Braschler, Curtis H.

Pfeiffer, Robert

English

Research Papers in Economics

SOUTHERN  JOURNAL OF AGRICULTURAL  ECONOMICS  JULY,  1977
AN ANALYSIS  OF FACTORS INFLUENCING
REGIONAL  LABOR FORCE MIGRATION,  1960-1970*
Robert Pfeiffer and Curtis Braschler
INTRODUCTION  METHODOLOGY  AND DATA SOURCES
The  migration  of labor  is  an  important  mechan-  The  major  objective  of  this  study  was  to  deter-
ism  through  which  an  efficient  spatial  allocation  of  mine  the  relationship  between  observed  rates  of
resources  is  achieved  and  maintained.  Many  studies  outmigration  and  rates  of economic  growth  among
were  designed  to  determine  the  efficacy  of  the  regions.  The  two  major  differences  between  this
market  as  an  allocator  of  labor  between  various  study  of  migration  and  others  using  the  push-pull
regions  of the economy.  However,  most of these  have  variation  of labor  migration  theory  was the choice  of
concentrated  on  the  allocation  mechanism  between  the  regional  unit  of  observation  and  the  source  of
major  metropolitan  labor  markets  or  major  sub-  data.
regions  in  the  general  economy.  This  study  was  Choice  of  the  regional  unit  of  observation  was
designed  to  ascertain  the  effectiveness  of  labor  contingent  upon  several  considerations.  Ideally,  such
allocation  between  rural  and  metro  regions  and  a  unit  should  comprise  a  single  labor  market.  If the
between  different  rural  regions in  the three-state  area  choice  of region  is too  small,  movement  within  may
of Missouri,  Kansas and Illinois.  be  local  residence  change  rather  than  economically-
Interregional  labor  force migration  has  generally  motivated  employment  change.  On  the  other  hand,
been  studied  in  the  context of neoclassical  economic  choice  of  large  regions  such  as  states  or  multi-state
theory,  with  its  emphasis  on  marginal  productivity  regions  may  ignore  large  differences  within  regions.
analysis.  In this concept,  regional differences  in wages  The  final  choice  was to use county data as basic units
and  employment  opportunities cause labor  to migrate  of  observation.  Data  on  these  units  were  then
to  areas  of economic  growth  and  away  from  lagging  combined  into  multi-county  labor market regions  in
regions [8].  order  to  minimize  the  observing  of noneconomically
Certain  migration  patterns  observed  in  the  motivated  movements  and  to  improve  the  sampling
United  States  in  recent  decades  have  not  been  validity  of  the  choice  of the  basic  data unit. Percent-
consistent  with  this  theory.  In  particular,  levels  of  age  changes  in  employment  were  used  as  basic
gross  outmigration  have  shown  little,  if  any,  rela-  variables  for  measuring  level  of  change  in  economic
tionship  to  economic  conditions  at areas of origin  [4,  activity  between  regions  because  of  availability  and
5].  This  has  led  to  formulation  of migration  theory  reliability.  Rates  of unemployment  were  considered
which  recognizes  factors  in  addition  to  economic  as  measures  of  regional  economic  activity,  but were
motives.  An  important  contribution  was  made  by  ruled  out because they reflect conditions at a point in
Everett  S.  Lee,  whose  theory explicitly  considers  the  time.  Thus,  changes  in  those  rates  over  a  5-year
personal  characteristics  of  migrants,  as  well  as  risk  period  may  not  accurately  reflect  actual  conditions
and uncertainty  as important factors [6].  within a region.1
Robert  Pfeiffer  is  Economist,  Economic  Research  Service,  USDA,  Little  Rock,  Arkansas;  and  Curtis  Braschler  is Professor  of
Agricultural Economics,  University  of Missouri, Columbia Missouri.
*Contribution from  the Missouri Agricultural Experiment Station, Journal Series Number 7678.
1Income  and  unemployment  data  were  used by the Area Redevelopment  Administration to designate  areas for public works
programs. Counties with low incomes  were distinctly rural, while high unemployment counties were essentially  urban places.
77Sources  of Data  was estimated  only for rural  areas  using employment
The  Continuous  Work  History  Sample  (CWHS)  data from  the CWHS file.
maintained  by the  Social Security  Administration  was  Model  I was specified  as follows:
the  source  of migration  data.  The  CWHS  file is  based
on  a  one  percent  sample  of  the  covered  work  force  Y  0 
- +  flX 1 +3 2 X 2 +  -3  X3 + :4X4 +  -5  X5 + u
and  has  been  in  development  since  1957.  The  file
contains  a  record  of each  job  held  by  each  worker  where
included  in  the  sample  and  data  on  age,  sex,  race,
industry  wage  class  and  location  by  county.  The  Y  outmigration  expressed  as  the  number  of
Bureau  of Economic  Analysis  has developed  a migra-  outmigrants  per  thousand  workers  from
tion  file  covering  the  intervals  of  1960-65  and  region i
1965-70  from  the  CWHS  file.  Using  this  file,  it  is  percentage  change  in employment in  region
possible  to estimate,  for any grouping of counties for  i
any  two  points  in  time,  the  number  of  inmigrants,  past  inmigration  into  region  i  in  the  pre-
outmigrants  and  nonmigrants  along  with  charac-  vious  time period
teristics relating to each  group.  X3  =  education  level  expressed  as the number  of
persons  25  years  and  older  having  com-
Classification of Areas  .pleted  four years of high school or more as
Employment  change  for  the  time  interval  of  a  proportion  of  the  total  population  in
1960  to  1970  was  used  as  a  measure  of  economic  region 
activity;  and  counties  in  the  three-state  study  region  X  number  of  persons  in  the  20-24  year  age
(Missouri,  Kansas and Illinois)  were grouped into four  cohort  as  a  percentage  of  the  total  labor
growth  classifications.2 The  classifications  were  as  force
follows:  X5  = rate of unemployment  in region i
u = random variable
County  Percent  Change In Em-  No. of  /o  = constant or intercept term
Class  ployment  1960-1970  Regions  i = population  parameters  in regression  model.
1  >30  8
Model  II used  the  same  dependent  variable  with
2 10;  30  1the  following independent variables:
3  >  0;<10  8
4  <0  10  X  = changes  in  the  size  of work  force  covered
by  Social  Security  (surrogate  for  employ-
Finally,  each  of  the  four  classes  was  subdivided  ment change)
into  regional  units  in  order  to  provide  as  much  X2  = same  as X2 in  Model I
geographic  continuity  as  possible.  A  total of 37  units  X3  = same as  X3 in Model  I
were  obtained,  18  of  which  were  urban  areas  X4  =  ratio  of  agricultural  employment  to  total
(SMSAs)  and  19  of  which  were  rural.  Of  the  rural  employment in region  i.
regions,  nine  experienced  decreases  in  total  employ-
ment while ten showed slow to moderate increases.  Several  considerations  were  paramount  in  selec-
tion  of these variables  for  the two models. A  number Regression  Analysis of  factors  could  theoretically  affect  an  individual's
Finally,  data  were  analyzed  in  the  framework  of  (or family's)  willingness  and  ability  to  move.  Two of
a  multiple  regression  model.  Regression  analysis  the  most  pervasive  are  age  (X4)  and  educational
provided  another  analytical  method  for  controlling  achievement  (X3). In the first place, young people are
for mobility difference  between regions.  believed  to  be  more  mobile  because  of having  more
Two  regression  models  were  estimated.  The  time  over  which  to  amortize  the  costs  of moving.
dependent  variable  in both models was migration rate  They  also  have  fewer  ties  to  their  present  area  of
determined  from  CWHS  data.  Model  I  was  estimated  residence;  i.e.,  moving  is  less  disruptive,  both
for all  areas  using employment  data  from  the Census  economically  and  socially.  It  is  generally  recognized
Bureau  Reports  as  the  primary  independent  variable  that  education  level  and  mobility  are  likely  to  be
relating  to the  level  of economic  activity.  Model  II  associated.  Well-educated  people  are  likely  to  have
2County  employment  figures  were  calculated  from  unpublished  data  compiled  by  the  Bureau  of  Economic  Analysis,
Department  of Commerce.  These data have been adjusted for commuting patterns.
78greater  access  to and  ability  to  act  upon information  ment.  This  influence  may  have  been  greater  during
relating  to  job  opportunities  in  areas  outside  their  the  latter  half  of  the  decade  with  respect  to  rural
current residence.  regions.  It  was  hypothesized  that  this  variable  pro-
A  third  independent  variable  (X 2)  relating  the  vides  a  means  of  accounting  for  the  increased
past  migration  patterns  of  a  given  population  to  its  mobility  status  of  people  who  had  prior  moving
current  mobility  status  was  included  in  the  model.  experience.  In  addition,  past  rates  of migration  are
Prior  research  has  shown  this  to  be  an  important  likely  to  serve  as  a  "carrier"  for  other  variables
indicator  of mobility  [7].  People who have moved at  affecting mobility of a given population.
least  once  are  thought  to  be more  mobile,  since they  Different  age  structures  of regional  populations,
would  be  expected  to have  fewer cultural  ties  and  a  as  specified  in  the  model, were  not significant  factors
lower  degree  of  locational  attachment.  In  terms  of  in  levels  of  outmovement.  Likewise,  educational
the  regression  model,  the  coefficient  of this  variable  achievement  did  not  appear  important.  Coefficients
was expected  to be positive.  for  these  variables  were  negative  when  urban  regions
were  included  in  the  model.  There  seems  to  be  no Model I
theoretical  basis  for expecting  a  negative  relationship
Results  of the  regression  analysis  for Model  I are  for either variable.
shown  in  Table 1.  The coefficient relating to employ-
ment  change  (X 1 ) was  not  significant  in  any  of the  Model  II
equations  for  either  period  of  time.  It  had  the  In  the second model, two changes were made and
expected  negative  sign  only  when  data  from  rural  equations  estimated  for  rural  areas.  Employment
regions  were  included  in the analysis.  This coefficient  change (X1 ) in  Model  II  was  specified  and defined as
appeared  to  be  reasonably  stable  over  time;  the  change  in  size  of covered  work  force  in  each  period.
negative  sign  indicates that  limited job  opportunities  Thus,  data relating  to  employment  and migration are
in  rural  areas exerted  some pressure for outmigration,  from  the  same  source  in  Model  II  (CWHS).  This was
but the relationship  appeared  weak.3 done  to  generate  a  more  compatible  data  set  with
Coefficients  attaching  to  previous  inmigration  regard  to  these  variables.  A  bias  in  the  CWHS
(X2)  were  significant  in  four  of  the  six  equations  migration  estimates  may exist, since agricultural  areas
estimated,  and  appear  to  be  the  most  consistent  are  likely  to have  a high  proportion of self-employed
factors  in  "explaining"  observed  rates  of  outmove-  workers (self-employed  people are not included  in the
TABLE 1.  REGRESSION  ANALYSIS  RESULTS  FOR MODEL  I, ALL  AREAS  AND  TWO TIME PERIODS
Equation  0 1  B2  3  B4  5  R2
All  Areas:
1960-65  114.9  -. 063  .866*  -. 103  -. 556*  .743  .530
(.043)  (.188)  (.102)  (.209)  (.862)
1965-70  156.2  -. 048  .624*  -. 049  -. 070  -. 107  .571
(.041)  (.126)  (.167)  (.131)  (.617)
Metropolitan
Areas:
1960-65  -51.2  .006  1.022*  -. 051  -. 515*  3.651*  .818
(.008)  (.242)  (.124)  (.233)  (1.700)
1965-70  28.0  .087  .399  .212  -. 129  1.393  .763
(.906)  (.235)  (.331)  (.161)  (1.224)
Rural  Areas:
1960-65  29.4  -. 074  .40.  .224  .057  .734  .242
(.103)  (.353)  (.208)  (.500)  (1.412)
1965-70  -64.3  -. 055  .661*  .339  .439  .773  .493
(.774)  (.219)  (.306)  (.309)  (1.022)
*Significant  at the  .05 level
Standard errors  are  shown in parentheses.
3It  may  well  be that employment  change  is significant  for specific  age  categories.  If  reliable  data existed  on  outmigration
rates  for the  young  adult  category  (16-25),  this hypothesis could be tested using  the framework of this model, omitting age  as an
independent variable.
79sample).  If  this  is  true,  outmigration  estimates  from  in  rural  areas.  Beyond  this,  few  cause  and  effect
rural  areas  would  be  understated.  Because  of  this  relationships  could  be  determined.  Thus,  the  study
possibility,  an  additional  variable  defined  as  ratio  of  demonstrates  difficulties  associated  with  establishing
agricultural  employment  (X 4)  to  total  employment  generalizations  concerning  any  single  facet  of  the
was  included  in  Model  II.  Age  and  unemployment  migration  process.  Rather,  it  indicates  the  necessity
was  deleted  after  earlier  analysis  produced  insignifi-  of  viewing migration  as  a  product  of a  wide  array  of
cant results.  Thus, each  region was weighed according  forces,  some  of  which  are  not  amendable  to  quan-
to  the  relative  importance  of agriculture  in its overall  tification.
economy.
Resulting  equations  are  somewhat  more  satis-
factory  in  that each coefficient has  the expected  signs  SUMMARY  AND CONCLUSIONS
for  both  periods  (Table  2).  Previous  inmigration  was  This  study  differs  from  earlier  ones  in  that  an
statistically  significant  in  both  equations.  If,  as  is  attempt  was  made  to define regions that are as  nearly
widely  believed,  the  migration  process  is  selective  of  homogeneous  as  possible  with  respect  to  economic
the  young  and  better  educated,  a  high  rate  of  growth forces.  The major objective was  to analyze  the
inmigration  into  an area would tend to alter composi-  relationship  between  employment  growth  and  levels
tion of  the population  in  favor  of these  groups.  This  of outmigration  in  rural  areas.  A  relatively  new  data
would  facilitate  higher  rates  of  outmigration  in  a  source,  the  Continuous Work History  Sample,  was  the
subsequent  time  period  than would  otherwise  be  the  source  of much migration  and employment data.
case.  The volume  of previous  inmigration  thus serves  Two  regression  models  were  used  to  analyze
as a proxy for mobility status.  data. The  following  conclusions  appear  warranted  on
While  the  coefficients  of employment  change  in  the basis of the regression equation estimated:
Model  II are  not significant, they  are larger relative  to
their  standard  errors.  This  means  that  employment  (1)  Employment  conditions  in  the  sample areas
opportunities  (or  lack  of  same)  exert  a  greater  did  not exert  a major  influence  on observed
influence  than that indicated in  Model I.  Both models  volume  of  outmigration.  This  was  true  for
were  in  agreement  concerning  the influence of educa-  both  urban  and  rural  areas.  In  rural  areas,
tion (not significant).  however,  employment  appears to  be  a more
A null hypothesis stating no relationship  between  important  factor,  since  coefficients  for  this
employment  change  and  opportunity  and  out-  variable  have  the  expected  sign  in both time
migration  could not  be  rejected.  Results  suggest  that  periods  and  are  larger  relative  to  their
much  of  the  movement  observed  among  regions  is  standard errors.
motivated  by  something  other  than  economic  con-  (2)  Variables  related  to  characteristics  of
siderations.  Mobility  status,  as  reflected  by  previous  migrants  themselves,  namely  age  levels  and
patterns  of inmigration,  was an important factor even  education  achievement,  were  not important
predictors  of  outmigration.  Variables
measuring  past  levels  of  migration  were
TABLE 2.  REGRESSION  ANALYSIS  RESULTS  important  factors  in  both  urban  and  rural
FOR MODEL  II,  RURAL AREAS ONLY  areas.  They  appeared  to  be  consistent  over
AND TWO TIME PERIODS  time.
Time  61  B 2 63  4  (3)  Finally,  certain  limitations  should  be  noted.
Period
1960-65  -.111  .421*  .005  -. 035  Since  the  sample  area  included  only  a
(.~4)  (.199)  (.135)  (.122) 114)  (.199)  (.135)  (122)  three-state  area,  it  would  be  unwise  to
1965-70  -.106  .716*  .107  -.128
(.098)  (.217)  (.164)  (.189)  generalize  the data to all regions. Also,  it was
*Significant  at.~  the  lvelassumed  that  employment  change  would *Significant at the .05  level
Standard  errors  are  shown  in  parentheses.  For  the  provide  the  most  perasive  description  of
1960-65  data,  the  constant  term  is 157.5 and the R
2 is .302.  economic  conditions  in  the  sample  areas.  A
The  equation  for  the  1965-70  data has  a  constant  of  127.7  number  of  other  indicators  might  be  used
and the R2 is  456.  number  of  other  indicators  might  be  used and the R
2 is .456.
for a more complete  description  of the areas.
80REFERENCES
[1]  Beale,  Calvin  L.  "Demographic  and  Social  Considerations  for  U.S.  Rural  Policy,"  American Journal of
Agricultural Economics, 51, May  1969, pp. 410-427.
[2]  Cartwright,  David  and  K.  Horowitz.  "Migration  Data from  the  Social  Security  Continuous  Work  History
Sample,"  Unpublished  Memorandum,  Bureau  of Economic  Analysis,  U.S. Department  of Commerce,
1973, mimeograph.
[3]  Ford, Thomas  R.  The Southern Appalachian Region, Lexington:  The University  Press,  1970.
[4]  Lansing,  John  B.  and  Eva  Mueller.  The  Geographic Mobility of Labor,  University  of  Michigan,  Survey
Research  Center,  1967.
[5]  Lowry,  Ira  S.  Migration and Metropolitan Growth:  Two  Analytical Models,  San  Francisco:  Chandler
Publishing Company,  1966.
[6]  Lee,  Everett  S.  "A Theory of Migration," Demography, 3  (1966),  pp. 47-57.
[7]  Miller,  Edward.  "Is  Outmigration  Affected  by  Economic  Conditions?"  Southern Economic Journal, 39,
January  1973, pp.  396-405.
[8]  Richardson,  Harry W. Regional Economics, New York:  Praeger Publishers,  1969, Chapter  12.
[9]  Census  of Population.  General Social and Economic Characteristics,  Department  of Commerce,  Bureau  of
the Census,  Missouri,  Kansas and  Illinois, 1960 and  1970.
[10]  U.S.  Department  of  Commerce,  Bureau  of  Economic  Analysis,  Regional  Economic  Analysis  Division.
Migration  Data from the  Social Security  Continuous  Work History  Sample  (Computer tapes, obtained
through  the  Public  Affairs  Information  Service,  College  of  Administration  and  Public  Affairs,
University of Missouri,  Columbia).
[11]  U.S.  Department  of  Commerce,  Bureau  of  Economic  Analysis.  Unpublished  County  Employment  data
prepared  from  the 1970 and  1960 Censuses of Population  (computer printouts).
81I

AN ANALYSIS OF FACTORS INFLUENCING REGIONAL LABOR FORCE MIGRATION, 1960-1970

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