Immigrant Assimilation in the United States: Economic and Cultural Factors - Prof. J. Garc, Study notes of Political Science

Download Immigrant Assimilation in the United States: Economic and Cultural Factors - Prof. J. Garc and more Study notes Political Science in PDF only on Docsity! C E N T E R F O R C I V I C I N N O V A T I O N A T T H E M A N H A T T A N I N S T I T U T E C C i C iv iC R ep o Rt N o. 5 3 M ay 2 00 8 Jacob L. Vigdor Associate Professor of Public Policy Studies and Economics, Duke University Faculty Research Fellow, National Bureau of Economic Research Measuring iMMigrant assiMilation in the united states Pu bl is he d by M an ha tt an In st itu te Measuring Immigrant Assimilation in the United States • Immigrant children born in Mexico are more distinct than immigrant children born in other foreign nations. This distinction is most obvious in terms of comparative naturalization rates, but extends to other dimensions as well. Mexican adolescents are imprisoned at rates approximately 80 percent greater than immigrant adolescents generally. • Naturalization rates among the foreign-born children of immigrants have been increasing. In this respect, the behavior of foreign-born, domestically educated immigrants resembles that of their parents educated abroad. Disaggregation by metropolitan area reveals widely varying rates of assimilation, due largely to the different combinations of immigrant groups that reside in each and the different characteristics of those groups. • Polyethnic New York City, which still attracts large numbers of European immigrants, has the second- highest assimilation index value among the metropolitan areas defined. • San Diego, despite its proximity to the Mexican border, has the highest. The methodology used to compute the assimilation index is outlined in the report and reviewed extensively in a more technical appendix. The method has been designed to take advantage of more than a century’s worth of historical data on the status of immigrants in the United States, made available to the public by the United States Census Bureau, and to provide the opportunity for annual updates. The assimilation index points to marks of success, to encouraging recent trends, and also to areas of concern. Within these areas of concern, the index provides some insight into the nature of the problem and the universe of appropriate potential policy responses. It is important to note, however, that this report neither proposes nor endorses any policy responses. Its sole purpose is to present information in a manner useful to concerned citizens and policymakers who hope to make informed decisions regarding the proper course of action. C iv ic R ep or t 53 May 2008 about the authoR Jacob Vigdor is Associate Professor of Public Policy Studies and Economics at Duke University, where he has taught since 1999, and a Faculty Research Fellow at the National Bureau of Economic Research. He received a B.S. in Policy Analysis from Cornell University in 1994 and a Ph.D. in Economics from Harvard University in 1999. His research interests are in the broad areas of education policy, housing policy, and political economy. Within those areas, he has published numerous scholarly articles on the topics of residential segregation, immigration, housing affordability, the consequences of gentrification, the determinants of student achievement in elementary school, the causes and consequences of delinquent behavior among adolescents, teacher turnover, civic participation and voting patterns, and racial inequality in the labor market. These articles have been published in outlets such as The Journal of Political Economy, The Review of Economics and Statistics, The Journal of Public Economics, The Journal of Human Resources, and The Journal of Policy Analysis and Management. Measuring Immigrant Assimilation in the United States chapter 1. introduction chapter 2. assimilation in 2006 chapter 3. assimilation in historical context chapter 4. case studies: Mexico, Vietnam, and italy chapter 5. the next generation chapter 6. conclusions appendix assimilation-index Values by birthplace, 2006 assimilation-index Values by Metropolitan area, 2006 endnotes CONTENTS 1 5 10 16 21 25 27 35 38 43 C iv ic R ep or t 53 May 2008 immigrants and natives today larger than they were in the recent or distant past? And how rapidly do these differences shrink as immigrants spend more time in the United States? The study of immigrant assimilation is not new, nor is it in a period of dormancy. Past studies of immi- grant assimilation range from detailed observation of particular immigrant enclaves to broader statistical analyses of nationally representative samples.4 The observational studies provide rich detail on the hab- its and interpersonal connections of actual people but can be criticized on the grounds that they don’t permit generalization about an entire population of immigrants. The broader statistical analyses are easily generalized but often focus on a limited set of mea- sures, the most prominent ones being earnings and other labor-force outcomes, English-speaking ability, naturalization, and intermarriage. The assimilation index builds on this previous litera- ture by using broad, nationally representative samples that include native-born Americans and by analyzing a wider array of measures. The index summarizes this large quantity of information in a form that can be applied to very broad and very narrow groups of a concise message and oversimplifying an inherently complex issue. The purpose of this report is to present information relevant to these ongoing debates by measuring the degree of distinction between the native- and foreign- born populations of the United States, or alternatively, their degree of assimilation.3 The analysis introduces a numeric index of assimilation, which measures the extent to which the foreign-born and native-born can be distinguished from each other on the basis of commonly observed social and economic data. The index measures the ability of a statistical algorithm to predict which individuals in a random sample of United States residents were born abroad. An appen- dix to this report provides both a general and a more technical overview of the method used to compute the index. The index can be computed for individual country-of-origin groups, sets of immigrants resid- ing in specific cities or regions, and for immigrants who have spent varying lengths of time in the United States. The index, which makes use of data provided by the Census Bureau, can be computed using data capturing conditions as recent as 2006, and as distant as 1900. The index can serve to answer two simple but important questions: Are the differences between 2 0 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 30,000,000 35,000,000 40,000,000 Fo re ig n- Bo rn P op ul at io n Me xico Ame rica s e xce p t Me xico Asia Eu ro p e Afr ica /O ce a n ia 1960 1970 1980 1990 2000 Year Figure 1. The Foreign-Born population of the united States, by region of Birth, 1960–2005 Measuring Immigrant Assimilation in the United States 3 immigrants. The method requires no prior assumptions regarding which characteristics are most effective in distinguishing immigrants from natives. Moreover, the inclusion of irrelevant characteristics—that is, ones that do not actually help distinguish immigrants from natives—has no impact on the index. The social and economic data used to compute the overall, or composite, assimilation index can be sepa- rated into three sets of factors, which in turn can be used in isolation to compute more narrowly focused component indexes: Economic assimilation describes the extent to which immigrants, or groups of immigrants, make produc- tive contributions to society indistinguishable in aggregate from the contributions of the native-born. Economic assimilation is low when immigrants clus- ter at certain points on the economic ladder—most notably, the low-skilled rungs—and high when their distribution on the economic ladder matches that of native-born Americans. The economic assimilation index is particularly rel- evant to two major areas of policy debate: the impact of immigration on the labor market; and the fiscal impact of immigration. A simple calculation suggests that immigrant participation in the labor market gener- ates net benefits, through lower consumer prices and higher shareholder returns, of $50 billion per year.5 But such benefits are accompanied by reductions in wages for native workers competing in the same market.6 It has also been argued that the immigration of highly skilled, entrepreneurial workers creates new jobs.7 The economic assimilation index can help track whether the skills of immigrants are matched to or mismatched with those of native workers. From a fiscal perspective,8 the economic assimilation index reveals information that can potentially address concerns that immigrants take up welfare benefits at disproportionate rates9 or rely on charitable provision of health care.10 Economic assimilation also correlates with immigrants’ contributions to the Social Security and Medicare trust funds11 and may help determine the impact of immigrants’ housing demand on property values and local property tax revenues.12 The following factors are used to measure economic assimilation: • Earned income in the year prior to the survey (not available for 1900–1930) • Labor-force participation • Unemployment (not available for 1900–1930) • A quantitative ranking of occupations by aver- age income in that occupation in 1950. • Educational attainment (not available for 1900–1930) • Home ownership (not available for 1900–1930) Since the labor-force participation and earnings pat- terns of males and females have historically been quite distinct, the index measures the immigrant-native dif- ferences in these factors separately by gender. Cultural assimilation is the extent to which immi- grants, or groups of immigrants, adopt customs and practices indistinguishable in aggregate from those of the native-born. Factors considered in the measure- ment of cultural assimilation include intermarriage and the ability to speak English, which have been the focus of many previous efforts to track immigrant assimilation in the United States. Cultural assimilation also incorporates information on marital status and childbearing. It is important to note that cultural as- similation is not a measure of a group’s conformity with any preconceived ideal. Changes in the customs and practices of the native-born can promote cultural assimilation just as easily as changes among the for- eign-born. Some of the most spirited charges in immigration policy debates concern the cultural aspects of immi- grants’ integration into American society. While some aspects of this debate, such as the value of traditional American culture, are relatively abstract, other aspects are very concrete. State and local governments, for example, often face cost burdens associated with pro- viding services—most notably, public education—to non-English-speaking immigrant groups.13 Incorporat- ing childbearing patterns into the index allows it to measure the potential impact of immigration on public schools in the near term, and on broader fiscal issues in the long term. Marital patterns, including the deci- C iv ic R ep or t 53 May 2008 sion to marry a native-born spouse, or the decision to reside in the United States without one’s spouse, provide clues as to immigrants’ long-term intentions, which are critical to understanding the long-term fiscal impact of immigration. The following factors are used to measure cultural assimilation: • Ability to speak English • Intermarriage (whether an individual’s spouse is native-born) • Number of children • Marital status Civic assimilation is a measure of immigrants’ formal participation in American society, primarily through naturalization. Since native-born residents of the United States are citizens by default, civic assimila- tion increases as the proportion of immigrants who are naturalized citizens increases. The index of civic assimilation also incorporates information on past or present military service, except in the years from 1900 to 1930. Since military service is more common among males than females, the index measures the immigrant-native difference separately by gender. Both naturalization and military service are signals of a strong commitment to the United States—though the power of these signals is directly influenced by government policy. The government sets standards for naturalization and, to some extent, determines the benefits of naturalization, by setting differential policies for citizens and noncitizens; military recruit- ment needs determine the number of opportunities for service in the armed forces. Changes in civic as- similation could, in theory, reflect either changes in immigrant civic attitudes or changes—perhaps even anticipated changes—in policy. It is important to note that the Census Bureau collects no information on immigrants’ legal status, which means that this study cannot use legal status as a factor in the computation of civic assimilation. 4 To some extent, civic assimilation is an even stronger indicator of immigrants’ intentions than cultural as- similation. The choice to become a naturalized citi- zen, or to serve in the United States military, shows a tangible dedication to this country. Civic assimilation may thus forecast the long-run impact of immigration, both in a concrete fiscal sense and in a more abstract cultural sense. The information in this report will not settle larger debates over immigration policy. Assimilation may not be necessary for immigrants to make net positive contributions to society. Assimilation may even be undesirable under certain circumstances. For example, immigration may have the most positive net impact on economic growth if immigrants are economically distinct from natives. Immigrants may choose to natu- ralize because they fear a change in immigration policy rather than because they wish to make a commitment to the United States. Detailed information on immigrant assimilation will help those wishing to make reasoned arguments in the immigration policy debate, but it will not resolve the controversies in and of itself. The remainder of this report is structured as follows. Chapter 2 reports basic results for 2006. Chapter 3 places these results in context by reporting additional index calculations for the period between 1900 and 2005. Whereas the assimilation index itself provides only a snapshot of immigrants’ status in the host society, analysis of data over time can actually illuminate the assimilation process itself and changes in that process over time. Chapter 4 augments the analysis by studying immigrants belonging to “Generation 1.5,” those indi- viduals born abroad but brought by their parents to the United States before they commenced their formal edu- cation. Chapter 5 presents an in-depth analysis of three immigrant groups: contemporary Mexican immigrants; contemporary Vietnamese immigrants; and the Italian immigrants of the early twentieth century. Chapter 6 summarizes the main conclusions of the study. The final chapter is a detailed methodological appendix. Measuring Immigrant Assimilation in the United States vadoran immigrants show the lowest degree of civic assimilation. More surprisingly, Canadians, despite their full economic and cultural integration with the native-born population, display only a modest degree of civic assimilation. Given the common border of Canada and the United States, Canadian immigrants may view their stay in this country as temporary and the naturalization process as unnecessary. The country-of-origin groups with the highest de- grees of civic assimilation have a common legacy of American military intervention at some point in the twentieth century. Foremost among them are im- migrants born in Vietnam, who are more assimilated along civic dimensions than any other large group in 2006. This achievement is particularly noteworthy given Vietnamese immigrants’ unremarkable degree of cultural assimilation, as well as their level of economic assimilation, which is slightly below that of natives of Canada, Cuba, Korea, and the Philippines. In addition to computing degrees of assimilation of individual country-of-origin groups, the index can evaluate all immigrants residing in a particular metro- politan area. A complete set of index numbers for areas with significant immigrant populations can be found in the Appendix. Figure 7 shows the index values for the ten largest immigrant destinations in 2006.17 To a large extent, variation across metropolitan areas can be explained by variation in the country-of-origin groups most strongly represented in the population. 7 Figure 4. Economic assimilation by Country of Origin: 2006 Figure 5. Cultural assimilation by Country of Origin: 2006 Figure 6. Civic assimilation by Country of Origin: 2006 Figure 7. assimilation by Metro area: 2006 C iv ic R ep or t 53 May 2008 Houston, with its proximity to Mexico, has the lowest assimilation-index value in this set of metro areas. Los Angeles, which has a very large Mexican population along with considerable numbers of Asian immigrants, is above Houston but below most other metropolitan areas. The polyethnic New York City area, which at- tracts a number of European immigrants in addition to people from the developing world, has the second- highest index value among the metropolitan areas shown here. Washington, D.C., also claims a relatively high index value. Miami, with its large concentration of immigrants from Cuba and other Caribbean nations, posts an index value slightly higher than the national average. Somewhat surprisingly, San Diego, in spite of its close proximity to the Mexican border, registers as the destination with the highest assimilation index among those listed here. To this point, reported index values have provided a simple snapshot of a dynamic process. Assimilation does not occur instantaneously but rather evolves as immigrants learn more about the host society and take steps, both formal and informal, toward more complete participation in it. Chapter 4, which expands the study of the assimilation index backward through time, will provide an opportunity to observe this process. Figure 8 presents a different type of opportunity by comparing the 2006 assimilation-index values of immigrants who report having arrived in the United States at varying points in time. There are several reasons that immigrants who ar- rived at varying points in time might exhibit varying degrees of assimilation in 2006. As stated above, one reason is that the assimilation process takes time. A second reason is selective return migration. Immigrants who experience difficulty in their transi- tion to the host society, and therefore look poorly assimilated when here, may be more likely to return to their origin country, or move on to a different host country.18 The set of immigrants who remain in the United States for an extended period of time will then appear more assimilated, even if their rate of assimilation has been quite modest. Finally, changes in immigration policy or world economic, social, and political conditions may change the composition of the immigrant population over time. Immigrants who arrived prior to 1965, for example, faced a differ- ent immigration policy from ones confronting more recent arrivals, and may differ for that reason. The trends in Figure 8 may reflect any of these explana- tions. Longitudinal analysis in the next chapter will be able to rule out the third explanation but will not distinguish between the first two. 8 Figure 8. assimilation by Years in the u.S.: 2006 Measuring Immigrant Assimilation in the United States Consistent with both the view that immigrants assimi- late over time and that immigrants who fare poorly are more likely to depart, there are several clear positive trends in Figure 8. In 2006, immigrants who arrived in the United States within the previous year or two are easily distinguished from the native-born, primar- ily because they are very unlikely to be citizens. The composite and civic assimilation indexes for this group are very close to zero. By comparison, immigrants who arrived ten years earlier, in the mid-1990s, post overall assimilation-index values of around 20 and civic assimilation-index values closer to 30. Immigrants who arrived in the mid-1980s had by 2006 attained a composite-index value of 30 or higher. The most as- similated immigrants shown here are those who arrived in the mid-to-late 1960s. This group posts composite- index values in the 60–70 range. There are interesting contrasts among the component assimilation indexes in Figure 8. Civic assimilation, un- surprisingly, begins close to zero but increases steadily, reaching values near 80 among immigrants who arrived a generation ago. Economic assimilation also shows an unmistakable upward trend, beginning in the mid-70s for recent arrivals and nearing the maximum value of 100. Cultural assimilation shows a comparatively weak trend among more recent immigrants; as of 2006, immigrants who arrived in the mid-1980s posted as- similation-index values only a few points higher than the most recent arrivals. A more recognizable upward trend appears among immigrants arriving prior to the mid-1980s. Some portion of this trend may be attribut- able to the experience of immigrants who arrived as youths in the 1960s or 1970s, learned English in the public schools, and married here in the United States rather than abroad. Chapter 5 will consider this type of first-generation immigrant in greater detail. While caveats apply to this analysis, as it is based on cross- sectional rather than truly longitudinal information, this evidence points once again to the conclusion that the process of cultural assimilation is not a necessary precursor of either economic or civic assimilation. 9 C iv ic R ep or t 53 May 2008 12 Canada. By 1990, these countries of origin represented a much smaller proportion of all immigrants. Second, the rapid growth of the immigrant population since 1990 has not occasioned a decline in assimilation comparable in scale with that witnessed between 1900 and 1920, when the immigrant population grew at a much slower rate. If the duration of immigrants’ stays in the United States were the only determinant of their degree of assimilation, we would expect periods of more rapid growth in the immigrant population to be periods of declining assimilation-index values because the proportion of that population that was newly arrived would be relatively large. The assimilation index is clearly influenced by other factors, however. Federal policy influences rates of naturalization and induction into the military; moreover, certain immigrant groups, notably those from English-speaking nations, arrive in the United States with a head start. The impact of new immigrant arrivals on the assimilation index, then, can be either diminished or augmented by changes in policy or changes in the composition of the flow of immigrants. Figure 12 shows how these factors can help explain both the low level of the assimilation index observed since 1990 and the stability of the index during this time period compared with earlier episodes of rapid growth in the immigrant population. It plots the as- similation-index value for immigrants who arrived in the United States within the past five years, for Census enumerations in 1900, 1910, 1920, 1980, 1990, and 2000, and for the American Community Survey for 2006. The shift in the composition of the immigrant population between 1900 and 1920, away from North- ern and Western Europe and toward Southern and Eastern Europe, is evident in the first three points on the chart. In 1900, newly arrived immigrants posted an assimilation index of over 20; by 1920, this value had fallen by more than two-thirds, to 7. In more recent years, the assimilation of newly arrived immigrants has been consistently low, ranging from around 8 in 1980 to just over 2 in 2000, but has not displayed the strong downward trend evident in the first two decades of the twentieth century. There has been, in fact, an uptick in the assimilation of newly arrived immigrants since 2000. Figure 12. The assimilation of Newly arrived Immigrants: 1900–2006 Figure 13. Composite assimilation by Years in the u.S. Year Measuring Immigrant Assimilation in the United States 13 A more complete picture of the change in the as- similation process that took place between 1900 and 1920 appears in Figure 13. This figure mirrors Figure 8 above, plotting the assimilation index for immigrants according to the number of years since their arrival. In all years, immigrants with more experience of the United States tend to be more assimilated. Note, however, that the assimilation “hill” representing the year 1920 is at almost every point lower than the hills representing 1910 and 1900. The hill representing 1910 is likewise lower than the 1900 hill for the first 20 years or so. Thus, the tendency of newly arrived immigrants to be less assimilated in 1920 than they were in 1900 or 1910 applies at other points in the assimilation process as well. Immigrants arriving in 1900 were consider- ably less assimilated in 1920 than the immigrants of 1880 were in 1900. Between 1900 and 1920, growth in the immigrant population was accompanied by a slowdown in the assimilation process. Figure 13 also includes assimilation hills for 2000 and 2006 (data from the 1980 and 1990 Census enumera- tions are not sufficiently rich to permit similar plots for those years). In contrast to the earlier period, when each decade’s hill lay below the one immediately preceding it, there is a substantial degree of overlap between the 2000 and 2006 hills at virtually all points. These two hills are also lower than those of the early twentieth century, which explains why contemporary composite assimilation is lower than it was in that earlier period. These “assimilation hills” show that at any given point in time, immigrants who have been in the United States for a longer period of time are more assimi- lated. One might also conclude from these graphs that the assimilation index tends to rise for individual cohorts as they spend more time in the United States. There is an alternative explanation, however, which graphs like Figure 13 and its earlier counterparts can- not rule out: that immigrants who entered long ago have always been more assimilated than those who arrived recently. There are a few clues in Figure 13 that this is not the case. The newly arrived immigrants of 2000, for example, are the immigrants who in 2006 had arrived six years earlier. It is difficult, however, to use a graph like Figure 13 to track one cohort’s progress. Figures 14 through 18 make the job easier. Rather than compare the experience of many different cohorts at a single point in time, these graphs follow the progress of individual cohorts across multiple points in time. Figure 14 presents true longitudinal information on the progress of immigrant cohorts between 1900 and 1930, focusing on three groups: those arriving Figure 14. The progress of Individual Cohorts 1900–1930 C iv ic R ep or t 53 May 2008 between 1895 and 1900, between 1905 and 1910, and between 1915 and 1920. Consistent with Figure 12, each cohort begins at a lower level of assimilation than the one immediately preceding it. Moreover, the cohorts exhibit differing rates of progress over their first full decade in the United States. The earli- est-arriving group posts a 20-point increase in the assimilation index between 1900 and 1910. This gain is followed by much weaker progress in the second decade. The second cohort shows a much smaller increase over its first decade. Between 1920 and 1930, assimilation accelerates for all three groups. The overall decline in assimilation between 1900 and 1920 reflects both the decline in initial position across cohorts and the tepid progress of all cohorts in the period 1910 to 1920. Figure 15 presents a comparable picture for the period 1980 to 2006.20 Consistent with the information in Figure 12, there is some evidence of a slight decline in the assimilation of newly arrived immigrants over this time period. Tracked over time, however, each cohort appears to show little slowdown in the rate of assimilation; each has either posted, or appears on track to post, an increase of 15 to 18 points over its first decade, followed by gains at the same rate or faster in the second decade. The newly arrived immigrants of 1975–80 appear much less assimilated than their Figure 15. The progress of Individual Cohorts: Composite Figure 16. The progress of Individual Cohorts: Economic Figure 17. The progress of Individual Cohorts: Civic Figure 18. The progress of Individual Cohorts: Cultural 14 Measuring Immigrant Assimilation in the United States and very similar, index values in the Census enu- merations of 1980 and 1990. These cohorts’ progress over the subsequent decade is far from uniform. The Vietnamese immigrants of the late 1970s attained a composite-index value of nearly 40 by 1990. Mexican immigrants of the same time period scarcely reached a value of 10 that same year, despite having started at a slightly higher level. An even stronger contrast can be seen among the arrivals of the late 1980s. By 2000, the Vietnamese immigrants in this cohort had once again neared an index value of 40, while their Mexican counterparts had posted very little improvement. Cohorts arriving after 1995 have been more distinct upon arrival, with Vietnamese immigrants tending to appear more assimilated at the entry point. The pat- tern for Vietnamese immigrants of swifter assimilation continues, however. It bears repeating at this point that the changes in the assimilation index viewed here could, in theory, reflect either of two mechanisms: Vietnamese immigrants may truly experience faster acclimation to American soci- ety over time; or they may be more likely to exit the country in the event that they assimilate poorly.22 How do these two polar cases compare with the ex- perience of Italian immigrants of the early twentieth Figure 20. assimilation of Italian Immigrants century? Figure 20 shows that Italians serve as some- thing of an intermediate case. Italian immigrants of 1895–1900, and of 1905–10, are very poorly assimilated upon arrival, with index values quite similar to those of newly arrived Mexicans and Vietnamese in 1980 and 1990. Their progress in the subsequent decade is faster than that of recent Mexican immigrants but slower than that of recent Vietnamese, with index values rising to the upper teens for both cohorts. Italian immigrants arriving between 1916 and 1920, a period when the overall flow of immigrants to the United States had slackened considerably, show signs of rapid assimilation between 1920 and 1930, though still not as rapid as that exhibited by recent cohorts of Vietnamese immigrants. If the long-run image of early-twentieth-century Italian immigrants is that they were successful in assimilating into American society, then a comparison of their early assimilation trajectory with the two recent cohorts now under analysis leads to some quick conclusions. Vietnamese immigrants, taken as a whole, are well on track to be considered successful. Mexican immigrants, by contrast, display much more worrisome patterns. If these two groups are indeed on different trajecto- ries, is there any policy solution that might encourage stronger assimilation on the part of Mexicans? Put dif- ferently, if we could change one aspect of Mexican 17 C iv ic R ep or t 53 May 2008 immigrants so as to make their experience more like that of the Vietnamese, what might that change be? To think about these hypothetical questions, it is use- ful to examine the component assimilation indexes for the cohorts studied in Figure 19. Figure 21 begins the process by plotting the economic assimilation of members of the two groups, by arrival cohort, between 1980 and 2006. Here, a strong contrast between groups appears. Vietnamese immigrants, particularly those in the first arrival cohort, display a much greater degree of economic assimilation upon arrival. Economic as- similation for newly arrived Mexicans in 1980 is around 50, whereas for Vietnamese immigrants it is over 85. Not only do immigrants born in Vietnam begin at a higher economic level; they show stronger signs of economic assimilation over time. The sole exception to this pattern is among those arriving in the United States between 2001 and 2005; in this group, Vietnamese im- migrants enjoy a clear starting advantage but appear to regress between 2005 and 2006, whereas there are signs of real progress among Mexican immigrants. Figure 21. Mexicans and Vietnamese: Economic assimilation Figure 22. Mexicans and Vietnamese: Civic assimilation 18 Measuring Immigrant Assimilation in the United States This intriguing contrast will merit further observation as more data become available in future years. Strong contrasts between groups appear once again in Figure 22, which examines trends in the civic as- similation index by country of origin and arrival cohort between 1980 and 2006. Immigrants from both na- tions start at low levels of assimilation in each cohort. Vietnamese immigrants arriving in the late 1970s, late 1980s, and late 1990s make considerable progress over their first full decade in the United States. Mexi- can-born immigrants make very little progress. This contrast does appear to extend to the cohorts arriving after 2000. Why do Vietnamese immigrants start at a higher eco- nomic level, and make more rapid progress along both economic and civic dimensions? While a complete discussion of the differences could consume an en- tire monograph, several easy explanations bear brief discussion. Vietnam, at least in the early part of the time period under study, was a Communist country lacking normal diplomatic and trade relations with the United States. The set of individuals choosing to flee a Communist nation to settle in a nation with a free- market economy likely included a high proportion of entrepreneurs or skilled workers seeking better com- pensation. The costs of exiting Vietnam and making the trip to the United States were substantial, and the costs of returning to Vietnam after settling here would also have been great. Vietnamese immigrants had relatively strong incentives to achieve full membership in American society. As political refugees, many also benefited from favorable naturalization rules. For Mexicans, the costs of moving to the United States from Mexico are not so substantial. While the United States is undoubtedly an attractive location for highly skilled and entrepreneurial Mexican-born workers, it also offers wages and living standards much higher than lower-skilled Mexican workers could expect in their own country. Those Mexicans who enter the country illegally stand no chance of progress along the lines of civic assimilation, and they surely face consid- erable barriers to significant economic advancement. Even if provided the opportunity to progress toward citizenship, Mexican immigrants’ incentives to do so may be muted should they intend to return to their home country after a brief stay in the United States. Do the contrasts in assimilation between Mexican and Vietnamese immigrants extend to the cultural dimen- sion? Figure 23 shows that the answer, perhaps surpris- ingly, is no. Among cohorts arriving in the late 1970s or late 1980s, an immediate upward trend in cultural assimilation appears only for Mexican immigrants. A Figure 23. Mexicans and Vietnamese: Cultural assimilation 19 C iv ic R ep or t 53 May 2008 not. Rather than divide the distinguishing characteris- tics into economic, cultural, and civic subgroups, this analysis will effectively partition the factors into “natu- ralization,” which basically mimics civic assimilation, and “all other,” denoting a combination of economic and cultural factors. It should be noted that citizenship cannot be used as a distinguishing characteristic in 1900 and 1910 because the Census questionnaire did not collect information on the citizenship of individuals under the age of 21 in those years. In 2006, the algorithm used to distinguish between adolescents and young adults born in this country or abroad takes advantage of the following patterns in the American Community Survey: • Adolescents and young adults born abroad, but brought to the United States by age five, are: u Perfectly distinguishable from natives when they are not citizens of the United States. u Much less likely to speak English. u Less likely to reside in group quarters. u More likely to have been married at any particular age. u Less likely to be enrolled in school when between the ages of 17 and 22. u More likely to be enrolled in school at the ages of 23 or 24. u Less likely to be a parent. u More likely to live with their own parents between the ages of 18 and 24. u Less likely to participate in the labor force at ages 16 through 19, and at ages 22 through 24. The distinctions between young immigrants and na- tive-born adults are troubling in some respects but not others. The higher tendency to drop out of school is a frequently analyzed concern regarding children of immigrants from Mexico and its neighbors. The lack of English ability in a group of young immigrants who have spent a minimum of seven years in the United States also warrants concern. The lower rates of teen parenthood and higher rates of school enroll- ment at ages typically associated with postgraduate education are encouraging, but this latter pattern in particular may reflect the experiences of a very dif- ferent subgroup of foreign-born but American-raised young adults. Perhaps the most important generalization to be made about the differences between native- and foreign-born adolescents and young adults is that they are relatively small. This conclusion is readily seen in Figure 24, which tracks the assimilation of Generation 1.5 using indexes that include and exclude citizenship as a dis- tinguishing characteristic for the years 1900 through 2006. For this 106-year time period, the assimilation 0 10 20 30 40 50 60 70 80 90 100 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 E x c luding c it iz ens hip 2005 Ye a r As si m ila tio n In de x Inc luding c it iz ens hip Figure 24. The assimilation of Generation 1.5 22 Measuring Immigrant Assimilation in the United States index excluding citizenship is consistently high, never falling below a value of 90. Without incorporating information on citizenship, it remains difficult to distinguish individuals raised in the United States but born in different countries. When citizenship is used as a distinguishing variable, it becomes much easier to differentiate the two groups. Assimilation-index values including citizenship information range from the mid- 40s in the early part of the twentieth century to a low of 18 in 1980, and have trended back upward into the 40s in recent years. While the assimilation index shows signs of increasing in recent years both among adult immigrants and their foreign-born children, the trend is more pronounced in Generation 1.5. This increase has been driven primarily by increased naturalization rates among individuals born abroad but raised in the United States. Chapter 3 presented evidence that immigrants arriving between 1975 and 1990 showed few signs of cultural assimilation over their first decade or more of residence in the United States, followed by clear increases. One possible explanation for this pattern, offered above, is that the Generation 1.5 group caused the observed increase in cultural assimilation, as it aged into the analysis sample of individuals between the ages of 22 and 65. The evidence in Figure 24 supports this explanation. Individuals born abroad but raised in the United States have consistently high assimilation-index values in all dimensions except citizenship. Moreover, these individuals will age into the analysis sample of adult immigrants after a lag of one to two decades. While assimilation is generally high in Generation 1.5, important variation exists within this group. Just as the analysis in Chapter 4 showed that first-genera- tion Mexican immigrants display a rate of assimilation much slower than that of other current or historical groups, foreign-born children of Mexican immigrants are less assimilated than the foreign-born children of immigrants born in other countries.25 In 2006, the Gen- eration 1.5 assimilation index excluding the question of citizenship status was 95 for those born in Mexico and 99 for those born in other countries. The index including citizenship was 18 for those born in Mexico and 62 for those born in other countries. Figure 25 shows that the children of Mexican immigrants have had below-average assimilation-index values for the entire period since 1980. As is the case in the overall population, there is some evidence of modest in- creases in assimilation for Generation 1.5 Mexicans in recent years. As low as the Generation 1.5 indexes are for Mexican-born children of immigrants, they may be overstated to some extent. This is because certain characteristics that are less pronounced in the immigrant population at large are actually dispropor- tionately common among young Mexican immigrants. Among girls aged 12–19 born in a country other than Figure 25. The assimilation of Generation 1.5: Mexico 23 C iv ic R ep or t 53 May 2008 the United States or Mexico but raised in the United States, roughly one in 100 lives with one or more of her biological children. This rate is lower than that found in the native-born population. Because of this pattern, the assimilation index treats this indicator of teen childbearing as a distinctively native-born char- acteristic. Mexican-born young immigrants, however, have a much higher rate of teen childbearing: nearly one in 20 Mexican-born girls aged 12–19 lives with one or more of her own children. Similarly, young immigrants born outside of Mexico are less likely to be incarcerated or otherwise institu- tionalized than natives in the same age group. Among those aged 12–24, the rate in the immigrant population is 1.0%, while in the native-born population it is 1.4%. Thus, the assimilation index treats institutionalization as a distinctively native characteristic. Mexican im- migrants, however, have an institutionalization rate of 1.8%. These contrasts raise one potential concern with the method of computing the assimilation index: the index looks at average differences between immigrants and natives, which can be misleading when some immi- grants are doing much better than, and others much worse than, natives. Fortunately, this type of concern is uncommon. Adjusting the assimilation index for Generation 1.5 Mexican immigrants to account for patterns that look not only different from those of natives but from those of other immigrants produces very little change. Because institutionalization and teenage childbearing are relatively uncommon, they contribute very little to the overall index. Taken as a whole, immigrants to the United States show consistent evidence of acclimating to American society over time, and between generations. There is also some evidence to suggest that the assimilation process, particularly along cultural dimensions, has strengthened over the past few years. As seen in this brief analysis, immigrants born in Mexico and most immigrants groups born elsewhere prove to be on a separate trajectory. 24 Measuring Immigrant Assimilation in the United States appendix This section begins with an intuitive description of the procedure used to compute the assimilation index and is followed by a more technical discussion of the sta- tistical model used to distinguish the native-born from the foreign-born. The process used to generate the assimilation index can be divided into four steps. Step 1: Build a Model That Predicts Immigrant Status Imagine having access to a wide array of information on the social and economic characteristics of a group of people but no information on their place of birth. On the basis of social and economic information, it might be possible for a well-informed person to guess which individuals in the group were born in the United States and which ones were born abroad. Knowing that an individual has difficulty speaking English, for example, or that he or she works as an unskilled laborer, may be sufficient to infer that a person was born abroad. The assimilation index is a measure of how easy it is to infer an individual’s place of birth, whether domestic or abroad, on the basis of common social and economic data. The more difficult it is to tell immigrants and na- tives apart, the higher the index is. Computation of the index begins with data on a representative sample of the American population, evenly split between native- and foreign-born individuals who are at least 25 but no more than 65 years of age. The data source and exact set of variables used are described below. Intuitively, the index is computed by guessing which individuals in the data set are native-born and which ones are foreign-born and seeing what proportion of the guesses are correct. The first step in the process is coming up with a method for making guesses. One could imagine many possible rules for guessing whether an individual is an immigrant on the basis of social and economic information; in practice, the index begins by employing a statistical procedure guaranteed to arrive at the most accurate guesses pos- sible. The procedure is known as a probit regression. This procedure automatically identifies the personal characteristics most strongly associated with immigrant status, as well as those with little relevance. With this statistical procedure at the heart of the index, there is no need to subjectively assign varying weights to particular characteristics, such as income or marital status. The use of this procedure distinguishes the index from many other popular measures, such as indexes used to rank colleges. As discussed in Chapter 2, the statistical model un- derlying the assimilation index considers three sets of factors: economic, cultural, and civic. The model considering all three sets produces the composite as- similation index. In addition to the composite index, this report analyzes three component assimilation indexes, which are derived from statistical models that analyze only one of the three sets of factors. Step 2: Use the Model to Make Educated Guesses Once the model is constructed, information on actual immigrant status is temporarily eliminated from the data set. Having removed this information, the model is then used to make educated guesses, or predictions, regarding which individuals are, in fact, foreign-born. The predictions take the form of probabilities. A pre- dicted value of zero indicates that there is virtually no chance that the individual in question is foreign-born. A predicted value close to 100% indicates that an in- dividual is almost certainly foreign-born.26 Complete assimilation is defined as a scenario in which it is impossible to distinguish immigrants from natives; that is, when the two groups are on average identical along all the dimensions incorporated into the probit model. In such a scenario, the model will assign each individual in the sample a 50% chance of being an im- migrant. The educated guess of which individuals are immigrants would be, in this case, no more accurate than a random coin flip. At the other extreme, when the model can predict perfectly which individuals are native- and foreign-born, immigrants will receive a predicted probability of 100% and natives a predicted probability of zero. 27 C iv ic R ep or t 53 May 2008 Table 1 presents educated guesses of immigrant status for three hypothetical individuals.27 While the sets of characteristics of each individual are contrived, and the set of characteristics included in Table 1 is far smaller than the set of characteristics incorporated in the probit model, the predicted probabilities are authentic and computed using the same formula used to determine the assimilation index in 2006. Case 1 concerns an individual who is not a U.S. citizen, is not married to a native-born American, does not speak English, and has not served in the U.S. military. The algorithm derived from the probit regression is used to predict this individual’s nativity. In this case, the model is able to predict with 100% certainty that the individual is foreign-born. Residents of the United States who are not citizens, are mar- ried to foreigners, do not speak English, and are not veterans of the U.S. military are always foreign-born. The algorithm derived from the probit model makes this guess about every individual with this particular set of characteristics. Case 2 is a more ambiguous scenario. The individual in question is a U.S. citizen and speaks English. However, this individual has not served in the military and is not married to a native-born American, which might indicate that the individual is married to a foreign-born spouse or that the individual is not married at all. While many foreign-born naturalized citizens undoubtedly fit this description, a number of native-born citizens would as well. The prediction offered by the model indicates that this scenario is less ambiguous than it might at first appear. Based on comparisons with the nativity of other individuals with similar characteristics, the model offers a 94% probability that the individual is foreign-born. In a sample evenly split between na- tive- and foreign-born residents, nearly 19 of every 20 English-speaking citizens with neither veteran service nor a native-born spouse are, in fact, immigrants. The best guess for this particular individual, then, is that he or she is an immigrant. Case 3 concerns a person who is a U.S. citizen, mar- ried to a native-born American, fluent in English, and with past or present service in the U.S. Armed Forces. While there are some foreign-born citizens who fit this description, the overwhelming majority of persons in this category are, in fact, native-born. The model thus indicates that the likelihood of such an individual being an immigrant is a relatively remote 8%. The best guess in this case is that the individual is native-born. Step 3: Determine the Accuracy of the Guesses Having built a model in Step 1, and having used that model to make educated guesses in Step 2, the next step is to determine just how accurate the guesses are. For this step, the actual information on birthplace is returned to the data set, and the actual information is compared with the educated guesses made using the algorithm derived from the probit regression model. If the guesses are right 100% of the time, the model can perfectly distinguish immigrants from natives, which will lead to an assimilation index of zero. If the guesses are right only half the time—that is, if the algorithm performed no better than random guessing—then it is impossible to distinguish immigrants from natives and the assimilation index will be 100%. The composite assimilation index will always make more accurate guesses than any of the component indexes—statistically, guesses made on the basis of more information are always more accurate. Thus the case 1 case 2 case 3 Individual is a U.S. Citizen No Yes Yes Individual is married to a native-born American No No Yes Individual speaks English No Yes Yes Individual is a veteran of the U.S. military No No Yes Result: Probability that individual is foreign-born 100% 94% 8% Table 1. probability Calculations Based on the probit regression Model 28 Measuring Immigrant Assimilation in the United States summary measure of accuracy for the composite index will always be superior to the measure of accuracy for the individual components. One useful summary measure of the model’s accuracy is the average predicted probability among all immi- grants in the data set. For example, suppose that the sample contains 100 foreign-born individuals, each of whom has a predicted probability of 100%. In this case, the model is perfectly accurate, as reflected by the group’s average predicted probability of 100%. The assimilation index will equal zero. As another example, suppose that there are 100 foreign-born individuals in the sample, and the model assigned a probability of 80% to half of them and 50% to the other. In this case, the model was not perfectly accurate, and the group’s average predicted probability is 65%. The model still performed better than random guessing, however, so the assimilation index will be less than 100%. The average predicted probability can be computed for all immigrants, or for subsets of the immigrant popula- tion divided along lines of country of birth, region of residence in the United States, number of years since immigration, or other factors. In theory, averages can also be computed for individual persons. Step 4: Convert the Average Accuracy Measure into an Index The final step in computing the assimilation index entails rescaling the average predictions so that high values indicate more assimilation and low values less. In the hypothetical example in which all foreign-born individuals are predicted to be immigrants with prob- ability 100%, the assimilation index takes on a value of zero. Immigrants who can be perfectly identified as such are defined as completely unassimilated. Conversely, a group of immigrants who cannot be distinguished from natives is defined as completely assimilated. The point of no distinction occurs when the probability assigned by the model equals the prob- ability obtained through a random coin flip, or 50%. Data For the years from 2000 to 2006, the composite assimi- lation index and its three components are computed using the Census Bureau’s American Community Sur- vey (ACS). The index is also computed for 1990, 1980, 1930, 1920, 1910, and 1900 using Public Use Microdata Samples of the decennial census. The index is com- puted by analyzing the characteristics of males and females between the ages of 22 and 65.28 An alternative version of the index that analyzes males and females age 12 to 24 is discussed in Chapter 5. A characteristic is incorporated into the predictive model according to the following guidelines: it must measure a factor that potentially distinguishes im- migrants from natives; it is commonly observed in the ACS and Census data; and it has inspired at least some interest in previous studies of immigration or current policy debates. This last guideline excludes certain indicators, such as the age of children in an immigrant’s household. While this indicator could distinguish immigrants from natives, previous literature has not focused on this factor as an indicator of as- similation, and no current policy debates hinge on it. The division of indicators into economic, cultural, and civic categories is largely intuitive; there are several examples of indicators, such as home ownership, that could fall into multiple categories. As noted in Chapter 1, not all these characteristics are available in Census data from 1900, 1910, 1920, and 1930. As a consequence, the probit model’s capacity to predict immigrant status is slightly lower in these earlier years. When comparing assimilation in the 1980–2006 period with that of the 1900–1930 period, the set of characteristics available in the later period but not the earlier period are excluded from the predic- tive model. This exclusion has only a modest impact on the assimilation-index computations for the most recent years. The Predictive Regression Model A probit regression model is based on the following conceptual model: Pr(Y=1) = Pr(X 1 b 1 + X 2 b 2 + ... + X n b n > e). In this context, the variable Y is an indicator set equal to 1 if an individual is an immigrant, and 0 otherwise. 29 C iv ic R ep or t 53 May 2008 time is a testament to the changing economic position of immigrants in society. While the probit coefficients suggest that immigrants on the whole have descended the economic ladder, there is also evidence that their attachment to the labor force has strengthened over time. Immigrants were more likely to be out of the labor force in 1910 than in 2006. Among the characteristics not available in the 1910 Census is military service, which is negatively associ- ated with being an immigrant. The association between educational attainment and the probability of being an immigrant is both positive and negative. When comparing two nearly identical individuals, one with an eighth-grade education and the other with a high school diploma, the more educated individual is more likely to be native-born. When comparing an indi- vidual with a high school diploma with an otherwise identical individual with a Ph.D., however, the less educated individual is more likely to be native-born. In other words, immigrants are most underrepresented at intermediate levels of education. As a final note, observe that when male and female coefficients are allowed to differ from each other, the female coefficients are almost always closer to zero. Thus, in a sense, females are consistently more as- similated than males. It is more difficult to distinguish foreign- from native-born females than to distinguish foreign- from native-born males. These coefficients can be used to illustrate the computation of predicted probabilities at the indi- vidual level. Suppose that in 2006, we observe a male high school graduate with no military service who works as a cashier earning $16,000 per year, and who speaks English, has never been married, has no children, is a U.S. citizen, and rents a unit in an apartment building. What is the likelihood that such an individual is foreign-born? First, we use the coefficients in Table 2 to compute an index number for this individual: 0.135 (constant term) - 0.333 (HS graduate) + 0.683 (never married) - 0.002*16 (coefficient on income in thousands*income in thousands) + 0.001*18 (coefficient on occupation score*occupation score for a cashier) = 0.471 The probability that this individual is an immigrant is equal to the probability of observing a draw from a standard normal distribution that is below 0.471. This is equal to 68.1%. In a sample split evenly between immi- grants and natives, about two of every three individuals matching these characteristics are foreign-born. Suppose we take another individual identical to the first, except that he is married to and lives with a foreign-born wife. All other characteristics remain the same. The index number becomes: 0.135 (constant term) - 0.333 (HS graduate) + 2.36 (spouse is foreign-born) - 0.001*16 (coefficient on income in thousands*income in thousands) - 0.002*18 (coefficient on occupation score* occupation score for a cashier) = 2.148 The probability of observing a draw from a standard normal distribution below 2.148 is 98.4%. In a sample evenly divided between immigrants and natives, we expect about 49 of every 50 individuals meeting this description to be foreign-born. Suppose we observe a similar individual in 1910 rather than 2006. The index number calculation uses the 1910 coefficients instead of the 2006 coefficients and omits those variables that are unobserved in the 1910 Census: - 1.01 (constant term) + 2.19 (spouse is foreign-born) + 0.008*18 (coefficient on occupation score*occupation score for a cashier) = 1.558 32 Measuring Immigrant Assimilation in the United States This index number translates into a 94% probability of being an immigrant. The lack of relevant data in 1910, coupled with altered patterns of differences between the native- and foreign-born in that earlier era, leads us to be a bit less certain that the individual we have observed is an immigrant. From Predictions to Index The probit regression models are used to compute pre- dicted probabilities for every individual in the sample. Samples generally consist of hundreds of thousands of individual observations. Computing the assimilation index for immigrants as a whole or for specific groups of immigrants begins by finding the average, or mean, predicted probability for sample individuals who belong to the group in question. To compute an index for all immigrants, the predicted values of all immigrants in the sample are averaged. To compute an index for Mexican immigrants who arrived in the United States within the last five years, for example, the predicted values of in- dividuals who meet that description are averaged. The averages are always weighted using sample weights made available by the IPUMS project. The averages are then converted into an index value by placing them on a scale between (a) the value that would be expected if the model could not distinguish immigrants from natives; and (b) the value that would be expected if the model could perfectly distinguish immigrants from natives. The conversion uses the following formula: Assimilation index = 2 × (100 – mean probability) When the mean predicted probability is 100%, that is, when all immigrants are identified as such in the pro- bit model with a probability of 100%, the assimilation index equals zero. A probit model that was completely ineffective in associating personal characteristics with immigrant status would assign all individuals a pre- dicted probability of being an immigrant equal to 50%, the proportion of immigrants in the sample. In such a scenario, the index will equal 2 ×(100 – 50) = 100%. There are occasions when the assimilation-index formula returns a value greater than 100%. This is most likely to occur when considering the economic assimilation of immigrant groups from developed na- tions. It occurs when individuals are overrepresented in the educational and occupational categories that are more commonly associated with natives rather than immigrants. In this type of scenario, the assimilation index is reset to its theoretical maximum of 100%. Component Indexes To compute the component indexes, probit regres- sions are recomputed, restricting the set of predictor variables to those associated with economic, civic, or cultural assimilation. Removing variables from the predictive model always has the impact of making the predictions less accurate. This is why the component assimilation indexes are always greater than the cor- responding composite index. The civic assimilation index, which is based on only two variables, tends to come closest to the composite index because citizen- ship and military service are very strongly associated with native-born status. The cultural assimilation index includes a broader array of variables, but in many cases these variables are weaker predictors of immi- grant status than citizenship and military service. Only groups with very low intermarriage rates, or low rates of speaking English, will have civic assimilation values higher than cultural assimilation values. Economic as- similation relies on educational attainment, occupation score, income, home ownership, and labor-force par- ticipation. As is shown above, the relationship between these factors and immigrant status is weak in recent data, and the association between educational attain- ment and immigrant status is complex. This explains the tendency of economic assimilation to approach 100% in many cases. Caveats The assimilation index and its components rely on publicly released data from the U.S. Census Bureau, both to build the probit model and to provide a set of individuals for whom predicted probabilities can be computed. While Census data sets provide clear advantages, including relatively large samples, relevant variables, and consistent measurement over a time span exceeding a century, there are important limita- 33 C iv ic R ep or t 53 May 2008 tions to the data. The Census Bureau intends each data set to be representative of the population of the United States, at least when proper statistical weighting techniques are employed, but there remain concerns that certain segments of the population are “under- counted” in each Census, primarily because they refuse to cooperate with survey enumerators. It is reasonable to believe that the undercounted population includes a disproportionate number of immigrants, particularly those who fear that their participation in the survey will lead to some form of government reprisal. In reality, the Census Bureau is statutorily prohibited from shar- ing information with any other government agency. Moreover, the Census does not inquire whether survey respondents are legal or illegal residents of the United States. However, it may be difficult to convince an il- legal immigrant of these protections. In part to address undercount concerns, the Census Bureau supplies “weights” with each survey. The weights attempt to correct any differences between the sample of individuals who complete the survey and the underlying population by attaching greater emphasis to groups with low response rates, and less emphasis to those with high response rates. If, for example, non-English-speaking Mexican natives liv- ing in Los Angeles were less likely to fill out a survey form, the Census Bureau will assign higher weights to those non-English-speaking Mexicans living in Los Angeles who did participate. In this analysis, Census Bureau weights are employed in the construction of the predictive probit model and the computation of average predicted probabilities for all immigrants and for groups of immigrants. If undercounted immigrants are less assimilated than those who appear in Census enumerations, and if the Census Bureau’s efforts to correct the undercount by supplying sample weights are insufficient, the “true” index of assimilation will be lower than the reported index. It is more difficult to assess the impact of un- dercounting on trends in assimilation. By some reports, the Census Bureau has reduced the magnitude of undercounting over time.29 If so, the trend in reported assimilation may appear too negative. While it is ulti- mately difficult to make definitive judgments regard- ing the impact of undercounting on the assimilation index, the problem is probably not sufficiently large to produce a significant effect. For example, the Census Bureau estimated that 5% of the Hispanic population was undercounted in the 1990 Census.30 The reported downward trend in undercounting implies that the problem was even less severe in 2000. A second caveat relates to the statistical properties of the assimilation index. The index and its components are estimates based on a sample of the U.S. popula- tion and, as such, are subject to sampling error. This error will be relatively inconsequential when describ- ing the entire population of foreign-born individuals in the United States but will be more important when describing smaller groups, such as the set of immi- grants from a relatively small foreign country or from a small metropolitan area. Small fluctuations over time, or small differences between groups, should not be regarded as having much significance. Finally, it should be noted that the index and its com- ponents are based on information that individuals themselves report to the Census Bureau. The Census Bureau makes few, if any, efforts to verify the accuracy of this information. Respondents may falsely state, for example, that they are U.S. citizens, or exaggerate their ability to speak English. The full extent of misreporting in the Census is not clear. The index and its components are computed under the assumption that all information reported to the Census Bureau is truthful. 34 Measuring Immigrant Assimilation in the United States Nigeria 34 100 68 49 Norway 50 100 100 40 Other United Kingdom 37 100 100 34 Pakistan 28 97 46 56 Panama 80 100 100 76 Peru 35 100 74 43 Philippines 49 100 72 65 Poland 36 100 60 55 Portugal 44 88 67 63 Romania 39 100 63 63 Russia 33 100 63 54 Scotland 67 100 100 54 Sierra Leone 25 93 71 39 Singapore 41 100 82 43 Slovakia 40 100 81 49 Somalia 18 70 53 34 South Africa 44 100 83 47 Spain 50 100 100 44 Sri Lanka (Ceylon) 20 100 53 33 St. Lucia 37 93 88 50 St. Vincent 45 95 88 53 Sudan 20 93 70 26 Sweden 48 100 100 38 Switerland 51 100 100 42 Syria 38 90 56 62 Taiwan 41 100 60 68 Thailand 49 100 91 55 Tonga 21 100 56 35 Trinidad and Tobago 46 100 84 55 Turkey 39 95 78 46 Uganda 26 100 69 43 Ukraine 28 100 51 53 Uruguay 23 89 60 29 Uzbekistan 25 96 50 55 Venezuela 28 100 77 28 Vietnam 41 99 53 72 Yemen Arab Republic (North) 17 72 48 42 Yugoslavia 30 98 56 52 Note: Only birthplace groups with 100 or more representatives in the 2006 American Community Survey sample used to compute the assimilation index are included in this table. assimilation-Index Values by Birthplace, 2006 contintued birthplace composite economic cultural civic 37 C iv ic R ep or t 53 May 2008 Metropolitan area composite economic cultural civic Akron, OH 47 100 84 46 Albany-Schenectady-Troy, NY 34 94 68 44 Albuquerque, NM 28 79 68 30 Allentown-Bethlehem-Easton, PA/NJ 36 97 69 49 Amarillo, TX 14 81 69 22 Anchorage, AK 51 99 78 57 Ann Arbor, MI 26 97 73 31 Atlanta, GA 22 90 62 31 Atlantic City, NJ 36 96 78 47 Augusta-Aiken, GA-SC 50 95 89 52 Austin, TX 22 78 60 27 Bakersfield, CA 18 70 49 29 Baltimore, MD 36 97 72 47 Baton Rouge, LA 30 87 63 39 Beaumont-Port Arthur-Orange, TX 25 78 60 35 Bellingham, WA 54 99 88 59 Bergen-Passaic, NJ 32 97 58 51 Birmingham, AL 19 78 66 23 Boise City, ID 27 80 66 36 Boston, MA 30 91 67 43 Boulder-Longmont, CO 17 78 63 24 Brazoria, TX 23 85 55 38 Bridgeport, CT 29 95 62 42 Brockton, MA 43 100 70 57 Brownsville-Harlingen-San Benito, TX 21 73 56 28 Bryan-College Station, TX 24 74 73 30 Buffalo-Niagara Falls, NY 40 100 83 48 Champaign-Urbana-Rantoul, IL 17 87 71 22 Charleston-North Charleston, SC 35 87 90 40 Charlotte-Gastonia-Rock Hill, SC 19 82 55 29 Chicago-Lake County, IL 27 90 55 43 Chico, CA 32 81 73 44 Cincinnati, OH/KY/IN 33 95 72 43 Cleveland, OH 47 100 73 57 Colorado Springs, CO 59 96 100 51 Columbia, SC 43 93 79 46 Columbus, OH 21 91 62 33 Corpus Christi, TX 45 84 89 46 Dallas, TX 17 73 52 26 Danbury, CT 29 98 61 38 Dayton-Springfield, OH 52 100 84 56 assimilation-Index Values by Metropolitan area, 2006 38 Measuring Immigrant Assimilation in the United States Daytona Beach, FL 40 100 77 50 Denver, CO 24 84 63 30 Des Moines, IA 16 83 53 25 Detroit, MI 33 98 60 50 Dutchess County, NY 45 92 84 50 El Paso, TX 30 82 60 41 Eugene-Springfield, OR 32 84 78 34 Fayetteville-Springdale, AR 20 69 61 26 Fayetteville, NC 78 100 100 65 Fort Lauderdale-Hollywood-Pompano Beach, FL 36 100 71 46 Fort Myers-Cape Coral, FL 25 88 61 32 Fort Pierce, FL 26 94 66 36 Fort Wayne, IN 36 85 68 37 Fort Worth-Arlington, TX 20 81 52 33 Fresno, CA 21 68 53 32 Gainesville, FL 34 93 81 43 Galveston-Texas City, TX 33 90 69 43 Gary-Hammond-East Chicago, IN 32 93 62 48 Grand Rapids, MI 30 87 65 40 Greeley, CO 17 78 56 31 Greensboro-Winston Salem-High Point, NC 18 72 56 23 Greenville-Spartanburg-Anderson, SC 30 86 65 37 Harrisburg-Lebanon-Carlisle, PA 38 97 78 47 Hartford-Bristol-Middleton-New Britain, CT 32 100 65 46 Hickory-Morgantown, NC 13 81 65 18 Honolulu, HI 48 99 79 61 Houston, TX 19 80 53 32 Huntsville, AL 34 90 59 54 Indianapolis, IN 25 85 73 30 Jacksonville, FL 42 100 77 50 Jersey City, NJ 28 87 62 42 Kalamazoo-Portage, MI 24 100 75 27 Kansas City, MO-KS 24 82 59 32 Kileen-Temple, TX 48 90 96 45 Knoxville, TN 29 89 77 40 Lafayette-West Lafayette, IN 18 84 70 23 Lakeland-Winterhaven, FL 24 86 59 31 Lancaster, PA 52 97 85 54 Lansing-East Lansing, MI 25 91 62 37 Laredo, TX 22 71 67 27 Metropolitan area composite economic cultural civic assimilation-Index Values by Metropolitan area, 2006 contintued 39 C iv ic R ep or t 53 May 2008 Tyler, TX 11 66 42 25 Utica-Rome, NY 34 96 73 42 Vallejo-Fairfield-Napa, CA 28 89 52 43 Ventura-Oxnard-Simi Valley, CA 25 80 55 38 Vineland-Milville-Bridgetown, NJ 25 71 62 34 Visalia-Tulare-Porterville, CA 15 61 43 26 Waco, TX 14 63 55 23 Washington, DC/MD/VA 30 95 64 41 West Palm Beach-Boca Raton-Delray Beach, FL 29 91 68 38 Wichita, KS 29 87 60 42 Wilmington, DE/NJ/MD 35 91 74 37 Worcester, MA 29 96 69 34 Yakima, WA 18 57 43 29 Yolo, CA 24 82 54 37 Yuba City, CA 20 75 44 38 Yuma, AZ 18 79 49 33 Note: Only metropolitan areas with 100 or more foreign-born representatives in the 2006 American Community Survey sample used to compute the assimilation index are included in this table. assimilation-Index Values by Metropolitan area, 2006 contintued Metropolitan area composite economic cultural civic 42 Measuring Immigrant Assimilation in the United States endnoteS 1. Data underlying this graph are taken from various official U.S. Census publications for 1960, 1970, 1980, and 1990, and from the American Community Survey (ACS) for 2000–2005. Immigrant population statistics are interpolated for intercensal years before 2000. 2. Joe Costanzo, Cynthia Davis, Caribert Irazi, Daniel Goodkind, and Roberto Ramirez, “Evaluating Components of International Migration: The Residual Foreign Born,” Population Division, U.S. Bureau of the Census, Working Paper Series no. 61 (2001). 3. The term “assimilation” carries negative connotations in certain circles, as it is often taken to imply the elevation of Anglo-Saxon, Protestant culture as an ideal and the judgment of individual groups by how well they conform to this ideal. The concept of assimilation employed in this report is quite distinct from this. The native-born population of the United States is now, and has always been, multicultural. Assimilation, in this context, refers to the degree of distinction between the foreign-born and native-born citizens regardless of race, religion, or ancestry. Immigrants are assimilated when it becomes impossible to distinguish them from the native-born population. This can occur either because immigrants become more like natives in certain respects, or because the native population itself changes. 4. For a conceptual discussion of assimilation and an overview of the ethnographic literature on immigrant assimilation in the first two-thirds of the twentieth century, see Milton Gordon, Assimilation in American Life: The Role of Race, Religion, and National Origins (New York: Oxford University Press, 1964). For more quantitatively oriented studies, see Stanley Lieberson, A Piece of the Pie: Blacks and White Immigrants Since 1880 (Berkeley: University of California Press, 1980), covering early-twentieth-century immigrants; and Richard D. Alba and Victor Nee, Remaking the American Mainstream: Assimilation and Contemporary Immigration (Cambridge, Mass.: Harvard University Press, 2003), covering more recent immigrants. See also Alejandro Portes and Ruben G. Rumbaut, Immigrant America: A Portrait, 3rd ed. (Berkeley: University of California Press, 2006), which discusses various forms of assimilation among recent immigrants. 5. This figure is based on the simple methodology used in George J. Borjas, “The Economic Benefits from Immigration,” Journal of Economic Perspectives 9, no. 2 (1995): 3–22. Immigrants currently form roughly 17% of the labor force; assuming that the elasticity of factor price for labor is -0.3 (a value widely supported in economic literature; see Daniel Hamermesh, Labor Demand [Princeton, N.J.: Princeton University Press, 1993]), immigrants reduce wages by about 5%. The economic benefit from immigration is the area of a triangle with height 5% and base 17%, 1/2*.17*.05 = 0.004, times GDP, which in 2006 was roughly $13 trillion. This is a larger estimate than that reported by Borjas because the share of immigrants in the labor market has increased over time and because nominal GDP has increased over time. Note that, as Borjas argues, this surplus may mark a much larger net transfer of wealth from labor to capital. This calculation also presumes that immigrants do not contribute to the nation’s capital stock, only to the labor supply, and that the economy is not marked by increasing returns. Both these assumptions are debatable. 6. For a discussion of the theoretical impact of immigration on wages, see Borjas, “The Economic Benefits from Immigration.” Empirical studies of the impact of immigration on wages arrive at varying conclusions. For example, George J. Borjas, “The Labor Demand Curve Is Downward Sloping: Reexamining the Impact of Immigration on the Labor Market,” Quarterly Journal of Economics 118, no. 4 (2003): 1335–74, presents estimates suggesting that a 10% increase in the labor force through immigration depresses wages by roughly 3%—a figure also consistent with assumptions in the Borjas (“The Economic Benefits from Immigration”) method of computing the benefits from immigration. David Card, “Is the New Immigration Really So Bad?,” The Economic Journal 115 (2005): 300–323, 43 C iv ic R ep or t 53 May 2008 argues that evidence of a negative impact on wages is “slight.” Some of the controversy reflects difficulties in measuring the impact of immigration on wages. In general, it is impossible to know what wage levels would prevail in the United States in the absence of immigration. Some studies use time-series data to study whether growth in the immigrant population is associated with declines in the earnings of natives with similar skill levels. Since 1980, for example, the immigrant population has grown and the earnings of the low-skilled have eroded relative to the earnings of the highly skilled. One might conclude from this that the former trend caused the latter. Other explanations have been proposed for the decline in relative earnings of the low-skilled, however. Another method of inferring the impact of immigration on wages is to compare the earnings of workers in local labor markets with higher and lower proportions of immigrant workers. These studies tend to show that earnings do not vary much across these types of labor markets. This method could be flawed, though, if immigrants tend to flock to cities with better labor-market opportunities or if natives depart cities that experience an inflow of immigrants. 7. See Gnanaraj Chellaraj, Keith E. Maskus, and Aaditya Mattoo, “The Contribution of Skilled Immigration and International Graduate Students to U.S. Innovation,” World Bank Policy Research, Working Paper no. 3588 (2005). 8. As summarized in Borjas (“The Economic Benefits from Immigration”), the question of net fiscal impact of immigrants depends on assumptions regarding the marginal cost of providing services such as national defense and highways to immigrants. Passel and Clark, “How Much Do Immigrants Really Cost? A Reappraisal of Huddle’s ‘The Cost of Immigrants’ ” (Washington D.C.: Urban Institute, 1994), estimate a net gain; Donald Huddle, The Net National Costs of Immigration (Washington, D.C.: Carrying Capacity Network, 1993), estimates a net loss. Ronald Lee and Timothy Miller, “Immigration, Social Security, and Broader Fiscal Impacts,” American Economic Review 90, no. 2 (2000): 350–54, estimate that the overall net impact of marginal increases in immigration is small and reflects a combination of net fiscal contributions to Social Security, little impact on the federal budget, and a net drain on state and local government. 9. See George J. Borjas and Lynette Hilton, “Immigration and the Welfare State: Immigrant Participation in Means-Tested Entitlement Programs,” Quarterly Journal of Economics 111, no. 2 (1996): 575–604, which shows that immigrant participation in cash or noncash welfare programs was roughly 50% higher than among native households, in data from the 1980s and early 1990s. George J. Borjas, “Immigration and Welfare Magnets,” Journal of Labor Economics 17, no. 4 (1999): 607–37, shows evidence that, prior to 1990, immigrants gravitated toward states with higher welfare benefits. See also George J. Borjas, “Welfare Reform, Labor Supply, and Health Insurance in the Immigrant Population,” National Bureau of Economic Research, Working Paper no. 9781 (2003), for an estimate of the impact of the 1996 welfare reform on the immigrant population. 10. See T. C. Buchmueller, A. T. Lo Sasso, I. Lurie, and S. Dolfin, “Immigrants and Employer-Sponsored Health Insurance,” Health Services Research 42, no. 1 (2007): 286–310; and O. Carrasquillo, A. I. Carrasquillo, and S. Shea, “Health Insurance Coverage of Immigrants Living in the United States: Differences by Citizenship Status and Country of Origin,” American Journal of Public Health 90, no. 6 (2000): 917–23. These studies document that noncitizen immigrants who work full-time are much less likely to receive health insurance from their employer, primarily because they are less likely to work for a firm that offers insurance benefits. Naturalized immigrants have insurance coverage rates very similar to those of the native-born. 11. See, for example, Kjetil Storesletten, “Sustaining Fiscal Policy through Immigration,” Journal of Political Economy 108, no. 2 (2000): 300–323; and Eduardo Porter, “Illegal Immigrants Are Bolstering Social Security With Billions,” New York Times, April 5, 2005. 44 The Center for Civic Innovation’s (CCI) mandate is to improve the quality of life in cities by shaping public policy and enriching public discourse on urban issues. The Center sponsors studies and conferences on issues including education reform, welfare reform, crime reduction, fiscal responsibility, counter-terrorism policy, housing and development, and prisoner reentry. CCI believes that good government alone cannot guarantee civic health, and that cities thrive only when power and responsibility devolve to the people closest to any problem, whether they are concerned parents, community leaders, or local police. The Center’s advisory board is chaired by former Indianapolis mayor Stephen Goldsmith. www.manhattan-institute.org/cci The Manhattan Institute is a 501(C)(3) nonprofit organization. Contributions are tax- deductible to the fullest extent of the law. EIN #13-2912529 CenteR foR CiviC innovation Stephen Goldsmith, Advisory Board Chairman Emeritus Howard Husock, Vice President, policy research Erin A. Crotty, Associate Director fellowS Edward Glaeser Jay P. Greene George L. Kelling Edmund J. McMahon Peter Salins Fred Siegel