Religions, Human Capital and Earning in Canada
Maryam Dilmaghani
McGill University, Department of Economics, CRIEQ and CIRANO
Abstract
Using Ethnic Diversity Survey (EDS) I examine how religious belief and practice relate to
earning in Canada. Besides the cross-religion differences in earning I also account for overall
religiosity using a composite scored-based variable constructed by means of several questions in
the survey. A negative correlation between religiosity and earnings is found controlling for
demographic and human capital variables. The difference between earning and human capital
return of different religions are economically and statistically significant where Jews enjoy a
premium and Muslims’ earnings are significantly lower than average Canadian. The most
plausible explanation of this discrepancy seems to be the return to experience.
Keywords: religiosity, earning
JEL Classification: Z12
Correspondence to the author:
maryam.esmaeilpourdilmaghani@mail.mcgill.ca
I. Introduction
Although the relationship between religiosity and labour market outcomes seems of
interest to labour economists, social economists and economic demographers it is
remarkable that the last study of this issue using Canadian data dates back to around 25
years ago (Tomes, 1983, 1984 and 1985; Meng and Sentence 1984).
This paper’s objective is to fill this gap by examining the question of labour market
impact of religions and religiosity in Canada. This study is carried out using Ethnic
Diversity Survey and its detailed data on religious affiliations, the extent of religious
belief and the frequency of religious practice along other relevant socioeconomic factors.
The conception of the question is twofold: not only I consider religiosity per se defined as
commitment to a religion and its tenets (with no distinction among religions) and its
relationship with earning but also I examine earning and human capital return
differentials among a number of religious denominations present in Canada.
Before going any further an introductory note reviewing recent economic literature on
this matter is in order. Religion is susceptible of impacting economic outcomes both
through institutions and by way of affecting individual agents’ incentives and behaviours.
The institutional channel is more suitable for historical studies (see: Dudley and Blum
2001; Boppart et al. 2008) or for researches focusing on the countries in which the
secularisation of institutions is still weak (see: Guiso et al. 2003). However the channel
of individual behaviour and incentives seems to be universally as relevant as in the past.
I believe one way of making a distinction among various parts of empirical literature on
the economic impact of religion is to distinguish between macro-data and micro-data
studies. The literature using micro-data itself can be divided into two categories based on
whether the economic impact of overall religiosity has been examined or the crossreligion differences in economic attainment were the subject matter of the research.
On the macroeconomics side McCleary and Barro (2003) examined the relationship
between economic growth and religion in a cross-section of countries through time. They
could show that while a country’s average religious belief is positively related to
economic growth the frequency of religious practice relates to economic growth through
a negative coefficient. Durlauf and colleagues (2006) made a critical review of the results
obtained by Barro and MacCleary through which they deduced that their finding may not
be robust to the specification’s modifications.
Within micro-data literature the impact of overall religiosity on a wide array of outcomes
such as educational attainment (Sander 2001; Saucerdote and Glaeser 2001; Blusch
2007), female labour force participation and fertility (see for a review: Lehrer 2008),
tendency towards entrepreneurship (e.g. Audretsch et al. 2007) and alike has been
examined. Focusing on data from the US the stylized facts derived from these studies can
be summarized as follows. Education is generally a positive predictor of religiosity;
income is a strong, positive predictor of religious contributions, but a very weak predictor
of most other measures of religious activity, such as church attendance and frequency of
prayer. More importantly than income however age, gender, and religious upbringing
predicts religious involvement (see also: Azzi and Ehrenberg 1975; Ehrenberg 1977,
Long and Settle 1977; Ulbrich and Wallace 1983 and 1984, and Biddle 1992).
Finally the studies that look at cross-religion differences in earning and human capital
return resulted in the finding of superior earning and educational attainment of Jews
compared to the rest of population in the US and in Canada (see: Steen 1996; Chiswick
1983 and 1985; Chiswick and Huang 2006; Meng and Sentence 1984; Tomes 1983, 1984
and 1985).
Meng and Sentence, using data from Canadian National Mobility Study 1973, found that
there is indeed a statistically significant difference among the earning of the devotees’ of
various religions as well as their human capital return; mainly they found that all others
equal Jews earn more than Catholics and Protestants. However the results reported by
Tomes, using 1971 Canadian Census, shed some doubts on the robustness of Meng and
Sentence’s results for he found a much smaller gap between Jewish earning and the rest
of the population.
This paper aims at covering both questions of economic impact of religiosity as well as
cross-religion differentials in earning and human capital return. In the first part of the
paper I consider religiosity as a continuous variable by construction of a score-based
religiosity index through a combination of several questions of the survey. The objective
is to see to what extent religiosity as an observable and measurable trait can predict
labour market outcomes. I also discuss the possible behavioural impacts of religion that
can serve as a mechanism linking religiosity and labour market attainment such as
honesty, trustworthiness, and discipline previously put forward by researchers. In the next
part I compare labour market attainment of Catholics, Protestants, Jews, Muslims,
individuals self-reporting with no religious affiliations and the residual group.
The reminder of paper is organized as follows. The next section is devoted to the
presentation of the dataset, the construction the Consolidated Religiosity Index (CRI) and
econometric methodology. In the third section I examine the impact of overall religiosity
(measured using CRI) by including it in an earning human capital function. And the forth
section deals with cross-religion differentials in earning and human capital return. The
concluding remarks are included in the last section.
II. Data and Methodology
The dataset used in this study is Ethnic Diversity Survey (EDS) of Statistic Canada
conducted between April and August 2002 and released in 2005. The dataset is a survey
of 41695 respondents of 15 years old and above male or female legal residents of Canada
including, besides Canadian nationals and permanent residents, people from another
country sojourning in Canada on employment or student authorizations, Minister's
permits as well as refugee claimants, and any family member living with them (see EDS
Guide, page72).
The survey incorporates more than 300 variables. There is precise information about
religious affiliation and ethnic background of the respondents covering several
generations. The advantage of this survey to labour market surveys is in that it contains
specific information about the self-reported importance of religion and the frequency of
religious practice. This feature makes it possible to treat religiosity as a quantitative
variable regardless of the denomination and in fact such conception constitutes the
subject matter of the next section of this paper.
The education measured by the highest degree attained by the respondent as well as that
of their parents and their spouses (if applicable) is surveyed. The hours worked per week
as well as annual personal income and annual household income of the respondents are
included in the survey. Although the survey’s information is on income rather than
earning it is been possible to know the source of the reported income. As such in order to
estimate the standard human capital earning function (at times abbreviated by HCEF in
this paper) I could exclude the respondents whose reported incomes were from sources
other than employment or self-employment (see EDS Guide, page-298).
In addition the dataset provides various interesting information such as ethnic
background up to several generations, the linguistic proficiency of the respondents, the
sector of professional activity, the structure of social network and family ties, trust and
social attitude and alike which enabled me to augment the human capital earning function
by extra control variables. I briefly report some descriptive statistics with regard to
religions and religiosity in Canada according to the data from EDS. Note that unless
otherwise is indicated all reported statistics are weighted by survey’s weights.
In Canada self-reported Catholics constitute 41.52% of sample followed by Protestants
with 27.19% and by the respondents with no religious affiliation (including atheists but
not limited to it1; abbreviated by NRA hereafter) with 16.23%. Among the minority
1
Note that it may be difficult to distinguish between sects and groups of philosophical thoughts and some
religions in the absence of a clear definition of religion. The variable NRA defined in the Table-2 explains
how this distinction is made in the EDS. It is interesting to note that this way of distinguishing between
having a religious affiliation and not having a religious affiliation is in accordance with the definition
religions we have Judaism and Islam coming close to each other in terms of the
percentage of the devotees with 1.02 and 1.58 respectively.
Table-1. Religious Affiliations’ Distribution
Sample
Frequency
Weighted
Percentage
NRA
7,851
16.23%
CATHOLIC
14,721
41.52%
PROTESTANT
11,565
27.19%
JEWISH
661
1.02%
MUSLIM
813
1.58%
RESIDRELIG
6,084
12.45%
SAMPLE
41,695
100%
Denomination
With respect to the self-reported importance of religion (the variable RELIGIMP, see
Table-3) the statistic is extracted from a question in the survey in which the respondents
were asked to express their opinion about the importance of religion by ranking it from 5
to 1 where 5 stands for very important and 1 for not important at all. Note that non
religious individuals have responded to this question by “not applicable”. Attributing the
value of zero to the respondents who have no religious affiliation (NRA) the average
score of the importance of religion in the survey computed by weighted data is 2.95.
There are two other questions dealing with religiosity and religious activity of the
respondent. In one question the respondents are asked to choose among different options
the one that corresponds to their frequency of religious practice with a group of people of
the same faith (variable PRAGRP). The other question contains the same options
however about the frequency of individual religious practice (variable PRAIND). For
both questions the options are: at least once a week, once a month, at least three times a
year, once or twice a year and not at all taking the values of 5 to 1. I noted that more than
50 percent of the individuals take time for religious practice at least once a month. And
overall the frequency of collective religious practice is noticeably lower than individual
religious practice (see Table-2).
proposed by Iannaccone (1998). He defines religion as “any shared set of beliefs, activities, and institutions
premised upon faith in supernatural forces”. His definition, he points out, excludes purely individualistic
spirituality and systems of metaphysical thoughts including some variants of Buddhism.
For sake of having a comprehensive measure of religiosity I defined the Consolidated
Religiosity Index (CRI) by summing the ranking numbers of the answers to the three
questions introduced in the previous paragraphs. Note that in the first questions the
respondents had to rank the importance of religion from1 to 5 while in the two others the
respondents’ answers were on the frequency of their individual and collective religious
practice bound by 5 predetermined categories. The problematic issue in the construction
of CRI was that the passage from one category to the next in the question of the
frequency of religious practice would not signify the same distance in a quantitative way.
More precisely in the first category the reported frequency of religious practice is 52
times a year while in the second it falls at 12 times and at 3 times in the third. On the
other hand any non-linear translation of categories into a quantitative measure has the
inconvenience of arbitrariness as such I opted for the simple summing method in order to
arrive at the religiosity index2.
CRI = RLIGIMP (between 0 and 5) + PRAGRP (between 0 and 5) + PRAIND (between
0 and 5)
Table-2. Mean Religiosity Indicators by Denomination
Denomination
RELIGIMP
PRAGRP
PRAIND
CRI
Catholic
3.48
2.99
3.63
10.09
Protestant
3.49
3.01
3.54
10.04
Jewish
4.04
3.14
3.22
10.40
Muslim
4.22
2.96
3.97
11.17
R-Subsample3
3.54
3.02
3.62
10.18
Sample
2.95
2.52
3.02
8.48
Table-2 contains describe statistics on the average religiosity indicators (RELIGIMP,
PRAGRP, PRAIND and CRI) of main religions in present in Canada along general
2
Note that William Sander, 2002, opted for the following quantification of a comparable question is the
General Social Survey (GSS): “The attendance variable is recoded from the GSS as follows: never equals
0, less than once a year equals 0.5, about once or twice a year equals 1, several times a year equals 3, about
once a month equals 12, two to three times per month equals 30, nearly every week equals 40, every week
or more often equals 52.” But he was using Attendance per se as the dependent variable which differs from
my methodology in using a composite measure.
3
R-Subsample is the subsample of individual self-reporting having a religious affiliation. In other words
respondent with NRA are not included in this subsample.
average. Before turning to the discussion of the methodology I also present the variables’
definition in the Table-3.
Table-3. Definition of Variables
Variable
Definition
CRI
The Consolidated Religiosity Index as defined in the text.
RELIGIMP
The EDS question is framed as: “Using a scale of 1 to 5, where 1 is not
important at all and 5 is very important, how important your religion to you
is?” The coverage of this question is Respondents who reported having a
religion. "Not applicable" includes respondents who did not report having a
religion.
PRAGRP
PRAIND
NONMETRO
TRUST
SELFEMP
LNINC
The EDS question is framed as: “In the past 12 months, how often did you
participate in religious activities or attend religious services or meetings with
other people, other than for events such as weddings and funerals?” Not
applicable" includes respondents who did not report having a religion.
The EDS question is framed as: “In the past 12 months, how often did you do
religious activities on your own? This may include prayer, meditation and
other forms of worship taking place at home or in any other location.” Not
applicable" includes respondents who did not report having a religion.
Takes the value of 1 if the area of residence of the respondent is not a Census
Metropolitan Area which is an area consisting of one or more adjacent
municipalities situated around a major urban core. To form a census
metropolitan area, the urban core must have a population of at least 100,000.
The EDS question is framed as: “Generally speaking, would you say that most
people can be trusted or that you cannot be too careful in dealing with people?” The
answers were binary.
Self-employed. The EDS defines self-employed as: A person who is 'self
employed' earns an income directly from their own business, trade or
profession, rather than being paid a specified salary or wage by an employer
(EDS page. 288)
Natural Logarithm of the respondents annual earning.
HOURS
Natural logarithm of hours worked per week.
EDUC
Education: Measured by years of schooling.
MEDUC
Mother’s education: Measured by years of schooling.
FEDUC
Father’s education: Measured by years of schooling.
UNIVDEG
Dichotomous variable taking the value of one of the respondents has obtained
a university (or college) degree.
EXPER
Potential experience (in absence of any better measure) computed by ageyears of education-6. The resulting number is truncated so that the potential
experience is smaller or equal 40.
EXPERSQ
Squared term of EXPER.
IMMIGRANT
The information is extracted from the question Genstat3 which allowed me to
identify individuals born outside Canada (first generation of immigrant) and
individuals born inside Canada from parents born outside Canada (second
generation of immigrants.
NRA
CATHOLIC
PROTESTANT
RESIDRELIG
No Religious Affiliation: It includes No religion, Agnostic, Atheist,
Humanist, Personal Faith, Free Thinker, Spiritual and Other "not included
elsewhere" (EDS Guide, p. 87.)
It includes the following denomination: Roman Catholic, Ukrainian Catholic,
Polish National Catholic Church, Other Catholic.
Anglican, Baptist, Jehovah's Witnesses, Lutheran, Mennonite, Pentecostal,
Presbyterian, United Church, Other Protestant.
Other religions including Buddhism, Hinduism, Sikh, Other Eastern religions,
Other Christian denominations such as Orthodox.
The equation that constitutes the basis of the estimation is human capital earning function
proposed by Mincer (1974) relating natural logarithm of earned income to natural
logarithm of hours worked, years of education, years of experience both in level and in
squared from, usually a dichotomous variable for having a university or college degree
known as credential effect as well as dichotomous variables for gender, marital status and
location.
Besides these controls I have also systematically included a few extra control variables
such as a dichotomous variable for self-employed status, the trusting behaviour indicator
(TRUST), parents’ education and nativity status. These variables are intended to stand for
unobservable individual and social characteristics that can interact with both human
capital variables and religiosity as it will be explained in the upcoming section.
The impact of overall religiosity is accounted for using CRI in a human capital earning
function which implies that the eventual impact of religiosity on earning is monotonous.
Note that Chiswick and Huang (2006) found that the impact of synagogue attendance is
not monotonous in an equation for Jewish males’ earning in the US. However since CRI
is itself an index more complex specification would not lead to fruitful interpretations.
The problem that usually arises within the estimation of human capital earning function
using such surveys is one of the missing data on income and earnings give that habitually
a considerable fraction of respondents refuse to answer the questions regarding their
income. And this issue raise the question of possibility of sample selectivity bias. The
standard way to deal with this problem is to use one of the estimation methods developed
by Heckman instead of mere OLS. In my data around 18% of the respondents of the EDS
have refused to declare their income to the interviewers hence I had more than 7,000
missing data for the variable on earning (LNINC). I used both OLS and Heckman 2Stage
estimation methods and both sets of results are reported.
Another problem less commonly considered by researchers is the problem of omitted
variable that is correlated with earning, human capital and religiosity indicators. Sander
(2001) shows that education in endogenous in the equations estimating the determinants
and the extent of religious activity. In order to lessen this eventual bias I added
complementary control variables of parents’ education (MEDUC and FEDUC) as well as
a dichotomous variable on the behavioural issue of trust and self-employed status. But I
recognise that this remedy does not eliminate the problem.
Finally given the limited number of categories of earning the OLS estimations are with
adjusted standard errors for intra-group correlations (see: Moulton 1990).
III. Religiosity and Earning
Religious affiliation and to some extent overall religiosity are highly persistent through
consecutive generations (according to Tomes 80% of US males followed the affiliation
they have been raised with). In my data within respondents with a religious affiliation
more than 87% adhere to same faith as at least one of their parents and even within
respondents with NRA more than 56% follow at least one of their parents in having no
religious affiliation4.
Therefore one can think of religious affiliation and religiosity as a part of family
background and by this virtue a constituent of a child’s endowment from family. And the
familial endowment is considered as means of intergenerational transmission of
economic status (for a formal model see Becker and Tomes 1979). From this stance
religious denomination and religiosity are the observable and somewhat measurable
aspects of this endowment that affect the child’s later economic and educational
attainment. Empirically this hypothesis implies the persistent of income inequality that is
serially correlated with religious denomination. While such study can be conceived and it
is of great interest my cross-sectional data is not suitable for this purpose.
It is also suggested that religiosity and earning can be related through the enhancing
impact of religion on some economically advantageous personality traits such as
discipline, diligence, trust and thrift. However recognising the possibility of the presence
4
EDS contains questions about how one’s religious faith compares to that of each of the parents. These
statistics are extracted from these questions. See: EDS Guide: p. 169-172.
of unobservable common cause(s) mainly of sociological and psychological order any
relationship found may also be spurious (for a survey see Iannoccone 1998). In the lines
below I go over some of these traits in more detail.
It has been suggested that religion enhance the trait of trust and cooperative behaviour,
which are economically advantageous traits (see for instance: Arrow 1972; Zak and
Knack 2001). There are a number of studies that report higher degrees other-regarding
behaviour in religious people. Anderson et al. (2008) through an experimental study
found some weak evidence that among subjects attending religious services, increased
participation is associated with cooperative behaviour in both public goods and trust
games controlling for income and other demographic attributes. Note that although
cooperation and trust are different issues cooperative behaviour presupposes trust. Hence
the evidences for more cooperative behaviour are an indirect evidence for trusting
behaviour as well.
A direct test of the relationship between religiosity and trust is provided by the
experimental study of Tan and Vogel (2006). They found that more religious trustees are
trusted more, and such behaviour is more pronounced in more religious trusters and
religious trustees are found to be trustworthier. A comparable study is the one by
Johansson-Stenman et al. (2008) examining the relationship between trust and religion in
Bangledesh both through survey questions and data from field experiment focusing on
the cross-religion difference between Muslims and Hindus. They found that the devotees
of the minority religion, Hindus, are less trusting. I looked at this question with my data
using the variable TRUST and I found that trusting individuals are less religious as score
slightly lower in terms of religiosity measured by CRI than not-trusting individuals.
The other possible channel is the enhancing impact of religion on tendency towards
entrepreneurship. This hypothesis recalls the classical thesis of Max Weber. Recently
Audretsch and colleagues (2007) looked at this question through data from India. Their
results suggest that certain denominations’ tenets and teaching impact negatively the
tendency towards entrepreneurship. I examined this question by comparing selfemployed and paid workers in terms of religiosity and I found that in fact self-employed
individuals on average score slightly lower in religiosity (measured by CRI).
In the present study I do not formulate any precise hypothesis about the nature of the
mechanism through which religiosity and earning may relate. I however control for these
suggested channels whenever it is possible. Before doing so I present a number of bivariant relationship extracted from the data that shed light on the relevance of some of
these hypothesis.
The descriptive statistics on trust and entrepreneurship along the religiosity score of the
residents of metropolitan areas compared to the rest of sample is presented in the Table-4.
Table-4. Bi-variant Relation with Religiosity Indicators
Mean Score
Trusting
Not Trusting
SELF
Paid
METRO
NONMETRO
RELIGIMP
2.85
3.05
2.65
2.80
2.93
3.00
PRAGRP
2.51
2.53
2.33
2.36
2.49
2.58
PRAIND
2.95
3.08
2.75
2.86
3.00
3.06
CRI
8.30
8.64
7.71
8.00
8.40
8.63
46.88%
53.12%
16.28%
83.72 %
65.88%
34.12%
SAMPLE
Education is another, probably more controversial issue that should be considered. The
popular belief prescribes that religiosity and the level of education are inversely related.
The basis of such belief is the “secularization hypothesis” promoted during the past
century. However data may not sufficiently support this popular belief and stark (1999)
has suggested that this hypothesis is a myth. Many studies, using US data, find that
education actually has a positive effect on religiosity (e.g. Ehrenberg, 1977; Hoge et al.,
1996; Iannaccone 1998).
The Canadian pattern seems however to be different. In the table below I report the mean
of the years of schooling (EDUC) for each level of self-reported importance of religion
along the weighted sample frequency of having a university or college degree. As we see
in the Table-5 the relationship between religiosity and education is at first positive then it
becomes negative.
Table-5. Religious Indicator and Educational Attainment
RELIGIMP
Mean EDUC
% of UNIVDEG
Not important at all: 1
2
3
4
Very important: 5
12.86
12.90
12.62
12.46
11.79
23.53%
21.93%
18.46%
19.27%
16.62%
NRA
12.85
22.45%
Sample
12.35
19.18%
Finally family structure has been suggested as another channel through which religiosity
may influence economic attainment. For instance religious sanctions on divorce may
increase the expected duration of marriage and hence encourage greater specialization
and division of labour between spouses raising the labour market skills and earnings of
one spouse (Tomes, 1984). Higher earnings of Jews were explained through their low
fertility levels influencing parental investments in the children. In contrast it has been
suggested that Roman Catholics face additional psychic costs of birth control, and this
lowers the price of numbers of children, the resulting larger family size would tend to
reduce investments in each child.
I also found that there are statistically significant differences among the mean household
size and number of children of religious groups under consideration. The comparative
descriptive statistics are reported in the Table-10 in the annex. Mainly I found that
Muslims’ average household size is larger than average Canadian while it seems that
Roman Catholics have converged to the mean number of children of Protestants and
Jews.
In order to account for the above mentioned channels I have augmented the human
capital earning function by supplementary control variables related to trust, selfemployment and marital status.
3.2. Regression Analysis
In order to examine the impact of religiosity on earning I started with comparing the
presence of religious belief with its absence therefore I included the dichotomous variable
of NRA in the human capital earning function (augmented by additional regressors as
explained in above, see also the note on Table-6). The equation is estimated by both OLS
and Heckman 2Stage. The coefficient on NRA turned out to be statistically significant
and positive of the magnitude of 0.039 with OLS (and 0.010 with Heckman 2Stage
however the latter estimate was not statistically significant at the usual levels). These two
regressions provide some evidence for slightly higher earning of respondents with NRA
in average all others equal.
Turning to the question of treating religiosity as a continuous variable I have augmented
the human capital earning function with CRI (recall that CRI takes the value of 0 for
respondents with NRA) estimating the equation again by OLS and Heckman 2Stage. The
results showed that each point increase in the CRI score reduces annual earnings, all
others equal, by 0.6% from the OLS estimation (and 0.4% from the Heckman estimation)
which means $260 annually. Hence the results provide some evidences for a negative
correlation between religiosity and earned income. The complete regressions are reported
in the Table-12 in the annex.
Finally I turned into examining the impact of degree of religiosity only on the individuals
with a religious affiliation excluding respondents with NRA from the sample. As it is also
interesting to compare the impact of religiosity across religions I have also estimated an
equation in which CRI is replaced by interaction terms between CRI and religious
denominations. The results of these regressions are partially reported in the Table-6 and
completely in the annex (Table-13).
These results suggest that higher degrees of religiosity contribute negatively to earned
income by 0.6% (the coefficient reported in the first column from the right).
Table-6. Human Capital Earning Function Augmented by Religiosity
Catholic
Protestant
Jewish
Muslim
Others
Pooled
CRI (OLS)
-0.008**
(0.003)
-0.004**
(0.001)
-0.003
(0.005)
-0.026*
(0.010)
-0.013**
(0.004)
-0.008**
(0.003)
CRI (Heckman
2Stage)
-0.006**
(0.001)
-0.005**
(0.001)
-0.003
(0.002)
-0.024**
(0.002)
-0.012**
(0.001)
-0.008**
(0.001)
Note: The sample is restricted to respondents having a religious affiliation. The regression contained:
EDUC, EXPER, EXPERSQ, MEDUC, FEDUC, HOURS, and dummies for gender, immigrant, locations,
married, and university degree. Note that * indicates 10% level of significance while ** stand for 0.05% or lower
levels of significance. N=14911. For complete report of the estimations see Table-13 in the annex.
With respect to religion-specific impact of degree of religiosity no important and
statistically significant difference is found between Protestants and Catholics. However
for Jews the coefficient on CRI is not significantly different from zero suggesting that the
degree of religiosity does not impact Jews’ earning. On the other hand the coefficient is
noticeably higher for Muslim: all others equal each point of CRI reduces the average
annual earnings by 2.6% (2.4% with Heckman 2Stage) which means $1130 per year.
IV. Religions and Earning
The question of interest in this section is an empirical one: is there any difference in
terms of earning and human capital returns among different religious groups in Canada?
As I have motioned in the introduction such difference has been found in the past studies
to the direction of lower earnings of Catholics and higher earning of Jews. The studies of
reference are the ones by Tomes and Meng and Sentence.
My study differs from the previous ones in that given the important movements of person
in Canada during the past two decades I have also included Muslims along Protestants,
Catholics and Jews as main comparable religions (note that the fraction of Muslims in the
population became more important that Jews; see Table-1). Moreover I did not exclude
females from the sample given that unlike 25 years ago their participation in the labour
force nowadays in important enough.
Perhaps it is interesting to start with a review of the bi-variant relationship between
religious denomination and annual income. As we see in the Table-7 the statistics suggest
sizable discrepancies among a subset of religious groups. Mainly one observes that
working Jews earn 23% more than average working Canadian and working Jewish males
earn 26% more than average working Canadian males while working Muslims earn 13%
less than average Canadian and working Muslim males earn 15% less than average
working Canadian males.
Obviously the discrepancies may be explained away through ceteris paribus analysis. But
before getting to it I like to take a close look as educational attainments of the groups
under consideration. From the Table-7 it can be noted that Jews enjoys a higher level of
education evident from both average years of schooling and the percentage of their
population that holds a university (college) degree. Working Jews have on average close
to 2 more years of schooling and 51% of them hold a university degree against 23% of
general working Canadians’ subsample. The perplexing issue is that Muslims also have
on average 1.3 more years of schooling and the university graduate percentage of them
primes the average by close to 20%.
With regard to the reasons behind Jews higher educational attainment Reuven Brenner
and Nicholas Kiefer (1981) proposed that because of their past cultural history of the
expropriation of material wealth, Jews make greater investments in human capital, which
is embodied and transportable. And with respect to Muslim I believe that the mains
reason behind Muslims’ higher educational attainment is that a high fraction of Muslims
are immigrants (71%) and taken together with Canadian immigration policy and its
requirement on academic qualification of the immigration candidates their education is
higher than average. It is noteworthy that unlike in previous studies dating back to 1980s
Catholics now have the same educational attainment as Protestants (see Tomes 1984).
To end this discussion I also looked at the age composition of the respondents in the
working subsamples. As a matter of fact the average age of working Muslims (38 years
old) is lower than average working Canadian (41 years old) while average working Jews
(45 years old) are noticeably older. Noting that the measure of experience in this study is
potential experience (see Table-3) it may be one of the reasons behind the observed
discrepancy in mean earning within groups especially Muslims versus Jews.
Table-7. Religious Affiliation, Earning and Educational Attainment
General
MALE
FEMALE
GR5
EDUC
UNIVDEG
NRA
44,108
47,768
37,502
1.27
13.58
26.19%
CATHOLIC
41,001
45,731
35,276
1.30
13.17
22.32%
PROTESTANT
44,059
49,903
37,058
1.35
13.21
21.27%
JEWISH
52,124
59,451
43,339
1.37
15.35
51.43%
MUSLIM
37,008
40,326
30,479
1.32
14.49
42.91%
OTHERS
40,774
45,558
33,738
1.35
12.37
24.32%
W-Subsample
42,390
47,209
35,983
1.31
13.19
23.60%
SAMPLE
32,984
39,475
26,439
1.49
12.35
19.18%
Note:
Date weights are applied. Reported income is in Canadian dollars. Note that this subsample included working
respondents only.
This fact, the age composition, added to the fact that the proportion of immigrants
compared to native born are much higher within Muslims subgroups suggest that the
noted mean earning differential between Muslims and the rest of the sample may be
explained away by potential experience and nativity status but not for the Jewish
differential. With this introduction I move to ceteris paribus analysis of the data.
5
GR stands for Gender Ratio, the ratio of male to female earning. This differentiation is made primarily for
informational reasons. It is also noteworthy that although previous studies (Tomes 1984, 1985) excluded
females from the sample Tomes suggested that the female side of labour market may make a substantial
difference especially with respect to Jewish earrings i.e. Jewish females, he suggested, earned less than
average Canadian female to the point that it could more than compensate the Jewish males’ premium. We
see that his insight preserved some relevance even after 25 years since Jews have the highest GR ratio (the
highest male-female earning inequality ratio) within all religious groups.
4.2. Regression Analysis
In the lines below I report two sets of regressions where in the first the religious
affiliation differential is assumed to be additive and in the second it is accounted for by
allowing differentiated returns to human capital variables. Regarding the first
specification, additive impact of religious groups, I report three separate regressions: in
the first regression the whole sample of working respondents is considered while the two
others concern the subsamples of self-employed respondents and paid-workers
respectively in separate regressions. The reason was to gain information about the
persistence of the earning differential in these two separate parts of labour market.
I opted to present the result from Heckman 2Stage estimation in the test while reporting
the OLS results in the annex (see Table-15). Note that they did not differ from each other
by a great degree.
Table 8. Heckman 2Stage Estimation of HCEF with Additive Impact of Religions
Denomination
POOLED
SELEMPLOYED
PAID WORKER
NRA
0.067**
(0.010)
0.024
(0.075)
0.077**
(0.010)
CATHOLIC
0.067**
(0.009)
0.022
(0.031)
0.073**
(0.009)
PROTESTANT
0.085**
(0.009)
0.053
(0.078)
0.088**
(0.010)
JEWISH
0.105**
(0.024)
0.120
(0.146)
0.093**
(0.027)
MUSLIM
-0.146**
(0.022)
-0.309*
(0.179)
-0.113**
(0.023)
26706
(Censored: 7756)
3686
(Censured: 799)
23020
(Censured: 6957)
-0.105**
(0.030)
-1.554
(1.750)
-0.073**
(0.028)
OBS.
LAMBDA
Note :
The reference group is other religions (RESIDRELIG). The regression contained: EDUC, EXPER,
EXPERSQ, MEDUC, FEDUC, HOURS, and dummies for gender, immigrant, locations, married status,
university degree, (and self-employed only in the pooled regression). Note that * indicates 10% level of
significance while ** stand for 0.05% or lower levels of significance.
It become clear that when the impact of religion is assumed to be additive all else equal
Jews earn more than 10% higher that reference group. While the earnings of NRA and
Catholics seem to be similar by being around 6% higher than the reference group
Protestants fare slightly better them (the difference is statistically significant) and slightly
worse than Jews. Muslims earn close 15% less than the references group which is by far
the largest statistically significant differential.
When I repeated the regression with two separate subsamples one for self-employed and
the other for paid worker I found that the earning differential drastically increases in the
subsample of self employed for Muslims and Jews and to some extent Protestants. The
noteworthy result is the coefficient on Muslims that shows 30% earnings less than the
references group. In the paid workers subsample however the earning differential of
Muslims declines to -11% while the coefficients on other religious groups are not
significantly different from each other.
In the second set of regressions I allowed the coefficients of human capital variables to
vary with religious affiliations.
Table-9. Heckman 2Stage Estimation of HCEF by Religions
NRA
Catholic
Protestant
Jewish
Muslim
Others
Pooled
EDUC
0.034**
(0.003)
0.029**
(0.002)
0.030**
(0.002)
0.024**
(0.006)
0.024**
(0.004)
0.026**
(0.001)
0.030**
(.002)
EXPER
0.028**
(0.002)
0.022**
(0.002)
0.020**
(0.002)
0.036**
(0.007)
0.001
(0.006)
0.020**
(0.003)
0.023**
(0.001)
EXPERSQ
-.0004**
(.0000)
-.0003*
(.0000)
-.0002
(.0000)
-.0007
(.0002)
.0002
(.0002)
-.0003**
(.0001)
-.0003**
(.0000)
UNIVDEGRE
0.099**
(0.019)
0.164**
(0.013)
0.157**
(0.016)
0.154**
(0.060)
0.166**
(0.052)
0.122**
(0.021)
0.137**
(0.009)
Note : The reference group is other religions (RESIDRELIG). The regression contained: EDUC, EXPER,
EXPERSQ, MEDUC, FEDUC, HOURS, and dummies for gender, immigrant, locations, married status,
university degree, and self-employed status. Note that * indicates 10% level of significance while ** stand for
0.05% or lower levels of significance. OBS=18950 (Censored: 7756)
The results show that there is no substantial difference on the return to education among
the religious groups under consideration. It contrasts Becker’s conjecture that the high
incomes and achievements of Jews are explained by high marginal returns to education
(1981). My results suggest that there is however a sizable difference on the return to
experience between Jews and others. The experience-earning profile of Jews is steeper
than respondents with NRA, Catholics and Protestant while the experience factor turns
out to have no economically and statistically significant impact on the Muslims earning.
On the other hand the credential effect (the coefficient of the variable UNIVDEG) is
significantly higher for Muslims (around 5% higher contribution to earnings compared to
the average of Catholic, Protestants and respondents with NRA). The credential effects
turns out to be quite smaller for Jews compared to other groups, moreover it is not
statistically significant. The absence of credential effect for Jews has been also found by
Tomes (1984). Note that the results from an OLS estimation of the same equation are
reported in the Table-16 in the annex.
Conclusion
Tomes said “The returns to research by economists on religion and earnings have been
small. One problem is that the lack of robust stylized facts leads to the rejection of most
simple hypotheses.” and the statement remains true after 25 years. Now that one should
mainly decide religion is still and may eventually remain an important aspect of human
culture it is legitimate to study its economic impact as systematically as other sociocultural factors such race, ethnicity and gender. This paper was conceived to contribute to
the creation of stylised facts regarding the relationship between religions, religiosity and
labour market’s main indicators.
Using Canadian Ethnic Diversity Survey I examined the relationship between religions,
religiosity and earning. With respect to the impact of overall religiosity on earning the
relationship uncovered, although quantitatively slight, was negative. This result is in
contrast with common belief hold about the situation is the US according to which the
relationship between religiosity and earning is positive. Examining the crossdenomination difference in earning and human capital return the results show that there
are indeed statistically significant discrepancies among religions. These differences are
more accentuated within the self-employed segment of Canadian labour market compared
to paid workers’ segment. In this paper I have also explicitly accounted for Muslims and
the results shows that their earning is significantly lower than average while Jews’
earnings are significantly higher. More precisely I found the experience-earning profile of
Jew is steeper than others while experience is not a relevant factor in explaining
Muslims’ earnings. An additional note might be in order with respect to Muslims’
earning and from there their economic condition in Canada. As it is reported in the Table11 in the annex the average family size and number of children are larger than average
Canadian (with respect to household size Muslims’ average is 3.872 persons against
2.933 persons in Canada). Taken together, the lower earnings and the larger family size
of Muslims make this affiliation a good predictor of economic disadvantage in Canada.
This paper was a first step in updating the knowledge of the relationship between earning
and religion in Canada. This paper also showed how Canadian pattern differ from the US
pattern in terms of the relationship between earning, educational attainment and
religiosity. However the paper was largely focused on uncovering descriptive
relationships. Certainly future studies on the same question using other data can help
showing the robustness of the results. Moreover various studies can be conceived aimed
at explaining the eventual reasons behind the relationships that I found in the present
paper.
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Annex: Additional Tables
Table-10. Descriptive Statistics on Variables6
Variable
Observation
Mean
Std. Dev
Min
Max
CRI
40551
8.975
4.536
3
15
RELIGIMP
40870
3.100
1.591
1
5
PRAGRP
40940
2.719
1.624
1
5
PRAIND
40747
3.167
3.167
1
5
MONTREAL
41695
0.100
---
---
---
TORONTO
41695
0.204
---
---
---
VANCOUVER
41695
0.088
---
---
---
METRO
41695
0.315
---
---
---
NONMETRO
41695
0.294
LNINC
19157
10.585
0.456
9.903
11.290
HOURS
22190
3.664
0.272
2.302
3.912
EDUC
41695
12.637
3.937
7
20
MEDUC
41695
10.092
3.530
7
16
FEDUC
41695
10.197
3.710
7
16
AGE
41695
41.275
15.546
16
65
FAMALE
41695
0.529
---
---
---
MARRIED
41695
0.575
---
---
---
IMMIGRANT
41695
0.256
---
---
---
EXPER
22185
21.259
14.004
0
40
NRA
41695
0.188
---
---
---
CATHOLIC
41695
0.353
---
---
---
PROTESTANT
41695
0.277
---
---
---
JEWISH
41695
0.016
---
---
---
6
The statistics are computed without weighting.
MUSLIM
41695
0.0195
---
---
Table 11. Descriptive Statistics on Family Structure by Religion
Number of Children
(Std. Dev.)
Household Size
(Std. Dev.)
NRA
0.601
(0.961)
2.902
(1.349)
CATHOLIC
0.715
(1.012)
2.893
(1.330)
PROTESTANT
0.713
(1.034)
2.791
(1.326)
JEWISH
0.659
(1.045)
2.853
(1.434)
MUSLIM
1.238
(1.335)
3.872
(1.422)
OTHERS
0.772
(1.056)
3.303
(1.459)
SAMPLE
0.711
(1.025)
2.933
(1.365)
Denomination
---
Table-12. Dependant Variable: Natural Logarithm of Annual Income
Indep. Variables
OLS
OLS
Heckman 2Stage
Heckman 2Stage
0.010
(0.007)
----
NRA
0.039**
(0.007)
CRI
----
-0.006**
(0.002)
----
HOURS
0.386**
(0.088)
0.383**
(0.087)
0.390**
(0.011)
0.388**
(0.010)
EDUC
0.032**
(0.008)
0.033**
(0.009)
0.030**
(0.002)
0.031**
(0.002)
MEDUC
0.006**
(0.001)
0.006**
(0.001)
0.004**
(0.001)
0.004**
(0.001)
FEDUC
0.001
(0.001)
0.001
(0.001)
0.001
(0.001)
0.002
(0.001)
EXPER
0.026**
(0.009)
0.026**
(0.009)
0.023**
(0.001)
0.024**
(0.001)
EXPERSQ
-0.0004**
(0.0001)
-0.0004**
(0.0001)
-0.0003**
(0.0000)
-0.0003**
(0.0000)
FEMALE
-0.212**
(0.078)
-0.205**
(0.075)
-0.207**
(0.007)
-0.205**
(0.007)
MARRIED
0.085**
(0.027)
0.089**
(0.027)
0.083**
(0.006)
0.087**
(0.006)
-0.038**
(0.020)
-0.033**
(0.019)
-0.024**
(0.006)
-0.021**
(0.006)
IMMIGRANT
-----
-0.004**
(0.001)
TRUST
0.047**
(0.017)
0.045*
(0.017)
0.041**
(0.007)
0.040**
(0.007)
SELFEMP
-0.030
(0.080)
-0.031
(0.081)
-0.031
(0.008)
-0.031
(0.008)
UNIVERSITY
0.138**
(0.038)
0.138**
(0.038)
0.137**
(0.009)
0.135**
(0.009)
TORONTO
0.072
(0.035)
0.074*
(0.036)
0.056**
(0.008)
0.058**
(0.008)
MONTREAL
-0.032
(0.023)
-0.039*
(0.022)
-0.032**
(0.010)
-0.036**
(0.010)
VANCOUVER
0.001
(0.020)
-0.004
(0.019)
-0.012
(0.010)
-0.015
(0.011)
NONMETRO
-0.046**
(0.011)
-0.047**
(0.011)
-0.053**
(0.007)
-0.053**
(0.007)
CONSTANT
8.307**
(0.471)
8.369**
(0.451)
8.420**
(0.057)
8.440**
(0.058)
OBS.
18950
18950
26706
(Censured: 7756)
26706
(Censured: 7756)
R2
0.3358
0.3392
----
----
LAMBDA
-0.102
(0 .030)
-0.090
(0.030)
Note
The reported coefficients are rounded to three decimal points. The standards errors are reported in the parentheses below.
Note that * indicates 10% level of significance while ** stand for 0.05% or lower levels of significance. The omitted variable
within locations category is Other Metropolitan areas.
Table-13. Dependant Variable: Natural Logarithm of Annual Income
Indep. Variables
OLS
HECKMAN 2Stage
OLS
HECKMAN 2Stage
HOURS
0.373**
(0.084)
0.393**
(0.012)
0.371**
(0.082)
0.391**
(0.012)
EDUC
0.031**
(0.008)
0.030**
(0.002)
0.031**
(0.008)
0.029**
(0.002)
MEDUC
0.006**
(0.001)
0.005**
(0.001)
0.005**
(0.001)
0.004**
(0.001)
FEDUC
0.001
(0.001)
0.001
(0.001)
0.002
(0.001)
0.001
(0.001)
EXPER
0.026**
(0.009)
0.022**
(0.002)
0.026**
(0.009)
0.021**
(0.002)
EXPERSQ
-0.0004*
(0.0002)
-0.0003**
(0.0001)
-0.0004*
(0.0002)
-0.0003**
(0.0001)
FEMALE
-0.206**
(0.080)
-0.208**
(0.007)
-0.212*
(0.081)
-0.213**
(0.007)
MARRIED
0.083**
(0.024)
0.083**
(0.007)
0.082**
(0.025)
0084**
(0.007)
IMMIGRANT
-0.021**
(0.019)
-0.012*
(0.007)
-0.007
(0.015)
-0.003**
(0.007)
TRUST
0.054**
(0.019)
0.104**
(0.013)
0.053**
(0.015)
0.043**
(0.007)
SELFEMP
-0.027
(0.081)
-0.024**
(0.009)
-0.027
(0.080)
-0.025**
(0.009)
UNIVERSITY
0.149**
(0.041)
0.088**
(0.015)
0.154**
(0.041)
0.151**
(0.011)
TORONTO
0.069
(0.036)
0.059**
(0.008)
0.077*
(0.040)
0.066**
(0.009)
MONTREAL
-0.042
(0.026)
-0.035**
(0.011)
-0.033
(0.028)
-0.033**
(0.011)
VANCOUVER
0.005
(0.028)
-0.008
(0.013)
0.019
(0.032)
0.008
(0.013)
NONMETRO
-0.049**
(0.010)
-0.053**
(0.008)
-0.052**
(0.011)
-0.057**
(0.008)
CRI
-0.008**
(0.003)
-0.008**
(0.001)
-----
-----
CRICATH
-------
-------
-0.008**
(0.003)
-0.006**
(0.001)
CRIPRO
------
------
-0.004**
(0.001)
-0.005**
(0.001)
CRIJEW
------
------
-0.003
(0.005)
-0.003
(0.002)
CRIMUS
-------
-------
-0.026**
(0.010)
-0.024**
(0.002)
CRIOTH
--------
--------
-0.013**
(0.004)
-0.012**
(0.001)
CONSTANT
8.442**
(0.432)
8.487**
(0.064)
8.462**
(0.422)
8.516**
(0.064)
OBS.
14911
21472
(6561)
14911
21472
(6561)
R2
0.3379
----
0.3453
-----
-----
-0.091**
(0.032)
-----
-0.099**
(0.032)
LAMBDA
Note
The reported coefficients are rounded to three decimal points. CRICATH is the interaction term between CRI and
Catholic. CRIPRO is the interaction term between CRI and Protestant. CRIJEW is the interaction term between
CRI and Jewish. CRIMUS is the interaction term between CRI and Muslim and CRIOTH is the interaction tern
between CRI and RESIDRELIG. The standards errors are reported in the parentheses below. Note that * indicates
10% level of significance while ** stand for 0.05% or lower levels of significance. The omitted variable within
locations category is Other Metropolitan areas.
Table 14. First Stage Regressions for Heckman 2-Satge Estimations
Independent Variable: Dichotomous Variable of Declaring One’s Income
Indep. Variables
(1)
(2)
(3)
(4)
EDUC
0.092**
(0.003)
0.092**
(0.003)
0.096**
(0.004)
0.096**
(0.004)
MEDUC
0.011**
(0.003)
0.011**
(0.003)
0.008**
(0.004)
0.008**
(0.004)
FEDUC
0.000
(0.003)
-0.000
(0.003)
0.002
(0.003)
0.002
(0.003)
AGE
0.170**
(0.045)
0.170**
(0.005)
0.175**
(0.005)
0.175**
(0.005)
AGESQ
-0.002**
(0.000)
-0.002**
(0.000)
-0.002**
(0.000)
-0.002**
(0.000)
FEMALE
-0.308**
(0.018)
-0.306**
(0.018)
-0.314**
(0.020)
-0.314**
(0.020)
MARRIED
0.010
(0.021)
0.010
(0.021)
0.019
(0.023)
0.019
(0.023)
IMMIGRANT
-0.066*
(0.018)
-0.070**
(0.019)
-0.067**
(0.021)
-0.067**
(0.021)
SELFEMP
0.077**
(0.027)
0.074**
(0.027)
0.104**
(0.030)
0.104**
(0.030)
UNIVERSITY
-0.310**
(0.029)
-0.310**
(0.029)
-0.338**
(0.033)
-0.338**
(0.033)
TRUST
0.299**
(0.018)
0.230**
(0.018)
0.303**
(0.021)
0.303**
(0.021)
-3.418**
(0.103)
-3.421**
(0.103)
-3.585**
(0.116)
-3.585**
(0.116)
OBS.
26706
26706
21472
21472
PSEUDO-R2
0.1997
0.2001
0.2122
0.2122
CONSTANT
Note
The first regression corresponds to the estimation of HCE function augmented by NRA and the second to the one augmented by
CRI. The third is augmented by CRI with the R-Subsample. The forth column is when the equation is augmented by interaction
terms of CRI and religion.
Table -15. OLS Estimation of HCEF with Additive Impact of Religions
Denomination
POOLED
SELEMPLOYED
PAID WORKER
NRA
0.097**
(0.028)
0.024
(0.028)
0.105**
(0.035)
CATHOLIC
0.058**
(0.019)
0.022
(0.031)
0.065**
(0.021)
PROTESTANT
0.092**
(0.032)
0.076
(0.026)
0.096*
(0.038)
JEWISH
0.123*
(0.060)
0.167
(0.086)
0.099
(0.050)
MUSLIM
-0.175*
(0.079)
-0.362*
(0.147)
-0.132
(0.068)
OBS.
18950
2887
16063
R2
0.3433
0.2325
0.3771
Note :
The reference group is other religions (RESIDRELIG). The regression contained: EDUC, EXPER,
EXPERSQ, MEDUC, FEDUC, HOURS, and dummies for gender, immigrant, locations, married status,
university degree, (and self-employed only in the pooled regression). Note that * indicates 10% level of
significance while ** stand for 0.05% or lower levels of significance.
Table-16. OLS Estimation of HCEF by Religions
NRA
Catholic
Protestant
Jewish
Muslim
Others
Pooled
EDUC
0.034**
(0.009)
0.031**
(0.008)
0.035**
(0.009)
0.023
(0.014)
0.026**
(0.004)
0.028**
(0.004)
.032**
(0.008)
EXPER
0.027**
(0.007)
0.027**
(0.008)
0.024**
(0.009)
0.043**
(0.016)
0.008
(0.011)
.029**
(0.014)
.026**
(0.009)
EXPERSQ
-.0004*
(.0001)
-.0004**
(.0001)
-.0003*
(.0000)
-.0007*
(.0003)
.0000
(.0003)
-.0005
(.0003)
-.0004**
(.0001)
UNIVDEGRE
0.102**
(0.035)
0.171**
(0.047)
0.131
(0.065)
0.193
(0.142)
0.156*
(0.100)
0.141**
(0.050)
0.139**
(0.038)
Note :
The reference group is other religions (RESIDRELIG). The regression contained: EDUC, EXPER,
EXPERSQ, MEDUC, FEDUC, HOURS, and dummies for gender, immigrant, locations, married status,
university degree, and self-employed status. Note that * indicates 10% level of significance while ** stand for
0.05% or lower levels of significance. . N=18950, R2=0.3445