Why People Stay

Why People Stay: The Impact of Community Context on Nonmigration in the USA Michael IRWIN,Troy BLANCI-ZARD, Charles TOLBERT, Alfred NUCCl and Thomas LYSON*

This article addresses t h crucial ~ question of the impacr on individual hehoviour uf n~i,qhhourhnod~ n dqf c o m m u n i ~ conrexi in g ~ n e r a l .By nnalysrng individrtal rnohility using rhe comprehensive data from the 1990 I/S C~nstrs.Michael IRWIN, Troy RLANCHARD,Charles TOLBERT, Alfred NUCCIand Thnmas LYSOH q f e r a newp~rsp~crive.firsr by ohservinff indiriduals not rnigmlitrg hetween 1985 and 1990. then h ~ f n c t r . e i n( ~~r tflw c i ~ i i ~ confexl, a factor assrrrned to Q ~ P residential C ~ srahiliry, rnea.wtr ~ by d variables de.rcribing cornmunip cohesion. This srudv of contextrraI P ~ ~ P C On I X individical rnobiliry yields resulrs that shobr, rnr~/tileveir n o d e t t i ~at~ i r s mflsr ~ficrive.

Why are individuals more prone to leave some communities than others? Migration studies consistent1 y find that out-migration behaviour is predicated both upon individual characteristics and events affecting those individuals. For instance. people in their 20s are more likely to leave an area in any given period. Likewise, adults with School-age children often consider moving to school districts that are perceived to be better. To be sure, the decision to migrate is influenced by individual characteristics, but is this all that determines migration?

* Michael ~ R W ~: Dliquesne M University. Tmy B~ANCHARD : Mississippi State University. Charles TOLRFRT : Raylor University. Alfred Nucct : Census Bureau. Thomas Z Y ~ O :NCornell Unlversi~. An earlier version of this paptr was presented at the 2002 Population As~ociationof America annual meeting. Atlanta GA, May 1 1 . 2IK)Z. This matenal is bawd on work supported by the Unizcd States Depanmenr o f Agriculture, Nai~onalResearch Initiative under award number 21K)3-35401-1?X92. and hy the Social and Rehavloural Sciences Division, National Science Foundar~onundcr award number 04903.1. The research i n [hi.; paper was conduc~cdwhile the aulhors wcre research azswiares at the Carnepie Mellon University Census Research Data Centre. Results and conclu~ionsexpressed are those of the authors and do not necessarily reflect aFecment by the Bureau nt the Censuk. This paper has k e n screened to emure that no confidcn[!;!I data a r e revealctl.

M.IRWIN et al. Aggregate analyses have shown that in some areas vihrant social and economic conditions are stemming the rates of out-rniyratinn. Previous

work has shown that communities with a civically engaged citizenry and w i t h locally oriented businesses also have h i g h e r proportions of nonrniyrants (Irwin. Tolbert and Lyson 1997; Irwin, Tol'bert and Lyson, 1999; Tolbert, Irwin. Lyson and Nucci, 20023. This civic community perspective highlights the importance of community-building social institutions, such as small retail shops and local associations. i n creating sustainable cornmuniny development. These elements of civic cornrnuni ties may well proY ide social conditions that moderate the probabilities of migration associated with individual characteristics like age and presence of children. However, empirical research on the effect of communities upon indiv i d u a l b e h a v i o u r h a s been rare despite a pervasive social science theoretical legacy which assumes that individuals' actions are influenced by the characteristics of the communities in which they live (Entwisle, Casterline and Sayed 1989; Young 19993. Lee. Oropesa and Kanan ( 1 994) note than despite the widespread theoretical appeal of this notion, rnethadolopical problems have made it difficult to verify empirically. I n the current study we overcome many of these methodological problems by using multilevel statistical techniques in conjunction with uniquely comprehensive data. We identify and evaluate local macro-level conditions tbat lead individuals to stay rather than leave their comrnunities. Our research objectives are to specify t h e relationship between community context and individual migration behaviour using a theoretically grounded approach based on those social and cultural factors suggested by t h e civic c o m m u n i t y theory. The civic c o m m u n i t y is one in which sesidents are bound to place by a plethora of local institutions and organizations (Irwin ea aI. 1997). Business enterprises are embedded in institutional and organizational networks (Piore a n d Sabel, 1984; Bagnasco and Sabel, 1995). And the community, not the corporation, is the source of personal identity, the topic of social discourse, and t h e foundation for social cohesion (Rarbea, 1995). Moorer and Suusmeijer (200 1 3 note a general lack of theoretically grounded multilevel analysis linking micro-level activity to larger cornrnunity contexts in this area i n urban studies. Geographers and epidemiologists have been more active i n specifying concepts applicable to the study of larger ecological structure (Jones and Duncan, 1995). These researchers have used multilevel modelling to develop hypotheses about the nature of relationships between individual behaviour and attitudes and community structure (Kalff e t a l . , 200 1; Pickett a n d Pearl, 2001 ; Subrarnanian, Duncan and Jones, 2001; Wiksttorn and Loeber, 2000). Their work highlights the study of multilevel interactions in illuminating relationships between individual behaviour (including mobility decisions) and community context. For policy makers, the identification and assessment of rnicromacro interactions are seen as a missing Pink in residential mobility and I

migration anaIyses that are central to residential housing policy (Li and Wu, 2004). As Theodosi noted: "'Littlejustification has been found for programs directed at strengthening community satisfaction andlor attachment: a possible reason is that little is known about their potential effects on the individual- and communitylevel issues" (Theodori, 200 1 ).

I

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i

By quantifying theoretically meaningful factors anchoring citizens to communities, then exploring the effects of these c i v i c factors an individual migration behaviour, this study advances migration theory. B y exploring the importance of community contextual effects, we also hope to provide an alternative approach for urban planners developing programs to enhance demographic stability and community sustainability. Although there have been attempts to explore these issues using state-level data (Gurak and Ksitx, 2000), these relationships cannot be assessed for sub-state geographies such as c o m m u n i t i e s with publicly available data. Public domain data sets simply lack geographic detail for comprehensive coverage. In this research, we use multilevel modelling of migration that relies on confidential internal microdata at the US Census Bureau. With over 19 mil lion adult long-form records and detailed geography, these data permit us to combine individual information with local community characteristics in a comprehensive manner. The detaiIed coverage for local geographic areas permits us to mode1 individual migration outcomes in the context of USA's large and small communities. Using a hierarchical modelling approach, w e estimate i n d i v i d u a l micro-level models for all counties i n she contiguous US. We examine the additive contextual effects of community structure upon these individual outcomes and assess the extent to which county characteristics interact with individual attributes. We are especially interested in the extent to which the civic context of a community influences an individual's propensity to migrate. Specifically, we argue that locally oriented business establishments are firmly integrated with local government and that IocaI churches and social associations are potentially i rnportant, though often neglected, structures for community development. We consider civic communities t o be those localities organized around smaller scale, locally controlled business enterprises that feature a more balanced socioeconomic life and h i g h levels of social welfare. Such communities are typified by social and cultural contexts which strengthen notions of community ernbeddedness. We believe that the strong local orientation In these places decouples individual migration behaviour from purely economic calculation. Importantly, these civic factors extend demographic theory beyond the somewhat narrow set of economic contextual variables currently highlighted in the migration literature. In the first section, we review theoretical approaches which explain the decision to stay. Following this, we shall present rhe data, strategy of

analysis and variables used in this research, CastIy. we shall show that community contexts, specifically the prevalence o f local meeting places, churches and long term businesses, do affect individuals' migration hehaviaur. People are more likely to stay when communities have relatively more of these types of institutions. These factors may operate generally in a community, or differentially affect specific types o f people i n a community.

I. ExpIaining migration: a social structural view Explanations o f migration typically focus on factors that determine i n and out-migration streams across localities, and the characteristics of individuals that inffu e n c e preferences amon2 all possible destinations. These explanations of migration tend to be grounded in neoclassical economic theory-the rational calculation of the costs and benefits of moving. In these neoclassical models. the decision to stay in one's community is under constant evaluation relative to the economic cost of moving and benefits that could accrue with a move. In this approach, migratin2 or not migrating are simply flip sides of the same phenomenon, and the factors that explain migration and nonmigration are the same (Da Vanzo, 1978: Greenwood, 1985). An alternative approach stresses that community attributes anchoring people to places are a disrinctly different set of factors. (Morrison, 1972; Petersen, 1958; Speare, 1974; U h l e n b e r g , 1973). These nonmigration factors are found i n t h e s o c i a l and culauraI m i l i e u o f the community (Tolbert et al., 2002: Irwin et al.. 1999; Kasarda and Janowitz, 1914; Uhlenberg, 1973). This line of research argues that-on a n aggregate level- nonmigrants and migrants are distinct subpopulations. One constitutes the stable core of a community's population. and the other comprises the flow of labour and human capital across communities. In terms of iadivldual migration decisions, persons residing in tightly integrated comrnunit i e s w i t h d e n s e local s o c i a l n e t w o r k s and e f f e c t i v e civic institutions (community oriented organizations such as churches or locally owned and long-standing businesses) may not engage in a solely economic calculns of migration, even when a purely economic rationality would warrant it. For persons residing in less civically cohesive communities, this community context would make li ttIe difference. For these residents the likelihood of migration is determined more by possible gains accrued with a move. In this. the differences befween nonmigrants and migrants lie i n their relationship to their community. While this commitment to cornmunity is influenced by individual characteristics (e.g. age, family situation), we argue that t h e effect of these individual: factors on the likelihood of migration are themselves contingent upon the nature of the community in

which one resides. Communities with more socially integrating institutions are more likely t o influence the calculus of migration. regardless of individual characteristics. t h a n a community with a dearth of thew institulions. Several lines of related research provide analytic support for this social structural view. Uhlenherg ( 1 9 7 3 ) and Speare, Kobrin and Kingkade ( 1 982) show that noneconomic factors are important in constraining migration decisions and tend to anchor populations in localities while economic push-pull explanations operate primarily after the decision to migrate has been made. Similarly, researchers using residential satisfaction models (Speare, 1974: Speare, Goldstein a n d Frey, 1975; Deane, 1991)) and residential stress models (Wolpest, 1965) argue that economic cost-benefit models fare poorly i n expla~ningthe decision to migrate and must bc supplemented with noneconomic factors. Both lines of research argue that local contexrual factors are important noneconomic deterrninanls o f individual intentions to remain i n a n area. Stress-threshold models argue t h a t shifts i n t h e balance between household needs (often life course changes) and local conditions (particularly ncighbourhood environments) create streqsnrs that motivate peopfe to consider moving. Only when activated by residential stress do individuals calculate migration destinations along cost-benefit tines (Deane, 1990). Recidential satisfaction explanations (Fugui tt and Zuiches. 1 975) hold that such stresses are necessary, but not sufficient conditions to explain the shift from stability to mobility. Social context acts to suppress migration consideration, t h u s precluding many individuals in such areas from even engaging in relative comparisons among other residential areas. The importance of these macro-level community contexts are exernplified in research on size of place preferences (Fuguitt and Brown, 1990; Heaton et al., 1979; Zuiches, 198 1 ). T h i s rcsearch indicates That satisfaction with current community context is a major determinant of the decision to migrate. Primary determinants of satisfaction include what may be construed as perceived civic emheddedness: recrkation or cultural access, being near friends and family, contacts with a variety af people [Fuguitt and Zuiches, F 975). However, these satisfaction rates vary by type of community. While community satisfaction is clearly related to social ernbeddedness, the specific institutional factors that anchor people to places have not been widely modelled (Kulkarni and Pol, 1994; Lee, Oropesa and Kanan, 1994). Conversely. in sociology and political science there is a rich theoretical tradition that highlights the role that institutions play in creating civic e n g q e r n e n t and cammunit y attachment. However, these factors are usually linked only tangentially to demographic stahi l i ty in local areas. We bring these two traditions together to specify the institutional rnechanixms influencing individual nonmigrationlmigsation behaviour. Specifically, w e argue that institutions which create and nurture civic engagement

M.IRWIN et al.

532

and link individuals into a larger community are central in altering individuals' propensities to stay in their communities. Simply. i n communities where these civic factors are strong, fewer people consider moving away, and the community maintains a strong core of long-term citizens.

IT. Empirical approach

We use hierarchical linear modelting (HLM) techniques t o anaiyse confidential individual-level migration data from the 1990 decennial Census. Use of confidential US Census data resources (at the Carnegie Mellon University Research Data Centre) aIlows u s to link individual migration behaviours and community (county-level) characteristics. These data have a numher of advantage? over public data sources, To ensure confidentiality, public-use data sources lack sufficient geographic derail to identify individuals or businesses i n smaller cernmnni ties. For example, the smallest area identified in a 1 9 9 0 Public-Use Microdata Sample (PUMS) o f household data must have 100,000 persons. It is not possible to conduct a spatially comprehensive migration analysis that finks micro-level migration hehaviour to characteristics of counties with such data. In 1990, only 454 of 3,064 continental US counties had populations o f a t least 100.000. A related problem with public domain data is that sufficiently large samples of individuals within c o m m u n i t i e s d o not exist. The largest 1990 P U M S represents 5% of all housing units. The internal ~ & s u s1990 longform data we employ contain information on 15.9% of American housing units. This larger sample size is much better suited t o the geographic resolution we employ here. To measure community context. we use microdata from various Economic Censuses to construct aspects of community economic and social structure. Unlike household censuses, there. are no microdata on business establishments made public from economic censuses. Most social scientists rely on data files, such as County Business Patterns, which summarize establishment totals at t h e county level. The C e n s u s Bureau maintains a national business register (the Standard Statistical Establishment List o r SSEL). which contains this same administrative data and i s updated annually. After each economic census, internal microdata files are generated as well. These establishment microdata files are available to researchers w h o agree t o follow Census Bureau disclosure procedures and are approved f o r access. Information includes size, payrofl, sales, age or longevity, sfnglevs. multi-unit status, legal form of organization (partnership. proprierorship, corporation), and detailed geography (state, county, place, zip code). From these data we construct our measures of community civic context for counties in the continental US.

O u r c h o i c e of c o u n t i e s , r a t h e r t h a n places o r n e i g h b o u r h o o d equivalent Census geography, is based on Togic and on consistency with o u r d e p e n d e n t variable ( w h e t h e r a n i n d i v i d u a l m i g r a t e s d u r i n g this period). In the US, migration i s defined using county geography. The US Census defines migration as a move from one county to another. Certainly, to maintain consistency with this definition, contextual factors explaining migration should likewise be defined at the county level. More than a statistical convenience, this definition logically differentiates local moves from non-local moves. Within a county, local transportation systems provide ready access t o a c o m m o n set of local economic and social goods. Residential shifts within these areas d o not alter potential daily access to these community goods. People may drive to the same church or commute to the same job regardless of their residence within a county. Thus intracounty movements usually d o not involve a shift in community context. However. the physical distance involved in inter-county residential rnovement often does force i n d i v i d u a l s to change d a i l y shopping locations. local meeting places, churches and associations. It is precisely this difference in local context on individuals that we are interested in modelling. Our individual analysis includes approximately 1 9 million individual long-form records f o r more than 3,000 counties. Migration horizons available in the Census are from 1985 t o 1990. The population examined includes all individuals at least 20 y e a r s of age but less than 60 in f 985 residing within the corhinental United States i n both 1985 and 1990. Thus, o u r population is t h e typical working-age population d u r i n g this five-year period. For several reasons, we use all persons in this group rather than restricting our analysis to heads of households. Conceptually, our community perspecrive implies that we should include a11 adult residents of working ages. Mathematically, this is important for t h e computation since all adults contribute to the calculated probabilities of migrationJnonmigration within each county. Eliminating adult cases skews these probabilities i n systematic ways. Our use of all persons also permits a spouse's personal attributes to be considered in the rnodelIing of herihis migration behavioua. From the decennial census microdata we have recreated the populations of these counties i n 1985 by putting people back in their covnty of origin. Thus, this research reconstructs the US population for these years (minus mortality a n d emigration) and then examines the probability that people would stay or leave d u r i n g these time periods. The main goal for this research is e v a l u a t i n g the e f f e c t s o f community structure i n these counties of origin on the individual probabilities o f nor migrating for these 19 million individuals. Specific factors for both individual and community context are discussed below. Table 1 provides means and standard deviations f o r independent variables used in this analysis.

TABLE 1 .- D ~ N I T I O AND N S SUMMARY STATISTICS FOR ENDEPENDENT VARIABLES TO BE USED IN MODELS PREDICFING NONMIGKATION, USA, 1 985- 19cK) Variable definitions

Standard

Mean

Individual-level variahleslN= 19,199,MO) Rlack ( l=Black. 0-Orhcrl H~spanic(1 = Hispanrc, O.=Other) Female (I =Female. O=Male). Married, spnuw present in 1990 l =Married. spouse present, {)=Other) Childrcn present in 1990 ( 1 =Children present. O=No children present) College graduate or higfier in 1 ( 1 =C'ullthge _mduaterrr higher dcgree, 0=Did not graduate cchlegc) Age (Log of age) Stayed in county 1985-90 in county, O=Left county) -( 1-=Stayed .-- - - County-level variables (N- 3,M2) County level contrnl~ + I Per capita income 1 9R9 Pemntage white collar occupation 1990 Petcenw2e in p v e r t y 19R9 Petcentage employed in manufacturing F 980 Percentztye cottnry urhan population 1W Labour l'orce growth 1980-X9 (% changc) A v c r a g unemployrnenl 19Xfl-89 (r+,change) Northcast Census region ( 1 =Nnnheast. O=Orher) Metropolitan county, 1993 Metropolitan Area classification ( 1 = Mctrnplitan county, O= N~onrnetropo!itan) -

-

deviation

0.m 0.08

0.29 0.27

0.52

0.50

0.69

0.46

0.48

0.50

0.41 1 1.40 (0.25)

0.21

42.23 (3.70) 0.8 1

0.39

1 11,093 45.36 16.7h

2.641 9.28 7.90

19.38

11.71

36.04 11.31 8.33 0.05

29.32

0.26

0.44

27.48 3.47

0.26

County level civic indicators d 0.13 0.24 N u m b of churches per capita 1990 0.10 0.15 Percentage of cwic adherents 1990 0.M2 0.003 h c a l associations pcr capita 1992 0.0 12 0.IFZT) Local third places per capita 1 9 2 6-20 61.47, Percentage of local cstablirhrnents 1992 6.33 ' 63.65 Percentape of small establishments 199997 6.44 24.39 Percenra~e of old establishments 1997, -- -. . Soumc.r: I 0 US Census of Population and Housing Internal Micmdata: US Economic Census Micmdata: Census nf Churches. 1992: Reg~onalEconomic Informarion Syctem. 1 Vh9-?(W)I .

.-

--

Depend~ntvariable Our dependent variable is measured as a contrast between migrants and nonrnigrants. Since our interest is in factors embedding people in places, w e categorire nonrnigrants as one and migrants as zero. Thus. associated probabilities and log-odds in the hierarchical model ate interpreted as the likelihood of nonrnigrorion. In our sample, about 81% of the i n d i v i d u a l s stayed i n their county of o r i g i n during the 1985 t o 1990

period. This corresponds to an 81% probability of staying i n one's county for five years. However, our analysis shows that there is considerabIe variation in this likelihood of nonmigration across counties and among different groups. What accounts for this variation?

Individual-[eveI mi~rafion factors Certainly individual factors affect the probability of migrating or staying. These are well established in the demographic literature and fall into two general types of variables, those that are characteristics of each person and their family status. Below we aperationalize these characteristics. To facilitate interpretation of the dependent variable (log odds of nonmigration). we categorize our individual-level independent variables as a series of binary variables.

Age i s a predominant predictor of nonmigrationlmigration. There i s genesalty a curvilinear relationship between age and migration, with the highest probabilities o f migration found among young adults and adults in the retirement ages of 65 to 75 (Jamieson, 2000; Long, 1972). For the purposes of out analyses, we measure age as the natural log of age i n years. Education also i s a reliable predictor of migration, with the probability o f rnigrat ing increasing with years of education beyond high school (Long, 1973; Long. 1992). We contrast persons who have graduated from college (those with a bathellor's. master's, professional, or doctorate degree) with those not graduating from college. Both gender and racelethnicilty exhi hit small but important influences i n determining nonmigration. Work by South, Deanne. Crowder and others indicates that race patterns of mobiIity differ (South and Crowder, f 998;South and Crowder, 1997a; South and Crowder, 1997h; South and Deane, 1993). Various authors (Breton, 1964; Clark, 1992; Kobrin and Speate 1983) have shown that an individual's race and ethnicity is likely to increase nonmigration, particularly when the sending area has a distinct minority or ethnic community identity. In our individual models we contrast blacks with all other races, and Hispanics with non-Hispanic ethnicity. A n o t h e r set of person-specific characteristics is an individual's farnilv status (Sandefur and Scott, 198 1 ). Foremost of these characteristics is the presence of children in 1990. Although the presence of children in specific age groups has been shown to reduce adult migration, children may also increase adult m o b i l i t y d u e to l i f e course decisions, such as housing changes and school district changes. We contrast individuals living with their children during t h e migration period t o all others('). Marital status is most important i n differentiating single adult households from ( " One notable caveat here. however. is that our data do not provide complete coverage of the presence of children through the 1985 to 19'10 pertod. Those families whose l n ~ child t left the hou~eholdby 1990 are categorized with those who d ~ d not have children through the migration penod a5 having no child precent. This excludes many children in the lowe\t mohrlity group. i.e. those in high school during this time period.

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others. Simply, this indicates whether migration decisions are exclusively the p u r v i e w of one adult or a negotiated decision ( c h i l d r e n " ~w i s h e s aside). We include a binary variable for maritah status in 1990 that is coded one if the person reported being married with a spouse present and zero if the person is widowed, divorced, separated, or never married. Unfortunately persons engaged in consensual unions are not identified i n the 1990 census and are therefore categorized as single. Our measure o f marital status refers to an individual's marital status i n 1990, since the Census questionnaire does not ask for the status in 1985. We believe this measure adequately captures those most likely to make migration decisions jointly during the 1985 to 1990 t i m e period, however. Individuals identified a s married in 1990 were either married i n 1985 or likely to have been in the process of entering a marital union during the 1985- 1990 time period. Each of these individual-level variables contrasts demographic groups (e.g. blac kinonblack, Hispaniclnon-Hispanic) with different migration! nonmigration propensities. I n the next section we discuss community-level factors that may create differqnz propensities for nonmigration.

Cornrnunity-level variah!es: Local capitnli.cm and civic engagement Our independent variables of interest include characteristics o f the Iocal business structure and aspects o f social organization. We base our measures on research in the civic community tradition (Lyson and Tolbert, T 996; Tolhert, Lyson and Irwin, 1998; ToFbert, Irwin, Ly son and Nucci, 2002). Local capitalism. Local capitalism refers ta the degree' to which businesses are embedded in the local community culture. We measure local capitalism as the percentage of all establishments that are small, old, or locally oriented. Small establishments are defined as establishments with fewer than five employees for retail and service establishments, and fewer than 20 for manufacturing and wholesale establishments. Old establishments are classified as establishments that have been in t h e same location for at least 15 years. Local orientation is defined as single establishments or multi-unit enterprises that operate within a single county. These local capitalism measures are derived from internal Census Bureau 1992 Economic Census establishment microdata. Civic engaRemenr. Civic engagement refers to the presence of Iocal institutions that offer contexts for individuals to engage in daily public life. We include per capita measures of local associations (voluntary associations, sports clubs, bowling leagues, etc.) and local third places (local gathering places such as bars, bowling alleys, restaurants, and beauty. parlours) calculated from Economic Census establishment microdata. We define local as above-i.e., single establishments or multi-unit enterprises located exclusively in a single county. We also include a per capita measure of churches and a measure of the ratio of the number o f adherents in

civic denominations to the county population from the Census of Churches

(Association of Statisticians of American Religious Bodies 1992).

Cnrnrnuni@-levelvariables: Control variahles We also control for economic and spatial dimensions of communities, such as labour force characteristics, local wealth, and residential characteristics. To measure labour force dynamics, we include percentage growth i n the labour force and the average unemployment rate during the 1980-1989 decade from the Regional Economic Information System (BEA 200 1 1. As Reisinger (2003) demonstrates, areas characterized by a whitecollar labour force have higher levels of out-migration than other areas. To control for this w e include a measure of the occupational composition of the labour force by including the percentage of the civilian labour force that is employed in white-collar occupations. These include executive, administrative, professional, technical, sales, and administrative support occupations. Occupational data come from the 1990 Census of Population and Housing. We also include a measure of the percentage employed i n manufacturing in 1980 as a n indicator of community industrial structure. Our indicators of local wealth include the poverty rate of persons in 1989 and per capita income in 198912,. Both measures are calculated from the I990 Census of Population and Housing. Our contrals for residential factors include the percentage of urban population i n the county, metropolitan status of the county (whether a county is part of a rnettopoIitan area), and US region. Urban populations in both metropolitan and nonmetropoli tan counties have been consistently more mobile than rural populations. Net of this, the often suburban remainder of mctropoli tan populations have lower migration rates than their nonrnetropolitan counterparts. These measures are included as controls necessary to model the relationship between community context, individual characteristics and individual nonmigration. We measure region based on the US Bureau of the Census regional c\assification. In our preliminary analyses, we found that only the Northeast was significantly different from other regions i n the probability of nonmigration. Thus, we measure region as a binary variable, where 1 =Northeast and 0= other regions.

the poverty rate in the 1990 US Census of Population and Housing is based on family income (or total income for unrelated individuals not i n a family ). Income threuholds in 1990 were ~dentifiedbased on the cost of living and arc adjusted for the size of the family and the age a i f;lmily members. For example. persons i n a family of four persons with an income below S 12.674 are classified as living In poverty. W t calculate the poverty rate as the numher nf prsans Iivinr hrlow the poverty threshold dividcd hy nhc total population for which poverty starus 1s dercrm~ncd.

3. Nonmigratiun in a multi-level framework IndividualJcornmunity relationships have long been understood to involve complex linkages between aspects of population composition, community context. and individual outcomes. I n a seminal article, Blau (1960) lays out a clear conceptual framework for understanding these microlrnacro stmctzlral: connections. Findley ( 1987) nperationalizes Blau's approach specifically i n a migration context, delineating relationships between individual characteristics, migrant selectivity, and community context. I n her analysis. she argues that contextual factors can exert three processes of inff uence upon individual hehaviour: additive. intervening, and interactional. Additive contextual processes are the direct effects o f community context on individual behaviours. These contextual influences uniformly affect all persons in the community. Intervening processes are rooted in compositional variations across communities. This can lead to selectivity biases that result i n incorrect inferences about t h e effect of community upon individual behaviour. Interactive or cross-level processes refer to the ways that community context ckan,qes t h e pattern of relationships between individual characteristics and the probability of an event. Findley (1987) and others (Bilsborrow, 1984; Lee, 1985; Lee et al., 1 994) have modelled these processes using ordinary least squares (OLS) techniques. However, these traditional contextual models do not deal well with contextual selectivity and other statistical problems that can lead to erroneous inferences (Kreft and DeLeeuw, 1998). Several: researchers have noted systematic problems with these traditional c o n t e ~ t u a lmodels and propose alternatives (Raudenhwsh and Bryk, 2002). We employ hierarchical linear modelling (HLM)techniques i n which we first model individuals' propensities to stay in their communities based upon personal and household characteristics. However, unlike tsadi tional models where individual characteristics are estimated across all cases, HLM allows for the coefficients of individual-level variables to vary across counties. By doing this, HLM controls for w i thin-county compositional differences since individual-level models are estimated within each county. This means that unmeasured intervening contextual effects do not bias individual-level coefficients. We then augment the model with economic, social, and cultural factots to test for the presence of additive contextual effects. These are additive effects that t e s t w h e t h e r local macro conditions affect micromigration behaviour net of the individual-level variables. Finally, we measure and evaluate interactive processes by introducing cross-level effects to determine the extent to which cornrnunity-level factors condition t h e inff uence of individual-level attributes on nonmigration. Using this modelling strategy, our final models simultaneously test additive a n d cress-level effects. A1 E models are estimated with Hierarchical General

Lincar hIodelling tochnirlucs f.or binary d c p c n d c n ~\,ariablc.s using the H1,M software package. Raudenbush er a ] . . 2000). 'l'he depcndsnt variahlc i l l our study i \ a binary variahlc cc-~dcdI if thc rcspcmdcnt i.c, 3 nv~~llligra a11d 0 if the individual is a migrant. Wu c s ~ i m a t ethree ~ n u d r l sin o u r a n a l y s i s . Our firs1 rnodel contains ~~ldividual-lcvcl c.h:~ractcri.sticso n l y . We specify this ~ n o d e las:

1 0 s

1

-

] refel-s l o Llir lug odds o l nonmigra~ionibr i indi-

iduals ncsred w ~ t h i nj counlies: X

..

411

yoo

arc thc individual-level variables y f o r case i nohled within and Yqn are fixed-eff'eol uoef-licienls l'or ihe inlcrcept and indi-

1,idual-level variahlr.; y: rt

0.1

and u (11. are randnnl el'l'ec~slor [he intercept and individual-love1

variables y. The intcrcept

IS

comprised of' a fixed eCft.c~(you) rt'prcsenting t h e

average of t h u county level5 o f nonmigration and a random effcr't ( u o j ) . u l l i c h is the i n c r e n ~ e n tto the intercept associated wilh county j. Coefficients f o r [he ind~vidual-lcvclvariables are also comprised of a fixed ( y, )

and random cffect ( u

.

41

1. In this instance. fixed effects refer l o the average

I-cgroz;aion coel'ficient ( s l o p e ) f o r a given independent variable across j counties in our analysis. T h e random offeut lerrn is the uniclue increment LO the coefficient associated with county j. Our second model cclntnins individual-lcvel variables along wilh our county- levei characteristics (such a s region, metropcrlitnn status, and civic u n ~ a g e n ~ c n t l l o c cnpirnlism al measures). We specify this nloclel as:

where y,j,y i x the coefficient for county level variable s for county j and M;-

?I

.

is county-level variable s for county j. All measures are popul~ition(?rand) nleall centred. This rnodel tests for addirive community (county) conlexlual effects. A third rnodcI estimates cross-level effeuls between the individual and cuunly-level variables. A cross-level effect identifies the degree 10 which 3 county-level variable c o n d i t i o n s the r e l a t i o n s h i p between a n ~ndividual-levelindependent variable and the dependent variable. In these

models the coefficients for the individual-level variables are treated a. outcomes predicted by county-level variables. We spccify these models as:

where gqs is the cross-level effect of county-level variable r and individual level variable ci for the j counties in our analysis. In our models we estimate three cross-level effects for each of the individual-level variables.

Because our models permit the effect of individual-level variabjes on nonm~grationto vary across counties, i t is important to have a suffioien number of individuals in each county to obtain reliable estimates. To o u knowledge, our internal long-form Census microdata are the only data available that can produce these nonmigration/migration estimates of individual and ct>ntextual effects for virtually all US continental countics~'!. Spatial autocorrelation is a concern here because our contextual oonditjons potentially could exhibit spalial dependence. That is, one count) potentially could influence outcomes in nearby counties. Following Land and Deane (1992), we ran spatial autoregressive models to test for thi: possibility, but found no statislically significant spatial autocorrelation. Thus, the results presented below are not biased by spatial dependencie: among counties in our analysis.

111. Results

HLM fixed-effect coefficients for the individual model and combinec individual/conlextual models respectively appear in T;ible 2. The log lineal individual-level effects reported in Models 1-3 o f Table 2 are largely con. sistent with other studies(4). Blacks have a Iower likelihood of staying ir the county of origin than other races (negative coefficient). Similarly Hispanics have a lower likelihood of staying. Net of other factors, such a > marital status and the presence of children. women are more likely to re. main in place than men. Likewise, currently married (spouse present) indi, viduals are more likely to remain in a county for this five-year period thar non-married (or those married whose spouses live elsewhere). The presence of children in the family increases the likelihood of re. rnaining in a county over this time period. The clear effects of educatior and of adult age upon the probability of staying in the county of origin art also consistent with other findings. College graduates are less likely to re. main in a county a s are younger adults. Our findings suggest that as per, -

-

'3I n two counlies, we found jnsufticien~sample sizes. Data for each cuunty were con1 bined with one adjacent county in these instances resulting in n=3,062. (41 Note [ha[, with 19 million rccords. many variables not consistently signiticant acros other studies are si~nificanthere.

sons progress towards retiremen1 age, they are less likely io leave their county of' residence. As discussed above, this individual-level model estimates likelihoods relative t o other individuals wirhirr each county. By doing so, the model holds c o n s t a n t the effects o f compositional differences between counties that introduce selectivity biases in our estimates. In the next section, we analy se t h e causes of between-county variations in these individual likelihoods of n o n m i g r a t i o n using both a d d i t i v e and interactive ( c r o s s - l e v e l ) m o d e l s e s t i m a t e d in H L M . Respectively, these s e c t i o n s delineate factors affecting all individuals within a county and those factors affecting groups differentially within each county. Together they specify contextual conditions that embed people in places and that operate apart or in conjunction with individual factors. The introduction of these county variables in Model 2 adds statistically significant county-level effects to the individual effects. T h e coeffic i e n t s for t h e i n d i v i d u a l - l e v e l c h a r a c t e r i s t i c s r e m a i n s t a t i s t i c a l l y significant and predictive in the same directions(5). Turning to our countylevel control variables. persons in high poverty counties are more likely to migrate than o t h e r s . T h e p e r c e n t a g e of the l a b o u r f o r c e e m p l o y e d i n white-collar occupations also h a s an important effect. Persons living in counties dominated by white-collar employment a r e more likely t o migrate. In c o n t r a q , counties with a large share o f blue-collar workers are rnore likely to retain individual residents. These findings suggest that t h e local employment structure may create an "environment of mobility" because persons employed i n certain white-collar occupations, such as professionals. executives, administrators, and managers, often compete in national rather than Local labour markets. These external labour markets frequently require interstate moves. Propensity In remain in o n e ' s community is enhanced in counties dominated by manufacturing, perhaps associated with a strong localist culture in these bIue-collar communities. Consistent with many migration studies, urbanization decreases t h e likelihood of 'staying in place. Less consistent is the increased likelihood of staying in communities where unemployment is higher. Aggregate local unemployment may well be associated with unspecified factors attaching people t o communities. Northeastern counties are more likely to retain individuals, as are metropolitan counties ( a finding supported in aggregate analyses). The percentage of the l o c a l e c o n o m y d o m i n a t e d by s m a l l b u s i n e s s e s o p e r a t e s opposite the direction we hypothesized, Small businesses are associated with decreased likelihood of staying in one's county of origin.

(3N a e ~ h athe l county characteristic standard rrrors (and t staristicsl are based upon the county population (7,000+) r a l h t r than upon the individual sample (19 million).

TABLE 2.- HIERARCHICAL LOGISTIC REGRESSION FIXED EFFECT COEFFICIENTS FOR THE ASSOCIATION BETWEEN INDIVIDUAL CHARACTERISTICS, COUNTY LEVEL FACTORS, AND NONMIGKATION, US ADULl'S AGED 25-64, 1990 -

.-

-

( 3 ) Cross-level effects rnode1ta) ... .----Cross-level eFfect

(Yy,)for:

Fixed effect coefficients

.-. .

I

.

L

Individual-level variables (yqo) Intercept, y,

I

Black, y j ~ (0.0194)

(0.0194 1

- 0.0565** : - 0 U8Y7*

Hispanic. *fro ,

Female, y,o Married. spouse present rn 1990. ydO

(0.0098) ' 0.02 196** (0.0016) 0.2032** (0.UU451

(0.0100) 0.0254** (0.0016) 0.2284** (0.0046)

1

(0.0206) 0.1087 (0.0103) 0.0189** (0.002 1 ) 0.2509** (0.0043)

(0.1669)

- 0.4714**

-

1

(0,0903) - 0.0465* (0.0168) 0.7648.' (0.0379)

Children present i n 1990,y50 (0.0329) College graduare i n 1990. Yhn Log of age, "~.m (U.OX03) C O I I I I ~ ~ - Icontr~mls PIJPI Per capita income 1989,yo,

9~white collar occupation 1940, yo2

% employed in manuhctur~ng1980, yo4 - --

-

j

i

County-level variables ( * I ~ )

-

(0.2 199) 0.2749* (0.0977) O.lOIOf* (0.0186) 0.0331 (0.04 I 0) - 0.10h9* (0.0355) 0.2439** (0.0539) 1 0.8876** -+ ! (O.OHX3) .-

- --

(0.0034)

- 0.0071**

-

11 1

(0.001t l ) - 0.0023** (0.0003) - 0.0015** (O.iI0081 - 0.0031** (0.0006) - 0.0152** (0.0010) - 0.0186** (0.0016)

, !

r

~

--

--

-

~

. . ....

~

-

- 7

- .

I .- .

,

12) Fixed effect coefficients

1 Individual

model 9'0 county urban population 1990. yn5

Labour force growth 1980-89 ('.dl. yo6 Unemployment rate 1980-89 ( 7 c h yo,

I

i - O.O06R** - 0.0067**

1

I

/

1

(0.0003) - 0.0007** (0.0002) 0.0140** (0.00 19) 0.2243**

(0.0003) - 0.0006* (0.0002) 0.0139** (0.00 1 9) 0.214R**

1

io.0211 O1046**

I

(0.01991

~

Local associalion per capita 1992. yOl2

1 Local third places per capita 1992,yOY3 1

7~local eslablishrnen~s1992, yOl4 % small es~ablishments1992, yuls

I

;

'

% old establishments 1992, YU16

Individual probabilities presented at the mean level or the contexlual effect. Nore: Numbers in parentheses are ~landarderrarr. * p < 0.01 ; ** p < O.Ml1. --

0.00 1 1 (0.0006) - 0.6370 (0.2715) - 0.0 1 I R (0.0614) 0,OO17 (0.0012) - 0.0085** (0.0011) 0.01 lo** (0.001 1 )

-

1

I

(o.02111 0.1042** (0.015q)

(0.0006) 0.6266 10.2719) - 0.3000** (0.0645 0.0014 (0.0012) - 0.0087** (0.0011) 0.012 5 * *

!

--

i

,

(0,0011)

er

I !

Yq? ! !


". Dcarrrogruph~.74(7), pp. 1 6 3 190. I:l'r;r.l~.rG. V,. U. L . BROWN.1090. "Residenti31 preferences and populaliurr rcdislribution: 1972- 198X", Drnrugraphy. 2714 1. pp. 589-600. I;[.fil!l7-r C . V . . J . J. Z~llcrrts, 1975. "Residential preferences and population distrr bu~ion". D r r r ~ o ~ r u l ~1 h3 ~3 1., pp. 44 1-504. I;KL~.NH'IIOL) M . J.. 1935. "Human migralion: Theory. models. and empirical studits". Jour~rulclJ Rc~.qiorirr/.Tr,irnc.e. 2 3 4 ). pp. 52 1 -544. G LK R D. ~ T.. M . M. KKIT'Z. 1000, "Tbe interstate migrnrion of US irnmigranls: Individual and contcrrunl determinan~s",Social Forr.rs. 78(3). pp. 101 7- 1039. I{l.n.rclh' T., G. V. Fucurrr. J . J . Z ~ ~ I C H E1074. S , "Residential preferences, conrmunity satisfact ~ o nand , the lotention ro move". Drrno,qruphy. t6(4), pp. 565-573. [nu 11hl. D.. C. M. TOLBEKT. T. A . LYSUN, 1997, "H~)H.lo build slrong home towns". An~rriccrr~ L)m-iogruphic,s. 19(2). pp. 43-47. I U L ~ I NM. D.. C. M . Tar H F R T . 1'. A . LYSVN.1999. "There's no place like home: norimigrat~onand civic engilgernent". Enl?imnrr~enrond Plurttring A. 3 I( 12). pp. 2223-2238. J , \ h l \ i SON 1 .., 2000, "M ~praiir>n, place and class: youth in rural areas", The Suc-irdngicul h'tmvi~u., pp. 203-223. J(INFSK., C D U N C A N 1995, . "lncfit~idualsand their ecologies; a n a l y s ~ n gthe geography of chrnnic illness wirhin a multrlcvel framework". Hfulrh ortd Pluce. l ( 1 ), pp. 27-40. t i , + ~ .A~. tC., M . KROES. J. S. H. VLES, J. G. M. HENDRIKSEN, F. J. M. FERON,J. STEYA~RT, T. M C. U . v . 4 ~ ZF-BEN. J. JOLLES,J. VAN0 s . 2001. "Neiphbourhood level and individual level SES effecls o n child problem behaviour: a mutlilevel analysis". Jorrr.rru1 of Epider~linlngynrrd C'oRl~rruniryHeulrh. 5544). pp. 246-250. li..\>.\ur)n J . D., M. JANOWIT%. 1974, "Community ar~achmenr in mass society". American Sur-ir~logirrrlHevtew. 39(3), pp. 328-339. LOUKIN F. E., A . SPEAKI:Jr.. 1983. "Out-migration and ethnic communitir>". Inrrrnarrortoi hfigruriorr Re v i ~ w 17(3 , 1, pp. 425-444. k ~ r ~ Ila l r G. G.. Jan DF.I.EECTW, 1408, l n l r o d u c i n ~M u l r i l e ~ ~Modelirrg. cl Londorr, Sage. I60 p. K ~ . L L A CM.. ~ N IL. G. POL. Iq94, "M~grationexpectancy revised: Results for the 1970's. 19BO's and I c)LIO's", Poj~ulafimnR ~ s e a r c hund Policv Revit~n: 13( 2), pp. 195-202. 1.' X N L ) K. C.. G . D. DI-ANE,1992. *'On the large-sample estimation of regression models with spatial- or network-effec~s rerms: a two stage least squares approach". S a c i n l o ~ i r u lMethodology. 22. pp. 3-2 1-248. I.EF: Barrrll A , . R. S . OROPESA, James W. KANAN. 1994. "Neighborhood conlrxl and residenlial mobility". l)emagruphy, 3 I ( ? ) , pp. 249-270. LEF.S.H.. 1985, Wh? People lntrnd to M o v e : lndividuol arrd Community-Lcvc,I F o c ~ o ro$. ~ OurM i ~ r u i i o nirr r h Philippines, ~ Boulder, CO. Westview Press, 155 p, Lr S.-M.. F. Wu, 2004, " C o ~ n e x ~ u a l i z i nresidential g mobility and housing choiee: evidence from urban China". Envirorrmrnr und Plannirr~A . 36, pp. 1-4. LUKL L., 1972, "The influence of number and ages of children on residential i~lohilicy",Demo~ r r r p h vY(3). , pp. 37 1-382. LONC~ L., 1973, "Migration differentials hy education and occupation: Trends and variations". Drrrro~ruphy,l o ( ? ) , pp. 243-258. ONrt Movernerrrs and the2 Shope c g Llrbon Grawrh: ltrtplico~iorrsfor P ~ ~ b lPoli~.v. ic Report R - 1072-CPG. Santa Monica, The Rand Corporation.

590

M. IRWINet al.

PETERSEN W.. 1958, "A general typology of migration". Anrrrican SocioIogj- Revirn.. 2 3 ( 3 ) . pp. 256-266. PICKETT K.E.. M. PEARL,200I. "Multilevel analyses of nrighbourhood socioeconomic conlent and health c~utcomes:a critical review". lrrurnaf ofEpidemiology a n d Cnmrnuniry Heulth. 55(2), pp. 1 1 1 - 122. PIOREM. J . , C F. SABEL.1984, The Second Industrial Divide, New York, Basic Books. RAUDENBUSH S. W., A. S . BRYK.2002, Hiararchicol Lineor Models: Applicaiinns and Du!u Anul w i s Methods, 2nd edirion, Newbury P a r k , CA, Sage Publications. 504 p. RAUDENRUSH S. W., A . S. BRYK, Y. F. CHEONG, R. CONGDON, 2000. ffierarchirol Lineor and Nanlinear Modeling. Lincolnwood, lL, Scientific Software International, Inc., 306 p. REISINGER M. E., 2003, "Sectoral shifts and occupational migration in the United States". PlaJt~ssionalGeographer, 55( 3 ) . pp. 383-395. SANDEFUR G. D., W. J . SCOTT,I98 I , "A dynamic analysis of migration: An assessmen[ of the effects of age, family alid career variables". Drrnogruphy, 18(3), pp. 355-368. SnvrH S . J., K. D. CKOWDER. l997a, "Escaping distressed neighborhoods: IndividuaI, communlry. and metropolitan influences", American Journal of Socirrlogy. 102(4). pp. 1040-1014. SOUTHS . J . . K . D. CROWDER, l997h, "Residential mobility between cities and suburbs: Race, suburbani~ation,and back-to-the-eiry moves", Demography, 34(4), pp. 525-538. 1998, "Leaving he 'hood': Residential mobility between black. SOUTHS. J., K. D. CRCSWDER, white. and inlegrated neighborhoods", Arrrerican Sociolo~icrllReview. 63(2), pp. 17-26. SOUTHS. I . , G. D. DEANE.1993, "Race and residential mobility: Individual determinants and siruc~uralconstrainrs",, Social Fnrce.~.72( 11, pp. 147- 167. SPEARE A.. 1974, "Residential satisfaction as an intervening variable in residential mobility", Demography. 1 1(2). pp. 173- 188. SPEARE A.. S GOLDSTEIN, W. H FREY,1975. Residenrral Mf~hiliry.Mtgrotion. and Metmpoli~on Change, Camllridge, MA, Ballinger, 316 p. SPEARE A.. F. KOBRIN, W. KINCKADE, 1982, "The influence of socioeconomic bonds and satisE~;rction on interstate migration", Social Forces, 61(2), pp. 55 1-574. SUHRAMANIAN S. V., C. DUNCAN. K. J O N ~ S2001, . "Multilevel perspecrives on modeling census data", Environment und Plunning A, 33(3), pp. 399-4 17. THEOWRIG. E., 2001. "Examining the effects of communily satisfaction and attachment on individual well-being". Rurul SOL-iology,66(4), pp. 618-628. TOLHERT C. M.; T. A. LYsoN, M. D. IRWIN,1998, "Local capitalism, civie engagement. and wcioeconornic well-being", Social Forcrs. 77(2), pp. 401 -428. TOLBERT C. M.. M. D. IRWIN, T. A. LYSON,A. NUCCI,2002, "Civie cam'munity in small town USA". Rural Sociology. 67(1), pp. g0- 1 15. UHLENBERC P.. 1973. "Noneconomic determinan~sof nonmigration: Social considerations for migration theory". Rural Sociology, 38(3), pp. 296-31 1. WIKSTROM P. 0. H., R . LOEBER, 2000, "Do disadvantaged neighborhoods cause well-adjusted children to become adolescenl delinquents? A s ~ u d ya f male juvenile serious offending, individual risk and protective factors, and neighborhood context", Criminology. 38(4), pp. 1109-1 142. WOLPERT J.. 1965, "Behavioral aspects of the decision to migrate". in Papers ofRegiunu1 Scien ce ..lsrot:iatian (vol. 15 1. Philadelphia. University of Pennsylvania Press, pp. 159- 169. Youxc F. W., 1999, Srnoll fiwn in M~rliilevelSnciery. New York. University Press of America. ZUICHESJ. J., 1981, "Residential preferences in the United States". in A. H. Hawley. S. M. Mazie (eds.), hhmcrropoliron Americn in Tmnsirion, Chapel Hill. NC. The University of North Carol~naPress, pp. 72- 1 15. ~

Michael D.~ R W I N 510 . College Hall, Dept. Sociology and G r a d u a ~ eCentre for Social and Public Policy, Duquesne University. Pittsburgh PA 15282, USA. Tel.; (412) 396-6488. e-mail: irwinrn@duq.edu

~ r Mtc i hael, BLANCHAH ti Troy, TOI.RERT Charles, NUCCIAlfred. LYSONTl~omaa.-Why Penplc Stay: The lmparl af C'omnrunity C o n f e x l on Nonmigration in t h e USA Most pritlr research on US migralirln has relied on individual-level explanation* 111- movin; I11 this study we huild on this l l ~ e r a t u r ehy examining the role ol'comrnunity strllcture upon Irbdivid~~a13' prob;~bilittrsof rrni n ~ i g r u r i t rhetween ~ 1985 and 1990. Using the full 1990 Censux 3:lrnplr. lorig-lorm microdata. we re-allocate adult persons ill 1990 to [heir 1085 counrieh of residcnce, Then. using county of origin macro-strurtural variables (derived from the Eeonomie Cell>us n ~ i c r o d a l aand ) individual chars l o u o n d ~ ~ i othe n et'feuls o f the individual characteristics on the likelihood of rnigra~ion. IKW

Michael. BL.ANC~IARI) Troy. TOLREKI. Charler. N u c c ~Alfred. LYSONThomas.-, Pnurquoi c e r t a i n s n e m i g r e n l pas : I'impact du contexte lnral sur la s i d e n t a r i t i aux E ~ a t s - U n i s L;I plupart d r s travaux sur les migrations amiricaines reposent sur d r s explications d c la rnoh~liteau niveau ~ n d i v i d u e l .Notre e t u d e va plus loin en examinant l'effet d u contexle local sur 1t.s probabilites d r nta pas migrrr rntre 1985 rt 1090. En uiilisanl les d o n n i e s individuelles extrailes d e s bulletins c o ~ n p l e l sdu recensement de 1990, nous rattachons les adultes rccensis en 1990 a ieur comte dc rksidence en 1985. E n s u i ~ een , exploitanr les r a r a c t i r i s ~ i q u e s macro-structurelIes d e s cornrds d'origine (fournies par le recensemenl de I'ictlnornrei e l les uaracriristiques d e s individus ( f o u r n ~ cpar ~ le recensrrnent d t c e n n a l ) . nous ilahorons un no dele l i n i a i r e hii-rarchique ideun niveaux- Au niveau I . nous construisons une iquation logistique qui rnodiltse les probabilitis individuelles de n e pas migrer. Au niveau 2. naus rnodklirons d ' a h o r d les cffets contextuels additifs de la localit6 d'oripine sur ces probabilitis. puis les effets .d'interacLion (inter-niveaux). Les facteurs locaux se classent en deux c;l~Pgories: I ) la s i t u a ~ i o neconomique, qui comprend les h a b i ~ u e l sfacteurs a repulsifs a a u niveau a g r e g e ; 2 ) les caracti2ristiques d e la collectivici locale qui tendent a retenir les pens 1 i ou i l h v i v e n ~ .Les r k s u l t a ~ spricisent le lien q u i exisre entre le coniexte local et les migrations individuelles er montrenl les effels d e s structures Cconomiques er sociales localrs sur ces cornpurrernents individuels. Nous constalons que. une fois contrdlis les tacteurs iconomtques locnux et les caracteristiques individuelles, les particularitis sociolopiqueb des localitis sonr associees sur un m o d e additif ? lai propension A rester ou I'on esr. Dr plus. nous ohservons q u e cerraines caractiristiques des communautks locales interagissenl avec les carac~eristiques i ~ i l l ~ v i d u e l l e st fian~ilialespour cunditionner les effets d e s factcurs individuels sur la probahilite de migrer. IRWIY

I K W ~ NMichael. B L A N C ~ ~ A Troy. U D TOLBERT Charles, Ntrccr Alfred. I,YSON Thomas.- Porque cier10sindividuos no migran: el impart0 del conlexto local s o b r e el s e d e n t a r i s m u e n 10s 1 E s t a d o s Unidos La mayc~riad e estudias sobre las rnigraciones en Eslados l!nidas intentan explisar la mnvilidad a nivel individual. Nuestro estudio va mas alla, ya que analiza el impacro del rontexto local subre las probahilidades d e n o migrar entre 1985 y 1990. Utilizando datos individuales procedent t s de 10.; cuestionarios completes del censo de 1990. vinculamos a 10s adultos censados en 1'390 con sus condados de residencia en 1985. A continuaci6n, u~ilizandolas caracredsticas macroestrr~c~urales de 10s c t ~ n d a d o sJ e origen (extraidas del censo econ6miuo) y las caracteris~icasd e 10s individuos (extraidas del censo decenal de poblacion) elaboramos un modelo lineal jerarquico a dos niveles. En el pnmer nivel construimos una ecuacion logisliua para estimar las probabilidadcs individuales d e no migrar. En el segunda nivel empezamos por nlodelizar los efectos contextuales ~ d i r i v o sdel lugar de origen sobre tales pmbabilidades. y a continuacibn modelizamos los efectos interactivos (enrre niveles). Clasificamos 10s fac~oreslocales en dos caregonas: 1 ) la si~uacioneconfimica, que incluye lo!, f a c ~ o r e shabituales de "expulsion" a nivel agregado; 7) las caracteristicas de la colec~ividadlocal que t ~ e n d e na retener a los individuos en el lugar donde viven. Los resulrados presentan la relaciiln que exisle enlre el contexlo local y las mipraciones individuales y muestran 10s efectos de las estrucIuras econdmicas y sociales locales sobre los cornportamienlos iadividuales. Conslatarnos que, una vez s e c o n ~ r o l ael efecto d e lo3 factures econrimicos locales y de las caracreristicas individnales, las particularidades sociol6gicas del lugar de residencia se asocian d e forma aditiva a la propension de quedarse. Tambitn observamos que cienas caracteristicas d r \as comunidades locales a c t ~ i a ninteractivamen~econ las caracteristicas individuales y familiares y condicionan el efecto de las caracterislicas individuales sobre la probabilidad de migrar.