Regional growth and interstate migration

Socio-Economic Planning Sciences 36 (2002) 239–265

Regional growth and interstate migration Ardeshir Anjomani* School of Urban & Public Affairs, University of Texas at Arlington, P.O. Box 19588, Arlington, TX 76019, USA

Abstract The current study examines a simultaneous equation model of interstate migration using income growth, employment growth, unemployment growth, population growth, gross migration, and employment in manufacturing as endogenous variables. The results show that neither the growth of employment nor the growth of income in the destination location has been directly important determinants of migration flow. However, an indirect effect through the population variable can be discerned for these variables, and this has important policy implications. A key feature of this migration model is that it incorporates most of the determinant factors as rates of change over time. The study sheds light on the joint and indirect effects of migration and other endogenous variables and draws some important policy implications pertaining to growth. r 2002 Elsevier Science Ltd. All rights reserved.

1. Introduction As a cause and a consequence of social change, human migration is regarded as one of the most important factors underlying the demographic and socioeconomic composition of regions. Thus, for anyone attempting to analyze the general process of regional change, an understanding of interregional migration is vital. Accordingly, policy makers have become increasingly aware of the role of migration in the context of such socioeconomic issues as regional growth, social well-being, and political representation [1]. Miller [2] believes decision makers, in order to make good decisions, need to be aware of the relation between changing patterns of interstate migration and changes in regional and national economic growth as well as spatial patterns of economic activity. The growth of states and regions relates closely to population growth, which is mostly a result of migration. Thus, it seems plausible that any study of migration flow would make it possible (1) to better understand the growth of regions, the factors causing this growth, and the interrelationship of these factors; (2) to more accurately anticipate and prepare for the future growth of different regions (especially growth that might cause problems or create new *Tel.: +1-817-272-3310; fax: +1-817-272-5008. E-mail address: [email protected] (A. Anjomani). 0038-0121/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 0 3 8 - 0 1 2 1 ( 0 2 ) 0 0 0 1 0 - 1

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A. Anjomani / Socio-Economic Planning Sciences 36 (2002) 239–265

opportunities in many sectors of the economy); and (3) to study policies that could increase (or induce) the growth. The overbuilding of commercial office buildings, residential housing, and other types of construction occurring during the 1980s constitute good examples of growth overestimation, contributing to the subsequent savings and loan crisis. Conversely, the case of areas experiencing underestimated immense growth, resulting in chaotic and unwieldy problems for inadequately prepared planners and public administrators, emphasizes the need for better understanding and anticipation of growth. Chun [3] believes that migration is a means of achieving both economic efficiency and equity. Thus, migration has implications for policy makers, especially those concerned with depressed areas. State and local governments seek to attract migration, because migrants increase employment and contribute to income equalization. A study of migration may show us not only how these aims can be achieved, but also may highlight other policies needed for inducing growth. 1.1. Study purpose This research attempts to find the determinant factors of interstate migratory flow in the United States. Achieving a better specification of the variables and a more appropriate model structure is one objective of the study. A new approach of looking at joint determinations and indirect effects of the variables, however, is one of the main objectives and contributions of this paper. It is hoped that we can shed light on some of the unresolved questions regarding migration, partly through this new approach, while improving on earlier work in the field. It is also hoped that understanding the pattern of migration and the major changes experienced in the study period will help with policy analysis and the growth anticipation process. Such findings might thus assist policy makers and practitioners in different levels of government while aiding private actors in their decision making regarding growth. In the absence of metropolitan level decision making in the United States, we selected state level analysis. Also, according to Miller [2], interstate migration needs greater attention due to its high degree of short run volatility. 1.2. Study period The study period here is 1975–1980. At the present time, the latest available data for the type of study we are proposing (1985–1990 data) is close to a decade old. As such, the intention here is not necessarily to put together a model so that the results can be extended to the immediate future. Rather, it is to study our recent era with the hope of finding interesting and useful information capable of supporting improved policy decisions while providing a better understanding of migration. The 1970s, in some respects, constituted the dawn of a new era for the United States, particularly after the oil embargo of 1973–1974. New major social and economic changes occurred after the end of the Vietnam War, Watergate and the ‘‘60s’’ phenomenon. Indeed, this period has been described as the beginning of post-industrial society, as a service economy began replacing a manufacturing economy. In terms of growth, the Sunbelt experienced new population movements and new employment growth. Any study on migration patterns must thus call special attention to this critical era. In this regard, Greenwood [4] argues that there has been a new change in migration pattern in the US since approximately 1970. Chun [3] agrees, asserting that the spatial


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distribution of population in the US underwent major changes, including net movement out of the Northeast and North Central regions into the South and West, which continued into the 1980s. Rogers and Henning [5], in reviewing the continued long term shift, assert that total regional net migration rates were lower in 1985–1990 than in 1975–1980. McHugh and Gober [6] suggest that annual interstate migration flows are highly volatile, and found a transformation of the migration system in the 1980s. Referring to this study, Miller [2] concludes that this was a transitory period and that the 1990s brought a reversion in the pattern of population redistribution back to that of the 1970s. Manson and Groop [7] assert that ‘‘while it is clear that the late 1970s to early 1980s was a period of systemic change in the origins and destinations of US migrants, it is equally clear that a new equilibrium has yet to emerge.’’ They also conclude that the ‘‘staunching of net out-migration from the American manufacturing belt’’ (Plane [8]) continued into the late 1980s and early 1990s. These studies draw attention to the significance of this period, which deserves further study. In particular, it would be interesting to see whether conventional variables explaining migration flow in past decades continue to be valid, considering these changes. In addition, it would be important to identify implications of such changes to the present and near future in terms of economic growth and resulting migration.

1.3. Methods Examination of related works will guide us in determining the structure and form of the model and, later, in variable selection and model specification. A vast body of literature exists in relation to the study of migration. Several disciplines have made numerous contributions to the migration literature since 1960. In relation to origin vs. destination, Lowry [9] concludes that the labor market characteristics of an origin locality make little difference to an individual who is contemplating a move to another area. On the other hand, destination characteristics do influence the locality to which migrants may move, and employment growth and greater income growth tend to induce net in-migration. Others have refuted the validity of these findings, stating that origin characteristics are important in explaining migration flow [10]. Thus, a thorough study would look at both origin and destination characteristics. Most early research used multivariate regression models in the study of migration [11]. Muth [12], however, argued that employment growth and migration are mutually dependent; he therefore estimated his model by means of appropriate simultaneous equations. Others have further developed the simultaneous equations approach by introducing structural equations for jointly dependent variables other than migration and employment change. Greenwood [13], for example, treated income growth as an endogenous variable in seeking to explain urban economic growth and migration over the decades of 1950–1960 and 1960–1970. In his ambitious 14equation, simultaneous system, he estimated the interrelationships between inter-metropolitan migration and growth rates of employment, unemployment, income, and the civilian labor force. He found that greater rates of employment growth encouraged in-migration and that inmigration, in turn, induced higher rates of employment growth. In another study of the 100 largest SMSAs, through application of a nine-equation simultaneous model, Greenwood [14] found the white civilian labor force to be more responsive to the growth of job opportunities than

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non-whites, while non-whites were more responsive than whites to high income levels and income growth. A common shortcoming of a single-equation, multiple regression model is the presence of ‘‘simultaneous equation bias,’’ i.e., migration that has occurred and has itself influenced the independent variables of the model. Mueller [15] provided a survey of research dealing with the simultaneous interaction between the causes and consequences of migration. It is argued that a simultaneous-equations representation of migration would improve the model. Several researchers have thus structured their models in this form, although only Crown [16] used an origin destination basis. More recently, to account for three-way causality amongst migration, employment and income, Chun [3] employed a full simultaneous equation model and found both disequilibrating and equilibrating effects for migration. These studies have shown convincing evidence for a joint causal effect between migration and economic growth. For this reason, the current study uses a simultaneous-equation approach for the study of interstate migration. In recent years, most migration research studies have used individual data from microsamples. These same studies have generally employed multivariable regression analysis. Considering the disaggregated characteristics of microsample data, it is not readily possible to study migration in simultaneous equation systems for determination of joint dependence of variables and systemic effects. This is because a good deal of the data for variables in such models is only available at aggregate levels. Selecting a simultaneous equation approach as a least-biased model thus prevents us from using microsample data in the current analysis. Bartik [17] argued, ‘‘that models that use the levels of local participation or employment rates as dependent variables may be biased by unobserved ‘fixed effects’ of local areas.’’ He then suggested that this can be avoided by first differencing all level variables. In line with this reasoning, most level variables in our model are selected in this form. Clark et al. [18], testing different models, found that difference models provide the best overall fit. Regarding the level of analysis, this study uses state level data since the goal here is to assist the public sector in terms of its policy decisions. Metropolitan government in the US is practically nonexistent and councils of government normally accommodate municipalities inside metropolitan areas. Likewise, county governments, in general, are less interested in influencing migration, especially in metropolitan areas. In contrast, state governments, for a variety of reasons, have been, and are, interested in the study of growth and migration. Indeed, some of the larger states regularly study interstate migration and are interested in forecasting growth, and, therefore, migration. Accounts of some of their models, most often as relatively large multilevel, sometimes complex, simultaneous equation systems, have been reported in the literature. (see, for example, Plaut [19], Bowman et al. [20] and Gabriel et al. [21].) One reason for such efforts is that some state governments can afford to construct and maintain the models, which might be too expensive a proposition at the county level. Also, through more structured legislative processes, states can adopt policies designed to attract or control growth. Another feature of our model is its consideration of both inflows and outflows of migrants from each state. This is noteworthy since most recent studies have attempted to explain net migration as opposed to gross migration. Frees [22,23] and Gabriel et al. [21] have also used gross migration flow. However, they did not use a simultaneous equation system. Our study is thus unique in that it includes gross migration flow, differenced level variables, and a multi-equation model, together with other characteristic described later in the paper.


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The paper is structured as follows: First, the proposed model is specified, including an explanation of the equations and key variables. Second, an analysis section examines the findings, after which major policy implications are discussed. A conclusion section closes the paper.

2. Specification of the model The basic model for this study consists of six jointly dependent endogenous variables. They include the rate of income growth (INCJ), the rate of employment growth (EMPJ), the rate of unemployment growth (UEMJ), the rate of population growth (POPJ), gross migration (GM), and employment in manufacturing (EMMJ). The first five equations are measured from 1975 to 1980. The last structured equation measures only 1975 data. All exogenous variables are lagged data (1975). Exogenous variables, or the truly independent variables, are all collected at the base year of 1975 in order to determine how they have or have not explained variations in interstate migration patterns. The model is of the following form: GM ¼FðEMPJ; POPJ; INCJ; PGM; INCI; DIST; TEMPJ; CRIMEJ; DENJ; NETI; NETJ; WAGEJ; EDUI; UEMI; TEMPI; and GRANTJÞ INCJ ¼F ðEMPJ; UEMJ; EMMJ; BLACKSJ; WAGEJ; EDUJ; DENJ; and AGEJÞ EMPJ ¼F ðINCJ; POPJ; GM; EMMJ; EDUJ; WAGEJ; AGEJ; DENJ; and D TAXJÞ UEMJ ¼FðGM; EMMJ; POPJ; EDOJ; BLACKJ; NETJ; and AGEJÞ EMMJ ¼F ðEDUJ; EMPJ; POPJ; CAPJ; WAGEJ; TAXJ; TEMPJ; VALUEJÞ POPJ ¼F ðEMPJ; UEMJ; GM; INCJ; BLACKJ; TEMPJ; DENJ; TAXJ; NETJ; EMMJ; and PGMÞ: All variables (except tax as dummy variables and net migration) are expressed as the logarithm of the variables. As such, the coefficients will constitute the elasticities. Generally speaking, the endogenous variables are in line with variables in similar studies (see, for example, [13]). However, here they are formulated within the context of our interstate (origin destination) study. Greenwood [13] points out that employment growth and migration have a simultaneous determination relationship. Haurin and Haurin [24] argue that migration and employment opportunities are both outcomes of the same process. Charney [25] suggests recognition of this simultaneous nature. The simultaneous consideration of migration and economic variables is reflected in our selection of endogenous variables. In addition to the two-way causation between these variables, the more intricate relationships should also be considered. For example, the effect of the destination’s manufacturing employment on migration is thought to be a function of employment and population variables. Similarly, it is hypothesized that unemployment at the destination affects other endogenous variables which, in turn, affect migration. More discussion of this issue is given below; however, it should be mentioned that this variable has not proven

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significant in most previous studies. A discussion of our equations will further clarify these endogenous variables as well as their relationships to other important variables.

3. Migration model Specification of the migration equation is straightforward, following Greenwood [13] and mirroring the rich literature of migration (for review of literature in migration see [4, 11]). Because the sample size is large, we attempted to include all theoretically significant variables in order to formulate a complete specification. The model has been examined for possible multicollinearity (for misspecification problems occurring when relevant variables are omitted, and/or there is correlation of model variables, see [26,27]). Important variables will be briefly discussed in this section. In discussing variables in relation to origin or destination state, as is conventional in the migration literature, i represents origin and j represents destination. Table 1, as a quick reference, highlights all model variables, including their expected signs. Complete variable definitions can be found in Appendix A. Considering the migration variable, Cushing [28] demonstrated that origin characteristics drop out of those models that use allocation rate. Thus, gross migration, GM, was selected instead of ‘‘allocation rate’’ as we intend to use some origin characteristics as explanatory variables. 3.1. Previous migration and distance variables In a planned move, a location is preferred if migrants have a strong attachment to the community and friends residing in that location [29]. People are thus more likely to move to places where relatives and friends have previously migrated so as to reduce uncertainty. Previous gross migration, PGM, is considered a proxy for the ‘‘availability of information’’ [13], where friends and relatives provide potential migrants with food, shelter, and jobs. It is also widely accepted that the availability of information concerning alternative places plays a prominent role in the potential migrant’s decision regarding a destination [11]. People are more likely to move to places about which they have at least some information rather than places about which they know little or nothing. Information decreases with increased distance from a person’s home. In addition, family and friends who have previously migrated from state i to state j may provide persons in their former place (state i) with an important source of information about their present location (state j). This information may, in turn, increase the propensity of people in state i to move to state j rather than to some other location. It can be said that the more people who have migrated from state i to state j in the past, the greater will be the quantity of information sent from j to i; and, therefore, Ceteris paribus, the greater is likely to be the current flow of migrants from i to j: Informational aspects of relations and friends are not, however, the only important factors associated with the potential migrant’s decision regarding his choice of a destination. The presence in state j of relatives and friends from state i may, in itself, increase the propensity of people in i to move to j: The nearness of close friends and relatives may influence many aspects of life from entertainment, to help in emergencies, to child care, to chance conversations that provide an exchange of information and feeling of closeness [30]. Relatives and friends may finance the


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Table 1 Summary information for variables in migration model Abbreviation Variable name

Description

GM PGM

Gross migration Previous gross migration

Flow of migration from origin state to destination state Dependent variable Proxy for ‘availability of information’; +

DIST POPJ INCJ INCI EMPJ EMPI UEMI UEMJ CRIMEI CRIMEJ DENJ GRANTJ BLACKJ TEMPJ TEMPI EDUI EDUJ NETJ TAXJ

Distance Population Ja Income J Income I Employment J Employment I Unemployment I Unemployment J Crime I Crime J Population density Grant J Black population J Temperance J Temperance I Education I Education J Net migration J Tax J

TAXI

Tax I

AGEJ WAGEJ

Age J Wage J

a b

Proxy for ‘social distance’ Distance between origins and destination states Changeb in population of destination states Change in income per capita of destination state Change in income per capita of origin state Change in employment of destination state Change in employment of origin state Change in % unemployment in origin state Change in % unemployment in destination state Crime rate per 100,000 in origin state Crime rate per 100,000 in destination state Population per square mile in destination state Per capital grant in destination state Percentage of African Americans in destination state Mean temperature in destination state Mean temperature in origin state Median education level in origin state Median education level in destination state Net migration of destination state Dummy variable for income tax of destination state (1 for states with income tax, 0 otherwise) Dummy variable for income tax of origin state (1 for states with income tax, 0 otherwise) Median population age of destination state Manufacturing wage in destination state

Expected signs according to literature

+ + + 7 + +/insignificant + 7 7 + 7 7 7 + 7 +

I and J represent origin and destination states, respectively. ‘‘Change’’ represent changes for the variable between 1975–1980.

journey of the recent migrant or provide him initial accommodation. Moreover, especially for less educated migrants, relatives and friends of similar background may help make the social transition easier. This variable is expected to have a positive relationship with gross migration. Distance (DIST) is expected to exhibit an inverse correlation with the dependent variable, gross migration. Earlier studies have consistently found that migration between two places decreases as distance increases [11]. This is due, in part, to the fact that information about opportunities at the destination is often more difficult to obtain the greater the distance. However, Pandit [31] has asserted that immigrants might be more knowledgeable about a distant location in certain contexts. Cadwallader [1] thus noted that distance is actually a surrogate for a number of other variables such as the amount of information about the physical and psychological costs of moving and employment prospects at the destination. More recently, with information transfer becoming easier, Clark and Ballard [32] found that the impact of distance on the volume of migrants has

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lessened during recent decades. This finding is consistent in both rapidly growing areas and depressed regions. 3.2. Economic variables In terms of economic (employment/income) factors, considerable migration research has found a direct relationship between these variables and interregional migration [33]. Chun [3], for example, found that economic opportunity variables are significantly more important determinants of migration than are location-specific climatic factors. Some believe that prospective unemployment explains a significant portion of the variance in interstate migration rates [34]. Others, however, have found interstate migration to be highly correlated with earnings levels and job growth [11,35]. Greenwood et al. [36], found that job search motives are highly significant. Treyz et al. [37], in specifying migration as a function of employment-to-labor force ratios, found similar results. Cushing [38,39], in a study of interstate gross out-migration, identified differences in economic opportunity as significant. According to neo-classical economic theory, as well as empirical research [11], migrants generally move from low wage and low employment growth areas to high wage and high employment opportunity areas. Higher income growth in an area implies greater affluence, which leads to in-migration. A positive sign between GM and increase of income of destination (INCJ) is thus expected. Assadian [40] found this relationship while suggesting that income might also be indicative of quality of life. With respect to origin, Clark and Ballard [32] found, contrary to theory, that migrants may come from very prosperous regions, and not simply from depressed areas. Low income in the origin state is thus not necessarily important as a determinant of out-migration. Migration, as a result of origin push, is either not significant or uncertain. INCI, therefore, takes either a plus or minus sign. Employment variables have been found to be significant by Gruidi and Pulver [41] and Preuhs [42] in explaining migration. Thus, a positive sign is expected for change in employment rate at destination, EMPJ, as the higher employment rate represents opportunity and thus attracts inmigration. According to Greenwood et al. [43], however, employment change plays an important role in determining migration in the short run. In the long run, however, it appears that regional amenities, among other factors, become more important. A large number of studies have included unemployment in migration models as an explanatory variable. It is expected that the greater the rate of increase in unemployment in i, the more there is out-migration. Basu [29] argued that the negative effect of origin unemployment could be based on the fact that it forces people to move. Fields [34] found that unemployment is an influential factor, and, combined with employment growth and wage rate, generates significant economic push and pull forces. However, this variable often has an incorrect sign or has proved to be statistically insignificant. Anjomani and Hariri [44] thus found both unemployment of destination and unemployment of origin statistically insignificant and destination unemployment to have an incorrect sign. Also, several migration studies have found unemployment rates insignificant in explaining migration as a result of simultaneous bias inherent in the single equation multivariate regression model [13]. They also suggest that the unemployed are confined to the least mobile groups who would not


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consider the option of migration. In this regard, Greenwood [13] concluded that the local unemployment rate is one of the most ‘‘perplexing problems’’ confronting migration scholars for its lack of significance in explaining migration. Therefore, UEMJ was not included in the proposed model. Moreover, variables measuring economic conditions at the origin, including income, unemployment, and wages are frequently found to be insignificant. Migrants from depressed regions may thus not be responsive to origin characteristics. State individual income tax, TAXJ, is expected to have a negative relationship with gross migration; this would represent higher costs for manufacturing and potential in-migration. Assadian [40] has found that migrants prefer low tax areas. Day [45] has shown that income tax rates have a significant impact on migration decisions. Schachter and Althaus [46] have also concluded that high taxes tend to discourage in-migration while encouraging out-migration. 3.3. Amenity variables Shelley and Koven’s [47] study indicates that amenity variables, combined with economic factors, are highly salient in predicting rates of net state migration in the United States. Regarding density variables, the hypothesis is that people prefer to live in densely populated areas because of economy of scale. However, high density might also indicate diseconomy of scale, congestion, and overcrowding that lead to out-migration, which might not be captured by population variables. Therefore, a negative sign is also expected between DENJ and GM. Porell [48] attempted to ascertain the relative importance of quality of life versus economic factors and concluded that both economic and quality of life factors are important determinants of migration. Greenwood et al. [36] also observed that amenities and similar factors become more important than economic factors in the long run. Clark et al. [18], measuring the impact of destination site characteristics for interstate mobility, found that amenity variables are jointly significant pull factors. Natural amenities influence the location to move [49]. Mean temperature, TEMPJ, is thus expected to be positive, with TEMPI being negative. It was hypothesized that warm climates are generally preferred by migrants. Cushing [38,39], in a study of state out-migration allocation rates, found climate amenity variables to be significant and correctly signed. If a migrant cannot find a job, he is forced to rely on unemployment compensation and welfare. Studies suggest that interstate migration of low-income people is determined by the availability of welfare benefits. Further, they perceive such benefits as the cause of high state and local taxes. In this regard, Schachter and Althaus [46] found that public assistance has a negative effect on inmigration. However, Nelson and Wyzan [50] and Shaw [51] have shown that equalization grants are important in migration. GRANTJ can, therefore, exhibit both plus and minus signs. Higher crime rate as a negative amenity is expected to have a negative relationship with gross migration. Low crime rate is thought to make destination more attractive to migrants. 3.4. Demographic variables Clark et al. [18] asserted that demographic characteristics act as determinants of mobility while influencing the labor market. For example, a high percentage of African Americans, BLACKJ, may represent poverty and thus discourage gross migration, lower the level of income per capita,

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and increase the unemployment rate. Studies have shown that growth has greater effects in African American employment than with other population groups [52,53]. More generally, higher population growth suggests more favorable opportunities as migrants are more attracted to fast growing regions. Gross migration, (GM) is thus expected to have a positive correlation with population of destination (POPJ). Those people who are more skilled and educated, whose affluence permits more travel, and who are familiar with different parts of the country, tend to migrate more. Linneman and Graves [54] thus concluded that more educated individuals are less likely to move, while Herzog and Schlottmann [55], using micro data, showed that education reduces the probability of outmigration. Considering education levels at the destination, Knapp and Graves [56] and Schachter and Althaus [46] found that education in receiving areas is a desirable location determinant. On the other hand, higher education levels at the destination may also suggest higher competition levels but easier communications. These conditions both discourage and encourage educated migrants. Age could have either a negative or positive impact. Myrdal [57] argued that migrants tend to be in the most productive age group, because of their higher return on human capital investment. However, Richardson [58] suggested that migrants who are younger tend to be selective. Newbold [59] asserted that age reduces the propensity to migrate. Further, those increasing in age are less likely to migrate while in the labor force, but, as Plaut [19] found, there is a retirement migration towards ‘‘good’’ climate areas, although the income effect may be negative. Net migration can thus be considered as an index for growth. The greater the rate of inmigration, the greater the rate of employment growth. Likewise, previous out-migration may lead to in-migration and vice versa. Therefore, plus or minus signs are possible [13]. Areas that experienced much in-migration also will experience out-migration because they include substantial segments of a ‘‘migration prone’’ population.

4. Other models It is generally expected that the lower the unemployment of destination (UEMJ), the higher is per capita income (INCJ); also, the higher the education of destination (EDUJ), the higher the per capita income. Further, it is believed that higher African American populations (BLACKJ) lead to lower income levels (INCJ); however, Bartik [60] found that when African Americans receive higher wages (WAGEJ), their population grows. Higher employment in manufacturing (EMMJ) results in a higher wage rate and higher INCJ, while in-migration leads to income growth since inmigrants are generally skilled, educated, and young. As noted, employment change has simultaneous interactions with both gross migration and income of destination. Therefore, GM and INCJ were included in the employment model. Greenwood [4] states that ‘‘ [H]opefully, far more attention will be directed at understanding the relationship between employment and migration.’’ He argues that this relationship is central to any debate concerning the causes or consequences of migration. Employment in manufacturing has been used as a proxy measure of industrial mix [60,61], while education and employment work together in two ways. First, higher education levels have a positive effect on employment while growth of employment has a greater effect on the less educated [62].


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The effects of wage on employment have been confirmed by Summers [63] and Moore and Laramore [53]. An increase in in-migration generally leads to growth in employment because of greater investment. This causes the demand curve for labor to shift upward, which, in turn, results in higher wages and attracts more labor and increases employment. Gross migration (GM) is included in our unemployment model because Walsh [64] found an important two-way interaction between migration and unemployment. Employment growth might have long-term impacts on unemployment rates due to hysteresis effects [60,65]. We would expect a large black population (BLACKJ) to lead to high unemployment (UEMJ), as would lower education levels (EDUJ). On the other hand, job growth should have a greater effect on blacks than on whites [52,53,66].

5. Analysis and findings A 100% sample (aside from the missing data) of 48 48 states (all possible flows except Alaska, Hawaii and Washington, DC) in the study period was collected for the analysis. With the exception of NET and TAX, which have negative and zero values, respectively, variables were tested in log-linear forms because these better fit the equations. Variables with negative and zero values were treated as power of the base of logarithms to be in conventional additive form after logarithmic conversion. The proposed model was estimated by a two-stage least squares technique, with results presented in Table 2. Note that most of the variables have the expected signs and are significant in the 0.01 level. The adjusted R2 values are reasonably high, except for the income and unemployment equations. In order to analyze the systemic effects, we first look at the joint and indirect effects. We believe that close scrutiny of these may help explain some of the puzzling findings of previous research. Subsequently, we examine the individual models. 5.1. Joint effects Based on the above findings, we felt it interesting to analyze some of the interactions between the endogenous variables. We thus found that the rate of employment (EMPJ) and manufacturing employment (EMMJ) are jointly determined, with employment in manufacturing determining more of employment in destination than the converse (Fig. 1a). Of particular interest is the fact that elasticity of manufacturing employment with respect to employment was nearly ten times that of elasticity of manufacturing employment growth with respect to employment. Similarly, employment (EMPJ) and population (POPJ) were jointly determined; however, elasticity of employment with respect to population growth was substantially higher than elasticity of the population with respect to employment (Fig. 1b). This can be interpreted as the service sector becoming a more important part of the economy, as discussed earlier. A similar two-way effect (Fig. 1c) was also found between population (POPJ) and manufacturing employment (EMMJ). Here, elasticity of manufacturing employment with respect to population was close to five times that of the reverse. Gross migration (GM) demonstrated a triangular inter-effect with the destination’s unemployment and population variables (Fig. 2a). However, the elasticity of migration with respect to population was higher than that of

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Table 2 Two stage least squares estimates Equations for

INTERCEPT

1 GM

2 INCJ

3 EMPJ

4 UEMJ

5 EMMJ

6 POPJ

0.356 0.29

0.026 0.21

4.477 2.79***

7.646 13.17*** 0.013 2.81***

0.113 0.33

0.150 8.87*** 0.0005 0.17 0.217 5.00*** 0.015 7.94*** 0.087 15.58*** 0.099 10.08***

GM INCJ EMPJ

0.763 1.05 0.010 0.53

UEMJ EMMJ POPJ

BLACKJ

DENJ NETI NETJ

0.039 2.50*** 0.140 7.63*** 0.004 4.43*** 0.006 3.64***

AGEJ EDUI TEMPI GRANTJ

DIST CAPJ

4.092 22.47*** 9.054 6.78*** 11.802 7.03***

0.502 4.70*** 0.0004 5.75*** 0.068 3.82*** 0.002 1.38

3.944 23.04*** 0.544 18.40***

0.183 9.08***

0.002 0.76 2.138 5.60***

0.400 9.37***

VALUEJ

INCI

0.433 12.01***

0.011 1.46* 0.138 0.66 6.793 13.97*** 0.003 8.02***

0.459 12.43*** 2.684 9.28***

0.550 26.68***

0.005 6.50*** 0.219 1.88*

0.0003 6.53*** 0.097 10.15*** 0.020 19.96***

0.001 15.15***

1.012 1.02 0.220 2.04** 0.108 1.44*

TAXJ

PGM

0.006 0.70 0.003 0.28 0.073 4.05***

1.137 1.98***

EDUJ

WAGEJ

3.611 2.38***

0.025 3.89*** 0.558 17.06***

1.014 88.64*** 1.506 4.23*** 0.010 0.46

0.023 25.13***

0.00 0.38

0.129


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Table 2 (continued) Equations for 1 GM TEMPJ CRIMEJ UEMI Adjusted R2

2 INCJ

0.289 2.76*** 0.139 2.63*** 0.286 5.39 0.9376

0.1382

3 EMPJ

0.4109

4 UEMJ

0.2229

5 EMMJ

6 POPJ

8.64*** 0.542 13.46***

0.060 8.35***

0.9869

0.8872

*Significant at 0.10 level. **Significant at 0.05 level. ***Significant at 0.01 level.

Fig. 1. Relationship of endogenous variables—joint effects.

unemployment rate with respect to migration. Similarly, there was a triangular effect amongst manufacturing employment, income, and population (Fig. 2b), and a joint triangular effect amongst employment, manufacturing employment, and population (Fig. 2c).

5.2. Indirect effects Fig. 3 helps us better understand some important indirect effects that may shed light on a series of unresolved questions raised in previous migration studies. Consider, for example, the effect that employment has on the destination population. One can view the effects of this variable, employment, on gross migration through the population variable (Fig. 3a). The direct effect was not proven to be significant in the model. In the same way, one can conjecture the effect of other important endogenous variables, e.g., INCJ and EMMJ, on migration through population (Fig. 3b and c, respectively).

252


Fig. 2. Relationship of endogenous variables—triangular effects.

Fig. 3. Relationship of endogenous variables—indirect effects.

In this sense, population plays an important role in the determination of migration, acting as a conduit to other important variables. Other three-way and more complex inter-effects were found between the economic endogenous variables can be observed from results of the model. Fig. 4 brings together these important effects related to the six endogenous variables in one place to help us visualize these relationships. Charney [25], taking points raised by Greenwood [13] and Haurin and Haurin [24] regarding the simultaneous interrelationship of migration and employment opportunities one step further, stated that a theoretical advancement is needed to identify the underlying process that influences both jobs and migration. The findings of the current paper shed some light on the underlying cross effects of this complex relationship. We now turn to a more detailed discussion of the results of each equation.


253

Fig. 4. Relationship of endogenous variables—full black lines represent significant relationships.

5.3. Results of migration model In the gross migration (GM) model (see Table 2, Column 1 and Fig. 5a), the selected exogenous and endogenous variables explain more than 93% variation of the logarithm of the dependent variable. Figs. 5a–f attempt to help visualize the model under discussion by illustrating the significant relationships, where the size of variable balls is proportionate to the size of the coefficients for the exogenous variables. The endogenous variables are depicted as large letter balls. Previous gross migration (PGM) has the highest contribution to the equation and is significant at the 99% level. Similar to Chun’s study [3] for the same period, both the growth of employment (EMPJ) and the growth of income (INCJ) for the destination are statistically insignificant. They are included in this gross model because they are widely accepted in previous theories. The indirect effect of these variables on migration was discussed above. In regard to the non-significance of EMPJ and INCJ, Gordon and Theobald [67] suggested that migrants may be sensitive to economic opportunities but lack precise information, as shown by the significance of population (POPJ). Differences in populations thus appear more easily understood than those of income and employment and may therefore be used as a proxy for anticipated economic opportunities. Growth of population for the destination (POPJ) was found significant at the 95% level, while several other variables were generally significant at the 99% level. Wage at destination (WAGEJ) was found significant with a positive sign, implying migration flow toward higher wage areas. This finding is contrary to the findings of Krumm [68]. The population density of the state of destination was also significant, but with a negative sign. Consistent with the findings of Anjomani and Hariri [44], this implies that states with lower population density were more attractive in the study period. It thus contradicts the theory that migrants have a propensity to move to high-density states. This may also be in line with

254


Fig. 5. (a) Migration model, (b) income model, (c) employment model, (d) unemployment model, (e) manufacturing model, (f) population model, and (g) composite of endogenous and exogenous variables (composite of Fig. 5a–f).

arguments made by Sternlieb and Hughes [69] that urban amenities are currently available in less dense areas. Further, this finding may be related to a shift in US migration patterns since approximately 1970 [4]. Physical distance between locations I and J, and education of origin (EDUI) were here found to be insignificant with correct signs. Income at the origin (INCI) was significant with the expected sign. Unemployment at the origin (UEMI) had the ‘‘wrong’’ sign, which is in line with much of previous migration research. (See [13] for a review of this research, and [4] for both a review and reasoning as to why this might be the case.) Herzog et al. [70], reviewing several microdata studies, concluded that personal unemployment significantly augments labor force migration. This suggests that the significance of unemployment may be masked in aggregate studies simply by the aggregation. Herzog and


255

Fig. 5 (continued).

Schlottmann [55], however, document why the unemployment rate is not the best measure of the labor market condition. The importance of previous gross migration (PGM) vs. distance (DIST) can be explained as follows: PGM includes the influences of economic variables as well as part of the distance effect, as previously noted. Lacking perfect information, migrants thus look to their friends and relatives for information, which adds significance to PGM.

256


Fig. 5 (continued).

5.4. Results of other models The results of other models are also shown in Table 2, by column. In this section, we briefly discuss each formulation.


257

Fig. 5 (continued).

5.4.1. Income model In this model, (see Table 2, Column 2 and Fig. 5b), only 14% variation in growth of income at destination was explained. This is similar to the findings of Rodgers and Rodgers [35] who tested the efficacy of moving for a 6-year period in a study of rural urban migration. Employment in manufacturing at the destination (EMMJ) realized a correct sign at the 99% significance level. The endogenous variable unemployment at destination (UEMJ) had the correct sign but was found insignificant. The endogenous variable employment at destination (EMPJ) was also found to be insignificant, while WAGEJ, manufacturing wage, was significant but with a negative sign. This means that the states with lower manufacturing wages were associated with higher levels of income growth. This phenomenon is in line with discussions of post-industrialization and the advent of the service economy (see, for example, [71]), i.e., that income growth is created in nonmanufacturing sectors. AGEJ had the highest T-value with 99% significance. Education and the black population of recipient states also had correct signs with 99% significance. 5.4.2. Employment model Our employment of the destination model, Table 2, Column 3, and Fig. 5c, had 41% of its variance explained. The joint effect of population and employment variables, and their elasticity, were discussed earlier. The population of destination variable was significant and had a negative sign. This means that either larger population increases occurred in states with lower increases in the employment rate, or that reduction in population corresponds to people without employment, such as the unemployed leaving the state. The income change variable was also found significant, but with a negative sign. This suggests that a higher per capita income increase was experienced in states with lower increases in the employment rate. We might also say that, on average, newly

258


added employees earned less than the average income, i.e., job creation occurred among lowerpaying jobs. Gross migration was here found not significant. The manufacturing wage variable was significant with a positive sign, meaning that states with a higher aggregate wage had higher employment growth rates. In sum, variations of employment growth at destination states were explained by employment in manufacturing, education, population density, tax and age, all at the 99% significance level. Elasticity of change in employment growth was found highest with respect to education. 5.4.3. Unemployment model Column 4 in Table 2 and Fig. 5d show the results of the unemployment model. Twenty-two percent of the unemployment model was explained by the selected variables. Gross migration (GM) showed a positive sign in explaining the variation. Employment in manufacturing was significant at the 90% level with the correct sign, while growth of population had the correct sign but was insignificant. Negative and significant signs for EDUJ and NETJ mean that growth of unemployment is explained by low education and out-migration from the destination. BLACKJ was significant at 0.99 with a negative sign, implying that a higher percentage of African Americans is associated with a decrease in the unemployment rate. This is in line with the findings of [52,53]. Change in the unemployment rate showed the highest elasticity with respect to education. 5.4.4. Manufacturing model In the manufacturing model, Table 2, Column 5 and Fig. 5e, 98% of variation was explained. New capital expenditure (CAPJ) on destination plants acquired a negative sign meaning that greater population increases occurred in those states with lower manufacturing employment. This suggests that states with higher manufacturing employment spend less on new capital equipment, etc. Wages and value added to manufacturing of destination had the highest t-values with signs in the correct directions. The joint effects of manufacturing employment and change in population were discussed earlier. The population level of destination had an unexpected negative sign, meaning that higher population increases occurred in states with lower manufacturing employment. This may imply a move toward post-industrialization and a service economy. All remaining variables were significant at the 99% level. 5.4.5. Population model The final model, population, Column 6, Table 2 and Fig. 5f, had 88% of its variation explained. Correct signs were recorded for all selected variables, along with the unexpected negative sign for employment in manufacturing. The latter suggested that states with a lower number for manufacturing employment experienced higher population growth in the study period. This, as explained above, verifies a move towards a service economy beginning in the 1970s in the United States. Only gross migration and previous gross migration did not achieve significant levels. All remaining variables were significant at the 99% level. In concluding this section, we present Fig. 5g as a super imposition of Figs. 5a–f, i.e., it displays all six models. The complex interrelationships of all endogenous and exogenous variables are thus illustrated as one complete system.


259

6. Policy implications In the absence of metropolitan level decision making in the United States, we selected state level analysis in order to better assist policy and decision makers. Recently, however, there has been interest in seeing a metropolitan level governments exercise some level of authority and power. If such efforts succeed, our model structure, with some modifications, can prove useful. Several variables were found to be significant in our simultaneous equation model. Indeed, some rather complex interrelationships amongst these variables were identified. For example, destination employment and income variables did not prove to be significant in directly explaining migration. However, an indirect pattern of influence was established for these variables, which may have important policy implications. Several significant endogenous variable interactions were identified, including those for employment rate and manufacturing employment, employment rate and population, and population and manufacturing employment. A triangular inter-effect amongst them was also found. Additional triangular inter-effects were found amongst population, manufacturing employment, and income, as well as amongst population, unemployment and migration. A cursory scrutiny of this intricate system of interrelationships (see Figs. 4 and 6) shows that policies increasing employment (or income) in destination states induce a chain of events starting with population increase; this, in turn, is related to an increase of migration from origin states. Unemployment in the destination state is thus increased, which subsequently decreases population growth, but at a lower rate than the first increase. This contributes to a further increase in employment and manufacturing employment, which increases income and further increases population growth. The key for growth and development thus lies in policies designed to induce employment (including manufacturing employment and income growth). However, the key policy variable for increasing employment and income (and, also reducing unemployment) is education (see Table 2, Columns 2–4). This important variable is significant at the 99% level in these three equations and has the largest coefficient (significantly larger than the second largest). This indicates high elasticity of the above listed endogenous variables with respect to education. Similarly, Fig. 4 (and the accentuated Fig. 7) show that if migration increases, unemployment increases, which results in population reduction. However, this, in turn, results in employment and manufacturing employment increases, which cause income to increase, which then causes an increase of employment as well as population and migration. Therefore, a viable policy to increase employment and income might involve inducing migration. This can be achieved through consideration of important determinants within our migration model. Clearly, population growth plays a central role in connecting the endogenous variables by contributing to the explanation of migration flow. In our study, the endogenous variables of growth of employment, unemployment, income, and manufacturing employment, as well as the exogenous variables of percentage of African Americans and state income tax at destination, contribute to variations in population growth. This indicates the indirect relationship between these variables and migration, which has important policy implications, as discussed above. This is in line with Preuhs’ [42] findings that low taxation levels, combined with less policy emphasis on dispersed benefits and high economic returns, experience more population growth via interstate migration.

260


Fig. 6. Chain of effects as result of increase in employment or income.

Fig. 7. Chain of effects as a result of increase in migration.


261

Previous gross migration was shown to be one of the most influential variables in the determination of migration flow. Unemployment and income of origin, as another set of variables contributing to migration flow, indicate that there is more migration flow from states with lower income growth. Density of destination as a significant variable implies that migration flow is higher in lower density states. The crime rate of the destination state indicates, understandably, that states with a lower crime rate attract more migrants. Climate is significant for both origin and destination with negative and positive signs, respectively, as was expected. Therefore, states with good climate, low density, and low crime rate are more prone to grow. Finally, net migration variables, as proxies for growth, show the importance of growth as a determining factor of migration.

7. Concluding remarks In an attempt to identify variables that influence interstate migration in the United States and to better understand the reasons for growth of some of the states in the study period, a simultaneous equation model of migration was constructed and studied. It was hoped that our analysis would identify variables and model structures that help us better understand the concept of migration. Such results could be used in policy analysis designed to forecast growth to improve planning efforts. We think that, to some degree, these goals have been achieved. Overall, results of the study indicate that states with lower income growth and larger increases in unemployment will produce out-migrants to states with lower crime rates, lower population densities, and high population growth. Aside from ‘‘shedding light’’ on more complex joint and indirect effects, and the effects of education as a major policy variable, the study clarified two important phenomena: reduction of the importance of manufacturing employment, and the move toward a service economy in the US. We note that the influence of these phenomena continues two decades after they were first observed. Finally, a relatively high adjusted R2 value, especially for the migration model, suggests the possibility of using this type of model for forecasting purposes. This paper also attempted to study the joint effects of migration and other endogenous variables, as well as the indirect effects these variables have on migration. This type of analysis may help to better understand the complex relationships and processes that influence these variables. Our study, however, has only taken a preliminary step in this regard. It is hoped that future research and analysis will further clarify the link between migration and these societal variables.

Acknowledgements For their extensive contributions the author thanks the referees and Dr. Barnett R. Parker, the Editor-in-Chief, with special gratitude to him for his many helpful editorial improvements.

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Appendix A. Variables definitions All variables, except the dummy variables TAXI and TAXJ and those with negative values, NETI and JETJ, are expressed as logarithms. The latter were treated as the power of the base of the logarithm. I, J=Origin and destination states, respectively. GM=Endogenous variable representing flow of migration from I to J. Represented by the number of persons in 1980, 5 years of age and older, who resided in I in 1975. PGM=Previous gross migration. Flow of migrants from state to state, 1967–1970 (the number of persons, 5 years of age and older, residing in state J in 1970, who resided in I in 1967). DIST=Distance between I and J. Obtained by using a map of the United States. POPJ=Endogenous variable representing change in state J population between 1975 and 1980. INCI, INCJ=Change in income per capita in state I, J, respectively, (in current dollars) between 1975 and 1980. INCJ is an endogenous variable. EMPI, EMPJ=Change in employment rate from 1975 to 1980 in state I, J, respectively. EMPJ is an endogenous variable. UEMI, UEMJ=Change in total unemployment as a percentage of the civilian labor force between 1975 and 1980, state I, J, respectively. UEMJ is an endogenous variable. CRIMEI, CRIMEJ=Total crime rate per 100,000 population. Based on estimated resident population of July 1, 1975 in states I, J, respectively. DENI, DENJ=Average number of inhabitants per square mile in 1975 in states I, J, respectively. GRANTI, GRANTJ=Total dollar grants per capita as a percentage of state and local government direct general revenues for fiscal year 1973–1974, by state. EMMI, EMMJ=Endogenous and exogenous variables, respectively, representing the total number of employees in manufacturing in 1975 in states I, J, respectively. WAGEI, WAGEJ=Exogenous variable representing wages for production workers in manufacturing in 1975 in state I, J, respectively. VALUEI, VALUEJ=Value added to manufacturing by subtracting the cost of materials, supplies, containers and fuel purchased in 1975, states I, J respectively. CAPI CAPJ=Exogenous variable for manufacturing equation. New capital expenditure on plants in states I and J, respectively, in 1975. BLACKI, BLACKJ=Percentage of blacks in total population by state I, J, respectively, in 1975. TEMPI, TEMPJ=Normal daily mean temperature, in Fahrenheit degrees, of selected cities by state I, J, respectively, (based on standard 30-year period, 1941–1970). EDUI, EDUJ=Exogenous variable measuring the median number of school years completed by residents of state I and J, respectively, in 1976. NETI, NETJ=Net migration. Represents a change in state population from 1970–1975 and is the difference between the number of persons moving away from the area and those moving into the area. A positive sign in the data means net in-migration. A negative sign indicates net outmigration.


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Appendix B Results of the first stage shown in Table 3. Table 3 Model

SSEa

F Ratio

Prob. F

R2

Migration Income Employment Unemployment Population Manufacturing

25.167 0.156 43.917 2.578 0.073 2.499

719.40 18.34 84.12 133.38 379.70 3922.02

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001

0.950 0.325 0.689 0.778 0.909 0.990

a

Sum of squares of the errors.

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