A comparative analysis of the nativity wealth gap.

Bauer, Thomas K. ; Cobb-Clark, Deborah A. ; Hildebrand, Vincent A. 等

I. INTRODUCTION

In many countries worldwide, substantial numbers of immigrants are at (or near) retirement age. In the United States and Germany, for example, immigrants are as likely as the native born to be over the age of 55 yr, while in Australia the foreign-born population is currently aging more rapidly than the native-born population (Bauer et al., 2007). We know very little about immigrants' capacity to provide for themselves in old age, although there are reasons to believe that both wealth levels and portfolio allocations depend on nativity (Amuedo-Dorantes and Pozo, 2002; Cobb-Clark and Hildebrand, 2006a, 2006b). Given this, it is important to know more about the way in which immigrants accumulate wealth and achieve economic security.

This paper addresses this issue by analyzing the wealth gap between immigrant and native households in Australia, Germany, and the United States. We take advantage of recent data collections in each of these countries using a unique opportunity to assess the wealth position of immigrant households. Differences in survey design and coverage will prevent us from directly comparing wealth levels across countries. However, following studies of cross-national differences in the gender wage gap (Blau and Kahn, 1992), we will assess how the nativity wealth gap differs across countries. We are particularly interested in addressing the following questions: First, how does household net worth vary by nativity status, region of origin, and immigration cohort? Second, how important are factors such as income differentials, disparities in educational attainment, and demographic characteristics in producing these wealth gaps? Finally, what can we say about the role that the institutional setting might play in generating any nativity wealth gap?

These are important questions given that wealth provides the resources necessary to maintain living standards in retirement or in times of economic hardship. Moreover, wealth is a fundamental component of overall economic wellbeing that directly influences immigrants' ability to integrate into host country society. Wealthier families have more political influence and live in neighborhoods with better schools, enhanced health facilities, and less crime (Altonji and Doraszelski, 2005; Gittleman and Wolff, 2004). At the same time, immigrants" economic wellbeing is almost certainly linked to the institutional setting, making it difficult to generalize research findings across national boundaries.

Australia, Germany, and the United States provide an interesting case study because they span the spectrum from a traditional immigration country accepting mainly permanent, skilled immigrants (Australia) to a country with a long history of accepting only temporary, predominantly unskilled workers (Germany). The results of our comparative analysis are necessarily inferential, but nonetheless provide some important insights into the ways that the institutional framework might influence the relative wealth position of immigrant families.

Our results indicate that in Germany and the United States wealth differentials are largely the result of disparity in the educational attainment and demographic composition of the native and immigrant populations, while income differentials are less important in understanding the nativity wealth gap. In contrast, the relatively small wealth gap between Australian- and foreign-born households cannot be explained by either the distribution of income or immigrants' characteristics. Indeed immigrants would have a wealth advantage if they accumulated wealth in the same way as the Australian born. On balance, our results point to substantial cross-national disparity in the economic well-being of immigrant and native families that is largely consistent with domestic labor markets and the selection policies used to shape the nature of the immigration flow.

II. THE INSTITUTIONAL SETTING

The comparative nature of our analysis provides an opportunity to shed light on the ways in which a country's institutional setting might lead to a nativity gap in wealth levels. Conceptually, variation in household wealth stems from differences in inherited wealth, rates of return, or savings behavior, which in turn is a function of both income and consumption patterns. Given this, there are several ways in which both the wealth levels and portfolio choices of immigrants may differ from those of their native-born counterparts. In this section, we briefly discuss some of the institutional differences in the three countries that may affect the incentives to accumulate wealth (see Bauer et al., 2007 for details).

Migration policy is designed to select the individuals, from the set of potential migrants, to be allowed to enter. Selection processes typically result in differences between natives and immigrants in terms of observable and unobservable characteristics generating disparity in wealth levels. As the nature of these selection processes varies across receiving countries, it is likely that the nativity wealth gap will differ across receiving countries as well. The United States, for example, places more weight on the reunification of families, while Australia mainly accepts economic migrants using numerical testing to evaluate skills (Birrell, 1990). In contrast, immigration to Germany has historically been predominantly temporary, although since 1973 German immigration policy has been almost exclusively based on family reunification and humanitarian considerations (Bauer et al., 2005: Schmidt and Zimmermann, 1992). This policy environment results in immigrant populations that are different in terms of their skills and settlement intentions. Immigrants to Australia are largely skilled and permanent. Immigrants to Germany, on the other hand, are mainly unskilled and, at least notionally, temporary. We expect these differences to contribute to producing a nativity wealth gap that is likely to be larger in Germany than in Australia.

Wealth accumulation is closely related to households' long-run earnings potential and hence to labor market institutions. The complex minimum wage system provides immigrants with some degree of earnings protection when they first enter the Australian labor market, while the greater flexibility of the U.S. labor market speeds the earnings adjustment of new arrivals. At the same time, the German labor market is considered to be quite rigid in comparison. Both labor costs and unemployment in Germany are relatively high and Germany follows a stronger redistribution policy through higher tax rates (OECD, 2006). Empirical research on the earnings assimilation of immigrants is consistent with these institutional differences. Immigrants to Australia have a lower earnings disadvantage upon entry as well as a slower rate of earnings assimilation than immigrants to the United States (Chiswick and Miller, 1985; Miller and Neo, 2003), while in Germany, immigrants face an even higher relative earnings disadvantage at arrival and experience no significant earnings assimilation over time (Bauer et al., 2005). These differences lead us to expect that the nativity wealth gap would be lower in Australia and the United States than in Germany, where immigrants remain at an earnings disadvantage.

Institutional barriers associated with ethnicity, nativity, legal status, or language skills may limit migrants' access to financial markets and hinder the purchase of certain assets like housing. Opening a bank account, for example, may be difficult for some immigrants due to a general lack of documentation, while other new arrivals may simply find it difficult to establish a credit rating (Osili and Paulson, 2004). Furthermore, legal systems and laws of land tenure, governmental housing and tax policy, and lenders' margins--all of which vary substantially between countries--combine to produce an institutional setting that may be more or less favorable to home ownership (Proxenos, 2002). The inability--or unwillingness--to fully participate in the host country's financial markets together with various constraints in the housing market are almost certainly related to the common finding that immigrant households have significantly lower home ownership rates than comparable native households (Borjas, 2002; Clark et al., 1997; Coulson, 1999; Myers and Lee, 1996; Painter et al., 2003; Sinning, 2006).

Limited access to social welfare programs may also affect wealth accumulation (Gruber and Yelowitz, 1997; Shamsuddin and DeVoretz, 1998). In 1996, for example, the United States restricted noncitizens' access to the welfare system (Fix and Passel, 2002), while Australia has subsequently extended the period that new immigrants must wait before becoming eligible for income support (Chiswick and Miller, 2006). In contrast, the German social assistance system does not differentiate between natives and immigrants although refugees and asylum seekers receive lower benefits until their asylum status is recognized (Nielsen, 2004). Despite these restrictions, immigrants have on average higher dependency rates on social welfare in all three countries considered in this paper. Note however, that these differences become very small or even disappear when controlling for socioeconomic characteristics. While the probability of receiving social welfare falls with duration of residence in Germany, it seems to increase in the United States. (1)

Finally, differences in pension systems are also of particular interest as these institutional differences affect the incentives to accumulate wealth. The Australian pension system has two components: a means-tested, aged pension financed through general tax revenue, and private pension plans financed by mandatory worker and employer contributions (Gong et al., 2006). The U.S. Social Security program is also financed through general tax revenue; however, unlike the Australian aged pension, benefits are paid under uniform formulas based on recipients' previous earnings histories without regard to wealth or nonwage income. Germany's pay-as-you-go state pension system, which is financed by contributions from employers and employees, is similar to U.S. Social Security in that it is not means tested. Other retirement savings vehicles are relatively unimportant in Germany. Comparing average pension wealth, that is, the present value of a future stream of pension payments can be a useful way of summarizing the effect of the various institutional aspects of specific pension systems (OECD, 2005). These calculations indicate that the net pension wealth for individuals with average earnings is highest in Germany ($262,000) followed by Australia ($189,000) and the United States ($183,000). (2)

We can only speculate about how these cross-country differences in welfare and pension systems affect the nativity wealth gap. Restricted access to the welfare system and the relatively low replacement rate of the U.S. pension system may give immigrants an incentive to accumulate personal wealth to cope with any financial difficulties and provide for their own retirement. Thus, we might expect the nativity wealth gap to be smaller in the United States than in Australia or Germany.

III. DATA AND DESCRIPTIVE ANALYSIS

A. Data Sources

This paper uses data from three nationally representative panels that all provide information about migration histories and comparable measures of household wealth. The Australian data come from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, which is a longitudinal survey of Australian households (Wooden et al., 2002). Our analysis exploits the 2002 release of HILDA (Wave 2) encompassing approximately 13,000 individual respondents living in more than 7,000 households. Wave 2 included a special module on household wealth making the HILDA Survey the only Australian microdata source that allows questions regarding nativity and the distribution of wealth to be examined.

The data used to study the nativity wealth gap in Germany have been extracted from the German Socioeconomic Panel (GSOEP). The GSOEP is a representative longitudinal survey that started in 1984 and includes German and immigrant households. In 2005, approximately 22,000 persons in nearly 12,000 households were sampled. Here we use data from the 2002 wave. (3) As less than 5% of the foreign-born population lives in East Germany (Statistisches Bundesamt, 2006), our analysis focuses on households residing in West Germany. Immigrants are defined as foreign-born individuals who immigrated to Germany after 1948 (including foreign-born persons with German citizenship).

Data for the United States come from the 2001 panel of the Survey of Income and Program Participation (SIPP). The SIPP is the only U.S. microdata set containing information about migration histories and wealth for a broad cross-section of the immigrant population (Cobb-Clark and Hildebrand, 2006a). The 2001 SIPP panel is a short rotating panel that is representative of the civilian, noninstitutionalized population living in the United States. Approximately 35,000 households were interviewed in the first wave of the 2001 panel. Each household in the sample was interviewed at 4-mo intervals over a period of roughly 3 yr beginning in February 2001. Information on immigrants is obtained from the Wave 2 migration history module and household wealth data are drawn from the Wave 3 module on assets and liabilities collected between October 2001 and January 2002. (4)

To facilitate comparisons across countries, we restrict the three estimation samples to native- and foreign-born, couple-headed households in which the reference person is between 25 and 75 yr old. Native-born households include all coupled-headed households in which both partners were born in Australia, Germany, or the United States, respectively. A couple-headed foreign-born household is a household in which at least one partner was born outside of the respective country. This definition allows an explicit consideration of "mixed households" in which one partner is native born and the other is foreign born as well as "immigrant-only" households. In the case of mixed households, the foreign-born partner is taken to be the reference person, regardless of whether this person was reported to be the head or the spouse of his or her respective household. In the case of native-born and immigrant-only couples, the reference person is the person who is designated as head of the household. Excluding all observations with missing values on one or more of the variables of interest results in samples of 2,235 native-born and 1,271 foreign-born households in Australia, 2,344 native-born and 691 foreign-born households in Germany, and 10,283 native-born and 1,671 foreign-born households in the United States.

B. The Level of Net Worth

Our measures of total household net worth are derived from wealth components that are either estimated at the household level or directly measured at the individual level and then aggregated to the household level. An overview of the specific components of each country's net worth measure is provided in Table A1. As the scope of financial investments differs across countries, each survey asks about a somewhat different range of asset types. Moreover, while information about many detailed forms of country-specific financial investments was gathered in both Australia and the United States, only a single broadly defined measure of the value of financial assets is observed for Germany. Fortunately, the resulting measure of financial wealth for Germany is comparable to those derived from both the Australian and U.S. data. Unlike financial wealth, the remaining components of our net worth measures (business equity, housing equity, vehicles equity, and total value of debt) are largely the same across all three countries. (5)

To assess the quality of HILDA's wealth information, Heady (2003) compares HILDA's estimate of aggregate net worth to national statistics compiled by the Reserve Bank of Australia (RBA). This comparison of aggregate values suggests that the overall household net worth derived from HILDA data is fairly accurate. Although the aggregate value of total debt derived from HILDA is 20% lower than the corresponding RBA figure, HILDA's overall estimate of household net worth is only 6.5% lower than the RBA estimate.

For Germany, we use a revised version of the 2002 wealth module of the GSOEP that accounts for measurement errors and was made available in 2007. Frick et al. (2007) provide an extensive description of editing and imputation procedures that were applied to obtain the revised wealth information. They also compare the wealth aggregates of the GSOEP with corresponding information from official national statistics (national accounts and data from the German central bank). On balance, their findings suggest that real estate--the largest wealth component--is captured well by the wealth module of the GSOEP 2002. At the same time, the coverage of more heterogeneous and diversified wealth holdings (such as financial assets) is much lower. Overall, the aggregated total net wealth in the GSOEP is about 80% of the corresponding macroinformation.

In general, SIPP data are not usually thought of as the best source of information for studying U.S. wealth. The Survey of Consumer Finance (SCF) provides a more comprehensive measure of wealth than do alternative data sources that measure the upper tail of the wealth distribution particularly poorly (Juster and Kuester, 1991; Juster et al., 1999; Wolff, 1998). Unfortunately, SCF data do not identify immigrants making it unsuitable for our purposes. In an effort to understand the limitations of SIPP data, the Social Security Administration (2003) compares SIPP wealth data to wealth levels in the SCF and the Panel Study of Income Dynamics (PSID). The results reveal that the SIPP estimate of median net worth is only about two-thirds that of the SCF and 74% that of the PSID. As estimates of liabilities are essentially the same across surveys, much of the underestimate of net worth results from assets being underestimated. Underestimation of assets in the upper tail of the wealth distribution accounts for about 72% of the difference in the SIPP and SCF estimates of asset levels, while asset categories not measured in the SIPP account for an additional 13%. Only a very small proportion (at most 15%) of the disparity in assets is attributable to underestimation of assets among the nonelderly (Social Security Administration 2003). Thus, we minimize the disadvantages of using SIPP data to assess wealth holdings in the United States by eliminating very wealthy households from our sample and focusing on median (rather than mean) wealth.

In addition to cross-national differences in the extent to which our data sources measure overall net worth, there are also important differences in the way in which pension wealth is measured in each survey. Information about pension entitlements in the GSOEP is limited to the cash surrender value of life insurance policies, private pensions plans, and building savings accounts. These assets are included as a component of financial wealth in our measure of net worth for Germany. In contrast to the GSOEP, HILDA also collects data about employer-provided pension wealth. In the United States, the total value of funds in Thrift/401K accounts is available in the assets and liabilities module of the SIPP, while the value of pension benefits in defined contribution plans could in principle be extracted from the SIPP's pension module (Wave 7). However, as in most wealth surveys, the value of assets held in defined-benefit pensions and social security entitlements are not available in the SIPP. Consequently, to make our measures of net worth as comparable as possible across countries, we do not include the available information about employer-provided pensions (HILDA), Thrift/401K (SIPP) accounts, or defined contribution pension wealth (SIPP) in our respective measures of net worth for Australia and the United States.

Given these differences in measurement, direct comparisons of the level of net worth across countries is not possible. This is not particularly problematic given our research interests. More importantly, we have no evidence that the merits of our net worth measure within a country differ substantially between native-born and foreign-born populations. Consequently, following the literature on international comparisons of the gender earnings gap (Blau and Kahn, 1992), our analysis concentrates on relative rather than absolute wealth measures. In particular, since wealth levels are not directly comparable across countries, we focus instead on comparing the proportion of the nativity wealth gap that is attributable to different (explained or unexplained) factors. To facilitate such a comparison, we convert our measures of net worth into U.S. dollars using purchasing power parity (PPPs) conversion factors provided by the OECD for the year 2002. (6)

Information about the level of wealth held by native-born and foreign-born households in each country is presented in Tables 1-3. Overall, native-born households have higher net worth than immigrant households, though the nativity wealth gap is considerably smaller in Australia than in Germany or the United States. Specifically, the net worth of immigrants to Australia is approximately 95% ($282,640) of the average wealth level of their Australian-born counterparts. Immigrant households in the United States hold about 68% ($111,868) of the net worth of native households, while the corresponding share in Germany is below 40% ($86,744). Moreover, there is very little difference in the proportion of Australian-born (96.4%) and immigrant households (95.9%) with positive net worth. In the United States, about 89% of the native-born and 84% of the foreign-born households report positive wealth levels. In contrast, while the net worth of 79% of the native households in Germany is positive, the corresponding proportion among foreign-born households is only 46%. The lower propensity to hold positive net worth is only a partial explanation for the overall nativity wealth gap in Germany and the United States however. Conditional on having positive net worth, immigrant households still hold only 56% ($154,291) of the wealth of native households in Germany and 73% ($136,797) in the United States. In contrast, those immigrant households with positive net worth hold approximately 96% ($295,515) of the wealth level of Australian-born households.

Figures 1-3 depict the unconditional nativity wealth gap along with a 95% confidence interval over the entire wealth distribution for all three countries. (7) These figures show that in all three countries native-born households have a wealth advantage over immigrant households at almost all points of the wealth distribution. In Australia, however, this wealth gap appears not to be statistically significant in most parts of the wealth distribution. In contrast, the nativity wealth gap appears to be increasing in wealth levels in Germany and the United States. At the 25th percentile, for example, the wealth gap is smallest in Australia with immigrants holding $11,152 less than natives (Figure 1), followed by the United States with a nativity gap of $15,036 (Figure 3) at the 75th percentile and Germany with a nativity gap of $37,956 (Figure 2). The wealth gap remains smallest in Australia ($12,220) compared to gaps of $72,519 in the United States and $223,334 in Germany.

Tables 1-3 further describe the relevant socioeconomic and demographic characteristics of native and immigrant households. The cross-country disparities in the characteristics of immigrants and natives are largely as expected given differences in immigration policies and institutional settings. In all three countries, immigrant households have a lower income on average than native-born households. Again, this disparity is smallest for Australia. Moreover, immigrants in Australia also have fewer children under the age of 18, are on average older, and have more education than natives. In Germany and the United States, these patterns are reversed. There are also differences in the distribution of the immigrant population across entry cohorts and sending regions. In Australia and Germany, the majority of the immigrant population arrived either before 1974 or after 1984, whereas in the United States almost half of the immigrant population captured in the SIPP arrived after 1984. Immigrants to Australia primarily stem from Europe and Asia, while in Germany most immigrant households originate in Central/Eastern Europe or Turkey and immigrants to the United States come predominantly from Asia and Mexico.

C. Determinants of Net Worth

To understand how wealth levels vary with household characteristics, it is first useful to analyze the determinants of household net worth. As the distribution of net worth is usually skewed to the right, linear regression models are typically estimated using a log transformation of wealth to obtain a log-normally distributed dependent variable (Shamsuddin and DeVoretz, 1998). A log transformation is not appropriate for households with zero or negative net worth, however. Consequently, we estimate a quantile regression model to analyze the determinants of household net worth at the median of the distribution.

Specifically, we estimate the following cross-sectional quantile regression models of the determinants of net worth ([W.sub.hc]) for native and immigrant households h residing in country c,

(1) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where q reflects a specific percentile of the distribution, [X.sub.hc] contains information about income (i.e., current net household income), (8) education (in years), and demographic characteristics (number of children younger than 18, age and age squared). Additionally, [I.sub.hc] is an indicator variable that equals 1 for both mixed and immigrant-only couples and 0 otherwise. This specification allows us to compare the total immigrant population (including mixed couples in which only one partner is foreign born) with the native-born population. We are also interested in variation in wealth within the immigrant population, however. Consequently, the model also includes vectors of exhaustive, mutually exclusive indicators of family type, entry cohort, and region of origin. Specifically, immigrant couples are classified into one of the three possible family types: mixed couple with a native-born head, mixed couple with a foreign-born head, and immigrant-only couple. [M.sub.hc] is a vector of three indicator variables capturing these family types, while [D.sub.hc] and [R.sub.hc] are vectors of indicator variables, capturing arrival cohort and region of origin, respectively. Finally, [[beta].sup.q.sub.c] is a vector of the model parameters to be estimated and [[epsilon].sup.q.sub.hc] is an error term with the usual properties.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

Inclusion of indicator variables that are both exhaustive and mutually exclusive facilitates interpretation of our results. We achieve identification of the overall constant by restricting the estimated coefficients to sum to zero, that is we restrict [[SIGMA].sub.m] [[beta].sup.q.sub.3cm] = 0, [[SIGMA].sub.n] [[beta].sup.q.sub.4cn] = 0, and [[SIGMA].sub.p] [[beta].sup.q.sub.5cp] = 0 whereto n, and p are the numbers of family types, immigration cohorts, and regions of origin, respectively. Consequently, the parameter [[beta].sup.q.sub.2c] can be interpreted as the overall difference in the wealth level of native-born and immigrant couples (i.e., the overall nativity wealth gap) conditional on characteristics, while [[beta].sup.q.sub.3c], [[beta].sup.q.sub.4c], and [[beta].sup.q.sub.5c] capture deviations across family types, entry cohorts, and regions of origin, respectively, from this overall nativity wealth gap.

Finally, we would also like to know whether the relationship between wealth and household characteristics differs by nativity. Consequently, we also estimate

[W.sub.hc] = [[gamma].sup.q.sub.0c] + [X.sub.hc] [[gamma].sup.q.sub.1c] + [I.sub.hc] [[Y.sub.hc][[gamma].sup.q.sub.Yc] + [Z.sub.hc][[gamma].sup.q.sub.Zc] + [[eta].sup.q.sub.hc],

where [Z.sub.hc] is a vector that includes net income, education, the number of children in the household younger than 18 yr of age, and a dummy variable indicating whether the reference person was younger than 18 yr old at migration. Although the estimated coefficients are restricted in the same way as in Equation (1), the additional interaction terms in Equation (2) alter the interpretation of the estimated coefficient on the immigrant dummy variable ([I.sub.hc]) so that it cannot be sensibly interpreted on its own and therefore will not be directly comparable to that estimated in Equation (1). (9) Still, this specification has the advantage of allowing us to assess the extent to which the pattern of wealth accumulation across income and education categories might differ between immigrant and native-born couples.

Estimation results at the median (q = 0.5) are presented in Table 4. The results of Model 1 indicate that--controlling for households' characteristics--the median net worth of immigrant households is significantly lower than the median net worth of native-born households in both Australia and Germany, whereas the conditional nativity wealth gap is positive in the United States. The relative wealth position of immigrants depends on their family type, however. The median net worth of immigrant-only households (in which both partners are foreign born) is substantially lower than the median net worth of immigrant couples as a whole in Australia, leaving mixed households with more wealth, regardless of whether the foreign-born partner is the head or the spouse of his or her respective household. Interestingly, while mixed couples in which the head is foreign born hold more wealth than other immigrant households in Germany, mixed households with native-born heads are wealthier than other immigrant households in the United States. The results also suggest that more established immigrants to Australia and the United States are wealthier than recent immigrants. In contrast, immigrants' relative wealth is unrelated to their immigration cohort in Germany. Finally, there are significant differences in the wealth disadvantage faced by immigrants arriving from different source countries or at different ages.

Estimation of Equation (2) reveals that the determinants of wealth differ for native- and foreign-born couples in each country. For example, immigrants to Australia accumulate more wealth for each dollar of household net income than do native-born Australians, though immigrants to the United States accumulate less. On the other hand, the wealth return to education is lower for immigrants in both the United States and Australia. In Germany, neither the education nor the income interaction is significant. Together these results highlight the large differences in the relative wealth position of different groups of immigrants in different receiving countries.

IV. DECOMPOSITION OF THE WEALTH GAP

A. Decomposition Method

We investigate the source of the nativity wealth gap using the semiparametric decomposition method proposed by DiNardo et al. (1996). This approach allows us to assess the relative impact of various sets of explanatory factors on differences in the wealth distribution between native and immigrant households (Cobb-Clark and Hildebrand, 2006c). In particular, we partition the determinants of wealth into three main factors: income (y); educational attainment (e); and household demographic composition (z). We can then write the wealth distribution of group j as follows:

(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where I is our indicator of immigrant status and j = (0, 1). Equation (2) comprises four conditional densities: the conditional wealth distribution f given the full set of wealth determinants and immigrant status I, the conditional income distribution ([f.sub.y|e,z]) given education, household demographic composition, and immigrant status, the conditional education distribution [f.sub.e|z] given demographics and immigrant status and finally the distribution [f.sub.z] of demographic characteristics conditional on immigration status.

Following DiNardo et al. (1996), the wealth gap between immigrant and native households can then be decomposed into three separate components explained by disparities in conditional income distributions, educational backgrounds, and demographic characteristics, respectively, and a fourth "unexplained" component arising from differences in the conditional (on y, e, and z) wealth distributions of immigrants and natives. This decomposition is achieved by constructing a series of counterfactual wealth distributions in which the conditional income, education, and demographic distributions among native-born households (j = 0) are selectively replaced with the distributions faced by their foreign-born counterparts (j = 1). (l0) These counterfactual distributions are then used to isolate the effect of various wealth determinants on the nativity wealth gap. (11)

B. Decomposition Results

To understand the source of the disparity in the wealth levels of natives and immigrants, we decompose the nativity wealth gap into three separate vectors of wealth determinants: (1) a quartic in income; (2) educational attainment, which includes years of education for the household head and the spouse; and (3) household composition, which includes an indicator variable for children less than age 18 yr in the household, as well as a cubic in the age of both partners. (12) Moreover, since the relative wealth position of mixed households differs substantially across countries (Table 4), we present separate decomposition results of two samples with and without mixed households for the three countries. The results of the decomposition analysis including mixed households are reported in Table 5. Table 6 contains the corresponding estimates of a sample without mixed households.

One advantage of the DiNardo et al. (1996) approach is that by estimating the entire counterfactual wealth distribution it is possible to decompose differences in summary statistics for these wealth distributions. In what follows, we consider two alternative statistics that are useful in describing the differences in the wealth holdings of natives and immigrants: first, the nativity wealth gap at various percentiles of the distribution and second, the disparity in wealth dispersion as reflected in the gap between the 75th-25th percentiles of the wealth distribution. Our results are obtained by calculating each of the relevant counterfactuals and then taking the simple average of these statistics over all of the possible decomposition sequences. Bootstrapping methods using a normal approximation with 1,000 replications are used to calculate standard errors.

We find that native-born households are wealthier than foreign-born households (see the second column of Table 5) throughout the entire wealth distribution. At the same time, the magnitude of the nativity wealth gap differs substantially across countries. Immigrants to Australia have approximately $9,000 less wealth at the median than native-born Australians--a gap that is not significantly different from 0. In the United States, the median nativity wealth gap is almost five times higher than that in Australia (approximately $44,000), while in Germany the median wealth gap exceeds $158,000. In contrast to Australia, where the largest wealth gaps appear to be at the tails of the wealth distribution, the nativity wealth gap is increasing with higher wealth levels in the United States and Germany reaching more than $72,000 in the United States and as much as $194,000 in Germany at the 75th percentile. These results point to substantial disparity in the economic wellbeing of immigrant and native families. Moreover, this disparity varies dramatically across receiving countries and is consistent with crossnational differences in the policies used to select immigrants. In particular, German selection policy has resulted in an immigrant population that is largely unskilled and, at least notionally, temporary, while immigrants to Australia are both skilled and permanent. Given this, it is not surprising that the nativity wealth gap is much larger in Germany than in Australia.

Interestingly, income differentials explain relatively little of this nativity wealth gap in the United States and Germany. In Germany, the income component of the decomposition is small in magnitude, explaining between 10% of the raw wealth gap at the 75th percentile and the median and 28% at the 25th percentile. The income component is even smaller in the United States. Income differentials appear to account for a much larger share of the wealth gap between immigrant and native households in Australia. For example, at the median, almost $7,000 of the overall $9,000 nativity wealth gap in Australia (79%) stems from the fact that foreign- and Australian-born households have different incomes. However, it is important to note that the wealth gap itself is much smaller in Australia.

Overall, these results suggest that--conditional on the household's educational attainment and demographic characteristics--income disparities do not themselves lead to a substantial higher nativity wealth gap. This result is particularly surprising in light of the empirical evidence demonstrating the large nativity earnings gap and the often slow earnings assimilation of immigrants in these countries. At the same time, economic theory suggests that it is permanent rather than current income that is most closely related to consumption, savings decisions, and ultimately wealth accumulation. Our data unfortunately do not provide us with a sensible permanent income measure and our results are consistent with the theoretically weaker relationship between current income and wealth. (13) Any effects of our inability to control for disparities in permanent income will be captured in the unexplained component of the nativity wealth gap.

In all three countries, differences in educational attainment account for as much or more of the nativity wealth as the conditional income functions. This is particularly important as educational qualifications are often one of the key factors underlying a nation's immigration selection process. Again, Australia stands out relative to the United States and Germany as immigrants have more education on average than native-born Australians. Our decomposition analysis indicates that this educational advantage contributes to reducing the nativity wealth gap in Australia. In other words, if immigrants to Australia had the same educational qualifications as the native-born population, we would expect that immigrants' wealth disadvantage at the median would be approximately $14,000 higher than we observe it to be. On the other hand, the relative educational disadvantage of immigrant households in the United States and Germany accounts for a substantial portion of their wealth disadvantage. Specifically, at the median, educational differences account for about 13% of the nativity wealth gap in Germany. In the United States, disparities in conditional education functions explain an increasingly large proportion of the nativity wealth gap as one moves up the wealth distribution with more than half of the gap attributable to education differences at the 75th percentile. Thus, selection policies that ultimately shape the nature of the immigration flow are likely to have a large effect on the relative wealth holdings of the immigrant population.

A household's demographic characteristics-in particular partners' ages and numbers of children--also explain a large portion of the nativity wealth gap in Australia and the United States. In Australia, immigrant households are older and less likely to have children than native-born households. Thus, their relative demographic characteristics, like their relative educational advantage, contribute to reducing the overall nativity wealth gap by approximately $17,000 at the median. In the United States, however, 32% of the median wealth gap is explained by households' demographic characteristics. In Germany, the estimated contributions of the demographic factor to the overall wealth gap is insignificant over the entire distribution explaining only between 3 and 13% of the observed wealth gap. To the extent that this simply reflects differences in households' position in the life cycle, the gap in wealth resulting from demographic composition may not be particularly worrying. At the same time, immigrants' fertility patterns can differ widely across regions of origin suggesting that the national-origin mix of the immigrant population is likely to be related to the magnitude of the nativity wealth gap.

We assess the robustness of our findings by replicating the analysis dropping mixed immigrant households (Table 6). We find that the unconditional wealth gap is larger in all three countries indicating that the relative wealth position of immigrant-only households is more problematic than that of mixed immigrant households. At the median, the wealth gap between immigrant-only couples reaches $24,000 in Australia, $165,000 in Germany, and $54,000 in the United States. As before, the income component explains relatively little in the United States and Germany. Educational attainment again appears to be an important factor in explaining wealth differentials in all three countries, whereas households' demographic characteristics contribute to explaining the nativity wealth gap in Australia and the United States.

Overall, these results indicate that wealth differentials are largely the result of disparity in the education level and demographic composition of the native and immigrant populations, while income differentials play a role that is relatively minor. Interestingly, Australia differs from the United States and Germany in that disparity in educational attainment and demographic characteristics serve to narrow rather than widen the nativity wealth gap. If immigrants to Australia accumulated wealth in the same way as the native-born population, we would expect that they would have a median wealth advantage of approximately $32,000 (Table 5). Instead, we observe a median wealth gap of more than $8,600 implying that immigrant couples in Australia accumulate substantially less wealth given their characteristics than do similar native-born couples. Thus, the relatively small wealth gap in Australia obscures the fact that immigrant households, especially mixed households, are relatively advantaged in their characteristics. Unfortunately, they do not translate their educational and demographic advantage into higher wealth levels. In Germany, as much as 74% of the observed median nativity wealth gap is unexplained by immigrants' income, education, and demographic characteristics, while in the United States at most 28% of the gap cannot be explained by the fact that immigrants to the United States have lower income, less education, and are on average younger with larger families.

V. CONCLUSIONS

Given the increasing numbers of individuals worldwide living outside their country of birth, it is imperative that researchers move beyond a narrow focus on the economic assimilation of immigrant workers to also consider the economic well-being of immigrant families and the economic integration of immigrant communities more broadly. Many traditional immigrant-receiving countries are facing aging immigrant populations as large numbers of immigrants reach retirement age. The extent to which these immigrants have accumulated sufficient wealth to provide for themselves in old age is, however, largely unknown. This is problematic as there are many reasons to believe that nativity status may affect wealth holdings and asset portfolios. This paper uses recent data collections that link wealth holdings to migration histories to analyze the relative wealth position of immigrant and native households in Australia, Germany, and the United States.

Our results indicate that native-born households are wealthier than immigrant households across the entire wealth distribution. At the median, immigrant households in Australia hold approximately $9,000 less wealth than native households. This nativity wealth gap is substantially higher in both Germany and the United States reaching approximately $44,000 in the United States and more than $158,000 in Germany at the median. Using the semiparametric decomposition method proposed by DiNardo et al. (1996), we demonstrate that income disparities between native- and foreign-born households are not the primary explanation of these wealth gaps. Rather, wealth differentials are largely the result of differences in the educational attainment and household composition of native and immigrant households. Once these differences are taken into account, the wealth gap becomes considerably smaller in the United States. In contrast, the relatively small wealth gap between Australian-born and foreign-born households becomes larger once differentials in household characteristics are considered, indicating that immigrants to Australia do not translate their relative educational and demographic advantage into a wealth advantage. In particular, we would expect immigrant households to have a wealth advantage if they accumulated wealth in the same way as the Australian born. In Germany, most of the wealth gap between immigrant and native households cannot be explained by the fact that immigrant households have lower income, less education, and are on average younger with larger families.

On balance, the substantial cross-national disparity in the economic well-being of immigrant and native families is largely consistent with the selection policies used to shape the skills and settlement intentions of the immigration population. These policies ensure, for example, that immigrants to Australia are generally both permanent and relatively skilled, while immigrants to Germany are mainly unskilled and notionally temporary. It is not surprising then that we observe the nativity wealth gap to be larger in Germany than in Australia. These wealth differentials are most likely reinforced by labor market differences that lead immigrants to earn higher relative wages at entry in Australia and enjoy faster earnings adjustment in the United States, but remain at an earnings disadvantage in Germany. At the same time, given the large empirical literature documenting these crossnational differences in the relative labor market outcomes of immigrants, we might have expected a larger role for income disparities in producing these nativity wealth gaps.

A number of issues remain important topics for future research. Unfortunately, given the nature of our data, we have been unable to directly assess how cross-national differences in welfare and pension systems affect the nativity wealth gap. Moreover, we can only speculate about how institutional barriers associated with ethnicity, nativity, legal status, or language skills might limit immigrants' ability to accumulate financial or housing wealth. Making progress in these areas is likely to be largely dependent on the development of data sets that allow detailed comparisons to be made across a number of institutional settings. At the same time, a deeper understanding of the role of immigration policy in shaping the nativity wealth gap is likely to come from a comparison of the wealth levels and asset portfolios of immigrants selected on the basis of productive skills or family relationships rather than out of humanitarian concerns. Such a comparison is likely to be particularly important in the case of Australia as our results suggest that the nativity wealth gap is largely driven by behavioral differences in the accumulation of wealth. Expanding our focus on first-generation immigrants to also consider the wealth holdings of second-generation immigrants is an important first step in understanding the extent to which the nativity wealth gaps we observe are driven by the relatively higher investments that immigrants make in the human capital of their children. Finally, future research should explore the extent to which nativity wealth gaps arise out of differentials in the consumption and saving patterns or bequest behavior of native-born and immigrant households.

ABBREVIATIONS

GSOEP: German Socioeconomic Panel

HILDA: Household, Income and Labour Dynamics in Australia

PSID: Panel Study of Income Dynamics

RBA: Reserve Bank of Australia (RBA)

SCF: Survey of Consumer Finance

SIPP: Survey of Income and Program Participation

APPENDIX

TABLE A1
Components of Net Worth

HILDA

 Total interest earning assets in banks

+ Total interest earning assets in other institutions

+ Total stocks and mutual funds

+ Total other investments (a)

- Total value of unsecured debt

= A. Net financial wealth
 Business equity

= B. Business equity
 Housing and real estate equity

= C. Housing equity
 Total value of vehicles (b)

= D. Vehicles equity

SOEP

 Value of financial assets

+ Equity in other assets (c)

- Value of financial debts

= A. Net financial wealth
 Business equity

= B. Business equity
 Housing and real estate equity
 (including vehicles)

= C. Housing equity
 (see C.)

= D. Vehicles equity

SIPP

 Total interest earning assets in banks

+ Total interest earnings in other institutions

+ Equity in stocks and mutual funds

+ Equity in other assets (d)

+ Equity in IRA and KEOGH accounts

- Total value of unsecured debt

= A. Net financial wealth
 Business equity

= B. Business equity
 Housing and real estate equity

= C. Housing equity
 Vehicles equity

= D. Vehicles equity
 A+B+C+D

= Net worth

(a) Life insurances + trust funds + collectibles.

(b) Car loan is included in the total value of unsecured debt
(see A) which also includes other loans, higher purchase, and
overdraft.

(c) Life insurances + equity in tangible assets.

(d) Including mortgages held from sale of real estate.

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(1.) See, for example, Blau (1984), Borjas and Trejo (1991), and Borjas and Hilton (1996) for the United States, Riphahn (1998) and Fertig and Schmidt (2002) for Germany, and Maani (1993) for Australia.

(2.) These numbers represent estimated pension entitlements based on preretirement earnings.

(3.) The data used in this paper were extracted from the GSOEP Database provided by the DIW Berlin (http://www.diw.de/GSOEP) using the Add-On package PanelWhiz v1.0 (October 2006) for Stata(R). PanelWhiz was written by Dr. John P. Haisken-DeNew (john@panelwhiz.eu). The PanelWhiz generated DO file to retrieve the GSOEP data used here and any Panelwhiz Plugins are available upon request. Any data or computational errors in this paper are our own. Haisken-DeNew and Hahn (2006) describe PanelWhiz in detail.

(4.) All calculations are weighted using the weights available in the respective data sets.

(5.) Marks et al. (2005) provide a comprehensive and detailed description of the household wealth module of HILDA. The components of net worth in the GSOEP are described in Frick et al., (2007). The Social Security Administration (2003) provides a description of the way wealth is measured in the SIPP.

(6.) The conversion factors are 1.340 for Australia, 0.959 for Germany, and 1.000 for the United States. See http:/www.oecd.org/std/ppp/.

(7.) Quantile regressions of our wealth measure on a constant term and a dummy variable for immigrants have been used to obtain these figures.

(8.) We also estimated an alternative specification including a quadratic in household net income. The quadratic term was insignificant in all cases and was dropped.

(9.) Specifically, the estimated coefficient on the immigrant dummy in Model 2 captures the wealth gap between native-born couples and immigrant couples that have no income, no education, no children younger than 18, and in which the reference person migrated as an adult.

(10.) The net worth distribution of immigrants is considerably narrower than that of natives in all three countries. Therefore, reweighting the immigrant wealth distribution would involve extrapolating the immigrant conditional wealth distribution beyond the income range actually observed in the data. For that reason, we have chosen to create our counterfactual distributions by reweighting the wealth distribution of native households (Barsky et al. 2002).

(11.) The proportion of the wealth gap attributable to each of the explanatory factors will depend on the sequence (or order) in which we consider them (DiNardo et al., 1996). As we have no particular preference for one sequence over another, we will calculate each in turn and present results based on the simple average across all possible sequences (Cobb-Clark and Hildebrand, 2006c). Gibson et al. (2007) adopt a similar approach for New Zealand.

(12.) To compare the decomposition results to the estimates of the quantile regression, we also estimated a more parsimonious model including only household net income, education, an indicator variable for children less than 18, and a quadratic function in age. The decomposition results from this alternative specification do not differ substantially from those presented in the paper and are available upon request.

(13.) More specifically, our analysis controls for current, after-tax household income because none of our data sets provide a measure of permanent income. As a robustness check, we proxied permanent income using a measure of predicted income following Blau and Graham (1990). We found that using predicted income did not affect the results of the decomposition analysis substantially. However, using predicted income makes identifying the effects of education and demographic characteristics more challenging. Consequently, we report only results based on current income.

THOMAS K. BAUER, DEBORAH A. COBB-CLARK, VINCENT A. HILDEBRAND and MATHIAS G. SINNING **

* This paper uses confidentialized unit record file data from the HILDA survey. The HILDA Project was initiated and is funded by the Department of Families, Community Services, and Indigenous Affairs (FaCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR). The findings and views reported in this paper, however, are those of the authors and should not be attributed to FaCSIA or MIAESR.

** The authors are grateful for support from the Social and Economic Dimensions of an Aging Population Research Program, a SSHRC Grant (#410-2007-1477), and funding from an Australian Research Council Discovery Grant (DP0666158). The authors would also like to thank Juan Bar6n, Markus Grabka, David Mare, the participants of the Immigration Workshop 2006 at the Australian National University, and two anonymous referees for very helpful comments.

Bauer: Professor, Rheinisch-Westfalisches Institut fur Wirtschaftsforschung (RWI) Essen, Hohenzollernstr. 1-3, 45128 Essen, Germany. Phone 49-201-8149264. Fax 49-201-8149284, E-mail bauer@rwi-essen.de

Cobb-Clark: Professor, Research School of Social Sciences, Australian National University, HC Coombs Building, Canberra ACT 0200, Australia. Phone 61-2-6125-3267, Fax 61-2-6125-0182, E-mail deborah. cobb-clark@anu.edu.au

Hildebrand: Professor, Glendon College, York University, 2275 Bayview Avenue, Toronto, Ontario, Canada M4N 3M6. Phone 416-736-2100, Fax 416-487-6852, E-mail vincent @econ.yorku.ca

Sinning: Research Fellow, Research School of Social Sciences, Australian

National University, HC Coombs Building, Canberra ACT 0200, Australia. Phone 61-2-6125-2216, Fax 61-2-6125-0182, E-mail mathias. sinning@anu.edu.au

doi: 10.1111/j.1465-7295.2009.00221.x

TABLE 1
Descriptive Statistics, Australia (a)

 Natives

 Variable SD N

Net worth

Mean net worth ($) 295,078 396,724 2,235
Median net worth ($) 181,567 128,257 2,235
Mean net worth ($) if >0 306,876 399,323 2,141
% of HH with net worth >0 0.964 0.187 2,235

Mean net worth by age group ($)

25-34 154,352 301,899 522
35-44 224,573 275,803 624
45-54 338,094 360,300 502
55-64 506,342 618,015 326
65-75 339,686 326,420 261

Mean components of net worth ($)

Net financial wealth 57,473 165.596 2,235
Business equity 36,269 186,344 2,235
Housing equity 181,743 235,880 2,235
Vehicles equity 19,592 27,068 2,235

Explanatory variables

Household net income (b)) ($) 48,102 40,894 2,235
Age (years) 46.7 13.2 2,235
Number of kids <18 0.9 1.1 2,235
Years of education 11.9 2.4 2,235

Place of residence (%)

New South Wales 0.306 0.461 2,235
Victoria 0.248 0.432 2,235
Queensland 0.230 0.421 2,235
South Australia 0.079 0.271 2,235
Western Australia 0.087 0.282 2,235
Tasmania 0.032 0.176 2,235
Northern Territories 0.004 0.065 2,235
Australian Capital Territory 0.013 0.114 2,235

Immigration cohort (%)

< 1965
1965-1974
1975-1984
> 1984

Place of origin (%)

English speaking
Europe
Asia
Other

 Immigrants

 Variable SD N

Net worth

Mean net worth ($) 282,640 383,011 1,271
Median net worth ($) 180,074 130,811 1,271
Mean net worth ($) if >0 295,515 385,696 1,219
% of HH with net worth >0 0.959 0.197 1,271

Mean net worth by age group ($)

25-34 112,602 208,093 197
35-44 231,226 320,537 345
45-54 334,413 441,129 334
55-64 376,203 404,528 237
65-75 338,712 413,850 158

Mean components of net worth ($)

Net financial wealth 49,598 157,331 1,271
Business equity 24,552 133,759 1,271
Housing equity 192,703 243,617 1,271
Vehicles equity 15,786 26,773 1,271

Explanatory variables

Household net income (b)) ($) 47,563 45,468 1,271
Age (years) 48.4 12.5 1,271
Number of kids <18 0.8 1.0 1,271
Years of education 12.1 2.6 1,271

Place of residence (%)

New South Wales 0.327 0.469 1,271
Victoria 0.264 0.441 1,271
Queensland 0.152 0.359 1,271
South Australia 0.082 0.274 1,271
Western Australia 0.125 0.331 1,271
Tasmania 0.016 0.126 1,271
Northern Territories 0.010 0.099 1,271
Australian Capital Territory 0.023 0.152 1,271

Immigration cohort (%)

< 1965 0.265 0.442 1,271
1965-1974 0.238 0.426 1,271
1975-1984 0.148 0.356 1,271
> 1984 0.348 0.476 1,271

Place of origin (%)

English speaking 0.471 0.499 1,271
Europe 0.241 0.428 1,271
Asia 0.192 0.394 1,271
Other 0.096 0.295 1,271

(a) Amounts in $US purchasing power parities.

(b) Annual income reported.

TABLE 2
Descriptive Statistics, Germany (a)

 Natives

 Variable SD N

Net worth

Mean net worth ($) 231,368 290,112 2,344
Median net worth ($) 175,501 139,839 2,344
Mean net worth ($) if >0 273,701 297,906 1,894
% of HH with net worth >0 0.790 0.407 2,344

Mean net worth by age group ($)

25-34 83,955 222,685 242
35-44 184,068 226,901 639
45-54 259,899 336.813 540
55-64 304,479 303,229 493
65-75 252,800 288,409 430

Mean components of net worth ($)

Net financial wealth 51.797 115,704 2,344
Business equity 15,208 104,373 2,344
Housing equity 164,362 208,002 2,344

Explanatory variables

Household net income (b) ($) 34,333 16,652 2,344
Age (years) 51.5 12.9 2,344
Number of kids < 18 0.6 0.9 2,344
Years of education 12.3 2.6 2,344

Place of residence (%)

Berlin 0.023 0.150 2,344
Schleswig-Holstein 0.047 0.211 2,344
Hamburg 0.021 0.142 2,344
Lower Saxony 0.140 0.347 2,344
Bremen 0.011 0.104 2,344
North Rhine-Westphalia 0.266 0.442 2,344
Hesse 0.077 0.266 2,344
Rhineland-Palatinate, Saarland 0.085 0.279 2,344
Baden-Wuerttemberg 0.144 0.351 2,344
Bavaria 0.180 0.384 2,344

Immigration cohort (%)

< 1965
1965-1974
1975-1984
> 1984

Region of origin (%)

OECD member country
Central and Eastern Europe
Turkey
Ex-Yugoslavia
Other

 Immigrants

 Variable SD N

Net worth

Mean net worth ($) 86,744 230,067 691
Median net worth ($) 7,627 10,830 691
Mean net worth ($) if >0 154,291 181,553 339
% of HH with net worth >0 0.460 0.499 691

Mean net worth by age group ($)

25-34 25,713 146,381 114
35-44 86,655 362,941 189
45-54 125,012 190,062 156
55-64 102,036 151,304 146
65-75 76.034 145,698 86

Mean components of net worth ($)

Net financial wealth 24,643 59,353 691
Business equity 8,354 163,192 691
Housing equity 53,746 122,972 691

Explanatory variables

Household net income (b) ($) 27,724 13,332 691
Age (years) 48.2 12.9 691
Number of kids < 18 1.0 1.2 691
Years of education 11.3 2.7 691

Place of residence (%)

Berlin 0.044 0.206 691
Schleswig-Holstein 0.026 0.159 691
Hamburg 0.009 0.096 691
Lower Saxony 0.093 0.291 691
Bremen 0.009 0.096 691
North Rhine-Westphalia 0.293 0.456 691
Hesse 0.094 0.291 691
Rhineland-Palatinate, Saarland 0.047 0.211 691
Baden-Wuerttemberg 0.230 0.421 691
Bavaria 0.155 0.362 691

Immigration cohort (%)

< 1965 0.109 0.312 691
1965-1974 0.282 0.450 691
1975-1984 0.174 0.379 691
> 1984 0.435 0.496 691

Region of origin (%)

OECD member country 0.262 0.440 691
Central and Eastern Europe 0.284 0.451 691
Turkey 0.228 0.420 691
Ex-Yugoslavia 0.105 0.307 691
Other 0.122 0.327 691

(a) Amounts in $US purchasing power parities.

(b) Annual income reported.

TABLE 3
Descriptive Statistics, United States (a)

 Natives

 Variable SD N

Net worth

Mean net worth ($) 163,479 268,938 10,283
Median net worth ($) 70,946 66,033 10,283
Mean net worth ($) if >0 186,943 276,553 9,103
% of HH with net worth >0 0.887 0.316 10,283

Mean net worth by age group ($)

25-34 48,108 116,109 1,744
35-44 115,871 205,622 2,780
45-54 173,237 263,908 2,573
55-64 250,801 342,642 1,766
65-75 268,793 329,513 1,420

Mean components of net worth ($)

Net financial wealth 55,871 151,847 10,283
Business equity 14,757 106,119 10,283
Housing equity 87,586 132,806 10,283
Vehicles equity 5,265 8,481 10,283

Explanatory variables

Household net income (b) ($) 17,313 13,843 10,283
Age (years) 48.0 13.0 10,283
Number of kids < 18 0.9 1.1 10,283
Years of education 13.6 2.3 10,283

Place of residence (%)

New England 0.048 0.213 10,283
Middle Atlantic 0.133 0.340 10,283
East North Central 0.173 0.378 10,283
West North Central 0.080 0.271 10,283
South Atlantic 0.191 0.393 10,283
East South Central 0.069 0.254 10,283
West South Central 0.114 0.318 10,283
Mountain 0.050 0.218 10,283
Pacific 0.114 0.318 10,283

Immigration cohort (%)

< 1965
1965-1974
1975-1984
> 1984

Place of origin (%)

Europe (c)
Asia
Mexico
C/S America
Other

 Immigrants

 Variable SD N

Net worth

Mean net worth ($) 111,868 219,330 1,671
Median net worth ($) 27,577 29,945 1,671
Mean net worth ($) if >0 136,797 231,617 1,382
% of HH with net worth >0 0.835 0.371 1,671

Mean net worth by age group ($)

25-34 40,373 120,635 425
35-44 96,897 214,846 507
45-54 129,388 232,662 397
55-64 194,675 262,981 218
65-75 231,446 276,161 124

Mean components of net worth ($)

Net financial wealth 27,508 99,646 1,671
Business equity 9,733 77,928 1,671
Housing equity 70,908 133,033 1,671
Vehicles equity 3,718 7,453 1,671

Explanatory variables

Household net income (b) ($) 14,610 13,171 1,671
Age (years) 44.1 12.0 1,671
Number of kids < 18 1.2 1.3 1,671
Years of education 12.4 3.9 1,671

Place of residence (%)

New England 0.050 0.217 1,671
Middle Atlantic 0.160 0.366 1,671
East North Central 0.088 0.283 1,671
West North Central 0.020 0.139 1,671
South Atlantic 0.168 0.374 1,671
East South Central 0.012 0.107 1,671
West South Central 0.121 0.327 1,671
Mountain 0.062 0.242 1,671
Pacific 0.312 0.464 1,671

Immigration cohort (%)

< 1965 0.117 0.322 1,671
1965-1974 0.146 0.353 1,671
1975-1984 0.249 0.433 1,671
> 1984 0.488 0.500 1,671

Place of origin (%)

Europe (c) 0.219 0.414 1,671
Asia 0.257 0.437 1,671
Mexico 0.274 0.446 1,671
C/S America 0.177 0.382 1,671
Other 0.073 0.261 1,671

(a) Amounts in $US.

(b) Quarterly income reported.

(c) Includes also Canada and Australia.

TABLE 4
Median Quantile Regressions

 Australia

 Model (1) Model (2)

Age 13248.43 (0.000) 12891.30 (0.000)
Age Squared -67.39 (0.000) -63.66 (0.000)
Household net income 1.89 (0.000) 1.77 (0.000)
Years of education 17077.09 (0.000) 19190.23 (0.000)
Number of kids <18 3773.62 (0.186) 1247.81 (0.698)
Immigrant (including -31833.34 (0.001) 1417.19 (0.965)
 mixed households)
Family type:
Immigrant only -86965.01 (0.000) -74349.10 (0.001)
Mixed native-born head 59266.65 (0.000) 49612.77 (0.000)
Mixed foreign-born head 27698.36 (0.034) 24736.33 (0.059)
Interaction terms:
Immigrant x
 Year of migration:
 <1965 27450.30 (0.005) 25422.91 (0.017)
 1965-1974 -3307.11 (0.717) -5441.94 (0.541)
 1975-1984 2736.53 (0.812) 2113.47 (0.847)
 >1984 -26879.72 (0.003) -22094.45 (0.031)
 Age at arrival < 18 1703.06 (0.899)
 Household net income 0.55 (0.000)
 Years of education -4936.85 (0.058)
 Number of kids <18 5518.74 (0.312)
 Regions of origin:
 English speaking -11031.62 (0.190) -9051.36 (0.267)
 Europe 22563.74 (0.032) 15958.98 (0.118)
 Asia -11180.45 (0.339) -6151.90 (0.582)
 Other -351.66 (0.980) -755.73 (0.957)
 OECD
 Central and Eastern
 Europe
 Ex-Yugoslavia
 Turkey
 Other
 Europe
 Asia
 Mexico
 C/S America
 Other
Constant -556789.65 (0.000) -566678.60 (0.000)
N 3506 3506

 Germany

 Model (1) Model (2)

Age 9740.20 (0.000) 9547.83 (0.000)
Age Squared -49.08 (0.040) -48.01 (0.024)
Household net income 4.09 (0.000) 4.33 (0.000)
Years of education 5947.24 (0.000) 5221.81 (0.002)
Number of kids <18 11825.56 (0.010) 2956.56 (0.522)
Immigrant (including -52455.12 (0.000) -74751.96 (0.054)
 mixed households)
Family type:
Immigrant only -45803.53 (0.001) -53562.27 (0.000)
Mixed native-born head 3818.24 (0.813) 6062.19 (0.663)
Mixed foreign-born head 41985.28 (0.027) 47500.08 (0.004)
Interaction terms:
Immigrant x
 Year of migration:
 <1965 -41407.68 (0.056) -35758.32 (0.066)
 1965-1974 -4671.82 (0.774) 20211.46 (0.158)
 1975-1984 31115.88 (0.067) -1162.33 (0.937)
 >1984 14963.63 (0.340) 16709.19 (0.277)
 Age at arrival < 18 63902.90 (0.001)
 Household net income -0.95 (0.132)
 Years of education 1942.15 (0.551)
 Number of kids <18 18364.34 (0.026)
 Regions of origin:
 English speaking
 Europe
 Asia
 Other
 OECD 49826.15 (0.012) 47517.87 (0.007)
 Central and Eastern -20319.89 (0.209) -1597.10 (0.912)
 Europe
 Ex-Yugoslavia -15798.63 (0.441) -21956.45 (0.244)
 Turkey 4224.55 (0.811) -8164.96 (0.601)
 Other -17932.17 (0.382) -15799.36 (0.371)
 Europe
 Asia
 Mexico
 C/S America
 Other
Constant -408328.90 (0.000) -395139.56 (0.000)
N 3035 3035

 United States

 Model (1) Model (2)

Age -1128.55 (0.034) -1260.91 (0.011)
Age Squared 52.18 (0.000) 52.96 (0.000)
Household net income 3.47 (0.000) 3.40 (0.000)
Years of education 8160.25 (0.000) 10992.49 (0.000)
Number of kids <18 3972.15 (0.000) 2390.59 (0.008)
Immigrant (including 13449.78 (0.000) 112162.27 (0.000)
 mixed households)
Family type:
Immigrant only -4212.21 (0.240) -6542.04 (0.054)
Mixed native-born head 9004.99 (0.027) 12937.45 (0.001)
Mixed foreign-born head -4792.78 (0.312) -6395.40 (0.148)
Interaction terms:
Immigrant x
 Year of migration:
 <1965 37671.01 (0.000) 35431.17 (0.000)
 1965-1974 -11474.28 (0.027) -14741.34 (0.003)
 1975-1984 -8954.27 (0.048) -10567.30 (0.014)
 >1984 -17242.46 (0.000) -10122.53 (0.000)
 Age at arrival < 18 13392.75 (0.040)
 Household net income -0.59 (0.003)
 Years of education -8213.27 (0.000)
 Number of kids <18 5208.64 (0.015)
 Regions of origin:
 English speaking
 Europe
 Asia
 Other
 OECD
 Central and Eastern
 Europe
 Ex-Yugoslavia
 Turkey
 Other
 Europe 11187.34 (0.031) 16886.77 (0.001)
 Asia -7824.41 (0.094) 3848.08 (0.390)
 Mexico 26635.82 (0.000) 3635.75 (0.458)
 C/S America -10583.86 (0.043) -12038.81 (0.014)
 Other -19414.88 (0.008) -12331.80 (0.072)
Constant -152335.24 (0.000) -182142.01 (0.000)
N 11954 11954

Note: Amounts in $U.S. Purchasing Power Parities. p-values are given
in parentheses.

TABLE 5
DFL Decomposition--Foreign-Born to Native-Born Households

 Raw Gap Income

Australia

25th 18,694.03 [7,232.89] 4,212.19 [1,442.40] (23)
50th 8,638.06 [9,186.04] 6,792.29 [2,292.40] (79)
75th 13,488.81 [19,710.91] 8,832.09 [5,138.49] (65)
P75-P25 -5,205.22 [18,471.24] 4,619.90 [4,464.98]

Germany

25th 29,968.72 [4.286.16] 8,505.98 [2.298.00] (28)
50th 158,870.49 [8,806.65] 15,443.41 [5,129.17] (10)
75th 194,383.94 [23,221.04] 20,051.89 [7,220.72] (10)
P75-P25 164,415.22 [22,841.57] 11,545.92 [6,648.22]

United States

25th 15,063.71 [915.00] 1,668.50 [365.57] (11)
50th 43,658.21 [3,481.53] 1,903.03 [942.52] (4)
75th 72,519.33 [6,833.101 -1,005.85 [2,294.16] (-1)
P75-P25 57,455.62 [6,644.68] -2,674.35 [2,085.90]

 Education

Australia

25th -8,295.27 [2,259.05] (-44)
50th -13,778.61 [3,455.44] (-160)
75th -26,347.01 [6,841.01] (-195)
P75-P25 -18,051.74 [5,558.46]

Germany

25th 8,519.22 [2,229.25] (28)
50th 20,313.59 [5,675.89] (13)
75th 24,567.43 [6,535.99] (13)
P75-P25 16,048.21 [5,754.24]

United States

25th 4,937.40 [443.23] (33)
50th 15,533.93 [1,270.00] (36)
75th 39,554.71 [3,026.24] (55)
P75-P25 34,617.31 [2,801.64]

 Demography

Australia

25th -13,193.03 [3,112.431 (-71)
50th -16,614.43 [3,953.62] (-192)
75th -25,097.01 [6,709.02] (-186)
P75-P25 -11,903.98 [5,484.60]

Germany

25th 3,912.44 [2,933.16] (13)
50th 11,186.27 [8,357.29] (7)
75th 6,495.37 [7,794.481 (3)
P75-P25 2,582.93 [6,111.83]

United States

25th 5,786.77 [576.41] (38)
50th 13,912.88 [1,475.61] (32)
75th 28,347.50 [2,800.88] (39)
P75-P25 22,560.74 [2,495.42]

 Unexplained

Australia

25th 35,970.15 [6,851.34] (192)
50th 32,238.81 [8,675.40] (373)
75th 56,100.75 [19,153.17] (416)
P75-P25 20,130.60 [18,943.12]

Germany

25th 9,031.07 [3,106.69] (30)
50th 111,927.22 [11,821.70] (70)
75th 143,269.24 [21,665.29] (74)
P75-P25 134,238.17 [21,745.02]

United States

25th 2,671.04 [631.30] (18)
50th 12,308.37 [2,929.43] (28)
75th 5,622.96 [5,820.62] (8)
P75-P25 2,951.92 [5,719.05]

Notes: Percentage of total variation explained in parentheses.
Standard errors of explained variation are reported in brackets.

TABLE 6
DFL Decomposition-Foreign-Born to Native-Born Households, Excluding
Mixed Households

 Raw Gap Income

Australia

25th 25,485.07 [10,648.40] 9,575.87 [2.307.40] (38)
50th 23,936.57 [12.754.53] 11,947.14 [3,307.21] (50)
75th 42,686.57 [18,785.23] 22,265.80 [6,984.02] (52)
P75-P25 17,201.49 [18.554.62] 12,689.93 [6,123.98]

Germany

25th 29,968.72 [4,321.17] 9,945.32 [2,500.27] (33)
50th 165,079.25 [8,108.23] 18,780.64 [7,744.37] (11)
75th 270,473.83 [18,844.53] 21,347.90 [11,563.63] (8)
P75-P25 240,505.11 [18,432.87] 11,402.57 [11,096.98]

United States

25th 15,813.54 [867.24] 2,513.95 [457.57] (16)
50th 54,148.14 [3,134.151 5,052.84 [1,139.91] (9)
75th 106,758.90 [8.563.86] 5,392.79 [2,793.42] (5)
P75-P25 90,945.36 [8,427.17] 2,878.84 [2.502.46]

 Education

Australia

25th -10,286.07 [3,152.49] (-40)
50th -15,677.86 [4,812.43] (-65)
75th -27,510.32 [8,789.15] (-64)
P75-P25 -17,224.25 [6,590.59]

Germany

25th 11,973.27 [2,892.46] (40)
50th 35,846.64 [9,671.87] (22)
75th 40,215.99 [9,525.18] (15)
P75-P25 28,242.72 [8,382.69]

United States

25th 6,199.09 [578.27] (39)
50th 20,067.04 [1,574.68] (37)
75th 52,002.94 [3,771.07] (49)
P75-P25 45,803.85 [3,500.72]

 Demography

Australia

25th -20,573.38 [4,701.20] (-81)
50th -25,840.17 [5,305.60] (-108)
75th -40,203.23 [9,229.86] (-94)
P75-P25 -19,629.85 [7,538.26]

Germany

25th 2,977.13 [2,833.99] ()0)
50th 6,705.98 [9,348.61] (4)
75th -391.62 [8,446.70] (-0)
P75-P25 -3,368.75 [6,785.12]

United States

25th 5,352.80 [649.15] (34)
50th 14,216.23 [1,675.41] (26)
75th 28,893.38 [3,199.64] (27)
P75-P25 23,540.58 [2,795.35]

 Unexplained

Australia

25th 46,768.66 [11,040.14] (184)
50th 53,507.46 [12,282.42] (224)
75th 88,134.33 [20,757.98] (206)
P75-P25 41,365.67 [21.111.25]

Germany

25th 5,072.99 [2,901.10] (17)
50th 103,745.99 [17.496.33] (63)
75th 209,301.57 [20,305.64] (77)
P75-P25 204,228.57 [19,858.09]

United States

25th 1,747.70 [620.50] (11)
50th 14,812.03 [2,831.15] (27)
75th 20,469.78 [7,617.71] (19)
P75-P25 18,722.09 [7,537.22]

Note: See the notes in Table 5.