THE IMPACT OF METROPOLITAN DOMINANCE
UPON THE STATUS STRUCTURE AND STATUS
CONSISTENCY OF RURAL-FARM AND

URBAN POPULATIONS

Thesis for the Degree of Ph. D.
MICHIGAN STATE UNIVERSITY
John Sfoeckel
196—6

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m—xests

This is to certify that the

thesis entitled
The Impact of MetrOpolitan Dominance Upon
the Status Structure and Status Con-
sistency of Rural-Farm and Urban
P0pulations

presented by

John Stoeckel

has been accepted towards fulfillment
of the requirements for l

*P h - D -___.._ degree in_§9_c i Q l ng

Date—Aw

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ABSTRACT

THE IMPACT OF METROPOLITAN DOMINANCE UPON
THE STATUS STRUCTURE AND STATUS
CONSISTENCY OF RURAL-FARM AND
URBAN POPULATIONS

by John Stoeckel

The impact of metropolitan dominance upon the status
structure (occupation. education. income) and status con-
sistency of the rural-farm and urban populations is analyzed.
Hypotheses are generated around the two factors that arti-
culate dominance. distance from urban centers, and size
of urban center. The general hypotheses formulated are:

1. There is a direct relation between metropolitan
dominanceanxithe occupational status of rural—
farm and urban populations.

(a) The occupational status of urban populations
will be higher than the occupational status
of rural-farm populations for all degrees
of metropolitan dominance.

2. There is a direct relation between metropolitan
dominance and the educational status of rural-
farm and urban populations.

(a) The educational status of urban populations
will be higher than the educational status of
rural—farm populations for all degrees of
metropolitan dominance.

3. There is a direct relation between metropolitan
dominance and the income status of rural-farm
and urban populations.

John Stoeckel

(a) The income status of urban populations will
be higher than the income status of rural—
farm populations for all degrees of metro-
politan dominance.

4. There is an inverse relation between metropolitan
dominance and the status consistency of rural-

farm and urban populations.

(a) The status consistency of urban populations
will be lower than the status consistency of
rural-farm populations for all degrees of
metropolitan dominance.

In addition two related issues are attended to:

(l) the predictive value of the indicators of dominance for
the status structure and status consistency of the rural—
farm and urban populations; and (2) the utility of the rural—
farm and urban residence categories for sociological research.

Four specific hypotheses were not supported. These

are: (l) the direct relationship between the occupational
status of the urban population and metropolitan dominance--
it was concluded that for the farm and urban populations
at the county level of anslysis the present occupational
status scale is adequate and that dominance simply exerts
little influence upon the urban occupational distribution.
(2) The direct relationship between the educational status
of the rural-farm and urban populations and metropolitan
dominance--it was concluded that uncontrolled factors in
the educational data such as sex, race. and age resulted
in a loss of homogeneity in the farm and urban populations
and produced sporadic variations in the educational status

distribution. (3) The inverse relationship between the

status consistency of the rural-farm population and

John Stoeckel

metropolitan dominance—-it was concluded that the lack of
controls for educational status produced enough of a de-
viation in the variable to effect the total status consis-
tency measure. (4) The higher status consistency for the
rural-farm population than for the urban at all degrees of
dominance--it was concluded that the extremely low status
score for the farm occupational categories reduced the
occupational status scores for the farm population. These
low scores when used to compute the status consistency
measure had the effect of reducing the total measure. A
new measure of status consistency was proposed that could
be used in current data and would take account of the
deviation of each of the status distributions from their
total intercorrelation. This minimizes the impact of the
low farm occupational scores.

Analysis of the two related issues produced the
following:

(1) the predictive value of the dominance indicators
for the status structure and status consistency of the
rural-farm and urban populations--it was concluded from a
multiple R analysis that dominance was an adequate predictor
for urban income status and status consistency. and rural-
farm occupational and income status. The variable of
functional specificity was proposed as an additional indi-
cator that may add to the predictive utility of dominance.

(2) the utility of the rural-farm and urban residence

categories for sociological research-—it was concluded

John Stoeckel

from an analysis of variance between the rural-farm and
urban categories for the status variables that for the

North Central region as a whole the residence categories

are mutually exclusive. Hewever, inSpection of mean

status scores under occupational and educational status,

and status consistency indicated close convergences be-
tween the residence categories at the highest degrees of
dominance. Hence, sociological research dealing with rural—
farm and urban status differentials must necessarily control
such factors as size of place and distance from SMSA's

if valid comparisons and generalizations are to be made.

THE IMPACT OF METROPOLITAN DOMINANCE UPON
THE STATUS STRUCTURE AND STATUS
CONSISTENCY OF RURAL-FARM AND

URBAN POPULATIONS

BY

John Stoeckel

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Sociology

1966

ACKNOWLEDGEMENTS

I wish to eXpress sincere appreciation to the members
of my guidance committee for their interest and constructive
criticisms. These members were Drs. J. Allan Beegle. Chair-
man. William Form, Jay Artis, and James McKee.

A special acknowledgement of deep appreciation and
gratitude must be extended to Dr. Beegle. At no time during
the preparation of the thesis was he too busy to provide
help for my problems. Without the most generous offering
of his valuable time and suggestions, this thesis could
never have been completed.

Lastly. to my wife who labored patiently through
many typings and provided me with moral support and under-

standing, I am deeply grateful.

ii

TABLE OF CONTENTS
Chapter Page

I. INTRODUCTION . . . . . . . . . . . . . . . . . l

The General Problem and Its Significance 1
Theoretical Framework 7
Theoretic Hypotheses 15
Thesis Organization 17

II. WTHODOLOGY O O O O 0 O O O O O O O O O O O O 19

Operational Definitions 19
Operational Hypotheses 32
Statistical Techniques 35

III. HYPOTHESIS-TESTING AND RESULTS . . . . . . . . 37

Summary of Findings 55
Multiple Correlation (R) Analysis 58
Analysis of Variance 62

IV. DISCUSSION AND CONCLUSIONS . . . . . . . . . . 64

Unexpected Results 64
Summary 76

LITERATURE CITED . . . . . . . . . . . . . . . . . . . 79

APPENDIX 0 O O O 0 O C O O O O O O O O O O O O 0 O O O 84

iii

Table

LIST OF TABLES
Page

Correlation (r) between distance from
nearest SMSA and rural—farm and urban
occupational status of counties and
mean occupational status scores by
distance from nearest SMSA and residence
for the North Central region, 1960 . . . . 39

Correlation (r) between urbanity and rural-
farm and urban occupational status of
counties and mean occupational status
scores by urbanity and residence for
the North Central region. 1960 . . . . . . 4O

Correlation (r) between size-distance and
rural-farm and urban occupational status
of counties and mean occupational status
scores by size—distance and residence
for the North Central region. 1960 . . . . 42

Correlation (r) between distance from
nearest SMSA and rural-farm and urban
educational status of counties and
mean educational status scores by
distance from nearest SMSA and residence
for the North Central region . . . . . . . 44

Correlation (r) between urbanity and rural—
farm and urban educational status of
counties and mean educational status
scores by urbanity and residence for
the North Central region. 1960 . . . . . . 45

Correlation (r) between size-distance and
rural-farm and urban educational status
of counties and mean educational status
scores by size—distance and residence
for the North Central region, 1960 . . . . 47

iv

Table

10.

ll.

12.

13.

Page

Correlation (r) between distance from nearest
SMSA and rural-farm and urban income
status of counties and mean income status
scores by distance from nearest SMSA and
residence for North Central region, 1960 . 48

Correlation (r) between urbanity and rural-
farm and urban income status of counties
and mean income status scores by urbanity
and residence for the North Central region,
1960 . . . . . . . . . . . . . . . . . . . 50

Correlation (r) between size-distance and
rural-farm and urban income status of
counties and mean income status scores
by size-distance and residence for the
North Central region, 1960 . . . . . . . . 51

Correlation (r) between distance from
nearest SMSA and rural-farm and urban
status consistency of counties and mean
status consistency scores by distance from
nearest SMSA and residence for the North
Central region, 1960 . . . . . . . . . . . 53

Correlation (r) between urbanity and rural-
farm and urban status consistency of
counties and mean status consistency
scores by urbanity and residence for
the North Central region. 1960 . . . . . . 54

Correlation (r) between size-distance and
rural—farm and urban status consistency of
counties and mean status consistency scores
by size-distance and residence for the
North Central region. 1960 . . . . . . . . 56

Summary of acceptance (+) and rejection (-)
of the hypotheses dealing with the
relationships between distance, size-
distance, urbanity, and rural-farm
and urban consistency for the North-
Central region, 1960 . . . . . . . . . . . 57

Table

14.

15.

16.

17.

18.

19.

20.

21.

22.

Summary of acceptance (+) and rejection (-)
of the sub-hypotheses dealing with the
differences between the rural-farm and
urban mean occupational, educational,
income. and status consistency scores
by distance, size-distance and urbanity
for the NOrth Central region, 1960 . . .

Multiple correlation coefficients and related
factors for the occupational status of the
rural—farm and urban populations and
distance, size-distance, and urbanity for
the North Central region, 1960 . . . . .

Multiple correlation coefficients and related
factors for the educational status of the
rural-farm and urban populations and
distances, size-distance. and urbanity.
for the North Central region. 1960 . . .

Multiple correlation coefficients and related
factors for the income status of the
rural-farm and urban populations and
distance. size-distance, and urbanity
for the North Central region. 1960 . . .

Multiple correlation coefficients and related
factors for the status consistency of
the rural—farm and urban populations
and distance, size-distance and urbanity
for the North Central region, 1960 . . .

Analysis of variance between the rural-farm
and urban mean occupational status scores
of counties in the North Central region.
1960 O O O O O O O O O O O O O O O O O 0

Analysis of variance between the rural—farm
and urban mean educational status scores
of counties in the NOrth Central region.
1960 O O O O O O O O O O O O O O O O O 0

Analysis of variance between rural-farm and
urban mean income status scores of
counties in the North Central region.
1960 O O O O O O O O O O O O O O 0 O O 0

Analysis of variance between the rural—farm
and urban status consistency scores of
counties in the North Central region.
1960 O O O O O O O O O I O O O O O O O 0

vi

Page

57

59

60

6O

61

62

62

63

63

CHAPTER I
INTRODUCTION

The General Problem and Its Significance

The general problem of this thesis falls into the
areas of population, ecology: and social stratification.
More specifically, it investigates the impact that large
urban centers1 have upon the status structure and status
consistency of the rural-farm and urban populations that
occupy the hinterland of the urban centers.

It should be made explicit at this point that this
study is concerned only with the population of two residence

categories, the urban2 and the rural-farm.3 Three major

 

Large urban center refers to Standard Metropolitan
Statistical Areas which are defined as one or more contigu—
ous counties containing at least one city of 50,000 or more
(or a pair of contiguous twin cities of at least this joint
size) and having a generally metropolitan character based
on the county's social and economic integration with the
central city. For more detailed discussion see U.S. Census
of Population, 1960. V01. PC (1) 1B. U.S. Summary.

2 . . . . .

Urban population comprises all persons liVing in
(a) places of 2,500 inhabitants or more incorporated as
cities. boroughs, villages, and towns; (b) the densely
settled urban fringe including both incorporated and unin-
corporated areas around cities of 50,000 or more; (c) unin—
corporated places of 2,500 inhabitants or more; (d) towns in
New England and townships in New Jersey and Pennsylvania
which contain no incorporated municipalities as subdivisions
and have either 25,000 inhabitants or more or a population
of 2.500 to 25,000 and a density of 1,500 persons or more

1

variables are used: the independent variable of urban impact
or dominance, and two dependent variables, status structure
and status consistency.

Essentially three points of significance underlie
this investigation. The first is that it attempts to pro-
vide further insight and answers to the criticisms that have
been aimed at the utility of the rural and urban residence
categories in sociological research. Critics have generally
taken the position that since American society is in an
era of rapid social and cultural change and of increasing
urban dominance. the differences between urban and rural have
become negligible and hence ”the concept rural in contra—

distinction to urban is not the homogeneous concept as

 

per square mile; and (e) counties in states other than the
New England states, New Jersey. and Pennsylvania that have
no incorporated municipalities within their boundaries

and have a density of 1,500 persons per square mile. U.S.
Census of Population: 1960. Numbers of Inhabitants. United
States Summary. Final Report PC (l) - 1A (1961).

3The farm population consists of all persons living
in rural territory on places of 10 or more acres if as
much as $50 worth of agricultural products were sold from
the place in the reporting year plus those living on
places of under 10 acres if as much as $250 worth of
agricultural products were sold from the place in the
reporting year. United States Bureau of the Census,
U.S. Department of Agriculture Series Census: AMS (P-27)
No. 29 "Estimates of the Rural-Farm Population of the
United States April 1960". (Washington, D.C.: United
States Government Printing Office. 1961). p. 5.

assumed by many sociologists."4 One answer to this criticism
is that the utility of the concepts would depend upon their
ability to discriminate between social characteristics

(e.g.. occupation. education. and income). Consequently.

if an operationalization would be successful in constructing
two mutually exclusive. homogeneous categories then their
utility would be supported. One objective of this thesis

is to test such an operationalization constructed by the
United States Bureau of the Census.5 If the operationali-

zation is to have utility for sociological research. then

 

4Neal Gross. "Sociological Variation in Contemporary
Rural Life." Rural Sociology. V01. 13 (Sept.. 1948). p. 269.
For additional commentary concerning the utility of the rural
and urban concepts see the following: R. C. Bealer. Fern K.
Willits. W. P. Kuvlesky. "The Meaning of Rurality in American
Society: Some Implications of Alternative Definitions."
Rural Sociology, V01. 30 (1965). pp. 255-266; Richard Dewey.
"The Rural-Urban Continuum: Real But Relatively Unimportant."
American Journal of Sociology. V01. LXVI (July. 1960).
pp. 60-66; Lewis W. Jones. "The Hinterland Reconsidered.“
American Sociological Review. Vol. 20 (Feb.. 1955). pp. 40-44:
Oscar Lewis. Life in a Mexican Village (Champaign: University
of Illinois Press. 1951); Charles K. Nichols. "A Suggested
Technique for Determining Whether a Community Can Be Classi—
fied as Rural or Urban." Rural Sociology. V01. 5 (Dec.. 1940).
pp. 454-460; Charles T. Stewart Jr.. "The Urban-Rural
Dichotomy: Concepts and Uses." American Journal of Sociology.
V01. 64 (Sept.. 1958). pp. 152-158.

 

 

5The operational definitions constructed by the U.S.
Bureau of Census have been refined considerably in the last
two decades. In 1950 the definition of urban was changed
from simply including persons living in incorporated places
of 2.500 inhabitants or more and in areas (usually minor
civil divisions) classified as urban under Special rules re—
lating to population size and density to the more inclusive
definition stated above. Similarly in 1960. the definition
of rural-farm was changed from persons living on farms as
determined by the respondents answer to the question "Is
this house on a farm (or ranch)?". to a more objective
criterion of size of place and value of produce. U.S. Census
of Population: 1950. Detailed Characteristics. United States
Summary. Report PC-l. pp. viii. ix.

traditional propositions concerning the status differentials
between the urban and rural-farm populations (e.g.. higher
occupational. educational. and income level for the urban
than for the rural-farm population) would become testable.
The second is that the investigation makes a contri-
bution to "the most neglected area of sociology--the analysis
of the determinants of social structure."6 Schnore main—
tains that since the time of Durkheim the trend in sociology
has been toward studies of social structure as the indepen-
dent variable and its effects upon the behavior of indivi-
duals. He points out that Durkheim. ("in the best tradition
of sociology") in his early work on social morphology was
primarily concerned with the factors that gave rise to social
structure and not to the behavior of individuals.7 Further.

Durkheim regarded such social phenomena as collective

 

6Leo Schnore. "Social Morphology and Human Ecology."
American Journal of Sociology. V01. LXIII (May. 1958): p- 632.

7Ibid.. p. 629. Sorokin makes a similar point in
his discussion of Durkheim. In the first portion of the
Division of Labor Durkheim discusses "the principal effects
of the variation of labor division. as a social factor. on
different sides of social life and psychology . . . but in
the second part of the book. he reverses his equation. and
asks: What are the causes (determinates) responsible for
an increase in the division of labor itself. . . ."
Pitikim Sorokin. Contemporary Sociological Theories (New
Y0rk: Harper and Row. 1928). pp. 467-71. This emphasis
is illuminated further by Schnore who points out that the
two major inquiries in Durkheim's social morphology focused
on the study of the environmental basis of social organi-
zation and the study of population phenomena especially.
size. density and spatial distribution.

representations. etc. (shared norms and values) as mere
"emanations" of underlying social morphology or structure.8
Hence. if one holds to this position it places the analysis
of structure itself as a logically prior problem9 to the
study of the human behavior as manifested in organizational
forms. Consequently. the second objective of this study is
to treat the status structures of the urban and rural-farm
populations as the dependent variables. and propose and
test possible determinants of them.

Finally. this study is significant in that it deals
with a dimension of the status structure. that is. status

consistency.10 as a dependent variable of populations (rural—

farm and urban) and not individuals. The major portion of

 

E. Durkheim. "Representatives Individuelles et
Representations Collectives." Revue de metaphysigue et de

moral. VI (1898). 273-302.

 

9Schnore. op. cit.. p. 632.

10Like many of the concepts in sociology. status con-
sistency lacks consensus of definition. H0wever. in general.
it is viewed as the relative correspondence of an individual's
positions on rankings along various status hierarchies. For
further commentary on the concept see the following:
Emile Benoit-Smullvan. "Status. Status Types. and Status
Interrelations." American Sociological Review. V01. 9 (April.
1944). pp. 151-161; Leonard Broom. "Social Differentiation
and Stratification." in R. K. Merton. L. Broom and L. S.
Cottrell Jr. (eds.) Sociology Today (New York: Basic Books.
1959). pp. 429-441; G. H. Fenchel. "Subjective Status and
Equilibration Hypothesis." Journal of Abnormal and Social
Psychology. V01. 46 (Oct.. 1951). pp. 476-79; Edward
Sampson. "Status Congruence and Cognitive Consistency."
Sociometry. V01. 32 (June. 1963).EP. 146-162; Gregory
Stone and William Form. "Instabilities in Status." American
Sociolggical Review. V01. 18 (April. 1953). pp. 149-62:
Herbert vermilye. "0n Measuring Status Consistency." American
Sociological Review. V01. 28 (June. 1963). pp. 455-461.

 

 

the research on status consistency has dealt only with the

individual's status consistency and its impact upon such

phenomena as an individual's performance in small groups.11

political attitudes.12 suicide.13 participation and non-

participation in voluntary associations.l4 preference for

change in various type organizations.15 psychological dis—
16

turbances such as "frustration" and "uncertainty.” class

. 17 . . .
consc10usness. and anXiety in reaction to status

 

llStuart Adams. "Status Congruency as a variable in
Small Group Performance." Social Forces. V01. 32 (Oct..
1953). pp. 16—22.

12William Kenkel. "The Relationship between Status
Consistency and Politico—Economic Attitudes." American
Sociological Review. Vol. 21 (June. 1956). pp. 365-368;
Gerhard Lenski. "Status Crystallization: A Non—Vertical
Dimension of Social Status." American Sociological Review.
V01. 19 (Aug.. 1954). pp. 405-413.

13Jack Gibbs and Walter Martin. "A Theory of Status
Integration and Its Relationships to Suicide." American
Sociological Review. V01. 23 (April. 1958). pp. 140—147;
Jack Gibbs. "On Status Integration and Suicide Rates in
Ceylon." American Journal of Sociology. V01. 64 (May. 1959).
pp. 585—591; Jack Gibbs. "On Status Integration and suiCide
Rates in Tulsa.“ American Sociological Review. V01. 24
(June. 1959). pp. 392—396.

l4Gerhard Lenski. "Social Participation and Status
Crystallization." American Sociological Review. V01. 21.
(Aug.. 1956). pp. 458—464.

 

 

 

5Irving Goffman. "Status Consistency and Preference
for Change in Power Distribution." American Sociological
Review. V01. 22 (June. 1957). PP. 275-281.

l6Elton Jackson. "Status Consistency and Symptoms of
Stress." American Sociological Review. V01. 27 (Aug.. 1962).

17Warner Landecker. “Class Crystallization and Class
Consciousness." American Sociological Review. Vol. 28
(April. 1963). pp. 219—229.

 

 

\__/

threats.18

By concentrating upon the internal consistency of
the individual's ranking. these studies have constructed
statistical groups of people with homogeneous or hetero-
geneous ranks. However. it is one thing to form such statis-
tical classes and quite another to assume that they are
meaningful social units. This continued attention to the
individual as the unit of analysis and the exclusion of the
aggregate aspects of social stratification only promotes
the "common social psychological delusion" that the indi-
vidual and not the group is the unit of social ranking.19
Hence. a third objective of this study is to focus upon the

status consistency of groups or aggregates as the unit of

analysis.

Theoretical Framework

 

The theoretical orientation that generates the hypo-
theses to provide an answer totfluaproblem is metropolitan
dominance. TWO of the earliest formulations of the

dominance theory were by N. S. B. Gras20 in An Introduction

 

 

18Saad Nagi. "Status Profile and Reactions to Status
Threats." American Sociological Review. vol. 28 (June. 1963).
pp. 440-443.

19Robert W. Hodge. "The Status Consistency of
Occupational Groups." ASR. V01. 27 (June. 1962). p. 336.

20N. S. B. Gras. An Introduction to Economic
History (New York: Harper and Brothers. 1922).

 

 

to Economic Histopy and by R. D. McKenzie21 in The
Metropolitan Community. However. probably the most syste-
matic formulation of the theory was made by D. J. Bogue in
his work on the structure of the metropolitan community.
Bogue maintains that the metropolis or modern large and
complex city exercises an organizing and integrative influ—
ence upon the social organization of a broad expanse of

territory far beyond its civil boundaries. and thereby domi-

nates populations within this area of influence.22 The

rationale upon which this position is based is provided by
Bogue in the following:

The metropolis is usually the largest and most
complex (the farthest removed from the "average"
city) of all of the cities in the territory. Be—
cause it is able to assemble cheaply a varied
array of raw materials and products from all

parts of the workd; because a large number of
specialized components and skills are required in
the production of the goods required to sustain
human beings at their present level of living:
because up to a certain point machine production
increases in efficiency with an increased scale of
operations; and because certain mutual benefits
appear to accrue to business enterprises from
their location in proximity to each other the
large city is able to produce and distribute more
varied goods and services than is a smaller city.
The more Specialized the goods. and the more the
goods are amenable to mass production. the greater

 

21R. D. McKenzie. The Metropolitan Community
(New York: McGraw—Hill. 1933).
22Donald J. Bogue. The Structure of the Metropolitan
Agommunity. A Study of Dominance and Subdominance (Ann
Arbor: Rackham School of Graduate Studies. University
of Michigan. 1950). p. 5.

these industrial and commercial advantages of
large cities seem to become.2

This formulation of metropolitan dominance is grounded in

a traditional ecological frame of reference which has been
utilized by Robert Park.24 Amos Hawley.25 O. D. Duncan.2
and Leo Schnore.27 This frame of reference is composed

of the following four major classes of variables: population.
defined as an aggregate of persons; technology. defined as
a set of techniques employed by a population to gain suste-
nance from its environment to facilitate the organization
of sustenance—producing activity;28 environment. defined as
the topography of land. natural resources. and the climatic
conditions of the region a population inhabits in addition

to the size of the population itself;29 and social organization

 

23;p;g.. pp. 5.6. The major assumption underlying

this position is that the metropolis and the hinterland are
interdependent and are parts of one organic whole.

24R. E. Park. "Human Ecology." American Journal of
Sociology. Vol. XLII (July. 1936). pp. 1-15.

25Amos H. Hawley. Human EcologyL A Theory of Com—
munity Structure (New Y0rk: The Ronald Press Co.. 1950).
Chapters 11 and 12.

260. D. Duncan. "Human Ecology and the Population
Studies." in P. Hauser and O. D. Duncan (eds.). The Study of
Population (Chicago: The University of Chicago Press. 1959).
pp. 678-716; "From Social System to Ecosystem." Sociological
Inguiry. V01. XXXI (Spring. 1961). pp. 140-149.

27

 

 

 

 

 

Schnore. op. cit.. pp. 620—634.

280. D. Duncan. Human Ecology and Population Studies.

op. cit.. p. 682.

29Schnore. op. cit.. p. 629.

10

defined as a collective adaptation of a population to the
character of its environment.30 Ecologists maintain that
it is the interaction of a population and its technology
within an environment that produces various forms of social
organization. Thus. the urban center with its superior
population size and technological advantage can be viewed
as exerting an influence within an environmental context
upon an aspect of the social structure of the organization
of its hinterland population. that is. status structure

and status consistency. Status structure refers to the
arrangement or pattern of the distribution of a population
on the three hierarchies of occupation. education. and
income: and status consistency refers to the extent to which
the distribution of a population on each of the three
hierarchies correspond to each other.

The extent or degree of the metropolitan influence
upon these variables is a function of two central factors.
1) the size of the metropolitan center and. 2) the accesSi-
bility (distance) of the hinterland population to the metro-
politan center.

The importance of size as an indicator of metropoli—
tan dominance centers around the general notion that the

size of the population covaries with its technological

 

300. D. Duncan and Leo Schnore. "Cultural.

Behavioral and Ecological Perspectives in the Study of
Social Organization." AJS. V01. VLXV (Sept.. 1959).
p. 135.

11

level.31 More specifically. Hawley points out that the
size of a population imposes limits on both the extent of
specialization and the number of different activities that
may be carried on simultaneously. Specialization pre—
supposes a sufficient number of users of the given service
to support a concentration of effort on its production.

In a small population the degree of specialization of
activity is necessarily slight. On the other hand every
increment in population size increases the extent to which
different types of specialization may be developed and more
efficient means of gaining sustenance can be achieved.
Obviously. a population characterized by small numbers can—
not adequately staff an establishment such as a modern
mass—production factory. nor can it support a great

variety of sustenance-producing activities. Hence. its

technology is restricted to what can be manufactured and

 

31The proposition that there is a correlation be—
tween population size and technological level or develop-
ment is by no means recent. Sorokin points out that such
theorists as M. Kovalevsky. A. Coste. E. Durkheim. F.
Ratzel. P. M0ngeolle. E. Levasseur. E. Drepreil. C. Gine.
F. Carli. W. Summer and A. Keller maintained that the growth
of a population and its consequence. an increase in its
density. have been responsible for an improvement in the
technique of economic production and for a transition from
less intensive forms of production to more intensive ones.
An increase in population size makes the methods of produc—
tion insufficient. which were quite satisfactory for a
smaller population. "Hence. the increasing pressure of
this factor. It urges the invention of more efficient
methods of production. which will be fit to satisfy the
needs of an increased population. This consequently leads
to inventions and through them. a betterment of the techniques
of production." Sorokin. op. cit. . p. 388.

12

operated by relatively few individuals. The increase of
manpower that results from population growth permits the
adoption of more complicated and efficient processes. and
to the extent that it does so the number and diversity of
sustenance—producing activities may also be increased.32

Since population size is directly related to tech-
nology it would be expected that communities of varying
sizes would exert varying degrees of influence upon their
hinterland populations. Empirical research tends to pro—
vide support for this general proposition. various studies
have found gradients of dominance in terms of size of

place with such population characteristics as wholesale

and retail trade.33 mobility and socioeconomic position.34

 

age-sex structure and fertility.35 and farm land tenure and
utilization.36
32 .
Hawley. op. c1t.. pp. 122. 123.
33

Donald J. Bogue. The Structure of the Metropolitan
Community_(Ann Arbor: University of Michigan Press. 1949);
Vance. Rupert and Sara Smith. "Metropolitan Dominance and
Integration." in Hatt and Reiss (eds.). Cities and Society
(New York: Glencoe Free Press. 1964).

34O. D. Duncan and A. Reiss. Social Characteristics
of Urban and Rural Communities (New York: John Wiley. 1950):
H. H. Winsborough. "Occupational Composition and the Urban
Hierarchy." ASR. V01. 25. No. 6 (Dec.. 1960). pp.

350. D. Duncan. "Gradients of Urban Influence on the
Rural Population.” Midwest Sociologist. V01. 18 (1956).
pp. 27-30.

36James D. Tarver. "Ecological Patterns of Land
Tenure. Farm Land Uses. and Farm Population Characteristics."
Rural Sociology. V01. 28 (June. 1963). pp. 128—145.

 

13

The importance of the second factor. accessibility
of the hinterland to the metropolitan center. rests upon
the following assumption: varying degrees of accessibility
of hinterland populations represent varying degrees of
interaction with the metropolitan center and hence accessi—
bility is taken to be a variable which will covary with
dominance.37 The rationale for this assumption is that
in terms of time. cost. and expenditure of energy a popu—
1ation can enter most easily into a division of labor with
a metropolitan center located at a highly accessible point.
namely at a shorter distance. Exchange and interaction with
a metropolitan center located at the most inaccessible
point can be achieved only at a maximum eXpenditure of time.
cost. and energy. Further. the distance to be traveled
limits the opportunity to transport goods. services. and
persons from the hinterland to the metropolitan center.38
A permanent requirement for changing the location of any
object is the necessity of overcoming distance.39 Con-
sequently. varying distances of a hinterland population

from a metropolitan center can be viewed as varying degrees

 

37Bogue. op. cit.. p. 21.
38Ibid.
39

This is Hawley's "friction of space" notion which
holds that "space itself is something to be passed over and
thus it calls for an expenditure of time and energy. In
other words the friction of Space is Small or great as
distance is short or long. . .". Hawley. op. cit.. p. 237.

14

of dominance. Empirical research tends to support this
proposition. Studies have found gradients of dominance
with distance and the following population characteristics:
retail and wholesale trade:40 average monthly rental value
of dwelling unit. democratic vote. and proportions of
operatives and kindred workers in the labor force;41
distribution of production centers and administration
centers;42 change in population size;43 newspaper
circulation;44 average level of farm living. number of
tractors per farm. and size of farm;45 farm land tenure and
farm land utilization;46 farm land value. and farm buildings

per acre;47 sex ratio and middle age of rural farm

 

4OBogue. op. cit.. pp. 153-173.

41Leslie KiSh. "Differentiation in MStropolitan
Areas." ASR. V01. 19. No. 4 (August. 1954).

42D. M. Pappenfort. "The Ecological Field and the
Metropolitan Community." AJS. V01. LXIV. No. 4 (Jan.. 1959).

43C. M. Grigg. "A Proposed Model for Measuring the
Ecological Process of Dominance." Social Forces. V01. 36.
No. 1 (Dec.. 1957).

44R. E. Park. "Urbanization as Measured by News-
paper Circulation." AJS. V01. XXXV (July. 1929). pp. 60-79.
J. A. Kinnemann. "Newspaper Circulation from Small Metro-
politan Centers." gpg. Vol. II (April. 1946). pp. 150-157.

45T. R. Anderson and Jane Collier. "Metropolitan
Dominance and the Hinterland." Rural Sociology. V01. 21
(June. 1956). PP- 152-157.

46Tarver. op. cit.. pp. 128-145.

 

 

47Harold Goldsmith and James H. Copp. "Metropolitan
Dominance and Agriculture." Rural Sociology. 1964.
pp. 385—395.

 

15

persons.

Theoretic Hypotheses

In light of the theoretical framework a logical
question follows. that is. what hypotheses can be generated
that will provide answers to the problem of what is the
effect of metropolitan dominance upon the status structure
and status consistency of rural-farm and urban populations?

In order to formulate the hypotheses additional
observations concerning metropolitan dominance and the status
structure and status consistency of the rural—farm and
urban populations must be made. It would generally follow
from the stated theory that hinterland populations under
the most intensive degree of dominance would resemble the
metropolitan population more than a hinterland population
under a less degree of dominance. For example. it would
be expected that a rural—farm population under extreme
metropolitan dominance would exhibit characteristics more
Similar to the urban population of the metropolitan center
than the rural-farm population that is under less dominance.
Similarly. it would be expected that an urban population
under less dominance would exhibit characteristics less

urban in character than the urban population under high

 

48Walter T. Martin. "Ecological Change in Satellite

Rural Areas." ASR. V01. XXII (April. 1957). pp. 173—183;
E. T. Hiller. "Extension of Urban Characteristics into Rural
Areas." Rural Sociology. V01. 6 (Sept.. 1941). pp. 242-257.

l6

dominance. Dominance then is viewed as an indicator of
"rural-farmness" and "urbaness". "Rural—farmness" and
"urbaness" refer to the notion that certain characteristics
peculiar to these populations will be more intense and

less intense. respectively. as metropolitan dominance de-
creases. For example. the traditional differences between
occupational. educational. and income status (i.e.. higher
overall status for the urban population). for the rural—
farm and urban populations would be expected to be main-
tained as dominance decreased. although the status level for
all three factors would tend to decrease. Hence. the fol-

lowing theoretic hypotheses are proposed:

Hypothesis I: There is a direct relation between metro-
politan dominance and the occupational
status of rural-farm and urban populations.

Sub-Hypothesis: (a) The occupational status of urban
populations will be higher than
the occupational status of rural-
farm populations for all degrees
of metropolitan dominance.

Hypothesis II: There is a direct relation between metro-
politan dominance and the educational status
of rural-farm and urban populations.

Sub Hypothesis: (a) The educational status of urban
populations will be higher than the
educational status of rural-farm
populations for all degrees of
metropolitan dominance.

Hypothesis III: There is a direct relation between metro-
politan dominance and the income status of
rural-farm and urban populations.

Sub Hypothesis: (a) The income status of urban
populations will be higher than
the income status of rural—farm
populations for all degrees of
metropolitan dominance.

17

From the above discussion it can be reasoned further
that the status consistency of urban and rural-farm popu-
lations would be expected to vary inversely with metropoli—
tan dominance. This would follow since the populations of
metropolitan areas are characterized by a high degree of
specialization. division of labor or social differentiation.
and racial and ethnic heterogeneity. As a result the popu-
lations under the most intense metropolitan influence would
have a lower status consistency than the populations under
the least metropolitan influence. More Specifically. since
urban populations are characterized by the factors mentioned
for the metropolitan area and the farm population tradition-
ally characterized as more homogeneous in composition. the
status consistency of the urban population would be ex—
pected to be lower than the status consistency for the farm
population under all degrees of metropolitan dominance.

Hence. the following theoretical hypothesis is proposed:

Hypothesis IV: There is an inverse relation between metro—
politan dominance and the status consistency
of rural—farm and urban populations.

Sub Hypothesis: (a) The Status consistency of urban
populations will be lower than the
status consistency of rural-farm

populations for all degrees of
metropolitan dominance.

Thesis Organization

The remainder of the thesis consists of Chapters

II through IV. Chapter II presents the methodological

18

framework utilized in the study. It deals with the
operational definitions of the independent and dependent
variables. the operational hypotheses. and the statistical
techniques utilized to test the hypotheses.

Chapter III reports the results of the tests of the
operational hypotheses. while Chapter IV discusses and draws

conclusions concerning the results.

CHAPTER II

METHODOLOGY*

This chapter presents the operational definitions
of the independent and dependent variables. the operational
hypotheses. and the statistical techniques utilized to

test the hypotheses.

Operational Definitions

 

The first variable operationalized is the independent

variable. metropolitan dominance. As stated above the effect

 

*The data in this study is taken from the rural-
farm and urban populations of counties for all twelve
states (Ohio. Indiana. Illinois. Michigan. Wisconsin. Minne-
sota. Iowa. Missouri. North Dakota. South Dakota. Nebraska.
Kansas) of the North Central region. The data used are from
the "Rural America Printouts." and were programmed by the
Armours in Chicago from the U.S. Bureau of Census PC-LCD
summary records for 1960. Scratch tapes of the printout
results were constructed for use in the CDC-3600 computer
to facilitate statistical analysis.

The North Central region was chosen because it makes
up approximately one—third of the total population of the
United States. and is considered essentially representative
of the majority of the national population. Representation
here refers specifically to the white population since the
nonwhite population was generally excluded from the analysis.
This was done since the proportion of nonwhites in the North
Central region is less than 7% while in the South nonwhites
comprise over 20% of the total. Furthermore. 90% of the non-
whites in the Southern states are categorized as rural-farm
while 90% in the North Central states are categorized as
urban. Consequently. nonwhites in the South and North Central
represent two very different types of populations and any
attempt to generalize from one to the other would only pro—
duce spurious results.

19

20

of dominance on a hinterland population is a function of the
distance the hinterland population is from the metropolitan
area and the size of that metropolitan area. Thus. opera-
tional definitions of these variables provides indicators

of dominance. The first indicator. distance. was operation-
alized in the following manner:

1. Locate the central city of each SMSA on a map which
includes the state and county boundaries as well as the
locations of SMSA'S.

2. Using the central city of each SMSA as the center.
draw concentric rings around each SMSA. The first or inner-
most circle will have a radius of 50 miles. the second
circle will have a radius of 100 miles. the third circle
will have a radius of 150 miles. etc. This creates bands
around each SMSA. each band being 50 miles wide.

3. Assign the value "1" to the first band, i.e.. the
band formed by the area of the innermost circle.

4. Assign the value "2" to the second band. i.e.. the
band formed by the area between the first and second
circles.

5. Continue assigning values to bands. Each band is
assigned one more than the value of the preceding band.

6. The major portion of each county will be covered
by one or more bands. (A county will be covered by more
than one band only when the bands from two or more SMSAs
overlap). Determine for each county the band or bands

which cover it.

21

7. (a) If only one band covers the county. assign the
value of that band to the county.
(b) If more than one band covers the county. assign
the value of the lowest valued band to the county.
The selection of the 50 mile distance band is essen-
tially arbitrary. However. several factors aided in the
selection. Fifty miles is assumed to approximate the maxi-

mum distance which an individual will travel to work on a

 

regular basis. Also. local TV stations do not have broad-
cast beams of much greater length than fifty miles. and few
newspapers excluding those of the very large metropolitan
centers have coverage beyond this distance. Therefore.
the distance of 50 miles seemed reasonable.1
The second indicator of dominance. size. was operation-

alized by ranking all SMSA counties on population Size and
all non—SMSA counties on the percent of their population
categorized as urban. The total scale is as follows:

SMSA Counties

1.000.000 and over

500.000 to 999.999

250.000 to 499.999

249.999 and under

Non-SMSA Counties

70% or more urban

55.0% to 69.9% urban

40.0% to 54.9% urban

25.0% to 39.9% urban
24.9% and under

 

lJohn Stoeckel and J. Allan Beegle. "The Relation—

ship between Rural-Farm Age-Structure and Distance from
Nearest Urban Center." Forthcoming in Rural Sociology.
septo' 1966.

 

22

The rationale for constructing the scale by popu—
lation size of SMSA'S and percent urban of non—SMSA'S
is that if SMSA'S were categorized on the basis of percent
urban then there would be little differentiation Since the
urban population comprises approximately 97 to 99 percent
of the total. Hence. a measure of the impact of SMSAS of
different sizes would be lost. Also. categorizing non-SMSA
counties on population size where there is a great deal of
differentiation in the percent urban and rural—farm would
obscure the influence of the urban population on these
small counties. Consequently. the total scale can be
viewed as a ranking of "urbaness" (hereafter referred to as
urbanity) where the highest influence is exerted by the
SMSA of 1.000.000 population and over and the lowest in-
fluence exerted by non—SMSA'S having 24.9% or less urban
population.

The third operational definition of dominance com—
bines the factors of size of metropolitan area and distance
of the hinterland from the metropolitan area. This measure
was constructed in the following manner: Each SMSA county
of the North Central region was assigned a numerical value
that was a linear function of the size of the population in
the county. up to a population of 2 million people. The
value assigned increased by l for each 100.000 population.
Hence each SMSA county was assigned a value from 20 down to

0. For example. a county with a population of 2.000.000

 

23

received a value of 20. a county with a population of
1.000.000 a value of 10. etc. After the assignment of these
size categories the distance measure of 50 mile bands was
drawn from the central city of each SMSA as in the Simple
distance measure. The resulting bands were then assigned

a value that declined as a function of the number of 50

mile units from the SMSA. This decline was by a numerical
value of 2 for each distance band.

Hence. each county within the distance bands was
assigned a numerical value which was a combined function of
the size of the SMSA influencing it and of the distance
from that SMSA. Necessarily. many counties received a value
from several SMSA'S. In this instance the value assigned
to the county was the highest value. Furthermore. if the
county was an SMSA county with a smaller value than the
influence value assigned it from a larger SMSA. the values
were added-—except that no influenced county could receive
a higher value than the influencing SMSA. For example. a
county is designated as an SMSA county with a 1960 popu-
lation of 1.131.483. It is assigned a size value of 11.
The counties included in the band having a radius of 50
miles from this SMSA are assigned a value of 9. counties
in the 50-100 mile band a value of 7. etc. Suppose that
in the band value of 50—100 miles one of the counties was
an SMSA county with a population of 600.000. The addition
of the 7 value from the large SMSA and the 6 from its own

population would give this county a total value of 13.

 

24

Since this value. however. exceeds the value of the larger
SMSA the value assigned the county was 11 rather than 13.2
The rationale for these procedures was as follows:
as stated above. dominance is a function of both size of
metropolitan area and the distance of the metropolitan area
from the hinterland population. However. in portions of

the North Central region cities of different size are rela—

tively close. It would appear that the social and economic

forces in counties with smaller cities close to large metro-

politan centers would be influenced by both the immediate
city and the larger metropolitan center. However. there
is no reason to assume that the results of this combination
would result in forces greater in the hinterland area than
in the county with the large metropolitan center. This
led to the restriction on the additive values of multiple
influence which prevented a county from having a larger
total value than the highest assigned for any county influ-
encing it.3

The rationale for assigning the maximum value of
population size at 2.000.000re1ates to the way in which
metropolitan dominance is transmitted. While it is ex—
pected that the Size of the dominant metropolitan area is a

significant factor in determining its influence. it was

 

2Dale Hathaway. J. A. Beegle. and Keith Bryant.

Rural America. Census Monograph. forthcoming. pp. 13. 14.

3Ibid.. p. 15.

25

felt that at some point increases in population merely were

duplications of technological functions and conditions that

existed in areas having a maximum population density. Thus.

it was arbitrarily decided that an SMSA having two million

population essentially would possess the technological base

that would exert the maximum influence over its hinterland 1”
populations.4 1“

Four dependent variables are operationalized. These
are. occupational status. educational status. income status.
and status consistency.

Occupational status was defined operationally in
terms of O. D. Duncan's "Socioeconomic Index for Major
Occupation Groups." Duncan constructed this index for
each broad occupation group for fully employed males
in the 1950 census by means of a multiple regression equation
in which the age-adjusted education and income character-
istics of a given occupation were used as predictor or es-

timator variables5 for the socioeconomic status of the

 

41bid.. p. 16.

5The regression equation used to predict occupational
status score was as follows: X.= 0.59 X2 + 0.55 X3 - 6.60
where X. is the predicted occupational status score; X2 is
the age adjusted percent of persons in the occupation with
an annual income of $3.500 or more: and X3 is the age ad-

justed percent of persons in the occupation that have
completed high school. i.e.. the percentage reported in the
census as "high school 4." "college 1 to 3." or "college 4
or more" years of school completed. Albert J. Reiss. Jr..
Occupations and Social Status (Glencoe: The Free Press.
1961). PP. 124-125.

26

occupation. The computed status scores for the broad
occupation groups are as follows:6

Occupational

Major Occupation Group Status Score

 

 

Professional. technical. and kindred
workers 75

Farmers and farm managers 14

Managers. officials. and proprietors.

exc. farm 57
Clerical and kindred workers 45
Sales workers 49
Craftsmen. foremen. and kindred workers 31
Operatives and kindred workers 18
Private-household workers 8
Service workers. exc. private household 17
Farm laborers and foremen 9
Laborers. except farm and mine 7

Duncan's rationale for the selection of education
and income as the strategic characteristics of occupations
revolve around the functional relationship between occu-
pation. education. and income. He makes this point in
the following:

The large majority of persons in the labor
force. engaged in one occupation or another.
have completed their formal education. Mbre-
over. from a functional standpoint. education
may be considered in large measure as a pre-
paration for the pursuit of an occupation. or
as the acquisition of qualifications for an

 

6Ibid.. p. 155.

27

occupation. Hence. there is both a functional

nexus and a temporal order in the relationship

of occupation to education. For most persons.

the bulk of the income received consists of com—

pensation for rendering the services of their

occupations. Income and occupation. therefore.

are functionally related. We have. therefore.

the following sequence: a man qualifies himself

for occupational life by obtaining an education;

as a consequence of pursuing his occupation. he

obtains income. Occupation. therefore. is the

intervening activity linking income to education.

Another factor contributing to Duncan's rationale is
that the occupational status measure derived from income
and education is directly related to the "prestige" of the
occupation. This is supported Since the occupational status
scores Duncan obtained were highly correlated with the NORC
occupational prestige ratings. As a result. Duncan main—
tains that his scale can be substituted in any research
for the NORC scale and provides a table of transformations
for all of the occupations in the detailed classification
of the Bureau of the Census for 1950.8
The Duncan scale was selected for use in this study

for several reasons. First. Duncan constructed this scale
from aggregate data from the categories of the U.S. Bureau
of Census. Since the present study deals with aggregate
data from the census his Scale would be more appropriate to
operationalize occupational status than other occupational

scales based on data for the individual. Second. his

scale correlated highly with the NORC scale for occupational

 

7Ibid.. pp- 116-117. 8Ibid.. pp. 263—275.

28

prestige. Hence. by using Duncan's scale a legitimate

measure of "prestige" can be inferred as an added dimension

of occupational status. Finally. Duncan's scale included

occupational status scores for the farm occupations (i.e..

farmers and farm managers. and farm laborers and foremen).

The only other occupational status scale of possible use. ‘

the U.S. Bureau of Census Socioeconomic Status Scale.

k \L a—_.—u—fi ’
I

C

does not contain scores computed for farm occupations.

‘41

Consequently. the Duncan Scale was adopted as the operational
measure of a population's occupational status.

The procedures for ascertaining the occupational
status of the rural-farm and urban populations for each
county of the North Central region were as follows:
the occupational distribution of the rural-farm and urban
white male populations in each county were standardized to
the Duncan scale. and a Single mean status score computed
for each county. This was accomplished by multiplying the
number of rural-farm and urban white persons of a county in
each occupational category by the appropriate status score.
These results were summed and a mean status score for the
county for each residence category computed.

The second and third dependent variables. educational
status and income status. were operationally defined in
terms of scales constructed by the U.S. Bureau of Census
for 1960. The status scores for education were derived by
the census as follows: The cumulative percentage distri—

bution by education of chief income recipients in families

29

as of 1959 were computed. The score assigned to each cate-
gory of education was the midpoint of the cumulative per-
centage interval for the category. (For example. persons
who had completed one or more years of college were found
to be distributed between the 83rd and 98th percentiles.

A score of 90 was thus assigned to persons who had completed 1
one or more years of college.) The Status scores for family 16
income were obtained in a similar manner.9 The resultant

status scores for education (total males and females 25 1

 

 

 

 

years and older) and income (total family) were as follows:10
Education Income

Category Status Score Category Status Score

One or More Years $25.000 and over 100

of College Completed 90 $15.000 - $24,999 98
$10.000 - $14.999 94

4 Years of High School $9.000 - $9.999 88

Completed 67 $8.000 - $8.999 83
$7.000 - $7.999 76

1-3 Years of High School $6.000 — $6.999 66

Completed 42 $5.000 - $5.999 53
$4.000 - $41999 38

8 Grades or Less $3.000 — $3.999 24

Completed 10 $2.000 — $2.999 15
$1.000 — $1.999 7

No School Years Under $1.000 2

Completed 1 (Including N0 Income)

9

U.S. Bureau of the Census. Methodology and Scores
of Socioeconomic Status. working Paper. No. 15. Washington.
D.C.. 1963. p. 4.

10These scores were computed from aggregates of the
education and income categories constructed by the U.S.
Census and used specifically for the data in this study.
For a more detailed description of education and income
categories see Appendix III and IV of the U.S. Bureau of
Census Working Paper No. 15.

 

30

Since the data used in this study were taken from
the 1960 census, the U.S. Bureau of Census Scales for
education and income seemed most applicable. More
specifically. the data for education and income in this
study is education for persons 25 years and older and income
for families. This matches with data used by the census to
construct the status Scores.

The procedures for computing the education and in—
come status of the rural-farm and urban populations for
counties is identical with the procedures for computing
occupational status. The educational distribution of rural-
farm and urban persons 25 years and older and the income
distribution of white rural-farm and urban families were
standardized to the U.S. Bureau of Census scales. This
was achieved by multiplying the number of persons in each
education and income category by the appropriate scale
score. These results were summed and mean educational and
income status scores computed for each county.

The fourth and final dependent variable. status
consistency. was operationalized in a way similar to the
technique used by Gerhard Lenski in his study of status
crystallization.ll However. in the present study the county
population and not the individual is the unit of analysis.
Status consistency is operationally defined as the square

root of the sum of the squared deviations from the average

 

llLenski. op. cit.. p. 406.

31

of the three means—-occupation. education and income. status
scores--subtracted from 10012 for the rural-farm and urban
populations of a given county.l3 For example. to compute
the status consistency of the rural—farm population for a
given county. find the mean status score for the farm popu-
lation on each of the three status scales. Then compute

the mean of these three Scores and their individual devia—
tions from the mean. Square each of these deviations and
sum them. Then take the square root of this quantity and
subtract it from 100. The resulting figure gives the extent
of the deviation or consistency of the mean status scores

of a county's rural-farm population around their total mean.
Thus. the more highly consistent a population's status.

the more nearly the consistency score will approach one—
hundred; conversely. the less consistent the population's
status. the more nearly the consistency score will approach

zero.

 

12The technique of subtracting the resulting figure

from one hundred was employed so that a population whose
status was highly consistent would have numerically higher
consistency scores than a population whose status was low
in consistency. This was done primarily to avoid semantic
difficulties.

l3T'he formula for the computation is as follows:

Status Consistency of a Population of a County =

 

3 -2
100- Z (Xi-X)
i=1
Where Xi for i = l . . . 3 refers to the three status
dimensions.

32

Operational Hypotheses

 

Given the operational definitions of the independent
(metropolitan dominance) and dependent (statusstructure
and status consistency) variables. the theoretic hypotheses
in Chapter I are stated in operational form for the
rural-farm and urban population of each county of each state
in the North Central region of the United States in the
following manner:

Hypothesis I: A. There is an inverse relation between the
mean occupational status Scores of rural-
farm and urban males of counties and the
distance the counties are from the nearest
SMSA.

Sub-Hypothesis: (a) The mean occupational status scores
of urban males will be higher than
the mean occupational status scores
of rural-farm males in all cate-
gories of distance of the counties
from the nearest SMSA.

B. There is a direct relation between the
urbanity values of counties and the mean
occupational status scores of rural—
farm and urban males of those counties.

Sub—Hypothesis: (b) The mean occupational status
scores of urban males will be
higher than the mean occupational
status scores of rural-farm males
in all urbanity categories of
counties.

C. There is a direct relation between the
Size-distance value of counties and the
mean occupational status scores of rural—
farm and urban males of those counties.

Sub—Hypothesis: (c) The mean occupational status scores
of urban males will be higher than
the mean occupational status scores
of rural—farm males in all size-
distance categories of counties.

Hypothesis II: A.

Sub-Hypothesis:

B.

Sub-Hypothesis:

C.

Sub-Hypothesis:

33

There is an inverse relation between the
mean educational status scores of rural-
farm and urban persons 25 years and older
of counties and the distance the counties
are from the nearest SMSA.

(a)

The mean educational status scores
of urban persons 25 years and older
will be higher than the mean edu-
cational status scores of rural-
farm persons 25 years and older

in all categories of distance of
the counties from the nearest SMSA.

There is a direct relation between the
urbanity values of counties and the mean
educational status scores of rural-farm
and urban persons 25 years and older of
those counties.

(b)

The mean educational status scores
of urban persons 25 years and
older will be higher than the
mean educational status scores of
rural-farm persons 25 years and
older in all urbanity categories
of counties.

There is a direct relation between the
size-distance values of counties and the
mean educational status score of rural-
farm and urban persons 25 years and older
of those counties.

(C)

The mean educational status scores
of urban persons 25 years and older
will be higher than the mean
educational status scores of rural-
farm persons 25 years and older

in all size-distance categories

of counties.

Hypothesis III: A. There is an inverse relation between the
mean income status scores of rural-farm
and urban families of counties and the
distance the counties are from the
nearest SMSA.

Sub—Hypothesis:

(a)

The mean income status scores of
urban families will be higher than
the mean income status scores of
rural-farm families in all categor-
ies of distance of the counties
from the nearest SMSA.

B.

Sub-Hypothesis:

C.

Sub—Hypothesis:

Hypothesis IV: A.

Sub-Hypothesis:

B.

Sub-Hypothesis:

C.

34

There is a direct relation between the
urbanity values of counties and the mean
income status scores of rural-farm and
urban families of those counties.

(b)

The mean income status Scores of
urban families will be higher than
the mean income status scores of
rural-farm families in all urbanity
categories of counties.

There is a direct relation between the
size-distance values of counties and

the mean income status scores of rural—
farm and urban families of those counties.

(C)

The mean income status scores of
urban families will be higher than
the mean income status scores

of rural-farm families in all size-
distance categories of counties.

There is direct relation between the
status consistency of rural—farm and
urban populations of counties and the
distance the counties are from the
nearest SMSA.

(a)

The status consistency scores of
the rural-farm population will be
higher than the status consistency
scores of the urban population in
all categories of distance of the
counties from the nearest SMSA.

There is an inverse relation between the
urbanity values of counties and the

status consistency scores of rural-farm
and urban populations of those counties.

(b)

The status consistency scores of

the rural—farm population will be
higher than the status consistency
scores of the urban population in
all urbanity categories of counties.

There is an inverse relation between the

size-distance values of counties and the
status consistency scores of the rural-
farm and urban populations of those
counties.

35

Sub—Hypothesis: (c) The status consistency scores
of the rural-farm population will
be higher than the status con-
sistency scores of the urban popu-
lation in all size—distance cate-
gories of counties.

Statistical Technigues

 

Pearson product moment correlation was employed to
test the hypotheses dealing with the relationship between
metropolitan dominance (distance. urbanity. and size-
distance) and rural—farm and urban status structure (status
scores for occupation. education. and income). and status
consistency. Fishers r to z transformation is utilized to
test the significance of these correlations.

In addition to the Pearson r's for each hypothesis
a multiple correlation analysis was performed utilizing
distance. urbanity. and Size-distance of each county for the
occupation. education. and income status scores and status
consistency measures of the rural—farm and urban population.
This will provide a composite measure of predicted variance
for each of the three status scores and the consistency
measure. It will also allow an assessment of the combined
importance of the indicators of dominance as predictors or
contributors to the variance of the status structure and
status consistency of the rural-farm and urban populations.

Simple inspection of the differences between mean
status scores and consistency scores by each independent
variable will be utilized to confirm or disconfirm the

hypotheses dealing with the status and status consistency

36

differences between the rural-farm and urban populations.
In addition. to provide an answer to the question posed
above. "Are the rural—farm and urban categories mutually
exclusive or different enough to be utilized as two separate
residence categories?". a Simple analysis of variance or

F test was performed on the data. The analysis of variance
design is referred to by E. F. Lindquist as the "groups
within treatments design." This was employed since the
unit of analysis is the county. Under this design the
residence categories of rural-farm and urban become the
main effects or treatments and the county's status and

status consistency scores the groups within.14

14For a lengthy discussion of this design see.

E. L. LindquiSt.Design and Analysis of Experiments in
Psychology and Education (Boston: Houghton Mifflin Co..
1956). Chapter 7.

CHAPTER III

HYPOTHESIS-TESTING AND RESULTS

This chapter reports the results of the tests of the
operational hypotheses. The format for this reporting is
as follows: first. a statement of the hypothesis and sub-
hypothesis dealing with the first indicator of dondnance.
distance, and the occupational status of the rural-farm
and urban populations; second. a brief statement of the
results of their correlations. and an inspection of the mean
score differences between the farm and urban populations:
and third the tabular presentation of these statistics.
This same format is followed for the remaining two indicators
of dominance. urbanity and Size-distance. and occupational
status. Following this presentation there is a brief summary
statement of the findings for all indicators of dominance
and occupational status and the mean score differences
between the farm and urban populations. The results for
educational and income status and status consistency are
reported in an identical manner. Upon completion of the
total presentation a summary of all findings in tabular form
is presented. In addition. the results of the multiple
correlation (R) analysis are reported to later provide an

assessment of the predictive utility of distance. urbanity.

37

38

and size-distance as determinants of rural—farm and urban

status structures. Also. the results of the analysis of

variance between the status and status consistency scores
of the rural-farm and urban populations of counties are
reported. This is done in order to later evaluate the
exclusivity of the residence categories and consequently
their utility for sociological research.

Hypothesis I: A: There is an inverse relation between
the mean occupational status scores of
rural-farm and urban males of counties
and the distance the counties are from
the nearest SMSA.

Sub—Hypothesis: (a) The mean occupational status
scores of urban males will be
higher than the mean occupational
status scores of rural—farm males
in all categories of distance of
the counties from the nearest
SMSA.

The expected relationship between rural-farm occu-
pational status and distance from nearest SMSA is supported
by a Significant inverse correlation. However. the cor-
relation for the occupational status of the urban popu-
lation is not in the expected direction and is not
significant (see Table 1).

In all distance categories the mean occupational
status of the urban population is higher than the mean occu-
pational status of the rural—farm population. thus support-
ing the hypothesis. Even though urban occupational status
is higher than the rural-farm the differences between them

are smaller in the categories where dominance is highest

(see Table 1).

39

Table 1. Correlations (r) between distance from nearest
SMSA and rural-farm and urban occupational status
of counties and mean occupational status scores
by distance from nearest SMSA and residence for
the North Central region. 1960.

 

 

 

 

 

 

Rural-Farm Urban
Distance -.44* .01

Mean Scores Mean Scores
SMSA 20.7 35.9
Less than 49 Miles 18.3 34.7
50—99 Miles 17.2 35.4
100-149 Miles 16.6 36.3
150-199 Miles 15.3 34.8
200-249 Miles 15.1 36.3
250-300 Miles 14.6 37.7

 

*p < .001.

B: There is a direct relation between the
urbanity values of counties and the mean
occupational status scores of rural-
farm and urban males of those counties.

Sub-Hypothesis: (b) The mean occupational status
scores of urban males will be higher
than the mean occupational status
scores of rural-farm males in all
urbanity categories of counties.

The correlations of rural-farm and urban occupational
status with urbanity are in the expected direction. How-
ever. only the relationship between rural-farm occupational
status and urbanity is significant. Hence. the hypothesis

is supported for the rural—farm population and rejected

for the urban (see Table 2).

40

The mean occupational status scores of the urban
population are higher than the rural—farm for all categories.
However. in the urbanity categories representing the highest
degree of dominance the Scores converge and the differences
between farm and urban are less than in the urbanity cate-
gories representing the lesser degrees of dominance (see
Table 2).

Table 2. Correlations (r) between urbanity and rural-farm
and urban occupational Status of counties and

mean occupational status scores by urbanity and
residence for the North Central region. 1960.

 

 

 

 

 

 

 

 

Rural—Farm Urban
Urbanity .36* .06

SMSA Counties Mean Scores Mean Scores
1.000.000 and over 22.7 36.6
500.000 to 999.999 23.5 34.2
250.000 to 499.999 19.7 35.6
249.999 and under 19.7 36.3
Non—SMSA Counties
70% or more urban 17.5 36.7
55.0% to 69.9% urban 17.4 35.1
40.0% to 54.9% urban 17.8 35.3
25.0% to 39.9% urban 17.3 35.3
24.9% and under urban 19.3 38.8

 

*p < .001.

41

C. There is a direct relation between the
size—distance values of counties and the
mean occupational status scores of rural-
farm and urban males of those counties.

Sub—Hypothesis: (c) The mean occupational status scores
of urban males will be higher than
the mean occupational status scores
of rural-farm males in all Size-
distance categories of counties.

The size-distance indicator of dominance correlates
directly with the occupational status of the rural-farm
population and indirectly with the occupational status of
the urban population. The correlation with the rural—farm
is Significant whereas the correlation with the urban is
not. Consequently. the hypothesized relationship is
supported for the farm population and rejected for the
urban (see Table 3).

As with the two previous indicators of dominance
mean occupational status for the urban is higher than the
rural-farm for all categories of size—distance. Also.
the differences between the urban and rural-farm are
greatest where dominance is least and lowest where dominance
is highest (see Table 3).

In sum. the correlations support the expected
relationships between all the indicators of dominance and
rural-farm occupational status. but fail to support the
expected relationship with urban occupational status. Also.
all three indicators Show convergences between the urban
and rural-farm at higher degrees of dominance even though

urban occupational status is always higher than the rural—

farm.

42

Table 3. Correlation (r) between size-distance and rural-
farm and urban occupational status of counties and
mean occupational status scores by Size-distance
and residence for the North Central region. 1960.

 

 

Rural-Farm Urban

 

Size—Distance .55* —.04

 

Mean Scores Mean Scores

 

 

20 23.0 35.4
19 21.9 36.4
18 20.3 35.9
17 20.9 34.8
16 18.7 34.6
15 20.0 36.5
14 18.9 35.3
13 19.6 33.7
12 18.8 34.8
11 16.4 36.9
10 18.0 35.1
9 16.0 36.6
8 17.3 35.4
7 15.3 36.7
6 17.3 36.3
5 15.6 35.6
4 16.4 35.9
3 15.5 37.0
2 15.6 36.0
1 14.8 37.1
0 14.9 36.4

 

*p < .001.

43

Hypothesis II: A. There is an inverse relation between
the mean educational status scores of
rural-farm and urban persons 25 years and
older of counties and the distance the
counties are from the nearest SMSA.

Sub—Hypothesis: (a) The mean educational status scores
of urban persons 25 years and older
will be higher than the mean
educational status scores of rural-
farm persons 25 years and older
in all categories of distance
of the counties from the nearest
SMSA.

The correlations between rural—farm and urban
educational status and distance are in the expected direction.
However. these correlations are extremely small (less than
.10) and are not Significant. Hence. the hypothesis is not
supported (see Table 4).

The mean educational status scores of the urban
population are higher than the farm in all distance
categories. There is no noticeable convergence or diver-
gence of differences between farm and urban educational
status (see Table 4).

B. There is a direct relation between the
urbanity values of counties and the
mean educational status scores of rural—
farm and urban persons 25 years and
older for those counties.

Sub-Hypothesis: (b) The mean educational status scores
of urban persons 25 years and
older will be higher than the mean
educational status scores of rural—
farm persons 25 years and older
in all urbanity categories of
counties.

Rural-farm and urban educational status Show positive

correlations with urbanity. However. only the correlation

44

Table 4. Correlation (r) between distance from nearest
SMSA and rural-farm and urban educational status
of counties and mean educational status scores
by distance from nearest SMSA and residence for

the North Central region.

 

 

 

 

Rural-Farm Urban
Distance -.06 -.08

Mean Scores Mean Scores
SMSA 36.9 46.6
Less than 49 Miles 39.3 44.5
50-99 Miles 36.0 43.4
100-149 Miles 33.5 43.5
150-199 Miles 37.9 45.2
200-249 Miles 41.7 49.3
250-300 Miles 36.8 46.4

 

 

 

between urban educational status and distance is Significant.

Thus. the hypothesis is upheld for the urban population and

rejected for the rural-farm (see Table 5).

All urbanity categories indicate higher mean edu-

cational status for the urban than for the farm. Also.

there is no evidence of a pattern of convergence between

farm and urban educational status (see Table 5).

C.

There is a direct relation between the
size-distance values of counties and
the mean educational Status score of
rural-farm and urban persons 25 years
and older of those counties.

45

Table 5. Correlation (r) between urbanity and rural-farm
and urban educational status of counties and
mean educational status scores by urbanity and

 

 

residence for the North Central region. 1960.
Rural-Farm Urban
Urbanity .07 .19*

 

 

SMSA Counties

 

 

Mean Scores

 

Mean Scores

 

1.000.000 and over 33.8 46.4
500.000 to 999.999 41.2 45.5
250.000 to 499.999 39.8 47.5
249.999 and under 38.8 47.0
Non—SMSA Counties

70% or more urban 39.7 48.0
55.0% to 69.9% urban 38.7 45.4
40.0% to 54.9% urban 37.4 43.6
25.0% to 39.9% urban 36.5 43.3
24.9% and under urban 38.6 47.7

 

*p < .01.

Sub—Hypothesis: (c) The mean educational status scores
of urban persons 25 years and older
will be higher than the mean
educational status Scores of rural-
farm persons 25 years and older
in all size-distance categories
of counties.

The hypothesized direct relationships between rural-
farm and urban educational status and size distance are
not supported. However. a rather low though significant
correlation indicates that an inverse relation exists be-

tween farm educational status and size-distance (see Table 6).

46

All size-distance categories from 20 through 7 indi-
<:ate higher mean educational status for the urban population
than for the rural-farm. H0wever. categories 6. 4. 2. and
0 show rural-farm educational status higher than the
urban. and categories 5. 3. and 1 Show extremely small
differences between the two residence categories even though
the urban is higher than the farm. Hence. a convergence
‘between farm and urban educational status occurs in the size-
distance categories representing the lowest intensity of
dominance (see Table 6).

In sum. the correlations do not support the expected

relationships between the distance and size-distance indi-
cators of dominance and rural-farm and urban educational
status. However. the expected relationship between urbanity
and urban educational status is supported but the relation-
ship with rural-farm educational status is rejected.
Further. urban educational status is higher than the rural-
farm for distance and urbanity. but several categories of
size-distance Show the opposite to be true.

Hypothesis III: A. There is an inverse relation between
the mean income status scores of rural-
farm and urban families of counties and
the distance the counties are from the
nearest SMSA.

Sub-Hypothesis: (a) The mean income status scores of
urban families will be higher than
the mean income status scores of
rural-farm families in all cate-

gories of distance of the counties
from the nearest SMSA.

47

 

 

 

 

Table 6. Correlation (r) between size-distance and rural-
farm and urban educational status of counties and
mean educational status scores by size—distance
and residence for the North Central region. 1960.

Rural-Farm Urban

Size-Distance -.16* -.11

Mean Scores Mean Scores
20 36.2 45.3
19 36.1 47.9
18 37.7 44.4
17 41.5 45.2
16 35.5 42.2
15 36.2 45.5
14 36.5 43.8
13 34.3 41.9
12 35.5 43.0
11 32.5 45.3
10 35.5 43.0
9 34.3 44.9
8 37.5 44.2
7 36.3 44.8
6 37.9 36.3
5 35.0 35.6
4 38.9 35.9
3 39.4 36.0
2 39.4 36.0
1 35.4 37.1
0 40.1 36.4

 

 

 

*p < .01.

48

Rural-farm and urban income status are negatively
correlated with distance from nearest SMSA. Further.
each of these correlations is highly significant. Hence.
the hypothesized inverse relation between farm and urban
income status and distance is supported (see Table 7).

In all of the distance categories the income status
of the urban population exceeds the income status of the
rural-farm population. Also. the differences between the
two residence categories are distributed fairly evenly through-
out all of the distance categories and give little evidence
of a pattern of convergence or divergence (see Table 7).
Table 7. Correlation (r) between distance from nearest SMSA

and rural-farm and urban income status of counties
and mean income status scores by distance from

nearest SMSA and residence for the North Central
region. 1960.

 

 

 

 

 

 

Rural-Farm Urban
Distance -.23* -.41*

Nban Scores ' Mean Scores
SMSA 45.5 61.1
Less than 49 Miles 37.9 52.7
50-99 Miles 32.2 48.4
100-149 Miles 30.6 48.2
150-199 Miles 35.3 48.2
200-249 Miles 38.5 53.6
250-300 Miles 36.4 50.5

 

*p < .001.

49

B. There is a direct relation between

the urbanity values of counties

and the mean income status scores of

rural-farm and urban families of

those counties.

Sub-Hypothesis: (b) The mean income status scores of

urban families will be higher than
the mean income status scores of

rural-farm families in all urbanity
categories of counties.

The correlations between rural-farm and urban income
status and urbanity indicate Significantly high direct
relationships. This supports the hypothesized relationship
(see Table 8).

Income status for the urban population is higher
than the rural-farm in all categories of urbanity. The
highest differences occur in two of the categories repre-
senting the lower range of dominance. but these differences
do not occur frequently enough to create a pattern of
divergence (see Table 8).

C. There is a direct relation between
the size-distance values of counties
and the mean income status scores
of rural-farm and urban families
of those counties.

Sub-Hypothesis: (c) The mean income status scores of
urban families will be higher than
the mean income status scores of
rural-farm families in all size-
distance categories of counties.

The hypothesized relation between farm and urban
income status and size distance is supported. Each of

the correlations is positive and is highly significant

(see Table 9).

50

Table 8. Correlation (r) between urbanity and rural-farm
and urban income status of counties and mean
income status scores by urbanity and residence

for the North Central region.

 

 

Urbanity

Rural-Farm

Urban

 

.46*

.52*

 

SMSA Counties

 

Mean Scores

Mean Scores

1.000.000 and over 50.1 65.0
500.000 to 999.999 50.2 61.2
250.000 to 499.999 43.9 60.1
249.999 and under 44.2 58.9
Non-SMSA Counties

70% or more urban 37.3 53.0
55.0% to 69.9% urban 38.0 52.7
40.0% to 54.9% urban 35.8 49.8
25.0% to 39.9% urban 34.0 48.5
24.9% and under urban 37.2 53.5

 

*p < .001.

All Size-distance categories possess urban income
status scores that are higher than those for the rural-
farm. There is no consistent monotonic trend of dif-
ferences between the scores and the size-distance categories.
However. the highest differences do occur in the lower
half of the distribution of size—distance values (see
Table 9).

In sum. the correlations support the hypothesized
relationships between distance. urbanity and size-distance
and the income status of the rural-farm and urban popu—
lations. Also. urban income status is higher than rural-

fann income status for all three of the independent variables.

51

Table 9. Correlation (r) between size-distance and rural-
farm and urban income status of counties and mean
income status scores by Size-distance and residence
for the North Central region. 1960.

 

 

Rural-Farm Urban

 

Size-Distance .26* .39*

 

Mean Scores Mean Scores

 

 

20 50.8 63.9
19 47.6 62.4
18 45.3 59.7
17 48.1 62.7
16 39.0 53.3
15 44.9 58.1
14 36.3 52.5
13 40.0 52.0
12 34.8 49.9
11 34.1 52.7
10 33.0 48.3
9 31.7 51.5
8 31.8 47.0
7 28.6 47.6
6 32.4 48.9
5 28.7 48.4
4 31.8 48.7
3 29.8 50.2
2 30.4 46.6
1 31.7 51.7
0 36.9 51.1

 

*p < .001.

52

Hypothesis IV: A. There is a direct relation between the
status consistency of rural-farm and
urban populations of counties and the
distance the counties are from the
nearest SMSA.

Sub-Hypothesis: (a) The status consistency scores of
the rural-farm population will be
higher than the status consistency
scores of the urban population in
all categories of distance of the
counties from the nearest SMSA.

The correlation between rural-farm status consis-
tency and distance indicates a non-Significant inverse re-
lation. However. the correlation for urban status con-
sistency indicates a highly significant. positive relation.
Hence. the hypothesis is supported for the urban population
and rejected for the rural-farm (see Table 10).

Contrary to the sub-hypothesis stated. the status
consistency of the urban population is higher than the
rural-farm in all distance categories. Further. a definite
monotonic trend in differences between urban and farm
consistency and distance is apparent. The differences are
smallest where distance is lowest and increase steadily as
distance increases (see Table 10).

B. There is an inverse relation between the
urbanity values of counties and the
status consistency Scores of rural-
farm and urban populations of those
counties.

Sub-Hypothesis: (b) The status consistency scores of
the rural-farm population will
be higher than the status consis-
tency scores of the urban popu-

lation in all urbanity categories
of counties.

53

Table 10. Correlation (r) between distance from nearest
SMSA and rural-farm and urban status consistency
of counties and mean status consistency scores
by distance from nearest SMSA and residence for
the North Central region. 1960.

 

 

 

 

 

 

Rural-Farm Urban
Distance -.03 .41*

Mean Scores Mean Scores
SMSA 78.6 81.9
Less than 49 NUles 82.5 86.7
50-99 Miles 84.7 89.5
100-149 Miles 85.7 90.7
150-199 Miles 81.6 86.2
200—249 Miles 78.1 86.4
250-300 Miles 81.3 88.8

 

*p < .001.

The correlation between urban status consistency
and urbanity is higher than the correlation between rural-
farm consistency and urbanity. However. both correlations
are significant and negative which supports the hypothesized
inverse relationships (see Table 11).

The status consistency of the urban population is
higher than the status consistency of the rural-farm through-
out all categories of urbanity. The differences between
them. however. are negligible in the categories repre-
senting high degrees of dominance. In the categories
representing low degrees of dominance there is a divergence
in the consistency scores and the differences are increased.

(see Table 11).

54

Table 11. Correlation (r) between urbanity and rural-farm
and urban status consistency of counties and
mean status consistency scores by urbanity and

 

 

residence for the North Central region. 1960.
Rural-Farm Urban
Urbanity -.20* -.51**

 

 

SMSA Counties

 

1.000.000 and over
500.000 to 999.999
250.000 to 499.999
249.999 and under

Non-SMSA Counties
70% or more urban
55.0% to 69.9% urban
40.0% to 54.9% urban
25.0% to 39.9%.urban
24.9% and under urba

1'1

Mean Scores

78.5
80.3
81.2
80.6

82.3
81.4
83.8
84.1
93.8

Mean Scores

79.4
80.7
82.4
83.6

87.7
85.7
88.3
89.3
98.6

 

*p i .01.
**p .001.

C.

Sub-Hypothesis:

There is an inverse relation between
the size-distance values of counties
and the status consistency scores of
the rural-farm and urban populations

of those counties.

(C)

The status consistency scores of the
rural-farm population will be higher
than the status consistency scores

of the urban population in all
size—distance categories of counties.

The correlation between urban status consistency

and size-distance is significant and negative.

However

the correlation between rural-farm status consistency is

significant and positive.

Consequently. the hypothesis is

supported only for the urban population (see Table 12).

55

The status consistency of the rural-farm population
falls below that of the urban throughout all values of the
Size-distance scale. H0wever. the differences between
them converge at the end of the Scale where dominance is
most intense and diverge where dominance is least intense
(see Table 12).

In sum. the correlations support the expected re-
lationship between urban status consistency and all three
independent variables. However. only the relationship be-
tween status consistency and urbanity is supported for the
rural-farm population.

Urban status consistency is higher than the rural-
farm in all categories of the three independent variables.
In addition to this unexpected outcome the three indicators
of dominance show convergences between urban and farm
status consistency at the higher degrees of dominance and

divergence at the lower degrees of dominance.

Summary of Findings

Tables 13 and 14 present an overall picture of the
results of the hypothesis testing. Table 13 presents
the findings for the hypotheses dealing with the relation-
ships between the indicators of dominance and farm and
urban status and status consistency. Table 14 presents
the findings for the sub-hypotheses dealing with the mean
status and status consistency score differences between

the farm and urban populations.

56

Table 12. Correlation (r) between size-distance and rural-

farm and urban status consistency of counties
and mean status consistency scores by size—
distance and residence for the North Central
region. 1960.

 

 

Rural-Farm Urban

 

Size-Distance .20* -.40*

 

H +4 H r4 H +4 H +4 H F4 N
o t4 N to 9 Ln m ~J m up 0

OHMWrHU'ImflCDKO

Mean Scores Mean Scores

 

79.0 79.3
79.5 81.5
81.5 82.6
79.4 79.7
83.9 86.2
81.0 84.3
85.0 87.2
84.6 86.8
84.8 88.6
85.7 88.2
85.5 89.8
85.4 89.1
84.1 90.8
84.3 91.4
82.8 90.1
84.1 89.9
82.7 89.9
82.7 89.0
81.7 90.8
83.7 89.3
79.5 88.2

 

*p < .001.

57

Table 13. Summary of acceptance (+) and rejection (-)
of the hypotheses dealing with the relationships
between distance. size-distance. urbanity. and
rural-farm and urban occupational. educational.
and income status and status consistency for the
North Central region. 1960.

 

 

 

Hypotheses
1 it 11.1. E
Status
Occupgtion Education Income Consistency

 

 

Farm Urban Farm Urban Farm Urban Farm Urban

 

Distance* + — - — + + - +
Size-Distance** + - - — + + _ +
Urbanity* + - — + + + + +

 

*Hypothesized direct relation with all variables
except status consistency.

**Hypothesized inverse relation with all variables
except status consistency.

Table 14. Summary of acceptance (+) and rejection (-) of
the sub-hypotheses dealing with the differences
between the rural-farm and urban mean occupational.
educational. income. and status consistency scores
by distance. size-distance and urbanity for the
North Central region. 1960.

 

 

 

 

Hypotheses
_I_ 11 111. 11/
Status
Occupation Education Income Consistency
Distance* + + + -
Size-Distance* + - + -
Urbanity* + + + —

 

*Hypothesized higher mean scores for the urban than
for the farm for all variables except status consistency.

58

Multiple Correlation (R) Analysis

 

Occupational Status

 

The multiple R for the combined effect of the three
independent variables on the occupational status of the
rural-farm and urban populations is highest for the rural-
farm. The relative contribution to the multiple R by size-
distance and urbanity is significant1 for rural-farm occu-
pational status while none of the three variables contribute
significantly to the variation in the urban occupational
status. Further. the size-distance variable alone accounts
for the highest percentage of variance2 in the farm occu—
pational status in addition to having the highest partial

correlation coefficient3 (see Table 15).

Educational Status

The multiple R's for rural—farm and urban educational

status are approximately identical. The relative contribution

 

1The relative contribution of each independent
variable to the multiple R is expressed by the beta weight
for each variable.

2The percentage of predicted variance attributable
to each independent variable is found by multiplying the
beta weight of the variable by the first order correlation
between that variable and the dependent variable.

3The partial correlation coefficients refer to the
correlation that occurs between the dependent variable and
one of the independent variables with the effect of the
other two independent variables removed.

59

Table 15. Multiple correlation coefficients and related
factors for the occupational status of the rural-
farm and urban populations and distance. size-
distance. and urbanity for the North Central
region. 1960.

 

 

 

Related Size—
Multiple R Factors Distance Distance Urbanity
Farm Urban Farm Urban Farm Urban Farm Urban

 

 

 

.57 .09 Beta Weights -.055 .004 .455* -.O65 .141* .084

Predicted
Variance in
Percent 2.4 .004 16.4 .02 7.8 0.5

Partial
Correlation
Coefficients -.046 .003 .363 —.049 .148 .078

 

*p < .001.

of all variables to the variation of both farm and urban
educational status is significant. Also. the size-distance
variable individually accounts for the highest percentage

of variance in the educational status of the farm and urban
populations. In addition. Size—distance exhibits the highest
partial correlation coefficient for both populations (see

Table 16).

Income Status

The income status of the urban populations exhibits
a higher multiple R than the income status of the rural-
farm. All variables contribute Significantly to the
variance ofifluaurban. while Size-distance and urbanity are

the significant contributors to the rural-farm. Urbanity

60

Table 16. Multiple correlation coefficients and related
factors for the educational status of the rural-
farm and urban populations and distance. size-
distance. and urbanity. for the North Central
region. 1960.

 

 

 

 

 

 

 

Related Size-

Multiple R Factors Distance Distance Urbanity
Farm Urban Farm Urban Farm Urban Farm Urban
.31 .30 Beta Weights —.312* -.193*-.425* -.294*.109* .208*

Predicted

Variance

in Percent 1.9 1.5 6.8 3.2 0.8 4.0

Partial

Correlation

Coefficients -.221 -.148 —.299 -.228 .099 .197

*p < .001.

alone accounts for the highest percentage of variance and
the highest partial correlation coefficients for both rural-

farm and urban income status (see Table 17).

Table 17. Multiple correlation coefficients and related
factors for the income status of the rural-
farm and urban populations and distance. Size-
distance. and urbanity for the North Central
region. 1960.

 

 

 

 

 

 

Related Size-

Multiple R Factors Distance Distance Urbanity
Farm Urban Farm Urban Farm Urban Farm Urban
.46 .59 Beta Weights .053 -.l43* .111* .175* .432* .415*

Predicted

Variance in

Percent 1.2 5.9 2.9 6.8 19.9 21.6

Partial

Correlation

Coefficients .041 -.l3O .088 .162 .391 .428

 

*p < .001.

61

Status Consistency

 

The multiple R for the status consistency of the
urban population exceeds that of the rural—farm. All
three independent variables contribute Significantly to the
variation in the status consistency of both the farm and
urban populations. Size-distance individually accounts
for the highest percent of variance and highest partial
correlation coefficient of the rural-farm. In the urban
population urbanity accounts for the highest percent of
variance and the highest partial correlation coefficient
(see Table 18).
Table 18. Multiple correlation coefficients and related
factors for the status consistency of the rural-
farm and urban populations and distance. size-

distance and urbanity for the North Central
region. 1960.

 

 

 

 

 

Related Size—

Multiple R Factors Distance Distance Urbanity
Farm Urban Farm Urban Farm Urban Farm Urban
.37 .58 Beta Weights .124* .138* .418* -.188* .303* -.401*

Predicted

Variance in

Percent 0.4 5.7 8.4 7.5 6.1 20.5

Partial

Correlation

Coefficients .092 .125 .301 -.l73 .273 -.413

 

*p < .001.

62

Analysis of Variance

 

The results of the analysis of variance between the
occupational. educational. and income status. and status
consistency of the rural-farm and urban populations are
shown in Tables 19 through 22. For every variable the
analysis shows a significant F test of differences between
the two residence categories.

Table 19. Analysis of variance between the rural-farm and

urban mean occupational status scores of counties
in the North Central region. 1960.

 

 

 

 

Source Sum of Squares df Mean Square F
Between Categories 139.895 1 139.895 11.749*
Within Categories 21.122 1774 12
Total 161.018 1775

*p < .005.

Table 20. Analysis of variance between the rural-farm and
urban mean educational status scores of counties
in the North Central region. 1960.

 

 

 

Source Sum of Squares df Mean Square F
Between Categories 24.960 1 24.960 474*
Within Categories 93.475 1774 53
Total 118.435 1775

 

*p < .005.

63

Table 21. Analysis of variance between the rural-farm and
urban mean income status scores of counties in
the North Central region. 1960.

 

 

 

 

Source Sum of Squares df Mean Square F
Between Categories 113.505 1 113.506 1702*
Within Categories 118.325 1774 67
Total 231.831 1775

*p < .005.

Table 22. Analysis of variance between the rural-farm and
urban status consistency scores of counties in
the North Central region. 1960.

 

 

 

Source Sum of Squares df Mean Square F
Between Categories 8.296 1 8.296 175*
Within Categories 84.102 1774 47
Total 92.398 1775

 

*p < .005.

CHAPTER IV

DISCUSSION AND CONCLUSIONS

Four unexpected results of the analysis merit dis-
cussion. These are: (1) the failure of the occupational
status of the urban population to vary directly with
dominance; (2) the failure of the educational status of
both the rural—farm and urban populations to vary directly
with dominance; (3) the failure of the status consistency
of the rural-farm population to vary inversely with domi-
nance; and (4) the failure of rural-farm status consistency
to be higher than the urban for all degrees of dominance.
Following this discussion the predictive value of the three
indicators of dominance for the status structure and status
consistency of the rural-farm and urban populations is
considered. In addition. the importance of the analysis
of variance and convergences and divergences of the mean
scores for the dependent variables will be treated as a means
of evaluating the utility of the rural-farm and urban

residence categories for sociological research.

Unexpected Results

The occupational status of the urban population
showed little or no relation with distance, urbanity. and

Size-distance. One explanation for this outcome is that

64

65

the categories of occupational status were not sufficiently
detailed to depict differences in various segments of the
urban distribution and a more refined and detailed scale

is needed. However, if this is the case then the question
arises. why was the occupational scale adequate for measur-
ing the occupational status of the rural-farm population
which showed a direct relation with dominance.1 A probable
answer to this question is that dominance simply makes
little or no difference for the occupational status of the
urban population. Perhaps mere urbanity on the part of
county units results in a similarity of occupational status
irrespective of size or proximity to other counties. This
is supported when the standard deviation of the urban
occupational status distribution is compared to the rural-
farm (see Appendix Table 1).

Since these standard deviations are approximately
equal. any notion is dismissed that there was insufficient
variation in the urban occupational distribution initially
to result in a relation with the dominance indicators.
Consequently. it would seem logical to conclude that for
data for the rural-farm and urban population at the county

level of analysis the present occupational status scale

 

1This result is accounted for by the fact that the
farm population under highest dominance has access to the
employment opportunities of the urban areas and hence hold
non—farm type occupations. although categorized as rural-
farm by residence. Conversely. the farm population under
less dominance has less access to urban employment oppor-
tunities and thus hold farm type occupations.

66

is adequate and that dominance Simply has little effect upon
the urban occupational distribution.2

The educational status of the rural-farm and urban
populations in general Showed little or no relation with
distance. urbanity. and size—distance. A probable explan—
ation for this unexpected outcome relates to the population
measured for educational status. The population included
the following: (a) both male and females: sex is a signifi-
cant variable in educational attainment particularly in the
rural-farm population where females reach higher educational
levels than males; (b) whites and nonwhites: race is
significant primarily in the urban population where edu-
cational attainment of nonwhites is considerably lower than
whites although nonwhite females generally reach higher
levels than nonwhite males. Race plays a relatively unim—
portant role in the rural—farm population of the North
Central region. since the proportion of nonwhites reaches
less than 2% of the total farm population: and (c) the
age-structure 25 years and older; the older portion of the
age-structure. i.e.. 65 years and over. will obviously have
lower educational attainment than the younger. since their

education was attained in periods when an education was not

 

2This conclusion is based on the available evidence
and is not intended to rule out conjecture on the possibility
that a more refined and extended occupational scale could
extract the necessary differences from the urban occupa-
tional distribution and indicate a relation with dominance.

67

easily acquired and not as important for acquisition of
employment.

The lack of control of these three factors results
in a considerable loss of homogeneity in the population and
increases the probability of Sporadic variations in the
distribution of educational status. This is evidenced
particularly in the relation between the rural-farm
educational status and size-distance. The inverse correlation
found was very low though significant and was the opposite
of what had been hypothesized. Also. in some of the lower
size-distance value categories the farm educational status
exceeded that of the urban. These two opposite and unex-
pected outcomes can be partially explained through the lack
of control of the factors mentioned above. First. the in-
verse correlation with size-distance can be partially ac-
counted for by the variation in the older segment of (65
and older) the farm age-structure.3 Inspection of the
proportions of this age group for all size distance cate—
gories indicates a direct relation with size-distance (see
Appendix Table 2). Hence. as the size-distance value de-
creases the proportions of persons 65 years and older de-
creases. Since the educational status of this age group

is lower than the remaining population. it contributes to a

 

3This age-group comprises only about 10% of the
total farm population but accounts for almost one-fifth of
the total number of farm persons over 25 years of age.
Hence. this age-group is sufficiently large to effect the
outcome of the relation between educational status and
size-distance.

68

reduction of the educational status in the higher size-
distance categories. and to an increase of educational status
in the lower Size-distance categories.

Second. the low mean educational status scores of
the urban population compared to that of the farm in the
lower size-distance categories may be accounted for by
race and the nature of the size-distance measure. As
mentioned above. the nonwhite population tends to have con-
siderably lower educational status than whites and is gener—
ally concentrated in the urban population of the SMSAS.

If the proportion of nonwhites is high enough in the
counties of a given size-distance category. then the overall
educational status of the category may be reduced. Further
it is possible for all of the Size-distance categories to
contain SMSAS. For example. an SMSA of 100.000 can be
categorized a 4 or a 3 if it falls in the bands of an SMSA
that has 300.000 population. Consequently. the combination
of the smaller SMSAS and their nonwhite population in the
lower size—distance categories may have the effect of
slightly reducing the overall educational status of the urban
population of that category. This could result in the

mean educational status scores of the farm population being
higher than the urban.

The status consistency of the rural-farm population
does not conform to the expected relation with distance and
size-distance. A probable explanation for both of these

outcomes concerns the variable of education in the consistency

69

measure. First. in the case of distance. it is evident

from an inspection of the mean scores that a fluctuation
occurs from category to category at times alternating from
high to low and low to high. It is possible that due to
this inconsistent fluctuation the overall status consistency
measure is in effect neutralized and shows no relation with
distance. From this an obvious question arises. If this
fluctuation can effect the status consistency of the farm
population. why does it not affect the status consistency

of the urban population? The answer is readily gleaned

from further inspection and comparison of the mean scores
for the farm and urban populations. It is evident that the
range of scores and hence the range of possible variation of
scores for the farm population is considerably higher than
the urban. Hence. it can be postulated that the higher
range of variation of the farm exerts a greater impact

upon the relation between status consistency and distance
than the lower range of the urban.

The size-distance variable did not conform to the
expected inverse relation with farm status consistency.
However. unlike distance. it showed a low. though signifi-
cant. direct relation with farm status consistency. As
mentioned above this outcome can be attributed to the
education variable in the consistency measure. While both
occupational and income status Show significant direct

relations with size distance. educational status shows a

70

significant inverse relation.4 Further, educational status
reaches its highest values consistently in the lower size-
distance categories even to the point of exceeding the
scores for the urban population. If these scores are

high enough. they could then have the effect of controlling
the deviation from the mean of the combined scores for
status consistency and create the rather low though signifi-
cant direct relation with size-distance.

In sum. the failure of farm status consistency to
vary in the expected direction with distance and size-
distance may be due to the lack of controls for the
education variable.

The status consistency of the farm population was
lower than the status consistency of the urban population
for all three dominance indicators. This unexpected out-
come is due primarily to several categories of the occupa-
tional scale utilized in the study. The occupational cate-
gories representing the farm occupations (farmers and farm
mangers. farm laborers and foremen) are the third and
fourth lowest categories in the scale preceded only by
private household workers. and laborers excluding farm
and mine. Since by definition the farm population will
have higher proportions in the farm categories than the

urban population the mean occupational status score of the

 

4It will be recalled that this was attributed to the

pattern of variation of size—distance and the older age
segment (65 years and older) of the farm population.

71

farm will be lower than the urban. Further. the deviation
from the mean of occupational. educational. and income
status scores produced by the farm occupational status
score is higher than the deviation produced by the urban
occupational status score. AS a result the farm status
consistency score falls below that of the urban. It is
obvious that any further comparison between the farm and
urban populations using the status consistency measure in
its present form would result in a higher score for the
urban population than for the farm. This finding generates
important considerations that should be attended to if
comparisons are to be made between rural-farm and urban
status consistency. Ideally one alternative is to con-
struct a new occupational scale which would take into account
the variation that exists in the farmers and farm managers
category. For example. factors such as type of farm.

size. and value. as well as total farm output or produce.
could be used to rank the occupation into various new
categories. Unfortunately. the available data for county
units do not contain the necessary information to construct
such a scale.

A second alternative is to keep the present scale.
and change the status consistency measure to a measure that
would be minimally affected by the scores of the farm
occupations and still provide a measure of relative
correspondence forifluapopulation of a county on the three

status variables. The procedures that could be used to

72

construct such a measure are as follows: compute the
Pearson product moment correlations between the standardized
status distributions for occupation and income. occupation
and education. and education and income. Take the re-
sulting three correlations and convert them to standard
scores (Z scores) by means of Fishers r to Z transformation.
Then compute the standard deviation of the three Z scores.
The resulting figure represents the extent to which the
intercorrelation (interrelationship) of the three status
variables deviate from their mean total intercorrelation.
Thus. the higher the status consistency of a population the
lower its standard deviation score (approaches zero) and

the lower the status consistency of a population the

higher its standard deviation score (approaches a quantity
of one or greater).

This new consistency measure is not intended to
solve the problem of the variation in the farm occupational
categories or the need for a new occupational scale.
However. given the present state of available data it
does provide a workable measure of a population's con-
sistency and minimizes the impact of the farm occupational
scores. This is done by taking account of the intercorre-

lation of the total occupational distribution with the

 

5The standard deviation of the Z scores is computed
by the following formula:

 

S.D.Z = % (Zi —'2)

i=1

 

3
where Zi for i=1 . . . 3 refers to the three status variables.

73

remaining status distributions instead of single means for

each distribution.

Dominance as a Determinant of Status
Structure and Status Consistency

 

 

Dominance is evaluated as a determinant of status
and status consistency in terms of the amount of variance
that the combined effect of the three indicators can account
for in each of the Status variables of the rural-farm
and urban populations. The three indicators attain their
highest predictive value for urban income status and status
consistency. and rural-farm occupational and income status.
In all four instances the amount of variance accounted for
reaches almost one-third of the total variation in each
variable. The remaining variables. farm educational status
and status consistency. and urban educational and occupation-
al status posed individual problems which were discussed
above. Hence. for these variables in their present form.
dominance shows little predictive utility.

Of the four dependent variables predicted satis-
factorily. the urbanity indicator individually predicted the
highest amount of variance in rural-farm income status.
and in urban income status and status consistency. In the
remaining variable. farm occupational status. the size-
distance indicator predicted the highest amount of
variance.

The superiority of the urbanity indicator in pre—

dicting variance generates speculation concerning an

74

additional indicator for dominance. In terms of the theo-
retical framework discussed in Chapter I. the technological
base of a population (the means used to acquire sustenance)
affects the structure of the social organization of a pop-
ulation. Technology in the present analysis is included in
the Size measures for SMSA and non-SMSA counties upon which
all three dominance indicators are based. Further. it
is viewed as directly related to the size of a population.
If a measure of technology could be extracted from the
Size measures to control the possible technological variation
that may occur within the size categories. then acontri-
bution to the predictive value of dominance may be made.
One such measure of technology that could be utilized is
based on the functional Specificity or economic function of
an area and operationalized in terms of the percent of
the population employed in different type industries. If
a scale of this type could be constructed and included in
the regression equation for farm and urban status structure.
the predictive utility of dominance may be further increased.
In sum. the dominance indicators adequately predict
variation in four of the eight status variables. It is
speculated that with refinement in the education data of the
rural-farm and urban populations and improvement of the
status consistency measure that the predictive value of the
indicators could be increased. Further. the inclusion of
an operational measure of technology might well increase

the predictive utility of dominance.

75

The Utility of the Rural-Farm and
Urban Residence Categories for
Sociological Research

 

 

The analysis of variance performed between the rural-
farm and urban populations on occupational. educational.
and income status and status consistency indicated that the
rural-farm and urban residence categories are significantly
different from each other. Hence. the two residence cate—
gories are mutually exclusive for the entire distribution
of counties for the North Central region. However. in-
spection of the differences between mean scores under the
influence of the three dominance indicators reveals that
there are convergences between rural-farm and urban for
occupational status and status consistency in addition to
somewhat lesser convergences for educational status. These
all take place in the dominance categories representing
highest influence while in the categories representing
lowest influence. the mean scores diverge and greater dif—
ferences appear. Thus. even though the rural-farm and
urban residence categories can be viewed as mutually ex-

. 6 . . .
cluSive for the region as a whole. in counties under extreme

 

6Ideally. to reach a solution to the problem of
mutual exclusiveness an analysis of variance would have to
be performed between the rural-farm and urban residence
categories for occupational. educational. and income status
and status consistency. The question arises then. why wasn't
this done? The answer is simply that to perform such a
task would require approximately 144 separate analysis of
variance runs on the computer. It is obvious that this
would be extremely time consuming as well as too costly.

76

dominance the exclusivity is diminished and differences
become more difficult to find. Consequently. sociological
research dealing with rural-farm and urban differentials
must necessarily take account or control such factors as
size ofplace and distance from SMSA to make valid compari-

sons and generalizations.

SW

Four unexpected outcomes of the analysis were dis-
cussed. These were the following:

(1) The failure<xfthe occupational status of the urban
population to vary directly with dominance. It was con—
cluded that for the rural-farm and urban populations at the
county level of analysis the present occupational status
scale is adequate and that dominance simply exerts little
influence upon the urban occupational distribution.

(2) The failure of the educational status of both the
rural-farm and urban populations to vary directly with
dominance. It was concluded that uncontrolled factors in
the educational data such as sex. race. and age resulted
in a loss of homogeneity in the rural-farm and urban popu-
lations and may have produced Sporadic variations in the
educational status distribution.

(3) The failure of the status consistency of the rural-
farm population to vary inversely with dominance. It was
concluded that the lack of controls for educational status

may have produced enough of a deviation in the variable

77

to affect the total status consistency measure.

(4) The failure of rural-farm status consistency to be
higher than the urban for all degrees of dominance. It
was concluded that the extremely low status score for the
rural-farm occupational categories reduced the occupational
status scores for this population. These low scores. when
used to compute the status consistency measure. had the
effect of reducing the total measure. A new measure of
status consistency was proposed that could be used on
current data and would take account of the deviation of
each of the status distributions from their total inter-
correlation. This minimizes the impact of the low rural-
farm occupational scores.

In addition to the discussion of these four un-
expected results two related issues were discussed. These
were the following:

(1) The predictive value of the dominance indicators
for the status structure and status consistency of the
rural-farm and urban populations. It was concluded from
the multiple R analysis that dominance was an adequate
predictor for urban income status and status consistency.
and rural-farm occupational and income status. The variable
of functional specificity was proposed as an additional indi-
cator that may add to the predictive utility of distance.
urbanity. and size-distance.

(2) The utility of the rural-farm and urban residence

categories for sociological research. It was concluded

78

from the analysis of variance between the rural-farm and
urban categories for the status variables that for the

North Central region as a whole the residence categories

are mutually exclusive. However. inspection of mean status
scores under occupational and educational status. and status
consistency indicated close convergences between the resi-
dence categories at the highest degrees of dominance. Hence.
sociological research dealing with rural-farm and urban
status differentials must necessarily control such factors

as size of place and distance from the SMSA if valid compari-

sons and generalizations are to be made.

 

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APPENDIX

 

 

 

 

Table 1. Standard deviations for the rural-farm and urban
occupational status distributions for the North
Central region. 1960.
Occupation
Rural-Farm 3.2
Urban 3.8
Table 2. Proportions of rural-farm persons 65 years and older

of the age group 25 years and above by size-distance
for the North Central region. 1960.

 

 

Percent 65 years
and older

Percent 65 years

Size-Distance Size-Distance and older

 

20 20.2 9 14.5
19 19.0 8 17.6
18 20.5 7 13.7
17 19.0 6 17.9
16 19.8 5 13.1
15 18.0 4 16.2
14 19.4 3 13.6
13 16.7 2 ‘15.5
12 18.8 1 11.5
11 15.6 0 12.6
10 18.4

 

84