Human Capital and the Economic Assimilation
of Recent Immigrants in Hong Kong
Dongshu Ou
The Chinese University of Hong Kong
413 Ho Tim Building, Shatin, N.T., Hong Kong
Email: [email protected]
Suet-ling Pong
Penn State University and The Chinese University of Hong Kong
Email: [email protected]
Abstract
Previous research has found an earnings divergence between native and Chinese-immigrant
workers in Hong Kong, thus creating an anomaly among immigrant countries in terms of
economic assimilation. Using more recent data, this paper found that the earnings divergence
between native and Chinese-immigrant workers continues for all workers. However, this
earnings divergence masked a reverse trend for low-skilled workers. Over time, low-skilled
immigrant workers gained earnings assimilation with low-skilled native workers, but highskilled immigrant workers did not gain assimilation with high-skilled native workers. This paper
also investigated nativity differences in the skill prices and in the distribution of occupations and
industries as explanations for the earnings divergence and convergence by skill level. The
decomposition analysis suggests that the relative skill prices cannot be a major explanation for
the relative mean-earnings differences between immigrant and native workers over time. Our
results for Hong Kong are consistent with the findings from recent research on the economic
assimilation of low-skilled immigrants in other countries.
JEL classification: J15, O15, J31, J40, J61
Keywords: immigrants, earnings differentials, returns to skills, Hong Kong
1. Introduction
Previous studies (Lam & Liu, 2002a, 2002b) found that economic assimilation—the
convergence of earnings between immigrant and native workers—was absent in Hong Kong
during the decade of 1981–1991, as immigrant workers earned increasingly less than did native
workers. This earnings divergence was very different from other major immigrant countries,
such as the United States, where immigrant workers tend to improve their economic position
over time relative to native workers (Borjas 1985, 1995; Chiswick 1978). The situation is even
more puzzling because the lack of economic assimilation in Hong Kong is found among Chinese
immigrants from mainland China, who are largely of the same racial, ethnic, and cultural
heritages as Hong Kong natives. The human capital of Chinese immigrants, as measured by their
educational attainment, has not only been on the rise since the decade studied, but also the
quality of mainland Chinese students’ education has improved substantially, as shown by the
outstanding achievement of Shanghai students in the 2009 Programme for International Students’
Assessment. Studies on secondary-school students have found that immigrant students in
secondary schools tend to attain higher test scores than do native students in all subjects except
the English language (Pong & Tsang, 2010), further suggesting that the education quality in
mainland China may not be inferior to that in Hong Kong. Thus, it is important to understand
whether the previous findings on earnings divergences by nativity were specific to a historical
period or represent a general trend.
Most studies on immigrant workers’ assimilation have treated immigrants as one type
of labor and have not examined the heterogeneity within this subgroup. On the one hand, many
immigrant workers are low skilled; they cross borders to seek higher pay for their labor. On the
other hand, immigration policies such as the 1990 Immigration Act in the United States, which
1
gave preference for economic immigrants based on skills and qualifications, encouraged highskilled individuals to immigrate. The patterns of assimilation likely differ between high- and
low-skilled workers. Because the concept of economic assimilation entails a comparison
between immigrants and natives, a better approach is to examine the nativity gap within skill
groups in which immigrants and natives can be regarded as perfect substitutes (Card, 2005). In
the US, high-skilled immigrant workers tend to experience occupational downgrading (Akresh,
2006), and some of them face the glass ceiling, unable to enjoy the same remuneration for their
work as do high-skilled native workers (Zeng & Xie, 2004). By contrast, low-skilled immigrant
workers in the United States have been found to obtain significant gains over time in earnings
relative to low-skilled native workers (Hall & Farkas, 2008). Differential returns to skills would
mean differential achievement in the labor market for high- and low-skilled immigrant workers,
relative to their native counterparts. In this paper we present evidence of systematic variations in
economic assimilation by skill levels, which can be masked by the overall immigrant–native
earnings differentials.
This study had three main objectives. The first concerned earnings inequality by
immigrant status in Hong Kong. We examined whether the earnings divergence found in 1981–
1991 between Hong Kong natives and immigrants from mainland China (Lam & Liu, 2002a)
remained in more recent years. The second objective was to investigate potential heterogeneity
of the nativity earnings gap by skill level. We explore whether high- and low-skilled workers
exhibited different assimilation patterns. Third, through a decomposition analysis, we analyzed
how returns to immigrants’ pre- and postmigration human capital contributed to the recent trends
in the nativity earnings gap.
2. Hong Kong Context
2
As one of the world’s financial centers, Hong Kong enjoys a real GDP growth rate of
about 6.8% (Bureau of East Asian and Pacific Affairs, 2011). Hong Kong’s economic success is
due in large part to the human resources of its immigrant population. According to data from
recent years of the Hong Kong Census, about one third of its population was born in mainland
China in 1996, 2001, and 2006 (Hong Kong Census and Statistics Department, 2006). Hong
Kong has a continuous population inflow from the mainland. Illegal immigrants started to arrive
after a quota system was in place in 1950. They continued coming through the 1960s. First
adopting a lassie faire policy toward illegal immigration, the Hong Kong government later
allowed illegal immigrants to become permanent residents by way of the “reach-base policy,”
which was implemented in 1974. Only until illegal immigration reached sky-high levels in late
1970s, during political turmoil in the mainland, did Hong Kong take serious measures to curb it.
In 1980, the reach-base policy was abolished. In 1983, with consent from the Chinese
government, Hong Kong sealed its border with China, enforced deportation of illegal
immigrants, issued Hong Kong identity cards, and imposed fines on businesses who hired illegal
workers (Lam & Liu, 1998).
Since 1983, Hong Kong has been admitting legal immigrants daily from China, and the
status of Chinese immigrants has changed from primarily illegal to primarily legal. After Hong
Kong’s handover to China in 1997, immigrants from the mainland to Hong Kong are no longer
“underground” people of a different country. Many speak the same language and enjoy the same
government benefits as native Chinese. In 2003, the Admission Scheme for Mainland Talents
and Professionals was implemented primarily to encourage high-skilled individuals from the
mainland to work in Hong Kong. Most mainland immigrants who were recruited under this
policy were university-educated professionals or accomplished athletes, artists, and musicians.
3
The policy objective was to fill needed professional positions to enhance Hong Kong’s status and
competitiveness with other countries (Hong Kong Immigration Department, 2011). This policy
has boosted the overall education levels among immigrant workers.
It is worth noting that Lam and Liu’s (2002a) finding of earnings divergence between
immigrant and native workers was based on an artificial cohort of adult workers who reported in
1981 that they had resided in Hong Kong within the preceding 5 years. The sample represented
a specific cohort of immigrants who most likely entered Hong Kong illegally. Thus, Lam and
Liu’s finding of economic divergence was based largely on Chinese immigrants who entered
Hong Kong without legal documents. It is necessary to investigate a more recent period of time
to determine whether Lam and Liu’s findings also apply to the more typical immigrants today
from mainland China through legal means. In this paper, we examine the nativity earnings gap
between immigrant and native Chinese male workers using newer Hong Kong census data from
1996, 2001, and 2006. We examined the economic assimilation of male employees of different
skill levels as defined by their educational attainment, focusing on both high- and low-skilled
employees. Our results provide evidence of an earnings convergence among low-skilled Chinese
immigrants in Hong Kong, which is consistent with the convergence of the nativity gap in the
returns to education among low-skilled workers. By contrast, the nativity earnings gap enlarged
over time among high-skilled workers, despite the fact that relative skill prices were actually in
favor of highly educated Chinese immigrants. These results point to differential patterns of
economic assimilation for high- and low-skilled workers.
3. Data and Summary Statistics
We used data from the 1996, 2001, and 2006 years of the Hong Kong Population Census
20% samples. Consistent with previous literature on immigrants’ assimilation in Hong Kong,
4
the samples were restricted to Chinese men who were employees and had nonzero earnings at the
time of interview. It is worth noting that the Chinese ethnicity made up 95% of Hong Kong’s
population in 2006.1 We constructed an artificial cohort of immigrants based on their place of
birth, age, and duration of residence in Hong Kong. The immigrant cohort includes Chinese
immigrants who were aged 15 to 50 in 1996 and were new to Hong Kong, residing in Hong
Kong for no more than five (0-5) years at the time of interview. This cohort was observed in
three time points that spanned 15 years: 1996, 2001, and 2006. The individuals were 20 to 55
years old in 2001, and they had resided in Hong Kong for 5 or more years but no more than 10
(5-10) years in 2001. Similarly, in 2006, they were 25 to 60 years old and had resided in Hong
Kong for 10 or more years but no more than 15 (10-15) years. The age restriction of 15 to 50 in
the base year was appropriate for our study of employees because 15 was the minimum age
required to work legally in Hong Kong2 and the upper age limit eliminated the potential bias of
attrition due to retirement at the final time point of observation.3 We included data from natives
of the same age as a comparison group.
Table 1 shows the percentage distribution of education levels by immigrant status in the
base year of 1996. The sample was divided into three skill groups according to their highest
levels of completed education.4 Low-skilled workers were defined as those who had attained no
1
The Chinese ethnicity made up 95% of its population in 2006. Other ethnic groups comprised 1.6% Filipinos, 1.3%
Indonesians, and 2% of a variety of ethnicities, including Caucasian, Indian, Nepalese, Japanese, Thai, Pakistani,
and other Asian (Hong Kong Census and Statistics Department, 2006).
2
The minimum work age was changed to 18 in 2008.
3
We also conducted the analysis using the age range of 25 to 50 to restrict the sample to individuals who had
completed their education and were economically active at the time they were first observed. The results were
virtually identical to those reported here.
4
Hong Kong’s education system contains 6 years of primary school, 3 years of lower secondary school, and 2 years
of upper secondary school. After secondary school, students can either study craft courses in technical institutions,
5
more than lower secondary school. Middle-skilled workers were defined as those who had
completed upper secondary school. High-skilled workers are defined as those who had attained
some postsecondary education or possessed credentials such as a higher diploma, first degree, or
postgraduate degree. Unlike samples of immigrant workers in previous studies (e.g., Capps,
2003), the cohort of immigrants in our study clearly had more education than natives. About
46% of immigrants were high-skilled workers, whereas barely 30% of native workers were in the
high-skilled category. In a descriptive analysis not reported here, we found that 30% of
immigrant workers had a first degree or postgraduate degree. The corresponding figure for
native workers was less than 15%.
When education was defined by the number of years in school, immigrant workers as a
whole had on average 12 years of education. Overall, native workers had about half a year less
education than immigrant workers in 1996. Native workers caught up and were slightly more
educated than immigrant workers in 2001 and 2006. This overall trend by nativity appears to
reflect the trend among high-skilled workers. One possible reason is sample selection by labor
force participation among these workers. However, our exploratory analysis suggests that labor
force participation rates were about the same for both nativity groups in each census year (see
Appendix Table 1). A more plausible reason is the different exit and entry rates of immigrant
and native workers. Hong Kong has been considered a stepping stone for highly educated
immigrants from mainland China. Some high-skilled immigrant workers in the 1996 cohort may
have left Hong Kong for other countries in subsequent years, hence the decreasing trend of
immigrants’ educational attainment. With respect to native workers, an exodus of highly
educated Hong Kong natives occurred before 1997, the year Hong Kong reunified with China.
or attend two years of matriculation courses, which prepare them for college (i.e. first degree programs). College
education spanned 3 years during the period we studied.
6
After the handover, many of these native workers returned as the political situation of Hong
Kong became more stable. This may explain the increasing trend of natives’ educational
attainment after 1996.
Panel A in Table 2 shows the average earnings of immigrant and native employees for
the full sample. All earnings are deflated at the 2006 level. Immigrant employees earned less
than native employees and this earnings gap increased over time. Immigrant employees earned
about 84% of what their native counterparts earned during their first 5 years in Hong Kong
(1996-2001). The nativity gap was 16.2% of native workers’ average earnings. The earnings gap
was enlarged in 2001 and 2006 as the cohort of immigrants stayed in Hong Kong for 5-10 and
10-15 years, and earned about71 and 63% of what natives earned 5 and 10 years after,
respectively. The corresponding nativity gaps were approximately 29% and 37%of native
workers’ average earnings, indicating earnings divergence.
However, when the sample was split into three groups by level of worker skill, the results
of the nativity earnings gap were quite different. The initial earnings gap by nativity among highskilled workers (Panel B) was about 19% of the native workers’ average earnings, and this gap
increased over the years of immigrants’ residence in Hong Kong. In contrast, the nativity
earnings gap for low-skilled male (Panel C) immigrants decreased over the 10-year period. Thus
we found earnings divergence for high-skilled workers but earnings convergence for low-skilled
workers for the 15-year period from 1996 to 2001. The middle-skilled group, not reported here,
exhibited divergence at first and then convergence in earnings. Because workers in this group
were homogenous in their education level, they were not further analyzed.5
4. Distribution of Immigrants in Industries and Occupations
5
Results are available from the authors upon request.
7
Workers’ earnings are tied to their skill levels, which, in Hong Kong, are generally
segregated by industries and occupations (Liu, Zhang, & Chong, 2003). This dynamic was true
in the study data as well (see Table 3). Columns 2, 6, and 10 show the distributions of
immigrants across industries in 1996, 2001, and 2006, respectively. Similar distributions can be
found in Columns 4, 8, and 12 for natives. The column labeled “% of white-collar jobs within
industry” shows the proportion of natives or immigrants who had white-collar jobs within
various industries. In 1996, the three top industries in which immigrant workers dominated were
manufacturing, utilities and construction, and wholesale. The top three industries for native
employees also included manufacturing and utilities and construction, but the third industry for
native workers was transportation and communication. In subsequent years, however, immigrant
workers appeared to follow the footsteps of native workers. In 2001, the concentration of
immigrant workers in wholesale industries declined, whereas their concentration in the
transportation-and-communications industry rose. Meanwhile, many native workers moved into
business services. In 2006, business services became one of the top three industries for
immigrant workers as well. Because native workers tended to concentrate in industries that
offered higher wages,6 such as business services, it is apparent that, over time, immigrant
workers achieved upward mobility in the labor market. This result is consistent with findings
from research in the United States (Duleep & Dowhan, 2002; Hall & Farkas, 2008; Newman,
2006).
However, even though manufacturing remained the biggest industry for both natives and
Chinese immigrants in 1996 and one of the top three industries in subsequent years, the
proportion of white-collar occupations within that industry was larger for natives than it was for
6
Average earnings for each industry in 1996, 2001, and 2006 are available upon request.
8
immigrants. For example, 46% of natives employed in the manufacturing industry in 2006 were
in white-collar occupations, compared to 25% of immigrants. The business sector is another
example. Although more immigrants than natives were employed in the business industry, the
proportion of immigrants holding white-collar jobs fell from 67% in 1996 to 30% in 2006, while
the proportion of white-collar occupations for natives remained high, at about 60% to 70%.
These large differences between the industry and occupation distributions of immigrant and
native workers likely confounded our analysis of skill prices on the earnings gap by nativity, so
the statistical models were adjusted to include these factors.
5. Returns to education and decomposition of earnings differences
The economic literature on immigrants’ economic assimilation has focused on two
aspects of immigrant human capital measured by their education. The first is the amount
(quantity) of human capital that immigrant workers bring from their home countries, which has
been referred to in the literature as the “quality” of immigrants. For example, Borjas (1985)
argued that the 1960s immigration reform in the United States led to a wave of new immigrants
of deteriorating quality. As discussed above, the reverse trend has been true in Hong Kong. Due
to political changes and immigration policy that favors high-skilled immigrants, the quality of
immigrants has been increasing over time.
The second aspect of human capital in workers’ assimilation is the returns to education or
“skill price.” Using data from Hong Kong in 1981 and 1991, Lam and Liu (2002a) found that
returns to education were the cause of the earnings divergence between immigrant and native
male workers. The reason was that human capital obtained locally is more adaptive to technical
change in the host country than human capital acquired in the immigrants’ home countries.
9
To understand the role of human capital on earnings differences between natives and
immigrants, we used a two-equation model to decompose the log earnings differences (Lam &
Liu, 2002a):
'
(1)
'
(2)
ln with = α ith + β th Edu ith + λ X ith + ε ith and
ln witc = α itc + β tc Edu itc + λ X itc + ε itc ,
where the superscripts h and c refer to Hong Kong natives and Chinese immigrants, respectively,
for individual i at time t. lnw is the logarithm of real monthly earnings from the individual’s main
source of employment in 2006 Hong Kong dollars. Edu represents years of formal education. X
is a vector for other characteristics, which include years of working experience and its squared
term. Working experience is derived by subtracting 6 years from the worker’s age and then
further subtracting the number of years of education for all workers who had at least 9 years of
education. For workers who had fewer than 9 years of education, their work experience was
derived by subtracting 15 years from their age.
To explore how the distribution of immigrants and natives in various industries and
occupations may confound the results, we also include in X a dummy variable of white-collar
occupations 7 and 13 dummy variables for industries: agriculture and mining; manufacturing;
utility and construction; wholesale (including export/import); retail, restaurants, and hotels;
transportation (including storage) and communication; finance; business services; public
administration; sanitary and similar services; social and related community services; amusement
7
We defined managers and administrators and professionals and associate professionals as white-collar
occupations. Non-white-collar occupations included clerks, service workers and shop sales workers, skilled
agricultural and fishery workers, craft and related workers, plant and machine operators and assemblers, and
elementary occupations.
10
and recreational services; and personal services. “Other” industries were used as the reference
group.
Equations 1 and 2 were estimated separately for immigrants and natives for 3 time points:
1996, 2001, and 2006, defined as Time 1, Time 2, and Time 3. Two sets of estimates were
obtained in a stepwise manner, first without and then with the industry and occupation variables.
The estimates for each time point, β th and β tc , represent the returns to education or skill prices of
Hong Kong natives and Chinese immigrants at time t, respectively. These coefficients were used
in the decomposition analysis.
We examined changes in earnings between two time points (t and t+1) and decomposed
the change in the mean earnings of immigrants relative to natives as shown in Equation 3:
^
^
^
c

 ^
− ln wih,t +1 − ln witc − ln with = Edu i ,t +1 ( β tc+1 − β th+1 ) − ( β tc − β th ) 
 44
1444
42444444
3
(ln w
)(
c
i ,t +1
)
( A)
c
^
h
t +1
h
^
+ ( Edu i ,t +1 − Edu i ,t +1 )( β − β th )
1444442444443
(3)
( B)
[
c
h
c
h
]
^
+ ( Edu i ,t +1 − Edu i ,t +1 ) − ( Edu it − Edu it ) β th
14444444244444443
(C )
c
i ,t +1
c
it
^
^
c
t
+ ( Edu
− Edu )( β − β th ) + other terms.
14444244443
(D)
Equation 3 contains four different types of education effects: Effect A is the “relative
price effect,” which measures the effect of the changes in relative skill prices for immigrants
compared to natives. A narrowing difference in the returns to education for immigrants and
natives over time would indicate an earnings convergence by nativity. B is the general price
effect. If immigrants have more education than natives and if the returns to education increase
over time, the earnings gap will converge. C is the relative quantity effect. If the education-level
11
differences between Chinese immigrants and natives do not vary over time, this term would be
close to zero.8 By our definitions of high-skilled and low-skilled workers, we expected that a
very small part of the earnings gap would be explained by C for the low-skilled workers because
they should have finished their highest level of education by age 15. D is the general quantity
effect. If immigrants’ mean level of education increases over time but they are consistently paid
less than natives for the same amount of education (i.e., lower returns to education), then the
earnings gap by nativity will increase. The “other terms” are a sum of the effects of all other
factors that could contribute to the earnings changes in two periods for immigrants and natives,
including the constant terms and experience variables (see Equation 1 and 2).
The returns to education obtained from Equation 1 for natives and Equation 2 for
immigrants are reported in Table 4 and illustrated in Figure 1. For all workers, regardless of skill
level (see Figure1a), the returns to education for natives were consistently higher than that for
immigrants. Without controlling for industries and occupations, the returns clearly diverged.
However, the divergence was not obvious after controlling for industries and occupations. A
closer examination of high- and low-skilled workers revealed a different pattern. High-skilled
immigrants had similar returns to education as their native counterparts at Time 1. They
surpassed natives at Time 2 and Time 3 (Figure 1b). Immigrant workers’ disadvantage at Time 1
was revealed when industries and occupations were controlled (Figure 1c), suggesting that these
8
As discussed earlier and shown in Table 1, there were some changes in the average years of education over time
for both immigrants and natives. Even if we restrict our sample to the cohort of workers ages 25 and above (results
not shown), who were likely to have completed their education, there are still some, albeit small differences
between t and t + 1. It is reasonable that the average levels would increase over time because individuals age 15
and above will continue education. However, sample selection could also change the mean levels of education of
the artificial cohorts we track. For example, if immigrants with more education left Hong Kong after a short period
of stay and only those with relatively less education remained, the average education level for immigrants should
become lower over time, which was the case in our data. It is also plausible that the pursuit of further education
(possibly due to occupational needs) for immigrants and natives are different. Nonetheless, the changes were less
than a year in our study.
12
high-skilled immigrants entered some relatively high-paying industries or occupations when they
first arrived in Hong Kong, which drove up average earnings and the returns to education.
Within industries and occupations, however, they earned less than natives with similar education
levels. For low-skilled workers, the returns to education were lower for immigrants than for
natives at Time 1, but the difference disappeared at Time 2 and Time 3. The occupational and
industrial differences did not change the results very much. In other words, when we take into
account the type of occupations and industry immigrants occupied, we observed a convergence
by nativity of the returns to education for both high- and low-skilled workers. Lumping all skill
levels in a general analysis obscured significant heterogeneity.
Table 5 reports the results of the decomposition analysis. As specified in Equation 3, the
change in the mean log earnings gap between immigrants and natives was decomposed into four
components, labeled as A, B, C, and D, which were calculated using the years of education
(quantity) and the returns to education (price effect) of different workers by nativity and skill
levels at various time points. Rows I and II show the results first without and then with
controlling the industry and occupation variables.
Among all four components, the relative price effect was the most useful in explaining
the change in the earnings gaps by nativity for all workers (Panel A), when industries and
occupations were not controlled (Row I). The relative price effect explains the widening
earnings gap to the advantage of natives between Time 1 (1996) and Time 2 (2001) and between
Time 2 and Time 3 (2006). The relative quantity effect also explains the earnings divergence,
albeit to a much lesser extent. However, the general quantity effect from Time 1 to Time 3 was
positive and cannot explain the diverging trend in relative mean earnings for immigrants and
natives. When industries and occupations are taken into account (Row II), the explanatory
13
power of the relative price effect was weak between Time 1 and Time 2. Also, between Time 2
and Time 3, both the relative price effect and the total education effect turned positive, and their
magnitudes became smaller after controlling for industry and occupation. This suggests a small
narrowing of the earnings gap between immigrants and natives. So, the diverging earnings gap
between Time 2 and Time 3 was due to factors not related to educational attainment.
Was Hong Kong’s earnings divergence by nativity driven by technical change that raised
the returns to local education relative to the returns to mainland education? The assumption that
nonlocal education is less adaptable to technical change needs to be tested. If technical change
was the key reason for the divergence in earnings and skill price, it would be informative to
examine the decomposition by skill level. We expected that technical change would have a
minimal impact on the skill price for low-skilled workers and that middle and high-skilled
workers would be more likely to be affected. Contrary to our expectations, we found that the
total education effects and relative price effects did not fully predict the earnings divergence
among high-skilled workers (Panel B), neither did they predict convergence among low-skilled
workers (Panel C). Our decomposition results showed that a clear trend of earnings divergence
continued despite the relatively higher skill prices for immigrants (Figure 1). This was most
pronounced for high-skilled immigrants in their first 10 years in Hong Kong. The relative price
effect predicted a .4455 positive log-earnings gap for immigrants and natives between Time 1
and Time 2, which was in the opposite direction of the mean log-earnings difference (-.1305).
The relative price effect was even larger (.9196) after controlling for industries and occupations,
which was about 7 times larger than the mean log earnings gap.
In fact, the earnings divergence observed among high-skilled workers from Time 1 to
Time 2 was captured well by the effect of the “other terms.” This was true even after the
14
industry and occupation variables were controlled in the regressions, and the magnitude of the
other terms was larger than the relative quantity effect, which also explained the earnings
divergence. These results suggest that unobserved skills or institutional factors in earnings
determination may be important factors behind the earnings divergence among high-skilled
workers.
As for low-skilled workers (Panel C), earnings convergence was observed between Time
1 and Time 2. This convergence can be explained by the convergence of the effect of relative
skill price for that period. The effect of “other terms” was also prominent for its sign and
magnitude in explaining earnings convergence for low-skilled workers between Time 1 and
Time 2 and between Time 2 and Time 3 (i.e., after they had been in Hong Kong for 5 to 15
years). It is likely that, with the increase of tourists from the mainland after Hong Kong’s
handover, low-skilled immigrant workers who speak Mandarin may have an increasing
advantage, particularly in the service sector.9
In sum, although the relative price effect had strong explanatory power for the earnings
gap between immigrants and natives, the effect was different for high- and low-skilled workers
in different periods. The “other terms” also played an important role in earnings inequality by
nativity, suggesting that other unobserved institutional factors need to be considered in future
research on the nativity earnings gap. In general, our decomposition results are different from
those reported in Lam and Liu’s study (2002a). One possible explanation for the difference is
that Lam and Liu’s (2002a) work captured a specific historical period when illegal immigrants
were prevalent. Although immigrants who entered Hong Kong illegally in the 1980s were
allowed to apply for permanent residency under the reach-base policy, discrimination against
9
Restaurant business hired the most immigrant male employees in all Census years we studied: about 11.8% in
1996, 14.4% in 2001, and 19.0% in 2006.
15
them likely happened when they first entered the Hong Kong labor market while having their
applications for legal status processed. The economic assimilation of these older cohorts may
not represent the economic assimilation of legal immigrants, who are more common in Hong
Kong today.
6. Returns to education and experiences obtained before and after immigration
Our main goals in this paper were to decompose the earnings gap among immigrants and
natives in different time periods and to compare the importance of the relative skill price effect
and total education effect on the earnings divergence or convergence we observed. However, in
this section we further examine the skill prices of immigrants by the place of education they
received because the imperfect transferability of education in one’s home country can possibly
explain the wage differentials between natives and immigrants (Basilio & Bauer, 2010;
Freidberg, 2000). Although most Chinese immigrants in this study, especially low-skilled
workers, had completed their education before they moved to Hong Kong, some continued to
invest education in Hong Kong. In this the previous section, we investigated the returns to two
types of education for immigrants: education received in mainland China and education received
in Hong Kong. We followed Friedberg (2000) to estimate the following equation:
h
ln wij = α + β 1 Edu h ij + β 2 Edu c ij + γ immigrant ij + φ1 Edu ij * immigrant ij +
µ1 Exp ijh + µ 2 Exp ijc + φ 2 Exp ijh * immigrant ij + λ X ij ' + ε ij ,
(4)
where X is a vector for a dummy variable of white-collar occupations10, and dummy variables
for 13 industries. Because language proficiency might affect the productivity of immigrants as
10
We defined white-collar occupations as managers and administrators; professionals and associate professionals
as white collar occupations. Other occupations include clerks, service workers and shop sales workers, skilled
16
service industries in Hong Kong become more developed, we also included in X a dummy
variable for spoken Cantonese proficiency and a dummy variable for spoken English proficiency.
This regression model not only allowed the returns to education received in China among
Chinese immigrants to be different from their education received in Hong Kong, it also allowed
the return to immigrants’ labor market experience in China to be different from that in Hong
Kong. The portability of education and work experience obtained in China to Hong Kong’s labor
market was measured by β2 and µ2. To the extent that immigrants gradually sort themselves into
occupations that reward their home-country education and work experience, this model would
show an increase in both coefficients.
Results in Table 6 are consistent with previous literature that compared immigrants and
natives in other countries (Basilio & Bauer, 2010; Freidberg, 2000). Returns to education and
experiences obtained in the country of origin (in this study, China) were lower than those
obtained in the host country (in this study, Hong Kong). For instance, in Panel A in Table 6,
controlling for industry and occupation differences, returns to education obtained in China were
about 5%, whereas the returns to education obtained in Hong Kong were 7% to 8% for all three
time points. However, returns to local education in Hong Kong are quite low for Chinese
immigrants. The interaction term of education and immigrant status implies that the gap in
returns to education between Hong Kong natives and Chinese immigrants became even larger in
the later periods11. In general, the variation of skill prices for high- and low-skilled immigrants
agricultural and fishery workers, craft and related workers, plant and machine operators and assemblers, and
elementary occupations.
11
Returns to experience in China were less than half of the returns to experience in Hong Kong in the first 5 years
after arrival. Furthermore, the returns to experience in China became statistically insignificant at the later time
points. It is interesting that the returns to experience in Hong Kong actually became negative for Chinese
immigrants. This seems to indicate a devaluation of experience by their employers, an improved quality of
education in the host society, a lack of internal labor market opportunities, an obsolescence of accumulated
17
in our results could imply that the quality of education received in home country are different for
low-skilled and high-skill workers.
As shown in Table 6, whether or not controlling for industry and occupation, high-skilled
immigrants had positive returns to education received in China. Taking into account the
interaction term between education in Hong Kong and immigration status, the returns to
education received in Hong Kong were actually lower than the returns to education received in
China. For example, at Time 1, without considering occupation and industry differences
(Column I), the return to education in Hong Kong was .05 (i.e., subtracting .101 from .151),
about .092 lower than their return to education received in China (.142).
It is worth noting that the difference of returns to education in Hong Kong for highskilled and low-skilled immigrants compared to the natives (i.e., φ1 in Equation 4) revealed the
same pattern of the relative price effect that was shown in the decomposition of changes in
relative mean earnings (Table 5). Here are some examples after controlling for industry and
occupation. In Panel B in Table 6, for high-skilled workers, the negative coefficient φ means
relatively lower returns to local education for the Chinese immigrants compared to Hong Kong
natives. The difference in the returns to local education became significantly smaller from Time
1 (-.076, with a standard error of .014) to Time 2 (-.045 with a standard error of .012) but
enlarged again from Time 2 to Time 3 (-.082, with a standard error of .011). Recall that in our
human capital and effects of aging (Casanova, 2010), or some combination of these factors. Just as we observed
for all immigrants, the returns to foreign work experience for high-skilled immigrants became very small and even
insignificant at Time 3. There was also a negative return to local work experience for high-skilled Chinese
immigrants. It is even more interesting that the return to local work experience was positive for low-skilled
Chinese immigrants in their first 5 years in Hong Kong. Even at Time 2 and Time 3, the estimated returns to work
experience, regardless of whether controls were included for industrial and occupational differences, were very
similar and positive for both Chinese-immigrant and native low-skilled workers. This might explain the earnings
convergence that we observed for this group.
18
decomposition analysis for high-skilled workers (Panel B of Table 5), the relative price effect
was positive on the change of relative mean earnings between Time 1 and Time 2 (.9196); but
the relative price effect was negative between Time 2 and Time 3 (-.1940). This means that the
skill price of immigrants has increased more than that of natives from Time 1 to Time 2 (i.e., a
narrowing nativity gap in the returns to education between Time 1 and Time 2), but the skill
price of immigrants has increased less than that of natives from Time 2 to Time 3 (i.e. an
enlarging nativity gap in the returns to education between Time 2 and Time 3). Therefore,
whichever method we used – whether estimating the returns to education received in Hong Kong
in the pooled sample for both natives and immigrants (Equation 4), or examining the returns to
education, local and nonlocal alike, in the two equation model (Equation 1 and 2), the changes in
the relative skill prices could not fully explain the earnings assimilation patterns we observed. In
other words, the place of education did not help to explain the earnings divergence for highskilled male employees.
Panel C (Table 6) illustrates a similar story for low-skilled workers. The negative
difference in the returns to education also shrank from Time 1 to Time 2 (although both
coefficients of -.02 and -.003 are not statistically significant) and became larger at Time 3 (-.035
with a standard error of .015). This pattern was the same in models without the controls for
industry and occupation as well. The changes of returns to local education between two time
points is similar to the changes of returns to education in the decomposition analysis (Panel C in
Table 5) that did not separate the education received in Hong Kong from the education received
in China. In short, our estimates from Equation (4) yield similar results as our estimates from the
two-equation model. The place where immigrants received their education did not appear to
matter for the overtime change in the nativity earnings gaps. Thus our results did not support the
19
hypothesis that human capital obtained locally is more adaptive to technical change in the host
country than human capital acquired in the immigrants’ home countries.
7. Conclusion
Using the three most recent, publicly available years of the Hong Kong Census (1996,
2001, and 2006), this study examined the economic assimilation of male Hong Kong immigrants
with different skill levels as defined by their human capital. We decomposed earnings
differentials to reveal whether the returns to immigrant men’s human capital contributed to an
earnings divergence or convergence with native male workers.
The results showed an earnings divergence between immigrant and native workers,
indicating an increase in economic disadvantage of recent Chinese immigrant workers in Hong
Kong. However, this general trend masked differences by skill level. Separate analyses of highand low-skilled workers led to different assimilation patterns. The nativity gap enlarged over
time for high-skilled immigrants even though their skill prices, or returns to education, were
higher than the skill prices of natives. By contrast, low-skilled immigrant workers achieved
earnings parity with low-skilled native workers. This finding is consistent with past literature on
economic assimilation of low-skilled immigrant workers (Hall & Farkas, 2008).
An unusual feature of Hong Kong is that Chinese immigrant male workers who entered
the country in the past 2 decades were on average more educated than their native counterparts.
In this respect, recent Chinese immigrants are similar to Asian Indian and African immigrants in
the United States, who generally have higher educational attainment than the average American
(Portes & Rumbaut, 2006; Massey et al., 2007). Over the 15-year period in the present study, the
immigrant–native gap in years of education narrowed. However, the immigrant–native
difference in the returns to education diverged. Separate analyses of workers with different skill
20
levels were revealing. Among male workers who had a lower secondary education or less, the
returns to education converged between immigrants and natives. Among male workers who had
a post-secondary education, high-skilled Chinese immigrants had lower returns in 1996 but
enjoyed higher returns to their education than did high-skilled native workers in subsequent
years. Only among male workers who had an upper secondary education did we observe a
divergence in the returns to education between immigrant and native workers.
Did the change in the returns to education over time explain the change in the nativity
gap in earnings? Although Lam and Liu (2002a, 2002b) found that the divergence in the returns
to education was the major cause of the earnings divergence for immigrants and natives in the
1980s, our analyses do not produce the same results. Specifically, converging returns to
education explained converging earnings among low-skilled workers from 2001 to 2006, but
converging returns to education were actually related to an earnings divergence among highskilled workers. We find that the recent Chinese immigrant cohort who possess more education
have gained relative earnings benefits due to rising skill prices. This pattern was especially clear
after the immigrants stayed in Hong Kong for 5 to 15 years and for low-skilled immigrants.
However, there seemed to be other unobserved factors affecting the high-skilled Chinese
immigrants’ earnings that enlarged the earnings gap. Other explanations, such as occupational
segregation or the glass ceiling, may offer more insight into the earnings-assimilation patterns
observed in this study.
This study highlights the usefulness of separating skill level in the study of economic
assimilation. According to Lam and Liu (2002b), Hong Kong’s earnings divergence by nativity
was driven by technical change that raised the returns to local education relative to the returns to
mainland education, with the assumption that nonlocal education is less adaptable to technical
21
change. The higher skill price among mainland immigrants than among Hong Kong natives
challenges the assumption that nonlocal education is less adaptable to technical change. In
addition, if technical change was the reason for divergence in earnings and skill price, the effect
on technical change should be minimal for low-skilled workers. We found both converging
returns to education and converging earnings for low-skilled immigrants and natives in this
paper.
We further estimated the returns to local (Hong Kong) and nonlocal (mainland China)
education for immigrants. The results revealed that the returns to nonlocal education were not
low, suggesting that the imperfect transferability of home country’s education found in previous
literature might not apply to Hong Kong’s recent Chinese immigrants.12 We conclude that
although the earnings divergence in Hong Kong continues to exist for all workers, the driving
forces of the divergence may be diverse and warrant further investigation. One area for future
research is to examine the factors that contribute to the skill-price differences for immigrants and
natives. Another area is to find out whether the influx of immigrants affects the skill price of
natives and the extent to which immigrant workers’ skill distribution matters (Jaeger, 2007).
Our results on low-skilled workers are consistent with findings in the U.S. on the
economic assimilation of low-skilled immigrant workers (Hall & Farkas, 2008). These results
support in part the view that immigrant groups who share the same ethnicity with the majority
group in the host country, and who receive government support are likely to achieve upward
socioeconomic mobility over time (Portes & Rumbaut, 2006). However, this same view cannot
explain our results on high-skilled workers. Zeng and Xie (2004) suggested that high-skilled
12
This might due to the high-skilled immigration policy, which ensured that only those who possessed skills that
would contribute substantially to Hong Kong’s labor force were recruited and permitted to enter Hong Kong’s
labor market.
22
Asian immigrant workers faced a glass ceiling that hindered their economic assimilation. If such
glass ceiling implies racial or ethnic bias, this would not apply to high-skilled mainland Chinese
workers in Hong Kong. Because an earnings gap was found between nativity groups (Hong
Kong vs. mainland) and not between ethnic groups (both groups are Chinese) in Hong Kong, one
questions whether the glass ceiling Asian immigrant workers faced in the U.S. have anything to
do with race or ethnicity. Other sources of the glass ceiling are more likely.
23
References
Akresh, I. R. (2006). Occupational mobility among legal immigrants to the United States.
International Migration Review, 40(4), 854-884.
Basilio, L., & Bauer, T. (2010). Transferability of human capital and immigrant assimilation: An
analysis for Germany. IZA Discussion Paper, 4716.
Borjas, G. J. (1985). Assimilation, changes in cohort quality, and the earnings of immigrants.
Journal of Labor Economics,3, 463–89.
Borjas, G. J. (1995). Assimilation and changes in cohort quality revisited: What happened to
immigrant earnings in the 1980s? Journal of LaborEconomics,13, 201–45.
Bureau of East Asian and Pacific Affairs, U.S. Department of State. (2011, March 15).
Background note: Hong Kong. Accessed on September 22, 2011 from
http://www.state.gov/r/pa/ei/bgn/2747.htm
Capps, R., Fix, M., Passel, J.S., Ost, J. & Perez-Lopez, D. (2003). A Profile of the low-wage
immigrant work force. Washington D.C.: Urban Institute
Card, D. (2005). Is the new immigration really so bad? The Economic Journal, 115, F300–F323.
Casanova, M. (2010). The wage process of older workers. Unpublished manuscript, Department
of Economics, University of California, Los Angeles.
Chiswick, B. R. (1978) The effect of Americanization on the earnings of foreign-born men.
Journal of Political Economy, 86, 893–921.
Duleep, H.O. and D.J. Dowhan. (2002). Longitudinal Data on Earnings Growth of U.S. ForeignBorn Men. Demography 39:485–506.
24
Friedberg, R. (2000). You can’t take it with you? Immigrant assimilation and the portability of
human capital. Journal of Labor Economics, 18(2), 221–251.
Hall, M., & Farkas, G. (2008). Does human capital rais earnings for immigrants in the low-skill
labor market? Demography, 45(3), 619–639.
Hong Kong Census and Statistics Department. (2006). 2006 population by-census: Summary
results. Hong Kong: Census and Statistics Department.
Hong Kong Immigration Department. Retrieved on December 7, 2011 from
http://www.immd.gov.hk/ehtml/faq_asmtp.htm
Jaeger, D.A. (2007). Skill Differences and the Effect of Immigrants on the Wages of. Natives,
College of William and Mary, mimeo.
Lam, K. C., & Liu P. W. (1998). Immigration and the economy of Hong Kong. Hong Kong: City
University of Hong Kong Press.
Lam, K. C., & Liu P. W. (2002a). Earnings divergence of immigrants. Journal of Labor
Economics, 20, 86–101.
Lam, K. C., & Liu P. W. (2002b). Relative returns to skills and assimilation of
immigrants. Pacific Economic Review, 7(2), 229–243.
Liu, P. W., Zhang, J. S., & Chong, S. C. (2004). Occupational segregation and wage differentials
between natives and immigrants: Evidence from Hong Kong. Journal of Development
Economics, 73(1), 395–413.
Massey, D. S., Mooney, M., Torres, K. C., & Charles, C. Z. (2007). Black immigrants and black
natives attending selective colleges and universities in the United States. American
Journal of Education, 113(2), 243-271.
25
Newman, K. (2006). Chutes and Ladders: Navigating the Low-Wage Labor Market. New York:
Russell Sage.
Pong, S.-L., & Tsang, W. K.. (2010). The educational progress of mainland Chinese immigrant
students in Hong Kong. Research in Sociology of Education, 17, 201–230.
Portes, A., & Rumbaut, R. G. (2006). Immigrant America: A portrait. Berkeley: University of
California Press.
Zeng, Z., & Xie, Y. (2004). Asian Americans’ earnings disadvantage reexamined: The role of
place of education. American Journal of Sociology, 109, 1075–1108.
26
Table 1
Percentage Distribution of Education Levels (1996) and Average Years of Education of Chinese
Males aged 15–50 by Immigrant status (1996–2006)
Education Level
Low-skilled
Middle-skilled
High-skilled
Total
Immigrant
41.07
20.84
38.09
100
Native
37.19
34.21
28.59
100
All
37.30
33.86
28.86
100
4760
174116
178926
N
Years of Education
1996
2001
2006
All
Immigrant
11.19
10.62
10.82
Native
10.72
11.04
11.48
Low-skilled
Immigrant Native
7.57
7.29
7.74
7.23
7.60
7.31
High-skilled
Immigrant Native
15.54
15.08
14.83
15.10
14.92
15.35
Middle-skilled
Immigrant Native
10.80
10.89
10.84
10.91
10.83
10.91
Note. High-skilled is defined as having an education of Form 6 or Form 7 or above. Middle-skilled is
defined as having at least lower secondary education but no more than a high school degree. Low-skilled
is defined as having a lower secondary education or below.
27
Table 2
The Nativity Earnings Gap by Skill Levels
A. All male workers
Duration in
Hong Kong
Immigrants
Natives
Raw nativity gap
Nativity gap as % of
native’s earnings
0-5 years
12099.99
14436.47
2336.48
16.18%
5-10 years
12747.51
17993.80
5246.29
29.16%
10-15 years
11603.03
18475.05
6872.02
37.20%
B. High-skilled male workers
Duration in
Hong Kong
Immigrants
Natives
Raw nativity gap
Nativity gap as % of
native’s earnings
0-5 years
18487.22
22752.20
4264.98
18.75%
5-10 years
20075.56
28374.43
8298.87
29.25%
10-15 years
15909.84
27689.27
11779.43
42.54%
C. Low-skilled male workers
Duration in
Hong Kong
Immigrants
Natives
Raw nativity gap
Nativity gap as % of
native’s earnings
0-5 years
7675.03
9948.29
2273.27
22.85%
5-10 years
9048.88
11194.03
2145.15
19.16%
10-15 years
9422.04
10968.79
1546.75
14.10%
Note. All earnings figures are at the 2006 price level. The number of observations is in parentheses in
Panel A.
28
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Total
Industry
(1)
U
0.29
26.82
15.50
11.18
7.66
8.45
4.53
7.23
1.11
0.76
7.80
0.25
3.67
3.46
100.00
U
1996
U
5.79
38.73
25.56
72.68
50.91
21.40
91.49
71.67
64.19
12.21
84.92
66.74
12.43
21.68
U
Natives
% of white-collar
jobs
within industry
(4)
U
0.34
20.79
11.29
6.04
7.09
17.14
5.82
10.44
3.84
0.56
7.21
2.16
3.23
4.04
100.00
Total
(5)
0.00
27.63
8.89
76.09
20.73
32.09
100.00
48.99
0.00
71.56
51.52
0.00
6.73
U
1.30
16.68
15.78
9.22
4.11
16.08
3.86
9.92
0.00
0.75
5.46
1.65
4.01
11.17
100
Immigrants
% of white-collar
jobs
within industry
Total
(6)
(7)
U
U
2001
U
3.42
47.04
30.02
71.58
54.03
23.44
92.74
69.94
64.73
13.60
80.22
65.95
9.57
22.20
U
Natives
% of white-collar
jobs
within industry
(8)
U
0.26
13.87
8.08
7.69
6.40
19.06
6.36
14.02
4.58
0.65
8.30
1.67
2.51
6.56
100
Total
(9)
0.00
23.44
1.62
55.91
50.00
13.62
77.50
37.37
33.33
0.00
52.94
0.00
4.61
U
0.28
9.69
28.62
5.89
6.49
11.92
1.86
13.78
1.39
1.39
6.31
0.00
2.32
10.06
100
Immigrants
% of white-collar
jobs
within industry
Total
(10)
(11)
U
U
2006
U
10.67
47.84
27.69
73.96
53.48
22.55
92.96
56.65
21.15
6.95
77.12
61.83
13.36
25.32
U
29
U
Natives
% of white-collar
jobs
within industry
(12)
Note. Industry codings: 1 = agriculture and mining; 2 = manufacturing; 3 = utility and construction; 4 = wholesale (including export/import); 5 =
retail, restaurants, hotels; 6 = transportation (including storage) and communication; 7 = finance; 8 = business services; 9 = public administration;
10 = sanitary and similar services; 11 = social and related community services; 12 = amusement and recreational services; 13 = personal services;
14 = others.
0.00
28.15
16.94
93.25
29.58
47.66
100.00
66.17
77.42
0.00
93.55
100.00
6.86
21.21
U
Immigrants
% of white-collar
jobs
within industry
Total
(2)
(3)
Distribution of Male Workers in Industries and Occupations by Time and Immigration Status
Table 3
0.20
11.45
10.56
7.71
6.31
19.97
5.79
16.57
1.57
1.26
8.69
1.99
2.58
5.35
100
Total
(13)
Table 4
The Returns to Education by Nativity for All Male Workers, High-Skilled Workers, and Low-Skilled
Workers, With and Without Industry and Occupation Variables
U
Time 1
(I)
U
U
(II)
Time 2
(I)
U
U
(II)
Time 3
(I)
U
(II)
All workers
Native
N
Immigrant
N
Immigrant - Native
0.115
0.069
(0.000)
(0.000)
174166
0.133
0.082
(0.000)
(0.000)
187774
0.127
0.074
(0.000)
(0.000)
177502
0.091
0.033
(0.002)
(0.003)
4760
0.087
0.040
(0.002)
(0.003)
4399
0.063
0.036
(0.002)
(0.002)
4951
-0.024
-0.046
-0.064
-0.036
-0.042
-0.038
High-skilled
workers
Native
N
Immigrant
N
Immigrant – Native
Low-skilled workers
Native
N
Immigrant
N
Immigrant - Native
0.147
0.107
(0.001)
(0.001)
49800
0.185
0.140
(0.001)
(0.001)
62314
0.154
0.107
(0.001)
(0.001)
67069
0.141
0.054
(0.006)
(0.007)
1813
0.209
0.149
(0.007)
(0.009)
1456
0.166
0.103
(0.007)
(0.007)
1681
-0.006
-.053
0.024
0.009
0.012
-0.004
0.030
0.025
(0.001)
(0.001)
64781
0.038
0.029
(0.001)
(0.001)
62397
0.039
0.030
(0.001)
(0.001)
54823
0.014
(0.005)
1955
0.013
(0.005
0.036
0.028
(0.005)
(0.004)
2119
0.031
0.026
(0.005)
(0.005)
2130
-0.016
-0.012
-0.002
-0.008
31
-0.001
-0.004
Table 5
Decomposition of Changes in Relative Mean Earnings, With and Without Controls for Workers’
Industry and Occupation (Cohort 2)
Panel A: All workers
Time 2-time 1
I
II
Relative
price
effect
(A)
-0.2347
-0.0640
Time 3-time 2
I
II
-0.1949
0.0433
General
price
effect
(B)
-0.0074
-0.0053
Relative
quantity
effect
(C)
-0.1081
-0.0649
General
quantity
effect
(D)
0.0146
0.0220
Total
education
effect
-0.3355
-0.1122
Other
terms
0.2633
0.0400
0.0039
0.0052
-0.0313
-0.0193
-0.0074
-0.0067
-0.2297
0.0225
0.1721
-0.0801
General
price
effect
B
-0.0102
-0.0089
Relative
quantity
effect
C
-0.1079
0.0786
General
quantity
effect
D
0.0043
0.0377
Total
education
effect
0.3311
0.9698
Other
terms
-0.4616
-1.0003
0.0133
0.0142
-0.0297
-0.0224
0.0021
0.0008
-0.1933
-0.2014
0.0394
0.0476
Change
of
relative
mean
earnings
-0.0723
-0.0576
Panel B: High-skilled workers
Time 2-time 1
I
II
Relative
price
effect
A
0.4455
0.9196
Time 3-time 2
I
II
-0.1790
-0.1940
Change
of
relative
mean
earnings
-0.1305
-0.1538
Panel C: Low-skilled workers
Time 2-time 1
I
II
Relative
price
effect
A
0.1083
0.0851
General
price
effect
B
0.0041
0.0020
Relative
quantity
effect
C
0.0071
0.0059
General
quantity
effect
D
-0.0028
-0.0021
Total
education
effect
0.1167
0.0910
Other
terms
-0.0190
0.0068
Time 3-time 2
I
II
-0.0456
-0.0228
0.0003
0.0003
-0.0083
-0.0063
0.0003
0.0001
-0.0533
-0.0287
0.1282
0.1036
Change
of
relative
mean
earnings
0.0977
0.0749
Note. Relative mean earnings were calculated by subtracting the earnings of natives from the earnings of
immigrants. The sample includes male employees age 15 and above with positive wages only. All
regressions include years of education, experience, and experience-squared. Estimates in all rows labeled
I do not control for industry and occupation; estimates in all rows labeled II do control for industry and
occupation.
32
Table 6
Regression of Log Earnings on Immigrants’ Pre- and Postmigration Education and Experience,
by Skill Level
Time 1
I
Time 2
Time 3
II
I
II
I
II
0.282***
(0.030)
0.195***
(0.028)
0.592***
(0.053)
0.462***
(0.049)
1.060***
(0.078)
0.853***
(0.070)
0.117***
(0.000)
0.091***
(0.002)
-0.050***
(0.009)
0.069*** 0.135*** 0.083*** 0.127***
(0.000)
(0.000)
(0.000)
(0.000)
0.053*** 0.088*** 0.051*** 0.063***
(0.002)
(0.002)
(0.002)
(0.002)
-0.032*** -0.073*** -0.058*** -0.102***
(0.008)
(0.007)
(0.006)
(0.007)
0.075***
(0.000)
0.036***
(0.002)
-0.077***
(0.006)
0.030***
(0.000)
0.014***
(0.001)
0.004
(0.006)
0.025***
(0.000)
0.010***
(0.001)
0.007
(0.005)
0.029***
(0.000)
-0.001
(0.001)
0.009
(0.006)
0.024***
(0.000)
-0.002
(0.001)
0.012*
(0.006)
0.021***
(0.000)
0.002
(0.001)
-0.037***
(0.006)
0.018***
(0.000)
0.003*
(0.001)
-0.034***
(0.005)
0.356
0.452
0.392
0.493
0.322
0.458
Panel B: High-skilled workers
0.206*
Immigrant
(0.086)
0.584***
(0.078)
-0.200
(0.131)
0.378**
(0.121)
0.368*
(0.157)
1.118***
(0.138)
0.151***
(0.001)
0.142***
(0.005)
-0.101***
(0.015)
0.107***
(0.001)
0.078***
(0.005)
-0.076***
(0.014)
0.192***
(0.001)
0.211***
(0.007)
-0.011
(0.013)
0.143***
(0.001)
0.128***
(0.006)
-0.045***
(0.012)
0.157***
(0.001)
0.166***
(0.007)
-0.042***
(0.012)
0.110***
(0.001)
0.097***
(0.006)
-0.082***
(0.011)
0.050***
(0.000)
0.032***
(0.002)
-0.069***
(0.010)
0.042***
(0.000)
0.022***
(0.002)
-0.054***
(0.009)
0.045***
(0.000)
0.014***
(0.003)
-0.022
(0.011)
0.039***
(0.000)
0.009***
(0.002)
-0.016
(0.011)
0.033***
(0.000)
0.004
(0.003)
-0.073***
(0.010)
0.029***
(0.000)
0.007*
(0.003)
-0.087***
(0.009)
0.370
0.486
0.410
0.498
0.260
0.429
Panel A: All workers
Immigrant
Education in HK
Education in China
Education_HK*Immigrant
Experience in HK
Experience in China
Experience_HK*Immigrant
Adj.R-squared
Education in HK
Education in China
Education_HK*Immigrant
Experience in HK
Experience in China
Experience_HK*Immigrant
Adj.R-squared
33
Time 1
I
Time 2
II
I
Time 3
II
I
II
Panel C: Low-skilled Workers
Immigrant
Education in HK
Education in China
Education_HK*Immigrant
Experience in HK
Experience in China
Experience_HK*Immigrant
Adj.R-squared
0.106*
(0.052)
0.082
(0.051)
0.073
(0.077)
-0.007
(0.073)
0.388**
(0.118)
0.145
(0.110)
0.030***
(0.001)
0.013*
(0.005)
-0.021
(0.013)
0.026***
(0.001)
0.011*
(0.005)
-0.020
(0.013)
0.037***
(0.001)
0.035***
(0.006)
-0.007
(0.013)
0.029***
(0.001)
0.032***
(0.005)
-0.003
(0.012)
0.041***
(0.001)
0.031***
(0.006)
-0.074***
(0.016)
0.031***
(0.001)
0.030***
(0.005)
-0.035*
(0.015)
0.017***
(0.000)
0.003*
(0.001)
0.030***
(0.008)
0.015***
(0.000)
0.002
(0.001)
0.032***
(0.008)
0.014***
(0.000)
-0.004**
(0.001)
0.014
(0.008)
0.012***
(0.000)
-0.003*
(0.001)
0.015
(0.008)
0.010***
(0.000)
-0.003
(0.002)
-0.018*
(0.009)
0.009***
(0.000)
-0.002
(0.002)
-0.003
(0.008)
0.089
0.135
0.064
0.147
0.031
0.160
Note. The sample includes male employees age 15 and above with positive wages only. In addition to the
variables shown in the table, both Model 1 and Model 2 include dummies for spoken Cantonese and
English proficiency. Additionally, Model 2 includes 13 industry dummies and a dummy for white-collar
jobs. Standard errors are in parentheses
***p < .001
**p < .01
*p < .05
34
Appendix Table A.1
Labor Force Participation of Chinese Male Immigrants and Natives
Year
ALL
High-skilled
Low-skilled
Chinese immigrants
Natives
Chinese immigrants
Natives
Chinese immigrants
Natives
83.43%
87.95%
91.00%
85.53%
91.75%
91.35%
92.32%
87.24%
93.42%
82.10%
89.14%
94.12%
77.76%
92.04%
90.90%
91.64%
91.61%
87.16%
1996
2001
2006
Note. All samples consisted of male employees with positive wages. The sample for 1996 consisted of those who
were 15 to 50 years old, the sample for 2001 included those who were 20 to 55 years old, and the sample for 2006
included those who were 25 to 60 years old.
Figure 1
35
36
Figure 1. The returns to education by skill levels and immigrant status, with and without taking into account
workers’ industry and occupation. All natives (N) are the estimates from Equation 1 without controls for industry
and occupation. All immigrants (N) are the estimates from Equation 2 without controls for industry and occupation.
All natives (Y) are the estimates from Equation 1 with controls for industry and occupation. All immigrants (Y) are
the estimates from Equation 2 with controls for industry and occupation.
37