Wage Mobility of Foreign-Born Workers in the United States

A. The CPS as Cross-Section and Panel Samples

The CPS is a collection of representative cross-sections. It is a monthly survey designed to collect information on demographic and labor force characteristics of the civilian noninstitutionalized population 16 years of age and older. As of July 2005, approximately 72,000 assigned housing units from 824 sample areas are in the sample. A housing unit is interviewed for four consecutive months, dropped out of the sample for the next eight months, interviewed again in the following four months, and then is retired from the sample. If the occupants of a dwelling unit move, the new occupants of the unit are interviewed. Nevertheless, the CPS provides a representative cross-section of each year’s population because the random sample of housing units remains fixed.

The outgoing rotation groups, or the individuals in the fourth and the eighth interviews, are of interest because interviewees are asked their labor market outcomes, such as usual weekly earnings and usual weekly hours worked. In the outgoing rotation groups, an individual appears only once in a year but may reappear in the following year if the individual does not move. One may append data from the two interviews and get repeated observations on the same individuals. The appended sample is called the Merged Outgoing Rotation Group (MORG) or the matched CPS.

The matched CPS is a collection of two-year panels. The 1996–97 panel, for instance, contains the individuals in the households that enter the survey scheme between October 1995 and September 1996. These two-year panels, however, are not representative of the U.S. population because they exclude those who move their residence. What makes it more complicated is missing foreign-born respondents in the second period because it is not possible to tell whether the person is in the United States or has gone back to his or her home country. If the person is still in the United States, we call it sample attrition because this person will have an equal probability of being selected in a cross-section as all other U.S. residents. However, if the person has emigrated from the United States, we call it population attrition since this person has no chance of being selected in the cross-section. The next section presents a method that accounts for sample attrition in the presence of population attrition.

B. Sample Attrition in the Presence of Population Attrition

The nonrepresentative two-year CPS panels, if combined properly, can mimic a regular representative longitudinal sample. Suppose that there is no population attrition. Then, attrition is caused by residential mobility within the United States. Since the CPS cross-sections are representative, a method developed by Hirano, Imbens, Ridder, and Rubin (2001) and Bhattacharya (2008) can be applied. Their method exploits the availability of representative cross-sections as the basis for weighting the persons in a balanced panel. The attrition-correcting weighting function is given by the inverse of one minus the probability of sample attrition. When there is attrition in the population, however, the second period cross-section is not representative of the first period population, and the existing method should not be applied. To account for sample attrition in the presence of population attrition, this paper uses a method developed by Kim (2012a).

The key estimation strategy is generating a counterfactual but representative second period cross-section (where there is no outmigration) prior to applying the existing sample attrition correcting scheme. For example, suppose that the two-year panel of 1996–97 is of interest. The CPS provides 1996 and 1997 cross-sections but the 1997 cross-section is not representative of the 1996 population due to population attrition. First, we use the 1996 cross-section as the basis for generating a representative counterfactual 1997 cross-section. The counterfactual sample is obtained by weighting the second period cross-section by one minus the probability of population attrition. Then the two representative cross-sections (the 1996 actual and 1997 counterfactual cross-sections) are used as the basis for estimating attrition-correcting weighting functions. This step is identical to Bhattacharya (2008). Finally, we assign weights to the persons in the balanced part of the 1996–97 panel. The resulting estimators are consistent.

The matched CPS with proper weights shares most of the advantages of usual panel data sets and is superior in some dimensions. First, it consists of multiple panel samples. Hence, usual panel data models, such as the first difference or the fixed effects models, can be used to control for individual-specific permanent components. Second, the sample has the crucial advantage of being much larger than alternative panel data sets such as the Panel Study of Income Dynamics (PSID) or the National Longitudinal Survey of Youth 1979 (NLSY79). Sample sizes matter in immigration studies because foreign-born persons, after all, are minorities and we want to disaggregate them by source countries. Finally, the CPS cross-section is representative of the U.S. population for any given year.³

C. Summary Statistics

Now we turn to specific data used in this analysis. Since 1994, the CPS has included information on international migration, such as year of entry into the United States and country of birth, along with demographic and labor market information such as age, schooling, marital status, earnings per hour or week, usual hours of work, and labor market status.⁴ The sample used in this analysis is drawn from the matched CPS between 1996 and 2008. We drop 1994 and 1995 because matching is not possible between June to December 1994 and 1995, and between January to August 1995 and 1996, due to the sample redesign of the CPS.

We take a sample of foreign-born and native-born men of ages 24–60. In order to examine differences based on ethnic origin, we divide the foreign sample into four groups: immigrants from Latin America (including Mexico), from Europe (including Australia, New Zealand, and Canada), from Asia, and from other countries.⁵ The group of other countries consists of immigrants from Africa, Oceania, and unclassified ones. The reference group consists of native-born non-Hispanic white men.⁶ Details on how the data are processed are explained in the Appendix. This section provides a general picture.

Table 1 reports summary statistics for cross-section / matched samples. The matched sample consists of two year panels. The wage information in the CPS sample is mostly self-reported but also involves imputed wages. As the imputation rule does not account for the country of origin, the imputed wages of immigrant workers tend to be biased toward the wages of native workers. Consequently, our preferred way to handle the imputed wages is simply dropping them.⁷ It is also worth noting that self-employed workers do not report wages or earnings in the CPS. Around 13 percent of natives and 11 percent of immigrants were self-employed in 2005.

We find substantial attrition. About 21 percent of native interviewees and 29 percent of immigrant interviewees drop out of the sample in the second period.⁸ The gap between natives and immigrants in the attrition rates may be partly explained by outmigration but it is also due to differential residential mobility within the United States. For these reasons, we estimate the attrition-correcting weighting functions for natives and immigrants separately. Moreover, attrition rates vary by year. According to Table A1 in the Appendix, the matching rates are 74–82 percent among the native samples and 67–73 percent among the immigrant samples between 1996 and 2008. Therefore, we estimate the weighting functions for 1996–2008 for each year and by immigration status.

The persons in the matched sample are a nonrandom subset of the cross-section sample. Table 1 reveals that persons in the matched samples, for all ethnic groups including natives, tend to earn more and work longer than those in the cross-section samples. It implies that more successful workers are more likely to be matched (or have lower residential mobility) than unsuccessful ones. In addition, individuals in the matched sample are older than those in the cross-section sample. Foreign-born persons from Latin America tend to attrite more than those from Europe and Asia.

Years of education provides a rough measure of skill endowment. Foreign-born persons have lower mean and a much larger standard deviation of education. In the cross-section sample, the average education level is 14.1 years for native-born persons and is 11.9 years for foreign-born persons. Immigrants from Latin America have 10.0 years of average education, those from Europe 14.5 years, and those from Asia 14.8 years. Estimates of years of education are virtually not different between the matched and the cross-section samples.

In the cross-section sample, the average hourly wage of native-born workers is $16.9, in 1994 dollars, while the average foreign-born worker earns $13.3. Immigrants from Latin America make $9.8 per hour, those from Europe $20.2, and those from Asia $18.0. Immigrant workers work 1.8–1.9 more hours per week than native workers. Although not reported in the table, 95.9 percent and 95.3 percent of the foreign-born and native-born populations are full-time workers, while 4.1 percent and 4.7 percent are part-time workers, respectively, among those who are employed. The proportions of full-time and part-time workers are relatively stable over the sampling period.

Among immigrants, 56.6–58.3 percent are from Latin America, 13.0–14.4 percent are from Europe, and 23.4–24.2 percent are from Asia. The estimates also indicate that foreign-born persons are about two years younger than native-born persons on average. An average native is 40.9 years old and an average immigrant is 39.0 years old in the cross-section sample. A larger proportion of the foreign-born population is married.

A. Conceptual Framework

Figure 1 presents the wage distributions of native-born and foreign-born workers in 1996 and 1997 using the 1996 and 1997 CPS cross-sections. Broken lines are the 1996 wage distributions and solid lines are the 1997 wage distributions. The native distributions are the ones with a mode around $10. The immigrant distributions are the ones with a mode around $7. Vertical lines indicate the decile points for the 1996 native wage distribution. For example, native-born workers with hourly wages between $8.5–$9.9 in 1996 are in the 20–30th percentile group. The decile points for the 1997 native wage distribution are omitted but are similar to those for the 1996 distribution. For example, to be in the 20–30th percentile group in 1997, the hourly wage has to be between $8.6–$10.2. We do not obtain decile points for immigrants. Instead, immigrants are assigned to the native decile groups. The wage distribution of natives is more dispersed and has a higher mean than that of immigrants. The majority of foreign-born workers are located at the bottom decile of the native wage distribution.

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Figure 1

Wage Distribution of Natives and Immigrants

In principle, we can obtain the foreign-native gap in year-to-year transition probabilities from one decile group to another of a wage distribution, where the deciles are determined by the native sample. It requires one to estimate a ten-by-ten transition matrix for every two-year pair.⁹ For illustration purposes, take native-born and foreign-born workers who were in the 20–30th percentile of native wage distribution in 1996. First, assign attrition-correcting weights to the matched CPS. Then, take native-born workers in the 20–30th percentile group in 1996 and observe which proportion of workers move to each of the ten decile groups in 1997. Finally, repeat the exercise for foreign-born workers and analyze the foreign-native gap in the proportions for each of the ten decile groups in 1997.

While it is not very difficult to estimate these matrices, we reduce its dimension by estimating the probabilities of moving up (moving to higher decile groups), moving down (moving to lower decile groups), and staying in the same decile group. This reduction is useful since the immigrant sample size per group is small. For example, for the workers in the 20–30th percentile group in 1996, one may observe which proportion moves to the 30–100th percentile group, which to the 0–20th percentile group, and which stay in the 20–30th percentile group in 1997. For the 1996–97 sample, we find that 36 percent of native-born workers moved to higher deciles, 21 percent moved to lower deciles, and 43 percent stayed. Among foreign-born workers, 25 percent moved to higher deciles, 32 percent moved to lower deciles, and 43 percent stayed.¹⁰

The results are visualized in Figure 2. The horizontal axis depicts percentile values representing the decile groups. The 20–30th percentile groups lie between 20 and 30. The solid line corresponds to native-born workers and the dashed line is for foreign-born workers. The length of these lines represents the probability of staying. Because the staying probabilities of native-born and foreign-born workers are identical, the two lines in Figure 2 are of the same length. The vertical distance between unity and the regular triangle (▲) indicates the probability of moving to higher decile groups. The triangle for foreign-born workers lies above of that of native-born workers, meaning that foreign-born workers between the 20–30th percentiles have a smaller probability of moving higher deciles than native-born workers. The vertical distance between zero and the inverted triangle (▼) indicates the probability of moving lower decile groups. The inverted triangle for foreign-born workers lies above of that of native-born workers, meaning that foreign-born workers between the 20–30th percentiles have a higher probability of moving to lower deciles than native-born workers in the same group.

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Figure 2

Probability of Moving Up, Down, and Staying

Figures are effective for summarizing wage mobility but have limitations. First, the estimates are unconditional transition probabilities. They do not involve covariates. In order to partly control for some key variables of interest, the next several sections present wage mobility figures by years since migration, continent of origin, and education. In addition, Section IV introduces a first-order Markov switching model to control for a full set of covariates. Second, we pool all the years together to produce one figure rather than presenting figures for each two-year panel but a key condition for this analysis is that the decile points for natives and immigrants are stable over time. Unless they are stable, pooling across years implies grouping individuals at different locations of the wage distribution. Later we demonstrate that the decile points are relatively stable.

B. Wage Mobility by Immigration Status

We apply the strategy discussed in the previous subsection to foreign-born and native-born workers for 1996–2008. For each of the first year decile groups of the two-year panels, the number of individuals who moved up, moved down, and stayed in the second year are counted and weighted by the attrition-correcting weights. These weighted individuals are then summed across all years, and their outcomes are summarized in Figure 3. Solid lines with squares and diamonds represent the share of native-born workers who moved to lower and higher decile groups, respectively. Dashed lines with squares and diamonds are the proportion of foreign-born workers who moved to lower and higher decile groups in the second year. Each of these four lines with symbols is surrounded by confidence bands indicated by two thinner lines. These thinner lines represent two times the standard errors of the thicker lines with symbols.

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Figure 3

Wage Mobility by Immigration Status

Overall, we find that foreign-born workers in lower decile groups are more likely to fall behind than native-born workers, and only those who are at the top of the wage distribution tend to outperform native-born workers. For example, foreign-born workers with wages below the median have lower chances of moving up decile groups than native-born workers. Foreign-born workers in the 70th percentile or below have higher chances of moving down decile groups than native-born workers. On the other hand, foreign-born workers in the eighth and ninth decile groups are move likely to move up than native-born workers, and those in the ninth and tenth decile groups are less likely to move down than native-born workers. Since most immigrants fall in the middle and bottom decile groups, we conclude that the majority of foreign-born workers fail to assimilate into the U.S. labor market.

To see the effect of attrition-correcting weights, Figure 4 illustrates nonweighted estimates and attrition-adjusted estimates. We find that the probability of moving up or down is affected by up to two percentage points due to the attrition-correcting weights but the differences between the nonweighted and weighted estimates of transition probability are not statistically significant. This is because wages enter as control variables in the attrition functions, and the transition probability estimates are conditional on wage deciles.¹¹ For example, individuals with lower wages, say, those in the second decile group, may be assigned larger weights. However, since individuals in the same decile group are likely to have similar weights, relative weights within the decile group would not be much affected.¹² The subsequent subsections present attrition-adjusted estimates since nonweighted estimates are not qualitatively different.

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Figure 4

Nonweighted and Attrition-Adjusted Wage Mobility for Natives / Immigrants

C. Wage Mobility by Years since Migration

In this subsection, we discuss transition probability estimates for immigrants with different years of working experience in the United States. The six figures in Figure 5 classify immigrants by years since migration: less than six years, six to less than 11 years, 11 to less than 16 years, 16 to less than 21 years, 21 to less than 26 years, and 26 years and above. We account for both sample attrition and population attrition in the two panel years to obtain these estimates. One can interpret the estimates as if there is no sample attrition and no population attrition in two year panels. No population attrition means that conditional on an immigrant is in the United States in the first panel year, the immigrant is in the United States in the second panel year. For example, for those who have stayed in the United States for five years and are in the sample in the first panel year, the counterfactual is that the immigrants are in the sample (and in the United States) in the second panel year.

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Figure 5

Wage Mobility by Years since Migration (Natives: Solid / Immigrants: Dashed)

The top left figure presents estimates for immigrants with less than six years of U.S. experience. The figure shows that immigrants in bottom decile groups have smaller probabilities of moving up and larger probabilities of moving down than their native counterparts. These results are statistically significant. Further, immigrants in middle decile groups have more or less the same probabilities of moving up as natives. They seem to have higher probabilities of moving down than native-born workers but the difference is not significant at the 5 percent level. Immigrants in top decile groups tend to stay in higher deciles relative to natives, although their probabilities of moving to lower deciles are not significantly smaller than those of natives.

Across all of the six figures, we find similar patterns. Immigrants in top decile groups are more likely to keep up or improve relative to natives, while those in middle and bottom decile groups tend to fall behind. These results, again, support the idea that the majority of foreign-born workers fail to assimilate into the U.S. labor market. However, it is unclear, at least from the figures, whether time spent in the United States is closely related to wage mobility of immigrants. This would be inconsistent with the economic assimilation hypothesis, and we will come back to this point later.

D. Wage Mobility by Continent of Origin

We conduct a similar analysis for immigrants from different continents in Figure 6.¹³ First, immigrants from Latin America tend to fall behind unless they are in the top two decile groups. In particular, the differences in the probability estimates of moving up and down for bottom or middle decile group Latin Americans are sizable and statistically significant. Second, immigrants from Asia exhibit clear divergence. Asian immigrants with above-median wages have a higher chance of moving up and a lower chance of moving down than natives with above-median wages. For Asian immigrants with below-median wages, the exact opposite is true. Finally, immigrants from Europe are very similar to natives in terms of wage mobility as the differences in the transition probability estimates for the two groups are within confidence bands.

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Figure 6

Wage Mobility by Continent of Origin (Natives: Solid / Immigrants: Dashed)

Overall, these results suggest that the widening foreign-native gap in mean wages with U.S. experience is mostly driven by middle and bottom decile group immigrants from Latin America and bottom decile group immigrants from Asia. This is confirmed later when we present conditional transition probability estimates.

E. Wage Mobility by Education

We conduct a similar analysis for natives and immigrants of different education levels. Individuals are assigned to four different groups of years of education: [0,8), [8,12), [12,16), and [16,∞). The results are presented in Figure 7. The first education group with less than eight years of education includes 2 percent of natives and 19 percent of immigrants. Due to the small size of the native sample, native results (the solid lines) in higher decile groups are poorly estimated. However, we do find that immigrant workers with wages below the median have higher probabilities of moving down and lower probabilities of moving up than their native counterparts.

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Figure 7

Wage Mobility by Education (Natives: Solid / Immigrants: Dashed)

The second education group with eight to less than 12 years of education consists of 6 percent of natives and 12 percent of immigrants. Immigrant workers with below-median wages are more likely to move to lower deciles than native workers in the same decile groups but the chances of moving to higher deciles are not statistically different from those of natives. Among the above-median wage workers, the foreign-native differences in the probabilities of moving up, moving down, and staying are small.

The members in the third education group have 12 to less than 16 years of education. 60 percent of natives and 41 percent of immigrants are in this group. Below-median wage immigrant workers have a smaller probability of moving to higher deciles than below-median wage native workers. Above-median wage immigrant workers have a similar probability of moving to higher deciles than above-median wage native workers. The probability of moving down is always larger for immigrants unless they are in the top two decile groups.

Finally, the highest education group with 16 or more years of education includes 32 percent of natives and 28 percent of immigrants. In lower decile groups, immigrants have a lower probability of moving up and a higher probability of moving down. In middle decile groups, wage mobility is not statistically different between native and immigrant workers. In upper decile groups, immigrants have a higher probability of moving up and a lower probability of moving down. Overall, the education results suggest that the below-median wage immigrant workers with less than 16 years of education can explain the widening foreign-native gap in mean wages.

F. Wage Deciles from Cross-Sections

This section presents native and immigrant wage distributions for 1996 to 2008 to examine whether there is cross-sectional evidence of assimilation at different deciles of the distributions. Transition probabilities are influenced by changes in the wage structure. If changes in wage structure affect natives and immigrants differently, then the probability of moving up (or down) will be different for natives and immigrants. For example, if immigrants in a certain decile group are disproportionately hurt by changes in the wage structure, they will be more likely to move down to lower decile groups than their native counterparts in the same decile group.

Figure 8 illustrates the time series of nine wage decile points for natives and immigrants. The real wages of a tenth percentile native worker are $6.61 in 1996, $6.93 in 1997, and so on, reaching $7.23 in 2008. We find that there is some widening of the native and immigrant wage distributions especially at the top tails of the distributions. For example, Figure 9 reveals that there is a lot of fluctuation and possibly an increasing trend in the 90 / 10 ratios for natives and immigrants. However, this problem is alleviated by the fact that both measures for natives and immigrants are rising. In addition, the distributions are more stable for the middle and bottom decile groups. In Figure 9, the 70 / 10, the 50 / 10, and the 30 / 10 ratios are nearly constant except for immigrants in 1996–98.

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Figure 8

Wage Decile Points for Natives / Immigrants by Year

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Figure 9

90 / 10, 70 / 10, 50 / 10, and 30 / 10 Ratios for Natives / Immigrants by Year

Wage decile points are relatively stable, which implies that Figures 4, 5, 6, and 7 are proper unconditional transition probability estimates by immigration status, years since migration, continent of origin, and years of education, respectively. We have seen that assimilation differs by continent of origin and education groups. In order to fully investigate assimilation patterns for each of the ten decile groups, the next section turns to estimation of transition probability functions conditional on covariates including years since migration, continent of origin, and years of education.

A. A First-Order Markov-Switching Model

Consider a first-order Markov-switching variable S_it that has ten states. The ten-state S_it represents the ten decile groups, where i is individual and t is calendar year. A standard first-order Markov-switching model defines a transition probability from state s_t−1 to state s_t by

(1)

for s_t−1, s_t ∈ {1, 2, …,10}. In principle, the joint probability, Pr [S_i,t−1 = s_t−1, S_it = s_t], can be estimated but what we need for our analysis is the transition probabilities of moving up, moving down, and staying, which are even simpler than estimating the entire ten-by-ten transition matrix given by Equation 1. The probability of moving up is given by

(2)

and the probability of moving down by

(3)

The probability of staying is simply the residual:

(4)

Now suppose that the probabilities in Equations 2–3 are functions of a vector of covariates, X, and are given in parametric forms. We estimate the transition probabilities for each of the ten decile groups. For any given state, S_it−1 = s, let the vector of parameters be θ_s. One may estimate the probabilities by maximum likelihood (ML) estimation. Conditional on S_t−1 = s, the ML estimator is given by the maximizer of

For each s = 1,2, …, 10, apply a separate maximum likelihood estimation procedure and obtain . Then, the estimated probabilities in Equations 2–4 are

While the first-order Markov-switching model is a well-defined methodology, we want to address some of the limitations of applying this approach to analyze wage mobility. First, this measure of wage mobility provides only limited information on the magnitude of wage changes. It may be the case that immigrants are less likely than natives to move up a decile in the wage distribution but when they do move up, immigrants tend to experience larger wage gains. One way of testing this is estimating the entire ten-by-ten transition matrix and examining whether improving immigrants land in higher decile groups than improving natives. Although not reported in the paper, we have unconditional transition probability results to use for this analysis but do not find significant differences between natives and immigrants.

Second, mainly due to the two-year panel structure, we can only estimate annual changes using a first-order model. However, under some conditions it is possible to make interpretations for longer-term mobility. For example, suppose that immigrants in middle and bottom decile groups are more likely to stay or move down than their native counterparts, and immigrants in top decile groups are more likely to stay or move up than natives in top decile groups. Then, of those in top decile groups, a larger share of immigrants is likely to remain in top decile groups in the following year than the share of natives. Repeating these outcomes over multiple years, immigrants in top decile groups are more likely to stay in top decile groups in the longer term. A similar logic will apply to those in middle and bottom decile groups.

B. Empirical Specification

A maximum likelihood estimation procedure can be used to estimate Equations 2–3 using a multinomial logit model. In our specific model, partition the parameter vector θ_s by θ_s = (γ′_s, δ′_s)′. The probability of moving up is given by

and the probability of moving down is given by

The vector of covariates include a constant, age, age squared, education, a dummy for marital status, and all these variables interacted with dummies for continent of birth. In addition, we include years since migration, years since migration squared, continent of birth, dummies for entry year, and calendar year dummies. Of the multinomial logit model estimates, the coefficients of age, marriage variables, and education are significant for some S_t−1 = s. These estimates are not directly interpretable but give the signs of the impact of corresponding covariates on the probabilities of moving up and down.

For the sake of space, Table 2 reports the multinomial logit model estimates, and , for education interacted with dummies for continent of birth. For S_t−1 = s, the estimate of education is positive (=0.148) and significant at the 1 percent significance level. In general, more educated individuals have a greater probability of moving up than less-educated ones for all S_t−1 = s. More educated individuals, however, also have a greater probability of moving down for S_t−1 = 4,5,6. For example, for S_t−1 = 4, the estimate of education is positive (= 0.034) and significant at the 1 percent significance level. These estimates imply that the wages of more educated individuals in middle decile groups have larger variance than the wages of less educated ones. More educated individuals have a smaller probability of moving down for S_t−1 = 9,10. It implies that more educated individuals in top decile groups have a greater tendency of staying in higher deciles than less educated ones.

Immigrants with higher education levels also have a greater probability of moving up than less educated natives for all S_t-1 = s. However, we also find that the effect of education on the probability of moving up for immigrants is not as great as the effect of education for natives because many of the coefficients of education interacted with an immigrant dummy are negative and significant. The positive effect of education on the probability of moving up is especially small for immigrants from Latin America. For instance, for S_t−1 = 1, the coefficient of education interacted with a dummy of Latin America is negative (= −0.092) and significant at the 1 percent significance level. Therefore, the sum of coefficients of education and education interacted with an immigrant dummy is 0.056 (= 0.148–0.092) and is significant at the 1 percent significance level (not shown in the Table). Foreign-born individuals do not benefit from higher education in terms of the probability of moving up in the wage distribution as compared to native-born individuals but at the same time they have a similar tendency of moving down in the wage distribution as their native counterparts do.

Although not reported in Table 2, age, age interacted with an immigrant dummy, and years since migration play a role in assimilation. We do not report these coefficient estimates because there is a more efficient way of examining these effects. In the next section, we turn to evaluate the estimated functions of moving up and down at different levels of age and years since migration using the coefficient estimates, and .

C. Evaluation of the Estimated Transition Probability Functions

We evaluate the probabilities of moving to higher / lower deciles for selected values of covariates. We consider hypothetical immigrants from Latin America, Europe, and Asia entering the United States at age 20. Hence, we compare a 24-year-old immigrant who has four years of U.S. experience with a 24-year-old native, a 36-year-old immigrant who has 16 years of U.S. experience with a 36-year-old native, a 48-year-old immigrant who has 28 years of U.S. experience with a 48-year-old native. Education is set to 8 when S_t−1 = 1,2,3, to 12 when S_t−1 = 4,5,6,7, and to 16 when S_t−1 = 8,9,10. We assume that all these individuals are married, since more people are married in the sample.

Tables 3 and 4 present the foreign-native difference in the probabilities of moving up and moving down. In Table 3, the first row of the first column corresponds to the difference in the probabilities of moving up between two individuals in the first decile group, S_t−1 = 1. The first individual is a 24-year-old immigrant worker with four years of U.S. experience and the other is a 24-year-old native person. The estimate −0.106 implies that a foreign-born individual is less likely to move to higher deciles in the following year than a native-born individual in the same decile group by 10.6 percentage points. It is significant at the 1 percent level. According to the first entry in Table 4, the foreign-native difference for immigrants, 0.197, is significant at the 1 percent level. The positive value implies that immigrants in the lowest decile group are more likely to move to lower deciles than natives in the same decile group.

Similarly, the first row of the last column in Table 3 compares the difference in the probabilities of moving up between a 24-year-old immigrant person with four years of U.S. experience and a 24-year-old native person in the ninth decile group. The estimates suggest that an immigrant person have a higher chance of moving up than a native person by 24.1 percentage points. The first row of the last column in Table 4 compares the difference in the probabilities of moving down among individuals in the tenth decile group. We find that an immigrant person is more likely to move down than a native person by 3.9 percentage points but the gap is statistically insignificant. To better understand these results for immigrants from different places, rather than discussing detailed results for all immigrants, we proceed with presenting results estimated separately for immigrants from different continent of origin.

Immigrants from Latin America exhibit a very similar pattern to the results for all immigrants especially those in bottom and middle decile groups. For example, a 24-year-old person from Latin America with four years of U.S. experience in the first decile group is 10.8 percentage points less likely to move to higher deciles in the following year than a 24 year-old native-born individual in the same decile group. Similarly, a 24-year-old Latin American immigrant is 22.1 percentage points more likely to move to lower deciles than his native counterparts. Overall, except for a few cases, the results in Tables 3 and 4 suggests that for all S_t−1 = s, immigrants from Latin America have smaller chances of moving to higher deciles and greater chances of moving to lower deciles than their native counterparts. This is evidenced in Table 2 by the large negative coefficients on the education-Latin America interaction terms for the probability of moving up.

The results for immigrants from Europe are mostly insignificant, meaning that they are not very different from natives. There are a few exceptions where Europeans have a smaller probability of moving down than their native counterparts. For example, S_t-1 = 2 in Table 4 shows that a foreign-born person from Europe at age 24 and four years of U.S. experience is less likely to move to lower deciles than observationally equivalent natives by 15.5 percentage points. Overall, immigrants from Europe are similar to natives and, in some cases, they have a smaller probability of moving down.

The wage distribution for Asian immigrants diverges as compared to those for others. Asians who are located in the below-median decile groups have lower chances of moving to higher deciles and higher chances of moving to lower deciles than natives. For example, an Asian immigrant in S_t-1 = 3 at age 36 and 16 years of U.S. experience has a lower chance of moving up by 11.2 percentage points and higher chance of moving down by 16.8 percentage points than natives. The results are persistent across below-median individuals. Asians located in the above-median decile groups, however, have lower chances of moving down than natives. For S_t-1 = 9, a 24-year-old Asian immigrant with four years of U.S. experience is less likely to move to lower deciles by 16.1 percentage points than observationally equivalent natives. The signs of these estimates support the hypothesis, although other estimates are not as significant as in the Latin American case probably due to the small size of the sample of Asian immigrants.

The results for all immigrants reflect the results for immigrants from different continents of origin. The findings for lower and middle decile groups are mostly explained by the results for Latin American and Asian immigrants, and those for higher deciles groups reflect the results for European and Asian immigrants. Foreign-born Latin American immigrants have smaller chances of moving to higher deciles and greater chances of moving to lower deciles than their native counterparts. These results are persistent across different age and decile groups, although there are a few exceptions. European immigrants, however, are not very different from natives in terms of wage mobility. The wage mobility of Asian immigrants is rather state-dependent. Asians who are located in the below-median decile groups have lower chances of moving to higher deciles and higher chances of moving to lower deciles than natives. Asians who are located in the above-median decile groups have lower chances of moving to lower deciles than natives and similar chances of moving to higher deciles.