The quality dimension of immigrant human capital has received little attention in the
economic assimilation literature. The objective of this paper is to demonstrate how
human capital acquired in different source countries may be adjusted according to its
quality in the Canadian labor market. This is achieved by deriving quality-adjustment
indices using data from the 2001 Canadian census. These indices are then used to
examine the role of schooling quality in explaining differential returns to schooling
and over-education rates by country-of-origin. The key finding is that accounting for
schooling quality virtually eliminates native-immigrant gaps in returns to schooling
and the incidence of over-education. The quality of human capital is important for
understanding the economic integration of immigrants.
JEL Codes
F22; I2; J15; J31
1. Introduction
Recent empirical evidence reveals that the quality of human capital plays an important
role in accounting for differences in growth rates across countries (e.g. Hanushek and
Kimko, 2000; Schoellman, 2012). However,
measuring the quality of human capital remains an empirical challenge, given that it is
not directly observed.
The principal objective of this paper is an explicit derivation of quality adjustment
indices which modify the human capital of immigrants acquired from different countries,
using information on their earnings in the Canadian labor market. The derived indices
are then used to examine the role of schooling quality in explaining differential
returns to schooling and over-education rates by nativity.
The derivation of the quality-adjustment indices is based on the following two steps.
First, the returns to immigrants’ foreign-obtained schooling are estimated, using
an augmented version of the Mincerian earnings function. Supporting evidence is then
provided of the ability of these estimated returns to measure the schooling quality of
their corresponding country of origin. Second, the estimated country-specific returns to
schooling are used to estimate a schooling quality production function whose fitted
returns are then used to derive a quality adjustment index for each country of
origin.
An important finding of this paper is that accounting for schooling quality virtually
eliminates the native-immigrant gaps in the returns to schooling and the incidence of
over-education. This implies that when human capital is measured by years of schooling
alone, as is standard in the literature, some of the discrepancy in the labor market
outcomes between immigrants and the native-born arise from differences in the quality of
human capital between the host and home countries. In sum, the findings of this paper
confirm that quality of human capital is important for understanding the economic
integration of immigrants.
This paper proceeds as follows. In Section 2 there is a brief description of the
background, followed, in Section 3, with a detailed description of the method of
evaluating quality adjustment indices. A description of the sample and data to be used
is carried out in Section 4. Section 5 presents and discusses the results and Section 6
summarizes the conclusion.
2. Background
2.1. Returns to schooling and over-education
The labor market performance of immigrants compared to the native-born has been the
subject of intensive research (e.g. Galarneau and Morissette, 2004; Li et al., 2006; Green et al., 2007; Wald and Fang, 2008). Earnings and
occupational placement have been commonly used to assess the degree of economic
integration of immigrants. A key factor directly related to this integration process
is the quality of foreign-acquired human capital brought into the host country
(Friedberg, 2000; Chiswick and Miller, 2009; Piracha et al., 2012).
There are two empirical regularities which emerge from the literature on the
assimilation of immigrants into a host country’s labor market. The first is
that foreign-obtained schooling is discounted in the host country’s labor
market. This is reflected by a lower return to an immigrant’s education
compared with the education of a corresponding native (see e.g. Chiswick, 1978; Baker and Benjamin, 1994; Friedberg,
2000; Alboim et al., 2005; Chiswick
and Miller, 2008). The second regularity is that
over-education rate is, on average, higher among immigrants than it is among
corresponding natives (see e.g. Li et al., 2006; Kler, 2007; Green et al., 2007; Galarneau and
Morissette, 2008; Wald and Fang, 2008;
Nielsen, 2011)1.
The first of these two regularities was established in the seminal paper on economic
assimilation of foreign-born males in the U.S. labor market by Chiswick’s
(1978) using 1970 census data. Chiswick (1978) estimated that an extra year of schooling for a foreign-educated
worker raises earnings by 5.7 percent, compared with 7.2 percent for a corresponding
American-born. Moreover, the return to schooling for immigrants from English-speaking
countries is 6.6 percent, whereas for other immigrants it is 5.2 percent. Similar
results hold in the Australian labor market (Beggs and Chapman, 1988), in the Canadian (Baker and Benjamin, 1994;
Alboim et al. 2005; Reitz, 2001; Ferrer
and Riddell 2008; Wald and Fang, 2008),
for the U.K. (Shields and Price, 1998), for Israel (Friedberg,
2000) and for Sweden (Nordin, 2011).
An example of the second regularity, namely over-education, is Li et al. (2006) who find that from 1993 to 2001 52 percent of immigrants
into Canada since 10 years or less were over-educated, compared with 28 percent of
corresponding Canadians. Similar results are reported by, for example, Galarneau and
Morissette (2008), Wald and Fang (2008)
and Sharaf (2013) for Canada, Nielsen (2011) for Denmark, Lindley and Lenton (2006) for
the U.K, Green et al. (2007) and Kler (2007) for Australia.
Several explanations have been suggested for a lower relative return to schooling for
foreign-acquired education and a higher incidence of over-education among immigrants.
These include lower quality of foreign schooling (Chiswick and Miller, 2009), “country-specific aspects” of the knowledge
acquired in schools (Chiswick, 1978), incompatibility of
foreign-obtained schooling with the requirements of the host country labor market
(Friedberg, 2000), lack of host country-specific human capital
such as language proficiency (Alboim et al., 2005).
2.2. Schooling quality
Given that education quality is not directly observed, previous studies mostly used
two main methods to infer it. The first approach uses student outcomes such as scores
on internationally standardized tests like the Programme for International Student
Assessment (PISA) as a direct measure for schooling quality. The second approach
involves estimating an education quality production function and relating educational
inputs, such as the pupil-teacher ratio and expenditures per pupil to an educational
outcome. For example, Lee and Barro (2001) estimated a
schooling quality production function that relates a set of educational outcomes such
as international test scores, repetition and dropout rates to a set of family inputs
and school resources.
In addition to the use of educational inputs to measure education quality, several
studies have used student scores in international achievement tests to infer the
quality of the educational system (e.g. Hanushek and Kimko, 2000; Sweetman, 2004; Chiswick and Miller, 2010). For example, Hanushek and Kimko (2000) used country scores from international achievement tests to
construct measures of the average quality of the labor force of each country, which
were then used to explain cross-country differences in growth rates. Other studies
(e.g. Chiswick and Miller, 2010) have used the PISA, an
international evaluation of 15-year-old school pupils' scholastic performance
conducted every three years by the Organization for Economic Co-operation and
Development (OECD), as a proxy for the average quality of schooling.
Though international test scores and educational resources have been commonly used to
measure schooling quality, they suffer from several shortcomings that affect their
validity and accuracy. For instance, several studies found weak correlation between
test scores of students and consequent labor market outcomes. The test scores also
suffer from selectivity bias and difficulties in standardizing the tests (Betts,
1995). In addition these test scores are not available for
many developing countries. For a more comprehensive review of the limitations of
these measures see, for example, Betts (1995), Ladd and Loeb
(2012).
This paper adds to the existing literature on cross-country differences in
educational quality by incorporating the idea that the earnings of foreign-educated
immigrants in the same host labor market can be used to measure the average quality
of schooling of each home country. The idea of using information on the labor market
outcomes of foreign-educated immigrants to infer the quality of human capital in
their country-of-origin derives from Hendricks (2002).
Hendricks (2002) used the average labor earnings of
immigrants, with identical measured skills in the US labor market, to estimate
unmeasured human-capital endowments across countries. In a similar fashion, Mattoo et
al. (2008) used the probability of placement of highly
educated immigrants in skilled jobs in the U.S. labor market as a measure of the
“average quality” of human capital of the source country. The authors
found that the occupational attainment of immigrants in the U.S. labor market is
largely affected by the characteristics of the source country that influence the
quality of human capital such as the amount of educational resources devoted to
schooling. They found that immigrants from source countries with low schooling
quality are more likely to end up working in unskilled jobs than immigrants from
source countries with better schooling quality. In a recent study, Schoellman (2012) estimated the return to schooling of immigrants in the US
labor market and used these returns as a measure of schooling quality of the source
countries. Schoellman (2012) demonstrated that cross-country
differences in education quality are as important as cross-country differences in the
quantity of schooling in accounting for differences in output per worker across
countries.
The novelty of the present paper is its explicit derivation of quality adjustment
indices which adjust human capital for cross-country differences in schooling
quality. The derived indices are then used to examine the role of schooling quality
in explaining differential returns to schooling and over-education rates by
nativity.
3. Method
The empirical exercise of the current study could be summarized as follow: First, the
returns to foreign-obtained schooling are estimated. Several robustness checks are then
undertaken which provide evidence as to the validity of the estimates to measure
schooling quality. Second, the estimated returns to schooling are used to derive
schooling quality adjustment indices. Third, the evidence on the differential
over-education rates and returns to schooling by nativity is then investigated.
3.1. Estimating the return to schooling
The first step in the analysis is to estimate the returns to foreign-obtained
schooling, which are used to measure schooling quality for each country-of-origin.
The method used here is an application of the idea developed by Card and Krueger
(1992) who used the cross-state returns to the schooling of
migrants to measure the schooling quality of states. The idea was also applied to
cross-country comparisons by: Betts and Lofstrom (2000);
Bratsberg and Terrell (2002); Sweetman (2004); Chiswick and Miller (2010), and Schoellman
(2012).
Returns to foreign-obtained schooling are estimated using an augmented Mincerian
earnings function:
(1)
Equation 1 allows the logarithm of the weekly wage of the
i,th immigrant from the
j,th country
(i = 1, 2, … …, nj; j = 1, 2, … …, m, ∑ jnj = n)
to be determined by: a country-specific fixed effect,
αj, total years of schooling,
,
of immigrant i from country j times a country-specific return to
schooling, βj, a row vector of common observed
covariates, ,
times a column vector of corresponding coefficients, φ, and a
stochastic error .
The control variables included in
are: potential experience, measured by age minus years of schooling minus 6, three
indicators for marital status (single, married and separated), four indicators for
cohort fixed effects (1960–69, 1970–79, 1980–89, 1990–2000),
ten indicators for provincial fixed effects and two indicators for language
proficiency. Mother tongue was used in the regression analysis rather than ability to
speak English or French since the former is exogenous and not affected by an
individual’s ability to learn a new language which may be positively correlated
with schooling quality (Sweetman, 2004). All estimation and
descriptive statistics are population weighted using the sampling weight provided in
the census.
3.2. Schooling quality adjustment indices
The country-specific returns to foreign-obtained schooling are used to estimate an
education quality production function similar to that of Lee and Barro (2001), and Bratsberg and Terrell (2002)2. A typical schooling quality production function relates
a schooling quality measure to a set of educational inputs as follow:
(2)
where Q denotes schooling quality, P represents parental
characteristics, I represents educational resources and ϵ
captures unmeasured factors affecting schooling quality. In a more technical term,
equation 2 can be specified as follow
(3)
where j indexes countries, Rj denotes the
country of origin-specific return to schooling, as a measure of schooling quality,
Pj denotes a set of parental factors,
Ij denotes a vector of educational resources
and ϵj captures country-specific unmeasured
factors affecting schooling quality.
In the analysis, educational resources are measured by the pupil-teacher ratio in
primary schools, log of real government educational expenditure per pupil in primary
schools, number of school days per year at primary schools (as a measure of the
intensity of education), and the log of real salary of primary school teachers (as a
measure of teachers’ quality). Data on educational resources are obtained from
Lee and Barro (1997). In addition to school resources, it has
been shown that the academic achievement of a student is largely affected by
non-school factors such as family background. Several studies have shown that family
background such as the parent’s education and income level are important
determinants of the educational outcomes of their children (Psacharopoulos and
Woodhall, 1985). Parental factors are measured by the log of
real per capita GDP (as a proxy for family income) obtained from Penn World Tables
(Heston et al., 2011), and average years of primary schooling
of adults aged 25 and above (as a proxy for the family education) obtained from Barro
and Lee (2010)3. To better capture the attributes of
the educational system and parental factors that were prevailing at the time
immigrants undertook their education, lagged data from the period 1975 to 1980 are
used4.
The fitted values of the returns to schooling of each country of origin, which are
obtained from the schooling quality production function, are then used to derive
schooling quality adjustment indices which convert years of schooling from different
countries into Canadian terms. Consider two workers, an immigrant from country
j and a native-born, who are identical in observed characteristics
= ,
apart from schooling level, and earn the same wage net of country fixed effects.
Since the country-of-origin fixed effect, αj, is potentially affected
by selection of immigrants based on unobserved characteristics, or may be affected by
other factors unrelated to schooling quality, the intercept, αj, in
equation (1) is discarded to focus only on the country-specific return to schooling,
. This permits equation (1) to be
estimated in re-written form:
(4)
Thus
and the schooling level of the immigrant is equivalent to the schooling level of the
Canadian-born. In other words, years of foreign-obtained schooling can be transformed
into Canadian-equivalent years using the relative return to schooling as
(5)
In equation (5),
denotes total years of schooling of immigrant i from country j
expressed in equivalent Canadian terms, called “quality-adjusted
schooling”. The term is a
country-specific adjustment index that converts foreign-obtained years of schooling
into Canadian terms and
is the quality unadjusted years of schooling5.
3.3. Over-education
The classification of a worker as over-educated is based on the realized match
method. According to this method an individual is considered to be over-educated if
his educational attainment is greater than a reference measure for the educational
requirements of the job (Hartog, 2000; Chiswick and Miller,
2008). This paper uses the modal years of schooling of
Canadian-born workers to determine required years of schooling in each of the 508
occupations identified in the census. As is standard, a job-education mismatch is
measured by ,
(6)
where is an
indicator variable as to whether a worker is over-educated or not,
represents total
years of schooling completed by worker i working in job j, and
represents years of
schooling required by job j. A worker is considered to be over-educated if
>.
4. Data
The empirical analysis uses data from the confidential master file of the 2001 Canadian
Census of Population. The merit of the census data is that it includes a large sample of
immigrants from a wide range of countries, along with information on a comprehensive set
of demographic and labor market variables for a nationally representative sample of
individuals6. In addition, the census file includes detailed occupational
codes for the job held by employed individuals at the time of an interview. The
occupational coding system is based on the 1980 Standard Occupational Classification.
This was used to determine the years of schooling required in each occupation and hence
determine the prevalence of over-education.
The analysis is restricted to males, aged 24 to 65 years who are paid workers and work
full time. In addition, the analysis excludes individuals with missing values on
relevant variables. Source countries with observations less than 500 immigrants are also
excluded from the sample. These restrictions produce a sample of 5,117,249 Canadians and
680,107 immigrants from 78 source countries. To control for the possibility that an
immigrant may have obtained some Canadian education after immigration, the analyses is
restricted to immigrants whose age at immigration is at least 24 years old as a baseline
specification. This threshold was raised to 30 years old as a robustness check on the
results. The rationale behind this exclusion is that any post immigration investment in
education is likely to raise the return to foreign-obtained schooling and hence will
bias upward the estimate of the source country schooling quality (Duleep and Regets,
1999).
Table 1 reports the characteristics of the baseline sample
that is used in the analysis. Immigrants represent 12.3 percent of the sample. On
average, immigrants are older, more educated, have more work experience and are more
likely to be married than the native-born7.
About one quarter of the immigrants reported English or French as their mother tongue.
Immigrants are concentrated in four provinces; Ontario (58 percent), British Columbia
(16.3 percent), Quebec (13.2 percent) and Alberta (8.5 percent). 53.88 percent of the
immigrants arrived to Canada during the period 1990–2001, 22.8 percent immigrated
during 1980–1989, 17.57 percent during 1970–1979 and 5.76 percent during
1960–1969.
India is the source of the largest percentage of immigrant males (8.73 percent) in the
baseline sample, followed by United Kingdom (7.8 percent), China (7.75 percent),
Philippines (5.49 percent), Hong Kong (4.42 percent), United States of America (3.80
percent) and Poland (3.67 percent).
5. Results
5.1. Baseline estimation results
Though not reported and are available upon request, estimates from the earnings
function reveal the discounting of foreign-obtained schooling in the Canadian labor
market. In particular, an extra year of Canadian schooling raises earnings by 7.06
percent, while the average return to a foreign-obtained year of schooling is
estimated at 5.90 percent. Results also show wide variation in the returns to
schooling across source countries (Standard deviation = 1.8), with a general
conclusion that the value of a year of schooling in the Canadian labor market depends
on its origin. Results also show that the returns to schooling for most of the source
countries are lower than the Canadian return of 7.06 percent. These results are
consistent with the findings of earlier studies on the discounting of
foreign-obtained schooling in the Canadian labor market (see for e.g. Baker and
Benjamin, 1994; Alboim et al., 2005) and
are also consistent with evidence for immigrants to U.S. (e.g. Chiswick, 1978; Friedberg, 2000). Nicaragua is the
source country with the lowest return to schooling, estimated at 1.66 percent,
followed by the Dominican Republic (2.4 percent), El Salvador (2.9 percent) and Syria
(3.1 percent). For source countries with a substantial number of immigrants, the
returns to schooling were 6.7 percent for China and India, Philippines (4 percent),
United Kingdom (6.5 percent) and Poland (4.6 percent). On the upper segment of the
returns distribution, the country-specific returns to schooling from 12 countries are
higher than the Canadian return. These include Switzerland (11.5 percent), South
Africa (9 percent), Hong Kong (8.7 percent), Denmark (8.3 percent), Belgium (8.1
percent), France (7.6 percent), Malaysia (7.5 percent), Australia (7.3 percent),
Czechoslovakia (7.2 percent), Sri Lanka (7.2 percent), Hungary (7.2 percent) and
Israel (7.1 percent)8. These results are consistent with the empirical
evidence on how the national origin of an individual’s education matters for
its return in the host labor market (Friedberg, 2000;
Bratsberg and Terrell, 2002; Sweetman, 2004; Chiswick and Miller, 2010).
5.2. Robustness checks
Several robustness checks are conducted to check the sensitivity of the estimated
returns to schooling to sample selection restrictions and to model specification. For
example, in a second specification, immigrants whose age at immigration was less than
30 years, instead of 24, are excluded to reduce further any bias that may result from
taking education in Canada. In a third specification, returns to schooling are
allowed to vary by immigration cohort and in a fourth specification by occupational
skill level (skilled and un-skilled). The 508 occupations identified in the census
are grouped into skilled and un-skilled categories based on the educational
requirement of each occupation; skilled if the occupation requires more than 12 years
of schooling, otherwise it is classified as unskilled. Given the finding of several
previous studies that foreign-acquired work experience has zero return in the
Canadian labor market (e.g. Alboim et al. 2005). Another
specification that was estimated includes only potential Canadian work experience.
The country-specific returns to schooling from these different specifications were in
general very close to the baseline model. For example, the difference between the
Canadian return to schooling and the average returns to foreign obtained schooling
was 1.16 percentage points, and ranges from 0.7 to 1.3 percentage points in the
alternative specifications.
Figure 1, compares estimated returns to schooling in this
paper with those obtained by Sweetman (2004). The goal of this
comparison is to check whether there is something special about the 2001 Canadian
census data set, or that it has particular features that may affect the empirical
findings. As shown in Figure 1, the estimated
country-specific returns to schooling in this paper are in general very close to
those obtained using other data sets. Sweetman (2004)
estimated the return to schooling of immigrants from a wide set of countries using a
pooled sample of the 1986, 1991 and 1996 Canadian census. Part of the deviation of my
estimated returns from the estimates of Sweetman (2004) may be
attributable to differences in the sample selection and model specification.
Figure 1
Comparing my estimated return to schooling to estimates based on 1986, 1991
and 1996 Canadian Census.
5.3. Supporting evidence for using returns to schooling to measure schooling
quality
It has been widely documented that immigrants from countries with high quality
educational systems receive higher economic returns to foreign-acquired schooling
than those from countries with low quality educational systems (Sweetman, 2004; Chiswick and Miller, 2010). This
implies that the return to schooling could be used as a
“productivity-based” or a “market-based” measure of schooling
quality (Bratsberg and Terrell, 2002).
To check the soundness of the returns to schooling as being a reasonable measure of
schooling quality, these returns are compared to a set of widely used measures of
schooling quality such as per-capita real GDP, international achievement test scores,
dropout rate at primary schools as well as a set of educational inputs such as
student-teacher ratio at primary schools, real government educational expenditure per
pupil at primary schools, and real average salary of primary school
teachers9.
Though not reported and are available upon request by the author, scatter plots show
that on average, immigrants from source countries with higher per-capita real GDP
earned higher returns on their foreign-obtained schooling than immigrants from
countries with lower per-capita real GDP (correlation coefficient = 0.41). In
addition, the estimated country-specific returns to schooling are plotted against
test scores from international achievement tests constructed by Hanushek and Kimko
(2000). Hanushek and Kimko (2000)
constructed a measure of the average schooling quality for a pool of countries based
on student performance on international tests of academic achievement in mathematics
and science conducted between 1965 and 1991 by two different international
educational testing organizations. The Hanushek and Kimko (2000) constructed index is an educational outcome, widely used in the
literature as a proxy for the average quality of schooling in each country. A
positive correlation (correlation coefficient = 0.39) is found between the estimated
country-specific returns to schooling and international achievement test scores. In a
recent study, Schoellman (2012) also found a positive
correlation between the return to schooling of immigrants in the US labor market and
another set of international test scores constructed by Hanushek and Woessmann (2012).
The estimated returns to schooling were also plotted against the dropout rate at
primary schools, a commonly used proxy for schooling quality. Data on dropout rates
are obtained from Lee and Barro (1997)10. As
expected, there is a negative correlation (correlation coefficient = −0.54)
between the estimated return to schooling and the dropout rate at primary
schools.
Additional evidence for the validity of schooling returns as a measure of schooling
quality can be observed by plotting these estimated returns to schooling against
several educational inputs that are directly related to schooling quality. To capture
the attributes of the educational system better, the educational inputs data were
lagged by 20 years to capture the time when immigrants were in school. These include
the student-teacher ratio at primary schools, real government educational expenditure
per pupil at primary schools, and real average salary of primary school teachers as a
proxy for teacher quality. Data for these variables are obtained from Lee and Barro
(1997).
On average, immigrants from countries with low pupil teacher ratios in primary
schools earn a higher return on their foreign obtained schooling in the Canadian
labor market (correlation coefficient = −0.35). In a similar fashion, estimated
schooling returns are positively correlated (correlation coefficient = 0.48) with log
of real government educational expenditure per pupil at primary schools. Similar
findings are reached when estimated returns to schooling are plotted against
educational input data related to secondary schooling. These relationships are in
general consistent with findings from other countries and data sets (e.g. Betts and
Lofstrom, 2000; Bratsberg and Terrell, 2002; Sweetman, 2004; Chiswick and Miller, 2010). There was also a positive correlation between
country-of-origin returns to schooling and teacher quality as measured by average
salary (correlation coefficient = 0.52) and average years in school (correlation
coefficient = 0.35)11.
5.4. Results for alternative specifications of the schooling quality production
function
The schooling quality production function is estimated under several model
specifications to check the sensitivity of the results as shown in Table 2. Model 1 includes only educational resources (pupil-teacher
ratio and real government educational expenditure per pupil); model 2 includes the
real salary of primary schools teachers in addition to the variables of model 1,
while parental factors (real per capita GDP and average years of primary schooling)
are added in models 3, 4 and 5.
Table 2
Estimates for the schooling quality production function
Given that source countries have different sample sizes, the schooling quality
production function is also estimated using weighted least squares, with the number
of individuals from each source country used as a weight. The results from the
weighted least squares are presented in model 5.
Results of model 5 show that pupil-teacher ratio has a negative relationship, though
not statistically significant, with schooling quality. This is consistent with the
expectation that smaller class size enhance quality of education. Results also show
that both the length of the school term, as a measure of education intensity, and
real government educational expenditure per pupil has a positive and statistically
significant effect on schooling quality. The regression also included the average
years of schooling as a measure of parental education level. This variable has the
correct sign but is not statistically significant.
The estimated schooling quality production function was used to generate predicted
values for the quality of schooling of each country of origin, which are then used to
derive quality adjustment indices to control for cross-country schooling quality
differences.
5.5. Deriving schooling quality adjustment indices
The derived country-specific quality adjustment indices are presented in
Table 3, which shows considerable variation among
source countries, ranging from 0.217 for Albania to 1.63 for Switzerland. Two useful
reference countries with a considerable number of immigrants are Philippines
(adjustment index = 0.57) and Hong Kong (adjustment index = 1.24). These adjustment
indices can be interpreted as follows: On average, ten years of schooling from
Philippines are equivalent to 5.7 years of Canadian schooling. Similarly, ten years
of schooling from Hong Kong are equivalent to 12.4 years when expressed in Canadian
terms.
Table 3
Quality adjustment indices and over-education rates by country of origin
5.6. Returns to schooling using quality-adjusted data
The Mincerian earnings function (1) is re-estimated using the quality-adjusted years
of schooling. Years of schooling from different source countries are now expressed in
the same Canadian quality units. Accordingly, if the quality adjustment indices
accurately capture differences in schooling quality across countries, then the return
to a year of quality-adjusted foreign-obtained schooling is expected to be close to
the return to a Canadian year of schooling. In line with this a priori
expectation, the average return to an extra year of quality-adjusted schooling of
immigrants is 6.87 percent, which is very close to the Canadian return of 7.06
percent given above12. This is in contrast to 5.9 percent, obtained before
the adjustment. The main conclusion from this exercise is that cross-country
differences in schooling quality substantially explain the lower return to immigrant
education in the Canadian labor market, and that the gap in returns to schooling
nearly disappears once the quality of schooling is taken into account. By the same
way, the lower return to immigrant schooling from many source countries, compared to
the native-born return, is mainly due to the lower quality of foreign-obtained
schooling.
5.7. Schooling quality and over-education prevalence by country of origin
Schooling quality adjustment reveals that immigrant quality-unadjusted years of
schooling, on average, overstate their earning capacity. Hence, an immigrant holding
a job that requires less schooling than the immigrant has may incorrectly appear to
be over-educated. Accordingly, the quality adjustment indices reported in
Table 3 can be used to re-examine the evidence on the
prevalence of over-education among immigrants.
Figure 2 depicts the aggregate incidence of over-education
by nativity, using the quality-adjusted and unadjusted years of schooling.
Figure 2
Over-education incidence among natives and immigrants.
It is evident from Figure 2 that the aggregate incidence
of over-education, using quality unadjusted years of schooling, is higher among
immigrants compared to native-born. Estimates show that 58.5 percent of immigrant
males were over-educated in 2001 compared to 43.85 percent of the Canadian born. The
high incidence of over-education among immigrants is in line with several previous
studies. For instance, using the survey of labor and income dynamics, Li et al.
(2006) found that during the 1993–2001 period, 52
percent of recent immigrants to Canada-those in Canada for 10 years or less-were
over-educated. Lindley and Lenton (2006) found that 63 percent
of male immigrants to the U.K. are overeducated compared to 37 percent of male
natives. In another study, Wald and Fang (2008) found that
about 50 percent of the immigrants arriving between 1989 and 1997 were overeducated
in 1999.
Another key result is that immigrants not only have a higher incidence of
over-education than their Canadian-born counterparts, but also a higher intensity of
over-education, measured in terms of years of surplus schooling above what is
required by the job. Estimates, using quality-unadjusted years of schooling, show
that immigrant males have, on average, 3.6 (standard deviation = 2.28) years of
surplus schooling, compared to 2.82 years (standard deviation = 1.84) for
natives.
As previously mentioned, these differential over-education rates by nativity may be
due to differences in schooling quality that are not captured by years of schooling
alone. Accordingly, the objective now is to see how these aggregate over-education
rates change when schooling quality is taken into account using the quality
adjustment indices derived in the previous section.
The fundamental result from this quality adjustment exercise is that accounting for
schooling quality virtually eliminates the native-immigrant gap in the incidence of
over-education. In particular, estimates show that the incidence of over-education
among immigrants, after adjusting for quality differences, becomes 44.4 percent,
which is very close to the Canadian incidence of 43.85 percent. This is in contrast
to 58.5 percent, obtained before adjusting for schooling quality. The intensity of
over-education among immigrants declined, but only a little, from 3.6 to 3.39 after
adjusting for quality differences. The general conclusion from these findings is that
when the job-education mismatch is measured by years of schooling alone, as is
standard in the literature, some of this apparent mismatch arises because of
differences in schooling quality between the host and home countries.
6. Conclusion
This paper explicitly derives schooling quality adjustment indices to account for
differences in cross-country schooling quality, using information on labor market
earnings. The derived indices are used to explain the differential returns to schooling
and over-education rates by nativity.
The fundamental finding is that accounting for schooling quality virtually eliminates
the native-immigrant gaps in the returns to schooling and in the incidence of
over-education. In particular, the return to an extra year of quality-adjusted schooling
of immigrants is 6.87 percent, which is very close to the Canadian return of 7.06
percent. This is in contrast to 5.9 percent, obtained before the adjustment. Estimates
also show that the incidence of over-education among immigrants, after adjusting for
quality differences, is 44.4 percent, which is very close to the Canadian incidence of
43.85 percent. This is in contrast to 58.5 percent, obtained before adjusting for
schooling quality. This implies that when human capital is measured by years of
schooling alone, as is standard in the literature, some of the apparent labor market
disadvantage of immigrants relative to natives arises due to differences in the
schooling quality between the host and home countries.
Results also show that cross-country differences in schooling quality account for over
90 percent of the variation in returns to foreign-obtained schooling. The findings of
this paper emphasize on the importance of controlling for source quality of human
capital when evaluating the economic integration of immigrants. “Quantity of
schooling alone is misleading”, it is important to account for quality too
(Behrman and Birdsall, 1983).
One potential limitation of the current study is the omission of some relevant variables
that may account for the cross-country differences in schooling returns. For instance,
different schooling distributions may also generate differences in returns to education
if returns are non-linear, even with the same quality of schooling, and immigrants are
selected into different segments of the schooling distributions in their home countries.
Due to the lack of data about schooling distribution in different countries, this was
not fully captured. However, the inclusion of the country-specific fixed effects in the
regression equation may partly have helped to account for these cross-country
differences in schooling distribution. This paper tried to control for the influences of
a number of earnings determinants to pin down the country of origin specific school
returns and focus on the variations that are caused by differences in schooling quality
across countries. Another limitation is the cross sectional nature of the census which
limits the ability to make causal inferences. In addition, further empirical evidence
from other countries and using longitudinal data will be needed before reaching a
generalized conclusion. In spite of these limitations, the current study is among the
first to control for cross-country differences in schooling quality by explicitly
deriving a set of quality adjustment indices and applying them to an over-education
exercise.
Endnotes
1Over-education occurs when the educational attainment of a worker is
greater than the educational requirement of the job.
2Lee and Barro (2001) estimated a schooling quality
production function that relates three measures of schooling quality (dropout rates,
repetition rates and test scores) to a set of parental factors and educational
resources. Bratsberg and Terrell (2002) related country of
origin specific return to immigrants’ education in the US labor market to
several schooling quality measures.
3In addition the average years of primary schooling could also reflect the
education level of the teachers.
4In a related study, Bratsberg and Terrell (2002)
lagged the educational quality data for 20 years.
5Here, years of schooling are quality-adjusted using adjustment indices
based on the predicted returns to schooling obtained from the quality production
function.
6For additional information on the 2001 Canadian Census see Statistic
Canada (2003).
7The fact that immigrants are on average more educated than their Canadian
counterparts may be due to the immigration point system which rewards education
highly with up to 25 points.
8Czechoslovakia includes immigrants from both Czech Republic and Slovak
Republic.
9Data on per-capita real GDP is obtained from the Penn World Tables
(Heston et al., 2011).
10Dropout rates are defined as the percentage of students who started
primary schools but did not attain the final grade of primary schools.
11Average years in school is also used in the literature as a measure of
family background.
12In another specification, the earnings function was re-estimated with
cluster robust standard errors by country of origin to account for the fact that the
predicted quality-adjusted years of schooling is obtained using a different number of
observations and the results were similar to those reported in the manuscript.
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This paper uses Statistics Canada confidential data, and the opinions expressed do
not represent the views of Statistics Canada. I would like to thank an anonymous
reviewer as well as the managing editor of this journal for the valuable comments and
suggestion. The usual disclaimer applies.
Responsible editor: Corrado Giulietti
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Department of Economics, Concordia University, Montreal, Canada
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Sharaf, M.F. The earnings of immigrants and the quality adjustment of immigrant human capital.
IZA J Migration2, 13 (2013). https://doi.org/10.1186/2193-9039-2-13