight 40 - Rubicon 27 - Fairhaven (Bay Port) 41 - Sand Beach 28 - Gore 42 - Sebewaing 29 - Grant 43 - Sheridan 30 - Hume 44 - Sherman (Ruth) 31 - Huron 45 - Cigel 32 - Lake 46 - Verona 33 - Lincoln 47 - Winsor 99 - Outside the Thumb Area 83 296 Question Column 49-50 (Cont.) Sanilac County - Incorporated Places 01 - Applegate 08 - Marlette 02 - Brown City 09 - Melvin 03 - Carsonville 10 - Minden City I 04 - Croswell 11 - Peck I I 05 - Deckerville 12 - Port Sanilac 06 - Forestville 13 - Sandusky II 07 - Lexington Sanilac County - Townships 20 - Argyle (Argyle) 33 - Lamotte (Decker) 34 - Lexington 21 - Austin 35 - Maple Valley 22 - Bridgehampton 23 - Buel 36 - Marion 37 - Marlette 24 - Custer 38 - Minden (Palms) 25 - Delaware 26 - Elk 39 - Moore (Snover) 40 - Sanilac 27 - Elmer 41 - Speaker 28 - Evergreen 29 - Flynn 42 - Washington 43 - Watertown 30 - Forester (Forester) 44 - Wheatland 45 - Worth 31 - Fremont 32 - Greenleaf 99 - Outside Thumb Area Tuscola County Incorporated Places 01 - Akron 07 - Mayville 02 - Caro 08 - Millington 03 - Cass City 09 - Reese 04 - Fairgrove 10 - Unionville 05 - Gagetown 11 - Vassar 06 - Kingston Original Variable Number 297 Question Column 49-50 (Cont.) Original Variable Number Tuscola County - Townships 20 - Akron 32 - Indianfields 21 - Aimer 33 22 - Arbela 34 - Kingston 23 35 - Koylton - Columbia - Juniata 24 - Dayton 36 - Millington 25 - Denmark 37 - Novesta (Deford) 26 - Elkland 38 - Tuscola (Tuscola) 27 - Ellington 39 - Vassar 28 - Elmwood 40 - Watertown (Fostoria) 29 - Fairgrove 41 - Wells 30 - Fremont 42 - Wisner 31 — Gilford 99 - Outside Thumb Area 51 G-9 Location of Residence X„. 84 0 - No response 1 - Open countryside 2 - Built up area not within city or village (unincorporated settlement) 3 - Within an incorporated village or city 52-53 G-10-a Years Lived in Township or Local Community X^,. 00 - No response (Actual number of years will be coded.) 54-55 G-10-b Years Lived in the County Xor oo (Actual number of years will be coded.) 56-57 G-10-c Years Lived in Thumb Area 00 - No response (Actual number of years will be coded.) X„_ 87 . 298 Original Variable Number Question G-ll-a X Previous Residence 88 0 - No response 1 - South Eastern Michigan Urban within 5 miles of a large city (S.M.S.A.) 2 - South Western Michigan Urban within 5 miles of a large city (S.M.S.A.) 3 - South Eastern Michigan Rural 4 - South Western Michigan Rural 5 - Michigan - Northern Lower Peninsula and Upper Peninsula 6 - Out of state 7 - Other G-ll-b Why Chose to Live in Thumb X 89 0 - No response 1 - Employment; business, transfer. 2 - Property; owned cottage here, property was cheap. 3 - Personal/family reasons: here; raised here. folks moved 4 - Retirement 5 - Positive attractions; amenities; liked area and people; enjoy the lake; peace and quiet; nature; small population; wanted to live in country. 6 - Rejection of city life; to get away from city; problems of city living; racial issues; crime; unsafe; too many people. 7 - Other miscellaneous reasons. G-12-a Less than School Age Children at Home X^Q (Actual number will be coded.) G-12-b School Age Children at Home (Actual number will be coded.) 299 Question Column 62 G-12-c Adults at Home Original Variable Number X, 92 (Actual number will be coded.) 63-64 Total Family Size 93 (Actual number will be coded) 65 65 Blank 66 G-13 Home Ownership X 94 0 - No response 1 - Own or buying 2 - Rent or leasing 67 G - 14 Property Owned or Buying X 95 0 - No response 1 - up to 1 acre 2 - Over 1 but less than 10 acres 3 - 11 to 40 acres 4 - 41 to 80 acres 5 - 81 to 160 acres 6 - 161 to 320 acres 7 - 321 to 640 acres 8 - Over 640 acres 68 G-14 Property Rented or Leased 96 (Same as G-14 Property Owned or Buying) 69 G-15 Education 0 - No response 1 - Some elementary school but did not complete (less than 6 years) 97 Original Variable 300 Question Column 69 (Cont.) Number 2 - Completed elementary school (6 years) 3 - Some junior high school but did not completed (less than eighth grade) 4 - Completed jurnior high school (eighth grade) 5 - Some high school (but did not complete: 1-3 years) 6 - Completed high school (4 years) 7 - Vocational School or other training 8 - College: 1-3 years 9 - College: 4 years or more 70 G-16 Family Income X yy 0 - No response 1 - Less than $3,000 2 - $3,000 to $6,000 3 - $6,001 to $9,000 4 - $9,001 to $12,000 5 - $12,001 to $15,000 6 - $15,001 to $25,000 7 - $25,001 to $50,000 8 - More than $50,000 71 H-l General Outlook - Tone of Response 0 - No response 1 - Con-response; not enough, need more, needs improvement. 2 - Con-response; bad changes, too much, changes for worse, problems. 3 - Neutral response 4 - Pro-response; good changes, changes for the better, O.K. 99 Original Variable Number JU1 Column 72-73 Question H—1 General Outlook - Subject of Response x ioo 00 - No response 01 - Little change or nochange; slow change 02 - Great change, fast change 03 - General response 04 - General growth, population growth,inmigration 05 - Farming; loss of land; preservation; decrease 06 - Land use planning and control code enforcement) < (zoning, 07 - Shoreline reservation for public use; harbor 08 - Industrial development 09 - Economic development; employment; standard of living 10 - Economy; inflation; prices; wages; cost of living 11 - Commercial services and facilities development 12 - Residential development; housing; home improve­ ment; land buying for homes 13 - Mobile homes increase 14 - Recreation development 15 - Tourism development 16 - Government; increased control 17 - Increased taxation; too high; need broader base; alternative system 18 - Schools; vocational education development 19 - Roads, streets development 20 - Sewers, septic systems development 21 - Water supply development 22 - Solid waste garbage service development 23 - Law enforcement; police; crime; vandalism; judicial system 24 - Youth needs and problems; recreation; employment; rehabilitation; delinquency; hippies 302 Column 72-73 (Cont.) Question Original Variable Number 25 - Morality; religion; care for others; values 26 - Health; hospital, clinic facilties; physicians; dentists 27 - Land prices, valuation increases 28 - Community appearance, attractiveness; appearance or housing and buildings 29 - Pollution littering 30 - Resistance to change, narrow mindedness, conservatism 31 - Planned organized approach to community change development 32 - Racial issues; bussing 33 - Welfare; subsidization for housing, lowincome needs, problems 34 - Competition, conflict in use of land 35 - Increased size 36 - Communication with government; public trust in government; government policy; cooperation in government; home rule; government responsiveness 37 - Drug abuse, including alcohol 38 - Area economic decline; population out­ migration (including youth); small business decline 39 - Senior citizens needs and problems; housing, transportation 40 - City people adaptation, rural urban conflicts 41 - Wildlife preservation, wildland preservation 42 - Transportation services; railroad, bus, air service 43 - Army Corps of Engineers Project 44 - Shortages of fuel 45 - Mental health; retardation; handicapped facilities 46 - Shoreline erosion 47 - Cultural development 48 - Drainage 49 - Fire protection 303 Question Column 72-73 (Cont.) Original Variable Number_ 50 - Farming; costs of inputs; returns on outputs; technology; productivity 51 - Commuting 52 - Vocational education; community college 90 - Don't know; not here long enough 99 - Other 74 (Same as H-l- General Outlook - Tone of Response) X 101 75-76 (Same as H-l - General Outlook - Subject of Response) X 102 77-78 (Same as H-l - General Outlook - Subject of Response) X 103 79-80 (Same as H-l - General Outlook - Subject of Response) X104 304 Coding Format for Completed Transformation Deck | First Card Column li 1-6 Identification Number S 7 Mailing wave 0 - not known 1 - first wave 2 - second wave 8 Blank 9 Card Number 1 = first card 10 Blank 11 Land Use Planning 0 - don't like the idea 1 - like the idea 12 Ordinance to Enforce Plan 0 - no 1 - yes 13 14-15 16 17-18 19-20 Zoning 0 - no 1 - yes Age Actual age coded (F2.0) Sex 1 - male 0 - female Martial Status single married separated, widowed,divorced +1 -1 0 Martial Status single married separated, widowed,divorced +1 +1 -2 Marital Status Single Married Separated, widowed, divorced 21 22-23 24-25 Blank Occupation 01 - 02 03 04 05 06 07 08 09 +1 +1 - +1 - +1 - +1 1 ---1 - -1 ---1 10 - - -1 Occupation 01 - -3 02 1 03 - -1 04 2 0 5 - 0 06 - +1 07 - +2 08 - +1 09 - +1 10 - +2 26-27 Occupation 01 - +1 02 - -1 03 - +1 04 - -1 05 - +1 06 - -1 07 - +1 08 1 09 - +1 10 - -1 28-29 Occupation 01 - - 1 02 - +1 0 3 - 0 04 - +1 05 - -1 06 - +1 07 - -2 08 - +1 09 - -1 10 - +1 1 2 3 x x +1 -1 0 +1 +1 -2 306 30-31 Occupation 0 1 - 0 02 - +1 03 - +1 04 - +1 05 - +1 0 6 - 0 07 - -1 08 - -1 09 ---1 10 - -1 32-33 Occupation 01 - +1 0 2 - 0 03 - -1 0 4 - 0 05 - +1 06 - -1 07 - +1 08 - -1 09 - +1 10 - -1 34-35 Occupation 01 - +1 02 - - 1 03 - -1 04 - +1 0 5 - 0 06 2 07 - -1 08 - +1 0 9 - 0 10 - +2 36-37 Occupation 01 - +2 02 - -1 03 - +1 0 4 --- 1 05 - +1 06 - -2 0 7 - 0 08 - +2 09 - -2 1 0 - 0 307 38-39 40 Occupation 01 - -2 02 - - 2 03 - +2 04 - +2 05 - -1 06 - +1 07 - +2 08 - 0 09 - -1 10 - - 1 Blank 41-42 Second Occupation 00 - +1 01 - +1 02 - 0 03 - +1 04 - +1 05 - + 1 06 - -1 07 - -1 08 - -1 09 - -1 10 - -1 43-44 Second Occupation 00 - -3 01 - -1 02 - -1 03 - -2 04 - 0 05 - +1 06 - 0 07 - +2 08 - +1 09 - +1 10 - +2 45-46 Second Occupation 0 0 - 0 01 - +1 02 - - 1 03 - +1 04 - -1 05 - +1 06 - -1 07 - +1 08 - -1 09 - +1 10 - - 1 308 47-48 Second Occupation 00 - - 1 01 - +1 0 2 - 0 03 - +1 0 4 --- 1 05 - +1 06 2 0 7 - 0 08 - +1 0 9 --- 1 10 - +1 49-50 Second Occupation 0 0 - 0 01 - +1 02 - +1 03 - +1 04 - +1 0 5 - 0 06 1 0 7 ---1 0 8 - 0 09 --- 1 10 1 51-52 Second Occupation 00 - +1 01 - - 1 02 - +2 03 2 04 - -1 05 - +1 06 - +2 07 - -2 08 - +2 09 - +1 10 - -3 53-54 Second Occupation 00 - +3 01 - - 1 02 - - 2 0 3 - 0 0 4 ---3 05 - +3 06 - +1 07 - -1 08 - +3 09 - -1 10 - - 2 309 55-56 Second Occupation 00 - - 2 01 - +2 02 - +3 03 3 04 - +1 05 - -1 06 - -1 07 - -1 08 - +1 09 - +1 1 0 - 0 57-58 Second Occupation 00 - +3 01 - +3 02 - +3 0 3 - 0 04 - -3 05 - -3 06 - -3 07 - +2 08 2 09 - +2 10 - - 2 59-60 Second Occupation 00 - -1 . 01 - -1 02 - -3 03 - +2 04 - +2 05 - +1 06 - -3 07 - +1 08 - +1 09 - +2 10 - - 1 61-62 Fathers Occupation 00 - +1 0 1 - 0 0 2 - 0 03 - +1 04 - +1 05 - +1 06 - -1 07 - -1 08 - -1 09 - -1 10 - - 1 310 63-64 Fathers Occupation 00 - -3 01 - - 1 02 - -1 03 - -2 0 4 - 0 05 - +1 0 6 - 0 07 - +2 08 - +1 09 - +1 10 - +2 65-66 Fathers Occupation 0 0 - 0 01 - +1 02 - - 1 03 - +1 04 - -1 05 - +1 06 - -1 § 1 | 07 ~ +1 08 - -1 09 - +1 'S 10 - -1 67-68 Fathers Occupation 00 - - 1 01 - +1 0 2 - 0 03 - +1 0 4 ---1 05 - +1 06 - -2 0 7 - 0 08 - +1 09 - -1 10 - +1 69-70 Fathers Occupation 0 0 - 0 01 - +1 02 - +1 03 - +1 04 - +1 0 5 - 0 06 - -1 07 - -1 0 8 - 0 09 - -1 10 - - 1 311 71-72 Fathers Occupation 00 - +1 01 - - 1 02 - +2 03 - -2 04 - -1 05 - +1 06 - +2 07 - -2 08 - +2 09 - +1 1 0 --- 3 73-74 Fathers Occupation 00 - +3 01 - - 1 02 2 0 3 - 0 0 4 --- 3 05 - +3 06 - +1 07 - -1 08 - +3 09 ---1 10 - - 2 75-76 Fathers Occupation 00 - - 2 01 - +2 02 - +3 03 - -3 04 - +1 0 5 ---1 06 - -1 0 7 --- 1 08 - +1 09 - +1 1 0 - 0 77-78 Fathers Occupation 00 - +3 01 - +3 02 - + 3 03 - 0 04 - -3 05 - -3 06 - -3 07 - +2 08 - -2 09 - +2 10 - -2 312 Fathers 00 - -1 01 - -1 02 - -3 03 - +2 04 - +2 05 - +1 06 - -3 07 - +1 08 - +1 09 - +2 10 - -1 79-80 01 - Professional, Technical, and Kindred Workers (Engineers, physicians, dentists, nurses, pharmacists, veterinarians, teachers (except administrators), technicians, accountants, librarians, reporters, lawyers, clergyman, social workers) 02 - Managers, Administrators, Self employed, Salaried (Assessors, bankers, wholesale and retail buyers, railroad conductors, school administrators, public administration inspectors, "Business Men", "Contractors", "Merchants") 03 - Sales and Clerical Workers (Real estate agents, insurance agents, brokers, sales clerks, bookkeepers, secretaries, bank tellers, cashiers, library attendants, mail carriers, mail handlers, mail clerks, teacher aids, telephone operators) 04 - Craftsmen and Foremen (Builders, mechanics, repairmen, mechinists, carpenters, masons, electricians, painters, road machine operators, plumbers) 05 - Operatives (Manufacturing, transportation, etc.) and Laborers (Gas station attendants, meat cutters, welders, bus drivers) 06 - Farmers 07 - Service Workers (Military, janitors, maids, bartenders, cooks, waiters, health aides, orderlies, LPN's, barbers, housekeepers, welfare aides, firemen, policemen, guards) 08 - Retired 09 - Unemployed or Handicapped 10 - Housewife Second Job (same as Occupation except, 02 indicates office some other primary occupation. 00 - NO RESPONSE) holder with Father's Occupation (same as Occupation except, 00 indicates both no response or deceased) 313 Occupation X X X X 0 +1 +1 +2 -2 +1 +1 0 -1 -1 -2 0 +1 -1 -1 +1 +2 +1 +1 0 +1 -1 +2 +1 -1 +1 +1 0 +1 -1 +1 -1 +1 0 -1 -2 -2 +1 -1 +2 +1 -2 -1 +1 -1 0 +2 08 -1 +1 -1 +1 -1 -1 +1 +2 0 09 -1 +1 +1 -1 -1 +1 0 -2 -1 10 -1 +2 -1 +1 -1 -1 +2 0 -1 X X X X X 01 +1 -3 +1 -1 02 +1 -1 -1 03 +1 -1 +1 04 +1 -2 -1 05 +1 0 06 -1 07 Second Occupation and Father's Occupation X X X X x 0 +1 +3 -2 +3 -1 +1 +1 -1 -1 +2 +3 -1 0 +1 +2 -2 +3 +3 -3 +1 +1 -2 0 -3 0 +2 -1 -1 +1 -1 -3 +1 -3 +2 +1 +1 +1 0 +1 +3 -1 -3 +1 -1 0 -1 -2 ~1 +2 +1 -1 -3 -3 07 -1 +2 +1 0 -1 -2 -1 -1 +2 +1 08 -1 +1 -1 +1 0 +2 +3 +1 -2 +1 09 -1 +1 +1 -1 -1 +1 -1 +1 +2 +2 10 -1 +2 -1 +1 -1 -3 -2 0 -2 -1 X X X X X 00 +1 -3 0 -1 01 +1 -1 +1 02 0 -1 -1 03 +1 -2 +1 04 +1 0 05 +1 06 314 [Second Card | Column 1-6 Identification Number Mailing wave 0 - not known 1 - first wave 2 - second wave § I 8 Blank 9 Card Number 2 - second card 10 Blank 11 Fraternal Organization Participation 0 - no response, no 1 - yes 12 Service Organization Participation 0 - no response, no 1 - yes 13 Farm Organization Participation 0 - no response, no 1 - yes 14 Formal Social or Recreation Organization Participation 0 - no response, no 1 - yes 15 Union Organization Participation 0 - no response, no 1 - yes 16 Professional Organization Participation 0 - no response, no 1 - yes 17 Political Organization Participation 0 - no response, no 1 - yes 18 Other Group Participation 0 - no response, no 1 - yes 315 19 20-21 22 Blank Total Number of Groups Actual Number of Groups Will Be Coded Blank 23-24 Political Party Identification 1 - +1 2 - +1 3 - +1 4 - -1 5 - -2 25-26 Political Party Identification 1 - -2 2 - -1 3 - 0 4 - +1 5 - +2 27-28 Political Party Identification 1 - +1 2 - -1 3 - +1 4 - 0 5 - -1 29-30 Political Party Identification 1 - +1 2 - 0 3 - -2 4 - -1 5 - +2 Political Party Identification Democrat Republican American Independent Other None X X X X 1 +1 -2 +1 +1 2 +1 -1 3 4 5 +1 0 -1 +1 -2 +2 -1 +1 0 -1 31 Blank 32 Voting in County Elections 0 - no 1 - yes 33 Voting in Local Elections 0 - no 1 - yes 0 -2 -1 +2 316 Blank General Voting Behavior 1 - +1 2 - +1 3 - -1 4 - -1 General Voting Behavior 1 - +2 2 - -1 3 - +1 4 - -2 General Voting Behavior 1 - -1 2 - -2 3 - +2 4 - +1 General Voting Behavior None Some Most All (0%) (1% - 50%) (51% - 99%) (100%) X X 1 +1 +2 -1 2 3 4 +1 -1 -1 +1 -2 +2 -1 -2 +1 X Blank Response of County Government Officials 0 - not responsive 1 - somewhat responsive, responsive, very responsive Response of Local Government Officials 0 - not responsive 1 - somewhat responsive, responsive, very responsive Blank County of Residence 1 - +1 2 - -1 3 - 0 County of Residence 1 - +1 2 - +1 3 - -2 Residence Location 1 - +1 317 Residence Location 1 - +1 2 - +1 3 - -2 51-52 Blank 53 Years Lived in Local Community Actual Years Will be Coded 54-55 County of Residence X Huron Sanilac Tuscola Open Country side Built-up Area City or Village 56-57 X +1 +1 1 +1 -1 2 -2 0 3 Residence Local 1 2 3 X X +1 -1 0 +1 +1 -2 Years Lived in County Actual Number Will be Coded 60 Blank 61 Home Ownership 1 - own or buy 0 - rent or lease 62 Blank 63-64 Property Owned 0 - +1 1 - +1 2 - +1 3 - 0 4 - -1 5 - -1 6 - -1 7 - 0 8 - 0 65-66 Property Owner 0 - +1 1 - -1 2 - +1 3 - -1 4 - 0 5 - -1 6 - +1 7 - -1 8 - +1 318 67_68 Property Owned 0 1 1 - 0 2 - +1 3 - 0 : 4 - -1 5 - 0 6 - +1 7 - +1 8 - -1 69-70 Property Owned 0 - -1 1 - +1 2 - 0 3 - +1 4 - -1 5 - +1 6 - 0 7 - 0 8 1 7 1 -7 2 Property 0 - +2 1 - -2 2 - +1 3 - -1 4 - -2 5 - +2 6 - -1 7 - -1 8 - +2 Owned 7 3 -7 4 Property 0 2 1 - +2 2 - -3 3 - +1 4 - +2 5 - -2 6 — +3 7 - +1 8 - -2 Owned 75-76 Property 0 - -2 1 - 0 2 - +2 3 - 0 4 - +2 5 - +2 6 - -2 7 - -2 8 - 0 Owned 319 77-78 Property Owned 0 - +2 1 - 0 2 2 3 --- 1 4 - -1 5 - +1 6 - +1 7 - -2 8 - +2 79-80 Blank Third Card Column 1-6 Identification Number 7 Mailing Wave 0 - not known 1 - first wave 2 - second wave 8 Blank 9 Card Number 3 - third card 10 Blank 11-12 Property Leased 0 - +1 1 - +1 2 - +1 3 - 0 4 - -1 5 - -1 6 - -1 7 - 0 8 - 0 13-14 Property Leased o - +1 1 - -1 2 - +1 3 - -1 4 - 0 5 - -1 6 - +1 7 - -1 8 - +1 320 15-16 Property Leased 0 - -1 1 - 0 2 - +1 3 - 0 4 - -1 5 - 0 6 - +1 7 - +1 8 - -1 17-18 Property Leased 0 - -1 1 - +1 2 - 0 3 - +1 4 - -1 5 - +1 6 - 0 7 - 0 8 - -1 19-20 Property Leased 0 - +2 1 - -2 2 - +1 3 ----- 1 4 - -2 5 - +2 6 - -1 7 - -1 8 - +2 21-22 Property Leased 0 - -2 1 - +2 2 - -3 3 - +1 4 - +2 5 - -2 6 - +3 7 - +1 8 - -2 321 23-24 Property Leased 0 -2 - 1 - 0 2 +2 - 3 - 0 4 - +2 5 - +2 6 -2 - 7 - -2 8 - 0 25-26 Property Leased 0 - +2 1 2 3 4 5 6 7 8 - 0 2 ---1 - -1 - +1 - +1 - -2 - +2 Property Owned and Leased • 0 X +1 X +1 X -1 X -1 X +2 X -2 X -2 X +2 1 acre 1 +1 -1 0 +1 -2 +2 0 0 1-10 acre 2 +1 +1 +1 0 +1 “3 +2 -2 11-40 acres 3 0 -1 0 +1 -1 +1 0 -1 41-80 acres 4 -1 0 -1 -1 -2 +2 +2 -1 81-160 acres 5 -1 -1 0 +1 +2 -2 +2 +1 161-320 acres 6 -1 +1 +1 0 -1 +3 -2 +1 321-640 acres 7 0 -1 +1 0 -1 +1 -2 -2 8 0 +1 -1 -1 +2 -2 0 +2 None 640 acres 27 28-29 Blank Education 1 2 3 4 5 6 7 8 9 +1 - +1 - - +1 - 0 - -1 - -1 - -1 - 0 - 0 322 Education 30-31 1 - +1 2 - -1 3 4 5 6 7 - +1 - -1 - 0 -1 - +1 8 - -1 9 - +1 32-33 Education 1 - -1 2 - 0 3 - +1 .4 - 0 5 - -1 6 - 0 7 - +1 8 - +1 9 - -1 34-35 Education 1 - -1 2 - +1 3 - 0 4 - +1 5 - -1 6 - +1 7 - 0 8 - 0 9 - -1 36-37 Education 1 2 - - +2 -2 3 - +1 4 - -1 5 - -2 6 - +2 7 - -1 8 - -1 9 - +2 38-39 Education 1 2 - -2 +2 3 - -3 4 - +1 5 - +2 6 - -2 7 - +3 8 - +1 9 - -2 323 Education 40-41 1 -2 - 2 - 0 3 - +2 4 - 0 5 — +2 6 - +2 7 ---2 8 2 9 - 0 Education 42-43 1 - +2 2 - 0 3 - -2 4 ---1 ' 5 - -1 6 - +1 7 - +1 8 2 9 - +2 Education 44 45-46 X X X X X X X X Some Elementary School 1 +1 +1 -1 -1 +2 -2 -2 +2 Elementary School 2 +1 -1 0 +1 -2 +2 0 0 Some Junior High School 3 +1 +1 +1 0 +1 -3 +2 -2 Completed Junior High School 4 0 -1 0 +1 -1 +1 0 -1 Some High School 5 -1 -1 -2 +2 +2 -1 Completed High School 6 -1 -1 +1 +2 -2 +2 +1 Vocatoinal School 7 -1 +1 +1 0 -1 +3 -2 +1 Some College 8 0 -1 +1 0 -1 +1 -2 -2 Completed College 9 0 +1 -1 +2 -2 0 +2 Blank Income 1 - +1 2 - +1 3 - +1 4 - 0 5 - -1 6 - -1 7 - -1 8 - 0 0 -1 0 -1 324 47-48 Income 1 - -1 49-50 2 - -1 3 4 5 6 7 - -1 +1 0 0 +1 8 - +1 Income 1 - +1 2 - -1 3 - +1 4 - -1 5 - +1 ' 6 - -1 7 - +1 8 51-52 - -1 Income 1 - +2 3 3 4 5 - -2 +2 -2 +2 6 - -2 7 - +2 8 53-54 - -2 Income 1 - 0 2 - -1 3 - -1 4 - +1 5 - +1 6 - +2 7 - -2 8 - 0 55-56 Income 1 - -2 2 - +2 3 - 0 4 - -1 5 - +1 6 - -2 7 - 0 8 - +2 325 Income 5 7 -5 8 1 - +2 2 - 0 3 - +2 4 - 0 5 - -2 6 - 0 7 - -2 8 - 0 Income X +1 X +2 X 0 X -2 X +2 $3000 1 $ 3 0 0 0 -$ 6 0 0 0 2 +1 -1 -1 -2 -1 +2 0 $ 6 0 0 1 -$ 9 0 0 0 3 +1 -1 +1 +2 -1 0 +2 $ 9 0 0 1 -$ 1 2 0 0 0 4 0 +1 -1 -2 +1 -1 0 $ 1 2 0 0 1 -$ 1 5 0 0 0 5 -1 0 +1 +2 +1 +1 -2 $ 1 5 0 0 1 -$ 2 5 0 0 0 6 -1 0 -1 -2 +2 -2 0 $ 2 5 0 0 1 -$ 5 0 0 0 0 7 -1 +1 +1 +2 -2 0 -2 $50000 + 8 0 +1 -1 -2 < 5 9 -8 0 X -1 X +1 0 +2 Blank I Tourtft Card Column 1-6 Identification Number 7 -8 Blank 9 1 }« m. 1 yJ t-; ‘ £3 Card Number 4 - fourth card 10-11 Blank 12 County 1 - Huron 2 - Sanilac 3 - Tuscola 13 Blank 1 4 -1 5 Minor Civil Division Code 0 326 Blank 16 6 1970 Minor Civil Division Population (F4.0) 17-20 Blank 21 I960 Minor Civil Division Population (F4.0) 2 2-25 Blank 26 1970 Minor Civil Division Population Density (F10.6) 2 7 -3 6 Blank 37 1960 Minor Civil Division Population Density (F10.6) 3 8-47 Blank 48 1960-1970 Minor Civil Division Population Density Change 49 Sign (+ or -) (F7.6) 4 9-55 Minor Civil Division Codes Huron County Incorporated Places $ 8 f! I it 01 02 03 04 05 06 Bad Axe Caseville Elkton Harbor Beach Kinde Owendale 07 08 09 10 11 Pigeon Port Austin Port Hope Sebewaing Ubly 34 35 36 37 38 39 40 41 42 43 44 45 46 47 McKinley Meade Oliver Paris Pointe Aux Barques Port Austin (Grindstone City) Rubicon Sand Beach Sebewaing Sheridan Sherman (Ruth) Sigel Verona Winsor Townships | -'I $ S i '•1 1 i "i ’f: 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Bingham Bloomfield Brookfield Caseville Chandler Colfax Dwight Fairhaven (Bay Port) Gore Grant Hume Huron Lake Lincoln 327 Sanilac County Incorporated Places 01 02 03 04 05 06 07 Applegate Brown City Carsonville Croswell Deckerville Forestville Lexington 08 09 10 11 12 13 Marlette Melvin Minden City Peck Port Sanilac Sandusky 33 34 35 36 37 38 39 40 41 42 43 44 45 Lamotte (Decker) Lexington Maple Valley Marion Marlette Minden (Palms) Moore (Snouer) Sanilac Speaker Washington Watertown Wheatland Worth 07 08 09 10 11 Mayvilie Millington Reese Unionville Vassar 32 33 34 35 36 37 38 39 40 41 42 Indianfields Juniata Kingston Koylton Millington Novesta (Deford) Tuscola (Tuscola) Vassar Watertown (Fostoria) Wells Wisner Townships 20 21 22 23 24 25 26 27 28 29 30 31 32 Argyle (Argyle) Austin Bridgehampton Buel Custer Delaware Elk Elmer Evergreen Flynn Forester (Forester) Freemont Greenleaf Tuscola County Incorporated Places ■& ii 01 02 03 04 05 06 Akron Caro Cass City Fairgrove Gagetown Townships & & I(>1 M § 1 fiv't I 20 21 22 23 24 25 26 27 28 29 30 31 Akron Aimer Arbela Columbia Dayton Denmark Elkland Ellington Elmwood Fairgrove Fremont Gilford 328 Blank Minor Civil Division Area - Square Miles (F3.1) Blank Identification Number Blank Card Number 5 - Fifth Card Blank County 1 - Huron 2 - Tuscola 3 - Sanilac Blank 1970 County Population (F5.0) Blank 1960 County Population (F5.0) Blank 1970 County Population Density (F8.6) Blank 1960 County Population Density (F8.6) Blank 1960-1970 County Population Density Change 44 Sign (+ or -) (F7.6) Blank County Area - Square Miles (F4.1) Blank