Labour Mobility and Returns to Education by Jiayuan

Transcription

Labour Mobility and Returns to Education
by
Jiayuan Teng
A Thesis
presented to
The University of Guelph
In partial fulfilment of requirements
for the degree of
Doctor of Philosophy
in
Economics
Guelph, Ontario, Canada
c
Jiayuan
Teng, April, 2015
ABSTRACT
LABOUR MOBILITY AND RETURNS TO EDUCATION
Jiayuan Teng
University of Guelph, 2015
Advisors: Dr. Miana Plesca, Dr. Louise Grogan
This dissertation applies statistical methods to understand labour market issues in China
and Canada. The first chapter uses an instrumental variable method to identify the causal
effect of migrant networks on the probability of rural-urban labour migration in China.
It uncovers a substantial heterogeneity in migrant network effects by gender, age groups,
and between people with and without migration experience. Evidence shows that migrant
networks affect migration decisions through increasing job tenure and improving work
environments of migrants. The second and third chapters answer research questions related
to gender wage gap and returns to postgraduate education in Canada. Using a broader set
of occupational characteristics than previous studies, the second chapter adopts a quantile
decomposition method to reveal that women with different educational levels experience
the gender gap for different reasons. DOT-skills used in previous studies are important in
explaining the gender gap for most workers in Canada, but not for high-school dropouts
and for the top 10% of wage earners among the university-educated workers. For the latter,
men working in more competitive jobs and taking more managerial responsibilities are
the explanations underlying Canada’s “glass-ceiling” phenomenon. By applying imputation techniques in a novel way, this chapter quantitatively demonstrates that correcting for
selection into work makes little difference in estimating the gender gap for individuals
with post-secondary education. For individuals without post-secondary education, the use
of observed characteristics is sufficient to capture the selection rule. The third chapter
documents up-to-date evidence on the decline in returns to postgraduate education relative
to four-year university degrees from 1995 to 2010. The return has declined in all major
fields of study except engineering and computer science in which workers with postgraduate
education have experienced a substantial gain over the same period of time. By focusing
on the supply side of the labour market, this paper provides an explanation for the decline
in returns to postgraduate education by exploring changes over time in the occupational
composition of workers with postgraduate education.
Keywords: Rural-urban migration, China, migrant networks, gender gap, quantile
analysis, workplace competitiveness, DOT-skills, duncan index, returns to a Master’s degree,
returns to a Doctorate, STEM fields
iv
ACKNOWLEDGEMENTS
First and foremost I want to thank my advisor Miana Plesca for her support and consistent
encouragement. She provided me with tremendous help in my research and also taught me
how to be a successful economist. The joy and enthusiasm she has for her research was
contagious and motivational for me, especially during tough times in the pursuit of my Ph.D.
I am also very thankful to my advisor Louise Grogan who has provided extremely useful
guidance in my dissertation. I appreciate the contribution of time, ideas, and funding from
both Miana Plesca and Louise Grogan to make my Ph.D. a gratifying experience.
I am truly grateful to Bram Cadsby who recommended me to the Master’s program in
Economics at the University of Guelph. As my best friend and mentor, Bram has been a
great support to my professional and personal life since we met in 2008. My achievement of
the Ph.D. degree in Economics would not be possible without him. I also want to thank Fei
Song for her continued support during my graduate studies at Guelph.
Other professors have contributed immensely to my graduate studies. I am especially
grateful to my dissertation committee member Alex Maynard for his advice on my research
and for making a graduate level econometrics course very enjoyable. I would like to
thank Chris McKenna, David Prescott, and Ana Ferrer for their helpful suggestions on
my dissertation. I also want to thank professors Francis Tapon, Thanasis Stengos, Ross
McKitrick, Steve Kosempel, René Kirkegaard, Yiguo Sun, Mei Li, Michael Hoy, and Asha
Sadanand for their contribution to my understanding of economic theory. I also gratefully
acknowledge access to data provided by the Statistics Canada Data Centre Network.
Lastly, I would like to thank my family and friends for all their love and encouragement.
For my parents who supported me in all my pursuits, for Esmond whose support during the
final stages of this Ph.D. is very appreciated, and for the time spent with my friends Diana
Alessandrini, Fraser Summerfield, and Joniada Milla at Guelph!
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Table of Contents
List of Tables
vii
List of Figures
ix
1
2
Social Networks and Migration Decisions: Evidence from China
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.1 Variable Construction . . . . . . . . . . . . . . . . . . . .
1.3.2 Probit Estimation and Instrumental Variable Method . . .
1.3.3 Unexpected Changes in Rainfall and Network Effect . . .
1.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4.1 Instrumental Variable Results . . . . . . . . . . . . . . .
1.4.2 Network Effects for First-time and Repeat Migrants . . . .
1.4.3 Employment Outcomes and the Size of Migrant Networks
1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Occupational Characteristics and Gender Wage Inequality: A Distributional
Analysis
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Variable Construction . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3 The Gender Gap Across the Wage Distribution . . . . . . . . . . .
2.3 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Quantile Decomposition Method . . . . . . . . . . . . . . . . . . .
2.3.2 Explained and Unexplained Proportion of Gender Gap . . . . . . .
2.3.3 Gender Differences in Work Experience, Union, Sector, Degree
Attainment, and Fields of Study . . . . . . . . . . . . . . . . . . .
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2.3.4 Gender Differences in Occupational Characteristics
2.3.5 Accounting for Selection into Paid Work . . . . .
2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . .
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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The Evolution of Returns to Education in the High-End Labor Market in
Canada
94
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
3.4 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.4.1 Wage Premium on Postgraduate Education Relative to BA . . . . . 102
3.4.2 Returns to Postgraduate Education by Age Group . . . . . . . . . . 106
3.4.3 Returns to Postgraduate Education by Major Field of Study . . . . 112
3.4.4 Returns to Professional Degrees by Major Field of Study,
Relative to MA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
3.4.5 Potential Explanations for the Decline in the PG-BA Wage Gap . . 125
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
References
136
vii
List of Tables
1.1
1.2
1.3
1.4
1.5
1.6
1.7
A1
A2
A3
A4
Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Network Effects on Migration with Alternative IV . . . . . . . . . . .
Migrant Network Effects . . . . . . . . . . . . . . . . . . . . . . . . .
Migrant Network Effects for First-time Migrants . . . . . . . . . . . .
Migrant Network Effects for Repeat Migrants . . . . . . . . . . . . .
The IV Estimate of Network Effects on Labour Market Outcomes for
Migrants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The IV Estimate of Network Effects on Labour Market Outcomes by
Migration Experience . . . . . . . . . . . . . . . . . . . . . . . . . . .
Geographical Information in Each Province . . . . . . . . . . . . . . . .
Migrants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
First-time Migrants . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Repeat Migrants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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. 26
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. 31
. 32
Sample Size by Educational Category and Gender . . . . . . . . . . . .
Skill Classifications and Examples of Occupations . . . . . . . . . . . .
Summary Statistics: Labour Market Attributes of Full-time Employees
Summary Statistics: Occupational Characteristics for Full-time Employees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5 Percentage of Full-time Employees in Each Occupation . . . . . . . . .
2.6 Gender Differences in Managerial Responsibilities . . . . . . . . . . .
2.7 The Proportion of Workers at Different Parts of the Wage Distribution
2.8 Variables Used in Decomposition Analysis . . . . . . . . . . . . . . . . .
2.9 Explained and Unexplained Proportion of Gender Wage Gap . . . . . .
2.10 The Contribution of Subsets of Covariates for the Low-Educated Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1
2.2
2.3
2.4
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2.11 The Contribution of Subsets of Covariates for the High-Educated Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.12 Fraction of Gender Gap Explained by Differences in Occupational
Characteristics and Industry (%) for Low-educated Workers . . . . . .
2.13 Fraction of Gender Gap Explained by Differences in Occupational
Characteristics and Industry (%) for High-educated Workers . . . . .
2.14 O*Net Characteristics in Service, Trade and Manufacturing Occupations
B1 O*Net Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B2 The Contribution of Subsets of Covariates in Model 1 for the LowEducated Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B3 The Contribution of Subsets of Covariates in Model 1 for the HighEducated Workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
The Evoluation of PG-BA Wage Gap from 1995 to 2010 . . . . . . . . . 105
The PG-BA Wage Gap by Age Group for Men . . . . . . . . . . . . . . 107
The PG-BA Wage Gap by Age Group for Women . . . . . . . . . . . . 108
The Difference in Average Job Tenure between BA and PG by Age Group113
The PG-BA Wage Gap by Major Field of Study for Men . . . . . . . . 115
The PG-BA Wage Gap by Major Field of Study for Women . . . . . . 116
Returns to Professional Degrees by Major Field of Study for Men, Relative to MA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
3.9 Returns to Professional Degrees by Major Field of Study for Women,
Relative to MA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
3.10 The PG-BA Wage Gap by Occupation for Men . . . . . . . . . . . . . . 129
3.11 The PG-BA Wage Gap by Occupation for Women . . . . . . . . . . . . 130
3.12 The PG-BA Wage Gap by Occupation for Young Workers . . . . . . . 131
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
ix
List of Figures
1.1
Response of Self-Migration and Networks to Alternative IV . . . . . . . 16
2.1
2.2
B1
B2
The Gender Gap at Various Points of the Wage Distribution . . . . .
Gender Gap Correcting for Selection . . . . . . . . . . . . . . . . . .
Gender Gap across the Wage Distribution by Employment Status . .
Gender Gap across the Wage Distribution by Employment Status with
95% Confidence Interval . . . . . . . . . . . . . . . . . . . . . . . . .
3.1
3.2
3.3
3.4
3.5
3.6
3.7
. 53
. 80
. 90
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The Evolution of PG-BA Wage Gap from 1995 to 2010 . . . . . . . . . . 104
Average Weekly Wage by Birth Year in 1995 and 2010 for Men . . . . . 109
Average Weekly Wage by Birth Year in 1995 and 2010 for Women . . . 110
Proportion of Men with the Same Education in Different Occupations . 123
Relative Wage Gap in 2010 for Men . . . . . . . . . . . . . . . . . . . . 123
Proportion of Women with the Same Education in Different Occupations124
Relative Wage Gap in 2010 for Women . . . . . . . . . . . . . . . . . . 124
1
Chapter 1
Social Networks and Migration
Decisions: Evidence from China
1.1
Introduction
In the China’s labour force a tremendous number of workers are from rural areas. They
are called “migrant workers” and they have made a significant contribution to the fast growth
of the Chinese economy since 1978. Migrant workers in China experience the difficulty
of establishing a life in destination cities because of China’s rigid rural-urban migration
system.1 They often rely on their migrant contacts for support in cities. While research on
1
China’s interregional migration system is based on a person’s hukou. The hukou system works as if it
were citizenship. It is determined by the person’s birth region, rural or urban, and birth place and affects
various aspects of people’s life. Examples are people’s own and children’s education, health care, and pension
plans.(Fan, 2008) When rural residents work in cities, they apply for a temporary residential permit (TRP) and
can only stay in cities with a valid TRP. Most migrants cannot obtain a permanent residential permit in their
destination cities.(Chen et al., 2010)
2
international immigration has shown a causal link between migrant contacts and individual
immigration decisions, a similar link between migrant contacts and rural-urban migration
has not been well established. Using Chinese household survey data in 2002, this chapter
contributes to the literature by examining how migrant networks affect rural residents’
migration decisions and their job market outcomes in cities.
In this study migrant networks are defined as interpersonal ties linking migrants to family
members, friends, and people in their villages of origin. Theoretically, these networks could
affect a person’s migration decision either positively or negatively. They could positively
affect the probability of migration by reducing the costs of migration because migrant
contacts offer information on the urban labour market and provide support to prospective
migrants in the workplace. They could negatively affect the probability if migrant contacts
share the challenges of living in cities and thereby influence village residents not to migrate.
I attempt to identify the causal effect of migrant networks on the migration decision of
rural residents using the Chinese Household Income Project Survey (CHIPS) 2002. I use the
ratio of migrants in the village of origin to the number of village residents to measure the
size of a person’s migrant network. I examine whether the network effects differ between
people with and without previous migration experience, and whether migrant networks
affect people’s migration decisions through the channel of improving people’s labour market
outcomes in cities.2
A difficulty in identifying the causal effect is that some village characteristics, such as
2
The data set for this study does allow me to observe whether rural residents reported living outside their
township for more than one year. I use this information to distinguish rural residents with migration experience
from rural residents without migration experience.
3
the size of arable land in the village, affect the migration decision of village residents. If
those characteristics are not observed by researchers, which is often the case, the measure of
migrant networks is endogenous. Within the literature examining rural-urban migration in
China, a few studies have attempted to identify migrant network effects, but the results are
mixed depending on the identification strategies used.
In this study the network effect is estimated using an instrumental variable method. I
construct the instrumental variable using unexpected changes in rainfall in 1999. I find
daily rainfall between April and October from 1996 to 2002 in a township and compute the
mean of daily rainfall over 7 years in the township. I calculate the average amount of daily
rainfall in the township in 1999 and subtract the average daily rainfall from the mean of
daily rainfall over 7 years. The instrumental variable is the absolute value of the difference.
I argue that this is a better identification strategy than those used in existing studies.3
Statistical tests show that my instrument is positively and strongly correlated with the
size of networks at the 1% level. The F-statistic value on the rainfall instrument is 30,
which is 3 times greater than the critical value under which the instrument is weak. The
validity of my instrumental variable estimates relies on the assumption that the instrument
does not have a direct impact on migration. The use of sudden changes in rainfall in
an earlier year reduces the possibility that rainfall affects people’s migration decisions.
Throughout the analysis I directly include unexpected changes in rainfall at the survey
time as a covariate. This accounts for the plausible correlation between the instrument
3
The idea of using average daily rainfall as the instrument comes from Munshi (2003). In the literature
review, I provide a detailed discussion on the identification strategy used in the previous studies and why my
identification strategy is better than the existing ones.
4
and the residual term of the migration determinants function. In comparison to household
characteristics used as the instruments in earlier studies, which could be correlated with
an individual’s migration decision, unexpected changes in rainfall in 1999 is a better
instrumental variable in identifying the network effect.
My results show that the magnitude of network effects on migration varies by age group,
gender, and between people with and without previous migration experience. Overall, an
increase of 10 percent in the proportion of migrants in a person’s village increases the
probability of migration by 8 percent. The network effect is smaller for people without
migration experience (first-time migrants) compared to people with migration experience
(repeat migrants). I find that migrant networks play a significant role in determining
migration decisions only for repeat migrants and first-time migrants younger than 30. For
everyone else, the impact is insignificant. To the best of my knowledge, my paper is the first
study providing evidence of heterogeneity in network effects between first-time and repeat
migrants.
Turning to the labour market outcomes of migrants, my results show that migrant
networks do not affect migrants’ annual earnings in cities, but they do help young firsttime migrants and repeat migrants in finding jobs that have longer tenures and better work
environments. There is no significant impact of networks on job quality for first-time
migrants older than 30. These findings support the conclusion that migrant contacts in the
villages affect migration decisions for some migrants, by improving their job quality after
they migrate to cities.
5
The chapter is organized as follows. Section 1.2 summarizes the existing studies on
migration network effects. Section 1.3 describes the data and identification strategy used in
this chapter. Section 1.4 presents the results. Section 1.5 concludes.
1.2
Literature Review
Microeconomic theory explains the choice of migration as an outcome of an individual’s
expectations regarding positive net returns from movement.(Massey et al., 1993) A number
of studies examining international immigration show that migrant networks, which consist of
people’s friends, family and community members in their countries of origin and destination,
affect migrants’ immigration decisions by reducing the cost of immigration through jobsearch assistance (Munshi, 2003), helping to find welfare and health care programs (Bertrand
et al., 2000; Devillanova, 2008), helping to find a destination location(Bartel, 1989), and
providing relational support (Schwartz, 1973; Berry, 1997).
In the context of rural-urban migration in China, previous studies have found a positive
relationship between migrant networks and the probability of rural residents migrating to
cities. Most of these studies attribute the impact to job-search assistance (Zhao and Li, 2003;
Du et al., 2005; Bao et al., 2007; Ioannides and Topa, 2010; Knight et al., 2011).4 Zhao
(2003) finds that the importance of early migrants comes from the guidance and assistance
that they offer to new migrants. In line with Zhao (2003), Knight and Song (2005) use a
4
This is also found in other countries such as India (Banerjee, 1983; Iversen et al., 2009) and Uganda
(Muto, 2012).
6
survey conducted in 8 provinces in 1995 and report that lack of contacts and information
about the labour market in cities are primary factors preventing rural labor from migrating.
Even though the positive relationship is consistent with social network theory, the causal
link of this relationship is subject to considerable debate. Since people living in the same
village will all experience any shocks that occur to the local labour market, shocks that
change the probability of neighbors migrating will similarly change the probability of survey
participants migrating. Empirical analysis, which fails to take account of this fact, would
generate a biased estimate of migrant network effects (Manski, 1995).
Two methodologies are used to identify the migrant network effect on China’s ruralurban migration. The first methodology is to estimate the proportion of migrants in the
current year with the proportion of migrants in people’s villages of origin in the previous
year (Zhao, 2003). However, Bertrand et al. (2000) empirically show that the ratio of
migrant workers in the prior year could still be correlated with omitted personal and group
characteristics that affect people’s migration decision in the current year.
The second methodology is to use an instrumental variable method (Lu et al., 2008;
Chen et al., 2010). The results in these studies are mixed. Let’s denote a village resident as
M, Lu et al. (2008) use the political identity of M’s father in the Mao era as the instrumental
variable for the size of migrant networks for M in the CHIPS 2002. Using the two-stage
least square (2SLS) estimator, they find that migrant networks do not affect the probability
of migration. Using the 2006 China Agricultural Census, Chen et al. (2010) use three
instrumental variables to estimate the migrant network effect for M: the percentage of adult
7
residents living in M’s village whose first birth has two or more children, the percentage of
female adults residing in households with a girl firstborn, and the percentage of male adults
residing in households with a girl firstborn. Their 2SLS estimates show that a 10% increase
in the migration rate raises the probability of migration by 7.3%. As explained in Chen et al.
(2010), the migrant network effect is equivalent to the impact of 7-8 years of education on
migration. This is not trivial, given that the average years of schooling in their sample is 7
years.
A better identification strategy is needed in defining the migrant network effect. Chen
et al. (2010) argues that M’s father’s political identity, which is used in Lu et al. (2008)
as the instrumental variable, affects M’s migration decision by affecting M’s social ties.
However, it is not clear why it affects M’s neighbours’ migration decisions. The latter is
the measure of the size of M’s migrant networks. Therefore, Chen et al. believe that the
political identity of M’s father should be used as a covariate in the main regression rather
than the instrumental variable. However, there are also concerns with the instruments in
Chen et al. (2010). The first concern, as discussed by the authors themselves, is that missing
information on adults’ own fertility may affect the accuracy of the analysis. The second
concern is related to the validity of the instruments. Other studies have found that in China
and other developing countries, the fertility rate of rural-urban migrants is significantly lower
than that of non-migrants.5 Thus, the proportion of first-born girls is directly correlated with
migration.
5
See Goldstein et al. (1997) for evidence in China, Lee and Farber (1984) for Korea, Chattopadhyaya et al.
(2006) for Ghana, and Lee and Pol (1993) for Korea and Mexico.
8
I argue that my instrument is better suited to identify the network effect on China’s
rural-urban migration. The idea of using rainfall information, so-called “distant-past rainfall,”
is borrowed from Munshi (2003), who uses distant-past rainfall as the instrument for the
size of immigrant networks, specially networks made up of Mexican immigrants who are
from a new immigrant’s community in Mexico and immigrated to the US.6 He finds that
having more established immigrant contacts, immigrants who are located continuously
at the destination for three or more years, in the new immigrant’s networks increases the
probability of the new immigrant finding a nonagricultural job. Munshi (2003) shows
that the amount of rainfall in a year only affects the local labour market in the same year.
Distant-past rainfall does not affect the migration decision in the year of interest. A similar
strategy is used to examine the impact of migration on household consumption growth
(Giles and Yoo, 2007) and educational attainment of youth (Brauw and Giles, 2008) in rural
China.
Empirical evidence suggests that migrant networks are implemented as a dynamic
process. Information on migration flows from experienced migrants to new migrants at a
point of time. The latter become experienced migrants in a few years and then help new
migrants find jobs at their places of destination (Banerjee, 1983; Shah and Menon, 1999;
Hooghe et al., 2008). This suggests that it is the new migrants, not the experienced migrants,
who benefit from migrant networks. This study will provide evidence on this issue by
6
Rainfall conditions are used in a number of studies to examine internal and international migration (Henry
et al., 2004; Deshingkar and Grimm, 2005; Choi and Yang, 2007; Giné et al., 2008). For example, Henry
et al. (2004) analyze how the rainfall condition affects internal migration in Burkina Faso villages. Their study
suggests that people from the drier regions are more likely than those from wetter regions to move to other
areas.
9
examining the migrant network effect separately for people who never migrated before 2002
and people who had migrated prior to 2002.
1.3
Data
The main source of data in this study comes from the Chinese Household Income Project
Survey (CHIPS) 2002. Questionnaires are designed separately for urban and rural residents
to account for the different geographic and demographic characteristics between rural and
urban regions. I use the rural household survey conducted during the period of Chinese New
Year in 2003 when most migrants go back home to meet their families. The survey questions
are related to various aspects of people’s life in 2002. In the rest of the paper the year 2002
is referred to as the survey time.
I use rural residents whose age is between 16 and 65 in 2002. They are from 22
provinces, which are Beijing representing the three metropolitan cities; Jiangsu, Zhejiang,
Guangdong, Shandong, Liaoning, and Hebei representing the eastern region; Shanxi, Jilin,
Anhui, Jiangxi, Hubei, Henan, and Hunan representing the central region; and Yunnan,
Gansu, Guizhou, Sichuan, Chongqing, Shaanxi, Guangxi, and Xinjiang representing the
western region.7 The use of broad geographical regions ensures that the results reported in
this study are representative of China.
7
Sample selection frames can be found in Knight and Gunatilaka (2010) and Knight et al. (2011).
10
1.3.1
Variable Construction
Using the definition from the National Bureau of Statistics of China, I define a migrant
worker as a rural resident who spent at least 30 days working/looking for jobs in cities that
are outside of their townships of origin. A person’s migrant network is measured using the
proportion of migrant workers over the number of residents in his/her village of origin.
The main question used to identify migrant workers is “how long did you stay out of
the household in 2002?” A potential measurement error in this exercise is that it classifies
people as migrant workers if they live in the township but do not live with their families in
2002. In order to mitigate this measurement error, I use the questions asking how much
time a person spent on agricultural and non-agricultural activities during the harvest time.8
People who spent 330 days or more on farm and/or home production in the countryside
during the survey time are not considered to be migrants.
Similar to previous studies, I cannot directly control for the labour market experience
of rural migrants in cities because the CHIPS does not report years of experience for rural
migrants. However, the survey does ask “have you lived outside of the township for at least
one year?” and I use this question to identify rural residents with and without migration
experience prior to 2002. 9 Throughout the paper, “repeat migrants” refers to rural residents
who reported living outside of the township for at least one year and were also migrants in
8
Survey participants were asked to answer during the harvest season, how many days you spent in planting,
raising livestock (including in the yard), and on nonproductive activities (schooling, housework, taking care of
sick family members and so forth.
9
If a person lived outside of township for at least one year, this person was a migrant before the year of
2002.
11
2002, while “first-time migrants” refers to rural residents who had not lived outside of the
township for one year but were migrants in 2002.
A disadvantage of the CHIPS is that I cannot observe which city a person moved to in
2002 or which city the person stayed in before 2002, thus I cannot observe the person’s
network in the destination cities. Network effects estimated here do not account for the
impact of migrant contacts in cities on the migration decision.10
For each township studied, I collect the size of arable land and the total population in
1989 from the provincial yearbooks that were published in 1990. The measure of migrant
networks is the proportion of migrants over the number of residents in a village. The use of
arable land per person a decade before the survey time is preferred to the arable land per
person in 2002 because the latter would cause multicollinearity in the analysis.
This paper uses two geographical units: villages and townships. There are a number of
villages in one township. A person’s migrant networks are measured as the proportion of
migrants from the person’s village of origin. Unfortunately, the yearbooks in 1990 do not
report information on land and population for people’s villages of origin. Thus, I construct
the arable land per person by dividing the size of arable land by the population in the
township where a person was born.
Daily rainfall information from 1996 to 2002 in townships is from the China Meteorological Administration Data Center.11 Data from 1996 is used because most townships in my
dataset do not have rainfall information before 1996. I first select the weather station that
10
11
For the same reason, I do not account for the labour market conditions in destination cities.
Details of climate data are available at http://cdc.cma.gov.cn/.
12
is nearest to a township by comparing the distance of all weather stations to the township
with their latitude/longitudinal points. Then I collect the amount of daily rainfall that was
recorded by the nearest weather station.
1.3.2
Probit Estimation and Instrumental Variable Method
This study uses a Probit model that is presented in equation (1),
Yi = α0 + α1 M(−i) + α2 Xi + ε1i
(1.1)
where Yi is a binary variable that describes a person’s migration status in 2002. It is 1
if person i migrates to cities, 0 otherwise. M(−i) is the migrant networks in the person’s
village of origin, which is the proportion of migrants in the village, excluding person i. Xi
is a set of explanatory variables that represent individual, household, village,and township
characteristics.12 Standard errors are clustered by villages.13
12
Specifically, individual characteristics are whether a person is female, age, whether a person is married,
and whether a person has high school or university education. A household characteristic is the years of
schooling for household heads. A village characteristic is the distance to the nearest bus/train/dock station.
The township characteristics are arable land per person in 1989 and the absolute value of deviation of average
daily rainfall in 2002 from the mean of daily rainfall over the years of 1996 - 2002.
13
Equation (1) is presented in a linear function format in order to help readers understand the functional
form. A formal Probit model is displayed as follows. Let y∗ be unobserved, a latent variable, and determined
by
yi∗ = γ0 + γ1 M(−i) + γ2 Xi + e,
yi = 1
[yi∗ > 0],
where e has the standard normal distribution. If income in countryside is normalized to be 0, an example of y∗
is expected income from working in a city. When expected income from migration is greater than the income
in countryside, a person decides to migrate. The response probability of y is
P (y = 1|X, M ) = P (yi∗ > 0|X, M ) = P (e > −(γ0 + γ1 M(−i) + γ2 Xi )|X, M )
= 1 − G(−(γ0 + γ1 M(−i) + γ2 Xi )) = G(γ0 + γ1 M(−i) + γ2 Xi ),
13
Table 1.1: Summary Statistics
Migrants
(2)
(3)
First-time Repeat
2,542
1,682
Nonmigrants
(4)
Obs(#)
(1)
All
4,224
Charateristics
Average size of migrant networks
Age
0.23
29.33
0.22
30.29
0.25
28.00
0.13
40.44
Female
0.31
0.30
0.33
0.48
Married
0.53
0.58
0.47
0.85
Completed high school or college
0.80
0.81
0.76
0.61
Schooling of household head
5.18
5.46
4.96
6.41
Distance from village to a nearest station(km)
5.29
5.15
5.20
5.35
Average arable land per person in 1989 (mu)
2.15
2.27
1.91
3.36
Distance of 2002’s rainfall from the mean over 7 years (mm)
4.24
4.27
4.19
2.3
12,571
I estimate a binary instrumental variable model with maximum likelihood estimator. The
proportion of migrants is estimated with the following equation, where Zi is the instrumental
variable.
M(−i) = β0 + +β1 Zi + β2 Xi + ε2i
(1.2)
In Table 1.1, I present the summary statistics of explanatory variables that are included
in the estimation.14 In total, 26% of rural residents are migrant workers. 60% of them are
first-time migrants. Overall, migrants are 10 years younger than non-migrants. They are
more likely to be men, less likely to be married, and are more educated than non-migrant
workers.15 Migrants live in townships that had less arable land per person in 1989 and a
where G is a function taking on values between zero and one: 0 < G(X, M ) < 1.
14
Appendix Table A1 presents the number of villages, townships, and migrants in each of the 22 provinces.
15
These findings are consistent with Zhao (2003).
14
Table 1.2: Network Effects on Migration with Alternative IV
(1)
(2)
(3)
(4)
(5)
0.56∗∗∗
(0.27)
0.84∗∗∗
(0.18)
1.64∗∗∗
(0.31)
1.13∗∗∗
(0.35)
Panel A: IV estimate of network effects on migration
0.82∗∗∗
(0.14)
Panel B: The effect of alternative instrument on the size of network
Migrant network
Excluded instrument
0.003∗∗∗
(0.0005)
0.002∗∗∗
(0.0005)
0.002∗∗∗
(0.0005)
0.02∗∗
(0.001)
0.002∗∗
(0.001)
F-statistics on the excluded instrument
28.58
14.30
15.81
3.97
5.12
Controls
demographical variables
abs. deviation from mean over time in 2002
abs. deviation from provincial mean in 2002
X
X
X
X
X
X
X
X
X
Excluded Instrument
abs. deviation from mean over time in 1996-1998
abs. deviation from provincial mean in 1996-1998
X
X
X
X
X
X
Notes: The table reports the IV estimate of network effects and the effect of excluded instrument on the size of network when a different
excluded instrument is used. The sample size is 14,211. Results are estimated with a Probit instrumental variable model. The Probit
estimate of network effect corresponding to the IV estimates in columns (1) - (5) is 0.82. Standard errors are clustered at village level and
reported in parentheses. Demographical variables are listed in Table 1.1.
larger amount of unexpected rainfall than non-migrants. This satisfies the hypothesis that
people are more likely to migrate to cities, when they live in townships where land and
rainfall conditions are less favorable to farming.
1.3.3
Unexpected Changes in Rainfall and Network Effect
I use unexpected changes in rainfall to identify exogenous variations in the ratio of
migrants across townships. To estimate an expected change in the amount of rainfall, I
collect data on daily rainfall between April and October and between 1996 and 2002. Using
this data, I calculate the mean of daily rainfall over the 7-year period in each township. I
also calculate the average daily rainfall in each year for each township. Then I calculate
the absolute difference between the 7-year mean and the yearly averages. The absolute
15
difference tells us how different average daily rainfall is in each year relative to the level of
rainfall that agricultural products have been adapted to. A critically large absolute value of
deviation would result in a lower quantity of agricultural products, leading to a decrease in a
rural person’s income and an increase in the person’s incentive to migrate.
I use the absolute value of the deviation of average daily rainfall in each year as the
excluded instrument and report the IV estimate of network effects on migration and the
effect of instrument on the size of migrant network in Panel A and B in Table 1.2. I find
that F-statistics on the absolute value of deviation between 1996 and 1998 (column (4))
are below 10, suggesting that this variable is weakly correlated with the size of migrant
networks.16 Among the three variables that pass the weak instrument test, the absolute value
of deviation in 1999, 2000, and 2001, the absolute value of deviation in 1999 has the largest
F-statistics and has a positive and significant impact on the size of network in the first stage.
Therefore, I choose the absolute value of the deviation in 1999 as the excluded instrument
for the study.17
Another reason why the year of 1999 is preferred over 2000 and 2001 is related to the
validity of the identification strategy that relies on the assumption that the instrument is
16
Stock et al. (2002) suggests that when F-statistics on the excluded instrument are smaller than 10, the
excluded instrument is weakly correlated with the endogenous variable and the IV estimate is biased towards
the estimate without accounting for endogeneity. The greater the F-statistics value, the better it is in ruling out
the weak instrument problem (Angrist and Pischke, 2008).
17
An earlier version of this chapter used average daily rainfall between 1996 and 1998 as the instrument.
The idea was to use variation in average daily rainfall across townships to predict the exogenous variation
in the proportion of migrants in 2002. To test whether this idea provides a good instrument, I used daily
rainfall between 1996 and 1998. I computed the mean of daily rainfall of all the townships located in the same
province and subtracted the average daily rainfall in a township between 1996 and 1998 from the provincial
mean. Row (5) shows that when I use the absolute value of the deviation between 1996 and 1998 as the
instrument, the instrument cannot pass the weak instrument test.
16
Figure 1.1: Response of Self-Migration and Networks to Alternative IV
not correlated with the residual term in the migration determinants equation (Equation (1)).
Suppose a person lives in a village that flooded in 2001 and decided to migrate in 2002. The
absolute value of deviation of average daily rainfall in 2001 is not an appropriate instrument,
since it directly causes the person to migrate. Since the year of 1999 is furthest away from
the survey year, the absolute value of deviation in 1999 is used to minimize the possibility
that the instrument has a direct impact on migration decisions in 2002.
The instrumental variable is constructed with daily rainfall between April and October
because many agricultural products (e.g. rice, wheat, and corn) are planted before April
and harvested by October. It is crucial to have rainfall conditions that are favorable to
17
farming between April and October. Since farming is a source of income for rural residents,
unexpected changes in average daily rainfall that occurred between April and October is an
ideal choice for the instrumental variable.
In Figure 1.1, I plot two graphs using a nonparametric estimator.18 The graph on the left
is the correlation of the self-migration status variable and the size of migrant networks with
the deviation of average daily rainfall from the time mean in 1999. The y-axis is deviation
of average daily rainfall in 1999, with the mean of daily rainfall over the 7-year period in
townships normalized to 0. The graph shows that when people live in townships that had
very little or a lot of rain in 1999 relative to the mean, they have a greater size of migrant
networks and are more likely to migrate.
The graph on the right is the correlation of the self-migration status variable and the
size of migrant networks with the instrumental variable. Points on the solid curve are
comparable to the reduced-form estimates and points on the dashed curve are comparable
to the first-stage estimates. It is clear that the probability of self-migration and the size
of migrant networks increase as the instrumental variable increases. It is the exogenous
changes in average daily rainfall three years prior to the survey that increased the number of
migrants at that time. This enables me to estimate the causal effect of network on migration.
18
For the graph on the left, I use the Epanechnikov kernel function and run a local polynomial kernel
regression of migration status variable and the size of migrant networks on the deviation of average daily
rainfall in 1999. For the graph on the right, I run the local polynomial kernel regressions on the absolute value
of the deviation.
18
1.4
1.4.1
Empirical Results
Instrumental Variable Results
In Table 1.3, I present the results of the migration network effects analysis of the Probit
model, the instrumental variable method (IV), and the reduced-form equation. In order to
explore the heterogeneity in migrant network effects for people in different age groups, I
report the estimates separately for rural residents in four age groups: 16-30, 31-40, 41-50,
and 51-65. The estimates are reported in columns (2)-(5), respectively.
The IV estimate of migrant networks is similar to the Probit estimate for most age groups.
Table 1.3 shows that an increase of 10 percent in the proportion of migrant workers increases
the probability of migration by 8.2 percent for all residents, 8.7 percent for men, and 7
percent for women. The corresponding figures that are estimated with the Probit model are
8.2, 10, and 6.1 percent.
In contrast to the Probit estimate, the IV estimate of network effects for workers in the
age group 51-65 and women in the age group 31-40 is not significantly different from zero.
This suggests that the significant impact of migrant networks for people in these age groups,
as estimated with the Probit model, only reflects the positive correlation between people’s
own migration decisions and the migration decision of their village fellows.
As expected, reduced-form results show that when people are making migration decisions, most of them respond strongly to the unexpected changes in rainfall three years
before the survey time. Specifically, the absolute value of deviation of average daily rainfall
19
Table 1.3: Migrant Network Effects
(1)
16-65
Panel A: All Residents
Proportion of Migrant Workers
Migrant Networks
(2)
16-30
0.26
0.48
Probit Model
0.82∗∗∗
(0.04)
1.43∗∗∗
(0.05)
(3)
31-40
(4)
41-50
(5)
51-65
0.23
0.12
0.06
0.87∗∗∗
(0.05)
0.49∗∗∗
(0.06)
0.16∗∗∗
(0.04)
0.002∗∗∗
(0.0004)
0.003∗∗∗
(0.0005)
0.81∗∗∗
(0.25)
0.44∗∗
(0.22)
0.11
(0.14)
0.001∗
(0.0006)
0.0003
(0.0004)
0.34
0.19
0.08
1.23∗∗∗
(0.08)
0.76∗∗∗
(0.09)
0.24∗∗∗
(0.06)
0.002∗∗∗
(0.0004)
0.003∗∗∗
(0.0005)
1.02∗∗∗
(0.42)
0.81∗∗∗
(0.31)
0.04
(0.22)
IV Maximum Likelihood Estimator: 1st stage
Absolute Value of Deviation in 1999
0.003∗∗∗
(0.0004)
0.002∗∗∗
(0.0005)
0.002∗∗∗
(0.0005)
IV Maximum Likelihood Estimator: 2nd stage
Migrant Networks
0.82∗∗∗
(0.13)
1.57∗∗∗
(0.17)
0.002∗∗∗
(0.001)
0.004∗∗∗
(0.001)
0.002∗∗
(0.0008)
# of obs
Panel B: Men
14,211
4,712
3,688
0.31
0.53
Probit Model
Migrant Networks
1.00∗∗∗
(0.05)
1.51∗∗∗
(0.06)
Reduced Form
Absolute Value Deviation in 1999
0.003∗∗∗
(0.0004)
0.002∗∗∗
(0.0005)
0.002∗∗∗
(0.0005)
Migrant Networks
0.87∗∗∗
(0.19)
1.51∗∗∗
(0.24)
0.002∗∗∗
(0.0008)
0.004∗∗∗
(0.001)
0.002∗
(0.001)
0.002∗∗
(0.0009)
0.0002
(0.0006)
# of obs
Panel C: Women
8,044
2,559
2,028
1,815
1,642
0.18
0.43
Probit Model
Migrant Networks
0.61∗∗∗
(0.04)
1.36∗∗∗
(0.07)
Reduced Form
0.09
0.04
0.01
0.45∗∗∗
(0.06)
0.18∗∗∗
(0.05)
0.05
(0.04)
0.002∗∗∗
(0.0006)
0.003∗∗∗
(0.0007)
0.55∗∗∗
(0.30)
0.08
(0.20)
0.34
(0.32)
0.001∗∗
(0.0006)
0.0002
(0.0005)
0.0006
(0.0004)
0.003∗∗∗
(0.0004)
0.003∗∗∗
(0.0005)
0.003∗∗∗
(0.0006)
Migrant Networks
0.70∗∗∗
(0.14)
0.002∗∗∗
(0.0005)
1.63∗∗∗
(0.20)
Reduced Form
0.004∗∗∗
(0.001)
# of obs
6,177
2,153
1,660
1,398
628
Notes: This table reports the marginal effects that are estimated with the Probit model and an instrumental variable method. Dependent
variable is 1 if one is a migrant, 0 otherwise. IV estimates in bold are significantly different the Probit estimates at the 10% level.
Standard errors are clustered by villages. The Probit model and the instrumental variable method control for the covariates that are
presented in Table 1.1.
20
in 1999 has a positive impact on the probability of migration and the impact is greatest for
people in the 16-30 age group.
1.4.2
Network Effects for First-time and Repeat Migrants
The previous section shows that migrant networks affect the probability of migration
differently for people in different age groups. It is also useful to investigate whether the
networks have different impacts for people with different levels of migration experience.
For people who never migrated before 2002, the migrant contacts in their villages could
be the only source of information about cities. However, if people who migrated before
2002 have contacts in the cities in which they stayed, their migration decision would be less
dependent on the migrant contacts who live in their villages.
In this section, I explore the network effects for two types of migrants. The first type
of migrants is first-time migrants. I investigate their migration decision relative to people
without migration experience. The second type of migrants is people who were migrants
before 2002. I investigate the migration decision for past migrants who also migrate in
2002, relative to past migrants who do not migrate in 2002. Overall, I find that people with
migration experience in all age groups and people with no migration experience prior 2002
who are younger than 30 are more likely to migrate when they have more migrant contacts.
The network effect is slightly greater for young first-time migrants than repeat migrants.
I repeat the same analysis shown in Table 1.3 for people without migration experience
and report the results in Table 1.4. As only 4% of people in the 50-65 age group are first-time
21
Table 1.4: Migrant Network Effects for First-time Migrants
(1)
16-65
Panel A: All Residents
Proportion of First-time Migrants
0.19
(2)
16-30
(3)
31-40
(4)
41-65
0.37
0.18
0.07
1.30∗∗∗
(0.07)
0.73∗∗∗
(0.06)
0.30∗∗∗
(0.04)
0.002∗∗∗
(0.0005)
0.003∗∗∗
(0.0005)
1.48∗∗∗
(0.23)
0.62∗∗
(0.31)
0.09
(0.15)
Probit Model
Migrant Networks
0.69∗∗∗
(0.04)
0.002∗∗∗
(0.0005)
0.002∗∗∗
(0.0005)
Migrant Networks
0.62∗∗∗
(0.16)
0.001∗∗
(0.0006)
0.003∗∗∗
(0.001)
0.001
(0.0008)
0.0002
(0.0004)
# of obs
Panel B: Men
11,704
3,454
3,145
5,105
Reduced Form
0.25
0.42
0.28
0.12
1.42∗∗∗
(0.08)
1.09∗∗∗
(0.10)
0.44∗∗∗
(0.06)
0.002∗∗∗
(0.0006)
0.003∗∗∗
(0.0005)
1.30∗∗∗
(0.42)
0.76
(0.58)
0.12
(0.26)
Probit Model
Migrant Networks
0.89∗∗∗
(0.06)
0.002∗∗∗
(0.0005)
0.002∗∗∗
(0.0005)
Migrant Networks
0.59∗∗
(0.27)
0.001∗
(0.0008)
0.003∗
(0.001)
0.001
(0.001)
0.003
(0.0007)
# of obs
Panel C: Women
6,386
1,835
1,653
2,898
Reduced Form
0.12
0.31
0.06
0.02
1.18∗∗∗
(0.08)
0.35∗∗∗
(0.05)
0.11∗∗∗
(0.03)
0.002∗∗∗
(0.0006)
0.003∗∗∗
(0.0005)
1.57∗∗∗
(0.22)
0.43
(0.31)
0.06
(0.10)
0.004∗∗∗
(0.001)
0.0009
(0.0005)
0.0001
(0.0003)
Probit Model
Migrant Networks
0.48∗∗∗
(0.04)
0.002∗∗∗
(0.0005)
0.002∗∗∗
(0.0005)
Migrant Networks
0.57∗∗∗
(0.15)
0.001∗∗∗
(0.0005)
Reduced Form
# of obs
5,318
1,619
1,492
2,130
variable is 1 if one is a first-time migrant in 2002, 0 if one has never migrated. The proportion of first-time migrants is the ratio of firsttime migrants over the number of people who have never migrated. IV estimates in bold are significantly different the Probit estimates
at the 10% level. Standard errors are clustered by villages. The Probit model and the instrumental variable method control for the
covariates that are presented in Table 1.1.
22
Table 1.5: Migrant Network Effects for Repeat Migrants
Proportion of Repeat Migrants
Migrant Networks
(1)
16-65
All Residents
0.57
0.86∗∗∗
(0.08)
(2)
(3)
16-30
31-40
All Residents
All Residents
0.80
0.52
Probit Model
(4)
41-65
All Residents
0.20
(5)
16-65
Men
0.58
(6)
16-65
Women
0.55
0.99∗∗∗
(0.09)
0.53∗∗∗
(0.13)
0.88∗∗∗
(0.09)
0.82∗∗∗
(0.12)
0.004∗∗∗
(0.0006)
0.004∗∗∗
(0.0006)
0.003∗∗∗
(0.0006)
1.60∗∗∗
(0.41)
1.17∗∗∗
(0.27)
1.38∗∗∗
(0.20)
1.21∗∗∗
(0.34)
0.005∗∗∗
(0.002)
0.004∗∗∗
(0.001)
0.005∗∗∗
(0.001)
0.004∗∗∗
(0.001)
1.08∗∗∗
(0.20)
Absolute Value of Deviation in 19990.003∗∗∗
(0.0006)
0.003∗∗∗
(0.0007)
0.003∗∗∗
(0.0007)
IV Maximum Likelihood Estimator:2nd stage
Migrant Networks
1.32∗∗∗
(0.20)
1.38∗∗∗
(0.30)
Reduced Form
Absolute Value of Deviation in 19990.005∗∗∗
(0.001)
0.004∗∗∗
(0.001)
# of obs
2,490
1,241
540
709
1,643
847
variable is 1 if one is a repeat migrant in 2002, 0 if one has migration experience but is not migrant in 2002. The proportion of repeat
migrants is the ratio of migrants who are not first-time migrants over the number of people who had migration experience prior to the
survey time. IV estimates in bold are significantly different the Probit estimates at the 10% level. Standard errors are clustered by
villages. The Probit model and the instrumental variable method control for the covariates that are presented in Table 1.1.
migrants, I combine the 41-50 and 51-65 age groups and report the migrant network effect
for the age group of 16-30, 31-40, and 41-65.
The most important finding in this table is that, compared to the Probit estimate, the IV
estimate of migrant network effects is significant only for first-time migrants younger than
30. An increase of 10 percent in the size of migration networks increases the probability
by 13 percent for men in the 16-30 age group and 15.7 percent for women in the same age
group. The IV estimate for young women is statistically larger than the Probit estimate,
suggesting that the Probit estimate of network effects for young women without migration
experience is downward-biased. For people older than 30, the probability of migration is
not significantly affected by the size of their migrant networks.
On the contrary, the IV estimate of network effects for people with migration experience
23
is positive and significant for both genders in all age groups.19 Table 1.5 shows that an
increase of 10 percent in the ratio of migrants in a person’s village increases the person’s
probability of returning to the urban labour market by 13.2 percent. The IV estimate for
men is statistically greater than the corresponding Probit estimate, while the IV estimate for
women is statistically the same as the Probit estimate.
To explain the difference between Probit and IV estimates, we need to consider what
these two methods estimate. The instrumental variable method estimates the impact of
migrant networks for people at the margin: rural residents who would not have migrated if
they did not know the migrant contacts who migrated to cities because of the unexpected
rainfall changes in 1999. The Probit estimates reflect the positive correlation between an
individual and the individual’s village cohort who migrate in 2002. Migrant contacts who
migrated in earlier years have more information related to the labour market in cities. When
we compare the influence of migrant contacts who migrated in 1999 with migrant contacts
in 2002, Tables 1.3 and 1.4 imply that the former, as estimated with the instrumental variable
method, have more influence on the migration decision of rural residents than the latter, as
estimated with the Probit method, for men with migration experience and young women
without migration experience.
Finally, when rural residents have migration experience, they likely have connections
in the cities where they worked. Their migration decisions are also affected by these
contacts. Table 1.5 shows that the proportion of repeat migrants among people with migration
19
The analysis by gender shows that the IV estimate is positive and significant for each of the three age
groups and the magnitude of IV estimates is greater than the magnitude of Probit estimates.
24
Table 1.6: The IV Estimate of Network Effects on Labour Market Outcomes for Migrants
(1)
Earnings
Panel A: All Migrants
Mean of DV
7.84
(1.00)
(2)
Days worked
(3)
Hours worked
(4)
Working indoors
(5)
High temperature
(6)
Toxics
219.8
(92.9)
8.48
(1.23)
0.55
(0.50)
0.10
(0.31)
0.09
(0.28)
Network Effect 0.24
(0.86)
243.56∗∗∗
(66.91)
0.15
(1.00)
0.75∗∗∗
(0.28)
0.06
(0.27)
-0.54∗∗∗
(0.20)
# of obs
3,796
Panel B: Male Migrants
Mean of DV
7.86
(1.02)
3,985
3,967
4,048
4,048
4,048
211.8
(93.0)
8.50
(1.52)
0.44
(0.50)
0.13
(0.33)
0.11
(0.32)
Network Effect 0.35
(0.90)
218.52∗∗∗
(73.5)
-0.31
(1.02)
0.80∗∗
(0.32)
0.12
(0.30)
-0.63∗∗∗
(0.24)
# of obs
2,633
Panel B: Female Migrants
Mean of DV
7.80
(0.95)
2,754
2,741
2,801
2,801
2,801
237.56
(89.0)
8.43
(1.55)
0.79
(0.41)
0.06
(0.23)
0.03
(0.17)
Network Effect 0.12
(1.11)
300.50∗∗∗
(84.06)
0.73
(1.63)
0.64
(0.41)
-0.07
(0.26)
-0.36∗∗
(0.16)
# of obs
1,163
1,231
1,226
1,247
1,247
1,247
Notes: This table presents the impact of migrant networks and other labour market characteristics on various labour market outcomes.
The results are estimated with the two-stage least square (2SLS) estimator using migrant workers in 2002. Standard errors are clustered
by villages. Omitted groups are male migrants and people who are not married and who have education below high school.
Columns (1) - (6) present the estimate of impacts on log annul earnings from taking a job that is not related to agricultural activities, the
number of days spent in working at the job in 2002, the number of hours spent at the job per day, whether the job requires the person to
work indoors, whether the job requires the person to work in a very hot environment, and whether the job involves exposure to toxics,
respectively. I present the mean and the standard deviation of dependent variables in the first row of each panel.
experience is substantially large relative to the proportion of first-time migrants among
people without migration experience. The impact of contacts in cities would decrease
the impact of migrant contacts in the countryside. Since the Probit estimator does not
control for the size of migrant networks in cities, the Probit estimates of network effects are
downward-biased.
1.4.3
Employment Outcomes and the Size of Migrant Networks
One potential reason for the effect of migrant networks on migration decisions is that
migrant contacts in the countryside help fellow villagers to find jobs in cities. In this section,
25
I focus on the migrants who are employed in 2002, which make up approximately 95%
of the migrants in the sample, and investigate the extent to which migrant networks in
the countryside affect their labour market outcomes in cities. Overall, I find that knowing
more migrant contacts in a person’s village of origin does not increase the person’s annual
earnings, but it does improve the quality of the person’s job. The positive impact on job
quality is particularly strong for people without migration experience.
Table 1.6 presents the second-stage results that are estimated with the 2SLS instrumental
variable method for migrants aged between 16 and 65 and separately for each gender.20
Column (1) shows that the size of migrant networks does not have a significant impact on
annual earnings in 2002. However, Panel A in Table 1.6 shows that an increase of 10 percent
in the proportion of migrants in the home village increases the number of days worked by
24 days and decreases the probability of working outdoors and exposure to toxics by 7.5%
and 5.4%, respectively. The impact of migrant networks on job tenure and the probability of
exposure to toxics are found for both genders. On top of that, a 10% increase in the size of
migrant networks also increases the probability of working indoors by 8%.
In Table 1.7 I show that migrant networks affect job market outcomes for first-time
migrants younger than 30 and for repeat migrants. For the former, a 10 percent increase
in the proportion of migrants in the home village increases the number of days worked by
approximately a month and decreases the probability of exposure to toxics at the workplace
20
In Appendix I present the 2SLS estimate of all covariates included in the analysis. The instrument has
a positive impact on the size of migrant networks at the 1% level. The first-stage results are available upon
request.
26
Table 1.7: The IV Estimate of Network Effects on Labour Market Outcomes by Migration Experience
(1)
Annual Earnings
Panel A: First-time Migrants
Mean of DV
7.70
(0.98)
(2)
Annual Days
(3)
Weekly Hours
(4)
Indoor
(5)
High temperature
(6)
Toxics
201.8
(94.3)
8.47
(1.57)
0.48
(0.50)
0.11
(0.31)
0.10
(0.30)
Network Effect 0.42
(1.14)
241.8∗∗
(95.70)
-0.33
(1.31)
1.22∗∗∗
(0.47)
0.44
(0.41)
-0.76∗∗
(0.31)
# of obs
2,300
Panel B: First-time Migrants, age≤30
Mean of DV
7.72
(0.94)
2,410
2,400
2,452
2,452
2,452
217.9
(92.1)
8.48
(1.43)
0.63
(0.48)
0.10
(0.29)
0.07
(0.26)
Network Effect 0.70
(1.50)
295.9∗∗∗
(110.50)
-0.09
(1.65)
1.40∗∗
(0.55)
0.45
(0.46)
-0.65∗∗
(0.29)
# of obs
1,314
Panel C: First-time Migrants, age>30
Mean of DV
7.84
(1.02)
1,389
1,384
1,405
1,405
1,405
179.5
(92.2)
8.56
(1.46)
0.30
(0.46)
0.14
(0.34)
0.13
(0.34)
Network Effect 0.05
(1.33)
168.96
(131.68)
-0.79
(1.77)
0.92
(0.58)
0.45
(0.53)
-0.92∗
(0.51)
# of obs
986
Panel D: Repeat Migrants
Mean of DV
7.95
(1.02)
1,021
1,016
1,047
1,047
1,047
247.21
(83.64)
8.54
(1.38)
0.63
(0.48)
0.96
(0.30)
0.07
(0.26)
Network Effect -0.17
(1.02)
212.11∗∗∗
(66.39)
0.41
(1.16)
0.18
(0.34)
-0.32
(0.23)
-0.35∗∗
(0.15)
# of obs
1,548
1,540
1,584
1,584
1,584
1,485
Notes: This table presents the 2SLS estimates on the labour market outcomes for first-time migrants and the repeat migrants. Standard
errors are clustered by villages. Omitted groups are male migrants and people who are not married and who have education below high
school. Columns (1) - (6) present the estimate of impacts on log annul earnings from taking a job that is not related to agricultural
activities, the number of days spent in working at the job in 2002, the number of hours spent at the job per day, whether the job requires
the person to work indoors, whether the job requires the person to work in a very hot environment, and whether the job involves exposure
to toxics, respectively. I present the mean and the standard deviation of dependent variables in the first row of each panel.
27
by 6.5%. The same pattern is found for repeat migrants, with an increase in days worked of
21 days and decrease in probability of toxic exposure of 4%. In addition, a 10% increase in
the size of migrant networks also increases the probability of working indoors for young
first-time migrants by 14%. For first-time migrants who are older than 30, migrant networks
have little impact on labour market outcomes. These results support the hypothesis that
migrant networks in the countryside affect migration through the channel of improving the
job quality of rural residents working in cities.21
Knowing more migrant contacts increases job arrival rate for all migrants.22 As repeat
migrants are likely to have contacts in cities, their labour market outcomes are not only
dependent on their networks in the countryside, but also migrant networks in cities. This
could explain why migrant networks in the countryside play a smaller role in affecting
labour market outcomes for repeat migrants than first-time migrants.
1.5
Conclusion
This chapter examines the effect of migrant networks on migration decisions of village
residents in 2002 at the village of origin. Using unexpected changes in average daily rainfall
in 1999 as an instrumental variable, this study identifies a substantially large network effect
21
In Table A4 I present the network effects on labour market outcomes for young repeat migrants and repeat
migrants older than 30. There is a significant impact of migrant networks on annual number of days worked
and the probability of exposure to toxics for both groups of repeat migrants. These impacts are smaller for
young repeat migrants than young first-time migrants.
22
Social network theory demonstrates that word-of-mouth communication among unemployed individuals
and their contacts reduces the search frictions, which in turn increases the number of vacancies learned
about by unemployed individuals. For a theoretical explanation, see Calvó-Armengol and Jackson (2004);
Calvó-Armengol (2004); Calvó-Armengol and Zenou (2005).
28
for male and female first-time migrants younger than 30 and for repeat migrants in all age
groups.
I explore whether migrant networks in the countryside affect migration decisions through
the channel of helping rural residents find a job. My results show that knowing more migrant
contacts does not have a significant impact on a person’s annual earnings, but it seems to
improve a first-time migrant’s job quality in terms of job tenure, the probability of working
indoors and exposure to toxics. The impact of migrant network on job tenure and the
probability of toxic exposure are also found for people with migration experience but the
impact is much smaller than that for first-time migrants.
A drawback of the chapter is that the CHIPS 2002 does not have information on which
city a person migrate to in 2002 and which cities repeat migrants stayed at before 2002. This
limits the ability to explore the network effect on labour market outcomes in cities, since a
migrant often interacts with other migrants residing in the same location. In order to link
migrant workers to their contacts in their destination cities, new datasets are needed.
My findings suggest that first-time migrants are worse off in terms of wage and work
environment relative to people with migration experience. Expanding public services for
new migrants, such as services that provide information about local labour market and
community supports, would be beneficial.
29
Appendix
Table A1: Geographical Information in Each Province
Province
Anhui
Beijing
Chongqing
Hebei
Henan
Hunan
Jiangsu
Jiangxi
Jilin
Liaoning
Gansu
Guangdong
Guizhou
Nanning
Shaanxi
Shandong
Shanxi
Sichuan
Xinjiang
Yunnan
Zhejiang
Wuhan
Migrants
Average Size of
Migrant Networks
in 2002
Average Amount of
Unexpected Rainfall
in 1999
Villages
Townships
971
291
297
546
848
765
660
1,122
707
647
382
1,380
842
999
643
1,202
568
855
918
527
896
729
0.28
0.08
0.20
0.08
0.15
0.20
0.20
0.28
0.04
0.09
0.16
0.18
0.20
0.15
0.15
0.07
0.04
0.21
0.02
0.08
0.13
0.16
36.50
12.77
2.95
4.10
9.03
21.23
16.78
20.26
3.24
9.40
9.27
8.92
10.73
12.51
4.48
4.70
3.09
6.77
1.90
13.60
13.04
19.19
44
16
20
37
53
45
44
43
48
45
27
53
40
40
37
63
40
49
75
26
50
50
5
2
2
5
7
5
5
6
6
6
5
7
6
5
6
7
6
6
8
5
6
6
30
Table A2: The IV Estimate of Network Effects on Labour Market Outcomes for Migrants
(1)
Earnings
Panel A: All Migrants
Mean of DV
7.84
(1.00)
(2)
Days worked
(3)
Hours worked
(4)
Working indoors
(5)
High temperature
(6)
Toxics
219.8
(92.9)
8.48
(1.23)
0.55
(0.50)
0.10
(0.31)
0.09
(0.28)
Explanatory Variables:
Migrant networks
0.24
(0.86)
243.56∗∗∗
(66.91)
0.15
(1.00)
0.75∗∗∗
(0.28)
0.06
(0.27)
-0.54∗∗∗
(0.20)
Female
0.01
(0.04)
14.41∗∗∗
(3.44)
-0.08
(0.05)
0.27∗∗∗
(0.02)
-0.07∗∗∗
(0.01)
-0.07∗∗∗
(0.01)
Age
0.006∗∗
(0.003)
-0.78∗∗∗
(0.24)
-0.007∗
(0.004)
-0.007∗∗∗
(0.001)
-0.001
(0.001)
-0.0003
(0.001)
Married
0.07
(0.05)
-15.10∗∗∗
(4.40)
0.21∗∗∗
(0.07)
-0.13∗∗∗
(0.02)
-0.009
(0.02)
0.02∗
(0.01)
High school
0.31∗∗∗
(0.06)
24.86∗∗∗
(4.80)
0.04
(0.07)
0.08∗∗∗
(0.02)
0.05∗∗
(0.02)
-0.02
(0.02)
3,985
3,967
4,048
4,048
4,048
211.8
(93.0)
8.50
(1.52)
0.44
(0.50)
0.13
(0.33)
0.11
(0.32)
# of obs
3,796
Panel B: Male Migrants
Mean of DV
7.86
(1.02)
Migrant networks
0.35
(0.90)
218.52∗∗∗
(73.5)
-0.31
(1.02)
0.80∗∗
(0.32)
0.12
(0.30)
-0.63∗∗∗
(0.24)
Age
0.004
(0.003)
-0.91∗∗∗
(0.25)
-0.008∗
(0.004)
-0.007∗∗∗
(0.001)
-0.001
(0.001)
-0.0003
(0.001)
Married
0.14∗∗
(0.06)
-13.18∗∗∗
(4.91)
0.25∗∗∗
(0.07)
-0.14∗∗∗
(0.03)
0.04∗∗
(0.02)
0.01
(0.02)
High school
0.30∗∗∗
(0.06)
-0.09
(5.60)
23.34∗∗∗
(0.09)
0.09∗∗∗
(0.03)
-0.005
(0.02)
-0.03
(0.02)
2,754
2,741
2,801
2,801
2,801
237.56
(89.0)
8.43
(1.55)
0.79
(0.41)
0.06
(0.23)
0.03
(0.17)
# of obs
2,633
Panel B: Female Migrants
Mean of DV
7.80
(0.95)
Migrant networks
0.12
(1.11)
300.50∗∗∗
(84.06)
0.73
(1.63)
0.64
(0.41)
-0.07
(0.26)
-0.36∗∗
(0.16)
Age
0.01∗∗
(0.006)
-0.31
(0.57)
-0.01
(0.01)
-0.004
(0.003)
-0.002
(0.002)
-0.0004
(0.001)
Married
-0.11
(0.11)
-24.07∗∗∗
(9.15)
0.18
(0.17)
-0.12∗∗∗
(0.04)
0.07∗∗
(0.03)
0.05∗∗
(0.02)
High school
0.32∗∗∗
(0.08)
27.70∗∗∗
(7.16)
0.25∗
(0.13)
0.08∗∗
(0.04)
-0.02
(0.02)
0.004
(0.02)
# of obs
1,163
1,231
1,226
1,247
1,247
1,247
Notes: This table presents the impact of migrant networks and other labour market characteristics on various labour market outcomes.
The results are estimated with the two-stage least square (2SLS) estimator using migrant workers in 2002. Standard errors are clustered
by villages. Omitted groups are male migrants and people who are not married and who have education below high school.
Columns (1) - (6) present the estimate of impacts on log annul earnings from taking a job that is not related to agricultural activities, the
number of days spent in working at the job in 2002, the number of hours spent at the job per day, whether the job requires the person to
work indoors, whether the job requires the person to work in a very hot environment, and whether the job involves exposure to toxics,
respectively. I present the mean and the standard deviation of dependent variables in the first row of each panel.
31
Table A3: The IV Estimate of Network Effects on Labour Market Outcomes for Firsttime Migrants
(1)
Earnings
Panel A: First-time Migrants
Mean of DV
7.70
(0.98)
(2)
Days worked
(3)
Hours worked
(4)
Working indoors
(5)
High temperature
(6)
Toxics
201.8
(94.3)
8.47
(1.57)
0.48
(0.50)
0.11
(0.31)
0.10
(0.30)
Migrant networks
0.42
(1.14)
241.8∗∗
(95.70)
-0.33
(1.31)
1.22∗∗∗
(0.47)
0.44
(0.41)
-0.76∗∗
(0.31)
Female
0.005
(0.05)
12.90∗∗
5.00
-0.12
(0.07)
0.28∗∗∗
(0.02)
-0.08∗∗∗
(0.02)
-0.07∗∗∗
(0.01)
Age
0.007∗
(0.003)
-0.57∗
(0.30)
-0.009∗∗
(0.005)
-0.004∗∗
(0.002)
-0.001
(0.001)
-0.0008
(0.001)
Married
0.06
(0.06)
-20.90∗∗∗
(5.87)
0.23∗∗
(0.09)
-0.18∗∗∗
(0.03)
0.04
(0.02)
0.05∗∗
(0.02)
High school
0.26∗∗∗
(0.07)
26.45∗∗∗
(6.32)
0.03
(0.09)
0.08∗∗
(0.03)
0.003
(0.02)
-0.02
(0.02)
2,400
2,452
2,452
2,452
8.48
(1.43)
0.63
(0.48)
0.10
(0.29)
0.07
(0.26)
# of obs
2,300
2,410
Panel B: First-time Migrants, age≤30
Mean of DV
7.72
217.9
(0.94)
(92.1)
Migrant networks
0.70
(1.50)
295.9∗∗∗
(110.50)
-0.09
(1.65)
1.40∗∗
(0.55)
0.45
(0.46)
-0.65∗∗
(0.29)
Female
0.09
(0.06)
17.40∗∗∗
(5.58)
-0.02
(0.08)
0.26∗∗∗
(0.02)
-0.08∗∗∗
(0.02)
-0.08∗∗∗
(0.02)
Age
0.04∗∗∗
(0.01)
2.11∗∗
(1.00)
-0.02
(0.01)
-0.004
(0.005)
-0.005
(0.004)
-0.000008
(0.003)
Married
-0.13
(0.08)
-31.16∗∗∗
(8.14)
0.22∗
(0.12)
-0.15∗∗∗
(0.04)
0.03
(0.03)
0.03
(0.03)
High school
0.17∗
(0.09)
11.44
(8.06)
0.19
(0.13)
0.08∗
(0.04)
-0.03
(0.03)
-0.04
(0.03)
# of obs
1,314
1,389
Panel C: First-time Migrants, age>30
Mean of DV
7.84
179.5
(1.02)
(92.2)
1,384
1,405
1,405
1,405
8.56
(1.46)
0.30
(0.46)
0.14
(0.34)
0.13
(0.34)
Migrant networks
0.05
(1.33)
168.96
(131.68)
-0.79
(1.77)
0.92
(0.58)
0.45
(0.53)
-0.92∗
(0.51)
Female
-0.14
(0.1)
7.77
(10.45)
-0.35∗∗
(0.17)
0.34∗∗∗
(0.05)
-0.07 ∗
(0.04)
-0.03
(0.03)
Age
0.0008
(0.005)
-0.20
(0.46)
-0.02∗∗∗
(0.007)
-0.004
(0.002)
-0.003
(0.002)
-0.003
(0.002)
Married
0.40∗∗
(0.18)
-3.01
(15.43)
0.19
(0.29)
0.07
(0.07)
-0.03
(0.08)
-0.06
(0.07)
High school
0.30∗∗∗
(0.08)
36.77
(7.77)
-0.12
(0.13)
0.07∗
(0.04)
0.03
(0.03)
0.005
(0.04)
# of obs
986
1,021
1,016
1,047
1,047
1,047
Notes: This table presents the 2SLS estimates on the labour market outcomes for first-time migrants. Standard errors are clustered by
villages. Omitted groups are male migrants and people who are not married and who have education below high school. Columns (1) (6) present the estimate of impacts on log annul earnings from taking a job that is not related to agricultural activities, the number of days
spent in working at the job in 2002, the number of hours spent at the job per day, whether the job requires the person to work indoors,
whether the job requires the person to work in a very hot environment, and whether the job involves exposure to toxics, respectively. I
present the mean and the standard deviation of dependent variables in the first row of each panel.
32
Table A4: The IV Estimate of Network Effects on Labour Market Outcomes for Repeat Migrants
Repeat Migrants
Mean of DV
(1)
Earnings
(2)
Days worked
(3)
Hours worked
(4)
Working indoors
(5)
High temperature
(6)
Toxics
7.95
(1.02)
247.21
(83.64)
8.54
(1.38)
0.63
(0.48)
0.96
(0.30)
0.07
(0.26)
Migrant networks
-0.17
(1.02)
212.11∗∗∗
(66.39)
0.41
(1.16)
0.18
(0.34)
-0.32
(0.23)
-0.35∗∗
(0.15)
Female
0.04
(0.05)
17.64∗∗∗
(4.32)
-0.002
(0.07)
0.24∗∗∗
(0.03)
-0.05∗∗∗
(0.02)
-0.07∗∗∗
(0.01)
Age
0.006
(0.005)
-0.67∗
(0.38)
-0.0007
(0.007)
-0.01∗∗∗
(0.002)
-0.001
(0.001)
0.0002
(0.001)
Married
0.11
(0.08)
-4.38
(6.43)
0.14
(0.10)
-0.05
(0.03)
0.05∗
(0.03)
-0.0007
(0.02)
High school
0.40∗∗∗
(0.09)
22.82∗∗∗
(6.2)
0.05
(0.11)
0.10∗∗∗
(0.03)
-0.02
(0.02)
-0.02
(0.02)
1,552
1,596
1,596
1,596
8.51
(1.25)
0.72
(0.45)
0.08
(0.27)
0.07
(0.26)
# of obs
1,497
1,560
Panel B: Repeat Migrants, age≤30
Mean of DV
7.91
255.25
(1.01)
(78.8)
Migrant networks
-0.38
(1.18)
250.26∗∗∗
(71.46)
-0.66
(1.32)
-0.03
(0.40)
0.72
(0.45)
-0.40∗∗
(0.19)
Female
0.08
(0.06)
14.90∗∗∗
(5.03)
-0.06
(0.08)
0.20∗∗∗
(0.03)
-0.38
(0.23)
-0.07∗∗∗
(0.02)
Age
0.03∗∗
(0.01)
1.21
(0.94)
-0.01
(0.01)
-0.02∗∗∗
(0.005)
-0.06∗∗∗
(0.02)
0.005
(0.003)
Married
-0.02
(0.10)
-9.59
(7.65)
0.19
(0.11)
-0.01
(0.04)
-0.003
(0.003)
-0.004
(0.02)
High school
0.38∗∗∗
(0.11)
16.24∗∗
(7.41)
0.09
(0.14)
0.12∗∗∗
(0.04)
-0.03
(0.03)
-0.03
(0.03)
# of obs
1,061
1,101
Panel C: Repeat Migrants, age>30
Mean of DV
8.05
227.94
(1.05)
(91.54)
1,095
1,130
1,130
1,130
8.60
(1.64)
0.42
(0.50)
0.13
(0.34)
0.08
(0.27)
Migrant networks
0.40
(1.17)
161.3∗
(88.75)
2.33
(1.66)
0.45
(0.53)
-0.16
(0.38)
-0.19∗
(0.24)
Female
-0.07
(0.12)
43.41∗∗∗
(11.20)
0.002
(0.01)
0.35∗∗∗
(0.06)
-0.006
(0.04)
-0.06∗∗
(0.03)
Age
-0.002
(0.007)
-0.50
(0.62)
0.002
(0.01)
-0.003
(0.003)
-0.003
(0.002)
0.001
(0.002)
Married
0.49
(0.30)
-15.12
(16.90)
0.23
(0.29)
-0.01
(0.10)
-0.01
(0.07)
-0.02
(0.06)
High school
0.38∗∗∗
(0.13)
40.93∗∗∗
(10.54)
-0.05
(0.17)
0.08
(0.05)
0.002
(0.04)
-0.02
(0.03)
# of obs
436
459
457
466
466
466
Notes: This table presents the 2SLS estimates on the labour market outcomes for repeat migrants. Standard errors are clustered by
villages. Omitted groups are male migrants and people who are not married and who have education below high school. Columns (1) (6) present the estimate of impacts on log annul earnings from taking a job that is not related to agricultural activities, the number of days
spent in working at the job in 2002, the number of hours spent at the job per day, whether the job requires the person to work indoors,
whether the job requires the person to work in a very hot environment, and whether the job involves exposure to toxics, respectively. I
present the mean and the standard deviation of dependent variables in the first row of each panel.
33
Chapter 2
Occupational Characteristics and
Gender Wage Inequality: A
Distributional Analysis
2.1
Introduction
Using a quantile wage decomposition method, this chapter explores the question of
why men and women are paid differently when they work in different occupations. Since
the 1970s, women have made great improvements in educational achievement and labour
market participation, but a male-female wage gap still persists in Canada (Baker and Drolet,
2010). By controlling for occupational dummy variables, several Canadian studies have
found that a considerable proportion of the gender gap can be attributed to men and women
34
working in different occupations (Fortin and Huberman, 2002; Drolet, 2002a; Boudarbat
and Connolly, 2013).1 A drawback of using the occupational dummies, however, is that
this approach does not reveal why gender-specific occupational distribution has an impact
on the gender wage gap. For this reason, a few studies replace the occupational dummies
with occupation-specific skills, which are extracted from sources such as the Dictionary of
Occupational Titles (DOT), and examine how the DOT-skills affect the average gender wage
gap (Baker and Fortin, 2001).2
This study adds to the literature in two respects. First, it constructs a broader set of
skill measures and examines how gender differences in these occupation-specific skills
affect the gender gap at different points of the wage distribution other than the mean. In
addition to the DOT-skills, such as verbal, numerical, and clerical skills, I include workplace
competitiveness and the ranking of an individual’s managerial position (i.e. non-manager,
junior manager, or senior manager.). This analysis shows that more of the gender gap at
various points of the wage distribution is explained when occupational dummy variables are
replaced with the DOT-skills, workplace competitiveness, and the ranking of managerial
positions. Moreover, gender differences relating to workplace competitiveness and the
ranking of managerial positions, which were not examined in the existing Canadian literature
1
The extent to which gender differences in occupation contribute to the gender wage gap varies at different
point of the wage distribution. Examining the wage gap for young post-secondary graduates, Boudarbat and
Connolly (2013) show that the inclusion of the occupational dummies reduces the gender gap by 37% at the
mean, 112% at the 10th percentile of the wage distribution, and 17.7% at the 90th percentile of the wage
distribution.
2
Macpherson and Hirsch (1995), Black and Spitz-Oener (2010), and Bacolod and Blum (2010) conduct a
similar analysis for the US. To simplify the explanation, I call the occupation-specific aptitude factors, as used
in the previous studies, the DOT-skills.
35
on the gender wage gap, explain 30.5% of the gender gap at the 95th percentile of the wage
distribution for university-educated workers, 27 percentage points greater than the 3.5%
of the gender gap explained by DOT-skill variables. 3 Workplace competitiveness and the
ranking of managerial positions therefore appear to be the principal determinants underlying
the “glass ceiling” phenomenon – high-paid women experience a greater wage gap than
low-paid women. Previous studies have documented this phenomenon, but reasons as to why
women are prevented from obtaining the wage levels of the highest-paid men are missing in
the literature (Baker et al., 1995; Drolet, 2002b; Boudarbat and Connolly, 2013).4
The second contribution of the study is to reveal that women with different educational
levels experience the gender wage gap for different reasons. In line with previous studies,
I find that gender differences in DOT-skills explain up to 50% of the gender gap for high
school and community college graduates, as well as most of the university graduates. The
extent to which differences in DOT-skills contribute to the gender gap varies at different
3
A number of studies use laboratory experiments to test the hypothesis that gender differences in attitudes
toward competition have a significant impact on the gender gap in productivity (Niederle and Vesterlund, 2011;
Cadsby et al., 2013). However, the hypothesis has not been tested using large-scale Canadian micro data yet.
The use of micro data has pros and cons in testing the competitiveness hypothesis, compared to laboratory
experiments. The main purpose of the study is not to demonstrate whether the use of micro data is more proper
in testing the hypothesis than laboratory experiments, but rather to test whether working in jobs with different
levels of competitive pressure is an explanation for the gender gap for highly-educated workers.
4
Albrecht et al. (2003) and Christofides et al. (2013) have found the evidence of the “glass ceiling”
phenomenon in European countries, and Blau and Kahn (2006) in the U.S. The glass ceiling phenomenon can
be defined in two ways. First, when men and women work in the same occupation, a number of studies have
found that high-skilled women are less likely to be promoted than high-skilled men because women experience
more career interruption due to child-rearing.(Wood et al., 1993; Bertrand and Hallock, 2001; Ginther and
Kahn, 2004; Bertrand et al., 2010; Goldin and Katz, 2011; Gicheva, 2013; Goldin, 2014). Second, when the
highest-paid women work in occupations different from the highest-paid men, the highest-paid women are
paid less than the highest-paid men. A typical example is the pay difference between male top executives and
female pharmacists, where pharmacists earn less than top executives, suggesting that the highest paid women
lack access to the highest paying jobs of men. Explanations for this type of glass ceiling, which were not
subject to comprehensive examination in the literature, are explored in this chapter.
36
points of the wage distribution for each educational group. However, there are two groups
of workers for whom the DOT-skills are not significantly different between men and women
but the gender gap still exists. The first group is university-educated workers above the
90th percentile of the wage distribution. In this group, the analysis shows that men are
compensated more because they work in more competitive jobs and take more managerial
responsibilities than women. The second group is workers without high school education.
In this group, the analysis show that men are compensated more because they experience
unpleasant work conditions more often than women.
Within previous work that has investigated the impact of occupational characteristics on
the gender wage gap for Canada (Baker and Fortin, 1999; Drolet, 2002b), the only Canadian
study I am aware of that addresses the relationship of the DOT-skills to pay differences
between male- and female-dominated occupations is Baker and Fortin (2001). Using Canadian data from 1987 and 1988, Baker and Fortin (2001) examine whether female-dominated
occupations on average pay less than male-dominated and mixed occupations, conditional
on occupational characteristics that are extracted from the Canadian Classification and
Dictionary of Occupations. They found that men were paid significantly less in femaledominated occupations than in other occupations; however, a significant penalty for women
in female-dominated work only exists among women with university education. This study
extends their work in two dimensions. First, my study uncovers heterogeneity in the impact
of detailed occupational attributes on the gender wage gap at different points of the wage
distribution. Second, the analysis for the university-educated workers provides some expla-
37
nations as to why university-educated women are paid significantly less in female-dominated
occupations than in other occupations, explanations that go beyond the analysis in Baker
and Fortin (2001).
An important secondary analysis for understanding the gender gaps relationship to
education levels is an examination of sample selection induced by non-employment. This
exercise is particularly important for examining the gender gap for workers without postsecondary education, because of the relatively low employment rate of women at these
educational levels. To account for selection effects, I use alternative imputation techniques to
recover the missing wage values of non-working individuals. The first approach that allows
for selection through unobserved characteristics is closely related to that of Olivetti and
Petrongolo (2008). Previous studies using this imputation approach examined the impact of
sample selection on the wage gap at the median of the wage distribution.5 This is the first
study that applies the approach to investigating the impact of selection on the gender wage
gap at various points of the wage distribution other than the median. The second approach
that allows for selection through observed characteristics is built upon the reweighting
method introduced by DiNardo, Fortin and Lemieux (1996). The DiNardo-Fortin-Lemieux
method is commonly used in analyzing the wage gap between demographic groups, but it
has not previously been applied for the purpose of correcting for sample selection. Different
from the first approach, the second approach does not rely on longitudinal data. Therefore,
it enables researchers who use cross-sectional data such as Current Population Survey (CPS)
5
See Johnson et al. (2000) and Neal (2004) for an application to the white-black wage gap, Blau and Kahn
(2006) and Olivetti and Petrongolo (2008) for an application to the gender wage gap.
38
to analyze data with missing wage values.
While these alternative imputation techniques reveal different economic channels of
selection, results with both imputation approaches confirm that correcting for sample
selection makes little difference in estimating the gender gap for individuals with postsecondary education. For individuals without post-secondary education (low-educated),
correction for sample selection on observables makes greater changes in the gender gap than
selection on unobservables, suggesting that the use of observed characteristics is sufficient
to capture the selection rule for low-educated individuals.
The rest of the chapter is organized as follows. Sections 2.2 and 2.3 introduce data and
variable construction, followed by empirical findings. Section 2.4 concludes.
2.2
Data
The data for this study are from the Survey of Labour and Income Dynamics (SLID)
for the years 1993 to 2010. The choice to use the SLID is motivated by the fact that it
contains rich information on individuals work history and educational attainment, including
both education levels and major fields of study. More importantly, the SLID provides a
longitudinal dimension that offers information on fluctuations in income and on changes in
labour market activity over time for up to 6 years. This data is needed to recover missing
wage values for individuals who worked in some years but did not work in other years.
The SLID 1993-2010 has 461,693 people in the age group 25-54 who were not enrolled
39
in school at the time of the survey. I restrict my analysis to full-time employees whose
highest education level is known. These restrictions result in a total sample size of 257,937
observations, where 49.6% are women and 50.4% are men.
2.2.1
Variable Construction
The measure of earnings used is the hourly wage.6 The hourly wage is measured using
the total annual earnings (including tips, bonuses and commissions) divided by the annual
hours worked. Hourly wage over the period 1993-2010 is evaluated in 1993 constant dollars.
I focus on the wage gap for full-time employees because it allows a comparison between similar types of workers. Part-time employees tend to have different labour market
characteristics than full-time employees.7
To define educational groups, I use the survey variable “highest level of education of a
person”. There are four educational categories: below high school, high school graduates
(low-educated workers), post-secondary education below a four-year university degree, and
Bachelor’s degree and graduate education (high-educated workers).8
6
Hourly wage is preferred to other measures such as weekly and yearly earnings because it eliminates the
impact of gender differences in working hours on the gender pay gap.
7
The use of full-time workforce data is common in previous studies. See Fortin and Huberman (2002),
Baker et al. (1995), and Boudarbat and Connolly (2013) as examples using Canadian data. In Figure B2 in
Appendix B, I plot the gender gap at each decile of the wage distribution for full-time employees and for
part-time employees with a 95% confidence interval. It is noticeable that the pattern of the gender gap is very
different between full-time and part-time employees, which indicates the potential dissimilarities between the
two types of employees.
8
The sample of observations with education below high school includes individuals who had zero to 13
years of schooling and did not complete a high school diploma. The category of “post-secondary education
without completing a four-year university degree” includes trade programs, community colleges (with or
without certificates), and some university education with no degree. “University education” includes four-year
university degrees, university certificate or diploma above BA but below Master’s degree, Master’s degree,
40
Table 2.1: Sample Size by Educational Category and Gender
Total
Observations
Percentage of
Full-time Workers
Share of
Missing Wage
Observations
Men
Below HS
HS
College
University
40,408
37,955
108,999
36,840
58%
69%
71%
72%
37%
26.6%
24.1%
21.3%
Women
Below HS
HS
College
University
33,059
41,694
119,475
43,263
37%
50%
56%
64%
50%
34.5%
26.8%
20.3%
Notes: Author’s calculations using SLID 1993-2010. Referenced population: 25-54 year old, not currently
attending school. College includes people who have attended trade programs, community colleges, or universities but did not complete a four-year university degree.
Table 2.1 summarizes the number of observations by educational category for men
and women. It shows that the employment gap between men and women varies across
educational groups. The second column tells us that, in all educational groups, the proportion
of full-time employees is larger for men than women, but the difference is reduced by
13% ((58%-37%)-(72%-64%)) when one compares the employment gap for the lowest
educational group with that for the highest educational group.9 The third column shows
that the difference in the proportion of non-working individuals between men and women
decreases as the level of education increases. Since the employment and non-employment
degree in medicine, dentistry, veterinary medicine, optometry or first professional degree in law, and Doctorate.
9
Compared to Boudarbat and Connolly (2013), full-time employees here have a smaller share amongst
the college, trade and university observations. This difference is due to the fact that Boudarbat and Connolly
(2013) examines the wage gap for employees 2 years and 5 years after graduation, while this study examines
the wage gap for all employees. As mature workers are more likely to have career interruptions than young
workers, the full-time employment rate for each gender is smaller in this study.
41
gaps are much larger for low-educated individuals, it makes sense to examine the impact of
sample selection on the gender gap for low-educated observations.
The proportion of missing wage observations within each education-gender group is
larger than the unemployment rate for that group because missing wage observations result
from both unemployment and nonresponse. If individuals who are not employed and individuals who refuse to answer income questions systematically differ from employed individuals
who are willing to supply this information, then the estimated gender wage gap based on the
the observed wage value would be biased.10 Therefore, when I account for sample selection,
I include missing wage observations due to unemployment and nonresponse.
To investigate the impact of gender differences in occupation on the wage gap, I use nine
aptitude factors that are extracted from the Career Handbook, which is the Canadian version
of Dictionary of Occupational Titles. The aptitude factors are general learning ability, verbal
ability, numerical ability, spatial perception, form perception, clerical perception, motor
coordination, finger dexterity, and manual dexterity.11 The level of requirement is between 1
and 5, with 1 denoting the lowest requirement.12 I match the skills that are required by an
occupation to the people who worked in the occupation in a given year. Table 2.2 provides
an example of occupations that require a specific aptitude skill.
10
Because middle-income people are more likely to be income respondents than low-income and highincome people, such selection is prone to have a larger impact on the estimates of gender gaps at the tails
of the distribution. Thus, correcting for a nonrandom selection amongst the nonrespondents is particularly
important in a distributional analysis.
11
Those variables were adopted in Baker and Fortin (2001) for Canada. Macpherson and Hirsch (1995) and
Bacolod and Blum (2010) adopted a similar set of variables in studying the gender wage gap in the US.
12
The decomposition results are estimated by treating the skill requirement as continuous variables. I also
conduct the analysis by treating the skills as categorical variables. Findings are robust to this change.
42
Table 2.2: Skill Classifications and Examples of Occupations
Aptitude Skills from the Career Handbook
General Learning Ability
Verbal Ability
Numerical Ability
Clerical Perception
Spatial Perception
Form Perception
Motor Co-ordination
Finger Dexterity
Manual Dexterity
Most needed in managerial and natural science occupations.
Most needed in occupations such as senior government
managers, judges, and university professors.
Most needed in occupations such as financial senior managers,
professionals in natural science, accountants, and economists.
Most needed in administrative work.
Most needed in occupations for engineers, computer programmers,
and graphic designers. Some blue-collar jobs require a high level
of this skill, e.g. machinist and aircraft mechanics.
Highest requirement is in landscape architects, physicists,
astronomers, and chemists.
Highest requirement is in dentists, jewellers, watch repairers,
electronics assemblers, and related occupations. Blue-collar jobs,
e.g., electrical cable workers, require a level above median.
Most needed in occupations such as aircraft technician. Other jobs
such as shoe repairers and hair dressers require a level above median.
Needed in occupations such as electronic technicians, physicians,
cabinetmakers, and craftspersons.
O*Net Characteristics
Workplace Competitiveness Jobs such as graphic designers, orthodontists, investment fund
managers, and top executives require workers to work under the
highest level of stress. Jobs such as kindergarten teachers,
librarians, cashiers, and general office clerks require workers
to work under the lowest level of competition.
43
In addition to the aptitude variables, I include the level of workplace competitiveness,
as extracted from the O*Net database,13 and the ranking of an individual’s managerial
position, as extracted from the SLID. Workplace competitiveness (referred to by O*Net
as “level of competition at workplace”) measures the extent to which a job requires the
worker to compete or to be aware of competitive pressures. It is a continuous variable and is
normalized between 0 and 1, with lower values meaning lower competitiveness. Ranking
of an individual’s managerial position is a categorical variable: 0 if the individual is not a
manager, 1 if he/she works at a lower/middle managerial position (junior manager), and 2 if
he/she works at an upper/top managerial position (senior manager).
When accounting for the impact of occupations on the gender wage gap, most studies
control for occupational binary variables, where jobs are grouped into a small number of
occupational categories. There are many reasons why aggregated occupational dummy
variables are adopted in those studies, e.g. disaggregated occupational categories are not
available in the dataset. In applying wage decomposition methods to study the gender
wage gap, a necessary condition to make the analysis valid is that men and women within
occupations must be comparable in observed labour market characteristics, which is called
the “common support assumption.”14 If disaggregated occupational dummy variables are
applied to the analysis, there are occupations in which 90% of the employees are men.15 The
common support assumption would be violated if the labour market characteristics were
13
The O*Net database is the new version of Dictionary of Occupational Titles. I use the O*Net database
because workplace competitiveness is not available in the Career Handbook.
14
See Fortin et al. (2011) for a detailed explanation.
15
An example of such occupations in the SLID is textile machinery mechanics, where only 3 women were
employed in the sample period.
44
very different between women and men in these occupations.
In contrast to those studies, this study controls for nine aptitude factors and the level
of competition, which reflect the characteristics of more than 500 occupational categories,
and the ranking of a managerial position, which is unique to each individual. Such detailed
occupational attributes are preferable to the occupational dummy variables because the
comparison in occupational attributes between men and women provides an informative
message underlying wage differences between male and female work. Moreover, the use
of occupational attributes rather than disaggregated occupational dummy variables yields a
sample size for men that is comparable to the sample size for women for each of the eleven
occupation-related variables. This enables the computation of meaningful decompositions
at deciles of the wage distribution. In the rest of the paper, “occupational attributes” refers
to the nine aptitude variables, workplace competitiveness, and the ranking of an individual’s
managerial position.
This study controls for the proportion of female workers in an occupation and 17
industrial categories. Baker and Fortin (2001) showed that conditional on occupational
characteristics, the proportion of female workers plays a significant role in determining wage
for men and university-educated women. For this reason, the decomposition analysis accounts for the proportion of female workers in order to capture the unobserved occupational
characteristics that are wage determinants. In reality, we see variation, such as office staff
in the oil and mining industry being paid very differently than in the finance and business
industry. Thus, I control for industry dummy variables in order to account for such pay
45
differences.
2.2.2
Summary Statistics
Table 2.3 offers sample mean statistics for men and women by educational group. To
keep the interpretation simple, I define the sample of employees with education below
high school as the HSD (high school dropouts) group, high school graduates as the HS
group, employees with some post-secondary education as the community college group, and
employees who have four-year university degrees or above as the university group.
The first row presents the proportion of women in the full-time labour force by educational group. Women make up close to half of the full-time employees in three of the
educational groups, with the exception of the HSD sample in which only 35% of the fulltime employees are women. This indicates that labour supply behaviour is particularly
different between the very low-educated women and women in other educational groups.
If selection into full-time employment is not random, it could cause a substantial bias in
estimating the gender gap for workers in the HSD group.
Job attributes suggest that even though the gender wage gap decreases as women achieve
more education, men on average earn more than women in four of the educational groups.
Table 2.3 shows that there is a larger proportion of women than men employed in the public
sector, but a smaller union coverage rate among women than men for educational levels
below university. For the university group, the fraction of women in the public sector and
the fraction of union members are both greater than the fractions for men. Previous studies
46
Table 2.3: Summary Statistics: Labour Market Attributes of Full-time Employees
HSD
M
% of women
in FT jobs
HS
F
0.35
College
M
F
0.45
M
University
F
0.45
M
F
0.47
Job Attributes
Hourly Wage∗
Experience∗
Union∗
Public
2.60
(0.39)
20.42
(9.79)
0.39
(0.49)
0.09
(0.27)
2.23
(0.37)
15.15
(9.76)
0.26
(0.44)
0.10
(0.30)
2.71
(0.40)
18.92
(9.02)
0.39
(0.49)
0.13
(0.34)
2.46
(0.40)
16.35
(9.07)
0.29
(0.45)
0.18
(0.38)
2.81
(0.42)
17.67
(9.1)
0.38
(0.48)
0.18
(0.39)
2.60
(0.41)
15.24
(8.55)
0.34
(0.47)
0.28
(0.45)
0.76
(0.43)
0.78
(0.42)
3.12
(0.47)
14.45
(8.8)
0.30
(0.46)
0.34
(0.47)
2.94
(0.44)
12.28
(8.16)
0.46
(0.50)
0.49
(0.50)
0.66
(0.47)
0.091
(0.29)
0.18
(0.39)
0.06
(0.24)
0.73
(0.45)
0.092
(0.29)
0.15
(0.35)
0.04
(0.19)
Highest Education
Certificates∗
BA
Below MA∗
MA
Professional
Notes: Standard deviations are reported in parentheses. SLID cross-sectional weights are applied to the analysis.
∗
Hourly wage is the log hourly wage that is converted to 1993 constant dollars. Experience is measured using
the number of years worked at all jobs (part-time and full-time) since the first full-time paid job. Union includes
individuals who are union members or covered by collective agreement. Certificates includes individuals with
community college degrees that are below four-year university degrees. Below MA includes individuals who attended Master’s programs but did not complete the degree, and BA graduates who completed additional courses
for jobs such as accountants and teachers.
See text for details.
47
have found that there is a positive premium for working in the public sector and being a
union member. This indicates that university-educated women have a smaller average wage
gap than other women partly because a greater proportion of university-educated women
work in the public sector and are union members.
For the degree/diploma attainment, Table 2.3 shows that women outperform men in
achieving college certificates and four-year university degrees; however, they still fall behind
men in completing graduate degrees. The finding that women are underrepresented among
workers with post-graduate education is particularly important in explaining the gender gap
for high-wage earners, since workers with higher education are more likely located on the
upper-part of wage distribution.
Table 2.4 presents the summary statistics of occupational characteristics. It is clear that
women work in jobs that have less competition and that women are more likely to work
with female coworkers than men. Furthermore, the percentage of female workers in the
occupations of university-educated men is 43%, about 20 percentage points greater than
that for other educational groups, suggesting that compared to women in other educational
groups, university-educated women are more likely to opt out of female work, e.g., administrative occupations, and to participate in male-dominated/gender-integrated occupations, e.g.
occupations in law.16
16
This finding is consistent with Blau et al. (2013) and Fortin and Huberman (2002), where they documented
that since 1970 the proportion of women working in jobs requiring postsecondary education, which used
to be male-dominated, has increased at the expense of fewer women working in clerical jobs. But among
low-educated workers, there is little change in female employment.
48
Table 2.4: Summary Statistics: Occupational Characteristics for Full-time Employees
HSD
HS
College
University
M
F
M
F
M
F
M
F
0.23∗∗∗
(0.19)
0.18
(0.20)
0.29
(0.22)
0.31
(0.22)
0.38
(0.24)
0.40∗
(0.23)
0.68∗∗∗
(0.27)
0.62
(0.22)
Verbal
0.34
(0.15)
0.36∗∗
(0.15)
0.40
(0.18)
0.46∗∗∗
(0.17)
0.47
(0.19)
0.53∗∗∗
(0.17)
0.70
(0.18)
0.70
(0.16)
Numerical
0.31
(0.17)
0.32
(0.19)
0.37
(0.20)
0.43∗∗∗
(0.21)
0.45
(0.21)
0.48∗
(0.20)
0.68∗∗∗
(0.23)
0.60
(0.19)
Clerical
Perception
0.23
(0.17)
0.28∗∗∗
(0.21)
0.30
(0.19)
0.44∗∗∗
(0.22)
0.35
(0.19)
0.48∗∗∗
(0.20)
0.49
(0.19)
0.51
(0.15)
Spatial
Perception
0.18∗∗∗
(0.20)
0.05
(0.12)
0.18∗∗∗
(0.21)
0.05
(0.14)
0.24∗∗∗
(0.25)
0.08
(0.17)
0.23∗∗∗
(0.30)
0.11
(0.21)
Form
Perception
0.12∗∗
(0.18)
0.09
(0.15)
0.13∗∗∗
(0.19)
0.08
(0.16)
0.20∗∗∗
(0.22)
0.11
(0.18)
0.22
(0.24)
0.15
(0.21)
Motor
Coordination
0.23∗∗∗
(0.17)
0.15
(0.18)
0.20∗∗∗
(0.19)
0.14
(0.18)
0.21∗∗∗
(0.20)
0.14
(0.18)
0.06
(0.15)
0.06
(0.14)
Finger
Dexterity
0.07
(0.14)
0.09∗
(0.17)
0.08
(0.16)
0.13∗∗∗
(0.18)
0.13
(0.20)
0.15∗∗
(0.18)
0.05
(0.15)
0.08∗∗
(0.16)
Manual
Dexterity
0.27∗∗∗
(0.15)
0.19
(0.18)
0.23∗∗∗
(0.18)
0.13
(0.18)
0.23∗∗∗
(0.20)
0.13
(0.17)
0.07
(0.15)
0.07
(0.14)
0.43
(0.10)
0.50∗∗∗
(0.10)
0.46
(0.11)
0.52∗∗∗
(0.11)
0.47
(0.13)
0.56∗∗∗
(0.11)
0.50
(0.13)
Aptitude Skills
General
Learning
O*Net Characteristics
Workplace
0.50∗∗∗
Competitiveness (0.10)
Ranking of Managerial Positions
0.888
(0.31)
0.889
(0.31)
0.802
(0.40)
0.819∗∗∗
(0.39)
0.758
(0.43)
0.803∗∗∗
(0.40)
0.583
(0.49)
0.734∗∗∗
(0.44)
Junior Manager 0.076
(0.26)
0.079
(0.27)
0.137
(0.34)
0.135
(0.34)
0.170∗∗∗
(0.37)
0.150
(0.36)
0.246∗∗∗
(0.43)
0.190
(0.40)
Senior Manager 0.036
(0.19)
0.032
(0.18)
0.060∗∗∗
(0.24)
0.046
(0.21)
0.072∗∗∗
(0.26)
0.047
(0.21)
0.170∗∗∗
(0.38)
0.075
(0.26)
0.65∗∗∗
(0.26)
0.27
(0.25)
0.70∗∗∗
(0.25)
0.28
(0.26)
0.72∗∗∗
(0.25)
0.43
(0.24)
0.65∗∗∗
(0.22)
Not a Manager
% of Women
0.22
(0.23)
Notes: Standard deviations are reported in parentheses. ∗ denotes that the requirement is significantly greater for
men (women) than for women (men) at 10% level, ∗∗ at 5% level, and ∗∗∗ at 1% level.
Aptitude Skills are extracted from the Career Handbook. An aptitude skill reflects the requirement for that skill
in occupations that are coded with National Occupational Classification for Statistics 2006 (four-digit NOC).
Aptitude skills in this table are normalized between 0 and 1.
Workplace Competitiveness is extracted from the O*Net database. O*Net occupations are matched to a four-digit
NOC in the SLID. When there is more than one O*Net occupation for an SLID occupation, the characteristic is
weighted by the fraction of workers in each of the O*Net occupations that comprise a single SLID occupation.
Each of the O*Net characteristics has a score between 0 and 1 (inclusive).
Ranking of Managerial Positions is extracted from the SLID. Junior Manager is 1 if one takes a middle or lower
management position; 0 otherwise. Senior Manager is 1 if one takes a upper or top management position; 0
otherwise.
% of Women is the fraction of female employees at each of the four-digit NOC by gender and educational group.
49
Table 2.5: Percentage of Full-time Employees in Each Occupation
HSD
HS
College
University
Management Occupations
3.69
8.49
8.92
16.98M
Business, Finance and Administrative
Occupations
9.53
23.72F
23.40F
18.46F
Natural and Applied Sciences and Related
Occupations
1.56
2.91
8.50
15.94M
Health Occupations
1.47
1.96
6.71
5.92
Occupations in Social Science, Education,
Government Service and Religion
0.80
1.64
4.85
25.80F
Occupations in Art, Culture, Recreation
and Sport
0.56
0.85
2.08
3.44
24.80F
23.83F
18.41F
8.89
Trades, Transport and Equipment Operators 29.72M
19.40M
18.22M
1.80
6.37
3.48
1.85
0.55
21.51M
13.72M
7.07
2.22
62.2
60.6
59.8
39.8
Occupations
Sales and Service Occupations
Occupations Unique to Primary Industry
Occupations Unique to Processing,
Manufacturing and Utilities
Duncan Segregation Index
Notes: Author’s calculation of the percentage of full-time employees in each of the ten occupational categories
within educational groups.
An occupation with a superscript F (M ) is considered as a female(male)-dominated occupation, where the proportion of female (male) workers in the occupation for an educational group exceeds 60% of the workforce in
the occupation for that educational group.
In this context, the Duncan Index is a demographic measure of the evenness with which two genders that belong
to the same educational group are distributed across the ten occupations. It is between 0 and 100. When it is
0, the proportion of female workers equal to the proportion of male workers in any of the ten occupations (no
segregation). When it is 100, men work in some occupations while women work in other occupations (complete
segregation).
50
Women are less likely to take managerial positions. This is particularly clear for
workers in the university group for whom the proportion of women taking junior and
senior managerial positions is 5.6 and 9.5 percentage points, respectively, lower than the
24.6% and the 17% of men taking junior and senior managerial positions, respectively. The
corresponding figures are 2 and 2.5 percentage points lower for women in the college group
taking junior and senior managerial positions, and 0.2 and 1.6 percentage points lower for
women in the high school group taking junior and senior managerial positions. For workers
in the HSD group, close to 89% of men and women do not take managerial positions. There
is no significant gender difference in the proportion of workers taking managerial positions
for the very low-educated workers.
Men and women are required to have different aptitude skills. Women are required to
have higher levels of verbal, numerical, and clerical abilities, as well as finger dexterity,
while men are required to have higher levels of spatial perception, form perception, motor
coordination, and manual dexterity. This is found for employees with education below
university. In the university-educated group, women are required to have a higher level of
finger dexterity, while men are required to have higher levels of general ability, numerical,
and spatial perception.
Workers in different educational groups are required to have different aptitude skills.
Very low-educated workers take jobs that require high levels of motor coordination, finger
dexterity, and manual dexterity, while university-educated workers take jobs that require
high levels of general learning, verbal, numerical abilities, and clerical perception.
51
In Table 2.5, I present the occupational distribution by education. While three quarters of
workers in the HSD group work in sales and service, trades, and manufacturing occupations,
the same fraction of workers with university education work in management, business and
finance , natural science, and social science. Workers with high school or college education
are mostly hired in administrative jobs, sales and service, and trades. On top of that, 13%
of high school graduates work in manufacturing. Individuals and aptitude skills are linked
through individuals’ occupations. Since people with different levels of education work in
different occupations, aptitude skill requirements are different across educational group.
The last row in Table 2.5 represents the Duncan segregation index, a measurement of
occupational segregation. The index is computed as, S =
1
2
P
j=1
|Mj − Fj |, where Mj
and Fj are the proportion of male and female workers in job j, respectively. It measures
the proportion of women (men) who would have to change occupations to obtain equal
distribution of occupations between men and women. The measure is between 0 and 100,
with 0 indicating no segregation and 100 indicating complete segregation.
The Duncan index falls over the four educational groups, but the clear drop appears
only when one examines the segregation for university-educated workers. For those without
university education, the Duncan index is close to 60%. For those with university education,
it drops to 40%. This means that university-educated men and women are more likely
to work in similar occupations than other workers. This is consistent with the finding
in Table 2.4 that university-educated men working in occupations with a greater average
52
Table 2.6: Gender Differences in Managerial Responsibilities
Highest-paid
in University Sample
Upper Level Management
-0.12∗∗∗
(0.028)
Budget
-0.085∗∗∗
(0.03)
Promotion
-0.063∗∗∗
(0.023)
Supervising
-0.10∗∗∗
(0.03)
FT Employees
in University Sample
-0.05∗∗∗
(0.005)
-0.075∗∗∗
(0.008)
-0.061∗∗∗
(0.013)
-0.081∗∗∗
(0.01)
Notes: In this table and the following tables, standard errors are reported in parentheses. ∗ denotes that the
coefficient is significantly different from zero at 10% level, ∗∗ at 5% level, and ∗∗∗ at 1% level.
The SLID provides information on a person’s managerial duties. This table reports Probit (marginal) estimates
of gender differences in the probability of taking the managerial duties. A negative value for a managerial duty
means that compared to men, women are less likely to be responsible for the duty. Probit model controls for
ten occupations, work experience, union status, sector of employment, marital status, major fields of study,
residential provinces, survey year, whether one is an immigrant, and whether one is handicapped.
Highest-paid in University Sample represents the numbers that are estimated using the full-time universityeducated employees whose wage is above the 90th percentile of the wage distribution. FT employees in
University Sample represents the numbers estimated using all full-time employees in university group.
A managerial duty is coded as a binary variable, 1 if one takes the duty, 0 otherwise. There are five of such
duties:
Upper Level Management: Whether one takes a upper/top level management position.
Budget: Whether one has an influence on budget or staffing.
Promotion: Whether one has an influence on pay raise or promotion.
Supervising: whether one’s job involves supervising employees.
percentage of females than other men.
Lastly, I examine the likelihood of university-educated women taking managerial responsibilities, relative to their male counterparts, and compare this with the likelihood of
the top 10% of the wage earners of university-educated women, relative to the top 10% of
university-educated men. Table 2.6 demonstrates that while compared to men, women on
average have a lower probability of being responsible for upper level management, supervising coworkers, determining coworkers’ promotion or pay raise, and planning a company’s
budget, these differences are even larger between highest-paid men and women than the
differences between average men and women in the university group. In particular, highestpaid men are 12% more likely to work in upper-level managerial positions than highest-paid
53
Figure 2.1: The Gender Gap at Various Points of the Wage Distribution
Notes: Author’s calculations of the logarithm of male-female wage ratio at each decile of the distribution.
The curve, FT, connects the wage gap at each decile of the wage distribution for the full-time employees.
Other four curves plot the wage gap along the wage distribution by educational groups: HSD for the full-time
employees whose education is below high school, HS for the full-time employees who graduated from high
school, College for the full-time employees who attended/completed some post-secondary education, and
University for the full-time employees who completed university degrees.
women. The corresponding figure is 5% when I use the entire university group. This implies
that the fact that women are underrepresented in the managerial positions is more important
in accounting for the gender gap at the 90th percentile of the wage distribution than the
average gender gap, which is supported by the decomposition results.
2.2.3
The Gender Gap Across the Wage Distribution
Figure 2.1 plots the gender gap at each decile of the distribution, where the solid curve
is the gender gap for the entire full-time sample and the other four curves plot the gender
gap by educational group. For example, the solid curve tells us that the gender gap at the
54
30th percentile is approximately 22%. This means that at the 30th percentile of the wage
distribution men earn approximately 25 cents more than women for every dollar earned.17
This figure shows that the pattern of the gender gap is strikingly different across educational groups, in particular between the HSD group and the university group. Contrary
to the HSD group for which the wage gap curve displays an inverse U-shape, the gender
gap for the university group increases throughout the wage distribution and the increase
accelerates above the 80th percentile. The gender wage gap changes from 12% at the bottom
of the wage distribution to 21% at the top of the wage distribution, increasing by nine
percentage points. This indicates the existence of a “glass ceiling” phenomenon: women
on the upper-tail of the wage distribution experience larger wage gaps than women on
the lower-tail of the wage distribution. For the HS and college groups, the gender gap
displays a small variation along the wage distribution. As workers with high school and
community college education compose 65% of the sample, the gender gap for the entire
sample displays a slightly declining trend along the wage distribution, which hides the glass
ceiling phenomenon because it only exists for university-educated women.
Another way to observe the existence of glass ceiling phenomenon is to examine the
underrepresentation of women among high-paid workers. In Table 2.7, I present the proportion of workers by educational group at different parts of the wage distribution for full-time
workers in Panel A, and the proportion of workers by gender in Panel B. Panel A shows
that low-educated workers and workers in the college group compose more than 50% of
17
Log-wage differentials reported throughout the paper are used as an approximation to percentage differences. The exact percentages can be obtained as the exponential of the log differential minus 1.
55
Table 2.7: The Proportion of Workers at Different Parts of the Wage Distribution
Below 1st
5th
10th
25th
50th
75th
90th
99th
Above 99th
Panel A: the proportion of workers by education at each part of the wage distribution
HSD
27.98
27.62
22.90
18.49
13.19
8.55
4.61
2.17
2.12
HS
19.11
22.07
21.11
21.34
19.04
15.37
10.12
5.95
4.25
College
44.51
43.76
47.40
50.33
54.34
53.97
48.97
34.31
23.31
University 8.40
6.56
8.59
9.84
13.44
22.11
36.29
57.57
70.33
Men
Women
Panel B: the proportion of workers by gender at each part of the wage distribution
35.13
28.05
40.57
45.51
51.65
61.29
67.46
71.80
78.26
64.87
71.95
59.43
54.49
48.35
38.71
32.54
28.20
21.74
Notes: The wage distribution for all full-time workers is divided into nine parts: (1) below the 1st percentage
(inclusive) of the wage distribution, (2) between the 1st (exclusive) and the 5th percentile (inclusive) of the wage
distribution, (3) between the 5th (exclusive) and the 10th percentile (inclusive) of the wage distribution, (4)
between the 10th (exclusive) and the 25th percentile (inclusive) of the wage distribution, (5) between the 25th
(exclusive) and the 50th percentile (inclusive) of the wage distribution, (6) between the 50th (exclusive) and
the 75th percentile (inclusive) of the wage distribution, (7) between the 75th (exclusive) and the 90th percentile
(inclusive) of the wage distribution, (8) between the 90th (exclusive) and the 99th percentile (inclusive) of the
wage distribution, (9) and above the 99th percentile of the wage distribution.
Panel A reports the proportion of full-time workers at each part of the wage distribution that belong to one of the
four educational groups. Panel B reports the proportion of full-time men and women at each part of the wage
distribution.
the workers below the 90th percentile of the wage distribution, whereas university-educated
workers compose more than half of the workers above the 90th percentile of the wage
distribution. Panel B shows that while more than half of the workers below the median
are women, the proportion of women drops substantially to 22% among the top 1% of
wage earners. This suggests that achievement in university education would help women
get into the high-paying occupations; however, it does not change the fact that women are
underrepresented among the highest-paid wage earners.
Furthermore, when I restrict my sample to the workers who are above the 90th percentile
of the wage distribution for full-time workers, I find that the women, who make it to the top
10% of wage earners, earn statistically the same as their male counterparts.18 This means
18
To do this, I pool men and women in one sample and use the workers whose log hourly wage is above the
90th percentile of the wage distribution for the pooled sample. Women in this high-wage group earn about 1%
56
that Canada’s glass ceiling exists not because of the gender gap among the highest-paid
individuals of all workers, but because of the highest-paid women of female workers earning
considerably less than the highest-paid men of male workers. Therefore, I examine the
explanations for the glass ceiling phenomenon by comparing male versus female labour
market characteristics and detailed occupational characteristics at top points of the wage
distribution.
2.3
2.3.1
Empirical Results
Quantile Decomposition Method
Using the regression-based decomposition approach developed by Firpo et al. (2009)
(RIF-regression-based decomposition, hereafter), I estimate how much of the gender gap
at a decile of the wage distribution is explained by gender differences in labour market
characteristics (which is called “composition effect”) and how much of the gender gap is
explained by gender differences in returns to labour market characteristics (which is called
“wage structure effect”).
A challenge of decomposing differences between the wage distribution for men and
women is that the average derivative of the distribution of explanatory variables with
respect to a covariate at a quantile of the distribution differs from the average derivative
of the unconditional wage distribution with respect to the covariate at that quantile. Firpo
less than men and the gender gap is statistically no different than zero.
57
et al. (2009) resolve this problem by estimating a gender-specific wage function with
the recentered influence function (RIF-regression). The coefficients estimated with RIFregression at a quantile of the wage distribution correspond to the marginal effects of the
covariates on the unconditional quantile of the wage distribution. Using RIF-regression
estimates, the unconditional decomposition method decomposes the wage gap into the
composition effect and wage structure effect at various points of the wage distribution as if
it were decomposing the wage gap at the mean.
Let γ̂g,v be the vector of the coefficients of the RIF-regression for group g at the v th
percentile of wage distribution. As shown in Fortin et al. (2011), the overall wage gap at the
v th percentile of wage distribution, δ̂Ov , can be decomposed with the RIF-regression-based
decomposition in the same way as for the wage gap at the mean, where the counterfactual
wage function is based on men’s covariates as the reference covariates and the coefficients
in women’s wage regression as the reference coefficients.19
δ̂Ov = (γ̂m0,v − γ̂w0,v ) +
K
X
X mk (γ̂mk,v − γ̂wk,v ) +
k=1
=
19
δ̂Sv
K
X
(X mk − X wk )γ̂wk,v
k=1
+
v
δ̂X
(2.1)
In lay terms, the counterfactual wage function estimates what hourly wage women would have earned if
they had the observed characteristics of men and their wage function remained unchanged.
58
where δ̂Sv is the wage structure effect at the v th percentile,
δ̂Sv = (γ̂m0,v − γ̂w0,v ) +
K
X
X mk (γ̂mk,v − γ̂wk,v ),
k=1
v
and δ̂X
is the composition effect at the v th percentile,
v
δ̂X
=
K
X
(X mk − X wk )γ̂wk,v .
k=1
Take work experience as an example. The composition effect of work experience at the
median is estimated by weighting the difference in the average number of years worked
between men and women with the coefficient of work experience for women at the median.
The wage structure effect is estimated by weighting the gender difference in coefficient at
the median of the wage distribution with the average number of years worked for men.
If the gender gap is fully explained by different labor market characteristics between men
and women, we would conclude that there is no unfair discrimination against women. Put in
another way, gender-pay-equity legislation addresses the gender gap that is not explained by
the composition effect.
2.3.2
Explained and Unexplained Proportion of Gender Gap
In Table 2.8, I present the independent variables in the decomposition analysis. Model 1
uses demographic characteristics (e.g. immigration status, marital status, etc), work experi-
59
Table 2.8: Variables Used in Decomposition Analysis
Variables
Dependent Variable
HSD and HS
College and University
Logarithm of Hourly Wage
Logarithm of Hourly Wage
immigrant, with disability, marital
status, # of children, province,
experience, union, public sector, year
immigrant, with disability, marital
status, # of children, province,
experience, union, public sector, year
fields of study, education
Model 2
M1 + occupational dummies
+ industry
M1 + occupational dummies
+ industry
Model 3
M1 + occupational attributes
+ % of women + industry
Model 1 + occupational attributes
+ % of women + industry
Independent Variables
Model 1 (M1)
Notes: An explanation of variable constructions is provided in the section of Data. Model 2 controls for 10
occupational dummy variables. The ten occupational categories are presented in Table 2.5. Occupational
attributes includes aptitude skills, workplace competitiveness, and the ranking of an individual’s managerial
position. For the college group, the variable of education accounts for whether one has completed a certificate
from a post-secondary educational institution. For the university group, variables of education account for
whether one has completed a four-year university degree, a Master’s degree, or a more advanced degree.
60
ence, union status, and the sector of employment. On top of that, Model 2 adds occupational
dummy variables and industry, while Model 3 controls for occupational attributes, the
percentage of female workers, and industry. Using different specifications, I estimate the
fraction of gender gap that is explained by the composition effect (fraction explained) and the
fraction of gender gap that is explained by the wage structure effect (fraction unexplained)
at various points of the wage distribution for each of the four educational groups. The
comparison between Models 2 and 3 reveals how much of the gender gap is explained
when I replace the occupational dummy variables, as commonly used in the literature, with
detailed occupational characteristics. Table 2.9 presents the results.
Relative to Model 1, the inclusion of occupation-attributes and the percentage of female
workers (Model 3) makes a larger contribution to the wage gap than the use of occupational
binary variables (Model 2). More importantly, while more than 50% of the wage gap on the
upper-tail of the wage distribution is due to the wage structure effect when I use Model 2,
the wage structure effect no longer plays a primary role in explaining the wage gap for most
of the workers on the upper-tail of the wage distribution when I use Model 3. For example,
for high-school dropouts at the 90th percentile of the wage distribution, 61.1% of the wage
gap is explained by composition effect, 12.3 percentage points greater than the proportion of
48.8% when I use Model 2. This suggests that gender differences in detailed occupational
characteristics are important in explaining the wage gap, in particular for workers who earn
more than 50% of the people in their gender-education group.
It is of interest to see that the “glass ceiling” phenomenon for university-educated
61
Table 2.9: Explained and Unexplained Proportion of Gender Wage Gap
10th
log hourly wage gap
Model 1:
Fraction explained
Fraction unexplained
Model 2:
Fraction explained
Model 3:
Fraction explained
log hourly wage gap
Model 1:
Fraction explained
Model 2:
Fraction explained
Model 3:
Fraction explained
log hourly wage gap
Model 1:
Fraction explained
Model 2:
Fraction explained
Model 3:
Fraction explained
30th
50th
70th
High-school Dropouts (HSD)
0.265
0.391
0.422
0.400
90th
95th
0.361
0.325
11.3%
89.7%
17.4%
82.6%
22.3%
77,7%
23.75%
76.25%
30.5%
69.5%
40.0%
60.0%
20.4%
79.6%
27.6%
72.4%
29.6%
70.4%
25.8%
74.2%
48.8%
51.2%
56.0%
44.0%
33.5%
42.7%
41.7%
66.5%
57.3%
58.3%
High School Graduates (HS)
0.240
0.262
0.255
0.235
61.1%
38.9%
64.6%
35.4%
0.22
0.228
17.1%
82.9%
23.7%
76.3%
19.2%
80.8%
17.4%
82.6%
21.9%
79.1%
11.0%
89.0%
5.8%
94.2%
-5.1%
105.1%
4.5%
95.5%
15.3%
84.7%
28.6%
71.4%
20.6%
79.4%
22.5%
22.9%
16.5%
28.5%
77.5%
77.1%
83.5%
71.5%
Community College Graduates (College)
0.206
0.225
0.220
0.232
47.7%
52.3%
36.4%
63.6%
0.200
0.188
27.9%
72.1%
14.6%
85.4%
9.6%
90.4%
7.7%
92.3%
9.06%
90.94%
15%
85%
18.6%
81.4%
-18.1%
118.1%
6.04%
93.96%
5.4%
94.6%
15.9%
84.1%
29.0%
71.0%
33.0%
67.0%
13.0%
9.8%
21.8%
31.0%
87.0%
91.2%
78.2%
69.0%
University Graduates (University)
0.121
0.180
0.172
0.183
55.0%
45.0%
53.1%
46.9%
0.214
0.230
24.0%
76.0%
28.0%
72.0%
34.8%
65.2%
45.9%
54.1%
43.9%
56.1%
48.3%
52.7%
55.7%
44.3%
63.8%
36.2%
72.6%
27.4%
log hourly wage gap
Model 1:
Fraction explained
-32.2%
-36.1%
8.1%
Fraction unexplained 132.2%
136.1%
91.9%
Model 2:
Fraction explained
-26.5%
23.3%
32.0%
76.7%
68.0%
Model 3:
Fraction explained
24.0%
37.0%
42.8%
63.0%
57.2%
Notes: See Table 2.8 for the explanation of three specifications.
62
women is mostly due to high-paid university-educated women having different occupational
characteristics than their male counterparts. The raw gap at the 95th percentile of the wage
distribution is 23%, 11 percentage points larger than the gender gap at the 10th percentile
of the wage distribution for university-educated workers. After accounting for differences
in labor market characteristics in Model 3, I find that the corresponding figures drop to
5.8% at the 95th percentile and 9.5% at the 10th percentile of the wage distribution. This
means that once gender differences in occupational characteristics are taken into account,
high-paid women with university education do not experience a greater wage gap than
low-paid women. On the contrary, when I use Model 2, which adopts occupational binary
variables, the unexplained gender gap is 11.3% at the 95th percentile and 12.4% at the 10th
percentile of the wage distribution. This shows that the use of occupational binary variables
is not sufficient to explain the glass ceiling for university-educated women.
A smaller explained proportion of the gender gap in Model 2 than in Model 3 arises from
aggregated occupational categories that are used to construct occupational binary variables.
Take two jobs as an example. Financial Managers and Restaurant Managers belong to the
same occupational category (Management Occupation), but the former on average are paid
significantly more than the latter. Financial managers are required to have more numerical
ability and to work in a more competitive environment than restaurant managers. This
example demonstrates how the use of occupational characteristics can reveal the impact of
such differences on the pay gap between the two jobs, whereas the use of occupational binary
variables cannot reveal it. Accounting for different occupational characteristics between
63
men and women is important in determining whether women are treated unfairly in the
workplace; since such differences are suppressed by aggregated occupational categories,
detailed occupational characteristics are needed in the analysis.
2.3.3
Gender Differences in Work Experience, Union, Sector, Degree
Attainment, and Fields of Study
While Table 2.9 presents evidence supporting the hypothesis that the inclusion of
detailed occupational characteristics is important in explaining the wage gap for all four of
the educational groups, it is also useful to know how gender differences in each covariate
contribute to the gender gap. For this purpose, I present the contribution of work experience,
union, sector, education, and detailed occupational characteristics in Tables 2.10 for HSD
and HS groups and Table 2.11 for college and university groups.20 I will start with the
composition effect of work experience, union, sector and education.
Gender differences in work experience make a positive and significant contribution to
the wage gap in each of the four educational groups, meaning that men work more than
women at all educational levels. Gender differences in union coverage are positive for
all educational groups except the university group for whom the differences are negative,
because for university-educated women, they are more likely to be unionized than men,
while for other educational groups, women are less likely to be union members. Women
20
Decomposition results in Tables 2.10 and 2.11 are estimated using Model 3. To save space, I do not
report the estimates for survey years, marital status, the number of children, age groups, residential provinces,
whether one is an immigrant, and whether one is handicapped. Full results are available upon request.
64
are more likely to work in the public sector than men, but this difference is very small for
low-educated workers compared to that for high-educated workers.
Men and women are equally likely to complete a college/trade program, but the proportion of women completing university degrees is lower than men above the 30th percentile of
the wage distribution. One explanation is that women fall behind men in the completion
of Master’s and professional degrees (degrees in medicine and Doctorate). Since workers
with more education are more likely located on the upper-tail of the wage distribution, fewer
women having post-graduate education than men is particularly important for the high-wage
earners. Table 2.11 shows that the wage gap would be diminished by 8.7% (0.02/0.23) at the
95th percentile of the university-educated workers if the proportion of women completing
graduate degrees were the same as that of men.
Gender differences in the fields of study play a small role in accounting for the gender
gap.21 The small impact is due to the finding that there is a considerable variation in fields
of study across genders. Men are more likely to graduate from architecture, engineering
and applied sciences while women are more likely to graduate from health and education.
Larger gender differences in architecture, engineering and applied sciences offset smaller
differences in health and education. Thus, differences in the fields of study, which are the
weighted sum of the difference in each field of study, are small.22
21
This study is not the only study that finds that major fields of study do not appear as important as jobrelated attributes.Drolet (2002a) uses the SLID 1997 and finds that while gender differences in actual work
experience explain up to 50% of the gender gap, only 5% of the gender gap at the mean is explained by gender
differences in major fields of study.
22
In Appendix B, I report the decomposition results when only covariates in Model 1 are included in the
0.05
(0.014)
Composition
Effects
0.012
(0.002)
-0.0006
(0.0005)
0.011
(0.004)
-0.01
(0.007)
0.004
(0.005)
0.00003
(0.0003)
0.03
(0.012)
0.03
(0.008)
0.07
(0.12)
Composition
Effects
Wage
Structure
Effects
0.2
(0.02)
0.027
(0.007)
0.003
(0.004)
0.20
(0.058)
0.12
(0.07)
-0.04
(0.06)
-0.04
(0.02)
-0.05
(0.013)
0.04
(0.02)
Wage
Structure
Effects
0.19
(0.014)
0.06
(0.02)
Composition
Effects
0.025
(0.003)
-0.0005
(0.0006)
0.026
(0.005)
-0.007
(0.01)
0.004
(0.005)
0.0004
(0.0006)
0.04
(0.016)
0.035
(0.01)
0.13
(0.015)
Composition
Effects
30th
Wage
Structure
Effects
0.22
(0.02)
0.037
(0.010)
0.003
(0.003)
0.11
(0.05)
-0.02
(0.07)
0.07
(0.06)
-0.03
(0.02)
-0.017
(0.013)
0.031
(0.02)
Wage
Structure
Effects
0.27
(0.017)
0.04
(0.015)
Composition
Effects
0.037
(0.005)
-0.0008
(0.001)
0.035
(0.005)
0.0002
(0.01)
-0.004
(0.008)
0.0008
(0.008)
0.06
(0.02)
0.05
(0.02)
0.18
(0.2)
Composition
Effects
50th
Wage
Structure
Effects
0.23
(0.02)
-0.0004
(0.01)
-0.009
(0.004)
-0.062
(0.04)
-0.12
(0.08)
0.09
(0.07)
0.035
(0.07)
-0.003
(0.02)
0.02
(0.02)
Wage
Structure
Effects
0.25
(0.2)
0.07
(0.017)
Composition
Effects
0.039
(0.005)
-0.003
(0.002)
0.03
(0.007)
0.007
(0.02)
0.005
(0.01)
0.0008
(0.001)
0.05
(0.02)
0.03
(0.02)
0.17
(0.025)
Composition
Effects
70th
Wage
Structure
Effects
0.17
(0.018)
-0.004
(0.01)
-0.02
(0.006)
-0.06
(0.04)
-0.20
(0.11)
0.04
(0.12)
0.005
(0.03)
0.005
(0.015)
0.014
(0.02)
Wage
Structure
Effects
0.24
(0.027)
0.11
(0.02)
Composition
Effects
0.035
(0.006)
-0.001
(0.002)
0.05
(0.01)
0.03
(0.03)
0.009
(0.008)
0.001
(0.002)
0.07
(0.04)
0.03
(0.02)
0.23
(0.038)
Composition
Effects
90th
Wage
Structure
Effects
0.11
(0.02)
-0.02
(0.02)
-0.02
(0.008)
-0.04
(0.05)
-0.32
(0.20)
0.08
(0.15)
0.04
(0.04)
0.02
(0.02)
-0.04
(0.03)
Wage
Structure
Effects
0.15
(0.044)
Composition
Effects
Effects
0.08
(0.03)
0.03
(0.007)
0.001
(0.001)
0.06
(0.012)
0.02
(0.02)
0.008
(0.02)
0.002
(0.003)
0.08
(0.08)
-0.006
(0.02)
Composition
Effects
Effects
0.21
(0.04)
95th
Wage
Structure
Effects
0.14
(0.03)
-0.03
(0.02)
-0.006
(0.008)
-0.04
(0.05)
-0.25
(0.23)
0.095
(0.15)
0.05
(0.06)
0.03
(0.03)
0.03
(0.04)
Wage
Structure
Effects
0.12
(0.05)
0.02
0.01
0.03
-0.009
0.03
-0.01
0.02
-0.001
0.005
-0.007
-0.004
0.01
(0.003)
(0.009)
(0.004)
(0.01)
(0.003)
(0.01)
(0.003)
(0.01)
(0.003)
(0.02)
(0.004)
(0.016)
Public
-0.0005
0.0006
-0.002
-0.0005
-0.005
-0.013
-0.006
-0.03
-0.005
-0.015
-0.007
-0.02
(0.0006)
(0.004)
(0.001)
(0.005)
(0.002)
(0.005)
(0.002)
(0.006)
(0.002)
(0.008)
(0.003)
(0.01)
Experience
0.016
0.14
0.025
-0.05
0.02
-0.06
0.03
-0.05
0.03
-0.05
0.03
-0.08
(0.003)
(0.08)
(0.004)
(0.06)
(0.003)
(0.04)
(0.004)
(0.04)
(0.006)
(0.046)
(0.008)
(0.06)
Aptitude skills
-0.07
0.13
-0.10
-0.001
-0.06
-0.04
-0.03
-0.19
0.02
-0.30
0.04
0.04
(0.01)
(0.11)
(0.01)
(0.09)
(0.01)
(0.07)
(0.01)
(0.09)
(0.02)
(0.12)
(0.02)
(0.13)
Competitiveness
0.002
0.13
-0.007
0.23
-0.001
0.12
0.003
0.05
0.007
-0.01
0.005
0.09
(0.003)
(0.06)
(0.003)
(0.06)
(0.003)
(0.05)
(0.003)
(0.06)
(0.005)
(0.08)
(0.006)
(0.10)
Managerial
0.0005
-0.01
0.001
-0.015
0.001
-0.03
0.002
-0.006
0.003
0.01
0.004
-0.003
ranking
(0.0005)
(0.015)
(0.001)
(0.14)
(0.0008)
(0.01)
(0.001)
(0.01)
(0.002)
(0.02)
(0.003)
(0.04)
% of women
0.08
-0.005
0.1
0.01
0.03
-0.02
0.03
-0.04
0.04
-0.007
0.01
-0.003
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.03)
(0.02)
Industry
0.02
0.03
0.01
0.03
0.02
0.03
0.03
0.02
0.002
0.02
0.006
-0.02
(0.006)
(0.01)
(0.008)
(0.01)
(0.008)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.014)
(0.024)
Notes: Estimation is conducted with the RIF-regression-based decomposition method and uses full-time salaried employees aged 25-54 who did not attend post-secondary education. Numbers in bold are
significantly different from 0 at 10% level. Covariates in Model 3 are included in the estimation. Variables that are not reported are survey years, marital status, the number of children, age groups, residential
provinces, whether one is an immigrant, and whether one is handicapped.
Accounted for by:
Union
Total
HS
Industry
Managerial
ranking
% of women
Competitiveness
Aptitude skills
Experience
Public
Accounted for by:
Union
Total
HSD
10th
Table 2.10: The Contribution of Subsets of Covariates for the Low-Educated Workers
65
0.03
(0.02)
Composition
Effects
-0.0003
(0.0003)
0.002
(0.008)
0.007
(0.001)
-0.001
(0.001)
0.02
(0.002)
-0.11
(0.01)
-0.007
(0.003)
0.004
(0.0007)
0.11
(0.02)
-0.002
(0.01)
0.03
(0.012)
Composition
Effects
10th
Wage
Structure
Effects
0.09
(0.02)
-0.01
(0.02)
0.03
(0.02)
0.02
(0.007)
0.02
(0.004)
0.20
(0.056)
-0.01
(0.06)
0.16
(0.04)
0.001
(0.001)
-0.0009
(0.02)
0.03
(0.006)
Wage
Structure
Effects
0.19
(0.014)
0.07
(0.01)
Composition
Effects
0.00005
(0.0003)
-0.007
(0.007)
0.009
(0.001)
-0.012
(0.001)
0.03
(0.002)
-0.05
(0.007)
-0.0007
(0.003)
0.004
(0.0007)
0.06
(0.01)
-0.006
(0.005)
0.02
(0.012)
Composition
Effects
30th
Wage
Structure
Effects
0.11
(0.01)
0.026
(0.014)
0.006
(0.016)
0.02
(0.006)
0.004
(0.004)
0.03
(0.03)
-0.11
(0.05)
0.17
(0.035)
-0.02
(0.007)
-0.01
(0.01)
0.04
(0.006)
Wage
Structure
Effects
0.21
(0.012)
0.07
(0.01)
Composition
Effects
-0.0001
(0.0002)
-0.014
(0.007)
0.006
(0.001)
-0.015
(0.001)
0.02
(0.002)
0.008
(0.006)
0.008
(0.002)
0.004
(0.001)
0.02
(0.01)
0.006
(0.004)
0.05
(0.01)
Composition
Effects
50th
Wage
Structure
Effects
0.10
(0.01)
0.03
(0.011)
0.007
(0.01)
0.01
(0.005)
-0.02
(0.004)
-0.045
(0.024)
-0.18
(0.04)
0.09
(0.03)
-0.02
(0.01)
-0.02
(0.009)
0.02
(0.005)
Wage
Structure
Effects
0.17
(0.01)
0.10
(0.01)
Composition
Effects
-0.0000
(0.0002)
-0.01
(0.007)
0.003
(0.0007)
-0.015
(0.002)
0.02
(0.002)
0.05
(0.008)
0.02
(0.004)
0.005
(0.0008)
-0.005
(0.01)
0.01
(0.005)
0.10
(0.01)
Composition
Effects
70th
Wage
Structure
Effects
0.08
(0.01)
0.018
(0.01)
0.005
(0.01)
0.009
(0.005)
-0.031
(0.005)
-0.01
(0.02)
-0.25
(0.06)
0.05
(0.05)
-0.025
(0.008)
-0.01
(0.01)
0.01
(0.006)
Wage
Structure
Effects
0.13
(0.13)
0.13
(0.01)
Composition
Effects
-0.0002
(0.0004)
-0.00004
(0.01)
0.0001
(0.0006)
-0.016
(0.002)
0.017
(0.003)
0.09
(0.01)
0.02
(0.004)
0.009
(0.001)
-0.03
(0.02)
0.02
(0.008)
0.14
(0.02)
Composition
Effects
90th
Wage
Structure
Effects
0.08
(0.02)
-0.004
(0.017)
-0.014
(0.02)
0.006
(0.007)
-0.03
(0.006)
0.02
(0.03)
-0.39
(0.07)
-0.06
(0.05)
-0.007
(0.008)
-0.03
(0.01)
-0.002
(0.009)
Wage
Structure
Effects
0.06
(0.02)
Composition
Effects
Effects
0.17
(0.02)
-0.0005
(0.0004)
0.01
(0.02)
-0.002
(0.0007)
-0.014
(0.003)
0.01
(0.003)
0.07
(0.01)
0.02
(0.004)
0.01
(0.002)
-0.02
(0.02)
0.02
(0.01)
Composition
Effects
Effects
0.10
(0.02)
95th
Wage
Structure
Effects
0.06
(0.03)
-0.02
(0.02)
-0.02
(0.02)
0.02
(0.008)
-0.02
(0.007)
0.05
(0.03)
-0.28
(0.07)
0.07
(0.06)
-0.02
(0.02)
-0.02
(0.01)
-0.006
(0.07)
Wage
Structure
Effects
0.09
(0.03)
-0.009
-0.02
0.002
-0.007
0.01
-0.002
0.01
-0.004
0.01
0.007
0.02
0.05
(0.003)
(0.02)
(0.001)
(0.01)
(0.001)
(0.01)
(0.002)
(0.01)
(0.003)
(0.02)
(0.004)
(0.02)
Major
-0.008
0.01
-0.003
-0.03
-0.008
-0.01
0.0004
-0.02
-0.006
-0.002
0.0007
0.01
(0.01)
(0.03)
(0.007)
(0.01)
(0.006)
(0.01)
(0.006)
(0.01)
(0.007)
(0.01)
(0.01)
(0.01)
Union
-0.04
0.007
-0.03
-0.02
-0.02
0.03
-0.008
-0.03
0.008
-0.02
0.01
-0.008
(0.006)
(0.01)
(0.004)
(0.008)
(0.003)
(0.006)
(0.002)
(0.007)
(0.003)
(0.008)
(0.004)
(0.01)
Public
-0.02
0.02
-0.03
-0.05
-0.02
-0.01
-0.02
-0.02
-0.01
-0.02
-0.01
-0.01
(0.006)
(0.02)
(0.005)
(0.01)
(0.004)
(0.01)
(0.004)
(0.01)
(0.004)
(0.02)
(0.006)
(0.02)
Experience
0.03
0.02
0.04
0.04
0.03
0.01
0.03
-0.05
0.02
-0.008
0.02
-0.03
(0.006)
(0.1)
(0.004)
(0.05)
(0.004)
(0.03)
(0.004)
(0.03)
(0.004)
(0.03)
(0.005)
(0.04)
Aptitude Skills
0.03
-0.14
0.06
-0.20
0.05
-0.11
0.05
-0.05
0.02
0.09
0.008
-0.06
(0.01)
(0.18)
(0.008)
(0.10)
(0.007)
(0.076)
(0.007)
(0.08)
(0.009)
(0.1)
(0.01)
(0.13)
Competitiveness -0.002
0.22
0.006
0.016
0.02
-0.12
0.02
-0.1
0.015
-0.07
0.01
-0.01
(0.009)
(0.11)
(0.005)
(0.07)
(0.004)
(0.05)
(0.004)
(0.05)
(0.004)
(0.07)
(0.006)
(0.09)
Managerial
0.01
-0.02
0.02
0.005
0.02
-0.006
0.02
-0.001
0.06
0.05
0.04
0.02
ranking
(0.005)
(0.01)
(0.003)
(0.006)
(0.003)
(0.005)
(0.003)
(0.006)
(0.005)
(0.01)
(0.008)
(0.015)
% of women
0.03
0.04
-0.002
-0.04
-0.01
-0.02
-0.02
-0.03
0.007
-0.04
0.02
-0.03
(0.02)
(0.06)
(0.01)
(0.03)
(0.01)
(0.02)
(0.01)
(0.03)
(0.01)
(0.03)
(0.015)
(0.04)
Industry
0.02
0.04
0.02
0.03
0.008
0.007
0.01
-0.02
0.03
-0.02
0.04
-0.03
(0.01)
(0.03)
(0.007)
(0.01)
(0.006)
(0.01)
(0.006)
(0.01)
(0.007)
(0.02)
(0.01)
(0.02)
Notes: Estimation is conducted with the RIF-regression-based decomposition method and uses full-time salaried employees aged 25-54 who completed post-secondary education. Numbers in bold are
significantly different from 0 at 10% level. Covariates in Model 3 are included in the estimation. Variables that are not reported are survey years, marital status, the number of children, age groups, residential
provinces, whether one is an immigrant, and whether one is handicapped.
Accounted for by:
Education
Total
University
Industry
Managerial
ranking
% of women
Competitiveness
Aptitude Skills
Experience
Public
Union
Major
Accounted for by:
Education
Total
College
Table 2.11: The Contribution of Subsets of Covariates for the High-Educated Workers
66
67
2.3.4
Gender Differences in Occupational Characteristics
Even though the inclusion of occupational characteristics and industry increases the
explained fraction substantially for high school dropouts (Table 2.9), Table 2.10 tells us
that it is mostly because very low-educated men and women work in different occupations.
The composition effect of aptitude skills, workplace competitiveness, and the ranking of
managerial positions is not statistically different from 0 along the wage distribution.23
In contrast, Tables 2.10 and 2.11 show that gender differences in aptitude skills play
a significant role for high school and community college graduates and for most of the
university graduates, and gender differences in workplace competitiveness and the ranking of
managerial positions are particularly important for high-educated workers on the upper-tail
of the wage distribution.
I present the fraction of the gender gap that is explained by the composition effect
of aptitude skills, workplace competitiveness, the ranking of managerial positions, the
percentage of women in the workplace, and industry in Tables 2.12 and 2.13 for loweducated and high-educated employees, respectively. I find that for high school dropouts, the
fraction of the gender gap explained by differences in aptitude skills is very small, ranging
between -5% and 2% below the 90th percentile of the wage distribution. The composition
effect of aptitude skills makes a slightly larger contribution above the 90th percentile of the
RIF-regression decomposition method. While composition effects of work experience, union coverage, sector
of employment, achievement of post-secondary education degrees, and major fields of study are greater when I
do not include detailed occupational characteristics and industry in the estimation, major findings are consistent
with the findings in Table 2.10 and 2.11.
23
The only exception is at the 10th percentile of the wage distribution, where the gender difference in
aptitude skills is significantly different from 0 at 10% level.
68
wage distribution; however, this contribution is modest, relative to the faction explained by
the percentage of women in the workplace.24 This finding suggests that differences between
male and female work at the low-end of educational distribution cannot be captured by
gender differences in aptitude skill requirements.
The extent to which the gender gap is attributable to gender differences in aptitude skills
varies at different points of the wage distribution. Examples are the contribution of aptitude
skills for community college graduates, which is -52.6% at the 10th percent of the wage
distribution and 46% at the 90th percentile, and the contribution for high school graduates,
which is -29% at the 10th percentile and 8% at the 90th percentile. A negative contribution on
the lower-tail of the wage distribution means that women are required to have more aptitude
skills than men. This finding arises from the fact that more than 60% of administrative
positions (e.g. Administrative Clerks) are taken by women with high school or community
college education and therefore women are required to have significantly more clerical skills
than men. A positive contribution on the upper-tail of the wage distribution comes from men
taking occupations that require more clerical and spatial perceptions. For the former, men
are more likely to take upper-level administrative positions such as Executive Assistants,
which require more clerical skills than office staff positions. For the latter, men are more
likely to work as mechanics, computer programmers and engineers, which require a high
24
In the HSD group, the percentage of women in the workplace on average is significantly lower for men than
for women. RIF regression results show that the percentage of women in the workplace is negatively correlated
with the wage values along the wage distribution for women in the HSD group. Thus, the composition
effect, which is the difference in the percentage of women at workplace between men and women (a negative
value) multiplied by the (negative) coefficient on the percentage of women at the τ th percentile of the wage
distribution, makes a positive contribution to the gender gap at the τ th percentile of the wage distribution.
69
level of spatial perception.
As workers with different education levels are concentrated in different occupations,
Tables 2.12 and 2.13 show that gender differences in specific types of skill play different
roles, depending on the educational group of interest. Specifically, clerical perception is
important in accounting for the wage gap along the wage distribution for workers in the
high school and community college groups. Spatial perception, which is most needed
in engineering and computer programming, is important in explaining the wage gap for
community college and university graduates. General learning ability, which is most needed
in managerial positions, is essential in explaining the wage gap for university-educated
workers.
Gender differences in aptitude skills explain a substantial proportion of the gender gap
for university-educated workers below the 90th percentile of the wage distribution, but not
for the top 10% of wage earners. Table 2.13 shows that the contribution of aptitude skills is
9% of the gender gap at the 90th percentile of the wage distribution for university-educated
workers and 3.5% at the 95th percentile of the wage distribution. This is trivial relative to
the contribution for the university-educated workers below the 90th percentile of the wage
distribution, which ranges between 26% and 31%. Moreover, the contribution of almost
all the aptitude skills above the 90th percentile is negligible, with the exception of general
learning ability, which explains about 8% of the gender gap. This finding implies that
gender differences in managerial positions play an important role in explaining the wage gap
for high-paid workers. However, not all levels of managerial ranking matter in explaining
70
the wage gap. It is gender differences in upper/top managerial positions that explain why
high-paid women earn much less than high-paid men.
Tables 2.12 and 2.13 tell us that gender differences in working at upper/top management
levels have little impact in explaining the gender gap for workers without university education; however, for workers in the university group, these differences explain an increasing
fraction of the gender gap as one moves from the bottom to the top of the wage distribution.
There is 4% of the gender gap explained by the composition effect of senior managerial
positions at the 10th percentile of the wage distribution for university-educated workers,
about 20 percentage points smaller than the fraction explained by aptitude skills. The
fraction explained by working at senior managerial positions increases to 14% at the 95th
percentile of the wage distribution, 10 percentage points greater than the fraction explained
by aptitude skills at the same point of the wage distribution. Turning to gender differences
in working middle/lower management levels, I find that they play little role in explaining
the gender gap for all of the four educational groups.
Another factor that explains the gender gap for the highest-paid university-educated
workers is workplace competitiveness. It explains 7% of the gender gap at the 90th percentile
and 5% at the 95th percentile of the wage distribution, greater than the contribution of
nine aptitude skills at the corresponding percentiles of the wage distribution. Thus, men
taking (or being given) upper/top levels of managerial responsibility and working in a
highly competitive environment are the major reasons that university-educated women are
underrepresented among the top 10% of university-educated wage earners.
71
Similar to the composition effect of senior managerial positions, the composition effect
of workplace competitiveness explains less than 1% of the gender gap for low-educated
workers and 1% - 3% of the gender gap for workers in the community college group.
Workplace competitiveness and working at senior managerial positions are high-wage
determinants. Thus, these two occupation attributes are critical in explaining the gender
gap at the high-end of the wage (and education) distribution, but are not important for other
workers.
We have seen that gender differences in occupational attributes contribute to the wage
gap for workers with high school education and post-secondary education, but they play
a very small role for workers with education below high school. An explanation is that
although men and women in the HSD group work in different occupations – men working
in blue-collar jobs and women working in sales and service jobs – they are not required to
have high levels of skills regardless of their gender and occupation.
In Table 2.14, I present the average wage for lowest-educated workers in the occupations
that hire 80% of the HSD sample. Service and sales occupation are female-dominated,
with 60% of the workforce with education below high school being women. Occupations
in trade and transport and occupations in manufacturing and utilities are male-dominated:
female workers make up 37% of the lowest-educated workforce in trade and transport, and
7% in manufacturing and utilities. Occupations in trade and transport and occupations in
manufacturing and utilities on average pay $14 (in 1993 constant dollars) and $12.50 and $3
72
Table 2.12: Fraction of Gender Gap Explained by Differences in Occupational Characteristics and Industry (%) for Low-educated Workers
10th
30th
50th
High-school Dropouts(HSD)
-4.86
-1.84
0.06
1.78
0.80
2.92
70th
90th
95th
1.73
4.55
8.30
2.56
6.77
3.40
Verbal Ability
-0.96
-1.17
-0.51
-0.21
-3.14
-1.72
Numerical Ability
0.09
0.10
0.12
0.07
0.05
0.03
Clerical Perception
-0.72
-3.14
-4.73
-5.18
-2.56
-2.46
Spatial Perception
-4.21
1.38
2.22
3.08
7.97
3.00
Form Perception
1.93
0.64
0.09
-0.53
-4.61
-6.10
Motor Coordination
-0.21
-2.46
-2.62
-3.29
4.15
5.87
Finger Dexterity
-1.05
-0.38
-0.48
-0.49
-0.44
-2.68
Manual Dexterity
-1.50
2.39
3.04
3.71
4.15
7.43
Workplace Competitiveness
1.41
0.99
-0.95
1.29
2.43
2.41
Managerial Ranking
Not a Manager
0.01
0.02
0.10
0.03
0.19
0.07
0.19
0.07
0.36
0.11
0.73
0.18
Junior Manager
-0.01
0.00
0.00
0.00
0.02
0.07
Senior Manager
0.01
0.06
0.12
0.13
0.03
0.48
% of Women
12.98
11.16
13.30
12.95
18.19
24.95
Industry
9.89
8.92
11.57
7.05
8.48
-1.95
High School Graduates (HS)
-28.74
-39.36
-22.74
-4.16
-5.13
-4.08
-13.87
-1.47
7.66
-2.33
15.83
-4.79
Verbal Ability
-2.16
-2.66
-0.67
-9.78
-10.96
-7.79
Numerical Ability
7.39
6.77
-0.14
-3.15
-6.51
-4.31
Clerical Perception
-21.17
-24.46
-11.85
-2.54
17.27
23.71
Spatial Perception
-5.55
-4.54
-2.07
6.86
16.75
14.91
Form Perception
-0.15
-0.16
0.74
0.89
-1.00
-2.03
Motor Coordination
-3.73
-5.16
-1.54
-1.41
0.64
2.42
Finger Dexterity
-2.65
-3.26
-1.24
0.50
1.62
3.02
Manual Dexterity
3.45
-0.76
-1.87
1.18
-7.81
-9.30
-1.14
-2.64
-0.56
1.18
3.23
2.41
Managerial Ranking
Not a Manager
0.20
0.10
0.45
0.19
0.43
0.17
0.69
0.32
1.19
0.53
1.60
0.65
Junior Manager
-0.02
-0.02
-0.01
-0.05
-0.07
-0.05
Senior Manager
0.12
0.28
0.27
0.42
0.73
1.00
% of Women
33.28
37.44
12.68
11.94
17.43
4.69
Industry
4.96
3.80
7.99
11.64
1.12
2.46
Aptitude Skills
General Learning
Aptitude Skills
General Learning
Notes: Estimation is conducted with the RIF-regression-based decomposition method and uses full-time
salaried employees aged 25-54 who did not attend post-secondary education. Covariates in Model 3 are
included in the estimation.
73
Table 2.13: Fraction of Gender Gap Explained by Differences in Occupational Characteristics and Industry (%) for High-educated Workers
10th
30th
50th
70th
Community College Graduates (College)
Aptitude Skills
General Learning
90th
95th
-52.64
-5.82
-22.41
-2.87
3.73
-1.63
17.21
-1.55
45.66
-3.11
36.20
-3.82
Verbal Ability
-2.61
-5.73
-8.52
-11.00
-10.33
-4.07
Numerical Ability
2.82
0.12
-1.12
-1.33
-0.80
26.85
Clerical Perception
-29.36
-17.75
-4.63
3.49
11.90
11.47
Spatial Perception
-10.92
7.95
14.55
21.03
34.92
26.85
Form Perception
-1.31
-3.61
-1.46
-2.05
3.97
1.51
Motor Coordination
-8.99
-3.85
1.87
2.77
5.15
3.30
Finger Dexterity
-2.16
-0.35
0.68
1.14
2.01
1.41
Manual Dexterity
5.70
3.68
3.97
4.71
1.97
0.42
-3.29
-0.31
3.72
7.69
12.10
9.74
Managerial Ranking
Not a Manager
1.86
1.22
1.87
1.23
1.88
1.23
2.13
1.38
4.48
2.86
5.23
3.25
Junior Manager
0.20
0.19
0.17
0.16
0.16
-0.12
Senior Manager
0.44
0.45
0.48
0.58
1.47
2.10
% of Women
51.86
24.94
7.56
-2.38
-14.14
-12.58
Industry
-0.79
-2.83
2.88
5.97
7.84
9.80
University Graduates (University)
Aptitude Skills
General Learning
24.57
31.21
30.74
17.75
29.89
12.43
25.74
11.21
8.94
8.42
3.55
7.68
Verbal Ability
1.14
0.57
0.31
-0.14
-0.19
0.10
Numerical Ability
-0.85
0.93
1.81
2.05
1.97
0.72
Clerical Perception
-6.31
-0.86
0.41
0.34
0.90
0.79
Spatial Perception
-3.89
3.12
9.14
10.98
-2.81
-5.38
Form Perception
10.08
3.97
1.28
-1.51
0.17
-1.80
Motor Coordination
-0.86
0.25
0.12
-0.04
-0.12
-0.13
Finger Dexterity
-3.99
4.19
3.32
2.00
0.06
0.17
Manual Dexterity
-1.96
0.82
1.07
0.84
0.54
1.39
-1.94
3.51
10.07
9.79
7.32
4.75
Managerial Ranking
Not a Manager
12.81
8.16
12.12
7.78
10.69
6.86
13.52
8.54
19.36
11.54
25.19
14.25
Junior Manager
0.70
0.80
0.70
0.56
-0.83
-2.89
Senior Manager
3.95
3.54
3.12
4.43
8.65
13.82
% of Women
21.14
-0.95
-8.13
-8.62
3.30
6.76
Industry
19.30
10.44
4.65
6.65
13.62
16.19
Notes: Estimation is conducted with RIF-regression-based decomposition method and uses full-time salaried
employees aged 25-54 who completed post-secondary education.
74
Table 2.14: O*Net Characteristics in Service, Trade and Manufacturing Occupations
(1)
Sales and Service
0.54
(2)
Trades and Transport
0.66
(3)
Manufacturing and Utilities
0.62
Noise
0.54
0.74
0.78
Contaminants
0.46
0.74
0.73
Hazardous Equipment
0.20
0.65
0.60
Indoors Without
Environmental Control
0.27
0.60
0.61
Very Hot or Cold
Temperatures
0.36
0.63
0.53
Wear Safety Equipment
0.35
0.82
0.86
Physical Activities
Attributes of Workers with Education Below High school
% of Women
0.60
0.37
0.07
Average Hourly Wage
2.22
2.63
2.46
Notes: Three categories of occupations are Sales and Service occupations, Trades, Transport and Equipment
Operators and Related occupations, and Occupations Unique to Processing, Manufacturing and Utilities.
O*Net occupations are matched to National occupational code in the SLID. When there is more than one
O*Net occupation for an SLID occupation, the characteristic is weighted by the fraction of workers in each of
the O*Net occupations that comprise a single SLID occupation. Each of the O*Net characteristics has a score
between 0 and 1 (inclusive). The questions of O*Net characteristics are listed in Table B1.
75
more than the average hourly wage in sales and service occupations, respectively. The pay
differences between male-dominated and female-dominated occupations partly explain the
wage gap for the lowest-educated workers.
As explained in hedonic wage theory, one potential reason for higher average wages
in male-dominated occupations is an unfavorable work environment in these occupations.
Table 2.14 presents the average score of O*Net attributes that reflect the physical work conditions in the occupations. A higher score means worse work conditions. Male-dominated
occupations have more physical activities and are more likely to involve exposure to contaminants and hazardous equipment. Workers in these occupations are required to have higher
endurance for unpleasant environments, e.g., noise and very hot/cold temperatures in the
workplace. To prevent injuries, trade and manufacturing occupations require workers to
wear safety equipment much more often than sales and services occupations.
Overall, this section presents evidence supporting the hypothesis that male-dominated
and female-dominated occupations pay differently largely because they require different
levels of aptitude skills and have different work environments such as physical work conditions and workplace competitiveness. Whether the pay difference arising from different
occupational characteristics should be a public concern depends on the reasons as to why
women do not work in male-dominated occupations (e.g. oil drilling or surgery) knowing
that “male” jobs pay more than “female” jobs. If women experience barriers that prevent
them from entering male-dominated occupations, the pay gap between male and female
jobs concerns the pay-equity legislations. However, if women dislike working in unpleas-
76
ant work conditions or women prefer less demanding jobs so that they can devote more
time to their family, then their occupational choices should be respected. Unfortunately,
I cannot observe the characteristics such as innate ability and preferences that determine
a person’s occupation. Thus, I cannot conclude whether observed gaps in pay are due to
unfair discrimination against women in Canada.
Finally, evidence in this section suggests that the use of the unexplained gender gap
as evidence for unfair discrimination is problematic. Table 2.9 shows that for high school
and college graduates, the explained fraction of the gender gap on the lower-tail of the
wage distribution is very small. One reason is that gender differences in aptitude skills
make a negative contribution to the gender gap on the lower-tail of the wage distribution,
while gender differences in the percentage of women in the workplace make a positive
contribution to the gender gap at the same position of the distribution. They cancel each other
out, which results in a small proportion of gender gap explained by gender differences in
labor market characteristics. In fact, the negative contribution of aptitude skills is substantial,
ranking between -20% and -50%. This suggests that for high school and community college
graduates, the gender gap on the lower-tail of the wage distribution largely arises from
men and women taking different jobs and their jobs requiring different aptitude skills. This
example shows that the presence of an unexplained gender gap is not conclusive evidence
for the presence of unfair discrimination against women in the labour market.
77
2.3.5
Accounting for Selection into Paid Work
This section investigates how sample selection that is induced by non-employment affects
the gender gap by educational group. This study uses alternative imputation techniques to
recover missing wages along the wage distribution. Using the quantile regression method by
Koenker and Bassett (1978), I estimate the wage gap by regressing observed and imputed
wage values on a gender dummy at various points of the wage distribution. An advantage of
the quantile estimator is that as long as the imputed wage value for an individual is at the
same side of the v th percentile of the wage distribution as the actual wage if he/she were
employed, the estimate of the wage gap at the v th percentile is unbiased. A proof is provided
in Appendix A.
I first exploit the panel nature of the SLID. For those not in work in a given year, t, the
imputation procedure searches backward and forward to recover wage observations from
the nearest wave, t0 , in the sample. In practice, it imputes yit for Iit = 0 with wit0 when
Iit0 = 1. This imputation implicitly assumes that for an individual i, his/her latent wage
position with respect to the v th percentile of the potential (gender-education-specific) wage
distribution in the year t can be predicted by his/her wage in the nearest wave t0 when he/she
was employed.25
This approach is called “imputation on unobservables.”26 Wage information is imputed
with wage values from another wave, regardless of the reasons a person did not work in year
25
This assumption is addressed formally with the equation, F (wv |Dg,i , Iit = 0) = F (wv |Dg,i , Iit0 = 1).
This equation is reasonable if one’s wage position with respect to the v th percentile does not change when
one’s employment status changes between t and t0 .
26
Olivetti and Petrongolo (2008) used this approach to estimate the median gender gap.
78
t but worked in t0 . Hence, selection into work is based on the persons characteristics that
are not observable to researchers. This imputation procedure can recover wage values for
individuals who worked at least once during the 6-year sample period. The estimate of the
gender gap at the v th percentile is unaffected by imputation whenever movements of one’s
wage position from t0 to t happen within either side of the v th percentile of the distributions.
In order to recover wage observations for those who are never observed in work during
the 6 years of longitudinal sample period, I develop an alternative approach, which is built
upon the DFL procedure and reveals a slightly different economic mechanism of selection
than the first approach. Specifically, for individuals within gender-education groups, I
construct a hypothetical wage distribution for the non-employed by reweighting the wage
distribution for the employed workers with labour market characteristics of the individuals
who never worked during the survey period. As the imputation is based on the observed
characteristics of the non-employed and the wage structure of the employed in a given year t,
I call it “imputation on observables”. Mathematical explanations are provided in Appendix
A.
This imputation is implemented in two steps. In the first step, I split observations within
gender-education groups into two samples: the employed sample and the non-employed
sample. The employed sample includes both the full-time and the part-time employees.
It weights the wage distribution of employed observations with the characteristics of the
non-employed to construct a hypothetical wage distribution for each gender-education
79
group.27 In the second step, I construct an imputed sample in which the employed have their
observed wage and the non-employed have wage values that are drawn randomly from their
gender-education hypothetical wage distributions. The statistic of interest is the gender wage
gap, which is estimated with the imputed sample at each decile of the wage distribution for
an educational group.
The wage gap that corrects for sample selection is presented in Figure 2.2. It has
four diagrams, each diagram representing one educational group. In each diagram, I plot
the gender gap with a 95% confidence interval for the full-time and part-time employees
combined, the gender gap that accounts for selection on the unobservables, and the gender
gap that accounts for selection on the observables. The difference between the actual gender
gap (the gender gap for full-time and part-time workers combined) and the potential gender
gap (the gender gap that accounts for selection) at each decile of the wage distribution
measures the impact of sample selection in estimating the gender gap for that educational
group at that point on the wage distribution.28
The gender wage gap responds more strongly to the adjustment of selection for loweducated groups because low-educated workers have higher employment gaps between men
and women than high-educated workers. In particular, Figure 2.2 tells us that the wage
gap along the wage distribution is largely unaffected for workers with college or university
education; however, the potential wage gap is substantially different than the actual wage gap
27
The observed characteristics are work experience prior to being unemployed, marital status, the number
of pre-school and school-aged children, parental education, age group, and immigration status.
28
Because sample selection is induced by individuals who were not employed, the comparison group is the
wage gap for the entire sample of wage earners who are either full-time or part-time workers.
80
Figure 2.2: Gender Gap Correcting for Selection
Notes: The sample consists of survey participants aged between 25-54 and not enrolled in school.
for low-educated workers. This quantitatively demonstrates that the inclusion of individuals
who did not work would affect the gender gap significantly for individuals without postsecondary education, but not for other individuals.
Among the low-educated individuals, correcting for selection on observables makes
greater changes in the estimate of gender gaps than selection on unobservables. When
imputing missing wage values using individuals’ observed characteristics, I include individuals who never worked during the longitudinal period. They had weaker labour market
attachment than employed individuals and non-employed individuals who worked in some
81
years over a 6-year period. Thus, the gender wage gap is more affected when I correct
for selection on observables. The largest adjustment occurs at the median of the wage
distribution for the HSD group: if non-employed individuals with education below high
school worked during the sample period, women at the median would have earned only 57
cents for every dollar paid to men.
An interesting finding appears when I examine the selection-adjusted wage gaps for
the HS group. The inclusion of the individuals who never worked reduces the wage gap
substantially on the lower-tail of the wage distribution. This finding is not contrary to the
assumption in studies that examine the impacts of sample selection: women are assumed
to opt out of the labour market when they have low-wage characteristics, relative to the
characteristics related to home production. Therefore, the imputed wage values would be
expected to be lower than observed wage values for women and selection-adjusted gender
gaps would be greater than observed gender gaps. I find that the non-employed men are
less likely to be married and have fewer children than women. Since marital status and the
number of children are used in constructing hypothetical wage distribution for non-working
individuals, the non-employed men have lower productivity characteristics than the nonemployed women. Therefore, the selection-adjusted gender gap is smaller than the actual
gender gap.
Overall, Figure 2.2 suggests that there is heterogeneity in supply behaviour across
educational groups. While alternative imputation approaches reveal different economic
channels of selection, results with both imputation approaches confirm that correcting for
82
sample selection makes little difference in estimating the gender gap for high-educated
workers. For low-educated workers, correction for sample selection on unobservables makes
a lesser difference in the gender gap than selection on observables, suggesting that the use of
unobserved characteristics is insufficient to capture the selection rule for individuals without
post-secondary education.
2.4
Conclusion
Previous studies have found that male jobs pay more than female jobs partly because
female jobs require different DOT-skills than male jobs. This chapter contributes to the
literature by demonstrating that the gender gap is explained in part by required DOT-skills,
workplace competitiveness, and degree of managerial responsibility. For workers in the
high school and community college groups, and for university-educated workers below
the 90th percentile of the wage distribution, men and women work in occupations that
require different levels of clerical and spatial perceptions and general learning ability, which
accounts for a substantial proportion of the gender gap. This is in line with the conclusion
that male-dominated occupations such as auto mechanics pay more than female-dominated
occupations such as secretarial work because these different occupations require different
aptitudes/ skills (DOT-skills).
However, skill requirements do not account for all of the gender gap, and additional
factors at the two ends of the education distribution are very different. In the low-paid and
83
very low-educated worker groups, men are compensated more than women for working in
unpleasant work conditions. Among the highest-paid university-educated workers, men are
compensated more than women for taking upper-level managerial duties and working in a
more competitive environment. For the latter group, we have seen that the wage structure
effect (adjusted gender gap) decreases as one moves to the top of the wage distribution. This
suggests that the “glass ceiling” phenomenon is explained by the finding that highest-paid
men work in more demanding jobs than highest-paid women.
A limitation of the study is that I cannot observe the characteristics that determine a
person’s occupation. Thus, I do not know whether women working in different occupations
than men are doing so because of gender differences in abilities (e.g. men are better managers), outside options (i.e. spouse’s wages), worker preferences, or unfair discrimination
based on stereotypes.
I examine how the selection into work affects the estimates of gender gap at various
points of the wage distribution. Compared to existing methodologies, my study accounts
for sample selection with less restrictive assumptions. It empirically demonstrates that
correcting for sample selection increases the gender gap substantially for low-educated
workers, but not for high-educated workers. This means that low-paid women who work are
very different than those who do not. For better-educated women, for whom participation
rates are much higher, selectivity into work is much less important. As the main analysis in
the study does not account for sample selection, caution should be taken when interpreting
the decomposition results for the low-educated workers.
84
In this chapter I discussed the potential reasons for the fact that men and women
working in different occupations affects the gender wage gap. Future research could try to
understand what accounts for the gender wage gap within occupations. While a number
of studies have contributed to understanding the gender wage gap within high-skilled
occupations, the gender wage gap within low-skilled occupations has not been subject to
such comprehensive investigation. Further exploration of the gender gap for low-educated
workers is of fundamental importance for policy makers to ensure gender pay equity in
blue-collar occupations.
Appendix
Appendix A
In what follows, I explain the idea underlying the imputation techniques used to recover
missing wages at various points of the wage distribution. The variable of interest is the
difference between (log) male and female wage at each decile of the distributions:
δ v = v(w|Dm ) − v(w|Dw )
(2.2)
where v(.) is the wage function at the v th percentile of the distributions, v = (10, 20, 30, 40,
50, 60, 70, 80, 90). The (log) wage distribution for each gender is defined by
85
F (w|Dg ) =F (w|Dg , I = 1)P r(I = 1|Dg )+
F (w|Dg , I = 0)[1 − (P r(I = 0|Dg ))],
(2.3)
where I is an indicator function that equals 1 if an individual is employed and zero otherwise.
Wage distributions are estimated on the basis of the F (w|Dg , I = 1)P r(I = 1|Dg )
term alone. It would be misleading if F (w|Dg , I = 1)P r(I = 1|Dg ) and F (w|Dg , I =
1)[1 − P r(I = 1|Dg )] were systematically different. This problem typically affects the
estimate of female wage offer distributions in the low-educated labour market, as the
unemployment rate of women,
1 − P r(I = 1|Dw ), declines over the levels of education.
The goal is to retrieve the gender gap in (potential) wages at the v th percentile of the
distribution. The log wage at the v th percentile, wv , for each gender is defined in equation
(4)
θ =F (wv |Dg , I = 1)P r(I = 1|Dg )+
F (wv |Dg , I = 0)[1 − (P r(I = 0|Dg ))],
(2.4)
where θ = v/100. To identify F (wv |Dg ), it needs to retrieve the information on F (wv |Dg , I =
0)[1 − (P r(I = 0|Dg )] that represents the probability that non-employed observations have
86
potential wage below the v th percentile of the distribution.
The approach of this study is based on some form of wage imputation for non-employed
individual, but it simply requires assumptions on the position of the imputed wage observations with respect to the v th percentile of the wage distribution, and not on their level of
potential wage offer.
To see it formally, the explanation below uses the gender wage gap at the median as an
example.29 It estimates the median wage gap in potential wage offers using median wage
regressions. Let’s consider the linear wage equation
wi = β0 + β1 Dm,i + i ,
(2.5)
where wi denotes the log hourly wage, Dm,i = 1 denotes a man, Dm,i = 0 denotes a woman,
β0 is a constant term, and β1 is the parameter of interest. The conditional median of given Dm,i is assumed to be zero. Denote β̂ as the hypothetical least absolute deviation
(LAD) estimator for a median regression. It is based on the potential wage offers, wi , where
β̂ = (βˆ0 , βˆ1 ).
β̂ = argminβ
N
X
|wi − β0 − β1 Dm,i |
i=1
29
The explanation for wage imputation at the median is a summary of methodology section in Olivetti and
Petrongolo (2008).
87
The wage offers are not observed for those who do not work, Ii = 0. Suppose that the
potential wage offers of the non-employed are categorized into two groups, L and U, such
that wi < ŵi = βˆ0 + βˆ1 Dm,i for i ∈ L, and wi > ŵi for i ∈ U . The imputation procedure
can construct a dependent variable yi that is equal to wi for Ii = 1 and to some arbitrary
wage offer wimputed,i for Ii = 0 such that wimputed,i < ŵi for i ∈ L and wimputed,i > ŵi for
i ∈ U , and then the following condition holds:
β̂imputed = argminβ
N
X
|yi − β0 − β1 Dm,i |
i=1
= β̂ = argminβ
N
X
|wi − β0 − β1 Dm,i |
(2.6)
i=1
Condition (11) states that the LAD estimator is not affected by imputation when the
missing wage observations are imputed on the “correct”side of the median of the potential
wage offers.30 That is to say, the LAD estimation using yi yields the same estimate of the
median gender gap as it would yield if potential wage offers, wi , were available for the
whole population. The LAD estimator is the solution to the quantile regression by Koenker
and Bassett (1978) when θ = 0.5 (v = 50).31 When v equals to values other than 0.5, one
can prove that the quantile regression estimate of the gender gap based on yi at the v th
percentile is valid whenever the imputed wage values are on the “correct” side of the v th
30
See Bloomfield and Steiger (1983), Chapter 2, for formal proof.
Koenker and Bassett (1978) show that the θth regression quantile,0 < θ < 1, is defined
as any solution to
P
P
the minimization problem: min
θ|yt − xt b| +
(1 − θ)|yt − xt b| . The LAD estimator
31
b∈Rk
t∈t:yt ≥xt b
t∈t:yt ≥xt b
is the regression median,i.e., the regression quantile for θ = 1/2.
88
percentile of the potential wage offers.32
Imputation on observables
These are the mathematic notes for estimating the wage distribution of people who were
never employed during a 6-year window.
For each gender-education group, it takes the form of equation (7),
h
FW
:X=x|Dg ,I=0 =
=
Z
Z
FW |X,Dg ,I=1 (w|X = x)dFX|Dg ,I=0 (x)
FW |X,Dg ,I=1 (w|X = x)τ (X)dFX|Dg ,I=1 (x),
(2.7)
where
τ (X) =
dFX|Dg ,I=0 (x)
P (I = 0|Dg , X) P (I = 1|Dg )
=
dFX|Dg ,I=1 (x)
P (I = 1|Dg , X) P (I = 0|Dg )
where P (I = 1|X, Dg ) and P (I = 0|X, Dg ) are the probability of one belonging to group
I = 1 and I = 0 conditional on X, respectively. P (I = 0|Dg ) and P (I = 1|Dg ) are the
sample proportions in group I = 0 and I = 1, respectively.
In this case, the imputation rule does not require an assumption of the identical rank
throughout the whole wage distribution between the matched pairs of the non-employed and
the employed but only with respect to the v th percentile. Formally, it takes the following
form
32
See Koenker and Bassett (1978) for formal proof of Theorem 3.5.
89
h
F (wv |Dg , I = 0) = FW
v :X=x|Dg ,I=0
(2.8)
Equation (13) states that if a non-employed individual were employed, his wage position
with respect to the v th percentile would have been the same as the wage position of an
employed worker who has the same labour market characteristics as the non-employed
one.33
33
The assumption underlying equation (8) is that the labour market characteristics of the non-employed
would have been rewarded the same as the employed. In other words, it does not account for the possibility
that the non-employed may have been paid lower than the equally productive employed because of his/her
unemployment duration.
90
Appendix B
Figure B1: Gender Gap across the Wage Distribution by Employment Status
Figure B2: Gender Gap across the Wage Distribution by Employment Status with 95%
Confidence Interval
91
Table B1: O*Net Characteristics
Physical Activities
Noise
Exposed to Contaminants
Exposed to Hazardous Equipment
Indoors, Not Environmentally Controlled
Very Hot or Very Cold Temperature
Wear Safety Equipment
To what extent this job requires considerable use of your arms
and legs and moving your whole body, such as climbing, lifting,
balancing, walking, stooping, and handling of materials?
Lower score means lower requirement of physical strength.
How often does this job require working exposed to sounds and
noise levels that are distracting or uncomfortable?
Lower score means less exposure to noise.
How often does this job require working exposed to contaminants?
Lower score means less exposure to contaminants.
How often does this job require exposure to hazardous equipment?
Lower score means a lower chance of working in such condition.
How often does this job require working indoors in non-controlled
environmental conditions (e.g., warehouse without heat)?
How often does this job require working in very hot (above 90 F
degrees) or very cold (below 32 F degrees) temperatures?
How much does this job require wearing common protective or
safety equipment such as safety shoes, glasses, gloves, or life jackets?
Lower score means less frequency of wearing safety equipments.
Notes: Explanations for O*Net characteristics are extracted from http://www.onetonline.org.
0.04
(0.007)
Composition
Effects
0.015
(0.002)
-0.002
(0.0006)
0.017
(0.004)
0.03
(0.007)
Composition
Effects
Wage
Structure
Effects
0.2
(0.012)
0.033
(0.008)
-0.003
(0.002)
0.22
(0.065)
Wage
Structure
Effects
0.23
(0.012)
0.062
(0.009)
Composition
Effects
0.026
(0.003)
-0.002
(0.001)
0.035
(0.009)
0.07
(0.007)
Composition
Effects
30th
Wage
Structure
Effects
0.2
(0.013)
0.045
(0.01)
-0.005
(0.002)
0.11
(0.05)
Wage
Structure
Effects
0.32
(0.012)
0.05
(0.007)
Composition
Effects
0.038
(0.005)
-0.004
(0.002)
0.048
(0.006)
0.094
(0.01)
Composition
Effects
50th
Wage
Structure
Effects
0.21
(0.011)
-0.003
(0.01)
-0.02
(0.003)
-0.07
(0.04)
Wage
Structure
Effects
0.33
(0.013)
0.04
(0.008)
Composition
Effects
0.036
(0.005)
-0.007
(0.003)
0.052
(0.008)
0.095
(0.012 )
Composition
Effects
70th
Wage
Structure
Effects
0.19
(0.013)
-0.008
(0.013)
-0.03
(0.004)
-0.09
(0.04)
Wage
Structure
Effects
0.30
(0.018 )
0.04
(0.01)
Composition
Effects
0.027
(0.007)
-0.004
(0.002)
0.069
(0.012)
0.11
(0.017)
Composition
Effects
90th
Wage
Structure
Effects
0.18
(0.02)
-0.016
(0.02)
-0.024
(0.007)
-0.12
(0.05)
Wage
Structure
Effects
0.25
(0.029 )
0.03
(0.009)
Composition
Effects
0.02
(0.008)
-0.002
(0.002)
0.09
(0.02)
0.12
(0.02)
Composition
Effects
95th
Wage
Structure
Effects
0.20
(0.02)
-0.02
(0.02)
-0.02
(0.008)
-0.08
(0.06)
Wage
Structure
Effects
0.20
(0.04)
0.017
0.013
0.025
-0.003
0.018
-0.007
0.01
0.006
-0.003
-0.006
-0.01
-0.005
(0.002)
(0.008)
(0.003)
(0.009)
(0.003)
(0.01)
(0.003)
(0.01)
(0.003)
(0.013)
(0.003)
(0.02)
Public
-0.002
-0.000
-0.007
-0.01
-0.009
-0.021
-0.01
-0.028
-0.007
-0.01
-0.006
0.0007
(0.0007)
(0.003)
(0.002)
(0.003)
(0.002)
(0.004)
(0.003)
(0.005)
(0.002)
(0.007)
(0.002)
(0.009)
Experience
0.027
0.089
0.039
-0.092
0.035
-0.096
0.035
-0.08
0.043
-0.083
0.04
-0.08
(0.004)
(0.089)
(0.005)
(0.064)
(0.004)
(0.044)
(0.005)
(0.04)
(0.007)
(0.013)
(0.008)
(0.060)
Notes: Estimation is conducted with RIF-regression-based decomposition method and uses full-time salaried employees aged 25-54 who did not attend post-secondary educational institutions. Numbers in
bold are significantly different from 0 at 10% level. Covariates in Model 1 in Table 2.8 are included in the estimation. Variables that are not reported are survey years, marital status, the number of children,
age groups, residential provinces, whether one is an immigrant, and whether one is handicapped.
Union
Explained by:
Aggregate
Decomposition
HS
Experience
Public
Union
Explained by:
Aggregated
Decomposition
Below HS
10th
Table B2: The Contribution of Subsets of Covariates in Model 1 for the Low-Educated Workers
92
-0.04
(0.018)
Composition
Effects
-0.001
(0.0005)
-0.001
(0.007)
0.007
(0.001)
-0.014
(0.001)
0.034
(0.003)
0.03
(0.008)
Composition
Effects
Wage
Structure
Effects
0.16
(0.027)
-0.008
(0.02)
0.025
(0.02)
0.015
(0.007)
-0.001
(0.003)
0.14
(0.06)
Wage
Structure
Effects
0.18
(0.01)
-0.007
(0.012)
Composition
Effects
-0.0006
(0.0004)
-0.007
(0.009)
0.007
(0.001)
-0.022
(0.002)
0.037
(0.003)
0.02
(0.009)
Composition
Effects
30th
Wage
Structure
Effects
0.19
(0.016)
0.027
(0.016)
0.01
(0.015)
0.007
(0.006)
-0.017
(0.003)
-0.036
(0.036)
Wage
Structure
Effects
0.20
(0.011)
0.014
(0.009)
Composition
Effects
-0.0005
(0.0003)
-0.004
(0.008)
0.005
(0.0009)
-0.023
(0.002)
0.032
(0.003)
0.017
(0.009)
Composition
Effects
50th
Wage
Structure
Effects
0.16
(0.013)
0.038
(0.012)
0.011
(0.012)
0.007
(0.005)
-0.025
(0.003)
-0.082
(0.026)
Wage
Structure
Effects
0.20
(0.01)
0.044
(0.009)
Composition
Effects
-0.0007
(0.0003)
0.003
(0.009)
0.002
(0.0006)
-0.02
(0.002)
0.031
(0.003)
0.021
(0.01)
Composition
Effects
70th
Wage
Structure
Effects
0.14
(0.013)
0.019
(0.013)
-0.001
(0.014)
0.006
(0.006)
-0.023
(0.003)
-0.06
(0.025)
Wage
Structure
Effects
0.21
(0.011)
0.06
(0.009)
Composition
Effects
-0.0009
(0.0005)
0.021
(0.015)
-0.002
(0.0008)
-0.02
(0.002)
0.028
(0.003)
0.03
(0.015)
Composition
Effects
90th
Wage
Structure
Effects
0.15
(0.016)
-0.022
(0.018)
-0.016
(0.022)
-0.008
(0.008)
-0.018
(0.005)
-0.029
(0.03)
Wage
Structure
Effects
0.17
(0.018)
0.08
(0.01)
Composition
Effects
-0.001
(0.0005)
0.03
(0.02)
-0.004
(0.001)
-0.014
(0.002)
0.02
(0.003)
0.04
(0.02)
Composition
Effects
95th
Wage
Structure
Effects
0.15
(0.02)
-0.036
(0.02)
-0.02
(0.025)
-0.002
(0.008)
-0.016
(0.005)
0.03
(0.03)
Wage
Structure
Effects
0.15
(0.02)
0.0005
-0.022
0.01
0.007
0.013
0.011
0.014
0.006
0.017
0.012
0.02
0.06
(0.003)
(0.022)
(0.002)
(0.012)
(0.002)
(0.01)
(0.002)
(0.011)
(0.003)
(0.018)
(0.005)
(0.03)
Major
0.009
0.002
0.01
0.006
0.001
0.003
0.011
-0.01
0.004
0.003
0.007
0.01
(0.015)
(0.029)
(0.009)
(0.015)
(0.007)
(0.01)
(0.007)
(0.01)
(0.006)
(0.01)
(0.01)
(0.02)
Union
-0.024
-0.016
-0.018
-0.032
-0.01
-0.044
0.004
-0.043
0.024
-0.024
0.03
-0.000
(0.004)
(0.012)
(0.003)
(0.008)
(0.003)
(0.007)
(0.003)
(0.007)
(0.003)
(0.009)
(0.005)
(0.01)
Public
-0.046
0.006
-0.04
-0.036
-0.024
-0.003
-0.019
-0.01
-0.012
-0.03
-0.004
-0.02
(0.006)
(0.014)
(0.004)
(0.01)
(0.003)
(0.008)
(0.003)
(0.008)
(0.003)
(0.011)
(0.004)
(0.02)
Experience
0.046
0.070
0.046
0.063
0.04
0.03
0.040
-0.035
0.033
-0.023
0.04
-0.06
(0.008)
(0.104)
(0.006)
(0.051)
(0.005)
(0.04)
(0.005)
(0.033)
(0.005)
(0.037)
(0.006)
(0.04)
Notes: Estimation is conducted with RIF-regression-based decomposition method and uses full-time salaried employees aged 25-54 who completed post-secondary education. Numbers in bold are
significantly different from 0 at 10% level. Covariates in Model 1 in Table 2.8 are included in the estimation. Variables that are not reported are survey years, marital status, the number of children, age
groups, residential provinces, whether one is an immigrant, and whether one is handicapped.
Education
Explained by:
Aggregate
Decomposition
University
Experience
Public
Union
Major
Education
Explained by:
Aggregate
Decomposition
College
10th
Table B3: The Contribution of Subsets of Covariates in Model 1 for the High-Educated Workers
93
94
Chapter 3
The Evolution of Returns to Education
in the High-End Labor Market in
Canada
3.1
Introduction
Facing a rise in skill requirements for many occupations and a growing number of
students who desire postgraduate education, both federal and provincial governments have
been encouraging increased access to graduate programs (Wiggers et al., 2011). For these
policies to be effective in acting as a guide for university students, we must first understand
how earnings have evolved for workers with postgraduate education, compared to their BA
counterparts. A number of studies have documented an increase in the return to four-year
95
university education (BA, hereafter) and postgraduate education (PG, hereafter), relative
to high school (HS, hereafter) since 1980, but less-known evidence shows that the PG-HS
wage gap has grown more slowly than the BA-HS wage gap. Among men, for example, the
BA-HS wage gap increased by nine percentage points and the PG-HS wage gap increased
by six percentage points in 2005 (Boudarbat et al., 2010). However, little is known about
direct pay differences between PG and BA workers. More importantly, to the best of my
knowledge, there is no research that examines the reasons for the decrease.
The major goal of this study is to provide evidence of the evolution of returns to
postgraduate education, relative to BA, over the years 1995 - 2010. This study adds to the
literature by not only examining the return to postgraduate education as a whole, but also
separately examining the return to different levels of postgraduate education. Furthermore,
it pays particular attention to the heterogeneity in the PG-BA wage gap by age, gender,
and major fields of study. Finally, it documents the wage gap between people with a
Master’s degree and people with a Doctorate by major fields of study. This information,
which is missing in the Canadian literature, would be useful for Master’s graduates who are
considering continuing to pursue doctoral degrees.
Using workers aged between 25 and 59 in the Canadian Census, this study shows that
on average, the PG-BA weekly wage gap decreased by seven percentage points for both
genders from 1995 to 2010. The declining trend is not because of PG workers earning less
than BA workers – the PG-BA weekly wage gap is 9% for men and 11% for women in
2010. Rather, the decline is, because of a slowdown in the rate of wage growth for PG
96
workers relative to BA workers. This trend is found for all workers, with a greater decrease
among workers in the 25-34 age group. Converting the decline into an annual earnings gap
with the census data, I find that the premium from completing postgraduate education is
approximately 1000 dollars less for women and 1700 dollars less for men in 2010 compared
to the premium in 1995, which is not a trivial difference.
There are substantial differences in the return to postgraduate education, depending on
the levels of postgraduate qualification and major fields of study. Overall, I find that the
decline in return to postgraduate education is greatest for women majoring in education and
health, men majoring in physics and life sciences, and humanities, and men and women
majoring in business and management, social sciences, and natural resources. The return
to education above BA but below MA (e.g. professional certificates in accounting) and the
return to MA decrease for people majoring in all these fields of study, while the return to a
Doctorate declines only for people majoring in health and social sciences.
On the positive side, my results show that women benefit from completing postgraduate
education in the STEM fields, relative to women in other fields. The return to a Master’s
degree in engineering and computer sciences went up by nine percentage points for women
in 2010. This is the only group of women for whom a significant increase in the PG-BA
wage gap was observed. Returns to a Doctorate in mathematics and computer sciences, and
physics and life sciences in 2010 were found to be stable between 1995 and 2010, with
returns varying between 23% and 30%.
Linking changes in the PG-BA wage gap to changes in occupational composition of
97
university-educated workers, my study finds that the proportion of PG workers in natural
sciences, business and finance, sales and services, and health has increased from 1995
to 2010, while the proportion of PG workers in government services and management
occupations has decreased over the same period of time. Turning to the PG-BA wage gap, I
find that the gap decreased for workers in almost all occupations; however, it declined by
more in business and finance, sales, and health occupations than in government services and
management occupations. This suggests that the slowdown of wage growth for PG workers
in business, sales, and health occupations, where a larger proportion of PG workers worked
in 2010 than in 1995, is a main reason for the decline in the PG-BA wage gap.
The rest of the chapter is organized as follows. Section 3.2 summarizes related work.
Section 3.3 introduces data. Section 3.4 interprets the empirical findings. Section 3.5
concludes.
3.2
Literature Review
This chapter is closely related to an extensive literature that examines the evolution of
return to human capital in Canada. Much of the literature has a strong focus on the BA-HS
wage gap. Among studies that use Canadian Census data, there is a consensus that, the
return to BA have increased substantially for men over the period 1980 - 2005. Much of
this rise emerges in the early 1980s and after 1995. The growth of return to education is
heavily concentrated among men in the age range 25-34; the growth rate of the return to BA
98
for older workers is relatively stable (Beaudry and Green, 1998; Card and Lemieux, 2001;
Buchinsky, 2002; Bourbeaue et al., 2012). Women have a larger return to BA than men, but
the growth rate is rather modest (Boudarbat et al., 2006, 2010).1
There are a small number of studies that document the evolution of the PG-HS wage
gap in Canada.2 Boudarbat et al. (2010) finds that the return to PG, after controlling for
potential work experience, was 6% larger in 2005 than in 1980 for men, and 4% for women.
The return to PG grew three percentage points less for men and 2 percentage points less
for women, compared to the return to BA over the same period of time. Bourbeaue et al.
(2012) document trends related return to return to education in Canada for 21- to 35-year
old workers from 1990 to 2005. They separate postgraduate education into four levels of
qualification: above BA but below MA, MA, Doctorate, and degrees in Medicine. They
show that, conditional on work experience, the BA-HS gap grows faster on average than all
levels of postgraduate education for both men and women. This study adds to the literature
by (1) providing a direct comparison in the wage growth between PG workers and BA
workers for not only young workers but also older workers, (2) uncovering the heterogeneity
1
For the comparison, I focus on studies that use census data. Using Survey of Consumer and Finances,
Labor Force Survey and Survey of Labor and Income Dynamics data, Burbidge et al. (2002) show that during
the 1980s and 1990s, the return to BA, relative to education below BA (including high school graduates and
high school dropouts), remained stable. Furthermore, Burbidge et al. (2002) find that the return to university
education remains stable for young men aged 25-35 throughout the 1980s and 1990s. This contradicts studies
by Beaudry and Green (2002), Card and Lemieux (2001), Boudarbat et al. (2010), and Bourbeaue et al. (2012)
that find evidence in the census that the return to education grew substantially for young men during the 1980s
and 1990s. These differences could arise from different data sources and different sets of covariates. Burbidge
et al. (2002) do not account for work experience, while other studies account for potential work experience in
their analysis.
2
Related work on other countries show that the return to postgraduate education, relative to high school,
varies substantially for workers with different levels of postgraduate qualifications, majoring in different fields
of study, and completing the program in different educational institutes.(O’Leary and Sloane, 2005; Kelly
et al., 2010)
99
in the PG-BA wage gap by field of study, and (3) investigating reasons behind the decline in
the PG-BA wage gap.
Turning to the studies that examine the relationship between returns to education and
labour market structure, Card and Lemieux (2001) use data from the United States, the
United Kingdom, and Canada from 1980 to 1995 to examine why the BA-HS wage gap
increases for young workers (26-30), but remains nearly constant for older men (46-60).
They show that educational attainment for the baby-boom generation has grown more slowly
than for people born before 1950. This results in a smaller labour supply of highly educated
young workers, relative to the labour supply of highly educated older workers. Drawn from a
theoretical model that takes account of imperfect substitutability between younger and older
workers, their results demonstrate that a slowdown in the intercohort trend in educational
attainment causes a relative increase in the BA-HS wage gap for younger workers. Using
the Canadian Census 1970 - 2006, Green and Sand (2013) find that job polarization was
present in the 1980s and 1990s, but not after the 2000s.3 This finding is in line with Beaudry
et al. (2013) who demonstrate that in contrast to the increase in demand for high skilled
workers before 2000 in the U.S., the demand experienced a reversal after 2000. In response
to the reversal, high-skilled workers have moved down the occupational ladder and have
begun to perform jobs traditionally performed by lower-skilled workers.
The existing evidence implies that Canada has been experiencing stagnation in labour
3
Job polarization is characterized by a pattern of employment growth where employment increases in the
high-paid (high-skilled) and the low-paid (low-skilled) occupations and decreases in occupations that pay in
the middle of the wage distribution (middle-skilled occupations).(Goos and Manning, 2007; Autor et al., 2006;
Autor and Acemoglu, 2010)
100
demand for university-educated workers since 2000. This would have an impact for BA
workers and PG workers born after the baby-boom generation. Due to the expansion of
graduate education since the early 1990s, there has been a rise in the labour supply of
workers with postgraduate education. If the labour supply grows faster than the demand
for PG workers, relative to the difference in supply and demand for BA workers, this could
result in a slowdown in wage growth for PG workers. For these reasons, I examine the
PG-BA wage gap by investigating the role of changes in age composition and occupational
composition of the university-educated labour force over time.
3.3
Data
This study uses Master Files of the Canadian Census for 1996, 2001, and 2006, and
the National Household Survey for 2011 (the NHS replaced the Census in 2010). Census
data provides annual earnings, the number of weeks worked, and occupations where people
worked in the past 12 months. This study uses weekly wage, evaluated in 1993 dollar values,
to measure earnings.4 People with a real weekly wage below $75 are excluded. The analysis
uses people aged 25 to 59 at the time of Census who had worked at least one week and had
some positive earnings in the previous 12 months. In the rest of the paper, I use the years
1995, 2000, 2005, and 2010 to refer to the 1996, 2001, 2006 Census and the 2011 NHS,
respectively. The PG-BA wage gap refers to the average (log) weekly wage gap between
4
It is common to use weekly wage in estimating returns to education. See for example, Boudarbat et al.
(2010), Boudarbat et al. (2006), Green and Sand (2013)
101
people with postgraduate education and people with a four-year university degree.
I start the analysis by documenting the PG-BA wage gap. The Census data provides
information on the highest level of education obtained. People with postgraduate education
can be further categorized into four levels of qualification: workers with education above BA
but below MA (ABA, hereafter), Master’s degree (MA, hereafter), Doctorate, and degrees
in medicine, dentistry, veterinary medicine, and optometry (MD, hereafter). People with
education above BA but below MA are people who attended a Master’s program but did not
complete the degree, and people who completed professional training that has a bachelor’s
degree as a prerequisite, for example, teachers and accountants. I estimate the return to each
of the four postgraduate qualifications, relative to BA.
Summary statistics are presented in Table 3.1. Column (1) presents the log weekly
wage at the mean, at the 10th percentile, and at the 90th percentile of the wage distribution.
Average weekly wage for men in the PG group increases by 7.5%, only half of the 14.6% of
growth rate for men in the BA group. Looking at the growth rate for people at the tails of
the wage distribution, I find that weekly wage at the 90th percentile increases by 24.3% for
BA men and 20% for PG men, while at the 10th percentile it increases by only 9.7% for BA
men and 0.5% for PG men. This evidence suggests that the average weekly wage for men in
the PG group increased by a smaller percentage than that in the BA group largely due to the
finding that PG men at the 10th percentile of the wage distribution have experienced since
1995.
Similar findings appear for women. Table 3.1 shows that the average weekly wage for
102
women in the PG group increased by 10.9%, 7 percentage points less than the 18% growth
rate for BA women. While PG women experience a smaller growth rate in weekly wage at
both tails of the wage distribution compared to women in the BA group, the growth rate for
PG women at the bottom of the wage distribution, relative to their BA counterparts, is even
smaller than at the 90th percentile of the wage distribution.
There is little variation in age composition of university-educated workers over time.
Column (2) in Table 3.1 shows that for both men and women, about 80% of BA workers
were aged below 50 in four of the census years, with a modest increase in the proportion of
workers in the age groups of 50-54 and 55-59 over time. PG workers are distributed almost
evenly over the seven age groups, with a relatively small proportion of men in the youngest
age group and women in the oldest age group. Since the proportion of BA workers and PG
workers in each age group changes little from 1995 to 2010, the slowdown in the growth
rate of weekly wage for PG workers, relative to for BA workers, is not due to changes in
age composition of university-educated workers.
3.4
3.4.1
Empirical Results
Wage Premium on Postgraduate Education Relative to BA
Figure 3.1 presents the change in returns to postgraduate education. The solid curve
connects the PG-BA wage gap for men for the years 1995, 2000, 2005, and 2010, and the
103
Table 3.1: Summary Statistics
(1)
Log Weekly Wage
Mean
10th
90th
MEN: BA
1995 6.698
(0.647)
2000 6.781
(0.680)
2005 6.789
(0.720)
2011 6.844
(0.718)
MEN: PG
1995 6.861
(0.679)
2000 6.906
(0.722)
2005 6.890
(0.776)
2011 6.936
(0.770)
WOMEN: BA
1995 6.401
(0.624)
2000 6.468
(0.633)
2005 6.492
(0.663)
2010 6.581
(0.678)
WOMEN: PG
1995 6.585
(0.655)
2000 6.626
(0.658)
2005 6.626
(0.696)
2010 6.694
(0.708)
25-29
(2)
Proportion of Workers by Age Group
30-34 35-39 40-44 45-49 50-54
55-59
5.878
7.364
0.202
0.200
0.172
0.156
0.143
0.084
0.043
5.959
7.502
0.178
0.185
0.177
0.151
0.136
0.116
0.057
5.906
7.554
0.164
0.171
0.171
0.160
0.131
0.116
0.087
5.970
7.607
0.170
0.163
0.160
0.155
0.142
0.117
0.093
5.968
7.530
0.097
0.144
0.157
0.167
0.190
0.154
0.090
6.019
7.655
0.090
0.144
0.164
0.160
0.163
0.168
0.111
5.904
7.677
0.089
0.144
0.164
0.174
0.158
0.146
0.126
5.973
7.737
0.102
0.143
0.164
0.169
0.163
0.142
0.117
5.538
7.044
0.248
0.208
0.167
0.149
0.127
0.070
0.030
5.629
7.109
0.223
0.196
0.171
0.143
0.125
0.100
0.042
5.607
7.179
0.206
0.187
0.165
0.147
0.123
0.103
0.068
5.693
7.268
0.200
0.177
0.161
0.148
0.131
0.105
0.078
5.670
7.206
0.143
0.169
0.167
0.181
0.182
0.107
0.051
5.742
7.274
0.142
0.165
0.159
0.159
0.160
0.146
0.070
5.671
7.320
0.140
0.169
0.159
0.154
0.144
0.135
0.099
5.740
7.402
0.149
0.174
0.164
0.151
0.140
0.125
0.096
Notes: Summary statistics are weighted with Census sampling weights. Standard errors are reported in parentheses. I use workers aged
25-59 with four-year university education or post-graduate education. Workers in the PG group have education above BA but below
MA, Master’s degree, Doctorate, or degrees in medicine, dentistry, veterinary medicine, and optometry. Column (1) presents the log
weekly wage at the mean, and at the 10th and the 90th percentile of the wage distribution for each census year. Column (2) presents the
proportion of workers in the age groups 25-29, 30-34, 35-39, 40-44, 45-54, and 55-59 in a census year.
104
.08
PG-BA Log Weekly Wage Gap
.1
.12
.14
.16
.18
Figure 3.1: The Evolution of PG-BA Wage Gap from 1995 to 2010
1995
2000
2005
2010
Year
Men
Women
Workers with university degree aged between 25 and 59
long-dashed curve connects the wage gap for women. Table 3.2 presents the corresponding
estimates for each gender.5
This figure suggests that the completion of postgraduate education yields a significant
rise in weekly wage for workers with postgraduate education, relative to workers in the BA
group, but the rise has shrunk since 1995. The wage gap for men in 2010 is seven percentage
points smaller than the 16% wage gap in 1995, while it is seven percentage points smaller
than the 18% wage gap for women in 1995.
5
Log weekly wage gap approximately equals to percentage changes in weekly wage for workers with
post-graduate education, relative to workers with four-year university education. For example, the log weekly
wage gap in 1995 for men is 0.162. This means that men with post-graduate education on average earn 16.2%
more per week than their BA counterparts.
105
Table 3.2: The Evoluation of PG-BA Wage Gap from 1995 to 2010
Men
Women
PG
0.16∗∗∗
( 0.003)
0.18∗∗∗
(.004)
2000
0.08∗∗∗
(0.003)
0.07∗∗∗
(0.003)
2005
.
0.09∗∗∗
(003)
0.09∗∗∗
(0.003)
2010
0.15∗∗∗
(.003)
0.18∗∗∗
(0.003)
PG*2000
-0.04∗∗∗
(.005)
-0.03∗∗∗
(0.005)
PG*2005
-0.06∗∗∗
(0.005)
-0.05∗∗∗
(0.005)
PG*2010
-0.07∗∗∗
(0.005)
-0.07∗∗∗
(0.005)
cons
6.698∗∗∗
(0.002)
6.401∗∗∗
(0.002)
N
992,748
1,079,967
∗
Notes: In this table and following tables, standard errors are reported in parentheses. denotes that the
coefficient is significantly different than 0 at 10% level, ∗∗ at 5% level, and ∗∗∗ at 1% level.
This table presents OLS estimates for workers aged 25-59. BA workers in 1995 are in the reference group. PG
is a dummy variable, 1 if a person has a postgraduate degree; 0 if the person has a four-year university degree.
The interaction of PG with year dummy variables measures returns to post-graduate education in 2000, 2005,
and 2010, relative to the return in 1995.
106
To better understand the magnitudes, I present the percentage changes in dollar units as
follows. In 1995, men with post-graduate education earned 144 dollars (in 1993 constant
dollars) more per week than their BA counterparts; however, this wage premium drops to 90
dollars per week in 2010, a difference of 54 dollars. Similarly, for women, the weekly wage
premium changes from 122 dollars in 1995 to 86 dollars in 2010, dropping by 35 dollars per
week. In line with the decline in weekly wage, census data shows that the annual earnings
premium on average is approximately 1700 dollars less for men and 1000 dollars less for
women in 2010 than in 1995. This suggests that the decrease in the return to postgraduate
education since 1995 is not trivial.
3.4.2
Returns to Postgraduate Education by Age Group
This section and the following sections present a breakdown of return to postgraduate
education by age group and major field of study. People are paid more as they achieve a
higher level of postgraduate education. This section takes account of this fact and presents
the return to different levels of postgraduate education.
The PG-BA wage gap by age group is presented in Tables 3.3 and 3.4 for men and
women, respectively. Two findings are important. First, there is an increase in weekly wage
for both men and women in all age categories in the BA group, with weekly wage increasing
by 10% - 14% for men and 13% - 17% for women in 2010 compared to that in 1995. Second,
there is a decline in the PG-BA wage gap in all age groups since 1995. The magnitude of
the decline in returns to postgraduate education varies slightly for workers in different age
107
Table 3.3: The PG-BA Wage Gap by Age Group for Men
(1)
25-29
PG
con
N
PG
con
N
PG
con
N
PG
con
N
(2)
30-34
(3)
35-39
(5)
45-49
(6)
50-54
(7)
55-59
0.03∗∗∗
(0.008)
6.79∗∗∗
(0.005)
32,670
(4)
40-44
1995
0.09∗∗∗
(0.008)
6.86∗∗∗
(0.005)
31,580
0.0006
(0.01)
6.27∗∗∗
(0.004)
32,025
-0.026∗∗∗
(0.008)
6.62∗∗∗
(0.004)
35,314
0.12∗∗∗
(0.007)
6.91∗∗∗
(0.005)
31,584
0.15∗∗∗
(0.009)
6.95∗∗∗
(0.007)
21,775
0.14∗∗∗
(0.014)
6.93∗∗∗
(0.01)
11,819
-0.02∗∗
(0.01)
6.42∗∗∗
(0.004)
32,377
0.02∗
(0.008)
6.68∗∗∗
(0.008)
37,576
0.02∗∗∗
(0.008)
6.85∗∗∗
(0.005)
38,195
2000
0.08∗∗∗
(0.009)
6.92∗∗∗
(0.005)
34,469
0.09∗∗∗
(0.009)
6.94∗∗∗
(0.005)
32,859
0.13∗∗∗
(0.008)
6.94∗∗∗
(0.006)
(30,500)
0.15∗∗∗
(0.013)
6.91∗∗∗
(0.01)
(17,391)
-0.06∗∗∗
(0.01)
6.35∗∗∗
(0.004)
(34,511)
-0.02∗∗
(0.008)
6.68∗∗∗
(0.004)
41,002
0.04∗∗∗
(0.008)
6.82∗∗∗
(0.004)
43,089
2005
0.053∗∗∗
(0.008)
6.92∗∗∗
(0.005)
42,358
0.06∗∗∗
(0.009)
6.98∗∗∗
(0.006)
36,483
0.08∗∗∗
(0.009)
7.00∗∗∗
(0.006)
32,902
0.12∗∗∗
(0.10)
6.94∗∗∗
(0.007)
26,402
-0.07∗∗∗
(0.01)
6.42∗∗∗
(0.005)
43,235
-0.04∗∗∗
(0.008)
6.72∗∗∗
(0.004)
48,608
0.04∗∗∗
(0.008)
6.89∗∗∗
(0.005)
51,145
2010
0.07∗∗∗
(0.008)
6.97∗∗∗
(0.005)
51,905
0.07∗∗∗
(0.008)
7.02∗∗∗
(0.005)
47,815
0.09∗∗∗
(0.01)
7.06∗∗∗
(0.006)
40,828
0.12∗∗∗
(0.01)
7.01∗∗∗
(0.007)
33,141
Notes: This table presents OLS estimates. For example, column (1) in the top panel represents OLS estimates
from a regression of log weekly wage on the postgraduate dummy variable for men aged between 25 and 29 in
1995. The constant term represents the average log weekly wage for BA male workers in the age 25-29 and
the coefficient of PG is the PG-BA wage gap for this age group in 1995.
108
Table 3.4: The PG-BA Wage Gap by Age Group for Women
PG
con
N
PG
con
N
PG
con
N
PG
con
N
(1)
25-29
(2)
30-34
(3)
35-39
(5)
45-49
(6)
50-54
(7)
55-59
0.15∗∗∗
(0.009)
6.46∗∗∗
(0.005)
30,660
(4)
40-44
1995
0.18∗∗∗
(0.008)
6.50∗∗∗
(0.005)
29,297
0.03∗∗∗
(0.009)
6.15∗∗∗
(0.004)
39,205
0.08∗∗∗
(0.008)
6.40∗∗∗
(0.004)
36,045
0.17∗∗∗
(0.008)
6.57∗∗∗
(0.005)
26,477
0.18∗∗∗
(0.01)
6.60∗∗∗
(0.007)
15,074
0.20∗∗∗
(0.02)
6.54∗∗∗
(0.011)
6,718
0.03∗∗∗
(0.008)
6.26∗∗∗
(0.003)
44,838
0.08∗∗∗
(0.008)
6.44∗∗∗
(0.004)
42,140
0.11∗∗∗
(0.008)
6.54∗∗∗
(0.005)
34,139
2000
0.15∗∗∗
(0.008)
6.53∗∗∗
(0.005)
34,139
0.20∗∗∗
(0.008)
6.57∗∗∗
(0.005)
31,521
0.19∗∗∗
(0.008)
6.60∗∗∗
(0.005)
26,351
0.19∗∗∗
(0.01)
6.53∗∗∗
(0.01)
11,767
0.004
(0.007)
6.24∗∗∗
(0.003)
52,354
0.05∗∗∗
(0.007)
6.46∗∗∗
(0.004)
51,953
0.10∗∗∗
(0.008)
6.52∗∗∗
(0.004)
47,075
2005
0.12∗∗∗
(0.008)
6.58∗∗∗
(0.004)
43,200
0.13∗∗∗
(0.008)
6.62∗∗∗
(0.005)
38,051
0.19∗∗∗
(0.008)
6.63∗∗∗
(0.008)
33,433
0.19∗∗∗
(0.01)
6.60∗∗∗
(0.006)
23,052
-0.006
(0.007)
6.31∗∗∗
(0.007)
66,775
0.03∗∗∗
(0.007)
6.53∗∗∗
(0.004)
61,921
0.07∗∗∗
(0.007)
6.63∗∗∗
(0.004)
61,921
2010
0.12∗∗∗
(0.008)
6.66∗∗∗
(0.005)
56,604
0.14∗∗∗
(0.008)
6.71∗∗∗
(0.008)
50,847
0.15∗∗∗
(0.008)
6.74∗∗∗
(0.005)
43,142
0.20∗∗∗
(0.10)
6.70∗∗∗
(0.007)
32,698
Notes: This table presents OLS estimates. For example, column (1) in the top panel represents OLS estimates
from a regression of log weekly wage on the postgraduate dummy variable for women aged between 25 and
29 in 1995. The constant term represents the average log weekly wage for BA female workers in the age 25-29
and the coefficient of PG is the PG-BA wage gap for this age group in 1995.
groups.
For men, the decline mostly happens for workers in the 25-29, 45-49, and 50-54 age
groups for which the return to postgraduate education decreased by seven percentage points
((-0.07)-0=-7%), five percentage points, and six percentage points, respectively between
1995 and 2010. Moreover, I find that young men with postgraduate education earn less than
BA workers in the same age group and that the negative wage gap has grown greater since
109
Figure 3.2: Average Weekly Wage by Birth Year in 1995 and 2010 for Men
1995. Table 3.3 shows that for men in the 25-29 age group, the PG-BA weekly wage gap,
which is statistically no different from zero in 1995, drops to -7% in 2010. For men in the
30-34 age group, the wage gap is -2.6% in 1995 and decreases to -4% in 2010.
For women, the decline in the return to postgraduate education happens mostly for workers aged between 30 and 44, ranging between five percentage points and eight percentage
points. In the youngest age group, women’s return to postgraduate education decreases from
3% in 1995 to statistically no different than 0 in 2010.
In Figure 3.2 and 3.3, I explore the return to different levels of postgraduate education
by age group for men and women, respectively. I plot the average weekly wage in 1995 and
110
Figure 3.3: Average Weekly Wage by Birth Year in 1995 and 2010 for Women
2010 for people with education above BA but below MA (ABA), Master’s degree (MA),
and professional degrees including Doctorate and MD (PROF) in three panels. Take the top
panel in Figure 3.2 as an example. Birth years are on the x-axis and log weekly wage is
on the y-axis. Curves in light blue plot the average weekly wage by birth year in 1995 and
curves in red plot the average weekly wage in 2010. Solid curves are average weekly wage
for men in the BA group in 1995 and 2010. Dashed curves are for men in the ABA group.
The distance between the light blue solid and the dashed curves measures the return to ABA
by birth year in 1995 and the distance between the two curves in red measures the return to
ABA in 2010. Similarly, I plot average weekly wage by birth year for the BA group in 1995
111
and 2010 in the other two panels, average weekly wage for the MA group in the middle
panel, and average weekly wage for men with professional degrees in the bottom panel.
The two figures tell us that the return to MA and professional degrees shrank substantially
for people aged 25-34 over the period studied. The middle panel in Figure 3.2 shows that in
1995, the average weekly wage for MA men aged 25-29, is very close to that for BA men
in their birth cohort, and average weekly wage for MA born men aged 30-34, is slightly
above that for their BA counterparts. However, in 2010, men in both age groups experience
a decline in return to MA: for the 25-29 age group, the average weekly MA wage is smaller
than the BA wage; for the 30-34 age group, the average weekly MA wage is almost the
same as the BA wage. Similarly, the bottom panel shows that the difference in average
weekly wage between young men with professional degrees and men in the BA group is
positive and quiet large in 1995; however, it is negligible in 2010. Figure 3.3 displays the
same finding for women: returns to MA and professional degrees have decreased for young
women since 1995.
However, there are gender differences relating to the return to ABA. For men, the returns
are trivial in both 1995 and 2010, indicating that men gain little from completing professional
certificates (e.g. teaching) on top of their BA degree. For women, the return to ABA is
relatively large in 1995, particularly for women born before 1950. By 2010, however, the
return has become much smaller for women in all age groups.
The age-earnings profile is closely related to job tenure of workers. PG workers have a
lower job tenure than BA workers who are in the same age groups. If there is a decrease in
112
the job tenure for young PG workers over the years 1995-2010 and it decreases more than
young BA workers, this could result in a slower growth rate of weekly wage for young PG
workers than their BA counterparts. In Table 3.5 I report differences in average job tenure
between BA and PG by age category for each gender.6 This table shows that for men in
the age 25-39 and women in the age 25-34, PG workers had significantly lower job tenure
than BA workers in 1995. However, the difference in average job tenure is not significantly
smaller in 2010 compared to 1995. This evidence suggests that the decline in the PG-BA
wage gap is not driven by changes in average job tenure over time.
3.4.3
Returns to Postgraduate Education by Major Field of Study
Tables 3.6 and 3.7 present the return to four levels of postgraduate qualification by major
field of study, conditional on potential work experience (age-6-years of schooling), for men
and women, respectively.7 Overall, I find that the return to the four levels of postgraduate
education is positive in 2010. However, the return is smaller than the return in 1995 for
women in most fields of the study. The extent to which the return to postgraduate education
changed since 1995 varies for people with different levels of postgraduate qualification in
6
OLS estimates in Table 3.5 are calculated using employed people aged 25-59 in the Labour Force Survey
in 1995, 2000, 2005, and 2010, as Census data does not have the measure of job tenure.
7
Census 1996 and 2001 use the Major Field of Study (MFS) classification. Census 2006 and NHS 2011
use the Classification of Instructional Programs (CIP) Canada 2000. This study matches a detailed class of the
Major Field of Study classification to a six-digit class of CIP Canada 2000 with the MFS-CIP concordances
that are provided by Statistics Canada. It reports the return to postgraduate education for ten fields of study:
(1) Education, (2) Visual and Performing Arts, and Communications Technologies, (3) Humanities, (4) Social
and Behavioural Sciences and Law, (5) Business, Management and Public Administration, (6) Physical and
Life Sciences and Technologies, (7) Mathematics, Computer and Information Sciences, (8) Architecture,
Engineering, and Related Technologies, (9) Agriculture, Natural Resources and Conservation, (10) Health,
Parks, Recreation and Fitness.
113
Table 3.5: The Difference in Average Job Tenure between BA and PG by Age Group
Survey Year
25-29
Panel A: Men
30-34
35-39
40-44
45-49
50-54
55-59
PG
-6.053∗
(-2.05)
-13.41∗∗∗
(-4.17)
-10.89∗
(-2.48)
-3.945
(-0.75)
3.383
(0.54)
14.17
(1.77)
10.20
(0.95)
2000
0.290
(0.15)
-5.253∗
(-1.97)
-3.308
(-0.87)
-6.157
(-1.23)
-12.93∗
(-2.20)
8.467
(1.12)
-26.44∗
(-2.55)
2005
-1.192
(-0.61)
-8.430∗∗
(-3.28)
-18.54∗∗∗
(-4.99)
-24.37∗∗∗
(-5.08)
-16.30∗∗
(-2.79)
-3.863
(-0.53)
-21.96∗
(-2.29)
2010
0.0680
(0.04)
-4.171
(-1.57)
-13.49∗∗∗
(-3.67)
-30.24∗∗∗
(-6.41)
-31.03∗∗∗
(-5.53)
-3.458
(-0.48)
-9.550
(-1.03)
PG*2000 0.461
(0.11)
1.951
(0.41)
-10.35
(-1.61)
-10.17
(-1.29)
-6.994
(-0.77)
-12.87
(-1.19)
9.596
(0.66)
PG*2005 1.992
(0.45)
6.459
(1.36)
2.935
(0.46)
-3.078
(-0.40)
-12.71
(-1.38)
-1.298
(-0.12)
2.802
(0.21)
PG*2010 1.290
(0.31)
4.295
(0.90)
-1.303
(-0.20)
-9.037
(-1.13)
-15.18
(-1.69)
-16.27
(-1.54)
-21.08
(-1.61)
58.58∗∗∗
(33.03)
2820
88.71∗∗∗
(33.36)
3000
125.6∗∗∗
(37.94)
3087
149.1∗∗∗
(36.57)
2975
148.2∗∗∗
(25.99)
2500
163.8∗∗∗
(20.67)
1726
cons
N
31.90∗∗∗
(25.17)
2261
Panel B: Women
PG
-6.217∗
(-2.12)
-11.32∗∗
(-2.88)
-10.32
(-1.87)
-4.505
(-0.66)
9.718
(1.17)
0.0585
(0.01)
-11.76
(-0.68)
2000
-2.383
(-1.39)
0.794
(0.28)
6.385
(1.57)
2.906
(0.55)
11.12
(1.73)
12.27
(1.49)
-1.268
(-0.10)
2005
1.205
(0.70)
-2.404
(-0.91)
-10.29∗∗
(-2.65)
-2.150
(-0.42)
9.379
(1.52)
10.43
(1.32)
1.224
(0.10)
2010
1.611
(0.99)
-0.638
(-0.24)
-8.635∗
(-2.32)
-10.26∗
(-2.10)
6.820
(1.15)
13.38
(1.73)
-1.057
(-0.09)
PG*2000 0.397
(0.10)
-2.125
(-0.38)
-7.124
(-0.92)
2.101
(0.21)
-17.32
(-1.53)
6.475
(0.45)
13.21
(0.60)
PG*2005 -0.294
(-0.07)
-1.558
(-0.29)
11.39
(1.46)
-4.190
(-0.44)
-4.434
(-0.39)
6.398
(0.46)
9.280
(0.46)
PG*2010 1.029
(0.26)
2.153
(0.40)
1.125
(0.16)
5.315
(0.57)
-15.01
(-1.39)
-8.901
(-0.66)
25.12
(1.32)
55.98∗∗∗
(29.39)
2758
84.29∗∗∗
(30.27)
2866
105.3∗∗∗
(28.97)
2727
114.3∗∗∗
(25.03)
2432
130.7∗∗∗
(20.18)
1972
139.5∗∗∗
(13.76)
1104
cons
N
32.50∗∗∗
(27.65)
2862
Notes: This table presents OLS estimates for workers aged 25-59 in the Labour Force Survey. BA workers in
1995 are the reference group. PG is a dummy variable, 1 if a person has a postgraduate degree; 0 if the person
has a four-year university degree. The interaction of PG with year dummy variables measures the difference in
average job tenure (in month) between PG and BA in 2000, 2005, and 2010, relative to the difference 1995.
114
different fields of study.
Workers with a four-year university degree experienced a wage increase in almost all
fields of study in 2010, relative to the weekly wage in 1995. Women majoring in education
and health experienced a larger growth in weekly wage from 1995 to 2010 than women
majoring in other fields of study, while men majoring in education had a larger growth than
men majoring in other fields of study. It is notable that the weekly wage grew more slowly
for all BA workers majoring in mathematics and computer sciences and for men majoring in
health from 2000 to 2010. For the former, the growth rate of weekly wage dropped seven
percentage points for men and 12 percentage points for women in 2010 compared to that
in 2000. For the latter, the growth rate of weekly wage declined substantially from 2% in
2000 to -7% in 2005 and increased thereafter. By 2010, the average weekly wage for men
majoring in health is statistically the same as that in 1995.
The return to postgraduate education is large in 1995 for people graduating from business
and management, and government and social sciences, the most common fields of study
among both men and women, but it has become smaller since 1995. The return to ABA
decreases by seven percentage points to 5% for men and 10 percentage points to 2% for
women. The return to MA decreases by four percentage points to 4% for men and four
percentage points to 8% for women. The return to a Doctorate decreases by eight percentage
points to 20% for men and seven percentage points to 31% for women.
For both men and women majoring in health and education, common fields of study
-0.03∗∗
(0.01)
0.05∗∗∗
(0.007)
0.12∗∗∗
(0.007)
0.011
(0.021)
-0.02
(0.01)
-0.03∗
(0.014)
0.007
(0.02)
-0.05∗∗∗
(0.01)
-0.03∗∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.05
(0.07)
-0.05
(0.04)
-0.009
(0.05)
0.89∗∗∗
(0.008)
0.20∗∗∗
(0.008)
−−
−−
0.24∗∗∗
(0.03)
0.10∗∗∗
(0.02)
0.003
(0.02)
0.11∗∗∗
(0.02)
-0.03
(0.05)
-0.05
(0.06)
-0.08∗∗
(0.06)
-0.05
(0.04)
-0.03
(0.04)
-0.06
(0.05)
−−
−−
−−
−−
−−
−−
-0.08
(0.08)
0.009
(0.07)
-0.02
(0.08)
0.1∗∗
(0.04)
0.09∗∗∗
(0.03)
−−
−−
0.37∗∗∗
(0.05)
(2)
Arts
0.06∗∗∗
(0.01)
0.08∗∗∗
(0.01)
0.14∗∗∗
(0.01)
0.01
0.03
-0.07∗∗
(0.03)
-0.10∗∗∗
(0.03)
-0.03∗∗
(0.02)
-0.04∗∗∗
(0.02)
-0.05∗∗∗
(0.02)
−−
−−
−−
−−
−−
−−
-0.16∗∗∗
(0.03)
-0.12∗∗∗
(0.03)
-0.1∗∗∗
(0.03)
0.07∗∗∗
(0.02)
-0.02
(0.01)
−−
−−
0.32∗∗∗
(0.02)
(3)
Humanities
0.05∗∗∗
(0.006)
0.08∗∗∗
(0.007)
0.13∗∗∗
(0.007)
-0.09∗∗∗
(0.02)
-0.10∗∗∗
(0.02)
-0.07∗∗∗
(0.02)
-0.004
(0.01)
-0.05∗∗∗
(0.02)
-0.04∗∗
(0.02)
−−
−−
−−
−−
−−
−−
-0.11∗∗∗
(0.02)
-0.07∗∗∗
(0.02)
-0.08∗∗∗
(0.02)
0.12∗∗∗
(0.01)
0.08∗∗∗
(0.01)
−−
−−
0.28∗∗∗
(0.01)
(4)
Social Sciences
0.07∗∗∗
(0.006)
0.08∗∗∗
(0.006)
0.11∗∗∗
(0.006)
-0.005
(0.02)
0.000
(0.02)
-0.008∗∗∗
(0.02)
-0.001
(0.01)
-0.05∗∗∗
(0.01)
-0.09∗∗∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.12∗∗∗
(0.04)
-0.05
(0.05)
0.04
(0.05)
0.14∗∗∗
(0.01)
0.30∗∗∗
(0.01)
−−
−−
0.28∗∗∗
(0.03)
(5)
Business
0.04∗∗∗
(0.01)
0.05∗∗∗
(0.01)
0.11∗∗∗
(0.01)
-0.03
(0.03)
0.004
(0.03)
-0.09∗∗∗
(0.03)
0.004
(0.02)
-0.01
(0.02)
0.01
(0.02)
-0.37∗∗∗
(0.13)
-0.47∗∗∗
(0.12)
-0.94∗∗∗
(0.2)
0.004
(0.02)
0.01
(0.02)
-0.008
(0.02)
0.02
(0.02)
0.006
(0.01)
0.55∗∗∗
(0.07)
0.20∗∗∗
(0.01)
(6)
Life Sciences
0.12∗∗∗
(0.01)
0.01
(0.01)
0.05∗∗∗
(0.01)
-0.02
(0.04)
-0.05∗
(0.03)
-0.01
(0.03)
0.08∗∗∗
(0.03)
-0.02
(0.02)
0.008
(0.02)
−−
−−
−−
−−
−−
−−
0.08∗
(0.05)
0.12∗∗∗
(0.04)
0.10∗∗∗
(0.04)
-0.03
(0.02)
-0.05∗∗∗
(0.02)
−−
−−
0.07∗∗
(0.03)
(7)
Computer Science
0.04∗∗∗
(0.006)
0.008
(0.006)
0.09∗∗∗
(0.006)
-0.01
(0.02)
0.07∗∗∗
(0.02)
0.03∗∗∗
(0.02)
0.02
(0.01)
-0.000
(0.01)
0.02
(0.01)
−−
−−
−−
−−
−−
−−
-0.03
(0.03)
0.002
(0.03)
0.02
(0.02)
-0.14∗∗∗
(0.02)
-0.001
(0.01)
−−
−−
0.13∗∗∗
(0.02)
(8)
Engineering
0.04∗∗
(0.02)
0.02∗
(0.02)
0.07∗∗∗
(0.02)
-0.02
(0.05)
-0.12∗∗
(0.05)
-0.15∗∗∗
(0.05)
-0.1∗∗∗
(0.04)
-0.08∗∗∗
(0.03)
-0.07∗∗
(0.03)
−−
−−
−−
−−
−−
−−
-0.1∗
(0.05)
-0.13∗∗∗
(0.05)
-0.18∗∗∗
(0.05)
0.02
(0.03)
0.06∗∗∗
(0.02)
−−
−−
0.19∗∗∗
(0.03)
(9)
Natural Resources
0.02∗∗
(0.01)
-0.07∗∗∗
(0.01)
-0.02
(0.01)
-0.01
(0.03)
0.05
(0.03)
0.03
(0.03)
-0.02
(0.02)
-0.04
(0.02)
-0.05∗∗
(0.02)
-0.01
(0.02)
-0.08∗∗∗
(0.03)
-0.07∗∗∗
(0.02)
-0.04
(0.03)
-0.08
(0.04)
0.000
(0.04)
-0.02
(0.02)
0.13∗∗∗
(0.01)
0.35∗∗∗
(0.02)
0.26∗∗∗
(0.03)
(10)
Health
N
80,798
20,514
77,158
169,367
209,933
82,639
65,210
181,060
23,587
71,215
Notes: This table presents the OLS estimates for men by field of study, conditional on a quadratic function of potential work experience (age-6-years of schooling). Men in the BA
group in 1995 are in the reference group. Major fields of study are: (1) Education, (2) Visual and Performing Arts, and Communications Technologies, (3) Humanities, (4) Social and
Behavioural Sciences and Law, (5) Business, Management and Public Administration, (6) Physical and Life Sciences and Technologies, (7) Mathematics, Computer and Information
Sciences, (8) Architecture, Engineering, and Related Technologies, (9) Agriculture, Natural Resources and Conservation, (10) Health, Parks, Recreation and Fitness.
2010
2005
Year
2000
Doctorate*2010
Doctorate*2005
Doctorate*2000
MD*2010
MD*2005
MD*2000
MA*2010
MA*2005
MA*2000
ABA*2010
ABA*2005
Interaction
ABA*2000
Doctorate
MD
MA
Degrees
ABA
(1)
Education
Table 3.6: The PG-BA Wage Gap by Major Field of Study for Men
115
0.007
(0.009)
0.06∗∗∗
(0.005)
0.15∗∗∗
(0.005)
0.003
(0.02)
-0.01
(0.01)
-0.03∗∗∗
(0.01)
-0.02
(0.02)
-0.04∗∗∗
(0.01)
-0.09∗∗∗
(0.01)
−−
−−
−−
−−
−−
−−
0.005
(0.08)
0.03
(0.05)
0.08
(0.05)
0.12∗∗∗
(0.007)
0.25∗∗∗
(0.008)
−−
−−
0.22∗∗∗
(0.04)
0.09∗∗∗
(0.02)
0.04∗∗
(0.02)
0.13∗∗∗
(0.02)
-0.04
(0.04)
-0.13∗∗∗
(0.05)
-0.15∗∗∗
(0.05)
-0.04
(0.04)
-0.10∗∗∗
(0.04)
-0.06
(0.04)
−−
−−
−−
−−
−−
−−
-0.17
(0.11)
-0.24∗∗
(0.12)
-0.05
(0.11)
0.20∗∗∗
(0.03)
0.08∗∗∗
(0.03)
−−
−−
0.38∗∗∗
(0.08)
(2)
Arts
0.05∗∗∗
(0.008)
0.005
(0.008)
0.10∗∗∗
(0.008)
-0.04
(0.02)
-0.05∗∗
(0.02)
-0.10∗∗∗
(0.02)
0.03∗
(0.02)
-0.02
(0.17)
0.01
(0.02)
−−
−−
−−
−−
−−
−−
-0.02
(0.04)
0.008
(0.04)
0.07
(0.05)
0.15∗∗∗
(0.02)
0.04∗∗∗
(0.01)
−−
−−
0.30∗∗∗
(0.03)
(3)
Humanities
0.08∗∗∗
(0.005)
0.09∗∗∗
(0.005)
0.17∗∗∗
(0.006)
-0.005
(0.02)
-0.09
(0.02)
-0.10∗∗∗
(0.02)
0.05∗∗∗
(0.01)
-0.03∗∗
(0.01)
-0.04∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.12∗∗∗
(0.03)
-0.06∗∗
(0.03)
-0.07∗∗
(0.03)
0.12∗∗∗
(0.01)
0.12∗∗∗
(0.01)
−−
−−
0.38∗∗∗
(0.02)
(4)
Social Science
0.08∗∗∗
(0.006)
0.07∗∗∗
(0.006)
0.14∗∗∗
(0.006)
0.02
(0.02)
0.03
(0.02)
-0.07∗∗∗
(0.02)
-0.07∗∗∗
(0.02)
-0.08∗∗∗
(0.01)
-0.14∗∗∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.18∗∗
(0.07)
-0.20∗∗∗
(0.07)
-0.05
(0.07)
0.12∗∗∗
(0.01)
0.37∗∗∗
(0.01)
−−
−−
0.43∗∗∗
(0.05)
(5)
Business
0.04∗∗∗
(0.01)
0.09∗∗∗
(0.01)
0.17∗∗∗
(0.01)
0.02
(0.04)
-0.06
(0.04)
-0.07∗∗
(0.03)
-0.004
(0.02)
0.004
(0.02)
0.001
(0.02)
-0.07
(0.16)
-0.33∗
(0.18)
0.15
(0.20)
0.02
(0.04)
0.02
(0.03)
-0.02
(0.03)
0.08∗∗∗
(0.03)
0.07∗∗∗
(0.02)
0.39∗∗∗
(0.11)
0.29∗∗∗
(0.03)
(6)
Life Science
0.16∗∗∗
(0.02)
0.01
(0.01)
0.04∗∗∗
(0.01)
-0.06
(0.06)
-0.07
(0.05)
-0.02
(0.04)
0.09∗∗∗
(0.02)
0.000
(0.02)
0.08∗∗∗
(0.02)
−−
−−
−−
−−
−−
−−
0.07
(0.12)
-0.05
(0.09)
0.01
(0.09)
-0.03
(0.03)
-0.02
(0.02)
−−
−−
0.24∗∗∗
(0.07)
(7)
Computer Science
0.06∗∗∗
(0.02)
0.04∗∗
(0.02)
0.16∗∗∗
(0.02)
-0.04
(0.06)
0.02
(0.05)
0.02
(0.05)
0.07∗
(0.04)
0.06∗
(0.03)
0.11∗∗∗
(0.03)
−−
−−
−−
−−
−−
−−
0.01
(0.09)
0.03
(0.08)
-0.05
(0.07)
-0.09∗∗
(0.05)
-0.03
(0.03)
−−
−−
0.23∗∗∗
(0.06)
(8)
Engineering
0.05∗∗
(0.02)
0.07∗∗∗
(0.02)
0.14∗∗∗
(0.02)
-0.05
(0.08)
-0.08
(0.06)
-0.18∗∗∗
(0.06)
-0.12∗∗∗
(0.04)
-0.07∗
(0.04)
-0.04
(0.04)
−−
−−
−−
−−
−−
−−
-0.18∗
(0.10)
-0.13∗
(0.07)
-0.22∗∗
(0.09)
0.08∗∗
(0.04)
0.17∗∗∗
(0.03)
−−
−−
0.37∗∗∗
(0.05)
(9)
Natural Resources
0.02∗∗∗
(0.006)
0.10∗∗∗
(0.006)
0.20∗∗∗
(0.006)
-0.01
(0.02)
-0.006
(0.02)
-0.02
(0.02)
-0.02
(0.01)
-0.05∗∗∗
(0.01)
-0.06∗∗∗
(0.01)
0.01
(0.03)
-0.21∗∗∗
(0.03)
-0.17∗∗∗
(0.02)
-0.06
(0.05)
-0.20∗∗∗
(0.05)
-0.08∗
(0.05)
-0.003
(0.02)
0.17∗∗∗
(0.01)
0.26∗∗∗
(0.02)
0.29∗∗∗
(0.04)
(10)
Health
N
201,642
30,501
105,614
240,211
182,479
63,639
31,553
38,990
16,076
160,243
Notes: This table presents the OLS estimates for women by field of study, conditional on a quadratic function of potential work experience (age-6-years of schooling). Women in
the BA group in 1995 are in the reference group. Major fields of study are: (1) Education, (2) Visual and Performing Arts, and Communications Technologies, (3) Humanities, (4)
Social and Behavioural Sciences and Law, (5) Business, Management and Public Administration, (6) Physical and Life Sciences and Technologies, (7) Mathematics, Computer and
Information Sciences, (8) Architecture, Engineering, and Related Technologies, (9) Agriculture, Natural Resources and Conservation, (10) Health, Parks, Recreation and Fitness.
2010
2005
Year
2000
Doctorate*2010
Doctorate*2005
Doctorate*2000
MD*2010
MD*2005
MD*2000
MA*2010
MA*2005
MA*2000
ABA*2010
ABA*2005
Interaction
ABA*2000
Doctorate
MD
MA
Degrees
ABA
(1)
Education
Table 3.7: The PG-BA Wage Gap by Major Field of Study for Women
116
117
among women, returns to MA and MD decreased in 2010 compared to that in 1995, while
returns to ABA and a Doctorate changed little between 1995 and 2010. Table 3.7 tells us
that even though women in the MD group earned 12% more than women in the BA group in
2010, this is 17 percentage points smaller than the returns of 29% for women majoring in
health in 1995. Similarly, for women majoring in education, the return to MA in 2010 is
nine percentage points smaller in 2010 than the 25% return in 1995. Returns to MA and
MD decreased for men by three percentage points and seven percentage points, respectively,
which is less of a decrease than that found for women.
For workers majoring in engineering, mathematics and computer sciences, and physics
and life sciences, common fields of study among men, results are mixed. Specifically,
women experience an increase in the return to MA, while except for men in the ABA
group, men majoring in engineering experience little change in the return to postgraduate
education. In 1995, men and women earn 13% and 23% more from completing a Doctorate
in engineering than their BA counterparts. These gains remained about the same through
to 2010. Women with a Master’s degree in engineering experience an 11 percent increase
in the return to postgraduate education from 1995 to 2010, while men in the MA group
earn statistically the same as men in the BA group over the four census years. Returns to
ABA are found to be negative for men and women in 1995 and 2010, with a slightly smaller
negative gain in 2010 compared to that in 1995.
Men and women both experience an increase in the return to postgraduate education in
mathematics and computer sciences. For men, the return to a Doctorate in 2010 is 17%, 10
118
percentage points greater than the 7% return in 1995. For women, the return to MA in 2010
is 8%, eight percentage points greater than the zero return in 1995.
There is a substantial decrease in returns to ABA and MD for men majoring in physics
and life sciences. By 2010, men in the ABA and MD groups earn 9% and 44% less than
men in the BA group. In contrast, women majoring in these two fields experience no change
in returns to postgraduate education. The only exception is the returns to ABA that drops
from 8% to 1% from 1995 to 2010 for women.
There is a significant decrease in the return to postgraduate education for men majoring
in humanities between 1995 and 2010. There is little change in returns to postgraduate
education for women. For men, the return to ABA drops 10 percentage points to -3%, the
return to MA drops four percentage points to 4%, and the return to a Doctorate drops 10
percentage points to 22% in 2010. For women, the return to MA, which is 4% in 1995, and
the return to a Doctorate, which is 30% in 1995, stay about the same in 2010; however, the
return to ABA decreases from 15% in 1995 to 5% in 2010.
The return to postgraduate education for people majoring in agriculture and natural
resources has decreased substantially since 1995. For men, returns to ABA and MA are
negative in 2010, dropping 15 percentage points and 7 percentage points, relative to returns
to ABA and MA in 1995. The return to a Doctorate decreases from 19% in 1995 to 1% in
2010. For women, the return to ABA drops 18 percentage points to -10% and the return to a
Doctorate drops 22 percentage points to 15% in 2010. The return to MA stays about the
same as the 18% return in 1995.
119
The return to postgraduate education for workers majoring in the arts show little change
between 1995 and 2010, except for workers in the ABA group, for whom men’s return
decreases from 10% in 1995 to 2% in 2010 and women’s return drops from 20% to 5% in
2010.
3.4.4
Returns to Professional Degrees by Major Field of Study,
Relative to MA
The return to professional degrees, in particular doctoral degrees, concerns people in
a Master’s program who are considering whether to pursue a more advanced degree. In
Tables 3.8 and 3.9, I present returns to a Doctorate and MD by major field of study, relative
to MA, for men and women, respectively. Tables 3.8 and 3.9 tell us that (1) for most workers
with a Master’s degree, average weekly wage increased significantly from 1995 to 2010,
(2) for most workers with a Doctoral degree, the return to a Doctorate, relative to MA, is
positive and has changed little from 1995 to 2010.
The fastest growth of weekly wage for workers with a Master’s degree is for men
majoring in physics and life sciences, for whom there is an 11% increase in average weekly
wage between 1995 and 2010, and for women majoring in engineering, for whom there is
a 26% increase in average weekly wage in the same time period. Average weekly wage
changes little for MA men in arts or in agriculture and natural resources, or for women in
business and management. Furthermore, MA men in health experience a 6% decrease in
-0.02
(0.02)
0.002
(0.01)
0.10∗∗∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.06
(0.07)
-0.002
(0.04)
0.01
(0.05)
−−
−−
0.05∗
(0.03)
0.04
-0.04
0.05
(0.01)
0.05
(0.05)
−−
−−
−−
−−
−−
−−
-0.02
(0.09)
0.05
(008)
0.05
(0.09)
−−
−−
0.27∗∗∗
(0.06)
(2)
Arts
0.02∗
(0.01)
0.04∗∗
(0.01)
0.09∗∗∗
(0.02)
−−
−−
−−
−−
−−
−−
-0.13∗∗∗
(0.03)
-0.07∗∗
(0.03)
-0.04
(0.04)
−−
−−
0.33∗∗∗
(0.02)
(3)
Humanities
0.04∗∗∗
(0.01)
0.03∗
(0.01)
0.09∗∗∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.10∗∗∗
(0.02)
-0.01
(0.03)
-0.04
(0.03)
−−
−−
0.20∗∗∗
(0.02)
(4)
Social Science
0.07∗∗∗
(0.01)
0.03∗∗∗
(0.01)
0.02∗∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.12∗∗∗
(0.04)
-0.005
(0.05)
0.13∗∗∗
(0.05)
−−
−−
-0.02
(0.03)
(5)
Business
0.04∗∗
(0.02)
0.03∗
(0.02)
0.11∗∗∗
(0.02)
-0.38∗∗∗
(0.13)
-0.46∗∗∗
(0.12)
-0.95∗∗∗
(0.21)
0.001
(0.02)
0.03
(0.02)
-0.02
(0.03)
0.55∗∗∗
(0.07)
0.19∗∗∗
(0.02)
(6)
Life Science
0.21∗∗∗
(0.02)
-0.01
(0.02)
0.06∗∗∗
(0.02)
−−
−−
−−
−−
−−
−−
-0.004
(0.05)
0.14∗∗∗
(0.04)
0.09∗∗
(0.04)
−−
−−
0.12∗∗∗
(0.03)
(7)
Computer Science
0.05∗∗∗
(0.01)
0.001
(0.01)
0.10∗∗∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.04
(0.03)
0.001
(0.03)
0.003
(0.03)
−−
−−
0.13∗∗∗
(0.02)
(8)
Engineering
-0.07∗∗
(0.03)
-0.07∗∗∗
(0.02)
-0.01
(0.02)
−−
−−
−−
−−
−−
−−
0.003
(0.06)
-0.04
(0.05)
-0.11∗
(0.06)
−−
−−
0.12∗∗∗
(0.03)
(9)
Natural Resources
0.005
(0.02)
-0.10∗∗∗
(0.02)
-0.06∗∗∗
(0.02)
0.003
(0.03)
-0.06∗
(0.03)
-0.02
(0.02)
-0.03
(0.04)
-0.04
(0.04)
0.05
(0.04)
0.23∗∗∗
(0.02)
0.14∗∗∗
(0.03)
(10)
Health
N
19,237
4,100
23,725
36,898
55,605
30,075
15,363
44,144
6,957
29,955
Notes: This table presents the OLS estimates for men by field of study, conditional on a quadratic function of potential work experience (age-6-years of schooling). Men in the MA
group in 1995 are in the reference group. Major fields of study are: (1) Education, (2) Visual and Performing Arts, and Communications Technologies, (3) Humanities, (4) Social and
Behavioural Sciences and Law, (5) Business, Management and Public Administration, (6) Physical and Life Sciences and Technologies, (7) Mathematics, Computer and Information
Sciences, (8) Architecture, Engineering, and Related Technologies, (9) Agriculture, Natural Resources and Conservation, (10) Health, Parks, Recreation and Fitness.
2010
2005
Year
2000
Doctorate*2010
Doctorate*2005
Doctorate*2000
MD*2010
MD*2005
Interaction
MD*2000
Doctorate
Degrees
MD
(1)
Education
Table 3.8: Returns to Professional Degrees by Major Field of Study for Men, Relative to MA
120
-0.01
(0.02)
0.02∗
(0.01)
0.07∗∗∗
(0.01)
−−
−−
−−
−−
−−
−−
0.02
(0.08)
0.07
(0.05)
0.16∗∗∗
(0.05)
−−
−−
-0.04
(0.04)
0.04
(0.04)
-0.07∗∗
(0.04)
0.07∗
(0.04)
−−
−−
−−
−−
−−
−−
-0.12
(0.12)
-0.12
(0.12)
0.01
(0.11)
−−
−−
0.28∗∗∗
(0.08)
(2)
Arts
0.08∗∗∗
(0.02)
-0.02
(0.02)
0.11∗∗∗
(0.02)
−−
−−
−−
−−
−−
−−
-0.05
(0.05)
0.03
(0.04)
0.06
(0.05)
−−
−−
0.24∗∗∗
(0.04)
(3)
Humanities
0.12∗∗∗
(0.01)
0.06∗∗∗
(0.01)
0.13∗∗∗
(0.01)
−−
−−
−−
−−
−−
−−
-0.17∗∗∗
(0.03)
-0.03
(0.03)
-0.03
(0.03)
−−
−−
0.25∗∗∗
(0.02)
(4)
Social Science
0.004
(0.02)
-0.03∗∗
(0.01)
-0.008
(0.01)
−−
−−
−−
−−
−−
−−
-0.10
(0.08)
-0.11
(0.07)
0.10
(0.07)
−−
−−
0.03
(0.06)
(5)
Business
0.03
(0.02)
0.10∗∗∗
(0.02)
0.17∗∗∗
(0.02)
-0.07
(0.15)
-0.34∗
(0.18)
0.14
(0.20)
0.03
(0.04)
0.007
(0.04)
-0.02
(0.04)
0.32∗∗∗
(0.11)
0.21∗∗∗
(0.03)
(6)
Life Science
0.27∗∗∗
(0.03)
0.008
(0.02)
0.12∗∗∗
(0.02)
−−
−−
−−
−−
−−
−−
-0.32
(0.12)
0.06
(0.09)
-0.07
(0.09)
−−
−−
0.26∗∗∗
(0.07)
(7)
Computer Science
0.12∗∗∗
(0.03)
0.08∗∗∗
(0.03)
0.26∗∗∗
(0.03)
−−
−−
−−
−−
−−
−−
-0.07
(0.10)
-0.04
(0.08)
-0.18∗∗
(0.08)
−−
−−
0.25∗∗∗
(0.07)
(8)
Engineering
-0.07∗∗
(0.04)
0.003
(0.03)
0.10∗∗∗
(0.03)
−−
−−
−−
−−
−−
−−
-0.06
(0.11)
-0.07
(0.08)
-0.18∗∗
(0.09)
−−
−−
0.19∗∗∗
(0.06)
(9)
Natural Resources
0.000
(0.01)
0.05∗∗∗
(0.01)
0.14∗∗∗
(0.01)
0.03
(0.03)
-0.17∗∗∗
(0.03)
-0.13∗∗∗
(0.03)
-0.04
(0.05)
-0.14∗∗
(0.05)
-0.03
(0.05)
0.12∗∗∗
(0.02)
0.13∗∗∗
(0.04)
(10)
Health
N
32,191
5,035
21,828
42,639
36,377
18,217
10,042
10,736
4,637
41,181
Notes: This table presents the OLS estimates for women by field of study, conditional on a quadratic function of potential work experience (age-6-years of schooling). Women in
the MA group in 1995 are in the reference group. Major fields of study are: (1) Education, (2) Visual and Performing Arts, and Communications Technologies, (3) Humanities, (4)
Social and Behavioural Sciences and Law, (5) Business, Management and Public Administration, (6) Physical and Life Sciences and Technologies, (7) Mathematics, Computer and
Information Sciences, (8) Architecture, Engineering, and Related Technologies, (9) Agriculture, Natural Resources and Conservation, (10) Health, Parks, Recreation and Fitness.
2010
2005
Year
2000
Doctorate*2010
Doctorate*2005
Doctorate*2000
MD*2010
MD*2005
Interaction
MD*2000
Doctorate
Degrees
MD
(1)
Education
Table 3.9: Returns to Professional Degrees by Major Field of Study for Women, Relative to MA
121
122
average weekly wage in 2010.
Turning to the return to a Doctorate, I find that men with Doctorates in humanities, arts,
and social sciences earn 33%, 27%, and 20%, respectively, more than men with Master’s
degrees in the same field. The return to a Doctorate in these fields of study is larger than in
other fields of study.
Women with Doctorate degrees in arts, humanities, government and social sciences,
mathematics and computer sciences, and physics and life sciences experience a substantial
increase in average weekly wage, relative to their counterparts in the MA group. Returns
range between 24% and 28%. The exceptions to this trend are men and women in natural
resources, for whom the return to a Doctorate decreases substantially to 1% in 2010, and
women in engineering, for whom the return decreases to 7% in 2010.
This section confirms that the reduction in average weekly wage gap between postgraduate education and BA degree between 1995 and 2010 is not because PG workers earn less
than BA workers, but because PG workers experienced a slower rate of wage growth.
123
Figure 3.4: Proportion of Men with the Same Education in Different Occupations
Figure 3.5: Relative Wage Gap in 2010 for Men
124
Figure 3.6: Proportion of Women with the Same Education in Different Occupations
Figure 3.7: Relative Wage Gap in 2010 for Women
125
3.4.5
Potential Explanations for the Decline in the PG-BA Wage Gap
This section attempts to explain why weekly wage has grown more slowly for PG workers
than for BA workers. To do so, I compare the occupational composition of universityeducated workers in 1995 to that in recent years. If PG workers have different occupations in
2010 than in 1995, the change in occupational composition could contribute to the slowdown
in weekly wage of PG workers, relative to their BA counterparts.8
In Figure 3.5, I present the proportion of men with the same education in seven occupational categories.9 There is a considerable increase in the proportion of men with
postgraduate education in the natural sciences occupations, at the expense of a decline in
the proportion of men in social services and management occupations. On top of that, the
proportion of workers in business and finance, sales and services, and occupations in art
have increased by two to three percentage points for men in the ABA or MA groups, while
the proportion in health occupations has increased by three percentage points for men with a
Doctorate. The majority of men with MD work in health, with the proportion decreasing by
3 percentage points to 80% in 2010. There is little change in the proportion of BA working
in business and finance, health, and sales occupations.
In Figure 3.5, I present the return to postgraduate education by occupation in 2010 in
8
Changes in occupational composition of university-educated workers could be due to either changes in
labour supply or changes in labour demand over time. In the following analysis I take the labour demand in a
year as given and document how the return to postgraduate education evolved by occupational category.
9
The seven occupational categories are based on the National Occupational Classification in 2011. Other
than the six occupations listed, I group occupations that have a small proportion of PG workers into one
occupational category. Those occupations are occupations in art, culture, recreation and sport; trades, transport
and equipment operators; natural resources, agriculture and related production, and manufacturing.
126
relation to that in 1995.10 I find that the slowdown in the return to postgraduate education
happens to workers in almost every occupational category, but there is more of a slowdown
in occupations that PG male workers are more likely to work at in 2010 than in 1995.
Figure 3.5 shows that for men in the ABA group, returns to postgraduate education in
business and finance occupations decrease by -5.2%, compared to a 4% decline in social
services occupations. For men with a Master’s degree, returns decrease by 7.3% in business
and finance, and 12.7% in sales and services, but only 2% in social services and management
occupations. For men with MD or a Doctorate, there is about a 16% decrease in returns in
health occupations, but there is no significant difference in returns in social services and
management occupations.
Parallel results for women are presented in Figures 3.6 and 3.7. Figure 3.6 shows that
there is a substantial decrease in the proportion of women with postgraduate education in
social services occupations, varying between 5% and 13%. The proportion of women in
business and finance, and sales and services occupations has increased. The proportion of
women in health occupations has not only increased for women with a Doctorate, but also
for women with postgraduate education below a Doctorate.
Similar to men, women experienced a greater decline in occupations where a larger
proportion of women with postgraduate education work in 2010. On the one hand, Figure 3.6
shows that there is a significant decrease in returns to postgraduate education in business
and finance occupations for women in the ABA and MA groups, and in health occupations
10
Relative returns in Figure 3.5 are the coefficients of interaction terms between four postgraduate qualifications and 2010 year variable, conditional on a quadratic function of potential work experience, four
postgraduate qualifications, the year dummy variable, and major fields of study.
127
for women with postgraduate education except the ABA group. On the other hand, there
is no significant difference in returns to postgraduate education in social services between
1995 and 2010. The return to MA in management occupations is significantly lower in 2010,
but the magnitude is smaller than that in business and finance and health occupations.
I test whether the PG-BA wage gap decreases by a greater magnitude in occupations
where PG workers were less likely to work in 1995 for men in Tables 3.10 and for women
in Tables 3.11. To do so, I use an approach close to that in Verdugo and Verdugo (1989) in
the literature of educational mismatch.11 Specifically, using the Census 1996, I first estimate
the propensity score of workers with postgraduate education, conditional on four-digit
occupational categories, age, gender, and marital status (married, single, and other), and
collapse the predicated probability by gender, four-digit occupational categories, and marital
status. I then merge the predicted probabilities in 1995 to workers with university education
in 2000, 2005, and 2010 by their gender, four-digit occupations, and marital status. Finally,
according to the predicted probabilities, I group workers in 1995 - 2010 into four categories.
Sales and service occupations and most occupations in business and finance belong to
categories (A) and (B). Occupations in health are separated into all four of the categories.
There are two advantages to separating occupations into four categories, using the
predicted probabilities in 1995. The first advantage is that it reflects the impact of educational
mismatch, a worker’s education relative to the educational requirement of the job in 1995,
on returns to postgraduate education. For example, in 1995 the probability of PG workers
11
Verdugo and Verdugo (1989) uses the average years of schooling of all workers in an occupation as the
baseline for the level of education that the occupation requires. I use the probability of PG workers holding the
same occupation in 1995 as the baseline for the educational requirement of the occupation in other years.
128
working as sales representatives was lower than 10%. If a PG worker in 1995 worked as
a sales representative, he/she would be considered overeducated for the job. Returns to
postgraduate education would decrease, if, over time, a growing number of workers with
postgraduate education were displaced to jobs that were held by BA workers in 1995.
The second advantage is that this approach captures the impact of changes in labour
supply of PG workers to certain occupations on changes in returns to postgraduate education.
An example is in health occupations that consist of professional occupations in nursing,
professional occupations in health, and technical occupations in health. The proportion of
PG workers in technical health occupations doubled in 2010, which is a faster growth rate
than the proportion of PG workers in professional occupations. PG workers in technical
occupations, e.g. medical laboratory technologists, on average are paid lower than those in
professional occupations, e.g. physicians. The shift to technical health occupations would
generate a slowdown in the wage growth of PG workers in health occupations.
In both tables, column (1) presents returns to postgraduate education, conditional on
seven occupational categories. I find that the return to four postgraduate qualifications has
decreased significantly since 1995, suggesting that the decline in the return to postgraduate
education exists within aggregated occupational categories. This finding is consistent
between men and women.
In columns (2) - (5), I present the return to postgraduate education, conditional on
four-digit occupational code for workers in occupational categories (A) - (D), respectively.
129
Table 3.10: The PG-BA Wage Gap by Occupation for Men
(1)
All Workers
Degrees
ABA
MA
MD
Doctorate
Interaction
ABA*2000
ABA*2005
ABA*2010
MA*2000
MA*2005
MA*2010
MD*2000
MD*2005
MD*2010
Doctorate*2000
Doctorate*2005
Doctorate*2010
Year
2000
2005
2010
(2)
Category A
Probability below 25
(3)
Category B
25-50
(4)
Category C
50-75
(5)
Category D
above 75
0.05∗∗∗
(0.005)
0.07∗∗∗
(0.004)
0.37∗∗∗
(0.01)
0.22∗∗∗
(0.007)
0.03∗∗
(0.01)
0.03∗∗∗
(0.01)
-0.06
(0.04)
0.03
(0.04)
0.04∗∗∗
(0.006)
0.08∗∗∗
(0.005)
0.17∗∗∗
(0.03)
0.05∗∗∗
(0.014)
0.04∗∗∗
(0.01)
0.09∗∗∗
(0.008)
0.26∗∗∗
(0.05)
0.26∗∗∗
(0,01)
0.13∗∗
(0.05)
0.14∗∗∗
(0.03)
0.52∗∗∗
(0.03)
0.52∗∗∗
(0.03)
-0.03∗∗∗
(0.007)
-0.02∗∗∗
(0.007)
-0.05∗∗∗
(0.007)
-0.007
(0.005)
-0.03∗∗∗
(0.005)
-0.02∗∗∗
(0.005)
-0.03
(0.02)
-0.18∗∗∗
(0.02)
-0.12∗∗∗
(0.02)
-0.05∗∗∗
(0.01)
-0.04∗∗∗
(0.01)
-0.03∗∗∗
(0.01)
-0.01
(0.015)
-0.03∗
(0.01)
-0.03∗∗
(0.01)
-0.003
(0.01)
-0.03∗∗
(0.01)
-0.01
(0.01)
-0.01
(0.05)
-0.09
(0.05)
0.001
(0.05)
-0.01
(0.05)
-0.04
(0.05)
-0.01
(0.05)
-0.02∗∗
(0.009)
-0.006
(0.009)
-0.02∗∗
(0.009)
-0.002
(0.007)
-0.02∗∗∗
(0.007)
-0.03∗∗∗
(0.007)
-0.13∗∗∗
(0.05)
-0.17∗∗∗
(0.05)
-0.14∗∗∗
(0.05)
0.004
(0.02)
-0.03
(0.02)
0.02
(0.02)
-0.03
(0.02)
-0.01
(0.02)
-0.03
(0.02)
-0.01
(0.02)
-0.004
(0.01)
-0.02
(0.01)
0.04
(0.07)
-0.10
(0.08)
-0.05
(0.08)
-0.01
(0.02)
-0.03∗
(0.02)
0.002
(0.02)
-0.06
(0.09)
-0.20∗
(0.10)
-0.20∗
(0.08)
0.08∗
(0.05)
-0.03
(0.05)
-0.14∗∗∗
(0.04)
0.005
(0.04)
-0.17∗∗∗
(0.05)
-0.32∗∗∗
(0.04)
-0.05
(0.04)
0.02
(0.04)
-0.15∗∗∗
(0.04)
0.05∗∗∗
(0.003)
0.05∗∗∗
(0.003)
0.11∗∗∗
(0.003)
0.05∗∗∗
(0.004)
0.06∗∗∗
(0.004)
0.12∗∗∗
(0.004)
0.04∗∗∗
(0.006)
0.08∗∗∗
(0.005)
0.05∗∗∗
(0.01)
0.02∗∗∗
(0.008)
0.04∗∗∗
(0.008)
0.12∗∗∗
(0.009)
0.04∗∗∗
(0.04)
0.02
(0.04)
0.30∗∗∗
(0.04)
6.31∗∗∗
6.41∗∗∗
6.37∗∗∗
6.08∗∗∗
5.71∗∗∗
(0.004)
(0.02)
(0.01)
(0.03)
(0.08)
N
1,080,635
314,500
521,171
102,584
51,887
Notes: This table presents the OLS estimates for men aged between 25-59. Each column represents an OLS estimation that controls
for a quadratic function of potential work experience (age-6-years of schooling) and major fields of study. On top of that, results in
column (1) are conditional on seven categories of occupations, while results in columns (2) - (5) are conditional on four-digit national
occupational code. Men in the BA group in 1995 are in the reference group. Occupations in categories A - D are grouped according to
the probability of an occupation hiring PG workers in 1995. Category A includes occupations that hired PG workers with the probability
less than 25 percent (inclusive), category B includes occupations with the probability between 26 and 50 percent, category C includes
occupations with the probability between 51 and 75 percent, and category D includes occupations with the probability above 75.
constant
130
Table 3.11: The PG-BA Wage Gap by Occupation for Women
(1)
All Workers
Degrees
ABA
MA
MD
Doctorate
Interaction
ABA*2000
ABA*2005
ABA*2010
MA*2000
MA*2005
MA*2010
MD*2000
MD*2005
MD*2010
Doctorate*2000
Doctorate*2005
Doctorate*2010
Year
2000
2005
2010
(2)
Category A
Probability below 25
(3)
Category B
25-50
(4)
Category C
50-75
(5)
Category D
above 75
0.08∗∗∗
(0.005)
0.10∗∗∗
(0.004)
0.24∗∗∗
(0.02)
0.22∗∗∗
(0.01)
0.04∗∗∗
(0.008)
0.08∗∗∗
(0.008)
-0.009
(0.04)
0.05
(0.04)
0.06∗∗∗
(0.006)
0.12∗∗∗
(0.006)
0.03
(0.03)
0.11∗∗∗
(0.02)
0.06∗∗
(0.03)
0.12∗∗∗
(0.01)
0.32∗∗∗
(0.10)
0.24∗∗∗
(0.03)
0.05
(0.05)
0.17∗∗∗
(0.02)
0.50∗∗∗
(0.03)
0.50∗∗∗
(0.03)
-0.005
(0.006)
-0.02∗∗∗
(0.006)
-0.05∗∗∗
(0.006)
-0.005
(0.006)
-0.02∗∗∗
(0.006)
-0.03∗∗∗
(0.006)
-0.004
(0.02)
-0.16∗∗∗
(0.02)
-0.09∗∗∗
(0.02)
-0.03
(0.02)
-0.03∗
(0.02)
-0.009
(0.02)
-0.006
(0.01)
-0.006
(0.01)
-0.03∗∗∗
(0.01)
-0.01
(0.01)
-0.03∗∗∗
(0.01)
-0.04∗∗∗
(0.01)
0.003
(0.05)
-0.08∗
(0.04)
-0.02
(0.05)
0.02
(0.06)
-0.05
(0.05)
-0.02
(0.05)
0.001
(0.008)
-0.01
(0.007)
-0.02∗∗∗
(0.008)
-0.004
(0.008)
-0.02∗∗∗
(0.007)
-0.04∗∗∗
(0.007)
0.05
(0.04)
-0.07
(0.05)
0.01
(0.04)
0.008
(0.03)
-0.02
(0.03)
0.01
(0.03)
0.008
(0.03)
0.01
(0.03)
-0.02
(0.03)
-0.02
(0.02)
-0.02
(0.02)
-0.01
(0.02)
-0.11
(0.15)
-0.10
(0.14)
-0.11
(0.13)
0.02
(0.04)
0.01
(0.04)
0.05
(0.03)
0.05
(0.08)
0.06
(0.07)
-0.01
(0.06)
0.04
(0.03)
0.02
(0.04)
-0.13∗∗∗
(0.03)
0.02
(0.04)
-0.16∗∗∗
(0.04)
-0.27∗∗∗
(0.04)
-0.03
(0.04)
0.04
(0.04)
-0.11∗∗∗
(0.04)
0.04∗∗∗
(0.002)
0.05∗∗∗
(0.002)
0.14∗∗∗
(0.002)
0.04∗∗∗
(0.003)
0.07∗∗∗
(0.003)
0.14∗∗∗
(0.003)
0.02∗∗∗
(0.004)
0.05∗∗∗
(0.003)
0.14∗∗∗
(0.003)
0.04∗∗∗
(0.01)
0.06∗∗∗
(0.01)
0.12∗∗∗
(0.01)
0.04
(0.03)
0.06∗
(0.03)
0.30∗∗∗
(0.03)
6.33∗∗∗
6.49∗∗∗
5.99∗∗∗
6.14∗∗∗
5.92∗∗∗
(0.004)
(0.03)
(0.04)
(0.02)
(0.04)
N
1,124,876
468,205
499,482
58,267
46,332
Notes: This table presents the OLS estimates for women aged between 25-59. Each column represents an OLS estimation that controls
for a quadratic function of potential work experience (age-6-years of schooling), and major fields of study. On top of that, results in
column (1) are conditional on seven categories of occupations, while results in columns (2) - (5) are conditional on four-digit national
occupational code. Women in the BA group in 1995 are in the reference group. Occupations in categories A - D are grouped according to
the probability of an occupation hiring PG workers in 1995. Category A includes occupations that hired PG workers with the probability
less than 25 percent (inclusive), category B includes occupations with the probability between 26 and 50 percent, category C includes
occupations with the probability between 51 and 75 percent, and category D includes occupations with the probability above 75.
constant
131
Table 3.12: The PG-BA Wage Gap by Occupation for Young Workers
(1)
Category A
Men 25-39
Degrees
ABA
MA
MD
Doctorate
Interaction
ABA*2010
MA*2010
MD*2010
Doctorate*2010
Year
2010
constant
N
Women 25-39
Degrees
ABA
MA
MD
Doctorate
Interaction
ABA*2010
MA*2010
MD*2010
Doctorate*2010
Year
2010
constant
N
(2)
Category B
(3)
Category C
(4)
Category D
0.03∗∗
(0.01)
0.06∗∗∗
(0.01)
0.05
(0.06)
0.11∗
(0.06)
0.04∗∗∗
(0.01)
0.09∗∗∗
(0.007)
0.24∗∗∗
(0.05)
0.02
(0.02)
0.04
(0.03)
0.06∗∗∗
(0.01)
0.20∗∗
(0.09)
0.27∗∗∗
(0.02)
0.13
(0.10)
0.08∗
(0.04)
0.63∗∗∗
(0.05)
0.60∗∗∗
(0.04)
-0.02
(0.005)
-0.02
(0.02)
0.03
(0.07)
0.04
(0.07)
-0.005
(0.01)
-0.009
(0.01)
-0.13∗
(0.07)
0.07∗∗
(0.04)
-0.03
(0.04)
-0.004
(0.02)
-0.005
(0.06)
0.03
(0.04)
-0.27∗∗
(0.13)
-0.10
(0.06)
-0.31∗∗∗
(0.06)
-0.11∗
(0.007)
0.11∗∗∗
(0.005)
0.12∗∗∗
(0.005)
0.13∗∗∗
(0.01)
0.31∗∗∗
(0.06)
6.22∗∗∗
(0.04)
94,080
6.18∗∗∗
(0.05)
114,002
6.10∗∗∗
(0.08)
19,171
5.76∗∗∗
(0.08)
9,573
0.04∗∗∗
(0.01)
0.07∗∗∗
(0.01)
0.03
(0.05)
0.09
(0.06)
0.07∗∗∗
(0.008)
0.12∗∗∗
(0.008)
0.06
(0.04)
0.09∗∗
(0.05)
-0.00005
(0.05)
0.10∗∗∗
(0.02)
0.40∗∗∗
(0.13)
0.24∗∗∗
(0.04)
-0.04
(0.07)
0.13∗∗∗
(0.03)
0.51∗∗∗
(0.04)
0.53∗∗∗
(0.04)
-0.04∗∗∗
(0.01)
-0.04∗∗∗
(0.01)
-0.05
(0.06)
-0.08
(0.08)
-0.03∗∗
(0.01)
-0.03∗∗∗
(0.01)
0.04
(0.06)
0.008
(0.05)
0.004
(0.05)
-0.001
(0.03)
-0.22
(0.18)
0.09
(0.05)
0.10
(0.10)
-0.10∗∗
(0.05)
-0.26∗∗∗
(0.06)
-0.14∗∗
(0.06)
0.12∗∗∗
(0.01)
0.13∗∗∗
(0.02)
0.13∗∗∗
(0.02)
0.27∗∗∗
(0.04)
6.30∗∗∗
(0.38)
142,859
5.92∗∗∗
(0.14)
129,481
6.03∗∗∗
(0.04)
14,371
5.88∗∗∗
(0.08)
11,792
Notes: This table presents the OLS estimates for men aged between 25-39. Each column represents an OLS estimation that controls for a
quadratic function of potential work experience (age-6-years of schooling), major fields of study, and four-digit occupational categories.
BA workers in 1995 are in the reference group. Occupations in categories A - D are grouped according to the probability of an occupation
hiring PG workers in 1995. Category A includes occupations that hired PG workers with the probability less than 25 percent (inclusive),
category B includes occupations with the probability between 26 and 50 percent, category C includes occupations with the probability
between 51 and 75 percent, and category D includes occupations with the probability above 75.
132
Table 3.11 shows that for women, the decrease in returns to ABA and MA occurs in
categories (A) and (B), the occupations that hired PG workers with probability less than
50 percent in 1995, whereas the decrease in returns to MD and a Doctorate occurs in
occupational category (D). While the former is mostly due to the decline in returns to
postgraduate education in business and finance and sales occupations, the latter is mostly
due to a substantial drop in returns to postgraduate education in health occupations.
Findings are similar for men, with two exceptions. One exception comes from the
decrease in the return to a Master’s degree in 2010 in category D, which is mainly due to
the decline of MA workers in management occupations. The other exception comes from
the decrease in returns to MD in category B. This corresponds to the example explained
earlier: the decline in return to MD in health occupations largely results from an increase in
the proportion of MD workers working in technical occupations in health.
As discussed earlier, a substantial proportion of the decline in the PG-BA weekly wage
gap arises from the finding that young PG workers experience a significantly smaller return
to postgraduate education in 2010 than young PG workers in 1995. Table 3.12 repeats the
analysis by four occupational categories for men and women younger than 39. I find that the
previous findings are robust to men and women younger than 39. Since people in the age
25-39 in 2010 were younger than 25 in 1995, they would not be in the reference group of
workers in 1995. Thus, I can conclude that the decline in returns to postgraduate education
in an occupational category is not driven by changes in returns to work experience in the
occupational category.
133
Specifically, the top panel in Table 3.12 suggests that for men younger than 39, the
decline in the return to postgraduate education is mostly caused by the decrease in returns to
MD, a Doctorate, and ABA in occupational category D that had more than 75% likelihood of
hiring workers with postgraduate education in 1995. In contrast, the bottom panel suggests
that for women younger than 39, the decline in returns to ABA and MA occur in occupational
category A and B, while the decline in returns to MD and a Doctorate occurs in occupational
category D. The former had less than 50% likelihood of hiring workers with postgraduate
education in 1995.
Overall, evidence in this section suggests that returns to postgraduate education have
decreased by a greater magnitude in business and finance, sales and services, and health
occupations, where the proportion of PG workers has increased since 1995, than in social
services and management occupations, where the proportion of PG workers has decreased
since 1995. Since PG workers are likely to work in health occupations throughout the period
of 1995 - 2010, I cannot conclude that the decline in returns to education is mainly caused
by PG workers moving down to occupations that were mainly occupied by BA workers in
1995.
3.5
Conclusion
This chapter examines the earnings of recent Master’s and PhD graduates in Canada.
In 2001 the Canadian federal government called upon Canadian universities to increase
134
admissions to Master’s and Doctoral programs by an average of 5 percent per year through
to 2010. In this chapter I employ census data spanning 1995-2010 to examine changes in
the relative wages of working adults possessing postgraduate and bachelor’s degrees.
Using Census data from 1995 to 2010, I document that the average weekly wage gap
between people with postgraduate education and people with four-year university degrees
has decreased by seven percentage points for both genders. This decline is explained by
the finding that the weekly wage for PG workers has grown more slowly than that for BA
workers. By separating postgraduate education into four levels of qualification, I show that
there is a decrease in the return to all four levels of postgraduate education in almost all
fields of study. For education above BA but below MA, as well as MA degrees, the decrease
in returns is mainly due to the slowdown in weekly wage for PG workers in business and
finance, sales and services, management, and government services occupations. For MD
education and Doctorates, the decline in returns is mostly due to the slowdown in weekly
wage growth in health occupations.
In contrast to workers in other major fields of study, workers majoring in engineering
and computer science do not experience a decline in the return to postgraduate education.
My results show that from 1995 to 2010, there is a 7% increase in the return to MA in
engineering for women and a 3% increase for men. The return to a Doctorate in computer
science increases by 10 percentage points for men. Returns to MA and Doctorate degrees for
women majoring in computer science, or physics and life sciences have remained sufficiently
large since 1995.
135
This chapter documents the return to postgraduate education by focusing on the supply
side of the labour market. My findings suggest that compared to BA workers, workers with
postgraduate education earn more over time. However, given the deterioration of returns to
postgraduate education , which varies by field of study and occupation, it is important to
consider in which fields new graduate students should be led into. To answer this question,
future research on both supply and demand sides of the labour market is needed.
136
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Labour Mobility and Returns to Education by Jiayuan

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