Differential Net Migration Patterns in the SMSAY of the Southern United States,

1950-1960

BY T. W. ROGERS

INTRODUCTION

The universal pervasiveness of the urbanization process is one of the most conspicuous population trends of the twentieth century. [ I ] It is hardly possible to point to any region of the world which is not faced, in greater or lesser degree, with those changes in social structure and organization which are concomitants of the process of urban development. The significance of migration as a necessary factor in the urbanization process is well documented and appears to be a universal phenomenon. [2]

Although the population of the census South has traditionally been a rural one, and is still less urban than the United States as a whole, the South has undergone rapid urban development in recent years. The South, which increased its metropolitan popu- lation by 35.6 percent between 1940 and 1950 and by another 36.2 percent between 1950 and 1960, added over 2.7 million to its metropolitan population during the 1950’s.

There were, however, wide differentials in the migration rates of the 80 Southern S M S A ’ S . [ ~ ] Although the average migration rate for the 1950-1960 decade was 15.4, few SMSA’S conformed to this average. The wide differential of migration rates is illustrated by the fact that one must specify a range of 15.4 plus or minus the standard deviation of 37.5 (22.1 percent to 52.9 percent) in order to encompass two-thirds of the SMSA’S. Thus the migration rates in one-third of the SMSA’S were so deviant that they fell either above or below this broad range.

D E S I G N A N D A N A L Y T I C A L P R O C E D U R E

If a scientific explanation of the factors associated with these varying rates is to be achieved, differences in the net migration rates must be related in a systematic manner to variations between individual SMSA’S in the degree to which they possess combinations of factors that are theoretically related to their overall migration experience. The specifi- cation of a set of independent variables which are measurements of phenomena hypo-

* SMSA = Standard Metropolitan Statistical Area.

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thesized as ‘related to’ or ‘factors in’ the S M S A migration rates is a first step toward this objective.

While a list of possible causal factors could be expanded indefinitely, at some point the researcher must be willing to limit the independent causal influences to an arbitrarily selected group of measurable variables whose influence he wishes to test and assume that they form a closed system. [4] A set of ten demographic and socio-economic variables, which are recurring measures frequently employed in studies of metropolitan growth and migration, [5] were selected as potentially fruitful lines of investigation.

The demographic factors hypothesized as potentially important explanatory factors included size of S M S A (1950), age (number of decades since 1900 areas would have quali- fied as SMSA’S under the 1960 definition), density (population per square mile, 1950), and distance from nearest S M S A in highway road miles. The socio-economic variables selected were degree of industrialization (percent of s MSA labor force engaged in manufacturing, 1950), change in the degree of industrialization (rate of change in the percentage of labor force engaged in manufacturing, 1950-1960), median family income (1950), unemployment (percent of labor force unemployed, 1950), median educational level (1950). The tenth variable consisted of the net migration rate for the previous decade, 1940-1950. [6]

The aggregate approach to such events as migration assembles the total cases involved into one lot and subclassifies them according to a number of characteristics, often without specific criteria for deciding on the particular way in which cases should be divided. [7] The maximum scientific meaningfulness for such categories can be obtained only when there is a moderate degree of variation among classes within the category, a condition which can rarely be assumed in studies of population change. [8] In the distributive or continuous variable approach, on the other hand, distances between any two numbers of the scale are of a known size since they may be expressed as quantitative indexes at the interval and ratio levels of measurement rather than as qualitative traits. For example, rather than classifying the SMSA’S as ‘highly industrialized,’ ‘moderately industrialized,’ and ‘little industrialized,’ it is possible to take the actual properties of each variable and construct a self-defining index which scores each S M S A along a continuum.

Statistical methodology includes techniques which are especially useful in determining which of several independent variables are most strongly related to the independent variable when the explanatory variables are expressed as indexes along a continuum. The Pearson r, a zero-order correlation measure, answers the question of existence of association between the dependent variable and the independent variables in a particular set of observations. [9] However, since correlations only measure covariation or the degree to which variables vary together, no implication of causality is involved. In order to move in the direction of causal inferences, a technique developed by Simon,[10] and amplified by Blalock, [ [ I ] was employed.

The Simon-Blalock technique represents a scheme for making causal inferences from correlational data provided we are willing to make certain a pviori assumptions. We must assume ( I ) one-way causality between a set of variables treated as a closed system, and (2) that variables not brought into the hypothesized causal system operate in random fashion. Tn using the Simon-Blalock technique, the researcher attempts to make use of causal presumptions to arrive at certain predictions which can be empirically evaluated

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Figure I . Relating Independent Variables to Migration

Predicted Value Zero-Order Equations Correlation

Model I Age Size C .695 - A ‘XA.B = O - .073 - .073

.796 ‘CB . A = O .124 .607 ‘XB . C = 0 .083 - .036

B r ~ ~ . ~ = ~ .036 - .036

J. Density

1 - .166

X NMR, 50-60

Model 2

Age Distance

NMR, 50-60 Size

Model 3 Ind. Inc

-.382 ix . i a r F G . E = o - .486

x- .394 - F NMR, 50-60 h a . Ch.

- .053 - .215

Model 4 Ind. Inc

.365 ___C G ‘ F G . E = O - .053 - .215

‘ X G . E = O .329 .I44 - .382 1 \- .486

F \ x- .394-

NMR, 5&60 Ind. Ch.

Model 5 Edu. ,675 Inc

1-G ‘ G H . I = O - .036 - .261

‘XG . I = 0 - .173 . I 4 4 ,389 i\-- .349

X L H ‘XH . I = 0 - .011 .037 NMR, 50-60 Employment

Predicted Value Zero-Order Equations Correlation

Model 6 Age Ind.

C ‘CG. E = o .249 .334

x ‘XG. E = 0 .329 .144 .052 - .166 2- .365 ‘ X C . E = O

NMR, 50-60 Inc

Model 7

Age Inc. C - y T -.215 ‘ X C . F = O - .017 - .166

‘XE. F = o - .256 .144 x- .394 - F

NMR, 50-60 Inc. Ch.

Mode I 8 Age Ind

C %- i -.468 ‘XC. F = 0

x t .394 - F

- .017 - .166

‘XE. F = o - .243 - .382

NMR, 50-60 Ind. Ch.

Model 9

Age Size I -.239 -.166

X - .609 - NMR, 50-60 NMR, 40-50

‘CD = 0 .020 .020

Model I0 NMR, 40-50 Distance

-.239 e D

.609 i\ i -.291 ‘XD. J = o - .043 - .178 ‘XG . J = 0 -.166 .144

NMR, 50-60 Edu.

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by comparing the values of the coefficients yielded by the model with the hypothesized values set equal to zero. The Simon-Blalock technique, by permitting tests concerning the genuineness or spuriousness of observed relationships, provides a rationale for em- pirically evaluating the consistency of the intercorrelations within a given hypothesized model.

R E L A T I O N S H I P OF I N D E P E N D E N T V A R I A B L E S T O M I G R A T I O N

Demographic Variables

Models 1 and 2 are concerned with interrelationships between the demographic variables and the 1950-1960 net migration rates of the Southern SMSA’S. As may be seen from Table 1 , which shows the zero-order correlations for each variable investigated, only two of the four demographic variables, age (-.166) and distance (-.178) indicated even a moderate association with migration. The argument in Model 1 is that age is antecedent and directly causal to size. Size is directly causal to density, while age exerts an indirect influence on density through age. Neither size nor density is hypothesized as directly linked to migration. Size, as predicted, largely explains the positive correlation between age and density (.607), although it does not reduce the partial sufficiently to justify an interpretation ofno re!ationship between the two variables. Age is shown to exert a weak but direct negative influence on migration.

Table I . Zero-Order Correlations for Independent Variables

Variables

Variables X A B C D E F G H I J

Net Migration Rates, 1950-1960 (X) _ - Size (A) Density (B)

Distance (D) Industrialization (E) Change Tndustrial- ization (F) Median Income (G) Unemployment (H) Median Education (I) Net Migration Rates,

Age (C)

1940-1950 (J)

.073 -.036 -.166 -.I78 -.382 .394 .144 .037 .389 .609 .796 .695 -.I42 ,127 -.223 .413 -.029 .226 .007

.607 -.128 .313 -.224 .383 .157 -.088 ,066 .020 .313 -.386 .334 -.009 .I84 -.166

-.233 .134 -.291 -.I20 .349 -.239 -.468 .365 .165 -.345 -.406

,215 .I11 .026 ,190 -.261 .675 .433

-.349 .034 .394

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Model 2 presumes an absence of relationship between age and distance. Age is hypothe- sized as directly related to both size and migration. This model shows that age and distance exerted direct influences on migration, but their influence was exercised independently. Model 2 indicates that those SMSA’S located at greater distances from neighboring SMSA’S exerted a lesser attraction to migrants than those SMSA’S located nearer to other s M s A’S.

Socio-Economic Variabtes

It is generally acknowledged that economic opportunity stimulates migration, while the absence of economic pull factors results in negative migration rates. The nature of the relationship between urbanization and industrialization is a complex one in economic and sociological theory, especially for countries that are already highly industrialized. [I21 Clark and others have shown that urbanization tends to increase in conjunction with increased industrial employment during earlier or historical periods in the growth of cities. [I31 Although it might seem probably that highly industrialized areas have a greater potential for prolonged and above average growth, and thereby should be characterized by above average migration rates, recent tests of this hypothesis have actually revealed an opposite association. Several investigations have found the percentage of the labor force employed in manufacturing to be higher in declining rather than increasing urban areas. [I41

Evidence of a greater incidence of employment in manufacturing in declining places raises the theoretically intriguing question of the conditions under which a high degree of industrialization is associated with urban growth. The fact that the necessity for industrial growth as a structural requisite for a positive net migration rate is generally recognized [I51 suggests that industrial expansion rather than degree of industrialization already attained is more likely to be associated with higher migration rates. Since degree of industrialization may be closely associated with a combination of other variables, these factors may combine to exert a negative influence on an area’s attractiveness to migrants.

Figures 3 and 4 hypothesize two sets of relationships concerning the influence of the three economic variables of industrialization, industrial change, and income in the 1950-1960 net migration rates. These models suggest that whereas degree of industriali- zation exerted a direct negative influence on migration, the variable of industrial change exerted a direct positive influence. Both models indicate the negative association between income and industrial change. (-.215) is a function of the negative tie between industriali- zation and industrial change and the positive tie between industrialization and income.

Model 4 shows that the low positive correlation (.144) between income and migration is raised to a higher value (.329) when the intervening influence of industriali- zation is partialed out. The fact that income is only weakly related to migration seems to be due to the positive link between income and industrialization. The indication is that income exerted a stronger tie to migration apart from the obscuring influence of industriali- zation due to the positive association between industrialization and income. These findings are consistent with the often noted tendency for more industrialized areas to be characterized by higher income levels, but less industrial expansion and lower migration rates. [I61

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In Model 5 the variables of education and unemployment are brought into the causal scheme along with income. The high correlation between education and income (.675) is an anticipated one since the nature of the relationship between these two variables has been well documented. The positive link between education and migration (.389) is also an expected one in view of the large body of studies which have found higher median educational levels to be characteristic of growing rather than declining places. [I71

Model 5 shows that the weak positive correlation between income and migration is reduced to a negative value (-.173) when the influence of education is partialed out. This supports Tarver’s observation that education probably accounts for much of the variation in urban area migration attributed to income. Model 5 also indicates that the variable of unemployment was not causally related to the 1950-1960 net migration rates of South- ern SMSA’S. This is consistent with Tarver’s finding that 1960 unemployment levels were not related to S M S A migration rates between 1955 and 1960. [I81

Models 6, 7, and 8 test the hypothesis that higher income levels, which have been con- sistently found to be characteristic of older and more industrialized urban areas, may be partially responsible for the negative relationship between industrialization and in- dustrial change and migration. Model 6 shows that the original negative correlation between age and migration is raised sufficiently to justify an interpretation of no relation- ship when the influence of industrialization is controlled. The causal influence of age is indirect through industrialization rather than direct.

Models 7 and 8 lend supportive evidence to the pattern indicated by Model 6. These models also indicate that the negative influence of age on migration reflected by the zero-order correlation is spurious since it is raised to a near zero value when the influence of industrial change is partialed out. Model 7 also shows that the weak positive correlation between migration and income (.144) is raised to a higher second-order value (.256) when controlled for the inverse association between income and industrial change.

Models 6, 7, and 8 suggest that the negative influence of S M S A age on migration is exerted indirectly through industrial status. The older, more industrialized, higher in- come areas proved less attractive to migrants during the fifties, whereas the younger less industrialized S M S A’S were the areas of greatest industrial expansion and experienced the highest migration rates.

Demographic and Socio-Economic Variables and Past Migration

In Models 9 and 10 the variable of net migration rates during the previous decade (1 940-1950) is combined with certain of the demographic and socio-economic variables in the causal diagrams. Models 9 and 10 show a direct link between 1940-1950 and 1950- 1960 migration. Hitt, who related the net migration rates of 57 metropolitan areas between 1949 and 1950 with the net migration rates between 1935 and 1940, found, in general, that the same areas characterized by high migration rates during the 1949-1950 period had also experienced high rates during 1935-1940, Hitt interpreted this uniformity as suggesting that there was a combined operation of longitudinal forces favorable to movement to large population centers. [I91

Model 9 shows that age and distance independently influence migration indirectly

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through past migration. Model 10 shows that the association between migration and distance tends to disappear when controlled for migration during the previous decade. The fact that the negative tie between distance and migration is raised to a near zero value, and that the positive tie between migration and education is decreased to a negative value, when the influence of the variable of past migration is controlled, is indicative of a longitudinal operation of the same causal influence during both decades.

C O N C L U S I O N S A N D I M P L I C A T I O N S

The objective of this research was to identify some of the demographic, economic, and social factors responsible for the differential net migration rates of the 80 SMSA’S of the census South between 1950 and 1960. In addition to the dependent variable, 1950-1960 net migration rates, ten independent variables were arbitrarily selected for investigation as possible causal factors. Analysis of the interrelationships obtained among the in- dependent variables by the Simon-Blalock technique for making decisions on the spuriousness or genuineness of observed correlations in alternative hypothesized causal models indicated that the causal factors influential in determining the 1950-1960 net migration rates were a composite of interrelated demographic and socio-economic factors rather than a corollary of a single unitary element.

The demographic factors of size, age, density, and distance from nearest S M S A exhibited very little direct association with migration. The small inverse correlation between age and migration seems explainable in terms of industrial change. Seemingly, the older more industrialized S M S A’S did not attract the industrial development necessary for high migration rates. The indication is that whatever the combination of factors were which made industrial change a causal variable in attracting migrants (economic opportunity, demand for personal services, etc.), they were not characteristic of the older industrialized areas.

The factor of past migration, as has been consistently noted in related research, [20] emerges as a primary explanatory variable in this study. This finding indicates, as Bogue has suggested, that those particular factors which account for migration have not yet been isolated. However, the fact that 1940-1950 migration rates generally disposed the SMSA’S to migration rates correspondingly above or below the average for the 1950-1960 decade suggests that the migration rates of metropolitan areas are responses to definite factors which exert their influence over several decades, and that S M S A migration rates are not completely unpatterned and unpredictable sets of events.

The study is beset with a variety of limitations. Conclusions reached via the Simon- Blalock technique are necessarily tentative rather than definitive or exhaustive. It is never possible to prove that any particular model is the correct one since there may be any number of alternative models which may fit the data equally well. However, the analytical technique formalized in the Simon-Blalock approach seems to be a suitable tool for attempting to assess the relative influence of combinations of independent variables. Furthermore, use of the Simon-Blalock procedure allows the researcher to deal with continuous variable data, thereby avoiding the necessity for placing cases in arbitrarily defined bundles.

In order to use the Simon-Blalock technique it is necessary to make certain assumptions

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about the operation of outside variables. One may, however, question whether it is realistic to assume that outside variables do, in fact, operate in a random fashion, al- though such an objection can always be raised about interpretations based on ordinary controlling procedures since they must inevitably involve assumptions about the operation of outside variables. In addition questions may be raised relative to the selection of the independent variables. Determining which variables to take into a theoretically closed causal scheme, even with the guidelines of related theory and research, is largely a matter of personal predeliction and arbitrary selection. While the list of possible causal factors can be expanded indefinitely, at some point the researcher must be willing to limit the independent influences to an arbitrarily selected group of variables whose effect he wishes to test and assume that they form a closed system. Secondary analysis is often exploratory. Replication, with more refined analytical measures, should be of considerable value in isolating specific causal influences on urban area migration.

N O T E S

1 . J. MEDINA ECHAVARRIA and PHILIP M. HAUSER, ‘Rapporteur’s Report’, Urbanization in Latin America, Philip M. Hauser, editor. (New York: Columbia University Press, 1961), p. 19. 2. DONALD J. BOGUE and K. C. ZACHARIAH, ‘Urbanization and Migration in India’, India’s Urban Future, RAY TURNER, editor. (Los Angeles: University of California Press, 1962), pp. 28-54; KINGSLEY DAVIS, ‘Human Fertility in India’, American Journal of Scciology, LII (November, 1946), pp. 246-254 ; JANICE DOSSELAER and ALFONSO GREGORY, Urbanization in Latin America (Bogota, Columbia: International Center for Sociological Investigation, 1962), pp. 93-1 13 ; United Nations Department of Social Affairs, Determinants and Consequences of Population Trends (New York: United Nations, Population Studies No. 17, 1953), pp. 85-86; MICHAEL KENNY, A Spanish Tapestry: Town and Country in Castile (New York: Harper and Row, Publish- ers, 1966), p. 125; DAVID YACKEY, ‘Some Immediate Determinants of Fertility in Lebannon’, MGrriage and Family Living, X X V I (February, 1963), p. 28; J. V. D. SAUNDERS, Diferential Fertility in Brazil (Gainesville: University of Florida Press, 1958), p. 84; KURT B. MAYER, The Population of Switzerland (New York: Columbia University Press, 1964), pp. 102-104; JOHN WEBB, ‘The Natural and Migrational Components of Population Changes in England and Wales’, Economic Geography, XXXII I (April, 1963), p. 130. 3. The standard metropolitan statistical area (SMSA) is the most widely used metropolitan concept in the United States. In general, each S M S A comprises a county containing a city of 50,000 or more inhabitants according to the 1960 census, plus contiguous counties which are sufficiently integrated with the city. A total of 212 SMSA’S were identified in 1960, with 80 of these located in the census South. The census South is inclusive of the sixteen states of Delaware, West Virginia, North and South Carolina, Tennessee, Virginia, Florida, Arkansas, Mississippi, Kentucky, Maryland, Alabama, Oklahoma, Louisiana, Texas and Georgia, plus the District of Columbia. 4. K. P. SCHWIRIAN and JOHN P. PREHN, ‘An Aviomatic Theory of Urbanization’, American Journal ofSocio/og.v, X X V I I (December, 1957), p. 813. 5. P t K Sr Wu, ‘The Social Characteristics of Increasing, Stable, and Decreasing Cities’, (un- published Ph.D. dissertation, University of Chicago, 1946) ; FENTON KEYES, ‘The Correlation of Social Phenomena with Community Size’, (unpublished Ph.D. dissertation, Yale University, 1942); WILLIAM F. OGBURN, The Social Characteristics of Cities (Chicago: City Managers Asso- ciation, 1937) ; CHAUNCY HARRIS, ‘A Functional Classification of American Cities’, Geographical

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Review, XXXIII (January, 1943), pp. 89-96; OTIS D. DUNCAN and ALBERT J. REISS, Social Cha- racteristics of Urban and Rural Commnnities, 1950 (New York: John Wiley and Sons, 1956); H. J. NELSON, ‘A Service Classification of American Cities’, Economic Geography, X X X I (Septem- ber, 1955), pp. 189-211. 6. Measures of the independent variables, as well as the SMSA net migration rates, were obtained from census data. Probably the most convenient arrangement of the data used in this study is contained in the County and City Data Books for 1952 and 1962, U. S. Census o j Population, 1950: U. S. Summary, General Social and Economic Characteristics and U. S. Census of the Population, 1960, Final Report PC(l)-lC. 7. DAVID GOLD, ‘A Note on Statistical Analysis in the American Sociological Review’, American Sociological Review, X X I I (June, 1957), p. 333. 8. DONALD J. BOGUE and DOROTHY L. HARRIS, Comparative Population and Urban Research Via Multiple Regression and Covariance Analysis (Miami, Ohio : Scripps Foundation, 1954), p. 2. 9. For discussion of Pearson r, see JOHN G. PEATMAN, Introduction to Applied Statistics (New York: Harper and Row, 1963), pp. 79-124. 10. HERBERT A. SIMON, ‘Spurious Correlation: A Causal Interpretation’, Journal of the American Statistical Association, XLIV (September, 1954), pp. 467479; Models of Man (New York: John Wiley and Sons, 1957), pp. 10-12. 11. H. M. BLALOCK, ‘Correlational Analysis and Causal Inferences’, American Anthropologist, LXII (August, 1960), pp. 246-251 ; ‘Four-Variable Causal Models and Partial Correlations’, American Journal ofSociology, XLVII I (September, 1962), pp. 182-192; Causal Inferences in Non- experimental Research (Chapel Hill: University of North Press, 1961), pp. 1426. 12. FOREST G. HILL, ‘Regional Aspects of Economic Development’, Land Economics, X X X V I I I

(May, 1962), p. 95. 13. W. J. MATHERLY, ‘The Emergence of the Metropolitan Community in the South’, Social Forces, X I V (March, 1936) pp. 318-328; ALLAN PRED, ‘Industrialization, Initial Advantage, and Metropolitan Growth’, Geographical Review, L V (April, 1965), pp. 165-177. 14. Wu, op. cit., p. 66; BOGUE and HARRIS, op. cit., p. 23; ALBERT J. REISS, ‘Research Problems in Metropolitan Population Redistribution’, American Sociological Review, X X I (October, 1956), p. 573. 15. J. M. GILLETTE, ‘Some Population Shifts in the U. S., 1930-1940’, American Sociological Review, V I (October, 1941), p. 624; HOMER HOYT, ‘The Utility of the Economic Base Method in Calculating Urban Growth’, Land Economics, X X X V I I (February, 1961), pp. 53-54. 16. DUNCAN and REISS, op. cit., p. 124; BOGUE and HARRIS, op. cit., pp. 19-20; GEORGE L. WILBER, ‘Growth of the South’s Population, 1950 to 1960’. Paper presented at the annual meeting of the Association of Southern Agricultural Workers, Jackson, Mississippi, February 7, 1961, pp. 8-10; JAMES D. TARVER, ‘Metropolitan Area Migration Rates’, Industrial and Labor Relations Review, xv i i i (January, 1965) p. 218; ALBERT J. REISS, ‘Community Specialization in Durable and Nondurable Goods Manufactures’, Land Economics, X X X I V (May, 1958), p. 133. 17. JOHN E. PEARSON, ‘The Significance of Urban Housing in Rural-Urban Migration’, Land Economics, X X X I X (August, 1963), p. 233. 18. TARVER, op. cit., pp. 220-222. 19. HOMER L. HITT, ‘Peopleing the City: Migration’, The Urban South, R. B. VANCE and N. J. DEMERATH, editors (Chapel Hill: University of North Carolina Press, 1954), p. 62. 20. For similar findings see A. J. J , % f f E and SEYMOUR WOLFBEIN, ‘Internal Migration and Full Employment in the United States’, American Journal of Sociology, X L (September, 1945), pp. 301-323 and GEORGE L. WILBER, ‘Growth of Metropolitan Areas in the South’, Social Forces, L X I I (May, 19651, p. 499.

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T A U X D I F F E R E N T I E L S NETS D E M I G R A T I O N

D A N S LES A I R E S STATISTIQUES M E T R O P O L I T A I N E S

D U S U D DES ETATS-UNIS D’AMERTQUE, 1950-1960

L‘objectif de cette recherche Ctait d’identifier certains des facteurs demographiques, Cconomiques et sociaux qui expliquent les differences constatees dans les taux nets de migration des 80 aires statistiques metropolitaines retenues pour le recensement du Sud, entre 1950 et 1960.

L’analyse des relations existant entre les variables exogknes montre que les taux nets de migration enregist& entre 1950 et 1960 ont CtC dCterminCs par une combinaison de facteurs dkmographiques et socio-Cconomiques mutuellement lies, plut6t que par un seul Clement unitaire.

Les facteurs demographiques tels que la dimension, l’dge, la densite et l’eloignement par rapport a I’aire statistique la plus proche n’ont fait apparaitre qu’une trks faible association directe avec les mouvements migratoires. I1 appert que, quelle que soit la combinaison de facteurs qui a fait de I’evolution industrielle une variable ayant contribuC a attirer les migrants (possibilites Cconomiques, demande de services personnels, etc.), ces facteurs n’Ctaient pas caracteristiques des zones industrialiskes depuis plus longtemps que les autres.

TASAS D I F E R E N C I A L E S NETAS DE M I G R A C I O N E N

LAS A R E A S ESTADISTICAS M E T R O P O L I T A N A S D E L S U R

D E LOS ESTADOS U N I D O S D E A M E R I C A , 1950-1960

El objeto de este estudio era identificar algunos de 10s factores demograficos, economicos y sociales que explican las diferencias apreciadas en las tasas netas de migracion de las 80 areas estadisticas metropolitanas utilizadas para el censo del Sur entre 1950 y 1960.

El analisis de las relaciones existentes entre las distintas variables demuestra que las tasas netas de migracion registradas durante el periodo considerado estuvieron deter- minadas por una combinacion de factores demograficos y socio-economicos relacionados entre si, mas que por un solo elemento unitario.

Los factores demograficos tales como la dimensih, la edad, la densidad y la distancia del area estadistica mas proxima no parecen tener mas que escasa relacion directa con 10s movimientos migratorios. Parece que, cualquiera que sea la combinacion de factores que ha hecho de la evolucion industrial una variable que ha contribuido a atraer a 10s migrantes (posibilidades economicas, demanda de servicios personales, etc.), esos factores no pueden considerarse caracteristicos de las areas industrializadas desde hace mas tiempo.

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