University at Albany, provided technical and administrative support through .... We investigate the New York and Los Angeles metropolitan regions in 1990. We.
2001-3
Immigrant Enclaves and Ethnic Communities in New York and Los Angeles
John R. Logan Richard D. Alba Wenquan Zhang Department of Sociology University at Albany
Draft May 2001 DRAFT COPY -- NOT FOR CITATION OR QUOTATION WITHOUT THE AUTHORS’ PERMISSION
This research was supported by a grant from National Science Foundation, SBR95-07920 and by the Lewis Mumford Center for Comparative Urban and Regional Research. The Center for Social and Demographic Analysis, University at Albany, provided technical and administrative support through grants from NICHD (P30 HD32041) and NSF (SBR-9512290).
Abstract The major post-1965 immigrant groups have established distinctive settlement areas in many American cities and suburbs. This study examines the residential patterns of several of the largest groups in New York and Los Angeles. It addresses three kinds of questions: To what degree do they settle together with other members of the same group? What are their ethnic neighborhoods like? And what are the distinguishing characteristics of those group members who live in neighborhoods of ethnic concentration compared to those who reside outside these areas? The results show that the model of immigrant enclaves, where initial settlement areas serve as a potential base for eventual spatial assimilation with the white majority, applies well to some groups. For others, an alternative model of ethnic community is advanced, reflecting the group’s choice of segregated settlement even when spatial assimilation is otherwise feasible.
Immigrant Enclaves and Ethnic Communities in New York and Los Angeles
The neighborhood has long been considered a key facet of immigrant life. Despite dispute over the importance of neighborhoods for the average urban resident (compare Wirth 1938 and Wellman 1979 with Kasarda and Janowitz 1974 and Logan and Molotch 1987), there is wide agreement that neighborhoods continue to have an important function for new arrivals. This is particularly evident for people whose customs or language set them apart from the majority population. Today as in the past it is believed that concentrated immigrant settlement areas arise and are maintained because they meet newcomers’ needs in such areas as affordable housing, family ties, familiar culture, and help in finding work (for example, Thomas and Znaniecki [1927] 1974). We will use the term “immigrant enclave” to refer to such areas of new settlement. (A similar term, ethnic enclave economy, has been used to designate certain forms of group concentration in the local labor market. We restrict our use of the term “immigrant enclave” to residential concentrations, though in the final section we study the relationship between living in ethnic neighborhoods and working in ethnic jobs.) Earlier in this century, Chicago School ecologists recognized these immigrant colonies and gave them names like The Ghetto, Little Sicily, Greektown, and Chinatown (Burgess [1925] 1967). In that literature and the model of spatial assimilation that grew out of it (Massey 1985), immigrant enclaves represent a practical and temporary phase in the incorporation of new groups into American society. According to this model, segregation is natural as a group enters the United States, but it is eventually overcome by processes of individual socioeconomic mobility and acculturation. Immigrant
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enclaves are avoided by people with more financial resources and mainstream jobs, and they are left behind by immigrants with more experience and by the second generation in search of the “Promised Land.” Even in the Miami region, where a vibrant Cuban enclave economy is highly concentrated in Hialeah and in the Little Havana section of Miami, Portes and Jensen (1987) emphasize that immigrant businessmen, professionals, and better-paid employees have abandoned the original settlement areas and moved toward more affluent suburbs. We draw attention to another type of immigrant neighborhood. Not only do some immigrant groups possess the material means of establishing neighborhoods in desirable locations, but they may also prefer the cultural setting of such an environment even when they have the means to live elsewhere. Their motives may be associated more with taste and preference than with economic necessity, leading them to create neighborhoods whose purpose is to symbolize and sustain ethnic identity. For example, Bonacich (1973, p. 586) suggests that residential self-segregation is typical of middleman minorities, which “form highly organized communities which resist assimilation.” This process creates what we will call the “ethnic community.” This is how Zhou (1992) interprets the satellite Chinatowns that have arisen in Flushing and other outlying parts of the New York region. Horton (1995) describes a similar pattern for suburban Monterey Park, located not far from downtown Los Angeles, which was aggressively marketed by Chinese American developers to new immigrants and investors from Taiwan and Hong Kong. Marcuse (1997, p. 242) calls such places enclaves, which he defines as areas “in which members of a particular population group, self-defined by ethnicity or religion or otherwise, congregate as a means of enhancing their economic, social, political and/or cultural development.”
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The notion of segregation by choice has a long history. The Chicago School ecologists recognized an element of preference as well as necessity in the creation of immigrant colonies. But such behavior was of secondary importance, in the Chicago School view, to the main trend of spatial assimilation. Another type of segregated neighborhood and social process is also well known in American cities: the “minority ghetto.” The ghetto demonstrates African Americans’ restricted choice in location. Massey and Denton (1993) refer to this as residential apartheid, in which even if group members otherwise have the means to live in non-segregated settings, collective efforts by other groups prevent them from doing so. How are immigrant enclaves and ethnic communities related to ghettos? Some studies (e.g., Alba, Logan and Stults 2000) show that the locational process underlying black neighborhoods has a key feature in common with that producing immigrant enclaves as we describe them: African Americans with higher income and education are more likely to live in suburbs, in areas with higher proportions of non-black residents, and in wealthier and safer neighborhoods. This suggests, as argued by Wilson (1987), that the black middle class can navigate the housing market to meet their needs more freely than can poor blacks. Those who have more market choice, exercise this choice to achieve a modicum of residential assimilation. Immigrant enclaves and minority ghettos may also be alike in other ways, such as cheap and densely populated housing stock, inner city location, poverty and other indicators of dependency. The difference is that the enclave is understood to be a temporary way-station, while the ghetto is thought to ensnare people in a system that “did not allow blacks to be immigrants” (Logan and Molotch 1987, p. 126, italics in the original). Even the most affluent African Americans have less residential mobility and live in less desirable neighborhoods than do comparable whites (South and Crowder 1997, Logan et al 1996). Thus if
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the black neighborhood is a platform for mobility for African Americans, as the enclave is posited to be for immigrants, it is a very limited one, and this circumstance justifies thinking of them as ghettos. (Wirth [(1928) 1965] used this same term to refer to the 2nd-generation Jewish settlements that he studied, at least partly because of the restrictions imposed on Jewish homeseekers at that time in many American cities.) Black neighborhoods may also have something in common with ethnic communities: an element of chosen self-segregation. Surveys of middle-class African Americans have revealed a preference for black communities or at least a reluctance to live in mostly white communities (Rose 1981, Feagin and Sikes 1994, pp. 264-65). Some researchers (e.g., Clark 1991, following Shelling 1971) have suggested that such preferences contribute strongly to racial segregation. But the prevailing view is that black residential choice is highly constrained by a dual housing market (Galster 1988,Yinger 1987). And since those African Americans with the most resources, and therefore the most options in the housing market, are the least likely to live in highly segregated settings, it does not seem likely that they have strong in-group preferences. Our research on immigrant neighborhoods analyzes what kinds of neighborhoods have ethnic concentrations, and what personal characteristics of group members are associated with living in an ethnic neighborhood vs. a mainstream location. We adopt the following logic, which is consistent with the traditional literature on spatial assimilation: 1) Suppose that for a given group we find that greater economic and social resources and cultural assimilation are associated with living outside of ethnic neighborhoods. We will take this to mean that the locational process is one of spatial assimilation, and we will interpret ethnic neighborhoods of this group as immigrant enclaves. 2) Suppose on the other hand that people with more resources are equally likely to live in ethnic neighborhoods, or even that they are more likely to live in these
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neighborhoods. Then we will interpret the resulting neighborhoods for this group as ethnic communities. In either case, future research should not ignore the possibility that group members experience housing discrimination, even if not as thorough as that suffered by African Americans. The more evidence that a group remains very highly segregated over an extended period, that few group members ever achieve sufficient resources to leave immigrant enclaves, and that those who try to leave are subject to unequal treatment in the housing market, the more reason there would be to interpret immigrant enclaves as ghettos. And even if the analysis suggests that a group’s neighborhoods are sustained by those with the most market choice, evidence that these group members suffer housing discrimination would give their ethnic communities a very different tone. Research design Quantitative researchers have relied on two approaches to study ethnic residential clustering. The first and best known is the tradition of segregation research, where various aggregate indices of spatial separation and isolation are calculated and compared across groups and metropolitan regions, and over time. The second strategy is to explore variation at the individual level, evaluating the predictors of living in different kinds of locations (i.e., locational attainment). We apply both of these approaches. At the aggregate level, we study the level of group clustering and its change over time. Our examination of individual-level processes uses novel methods to identify the particular neighborhoods in which group members are concentrated, to ask what these neighborhoods are like, and to learn what kind of person lives within an ethnic neighborhood.
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Past locational attainment studies (e.g., Alba, Logan and Stults 2000) have focused especially on the question of group members’ exposure to whites, not addressing the predictors of living in their own ethnic areas. And, with the exception of studies of suburbanization, they have estimated models for Hispanics or Asians in general, rather than separate models for each ethnic group among them. Differences in residential patterns within broad racial/ethnic categories are now attracting more attention (see, for example, the detailed descriptions of the many immigrant groups in Los Angeles in Waldinger and Bozorghmehr 1996). Our research will focus on just such differences. We investigate the New York and Los Angeles metropolitan regions in 1990. We systematically review the residential patterns of the seven or eight largest immigrant groups in each region, including group members born abroad and those in the second and later generations in the United States. We treat several Hispanic and Asian groups defined by national origin, as well as Afro-Caribbeans in New York. Because the residential experiences of African Americans and the processes creating minority ghettos have been extensively documented, we limit our investigation to immigrant groups, and we give most attention to the models of the immigrant enclave and ethnic community. It is left to future research to consider whether and how the notion of minority ghetto may apply to immigrants. Sample metropolitan regions and immigrant groups New York and Los Angeles are natural laboratories for studying the residential patterns of immigrant groups in the American metropolis. They have in common the extraordinary size and diversity of their immigrant populations, but they represent distinct eras of urban development – the 19th century walking city with a hundred-year history of immigrant
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neighborhoods, and the California automobile city settled mainly by second- and thirdgeneration Americans. New York, like most large American cities in the Northeast and Midwest, is a product of immigration, and every successive wave of immigrants since the mid-19th century has left its mark on its neighborhoods. Some white ethnic neighborhoods from the turn of the century lasted less than a generation: this was the case of the important Jewish settlement in Central Harlem between 1910 and 1925 (Gurock 1979). Others, like Italian Bensonhurst in Brooklyn (Alba, Logan and Crowder 1997), remain singular ethnic enclaves even now. Today’s new immigrants therefore recreate an established pattern of segregated living, whether in neighborhoods with a tradition of passage from one group to another (like Manhattan’s Lower East Side, which was German Deutschland early in its history, then Jewish, and more recently Puerto Rican and Dominican), or in new, even suburban locations (such as Asian neighborhoods around Fort Lee, NJ). As shown in Table 1 in New York-New Jersey metropolitan region (CMSA) there are seven Latino and Asian groups with over 100,000 population in 1990, counting group members in both the first and later generations. In New York, the largest new immigrant group is AfroCaribbean, from a set of islands in the English-speaking West Indies and Haiti, with over half a million by 1990. Nearly as numerous are the Dominicans (over 400,000) and Chinese (over 300,000). The remaining groups in New York are Asian Indians (nearly 200,000), Cubans (about 150,000), and Korean and Filipinos (both just over 100,000). Larger than any of these groups is the Puerto Rican population (close to 1,250,000). We do not study Puerto Ricans here because they arrived mainly before 1970, representing a different era of population movements
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to New York. Table 1 documents the rapid growth of the new immigrant groups over the previous decade, approximately doubling in most cases. Table 1 about here Los Angeles, by contrast, is a 20th century creation. Although it inherited small numbers of Mexicans and Japanese from an earlier era, it was first catapulted into the ranks of major cities by the arrival of 2nd and later generation European-origin whites from other regions of the United States, many of whom moved directly into outlying neighborhoods and suburbs (Laslett 1996). Its white ethnic and Mexican enclaves were relatively small at mid-century, while many other Mexicans – mostly the result of immigration after 1920 – were concentrated in former agricultural districts outside the city (Sanchez 1993). In the Los Angeles region, therefore, most of today’s immigrant neighborhoods are of relatively recent vintage. In Los Angeles-Long Beach there are eight immigrant groups with more than 100,000 population in 1990. By far the largest is Mexicans, nearing 4 million by 1990 – a full quarter of the region’s population. Salvadorans, Chinese, and Filipinos were in the vicinity of 300,000 by 1990 (more than doubling since 1980). Koreans (about 200,000) and Vietnamese and Guatemalans (about 150,000) had also more than doubled, while the Japanese (about 175,000 and mainly of the third and fourth generations) had grown more moderately. Despite their differences of history and geography, New York and Los Angeles now stand together as the location of the largest and most diverse arrays of new immigrant groups in the nation. Both have large numbers of Asians (Chinese, Indians, Koreans, and others). New York has the nation’s primary settlements of Dominicans and people from the English-speaking Caribbean. Los Angeles has a vast and growing Mexican-origin population, as well as many Spanish-speaking immigrants from Central and South America. How do settlements in these two
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regions, at opposite ends of the continent and rooted in different centuries of urban development, compare to one another? We suspect that the main differences are associated with LA’s greater suburbanization. Table 1 distinguishes between the central city and suburban portions of NY and LA, exposing this difference in the regions’ spatial structure. New York is about evenly split between city and suburb, while LA is heavily suburban and its suburban Mexicans, Chinese, Filipinos, Koreans, Japanese, and Vietnamese actually outnumber their city counterparts. One might expect suburbanization to have a large impact on the degree of group segregation, since a long-standing hypothesis of the spatial-assimilation model is that segregation is weaker in suburban settings than in urban ones (Massey, 1985). It may also affect the character of group neighborhoods (more desirable in suburbia), and perhaps even what kinds of people live in group neighborhoods (suburban ethnic neighborhoods may be less like the typical immigrant enclave that sociologists have described in central cities). Our sample allows us to study the effects of suburbanization in two ways. First, within each region we can compare central city and suburban patterns. Second, we can compare the overall pattern in more traditionally “urban” New York with the one in the newer “suburban” Los Angeles metropolis. Methods of analysis We study residential patterns using a combination of aggregate data from the population census (STF3A) and microdata (PUMS). 1 The analysis is divided into three parts. First and most familiar is the calculation of segregation indices for 1980 and 1990. Given our interest in the creation of ethnic neighborhoods, the most pertinent measure is the Index of Isolation (p*). This is the group’s share of the population in the census tract of the average group member; for example, a p* of .15 means that the average group member lives in a
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tract where 15% of the residents are coethnics. It is well known that p* depends partly on the size of the group. If a group grows in size, even without changing its distribution across tracts, the Index of Isolation will necessarily increase in the same proportion. P* will also change if a group changes its distribution across tracts, increasing if the group becomes more concentrated in a small number of areas. We calculate this index for the metropolitan region as a whole, and for the city and suburban portions separately. Any such index is aggregate in nature. To examine in finer detail the residential distribution of each group, our second step is to identify those neighborhoods in which a group is substantially over-represented. We will refer to them throughout with the generic term, “ethnic neighborhoods.” We describe these neighborhoods for every group, which is a necessary intermediary step toward analyzing who lives in such places. In a comparable analysis of white ethnic neighborhoods, Alba, Logan and Crowder (1997) treated groups as over-represented when the group’s share of the tract was double or more its share of the regional population. We experimented with alternative classification schemes. In one, we adopted the “double share” criterion. Applied to Mexicans in Los Angeles, this led to a cutting point of 50% for “Mexican” neighborhoods. No other group studied here includes more than 3% of the metropolitan population, and for all of them we applied a minimum threshold of 10% (our intent was to err, if necessary, on the side of a demanding definition of what is an ethnic concentration). We report here the results from an alternative approach based on odds-ratios (the odds of a group member’s living in a particular tract divided by the odds of a non-group member’s living in the tract). The use of an odds ratio is already well established in the ethnic economy literature as a measure of group concentration in industry sectors (Logan, Alba and McNulty 1994).
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Having experimented with several possible cutting points, we established an odds-ratio of 5.0 as our criterion for an ethnic neighborhood. For Mexicans, this level is reached for census tracts that are more than 63.6% Mexican. For other groups, it is reached where group members make up between 3.0% (for the smallest group, Filipinos in New York) and 13.4% (for the largest remaining group, Afro-Caribbeans in New York) of the population. Since there is no established procedure for classifying neighborhoods, our choice may appear arbitrary. Fortunately, we found that most results (other than the number of ethnic neighborhoods, which is directly derived from the classification criterion) are stable regardless of the procedure. This robustness stems from two related sources. First, for all groups there is a high correlation between group members’ percentage of the tract population and the group’s odds-ratio (over .90 in all but two cases). In practical terms, it makes little difference which of these indicators is used. Second, the vast majority of census tracts have only tiny shares of any of these groups. Consider the Mexicans, with 25% of the Los Angeles population: less than one tract in five is as high as 5% Mexican. Turning to the remaining groups, no more than 30% of tracts have above 2% of group members among their residents. Hence most tracts will be classified as “non-group” under any classification scheme, and the specific cutting point selected has only a marginal impact. We define a group neighborhood as a census tract with higher than the threshold level of group members. In a few instances an isolated tract reaches this level; most, however, are found in larger clusters. There may in some cases also be “micro-neighborhoods” of a few blocks within a tract where group members are concentrated, but we do not study within-tract distributions. As a concise method of describing ethnic neighborhoods we have classified all census tracts in one of four categories for every group: city and suburban tracts that are within an
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ethnic neighborhood, and city and suburban tracts that are not ethnic neighborhoods. For all four categories we provide the following information (where aggregate figures are weighted by the number of group members in the tract): the total group population and its share of the group’s population in the region, the average group percentage and the average non-Hispanic white percentage of residents, and the median household income of all residents of the neighborhood. Having introduced the use of odds-ratios to identify ethnic neighborhoods, we can also now comment on its use in Table 1. The odds-ratio in the tract of the average group member is an indicator of the overall degree of group clustering in a metropolitan region. It is analogous to p*, which is the group percentage in the tract of the average member, except that it standardizes for the size of the group. Together the p* and average odds-ratio presented in Table 1 tell us the extent to which a group is clustered into ethnic neighborhoods. Our third and final step is to estimate models predicting the probability that a group member resides in an ethnic neighborhood. This analysis cannot be based directly on characteristics of the person’s census tract, because the smallest unit of geography for which the 1990 PUMS identifies individuals’ location is an area of approximately 100,000 persons, termed a PUMA (Public Use Microdata Area). (One publicly available national data set, the PUMS-F, contains census microdata matched to tract characteristics; however, this file does not identify specific cities or metropolitan areas, and it is therefore not useful for our purpose.) A PUMA is much larger than a census tract, and in most cases larger than any ethnic neighborhood. Nonetheless, it is possible to use a person’s PUMA location as an indicator of the probability of living in an ethnic neighborhood. Our procedure is described in detail below, following analysis of aggregate residential patterns.
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Results The segregation of immigrant groups The isolation indices (p*) presented in Table 2 reveal the extent to which people are surrounded by other group members in their neighborhoods. (Readers may prefer to multiply these values by 100, and to think of the result as the percentage of co-ethnics in the census tract of the average group member.) Because Mexicans comprise such a large share of the total metropolitan population in Los Angeles, they are the one group with very high values of p*. Whether in cities or suburbs, Mexicans in Los Angeles typically live in tracts that are nearly 50% Mexican (p* for the whole region in 1990 is .47). No other group approaches this level of isolation. Otherwise the highest values are found for Afro-Caribbeans and Dominicans in New York (.18 and .22, respectively) and for Chinese (.17 in New York and .14 in Los Angeles). All others are below .10, with the minimum value for Filipinos in New York (.04). Table 2 about here These values of isolation are not exceptionally high. Yet if we take group size into account, we can see that every group is much more isolated than it would be if it did not tend to be concentrated in certain areas. Filipinos in New York, whose p* value of .037 is the lowest of any group studied here in 1990, are only .006 of the New York population; their isolation is 6 times what it would be if the group were spread evenly across space. As an example at the higher end of isolation, Chinese in Los Angeles are 2.1% of the population but the average Chinese lives in a tract that is 13.5% Chinese. Logically, p* should vary with changes in group size, and we would expect it to increase sharply between 1980 and 1990 for most groups (other than Cubans and Japanese), reflecting their heavy volume of immigration. Indeed, in many cases this did occur: p* increased by half or
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more in New York for Indians, Koreans, and Filipinos and in Los Angeles for Chinese, Filipinos, Koreans, and Vietnamese. We are unable to assess what happened to Dominicans, Salvadorans, and Guatemalans due to their omission from the summary tape files of the 1980 census. But there are two cases with significant population growth but little change in p*: the Chinese in New York and Mexicans in Los Angeles. In both of these latter cases the values of p* were already at high levels in 1980. Perhaps, then, when a group reaches a high average level of concentration in certain neighborhoods, there are countervailing tendencies to spread into new territories, either to disperse or to establish new ethnic neighborhoods. Group size has such a strong effect on p* values that it makes sense to control for it when comparing the degree of isolation across groups. We do this in Table 2 by calculating the average odds ratio for each group. This value is above 5.0 for almost all groups, in both regions and in cities and suburbs. Few census tracts reach this level, but evidently group members are highly concentrated in those few tracts. Values tend to be higher in the cities than in the suburbs. In the majority of cases, the odds ratio declined somewhat between 1980 and 1990. This indicates that although most groups were living in tracts with higher proportions of coethnics in 1990 than in 1980 (shown by p* values), the increases were not commensurate with growth in their overall numbers. Decline was not universal, however – nearly half the groups experienced an increase in their suburban portion. Making comparisons across regions in 1990, we note first that values tend to be higher in New York than in LA. But this pattern is contingent on the portion of the metropolis that is considered: in the central cities, the New York values are about twice as high as those in LA, but the averages are about the same in the suburbs of the two regions. The pattern is even more telling for those groups that appear in both regions. For example, the New York Chinese are
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much more residentially clustered than LA Chinese in the central cities (36.2 vs 10.4), but much less clustered in the suburbs (1.5 vs 9.3). New immigrant neighborhoods These indices hint at the existence of immigrant neighborhoods, neighborhoods that may encompass a substantial share of members of all the groups in our sample, that are more common in cities than in suburbs (though perhaps deconcentrating in cities while sometimes intensifying in suburbia). The NY/LA comparison also suggests what may be a changing spatial pattern: a predominance of ethnic neighborhoods in the city (as in NY), giving way to a greater equality between cities and suburbs (as in LA.). We can assess these inferences directly, asking where neighborhoods are located, what proportion of group members live in them, and what are they like. Tables 3 and 4 describe four kinds of census tracts: those that are part of ethnic neighborhoods and those that are outside of them, both within and outside the central cities. Table 3 lists the number of tracts in each category for both metropolitan regions, as well as the number of group members and the percentage of the region’s group members in those tracts. Table 4 describes these tracts as they are experienced by group members (that is, they give average tract characteristics, weighted by the group population in the tract). The table includes the percentage of group members and of non-Hispanic whites in the tracts. It also reports the tracts’ median household income and percentage of immigrants in the population (both these latter statistics refer to all persons in the tract, not only group members). 1. Size and location of neighborhoods As shown in Table 3, the New York groups vary substantially in the share who live in their neighborhoods. Two have a majority of their members in ethnic neighborhoods:
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Dominicans (58.7%) and Afro-Caribbeans (51.8%). Koreans (49.3%), Chinese (44.9%), and Cubans (41.3%) are also quite high. Filipinos (36.6%) and Indians (30.4%) are at the lower end. These neighborhoods tend to be located in the central cities rather than in suburbs (at the extreme, only about 8000 Filipinos live in suburban tracts where the Filipino is above 5.0). Cubans are an exception: the big Cuban neighborhoods are suburban. Table 3 about here In Los Angeles, too, there is considerable variety in the shares of group members who live in ethnic neighborhoods; on average, these are lower than in New York. At the top end are the Chinese (42.9%), Salvadorans (42.3%), and Vietnamese (42.2%). The Filipinos are at the lower end (23.8%), and the other groups – Guatemalans, Koreans, Mexicans, and Japanese – fall in between. Because Los Angeles itself is so highly suburban (its suburbs have about twice as many residents as the city areas), its ethnic neighborhoods are not so strongly centered in the city as is the case in New York. And consistent with what we inferred from Table 2, several groups’ neighborhoods are predominantly suburban: the Mexicans, Chinese, Japanese, and Vietnamese. The Salvadoran and Guatemalan neighborhoods are overwhelmingly found in the city, and to a lesser extent so are Korean and Filipino neighborhoods. 2. Characteristics of neighborhoods Table 4 describes several characteristics of tracts within and outside of ethnic neighborhoods (these are the averages of tract values weighted by the number of group members in each tract). Although a finer tally shows that groups in a few neighborhoods (like Manhattan’s Chinatown and Dominican Washington Heights) reach densities of 80-90%, the average member in an ethnic neighborhood for most groups lives in a census tract where between 10% and 35% are of the same background. In this respect, today’s “ethnic”
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neighborhoods are like those established by European immigrants in an earlier period, where the largest group in a neighborhood was not a majority (Philpott (1978, p. 131). Of course the Mexican population is so large that Mexicans are a population majority in their neighborhoods (about 75%). Even outside these areas, Mexicans live in tracts with a considerable coethnic presence, if at much reduced levels (30-35%). Exposure to non-Hispanic whites in ethnic neighborhoods is also highly variable. At one extreme, Afro-Caribbeans, Dominicans, and Mexicans live in city tracts where whites are less than 10% of the population. LA’s city neighborhoods of Salvadorans, Chinese, and Guatemalans are barely above this level. Other groups have more exposure to whites, especially in their suburban neighborhoods: as high as 60-75% for the four Asian groups in suburban New York. Table 4 about here New York’s urban ethnic neighborhoods for every group include a high proportion of immigrants (40-50%), more so (except in the Cuban case) than for suburban ethnic neighborhoods (20-40%). The same is true in Los Angeles, though here the immigrant percentages range higher, as high as 55-60% in both city and suburbs. There is also variation in the income level of neighborhoods, which is a useful general indicator of the quality of the residential environment. (Note that our interest here is in the characteristics of the neighborhood itself, not the income level of group members in the neighborhood.) In New York, the situation is fairly similar for all groups in the city outside of their ethnic neighborhoods: they live on average in city tracts with a median household income of $25,000-$35,000. This is, in fact, near the average income level for the city, meaning that these dispersed members of new immigrant groups appear to live in socio-economically typical urban communities. (The average white urban resident, though, lives in slightly more affluent
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areas – $37,100). In non-group suburban areas there is greater variation: from about $40,000 for Afro-Caribbeans and Dominicans to nearly $60,000 for Chinese and Koreans. The latter two groups actually live in wealthier areas than the average white suburbanite ($52,700). Compared to the situation of group members outside of ethnic neighborhoods, a natural finding from the standpoint of the assimilation model would be for ethnic neighborhoods to be much poorer than average. From this viewpoint, the more advantaged group members in either zone of the metropolis would tend to leave ethnic neighborhoods for higher status non-ethnic areas. This seems to be the case for Dominicans in the city, for example: the average income level of Dominican neighborhoods (weighted by the size of the Dominican population) is under $20,000 and is about $5000 less than the non-group neighborhoods where Dominicans tend to live. But this is not a uniform pattern: the city neighborhoods of two New York groups (AfroCaribbeans and Indians) are more affluent than the non-ethnic neighborhoods where group members live, and there is very little difference for Koreans and Filipinos. This result might be interpreted in terms of Indians’ high personal incomes in New York (a high-income group can create a high-income enclave). But the same explanation could not be offered for AfroCaribbeans. In Los Angeles there are two suburban cases where the ethnic neighborhood is more affluent than non-ethnic neighborhoods where members live (true for the Filipinos and Koreans). Indeed, these communities have a higher average income than do the places in which the average suburban white person lives ($46,100). In the city, Vietnamese neighborhoods have much higher incomes than the non-ethnic neighborhoods where Vietnamese live. Hence it is often true, as presumed by the assimilation model, that upward mobility implies leaving ethnic locales. Yet
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the immigrant neighborhood is not necessarily a low-income area; sometimes it actually represents higher class standing and in this way a more desirable living environment. Models of the location process: predicting residence in an ethnic neighborhood Having discovered what these neighborhoods are like, we turn now to the question of who lives in them. For each group, we analyze a 5% sample of households, and we select one group member in the household for study (choosing randomly between the householder and the householder’s spouse where both belong to the group in question). We evaluate the following variables whose effects are anticipated by the spatial assimilation model: 1. Nativity. Group members born in the U.S. are expected to be less likely to live in ethnic neighborhoods than are immigrants; among immigrants, the most recent arrivals are expected to be most likely to live in residential enclaves. Nativity is represented by three dummy variables, with U.S.-born treated as the reference category: immigrated after 1985, between 1965 and 1985, and before 1965. 2. Language. In tandem with nativity, language is considered to be an indicator of cultural assimilation. Bilingual persons who speak English poorly are most likely to live in residential enclaves (while at the same time, residential segregation could itself impede learning or using English). Language is represented by two dummy variables, with “Speaking only English at home” is treated as the reference category for language. Two dummy variables refer to those who speak another language at home: speaking English well and speaking English poorly. 3. Education. Education (years of schooling completed) is understood as an indicator of socioeconomic status. For those educated in the U.S., it may also be an indicator of cultural adaptability or cultural experience.
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4. Household income and homeownership. Household income (expressed in thousands of dollars) and homeownership (a dummy variable) are both considered to be indicators of socioeconomic achievement, presumed to be negatively associated with living in an ethnic neighborhood. 5. Ethnic employment. Responsive to the literature on ethnic economies, we include two indicators of position in the labor force. The first is whether any household member is selfemployed. Business owners among immigrant groups (net of the effect of their possibly higher income) may depend on connections with co-ethnics as consumers or as sources of supplies or labor; this consideration advances the hypothesis that owners are more likely to live in ethnic neighborhoods. But workers may be equally dependent on such ties in finding employment. Hence self-employment is not in itself a convincing indicator of ethnic dependency. Better are measures of the industry sectors in which people work, because ethnic economies are so often concentrated in certain sectors. Following procedures established in prior work (Logan et al 2000), we identify ethnic sectors of three types: those in which the group is over-represented as both owners and workers (an enclave sector), those in which the group is over-represented only as owners (an entrepreneurial niche), and those in which the group is over-represented only as workers (a labor niche). Following the assimilation model, we would hypothesize that group members in any of these types of ethnic sectors would be more likely to live in ethnic neighborhoods. If, on the other hand, we find that they are not – that is, if people whose jobs do not limit their range of residential choice are equally likely to live in ethnic neighborhoods – we would treat this as evidence in favor of the ethnic community model.
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Two life cycle indicators are included as control variables: the person’s age and whether the person lives in a married-couple household. The theoretical models offer no clear expectations about the effects of these variables. It could be presumed that young adults will be more likely than older people of the same immigrant generation to wish to leave the enclave. But it could also be argued that older people have had more time in which to exercise this option. Married-couple households may have more residential options than single persons, although in some instances it might be expected that they would prefer – in raising their children – to live in the enclave. We also include a variable representing city vs. suburban location. Because a few PUMAs cross city boundaries and therefore include both city and suburban portions, we define this variable as the percentage of PUMA residents in its suburban portion (ranging from 0 to 100). (Where this percentage was less than 1 or greater than 99, it was rounded to 0 or 100.) Its inclusion is subject to criticism because suburbanization itself is an important residential outcome, likely to be related to other variables in the model. However, we examined the results for equations with and without suburban location as a predictor, and we found that its inclusion does not substantially change the interpretation of effects of other variables. The purpose of this variable is to test whether, having controlled for other factors, group members who live in the suburbs are less likely than those in the central city to live in residential enclaves, as traditionally supposed. The dependent variable in this analysis is the probability that a person lives in an ethnic neighborhood, ranging from 0 to 1. Of course, the census microdata on individuals do not reveal the tract of residence (if it did, our dependent variable could be simply whether the person lives in an ethnic tract, or alternatively the proportion of ethnic group members in the person’s tract).
22
Instead, we use the following procedure. First, we link census tracts to their corresponding PUMAs. Then, we identify which, if any, tracts within the PUMA are part of an ethnic neighborhood. Finally, we calculate the proportion of group members in the PUMA who live in such tracts. To illustrate this procedure, Figure 1 provides maps showing the principal settlement areas of two groups: Afro-Caribbeans in New York and Chinese in Los Angeles. Similar published maps of many of the settlement patterns of other groups are available for New York City (Mollenkopf 1993) and for the Los Angeles metropolis (Allen and Turner 1997). Ethnic neighborhoods (that is, areas where a group’s odds-ratio is at least 5.0) are darkly shaded . The surrounding lines identify the boundaries of those PUMAs that encompass these neighborhoods. Non-neighborhood census tracts are shown in two ways: the lightest shaded areas are suburban, and the moderately shaded areas are within the central cities. Figure 1 about here There are four large concentrations of Afro-Caribbeans in the New York metropolis. Crown Heights/Flatbush is located in the center of Brooklyn. Jamaica is in the borough of Queens. Mt. Vernon/Eastchester straddles the boundary between the Bronx and Westchester County, and Hempstead/Uniondale is in suburban Nassau County. Because it has the largest number of Afro-Caribbean residents, we use Crown Heights/Flatbush to show the relationship between the neighborhood, as defined by census tracts, and the PUMAs of which they are a part. This neighborhood (the darkly shaded area) has 104,000 Afro-Caribbean residents. These persons represent 84.5% of the total of 123,000 Afro-Caribbeans in the 3 PUMAs that contain the neighborhood. Thus Afro-Caribbeans in all 3 of these PUMAs are very likely (with odds of greater than 4 to 1) to be living in an ethnic neighborhood. The same can be said of those living
23
in the PUMAs that include the other major Afro-Caribbean neighborhoods. The PUMA itself is not the ethnic neighborhood; but knowing which PUMA a person lives in does provide a very clear idea of the probability that the person lives in an ethnic neighborhood. Those living in most PUMAs in the region have zero probability of living in an ethnic neighborhood, because their PUMA includes none. Our Los Angeles example is the Chinese. They have small concentrations in the traditional Chinatown adjacent to downtown and a suburban neighborhood centered on Hacienda Heights. Their main concentration is found in Monterey Park/San Gabriel, which is often described as a suburban Chinatown (Horton 1995). Here live nearly 79,000 Chinese, 95% of the total 83,000 Chinese in the 3 PUMAs that include the neighborhood. Chinese residents in these PUMAs have an unusually high odds (nearly 20 to 1) of living in an ethnic neighborhood. For most groups the probability of living in an ethnic neighborhood has a bimodal distribution among PUMAs: some PUMAs with very high values (in the range of .80 and .90), many with low values (most of which are near 0), and a smaller number in between. In light of this distribution, we treat the probability of living in an ethnic neighborhood as a 3-category variable: high (.75 and above), medium (between .25 and .75), and low (.25 and below). Multinomial logistic regression is an appropriate method for modeling such a variable. We treat the low category as the reference category, and ask what personal characteristics of group members predict a medium or high probability of living in an ethnic neighborhood. For the sake of parsimony, we present and discuss the logit models that predict living in the “most ethnic” category of PUMA compared to the “least ethnic.” We also estimated models for living in the middle category vs. the least ethnic category; the direction of results is consistent with those presented here.
24
Table 5 presents results for New York groups, and Table 6 for Los Angeles. The tables report many coefficients. We summarize them in two ways: first, by reviewing the consistency of effects of each set of predictor variables, and second, by identifying clusters of groups which have similar patterns of effects. Tables 5-6 about here The most successful predictor is language: in all but two cases, those speaking only English at home are significantly less likely than those speaking English “well” or “poorly” to live in an ethnic neighborhood (and one of the exceptions is Indians, for whom English is commonly a first or second language). We expected a similarly consistent effect for nativity, but in only 7 of 15 cases is there a statistically significant coefficient indicating that immigrants are more likely than the U.S.-born to live in ethnic neighborhoods. (In one other case, Japanese in Los Angeles, there is a significant effect in the opposite direction: immigrants are less likely.) And, though this pattern is more difficult to judge, there are only 5 or 6 cases where it appears that recent immigrants are more likely than more established immigrants to be found in ethnic neighborhoods. Socioeconomic variables often have the effects anticipated by assimilation theory. Homeowners (in 11 cases), persons with higher education (in 9 cases) and higher income persons (in 7 cases) are less likely to live in ethnic neighborhoods. But many coefficients are not significant, and there are also some significant effects in the other direction. Afro-Caribbean and Indian renters (in NY) and Filipino renters (in both regions) are less likely than homeowners to live in ethnic neighborhoods. Higher income Koreans in LA are more likely to live in Korean neighborhoods.
25
Labor market effects are less common and more mixed. Self-employment is mostly not significant (with three significant negative effects, and only one that is positive). Working in ethnic industry sectors in several cases is associated with living in ethnic neighborhoods (there is at least one significant positive coefficient among these variables for nine groups, though most coefficients are not significant). In two cases, though, the effect is unexpectedly in the opposite direction (Filipino enclaves in NY and Chinese enclaves in LA). Suburbanization has few effects, and these too are mixed. In New York, four of the significant coefficients are negative (as traditionally expected), and only one is positive (for Cubans, whose Jersey City neighborhoods have an urban appearance despite their classification by the Census Bureau as suburban). In Los Angeles there are three significant effects, and all are positive. The contrast between regions reflects their differences in spatial structure. But the main result here is that suburban vs city location has little predictive power for living in an ethnic neighborhood, and this finding contradicts the usual role attributed to suburbanization. Who, then, lives in ethnic neighborhoods? Almost always this is associated with less English language facility. This does not mean simply that people with more experience and ability to navigate mainstream society are more likely to assimilate residentially, because our other measure of acculturation – nativity – has little impact. Possibly the language effect is really a reciprocal one – not speaking English well may be not so much the cause but the consequence of living in a segregated setting. Living in ethnic neighborhoods is also frequently associated with lower socioeconomic standing, but with important exceptions. And the effects of ethnic employment and suburbanization are too few and mixed to offer much support for the spatial assimilation model. Our account is replete with exceptions: what is true for one group is
26
not true for another; a pattern consistent with assimilation in one respect is inconsistent in another (on this point, see also Galster, Metzger and Waite 1999). Which groups’ neighborhoods fit better into the model of the immigrant enclave, where the neighborhood is a starting point from which people leave when they are able? Which are consistent with the model of ethnic community in which group members who could live elsewhere choose to reside in the ethnic neighborhood? Mexicans and Cubans are close to the ideal type for the immigrant enclave model: both language and nativity, among the acculturation measures, have the predicted effects, as do education, income, housing tenure, and ethnic employment. Indians offer the profile most supportive of the ethnic community model: neither nativity nor language affects living in Indian neighborhoods, nor does income or ethnic employment. The more educated are less likely to be in Indian neighborhoods, but homeowners are more likely. Filipinos in NY also fit the ethnic community model well: while higher income is negatively associated with living in Filipino neighborhoods, homeowners and those in the mainstream labor force are more likely to do so. How shall we classify the 11 remaining groups, for whom findings are less clear-cut? In almost all of them (Salvadorans are the exception), language has the effect anticipated by the immigrant enclave model, and there is either a positive effect or no effect of nativity. But when we consider the socioeconomic effects, this model loses force. In only half of these cases is there at least one socioeconomic effect in the predicted direction without a contradictory socioeconomic effect in the opposite direction. The strongest case is Chinese in New York, where education, income, housing tenure, and ethnic employment are all significant. Three of these variables are significant for Dominicans and Guatemalans, and 2 for Vietnamese. We consider these cases to provide some support for the immigrant enclave model. Only one
27
socioeconomic variable is significant for Koreans in NY. Educated Koreans are no more likely than the uneducated, the wealthy no more than the poor, and those in ethnic sectors no more likely than those in the mainstream economy to live in Korean neighborhoods. In these respects, this case may better fit the model of ethnic community. There are five other cases in which there is a significant effect in the opposite direction to that predicted by assimilation: Afro-Caribbean renters are less likely, Chinese enclave workers in LA are less likely, Filipino renters in LA are less likely, higher income Koreans in LA are more likely to live in ethnic neighborhoods. An unusual case is the Japanese, the only instance in which immigrants are actually less likely than native-born to live in an ethnic setting. Since much of the contemporary Japanese immigration is temporary and business-related, it is plausible that there is little connection between these immigrants and Japanese neighborhoods based on descendants of earlier immigrants. Because the evidence in all five cases is contradictory, we cannot assign them fully to either model. Discussion and conclusion We have evaluated these groups’ residential patterns in three different ways, providing information on levels of isolation and segregation, on the characteristics of ethnic and non-ethnic neighborhoods, and on individuals’ location within or outside of ethnic neighborhoods. Though there is some support for the model of spatial assimilation, which is the starting point for most studies of this topic, we have shown that no single theoretical model can encompass the diversity of immigrant residential patterns. There is wide variation among groups in residential clustering (that is, in the degree to which people’s neighbors in the same census tract are members of the same group). We have stressed that this is associated with variation in group size. Larger groups are more isolated,
28
groups that have grown more (up to a certain point) show larger increases in isolation, and groups are more isolated in that sector of the metropolis (city or suburb) where they live in larger numbers. Reflecting the continuing rapid growth of most groups, values of the isolation index soared for most of them. Their ethnic neighborhoods certainly grew during the 1980s. But average odds ratios tended to decline, suggesting that – when we control for population growth – there was some dispersion in this period. Consider now the descriptions of ethnic and non-ethnic neighborhoods for every group. The expected pattern is found for some groups: their neighborhoods are predominantly in the central cities, living in an ethnic neighborhood means also living in a poorer neighborhood, and both suburbanization and living outside ethnic neighborhoods result in lower isolation, in greater exposure to whites and residence in a higher income area. Though there has been no empirical evidence before now on these points, this is the pattern anticipated by the assimilation model and taken for granted by most researchers. Yet we have seen that some groups’ neighborhoods are predominantly suburban. In some cases the suburban neighborhoods of a group have higher concentrations of group members than do their city neighborhoods. In some cases living in an ethnic neighborhood means living in a higher income area, compared to group members who live dispersed in the same portion of the metropolis. Thus the depressed central city enclave is not the only form of immigrant ethnic settlement. This conclusion is reinforced by analyses at the individual level. Indicators of acculturation are inversely associated with residence in ethnic neighborhoods for most groups. In some cases, the ethnic neighborhood tends to be chosen by those for whom it serves their practical needs (as indicated by their socioeconomic position) for an inexpensive and congenial
29
setting. And for several groups, it may also link members into ethnic employment. These are the functions of what we have called immigrant enclaves. Mexicans and Cubans in our study fit this model quite well. In most cases, however, the results diverge in some important way from the expectations of this model. Immigration and language play no role whatsoever in the locational patterns of some groups. Socioeconomic advancement and participation in the mainstream labor force in some cases have the opposite of the expected effects. For two New York groups, Indians and Filipinos, the preponderance of evidence suggests that ethnic neighborhoods fit the alternative model of ethnic community. In designing this study, we set up a comparison between New York, as a representative of an older style of urban development, and Los Angeles, as a newer and more decentralized form. We did find greater suburbanization of immigrant neighborhoods in LA. Our analyses also revealed that it was more likely in NY for suburban location to have a negative effect on the probability of living in an ethnic neighborhood, while in several LA cases the opposite is true. Hence there are certainly differences in the spatial organization of these two metropolitan regions. Yet in other ways the regions seem very similar: there are roughly similar levels of ethnic clustering, the differences between ethnic and non-ethnic neighborhoods and between city and suburban neighborhoods are similar, and there are no clear differences between regions in the predictors of living in ethnic neighborhoods. The contrast between them, therefore, is more in metropolitan form than in substance. We have discussed alternative models of the ethnic neighborhood as though each separate group that we studied could be interpreted, more or less, through the lens of one type of ethnic neighborhood. This is only a first approximation. There are processes of assimilation, self-
30
segregation, and stratification operating upon every group, to varying degrees. Probably this is why there are contradictory findings for many groups that we studied. If we were to compare different neighborhoods of a single group, we might well discover that some are better understood as immigrant enclaves, others as ethnic communities, and still others as minority ghettos. In any single neighborhood, whatever its overall qualities, we might well find that some residents are trapped within it, others use it as a temporary base from which to rise, and others – those with the most choice – prefer it as a culturally agreeable environment. Such possibilities call for different research strategies than we have used here, especially for intensive comparative field studies and original surveys. We are near the limit of what can be accomplished through the analysis of census data. The assimilation model is based on a conception of the ethnic neighborhood as a point of reception for new arrivals and an entry point into the ethnic labor market. But the process in which both the neighborhood and the niche job tend to be left behind by the next generation is applicable in only a minority of cases. We conclude that this is not a time, if ever there were a time, for a one-pattern-fits-all theory of residential location. The challenge now is to develop a theory of ethnic diversity, of contradictory processes of assimilation and separation, and of the conditions under which one or the other direction prevails.
31
Footnotes 1. The U.S. Census provides several different ways of identifying these population groups, each of which yields a different estimate of their size. We have employed definitions that allow us to identify groups consistently with both the 1990 Summary Tape Files and the 1990 Public Use Microdata that are required for our analyses. Asian groups are identified by the racial categories in the census. Latino groups are identified by the census’s Hispanic origin categories. And Afro-Caribbeans are defined by the ancestry category of “West Indian, except Hispanic origin groups.” The latter does not include Guyana. The 1980 STF and PUMS files allow the same Asian groups to be identified by race and Afro-Caribbeans by ancestry. Mexicans and Cubans are listed in the 1980 Hispanic origin categories. But neither Dominicans, Salvadorans, nor Guatemalans can be identified in the 1980 STF by Hispanic origin or by ancestry. For this reason, it is not possible to calculate segregation indices for these groups in 1980. Their total number (in Table 1) is drawn from the 1980 PUMS ancestry categories.
32
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Table 1. Population distribution by race and ethnicity for the Los Angeles and New York metropolitan regions, 1980 and 1990
1980 Suburbs
Cities
Total
Cities
1990 Suburbs
Total
New York CMSA Total Population Non-Hispanic Whites Afro-Caribbeans Dominicans Chinese Asian Indians Cubans Koreans Filipinos
8,019,328 4,154,025 262,996 167,840 125,814 51,513 75,578 23,323 33,537
100% 51.8% 3.3% 2.1% 1.6% 0.6% 0.9% 0.3% 0.4%
8,513,544 7,378,325 44,032 16,960 31,518 32,286 82,904 14,559 20,481
100% 86.7% 0.5% 0.2% 0.4% 0.4% 1.0% 0.2% 0.2%
16,604,872 11,532,350 307,028 184,800 157,332 83,799 158,482 37,882 54,018
100% 69.5% 1.8% 1.1% 0.9% 0.5% 1.0% 0.2% 0.3%
8,243,008 100% 3,477,049 42.2% 408,146 5.0% 359,192 4.4% 244,398 3.0% 100,061 1.2% 78,158 0.9% 74,242 0.9% 60,376 0.7%
8,882,719 100% 7,206,489 81.1% 107,191 1.2% 44,738 0.5% 72,712 0.8% 87,283 1.0% 78,123 0.9% 43,364 0.5% 44,203 0.5%
17,125,727 100% 10,683,538 62.4% 515,337 3.0% 403,930 2.4% 317,110 1.9% 187,344 1.1% 156,281 0.9% 117,606 0.7% 104,579 0.6%
4,511,789 2,463,786 904,624 45,300 51,366 62,628 39,494 61,785 23,780 25,520
100% 54.6% 20.1% 1.0% 1.1% 1.4% 0.9% 1.4% 0.5% 0.6%
6,985,779 4,568,912 1,319,751 16,060 63,881 62,937 34,648 85,838 24,213 13,360
100% 65.4% 18.9% 0.2% 0.9% 0.9% 0.5% 1.2% 0.3% 0.2%
11,497,568 7,032,698 2,224,375 61,360 115,247 125,565 74,142 147,623 47,993 38,880
100% 61.2% 19.3% 0.5% 1.0% 1.1% 0.6% 1.3% 0.4% 0.3%
5,497,553 100% 2,263,870 41.2% 1,543,266 28.1% 201,240 3.7% 88,678 1.6% 133,740 2.4% 85,933 1.6% 60,416 1.1% 48,609 0.9% 95,820 1.7%
9,033,976 100% 4,993,362 55.3% 2,193,177 24.3% 73,548 0.8% 219,103 2.4% 161,374 1.8% 108,265 1.2% 116,464 1.3% 96,855 1.1% 43,830 0.5%
14,531,529 100% 7,257,232 49.9% 3,736,443 25.7% 274,788 1.9% 307,781 2.1% 295,114 2.0% 194,198 1.3% 176,880 1.2% 145,464 1.0% 139,650 1.0%
Los Angeles CMSA Total Population Non-Hispanic Whites Mexicans Salvadorans Chinese Filipinos Koreans Japanese Vietnamese Guatemalans
Table 2.
Isolation Indices and Mean Odds-Ratios for NY and LA, 1980 and 1990
New York CMSA
1980 Cities OddsP* Ratio
Afro-Caribbeans Dominicans Chinese Asian Indians Cubans Koreans Filipinos
0.165 * 0.215 0.032 0.036 0.030 0.030
12.24 * 79.67 6.83 4 13.86 9.8
Los Angeles CMSA
Mexicans Salvadorans Chinese Filipinos Koreans Japanese Vietnamese Guatemalans
Cities OddsP* Ratio 0.435 6.99 * * 0.098 18.37 0.063 6.5 0.056 9.84 0.068 6.87 0.040 10.58 * *
* Data not available in 1980.
1990 Whole Area
Suburbs
Cities
P*
Odds-Ratio
P*
Odds-Ratio
P*
Odds-Ratio
0.042 * 0.014 0.017 0.206 0.009 0.012
2.35 * 1.44 3.32 34.45 4.08 3.92
0.147 * 0.179 0.027 0.124 0.022 0.024
10.82 * 63.99 5.48 19.94 10.1 7.56
0.202 0.235 0.209 0.055 0.040 0.086 0.048
9.64 18.85 36.18 5.49 4.83 15.11 8.85
1980
Suburbs OddsP* Ratio 0.087 0.075 0.028 0.044 0.147 0.032 0.021
3.26 3.64 1.5 4.75 23.51 4.97 3.21
Whole Area P*
Odds-Ratio
0.178 0.217 0.167 0.050 0.093 0.066 0.037
8.31 17.16 28.23 5.15 14.17 11.37 6.47
1990 Whole Area
Suburbs P* 0.424 * 0.057 0.048 0.025 0.083 0.031 *
Odds-Ratio 7.83 * 6.4 4.72 3.63 8.1 6.68 *
P* 0.429 * 0.075 0.055 0.041 0.077 0.036 *
Odds-Ratio 7.49 * 11.72 5.61 6.94 7.58 8.60 *
Cities P* 0.477 0.121 0.114 0.084 0.109 0.050 0.056 0.062
Odds-Ratio 5.31 7.82 10.42 4.92 10.7 5.19 5.95 7.12
Suburbs OddsP* Ratio 0.458 5.38 0.033 1.76 0.144 9.26 0.064 3.44 0.054 4.17 0.075 7.51 0.081 9.54 0.023 2.24
Whole Area P* 0.466 0.098 0.135 0.073 0.078 0.067 0.073 0.050
Odds-Ratio 5.35 6.2 9.6 4.11 7.06 6.71 8.38 5.58
Table 3. Distribution of group members across different kinds of neighborhoods: NY and LA, 1990
Neighborhood type
New York CMSA
Afro-Caribbeans N of tracts N of group members % of region's group members Dominicans N of tracts N of group members % of region's group members Chinese N of tracts N of group members % of region's group members Asian Indians N of tracts N of group members % of region's group members Cubans N of tracts N of group members % of region's group members Koreans N of tracts N of group members % of region's group members Filipinos N of tracts N of group members % of region's group members
Group Suburb
Non-group Suburb
Group Central City
Non-group Central City
30 24,226 4.7%
1990 82,965 16.1%
297 242,846 47.1%
2143 165,300 32.1%
17 14,268 3.5%
2003 30,470 7.5%
206 222,821 55.2%
2234 136,371 33.8%
6 2,880 0.9%
2014 69,832 22.0%
232 139,400 44.0%
2208 104,998 33.1%
37 16,245 8.7%
1983 71,038 37.9%
142 40,606 21.7%
2298 59,455 31.7%
59 42,039 26.9%
1961 36,084 23.1%
72 22,443 14.4%
2368 55,715 35.7%
36 12,204 10.4%
1984 31,160 26.5%
150 45,755 38.9%
2290 28,487 24.2%
48 7,925 7.6%
1972 36,278 34.7%
157 30,349 29.0%
2283 30,027 28.7%
Los Angeles CMSA
Mexicans N of tracts N of group members % of region's group members Salvadorans N of tracts N of group members % of region's group members Chinese N of tracts N of group members % of region's group members Filipinos N of tracts N of group members % of region's group members Koreans N of tracts N of group members % of region's group members Japanese N of tracts N of group members % of region's group members Vietnamese N of tracts N of group members % of region's group members Guatemalans N of tracts N of group members % of region's group members
Group Suburb
Non-group Suburb
Group Central City
Non-group Central City
137 704,203 18.8%
1343 1,491,862 39.9%
104 454,040 12.2%
968 1,086,338 29.1%
5 2,212 0.8%
1475 71,434 26.0%
122 114,150 41.5%
950 86,992 31.7%
88 108,550 35.3%
1392 110,188 35.8%
24 23,325 7.6%
1048 65,718 21.4%
31 28,883 9.8%
1449 131,895 44.7%
51 41,264 14.0%
1021 93,072 31.5%
50 28,645 14.8%
1430 79,559 41.0%
47 39,801 20.5%
1025 46,193 23.8%
60 37,917 21.4%
1420 78,201 44.2%
29 12,090 6.8%
1043 48,672 27.5%
91 46,702 32.1%
1389 51,469 35.4%
24 14,673 10.1%
1048 32,620 22.4%
10 3,862 2.8%
1470 39,985 28.6%
106 50,938 36.5%
966 44,865 32.1%
Table 4. Ethnic composition and income levels of different kinds of neighborhoods: NY and LA, 1990
Neighborhood type New York CMSA Afro-Caribbeans Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Dominicans Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Chinese Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Asian Indians Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Cubans Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Koreans Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Filipinos Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants
Group Suburb
Non-group Suburb
Group Central City
Non-group Central City
19.3 27.3 $41,154 26.2
5.4 49.9 $43,285 17.1
29.9 7.9 $32,414 41.5
6.0 22.3 $25,134 25.2
16.6 26.3 $27,179 38.9
3.2 52.9 $37,644 26.4
34.7 9.8 $19,368 44.9
5.3 26.3 $24,549 28.9
13.1 59.8 $32,523 29.5
2.2 83.9 $59,220 14.6
33.5 34.7 $26,168 51.3
4.1 58.3 $33,156 31.6
13.6 58.6 $44,827 29.1
2.2 80.4 $53,647 15.5
9.8 33.7 $33,275 48.2
2.6 45.4 $31,405 34.6
26.1 31.8 $28,503 52.2
1.3 75.3 $47,155 16.6
9.7 34.4 $26,441 43.9
1.7 40.6 $29,777 33.0
7.9 75.2 $53,743 28.0
1.3 84.1 $56,775 14.3
12.9 42.7 $32,998 51.8
1.6 62.1 $34,875 31.4
4.6 68.2 $47,641 20.7
1.3 78.7 $50,706 15.3
8.2 41.1 $33,606 39.0
1.5 53.1 $33,883 32.9
Los Angeles CMSA Mexicans Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Salvadorans Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Chinese Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Filipinos Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Koreans Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Japanese Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Vietnamese Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants Guatemalans Mean % of group members Mean % of non-Hispanic white Median household income Mean % of immigrants
Group Suburb
Non-group Suburb
Group Central City
Non-group Central City
76.6 9.4 $26,435 46.7
30.7 45.9 $36,743 25.5
78.0 6.6 $26,036 54.2
34.9 30.0 $28,848 38.9
9.8 6.6 $28,213 49.6
3.0 28.6 $31,538 38.4
18.1 12.7 $19,857 63.4
4.3 26.6 $28,584 39.9
24.8 29.8 $42,717 43.0
3.7 59.0 $49,550 23.7
34.2 10.8 $22,526 60.5
3.3 46.0 $37,845 34.6
18.4 30.6 $48,788 34.7
3.3 52.4 $42,170 25.6
18.2 24.6 $30,795 50.0
3.9 41.7 $34,445 36.6
11.4 48.4 $49,061 35.5
2.8 59.3 $47,536 24.3
20.7 19.9 $27,860 62.2
2.5 50.9 $37,496 34.2
17.5 40.8 $46,396 30.4
2.1 61.2 $48,686 22.0
17.0 28.7 $34,370 34.5
2.0 50.1 $39,095 30.7
14.0 41.7 $37,937 38.6
2.1 53.2 $43,630 25.7
12.5 29.5 $38,534 45.2
1.9 39.5 $32,185 36.6
6.7 6.9 $24,890 54.1
1.6 34.3 $33,807 34.7
9.6 13.0 $19,411 64.2
2.3 27.2 $28,230 40.3
Table 5. Predicting residence in an ethnic neighborhood: New York. (Logistic regression with unstandardized coefficients and standard errors)
Afro-Caribbeans Nativity US born ~ Post-1985 immigrant 1.038 *** (0.144) 1965-1985 immigrant 1.237 *** (0.099) Pre-1965 immigrant 0.366 ** (0.127)
~ 0.852 0.961 0.514
*** (0.222) *** (0.200) * (0.229)
~ 0.134 0.280 -0.263
(0.229) (0.196) (0.244)
~ 0.116 -0.445 -1.073
(0.598) (0.585) (1.147)
~ 1.654 1.609 0.757
*** (0.386) *** (0.211) *** (0.222)
~ 1.296 0.631 -0.439
Language Speaks English only Speaks English well Speaks English poorly
~ -0.016 0.649
~ 0.075 0.468
(0.203) (0.206)
~ 0.915 1.406
*** (0.204) *** (0.231)
~ 0.042 -0.418
(0.244) (0.412)
~ 1.195 1.699
*** (0.230) *** (0.250)
Education
-0.025
(0.011)
-0.053
*** (0.014)
-0.057
* (0.027)
-0.039
Household income
(0.002)
-0.006
*** (0.002)
-0.006
(0.004)
-0.565
(0.083) *** (0.185) *
Dominicans
*
Chinese
Asian Indians
Cubans
~ 0.695 0.936 -0.554
(0.590) (0.545) (0.816)
~ 1.676 1.779
*** (0.375) *** (0.390)
~ 1.115 2.059
* (0.437) ** (0.657)
** (0.014)
-0.014
(0.022)
-0.066
(0.042)
-0.005
** (0.002)
-0.001
(0.002)
-0.007
* (0.254)
0.586
*** (0.120)
0.396
(0.180)
-1.415
*** (0.235)
(0.433) (0.530) (0.240)
~ 0.837 0.302 0.256
*** (0.183) (0.466) * (0.130)
~ 0.032
(0.168)
~ -0.927
*** (0.232)
0.192
(0.312)
-0.509
(0.336)
(0.426)
0.033
(0.158)
0.085
(0.175)
-0.334
(0.376)
(0.004)
0.018
**
(0.007)
-0.009
(0.009)
(0.116)
0.496
**
(0.186)
0.747
*** (0.227)
-0.013
0.001
(0.001)
-0.004
Renter
-0.963
*** (0.085)
0.956
*** (0.137)
0.268
Employment Mainstream economy Enclave sector Worker Sector Owner Sector
~ 0.310 0.185 0.209
** (0.099) (0.121) * (0.087)
~ 0.033 0.598 -0.190
(0.107) *** (0.175) (0.143)
~ 0.438 0.191 -0.343
Self-employed
0.028
(0.137)
0.061
(0.152)
0.332
(0.003)
-0.002
(0.004)
0.011
*
(0.004)
0.008
(0.010)
0.004
(0.124)
**
(0.154) (0.846) (0.198)
~ -0.737 -0.556 0.280
(0.177)
-0.889
*
*
*
*
(0.003)
Age
-0.007
Married
0.039
(0.072)
-0.441
*** (0.085)
0.324
**
(0.124)
0.558
(0.286)
0.276
Suburban location
-0.046
*** (0.002)
-0.011
*** (0.001)
-0.109
*
(0.054)
-0.100
(0.104)
0.019
*** (0.001)
-0.022
*** (0.002)
-0.128
(0.142)
Constant
0.697
** (0.243)
-0.729
(0.380)
-0.568
(0.825)
-4.260
*** (0.402)
-2.836
*** (0.748)
0.334
(0.909)
Model Chi-Square
*
*
Filipinos
(0.617) (0.606) (0.748)
(0.010)
*
Koreans
1624.9
*
439.7
Note: ~, omitted category; ***, p
