Carruthers, Garrey E.

Eubank, Randy

Renner, Kathryn

Urquhart, N. Scott

English

Research Papers in Economics

Assessing  Rural  Community  Viability:
An  Experimental  Model
Garrey Carruthers, Randy Eubank, Kathryn Renner and N.  Scott  Urquhart
Governors,  legislators,  COG  Directors,  county
commissioners  and  other  assorted  public  decision
makers are confronted with the textbook definition
of an  economic problem - the allocation  of scarce
public  resources  to an increasingly  larger and more
demanding  set  of  communities.  Although  New
York City  and  its problems grabbed the headlines,
the  budget crunch is just as acute but not as visible
in  most  rural  communities.  Scarce  funds  and/or
public  services  must  be  targeted  in  these  rural
communities  if  the  public  is to  receive  maximum
benefits per dollar expended. The general objectives
of a New Mexico  State University  rural community
viability  research  project  - the basis  for this paper
- were  to  develop  a  model for  assessing the  rela-
tive  viability  of rural  communities,  and then using
the  model  results,  to  specify  alternatives  for
modifying  a  community's  relative  status.  Past
rural  development  programs  treated  single  com-
munity  problems  according  to  statutory  pre-
scription.  Knowledge  of  relative  viability  and the
causes  of  current  viability  status  would  permit
rural  development  investors  to  target  dollars  and
services.  One  community  might  need  funds  to
improve  its utility system  before  it  could  become
more  viable.  Another  might  discover  current land
holding  patterns  inhibit  growth  and,  therefore,
changes  in  the  status  of  public  lands  would  be  a
first  objective  in  improving  community  viability.
Given  a  quantitative  measure  of  relative  viability
and  an understanding  of the related factors,  public
decision  makers  will  be  much more  responsive  to
each  community  as  they  allocate  public  funds
or  services.  Politically  sensitive  outcomes  might
Garrey  Carruthers  is  an  associate  professor,  Randy
Eubanks  is  a  former  research  assistant,  and  Kathryn
Renner  is  a  former  research  specialist  in  the  Department
of Agricultural  Economics  and  Agricultural  Business.  N.
Scott Urquhart  is a professor  in Experimental Statistics.
This is journal article  number  605  of the  Agricultural
Experiment  Station,  New  Mexico  State  University,  Las
Cruces, New Mexico.
be  the  withdrawal  of some  public  programs  if  a
community  is  not  viable  or  reallocation  of funds
toward  the  more  viable  to  obtain  more  "bang
for the buck."
The  focus  of  this  paper  is  on an  experimental
model  which will  be  the  basis for assessing relative
community  viability.  Reference  is  made  to past
research  in community  taxonomy and community
viability,  but emphasis  is  on a combined statistical
procedure  which  yields  a  differentiation  of  com-
munities  with respect  to  relative  viability.  Results
are  presented  for  all  communities  (29)  in  New
Mexico  with  population  from  2,500  to  50,000
except  those communities  created  for government
purposes,  i.e.  Los  Alamos  and  various  air  bases.
Data were unavailable  for smaller towns.
Procedure
Attempts  to  categorize  communities  into
distinct  typologies  is  far  from  new  in  the  social
sciences.  Numerous  analytical  devices  have  been
employed  to  achieve  this  objective.  Among  the
earliest  was  factor  analysis.  The  premise  behind
such  an  approach  was  that  the  factors  would
represent  unique  dimensions  of  community
structure  and  therefore  a  community  which  had
a large  factor  score  on  a  specific  factor  belonged
to  a  community  group  typified  by  that  factor.
However,  since  it  was  possible  for  communities
to  rank highly  on more  than one factor, the  group-
ings  thus  devised  did  not  necessarily  represent
disjoint  sets.  This  approach  has  been  taken  by
several  researchers  [Price  pp.  449-455;  Jonassen
and  Peres;  and  Hadden  and  Borgatta].
Other  approaches  have  attempted  to  identify
non-intersecting  groups  through  cluster  analysis
[Bruce,  pp.  48-53;  Bruce  and  Witt,  pp.  238-245;
and  Kernan  and  Bruce, pp.  15-18].  Finally,  some
206Carruthers,  Eubank, Renner, and Urquhart
studies have  used  objective  or  subjective  measures
to place communities  in groups  and then employed
discriminant  analysis  to  provide  a  classification
function  [Bean,  Poston, and Winsborough,  pp.20-
32;  Bromley,  pp.  319-322].  The  validity of such
groupings  was  then  indicated  by  the  function's
probability  of misclassification.
The  experimental  community  taxonomy
procedure  used  in  the  New  Mexico  Community
Viability  Study  is  outlined  in  figure  1.  In  the
first  phase,  the initial  step was an accumulation of
secondary  data describing  the sample communities.
Viability  was  defined  as  a community's ability  to
attract  and  hold  mobile  resources.  Based  on  this
definition,  and  on  previous  work  in  the  area  of
community  viability  [Beers,  pp.  13-24; Hodge,  pp.
87-115;  Keele,  pp.  3-10; McGranahan,  pp. 61-77;
Swackhamer,  pp.  3-10;  and  Williams]  variables
were  selected  measuring  economic,  demographic,
locational,  resource,  infrastructure,  and  political
factors.  The  focus  in the  selection  process  was  on
causal  and/or  manipulatable  factors  to  facilitate
the  formulation  of policy recommendations based
on  the  results  of  the  model.  However,  a  few
descriptive  characteristics  were  included  because
of their hypothesized importance  in differentiating
communities.  The  following  variables  were  anal-
yzed  in the model:  employment  dispersion  among
Fig.  1.  Community  taxonomy  procedure,  New  Mexico Community  Viability Study,  1976
Phase I
Cluster Analysis
Phase  II
Discriminant  Analysis
,----------
I
207
Phase  III
Results
Rural Community Viability
I
I
I
I
I
I
IWestern J. Agr. Econ.
industrial  sectors measured  by the standard  devia-
tion  of  the  numbers  employed  among  sectors;
employment  specialization  based  upon  the  index
3(WC) + 2(BC) + (AG)
3
where  WC,  BC,  and  AG  are  percentages  of  the
labor  force  detailed  as  white  collar,  blue  collar,
and  agricultural;  importance  of  agriculture  as
measured  by  the  percent  of  community  and
hinterland  in irrigated  acreage; relative  importance
of  various  sources  of  community  general  fund
revenue,  using  the  percent  of  revenue  obtained
from  gross  receipts  tax,  gasoline  taxes,  property
taxes,  and  federal  revenue  sharing; basic  industry
employment,  measured  by  the  proportion  of the
employed  labor  force  in  basic  industries;  trans-
portation-communications  complexity  as  mea-
sured  by  Guttman  scaling;  per  capita  assessed
valuation;  availability  of  community  land  for
new  industry;  distance  to  a  central  place  of
100,000  or  more  population;  per  capita  water
availability,  measured  by  municipal  water  rights
in  acre-feet  per  year  per  person;  source  of water
for  irrigation,  percent  from  surface  sources  only;
proportion  of  community  and  hinterland  in
private ownership; and voter participation,  reflected
by  the  percent  of  registered  voters  who voted  in
1974  general  elections.  These  variables,  in  the
majority  of cases,  standardize  out population  size
by  use  of  percent,  per  capita,  and  index  forms.
These  forms  shift  focus  toward  community
structure,  thus  minimizing  the  direct  effect  of
size  on subsequent  analysis.
The  cluster  analysis  grouped  observations  by
similar characteristics.  Defining the 29 communities
as  29  groups,  Ward's  hierarchical  clustering  algo-
rithm  was  used  to  reduce  the  29  groups  to  one.
Ward's  algorithm  reduces  groups  from  n to  one in
a manner  that  minimizes  the  loss  of information
associated  with each  grouping and  permits quanti-
fication  of that  loss  in a form  that can  be  readily
interpreted.  Loss of information is expressed as an
increase  in the  error  sum of squares  for character-
istics  of  communities  as  they  are  clustered  with
other  communities  or  groups.  A  large  increase  in
error  sum  of squares  at  any  step  in  the  clustering
process  indicates combination of dissimilar groups.
If  there  is  no  marked  change  in  error  sum  of
squares  when  all  n  groups  have  been  reduced  to
one,  then  all  29  observations  are  probably  mem-
bers  of the  one  remaining group.  If the  largest  in-
crease  in error sum of squares occurs  when the last
two groups  are joined, then there are two definable
groups,  and  so  on. Note  in figure  1, that a require-
ment for moving into Phase II of the analysis is the
identification  of at least two distinct groups.
The  objective  of  using  stepwise  discriminant
analysis was  to reduce the number of characteristics
for describing  rural  communities  and to generate  a
reduced  discriminant  function  for  general  use  in
defining  the  relative  position  of a community  vis
a vis  all  other  communities.  Discriminant  analysis
yields  one  or  more  linear  combinations  of  the
discriminating  variables;  if the  analysis  is used  to
distinguish  between  two  groups,  then  one  discri-
minant  function  will  be  formed.  If  there  are  n
groups,  the  analysis  will  yield  n-1  discriminant
functions.  The functions take the form:
Dim = dilZi2 + di2Zm2 + ---  + dipZmp
where Dim  = discriminant  score,  community  m,
using discriminant  function i.
Dip  = weighted  coefficient,  discriminant
function i, characteristic  p,
Zmp=  characteristic  (p)  of  the  community
(m) being classified.
Results
Cluster  analysis  indicated  the  existence  of two
distinct  groups  of communities.  In  the last step of
the  clustering  process,  the  joining  of  two  groups
was accompanied  by a marked increase in the error
sum  of  squares,  suggesting  that  these  are  two
somewhat  distinct  groups.  The  communities  in
Group  1, evaluated  by  the  researchers  to be  the
more  viable  group  based  upon  the  discriminant
analysis  (see  fig.  1)  are:  Alamogordo,  Artesia,
Carlsbad,  Clovis,  Farmington,  Gallup,  Hobbs,  Las
Cruces,  Lovington,  Portales,  Roswell,  Silver  City
and  Santa  Fe.  Group  2  consists  of Aztec,  Bayard,
Belen,  Clayton,  Deming, Espanola, Eunice,  Grants,
Jal,  Las  Vegas,  Lordsburg,  Raton,  Socorro,  Truth
or Consequences,  Tucumcari and Tularosa.
Stepwise  discriminant  analysis  revealed  that
nine  variables  were  significant  in  differentiating
between  the  two  groups  of communities:  Trans-
portation-communication  complexity;  percentage
of  general  fund  revenue  from  gross  receipts  tax,
and  from gasoline  taxes; percentage  of labor force
in  basic  industries;  per  capita  assessed  valuation;
employment  dispersion  among  industrial  sectors;
208
June 1977Carruthers,  Eubank, Renner, and Urquhart
percentage  of  the  community  and its  hinterland
in  irrigated  acreage;  percentage  of  general  fund
revenue  obtained  from  federal  revenue  sharing;
and  employment  specialization.  Table  1 presents
these  variables  with  their  respective  F-values  and
standardized  discriminant  function  coefficients.
The  F-values  are  important  for policy  evaluation
because  they  reflect  the  relative  significance  of
each  variable  in  discriminating  between  the  two
viability  groups.  The  discriminant  coefficients  in-
dicate  the  relative  weights and  direction of contri-
bution  of the  variables  in  the  calculation  of  dis-
criminant  scores for each community.
The  ordinal  ranking  of  communities  by  dis-
criminant  scores  is  shown  in  table  2.  The  com-
munities  are  numbered  rather  than  named  in  the
ordinal  ranking,  because  this  ranking  is  not  con-
sidered  a  final  product  by  the  research  team.
Future  plans  are  to  include  more viability  factors
in the model and to enlarge  sample  size.
In  general,  Group  1 communities,  compared to
Group  2,  are  characterized  by  a  greater  degree
of  transportation-communication  complexity,  a
greater percentage  of general fund revenue obtained
from  gross  receipts  tax,  and  a  lower  proportion
Table  1. Significant  variables,  F-values,  and  stand-
ardized  discriminant function coefficients,
stepwise  discriminant  analysis,  viability
study,  New  Mexico,  1976
Standardized
Discriminant
Variable  F-Value  Coefficient
Transportation-communications
complexity  22.65  0.38
Percentage of general  revenue
fund from gross  receipts tax  19.82  0.36
Percentage of general  revenue
fund  from gasoline taxes  19.68  -0.38
Percentage of labor force in  basic  industry
industry  17.98  0.44
Per capita assessed valuation  12.36  0.32
Employment  dispersion  among
industrial  sectors  8.19  0.31
Percentage  of community and
h  hinterland  in  irrigated  acreage  6.41  0.20
Percentage  of general  fund  revenue
from  federal  revenue sharing  5.25  -0.19
Employment  specialization  2.32  0.14
from  gasoline  taxes  and  from  federal  revenue
sharing,  a  higher  per  capita  assessed  valuation,  a
greater dispersion of employment among industrial
sectors,  a  larger  proportion  of the community  and
its  hinterland  in  irrigated  acreage,  and  a greater
degree  of  employment  specialization.  These  find-
ings  suggest  that  transportation  and  communica-
tion  service  availability,  revenue  sources,  diversity
and  type  of economic activity, importance of agri-
culture,  and  community  wealth  are  the  most im-
portant viability factors in New Mexico.
Even  though  the  variables  were  constructed  to
minimize  the  effects  of  community  size,  the two
groups  consist  of larger  and  smaller  communities
respectively.  However,  three  cities  in Group  2  are
larger  than cities in Group 1. The apparent relation-
ship  between  size  and  viability  grouping  does not
directly  reflect  size  since  the  rank  correlation
between  population  size  and  viability  score  is
Table  2. Ordinal  ranking  of  communities,  viability
study, New  Mexico, 1976
Group and Community
Group 1
1
2
3
4
5
6
7
8
9
10
11
12
13
Group 2
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
Discriminant Score
1.651
1.555
1.254
1.191
1.163
1.001
0.997
0.969
0.946
0.875
0.794
0.648
0.506
-0.499
-0.591
-0.622
-0.676
-0.712
-0.750
-0.760
-0.773
-0.784
-0.788
-0.845
-0.907
-0.920
-1.123
-1.161
-1.639
209
Rural  Community  ViabilityWestern J. Agr. Econ.
0.22 and -0.07 with  Groups  1 and 2, respectively.
Only when  the  two  groups  are  combined does the
correlation increase  (to 0.75) indicating that Group
1 has  higher  average  size  and  viability  score  than
Group  2. This leads to the (perhaps)  obvious state-
ment that the more  viable (and larger) communities
have  a  different  composition  than  the  less viable
(and smaller) communities.
The success of the reduced discriminant function
depends  on  its  ability  to  reclassify  communities
into  the  original  groups.  Using  the  nine variables
shown  in table  1,  100 percent of the communities
were  reclassified  into  the  two  groups  revealed  by
cluster analysis.  This outcome  suggests the model is
useful in  reducing the number  of variables needed
to differentiate  viability  groupings.
This  analysis  will  be  extended  to communities
from  2,500  to  50,000  population  in  the  Four
Corner states.  If this experimental  model is success-
fully  retested  with  the  larger  number  of  obser-
vations,  it  will  serve  as  the  basis for  a  generalized
scorecard  which  will  be  used  at  the  community
level  to  assess  community  strengths  and  weak-
nesses, and  derive strategies to alter weaknesses.
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ASSESSING RURAL COMMUNITY VIABILITY AN EXPERIMENTAL MODEL

ASSESSING RURAL COMMUNITY VIABILITY AN EXPERIMENTAL MODEL

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