Metropolitan growth in transition

JOURNAL

OF URBAN

ECONOMICS

1 t,

I12-

Metropolitan

129 ( 1982)

Growth LELAND

in Transition

S. BURNS

School of Archttecture and Urban Plannrng, Unrverstty of Caltfornia, LAS Angeles, Ctdtfornia 90024 Received October 1, 1980 Two hypotheses are tested in this exploratron of how factors influencing metropolitan migration have changed. Results show that the traditional model of economic opportunity fails to meet the criteria of “symmetry” and “timelessness.” Determinants of migration differ substantially between samples of rapidly growing and relatively declining metropolitan areas and between time periods examined.

No less than a decade ago it was commonly believed that urbanization was a well-understood phenomenon. Migrants, the principal components of variations among urban areas in population growth, responded to opportunities described principally in economic terms and measured as differentials in job availability and wages. As Shaw [14] points out in his comprehensive literature review, empirical studies time and again verified the neoclassical model. Research by Blanco [4], Cebula and Vedder [6], Greenwood and Gormely [8], Lowry [9], Miller [ll], and Rogers [ 131 rank importantly among the examples. During the 1970s a new settlement pattern emerged in the United States. Described by terms like “counterurbanization” [2], growth of the metropolitan sector slowed and the distribution of intra-metropolitan migration shifted toward smaller metropolitan areas where before it had favored the largest, and the rate of natural increase, the other component of metropolitan growth, dropped. The question for research is this: Can the theory, that has served so well to explain past migratory flows, remain viable under the radically different conditions characterizing recent history? The answers to two specific questions challenge the theory’s resilience. Is the theory “timeless;” that is, is it immune to shifts over time in these forces? Second, is the theory “symmetrical;” that is, does it explain relative decline as well as growth? If not, then the traditional model of economic opportunity is particular to time periods and to direction of change, and its usefulness is called into question. This paper addresses these questions. Answers are found by comparing the determinants of migration to U.S. metropolitan areas (SMSAs) during two recent time periods and for two varieties of change, relative growth and decline. The tests are prefaced by a brief description of the long-term trends 112 0094-1190/82/010112-18$02.00/O Copyright AI1 nghrs

0 1982 by Academc Press, Inc of reproducrmn L” any for,,, reserved

METROPOLITAN

GROWTH

113

that have shaped the present urban settlement pattern, and the recent breaks with those traditions. THE CHANGING METROPOLITAN SYSTEM: A STATISTICAL SYNOPSIS During the 1970sthe size of the metropolitan sector shrank relative to the rest of the nation. From 1970 to 1978, the most recent year for which data are available, the average SMSA grew at only one-third the annual rate of the 1960s. The nonmetropolitan sector, which traditionally has fed metropolitan America, is now expanding at double the rates of the sixties and at rates higher than those for the metropolitan system as a whole. Put another way, the nonmetropolitan population grew at under half the metropolitan rate during the earlier decade,but at triple that rate in the years since. With 31 SMSAs losing population between 1970 and 1977, about one metropolitan resident in four-one-fifth of the nation’s total population-lived in declining areas.’ Population figures for the biggest units in the metropolitan system changed most dramatically. The large SMSAs, those over 1.5 million persons, grew by 1.8%per year during the 196Os,an annual rate which dropped to 0.34% between 1970 and 1977. During the two time periods, the remaining SMSAs showed annual growth rates of 1.7%and 1.3X, respectively. The very largest, those over three million persons, increased in size by 1.14% yearly in the 196Os,but registered annual absolute declines of 0.14% during the 1970s. Of the nation’s 25 largest metropolitan areas, only Pittsburgh lost population between 1960 and 1970. Since then, eight of the largest have experienced absolute declines: Cincinnati, Cleveland, Detroit, Newark, New York, Philadelphia, Los Angeles, and St. Louis. The size distribution of migration has altered as well. Morrison’s [12] comparative data for 1960- 1970 and 1970- 1975 show the complete reversal in the relation between SMSA size and both total population growth and its migratory component. With increases in size groups from the smallest (under one-quarter million persons) to the largest (over one million), total population growth rose from 1.4% to 1.6% annually and net migration climbed from 0.2% to 0.6%. In contrast, from 1970 to 1975, the smallest and largest groups grew by 1.5% to 0.5%, respectively, and the net migration rates dropped with increasing SMSA size from 0.5% to -0.2%. In short, population has shifted in relative terms away from the metropolitan system, and in absolute terms, from its largest units. If economic opportunity has shifted in tandem, then migration theory which posits ‘Unless indicated otherwise, the source of data in this section is U.S. Bureau of the Census [ 161;for elaborations, see Bums [5] and Stemlieb and Hughes [15].

114

LELAND S. BURNS TABLE I SelectedCharacteristics of Rapidly Growing and Declinmg Metropolitan Areas, 1960and 1970 1960

Population size ( X 1,000) Annual net migration rate” Median annual income Population below poverty level Labor force unemployed Labor force employed in manufacturing Labor force employed in armed forces Resident college students relative to population Population with high school education Population foreign born Population age 65 and over Population black or nonwhite*

Growing

Declining

657.4 1.4% $6024 17.5% 4.5% 23.0% 4.6% 3.1% 48.1% 4.1% 8.3% 11.3%

290.0 -0.88% $5556 19.6% 5.9% 26.6% 4.7% 1.6% 42.2% 3.3% 8.4% 9.1%

1970 Growing

Declining

420.8 I ,260.O 2.3% -0.90% $8953 $9844 12.8% 9.0% 4.6% 4.6% 15.9% 30.2% 8.3% 6.1% 5.0% 3.4% 57.3% 52.6% 4.2% 4.0% 8.7% 9.5% 10.7% 10.6%

Source: Author’s calculations from U.S. Censusesof Population, 1960 and 1970. “For the periods l%O- 1970and 1970- 1975,respectively; the rate covering the entire period was divided by the number of years in the period to obtain the simple annual rate. *The 1960data are reported for nonwhites, which includes Orientals as well as Negroes; the 1970 data cover Negroes only. Although Negroes constitute by far the largest component of nonwhites, the data are not strictly comparable between years

opportunity as its driving force maintains its viability. If not, then the generality of the theory as explanation of the past and predictor of the future is clouded by doubt. Whether the determinants of migration have shifted is hinted at by comparisons of traditional indicators for growing and declining metropolitan areas during the 1960sand 1970s.Table 1 lists the means for samples of the two types of areas during the two periods. Each of the four groups consists of 50 SMSAs. The “growing” and “declining” samples are made up of the metropolitan areas experiencing the highest and lowest net migration rates, respectively. The latter group includes SMSAs with low positive as well as negative net migration rates and, obviously, net migration was positive during both periods for alI sample units of the growth group. The tabulations reveal striking dissimilarities among groups. The most substantial differences include those pertaining to population size. In 1960, the averagemetropolis in the rapid-growth sample was over twice as large as the average declining SMSA; but in 1970, the average SMSA in the growth group had only a third the inhabitants of its declining counterpart. Differences in economic characteristics also appear in these averages.The data for the 1960s are congruent with the traditional economic model of

METROPOLITAN

GROWTH

115

migration where differentials in employment opportunities and in wages or income explain moves toward more prosperous areas. On average, median annual income in the growth group exceededby over 8% the average of the declining group. The situation was reversed in 1970 when median income in the typical declining SMSA stood 10% above that of the average growing metropolitan area. The possibility that income level may bear differently on current migration is reinforced by statistics describing low-income groups. The differences are as expected for the earlier period. The poverty range embraced a larger proportion of the populations of declining areas in 1960. The situation reversed in 1970 with a greater share of the population in the growing communities falling below the poverty threshold. The empirical relationships between migration and employment shown in Table 1 run parallel to those between income and migration. In the earlier period, growing metropolitan areas had lower unemployment rates than did declining areas. The rates were equal, however, in 1970. If these data are indicative, overall employment level appears to be disappearing as a factor influencing the decision to migrate. On balance, according to these statistics and recent research by Beale [l] and McCarthy and Morrison [lo], the three economic variables as they appear in the traditional migration models appear to have lost much of their explanatory power. In fact, migratory behavior with respect to economic opportunities has more or less reversed itself over the period of this analysis. Perhaps second in importance to aggregate economic factors as determinants of migration are measures of occupational mix. Averages for three types of employment-manufacturing, military, and higher education-are shown in Table 1. As with the economic factors, the influence of manufacturing-dependent economic bases also appears to have changed. Here it is a matter of degree though not of direction. For both time periods, concentration of employment in manufacturing activity is associated, not surprisingly, with decline. Contrasting growing with declining SMSAs shows that the relative concentration of employment has shifted over the years. The difference was marginal in 1960, but by 1970, twice the proportion of the labor force was engaged in manufacturing in declining areas as in growing SMSAs. The two variables measuring the proportions of resident college students in the population and proportions of military in the local labor force identify the presence of two types of institutional employment commonly associated with growth. The statistics show that a military base apparently generated little growth in the 1960sbut distinguished the rapidgrowth from the declining areas in the more recent period. During both periods, higher education seemed to be propulsive. Rapid-growth SMSAs generally had higher proportions of college students in their populations than did declining areas.

116

LELAND

S. BURNS

The tabulations include four other demographic characteristics associated with growth. Of these, level of education appears to be the most diff&entiated between periods and growth types. Only marginal differences are apparent in the proportion of the population foreign born, nonwhite, and aged. The differences noted, and particularly the dissimilarities on those dimensions tested in traditional models of migration behavior, are sufficiently sharp to warrant more systematic testing, as well as a more precise specification of hypotheses. Two hypotheses shape this investigation: 1. The determinants of metropolitan growth differ significantly from the determinants of relative decline. 2. The “causal” structure of growth and decline has shifted significantly over time. With respect to the first hypothesis, a “difference” can be identified in any of three ways. First, the variables that significantly explain population change in the rapid-growth sample differ from the variables which significantly explain change in the sample of declining SMSAs. Second, the relevant weights of the significant variables differ substantially between samples. Third, the direction of the relationship, as indicated by sign, can change. The same generalizations about acceptance apply as well to the second hypothesis. Variables explaining growth or decline may differ in significance, weight, or sign between analysis periods.

THREE EMPIRICAL

TESTS

The hypotheses will be tested three ways: first, by a comparison of zero-order correlations between migration and variables postulated as determinants of migration; second, by the estimation of multiple regression equations for those variables drawing on data for each of the four samples; and third, by a factor analysis which generates indices for the reestimation of equations to explain migration. Two samples of metropolitan areas for each of two time periods provide the empirical base for the tests. Each sample consists of 50 SMSAs as previously described. Migration, the dependent term, is defined as the absolute difference between in- and out-migrants during the analysis period as a percentage of population size at the start of the period. Independent variables were chosen to represent factors that influence migration. In addition to those common among economic opportunity models, such as level of income and employment, the variables include components of population and the employment mix. With predetermined variables for 1960 and 1970 explaining migration during subsequent periods, causation is

METROPOLITAN

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117

inferred. Definitions of the independent variables and their expected relationships to migration (+ or -) are as follows: SIZE = population of the SMSA at the beginning of the analysis period; 1960 for the first period, 1970 for the second. Given the size distribution of migration and its reversal observed between periods, a + sign is expected for the 1960s and a - for the 1970s. INCOME = median family income of the local population. According to the conventional wisdom, a major determinant of migration is the opportunity for earning higher incomes. If so, a + is anticipated. POOR = percent of local families below the poverty threshold. If the variable is colinear with INCOME, a - would be the result. If, however, poor families move in search of the most attractive welfare provisions and those areas already are the homes of substantial numbers of welfare recipients, the result could be +. UNEMP = percent of the local civilian labor force unemployed. If the classical migration model is operative, low unemployment should attract migrants; consequently a - is anticipated. MFG = percent of the local labor force employed in the manufacturing industry. With employment declines characterizing most manufacturing sectors, the relative immobility of plant and equipment, and the interregional labor movement during the past several decades, a - sign is anticipated. DEFENSE = number of persons on active duty with the armed forces as a percent of total employment. Because local employment multipliers for armed forces employment may be fairly large with the creation of ancillary civilian jobs, as Beale [l] and others have shown, a + is expected. COLLEGE = number of persons currently enrolled in a local college as a percent of the local population. Institutions of higher learning may also have fairly high employment multipliers. Moreover, as Beale [l] and McCarthy and Morrison [lo] argue, by contributing to the quality of the environment, colleges and universities offer amenities which attract migrants. A + is anticipated. EDUC = percent of local population 25 years and over completing high school educations. With years of schooling serving as a proxy for the quality of human resources, a component of life quality viewed by migrants as desirable, a + is anticipated. FOREIGN = percent of the local population born abroad. Historically, the size of the metropolitan system has swelled with international immigration. A + sign is anticipated since it is likely that immigrants settle where other foreign born are situated. AGED = percent of the local population age 65 and over. The propensity of the aged to move has increased since 1960 as data published by Biggar [3], and Stemlieb and Hughes [ 151have shown. As with the foreign born, it

118

LELAND S. BURNS

seemslikely that their destinations are areas already populated with elderly persons, a supposition that seemsparticularly true of the Sunbelt. Consequently, a + is anticipated. BLACK = percent of the local population nonwhite (1960) or negro (1970). As with the above two variables, the proportion of the minority population in receiving areas should be related positively to migration and immigration. EXPECT = percent population change during previous decade. If current migration decisions are based on past trends, as Fabricant [7] argues, the fastest growing areas should continue along their growth paths and migrants would be discouraged from moving to slow-, no-, or negativegrowth areas. A + is anticipated. SOUTH = a regional code where 1 = a southern state and 0 = another state, as defined by the U.S. Department of Commerce. The variable partitions the sample between the rapidly growing South and the rest of the nation to take account of variance distinguishing the region not captured by other variables. a. Analysis of Simple Correlations. Of the 13 migration correlates for which simple correlation coefficients to migration were estimated (Table 2), five were either not statistically significant in any of the four samples, or were significant at the 5% level in only a single sample. These consisted of SIZE, FOREIGN, EDUC, COLLEGE, and UNEMP. Two others, MFG TABLE 2 Correlation Coefficients, Migration and SelectedVariables, Growing and Declining SMSAs, 1960sand 1970s 1960s Variable SIZE INCOME POOR UNEMP MFG DEFENSE COLLEGE EDUC FOREIGN AGED BLACK EXPECT SOUTH

1970s

Growing

Declining

Growing

Declining

-0.12 -0.24* 0.25* 0.25’ -0.58** 0.32* -0.04 0.30* 0.03 0.04 -0.05 0.64” 0.11

0.15 0.32* -0.34** -0.05 0.34** -0.06 0.15 0.13 0 32; 0.44’. -0.21 -0.34** -0.60**

0.06 0.07 -0.17 0.17 -0.24. -0.31* 0.07 0.08 0.13 0.558’ -0.02 0.42** 0.12

-0.03 0.28* -0.43;’ 0.15 0.31* -0.76** 0.02 - 0.07 0.04 0.57” -0.48** -0.30* -0.38**

*Statistically significant at the 0.01 level or higher. **Statistically significant at the 0.05 level or higher.

METROPOLITAN

GROWTH

119

and EXPECT, showed consistency over samples; that is, their signs were the same and significance levels were similar comparing growing with growing and declining with declining samples. The behavior of the remaining six variables differed among samples, either in sign or acceptable levels of statistical significance. Pairing up samples of growing with declining SMSAs shows little consistency. For the two samples drawn from data for the 1960s no pairs of statistically significant terms retained the same sign. The signs of only two of the significant variables in the pairings of 1970 samples remained the same. In other words, for the great majority of variables, the signs of their coefficients changed with movement along the spectrum from high to low migration rates. Parenthetically, if continuous functions had been estimated for all SMSAs, rather than for samples drawn from the extremes of the growth distribution, U-shaped functions rather than strictly linear ones might have produced the best fits. Nonetheless, these simple comparisons provide preliminary support for the two hypotheses. b. Multiple RegressionAnalysis. Whereas bivariate correlations assume “all else equal,” multivariate regressions explicitly and simultaneously consider a seriesof determinants. All variables save three were estimated for the four samples. Since preliminary runs showed that the explanatory power of FOREIGN was almost consistently low, POOR was highly colinear with INCOME, and the regional dummy SOUTH tended to swamp explanation and was colinear with others, these variables were omitted. Because of multicolinearity, regressions containing the remaining ten variables still included statistically nonsignificant terms. Equations were swept of these and reestimated. For three of the four samples, several alternate specifications were estimated. The results, which include a few variables that turned nonsignificant in the cleared equations, explain from 29% to 65% of the variance in migration (Table 3). While the coefficients of determination were not high, neither were they low for cross sections. Moreover, it is the significance of individual variables, rather than the total explanatory power of any equation, that is crucial to the tests. The behavior of INCOME was consistent between the 1960 and 1970 rapid-growth groups but generally contrary to theory. In both of these samples, migration and income were negatively associated. The signs of the variable indicated that higher income not only failed to attract but actually discouraged migration to rapid-growth areas.The evidence for the declining areas in the 1960s sample, however, accorded with traditional migration models showing the direction of migration toward higher income areas. The companion economic determinant, UNEMP, remained nonsignificant across groups. For the growth areas of both periods, EXPECT was highly significant and positive indicating that knowledge of the distribution of demographic

IA

0.17 (4.8)*** 7.9 0.47

0.43 (2.4)**

-0.36 (2.8)***

2A

0.18 (4.8)*** -0.73 0.48

0.46 (2.5)** 0.44 (0.91)

-0.13 (1.3) - 0.30 (2.2)**

3A

1960

-30.1 0.32

0.75 (3.2)***

0.11 (2.1)** 0.11 (2.3)** 0.87 (2.1)**

1.04 (3.9)*** 0.12 (1.7)*

0.10 (2. I)** 0 83 (2.0)*

0.37 (3.1)*** 0.33 (0 97)

2B

-42.7 0.29

Declining

0.19 (2.1)**

1B

significant at the 0.01 level or higher. significant at the 0.05 level or higher. significant at the 0.10 level or higher.

0.16 (4.6)*** 15.5 0.52

-0.35 (3.6)**

***Statistically **StatMically *Statistically

Intercept Adj. R2

EXPECT

BLACK

AGED

ED UC

COLLEGE

DEFENSE

MFG

UNEMP

INCOME

SIZE

Variable

Growing

0.20 (5.2)*** 2.8 0.56

1.4 (6 3)***

0.39 (2.3)**

-0.13 (2. I)**

IC

TABLE 3 Regression on Migration, Original Estimates (I values 111parentheses)

0.19 (5.4)**11.7 0.62

1.2 (5 5)***

-0.34 (3.2)*** -0.87 (1.9)* 0.37 (1.9)*

-0.12 (2 I)**

2c

Growing 3c

0.20 (5.2)*** 23 0.55

0.30 (0.22) 1.4 (6.2)***

-0.15 (1.7)’

1970

-30 0.65

-0.85 (3.6)***

-0.10 (7.9)***

ID

Declining

E Z CA

METROPOLITAN

GROWTH

121

growth influenced migrants’ decisions. Expanding SMSAs in these groups tended to continue growing. The same generalization failed to apply to the declining group where trends appeared to exert little influence on subsequent growth or decline. AGED was another of the most significant variables in two of the four samples. The relationship to net migration was positive and statistically significant for the 1960s declining and 1970s growing areas. The positive relationship with migration in both samples ran as expected. Beyond this, there was virtually no consistency over samples in the signs or significance levels of the remaining variables. MFG negatively influenced migration toward areas in the rapid growth samples, induced growth in the declining samples during the 1960s but disappeared as a significant determinant in the 1970s. Employment in the armed forces and higher education had a positive effect on migration in the areas of decline during the 1960s but the two variables behaved erratically in the 1970s. EDUC, positive and significant in the rapid growth sample of the 196Os,apparently lost its influence in the 1970s. While there was a modicum of agreement in the composition of equations for different samples, the substantial differences among samples in levels of significance and direction of correlation was far more striking. If this evidence is indicative, the causal structure of metropolitan migration has shifted. The technique of conditional forecasting, where the data for one period or sample are substituted in an equation estimated from data of another period or sample, offers a superior test of stability. Two types of comparisons yield tests of the two hypotheses. First, growing and declining areas are compared for the same analysis period by switching data and equations. The basis for comparison is the significance of coefficients. If, for example, the structure of the 1960 sample of growing SMSAs also describes the structure of the 1960 sample of declining SMSAs, the set of determinants would be seen as stable between the ranges of migration levels being statistically explained. Table 4 presents the results as ratios of significance levels between the equations as originally estimated and as reestimated. The same procedure provides a test of the second hypothesis as well. The growing areas of the two analysis periods are compared with each other and the declining areas of each period are compared by reestimating the equation of one with the data for the other. Table 5 shows the ratios of significance levels for each variable. Even a cursory examination of results in this table and in Table 4 indicates little similarity between any comparison pair, suggesting again that structural shifts have occurred and at rather large magnitudes. One last test, based on the estimated and reestimated equations, compares beta coefficients between samples for those variables significant at 10% or higher in the originally estimated equations. The numerator of each fraction

lA/3B

2A/4B

3A/5B

1B/4A

2B/5A

Declining SMSAs and re-estimated as growing lC/2D

2C/3D

3C/4D

Growing SMSAs and re-estimated as declining

1970

***Statistically significant at the 0.01 level or higher **StatisticaBy significant at the 0.05 level or higher l StatisticaBy significant at the 0.10 level or higher ‘Not statistically significant at the 0.10 level

SIZE o/o *** ** *** 0 ** / *** * / *** **/0 ** / 0 INCOME ** / 0 / / UNEMP o/o ** *** *** / 0 *** / 0 MFG ; ..,. * *** ** 0 DEFENSE / **/o */o ** / 0 COLLEGE */o ** / 0 ** / 0 EDUC o/o *** / 0 ***,, ***,*** .... . ***/*.+ AGED BLACK */0 l **,*** ***,, ***/ * ***/ 0 EXPECT ***,, ***/0 0.52/0.12 0.47/0.52 0.48/0.22 0.32/0.32 0.29/0.07 0.56/0.45 0.62/0.57 0.55/0.44 AdJ. R2

Variable

Growing SMSAs and re-estimated as declining

1960

.0.75/0.06

***/0

*** / **

I D/4C

Declining SMSAs and re-estimated as growing

Significance Levels of Regressions as Originally Estimated (Numerator) and Reestimated (Denominator), Comparisons between Growing and Declining SMSAs

TABLE 4

**/0

***/o

2A/6C

Growing

**/0 0/***

o/o **/*

3A/7C

*** *** **t *** *** *** / / / 0.52/O 20 0 47/O. 19 0.48/0.52

***/ *

IA/5C

0.32/0.57

** 0 **$*** **/ 0

0 29/0.63

/

*/o * ***

*t*/ 0 o/o

2B/6D

Declining lB/5D

‘“Statistically significant at the 0.01 level or higher l *StatisticaBy significant at the 0.05 level or higher *Statistically significant at the 0.10 level or higher ‘Not statistically significant at the 0. IO level

SIZE INCOME UNEMP MFG DEFENSE COLLEGE EDUC AGED BLACK EXPECT AdJ. R=

Variable

1960equations with 1970data

*** *** / 0.56/0.43

***/0

**/ *

IC/6A

*/**

3C/8A

*t*/ 0

1D/6B

Declining

0.62/0.50

0.55/0.46

0.65/0.01

*/o o/* ***/ 0 *** /0 ***/ 0 ***/ *** ***/ ***

**/ 0

2C/7A

Growing

1970equations with 1960data

Significance Levels of Regressions as Originally Estimated (Numerator) and Re-estimated (Denominator), Comparisons of Growing with Growing SMSAs, and Declining with Declining SMSAs for Two Periods

TABLE 5

Dechmng

EXPECT -

BUCK

COLLbGt CDUC .4 GED

MFG DEFENSE

INCOME UNEMP

-0

1960

-0.50

0.53/O

0.2Y/lJ

13/o

6I

Growing

2A/6C

o.53/

0.29/o

-0.33,‘0.29

2A/4B

3A,‘7C

0 54/0.6

0.31/o 0/0.5x

o/o -n28/-036

equations

0.54/o

o/o

031/n

-0.15/o ~ 0.28/O

with

I960

3A/5B

Growing SMSAs and re-estimated as dechmna

0 49,‘0.42

-0.39/-0.23

I A/5C

Variable

0 39/o

lA,‘3B

0.4Y/O

SIZE

TABLE

I

1

6

IB/5D

data

4 I ,‘0.25

0.57/o 0.29/-o

0 25/n

0 27/n

o/o

0 55/o

2B/bD

n30pobb

Declining

0 57/o 0 29/n

0 25/O

0 30/0.27

o/o

0.55/o

2B,‘5A

n 3010 032/pn6n

028/o

1970

041/n

0.30/ ~ 0.55 0 32/O 0 27/O

0.28/O

1 B,‘4A

I

31 0.63/o

061/O

0 22/o

~ 0.26,’

I C/bA

68

-~ 0.19

063p0225

061,‘052

0.22/o

-0.26/0.45

I C/2D

and Re-estimated

Declimng SMSAs and re-esttmatecl as erowine

of Regressions as Originally Estimated (Numerator) SMSAs and between Years for Each Growth Type

SLACK EXPECT

SIZE INCOME UNEMP MFG DEFENSE COLLEGE EDUC A GED

Vanable

and

Beta Coefficients

24/O

0.62/O

0 53/o

~ 0 2R/ -0 19/o

~ 0.24/O

2C/7A

GlTW.Wtg

47

I

45

3C/UA

0 63/o

55

O/O.27 061/O

0.2 I/O

32

68/-0.32

I D/4C

-031/o

~ 0.68/O

I D/6B

Declining

-0.31/o

-0

cirowing

Declining SM!L% and re-estimated as growing

between

1970 data

-0.29/-O

with

0.63/O

o/o 0.6 I /0.5

0.2 I/O

- 0 29/O

3C/4D

Comparisons 1970

0.35

1960 equations

0 62/O

0.53,‘0.24

-0.28/O -0.19/-060

-0

2C/3D

Growing SMSAs and re-estimated as declining

(Denominator),

METROPOLITAN

GROWTH

125

displayed in Table 6 is the beta coefficient of the originally estimated variable and the denominator is the beta for the variable as reestimated. These results, like the analysis of signs and significance levels, reveal more inconsistencies than consistencies. First, the “bivalance” of INCOME, negative for the growing sample and positive for one of the two declining samples, and its rather low weight throughout, is puzzling. As noted before, high income apparently stands in a nonlinear relationship to migration, attracting population to those metropolitan areas which are either modestly growing or declining, and deflecting migrants from higher-growth areas.The underlying reasons clearly bear further analysis. Second, the bivalance of MFG is less mysterious. Manufacturing has long since disappeared from the list of propulsive industries, as reflected by the exodus from the industrial Northeast during recent decades.As the beta coefficients indicate, employment concentration in manufacturing has a fairly strong negative influence on migration for the growth samples. Among areas growing at most modest rates, at least during the decade of the 1960s jobs in manufacturing attracted migrants. Third, EXPECT had a large weight in both samples of rapid growth SMSAs. For these groups, history repeats and growth begets growth. Fourth, the weight of AGED is also high in the samplesof declining SMSAs during the 1960sand growing SMSAs during the 1970s.While there is no obvious reason why the variable would be important for two different growth classes,the rather large betas probably signal the emergenceof the Sunbelt migrations during the seventies and the increasing share of retirement age persons in the migratory streams. But, in contrast to earlier flows, rather than the areas with the young and vigorous populations acting as magnets attracting others, it is the presence of older persons and perhaps the character of the areas originally attracting this group that seemsto pull others. The two types of institutional employment COLLEGE and DEFENSE deserve some mention for the rather special effects each has had on migration. As revealed by their beta coefficients, institutions of higher learning positively affected migration and were fairly important in both analysis periods, but for different growth ranges. In the 196Os,the impact bore on slowly growing metropolitan areas; the weight of the variable was marginally less in the 197Os,but the SMSAs affected were instead those growing fastest. Although the sign of the coefficient for the COLLEGE variable was consistent, it differed between analysis periods for DEFENSE. In the 196Os,employment generated by the armed forces was apparently a major source of growth and had positive multiplier effects on the slower growing local economies. The impact on both the fast- and slow-growth areas during the 1970s however, was negative. The winding down of the military establishment in the current decade apparently has had the not unanticipated effect of depleting local labor forces through out-migration. If

126

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out-migration were not the consequence, unemployment should rise. The coefficients of correlation between UNEMP and DEFENSE were not statistically significant in any of the four samples, consequently adjustment apparently took place through migration. In sum, the beta coefficients demonstrate that remarkable shifts have occurred over recent history in the importance attached to migration determinants. Moreover, the predictors of growth among slow growing areas differ from those explaining more rapid growth. TABLE

7

Factor Structures, Growing and Declining SMSAs, 1960s and 1970s Growing Variable

I

II

Declining III

IV

I

II

III

IV

0.70 -0.82 -0.06 0.41 -0.21 -0.05 0.18 0.72 0.55 -073 -0.24 -0.83

-024 0.18 -0.13 -0.61 0.65 0.02 0.31 -0.22 -0.75 0.28 0.79 0.56

-021 0.18 -0.30 -0.69 0.21 0.44 0.65 -0.24 0.16 - 0.02 0.04 0.03

-0.15*

0.07

043 -0.19 0.16 -0.07 0.42 0.21 0.54 0.01 -0.85 0.26 0.75 - 0.08

-0.22 0.20 041 -091 0.47 0.36 0.63 0.02 -0.23 -0.07 0.24 0.14

-0.24**

-0.07

1960s INCOME POOR UNEMP MFG DEFENSE COLLEGE EDUC FOREIGN AGED BLACK EXPECT SOUTH Reg. coef ”

-0.07 0.1 I 0.33 -0.65 0.49 0.01 0.66 0.13 -0.11 -0.09 0.68 -0.03 062**

0.99 -0.97 0.02 0.30 -0.03 0.08 0.48 0.51 -0.03 -0.41 -0.03 -065

-0.26 0.37 -0.36 -0.19 0.02 008 -0 17 -0.53 -0.47 0 83 -0.14 0.75

0.09 -0.17 -0.39 -0.22 0.01 0.67 0.51 -0.32 -0.37 -0.07 -0.34 -0.19

0.73 -0.64 -0.64 -0.12 -0.10 0.06 0.68 -0.22 -0.26 -0.17 0.56 -0.21

-0.13

-0.02

-0 14

0.02

0 151

1970s INCOME POOR UNEMP MFG DEFENSE COLLEGE EDUC FOREIGN AGED BLACK EXPECT SOUTH

-0.01 0.17 0.15 -0.07 0.57 -0.07 0.14 -0.17 -0.74 0.3 I 0.09 004

091 -095 004 005 -002 0.17 0.87 -0.11 -0.07 -0.24 0.56 -0.62

-0.14 0.06 -0.70 -0.03 0.09 0.12 -0.25 -0.26 -0.01 0.92 -0.08 0.72

0.02 -0.04 -0.13 -0.30 -0.11 0.89 0.41 -0.22 -0.34 - 0.05 - 0.05 -0.14

0 87 -0.57 -0.02 0.16 -0.40 0.11 0.20 0.69 0.12 -0.04 0.19 -0.61

Reg. coef.”

-0.89**

0 I8

0.25.

0.25

0.09

0.55 -0.90 0.05 0.21 -0.40 0.28 0.53 0.14 0.25 -0.76 0.15 -0.78 0.22**

significant “Coefficient of the factor regressed on migration; **stahstully 0.01 level or higher, and ‘statistically significant at the 0.05 level or higher.

at the

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c. Fucror Analysis. The problem of multicolinearity, which bedevils multiple regression, is neatly handled in factor analysis. While multiple regression analysis requires the independence of determinants, factor analysis seeksthe opposite. Given the multicolinearities apparent in our data, the data for each of the four samples have been factor analyzed using the principal components rotation to determine the extent and nature of shifts in the causal structure. A maximum of four factors was specified for each sample. All variables save the dependent term, migration, and SIZE, which seriously distorted the distribution of factors, were included. The factor structures are shown for each sample in Table 7. The relation of the factors to migration was estimated by multiple regression. Each factor, orthogonal by definition with the other three, is an artificial independent variable. The regression coefficients and their significance levels are shown at the bottom of each column in the table. In contrast to the findings of the two previous tests, the factor structures displayed reasonable similarity between samples. Pairing factors between growth samples, 41 of the 48 variables showed up with the same signs. A comparison between 1960 and 1970 samples of the distribution of signs in Factor I illustrates: 8 of the 12 variables maintained their signs and the remaining four changed sign between samples. Comparing the two samples of declining SMSAs, two-thirds of the variables kept the same sign. Less similarity was apparent in comparisons of growing with declining samples for the same analysis periods. Sixty-nine percent and 46% of variables in the 1960 and 1970 sample pairings, respectively, maintained their signs. Overall, these results indicate that the structure of determinants, as defined by the 12 variables, remained remarkably constant between analysis periods. The structure of the two samples of growing SMSAs, in particular, stayed relatively constant. The signs of the regression coefficients, however, qualify this conclusion. Comparison of the 1960 and 1970 rapid growth samples shows that, although the structure of determinants remains relatively intact, the signs of each regression coefficient reversed between analysis periods. Thus, a factor that positively influences migration in one period is negative in the other. Consequently, for the growth samples, the structure stayed relatively unaltered over time but the effect of that structure on migration reversed. This did not hold true for the samples of declining areas where the signs of the regression coefficients remained the same for both time periods. While there was some similarity in pairs of samples compared by growth type, the distribution of regression coefficients was completely different; that is, the regression coefficients of Factors II and III of the declining samples were statistically significant in both periods but only Factor I of the two growth samples was significant. These findings lend further support to the two hypotheses.

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CONCLUSION Urban growth theory, one of the key elements in the corpus of urban economics, was formulated during a period when growth was the rule rather than the exception. The rare casesof stagnation or decline were managed by applying growth theory on the presumptions that, first, the theory was symmetrical, second, that a theory that worked in the past worked as well in characterizing the present and predicting the future, and third, that the lagging areas could, and should, grow as did the others. Whether growth theories continue to offer generalized explanations of change during a transition to stability, to stagnation, and even to absolute decline prompted this research. The sketchy evidence presented here suggeststhat traditional theory fails on two crucial counts, “symmetry” and “timelessness,” to explain recent patterns of metropolitan growth. Three types of statistical tests were applied to explain net migration in four samples of metropolitan areas, a rapidly growing group and a slow (or negative) growth group during the 1960sand 1970s.The tests indicated that the determinants of growth varied substantially among groups in terms of statistical significance and weight. Variables that explained rapid growth or decline in one period were uncorrelated to the samephenomena in the next. For the rapid growth samples in particular, structures comprising area characteristics believed to determine migration levels remained relatively constant, yet predicted net migration in opposite directions between analysis periods. The results of the tests, if accurate, offer a challenge to theory and policy. Urban growth theories, if empirically verifiable, are not timeless. Today’s propulsive industries are not tomorrow’s and yesterday’s multiplier differs from today’s as tastes, production processes,and prices change. Comparisons of migration determinants between metropolitan areas expanding at different rates suggests that the generality of theory fails to span the full growth spectrum. Growth policy as well may be ripe for reconsideration. The points at which leverage is effectively applied may be differently located today. The instruments themselves are perhaps no longer appropriate. These conclusions spring not from a careful review of existing policies, but rather from the realization that the current structure of the metropolitan system, and particularly the policy elements in the structure, explains and determines migration in very different forms. Since policymaking relies on the evidence of what works, where and when, changes in that evidence requires the appropriate policy response. The sharp differences uncovered in our explorations argue for a reconsideration and possible reshaping of policies for the new era so dramatically apparent.

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ACKNOWLEDGMENTS The author is indebted to Kathy Van Ness and Joe Okyere-Conduah for research assistance, to Donald Shoup for comments, and to the UCLA Senate Research Committee for financial support.

REFERENCES I. C. L. Beale, The recent shift of United States population to nonmetropolitan areas, 1970-75, Internal. Reg. Sci. Rev., 2, 113-122 (1977). 2. B. J. L. Berry, The counterurbanization process: How general?, in “Human Settlement Systems: International Perspectives on Structure, Change and Public Policy” (Hansen, Ed.), Ballinger, Cambridge (1978). 3. J. C. Biggar, The sunning of America: Migration to the Sunbelt, Population Bull., 34 (1979). 4. C. Blanco, The determinants of interstate population movements, J. Reg. Sci., 5, 77-84 (1963). 5. L. S. Burns, The Metropolitan Population of the United States: Historical and Emerging Trends, in “The Dynamics of Urban Development” (Klaassen, Molle, and Paelinck, Eds.), Saxon House, Westmead, in press. 6 R. J. Cebula and R. K. Vedder, A note on migration, economic opportunity and the quality of life, J. Reg. SCL, 13, 205-211 (1973). 7. R. Fabricant, An expectational model of migration, J. Reg. SCL, 10, 13-25 (1970). 8. M. J. Greenwood and P. T. Gormely, A comparison of the determinants of white and non-white interstate migration, Demography, 8, 141-154 (1971). 9. I. W. Lowry, “Migration and Metropolitan Growth,” Chandler, San Francisco (1966). IO. K. F. McCarthy and P. A. Morrison, The changing demographic and economic structure of nonmetropolitan areas in the United States, Internat. Reg. Sci. Rev., 2, 123-142 (1977). I I. E. Miller, Is outmigration affected by economic conditions?, Southern &on. J., 39, 396-405 (1973). 12. P. A. Morrison, “Emerging Public Concerns over U.S. Population Movement in an Era of Slowing Growth,” P-5873, Rand, Santa Monica (1977). 13. T. W. Rogers, Differential net migration patterns in the standard metropolitan statistical areas of the southern U.S.: 1950-60, Internat. Mgration, 6, 22-32 (1968). 14. R. P. Shaw, “Migration Theory and Fact: A Review and Bibliography of Current Literature,” Regional Science Research Institute, Philadelphia (1975). 15. G. Stemlieb and J. W. Hughes, “Current Population Trends in the United States,” Center for Urban Policy Research, New Brunswick (1978). 16. U.S. Bureau of the Census, “Statistical Abstract of the United States: 1979” (100th ed.), USGPO, Washington, D. C.