Is Cultural Diversity
Good for the Economy?
Wesley Sze
Honours Undergraduate Thesis
Written under the supervision of:
Dr. Nicole Fortin
Dr. Florian Hoffmann
University of British Columbia
Abstract. This paper describes the relationship between cultural
diversity and the mean earnings and rents of native-born residents of
Canada’s metropolitan areas from 1981-2006. A highly robust positive
correlation is found, where native-born workers living in more diverse
cities received higher earnings and paid higher housing rents, even
after controlling for relevant explanatory variables and city and time
fixed effects. The relationship is not dependent on any particular city
and is remarkably stable to robustness checks and time trend
controls. Using instrumental variable estimation and economic
reasoning, I argue that the correlation is at least partly driven by a
causal effect stemming from diversity. Using a simple equilibrium
model of earnings and rents, this causal effect is consistent with a
dominant positive productivity effect of diversity on the native-born.
An important new finding is that this diversity effect is almost
entirely driven by the highly educated class—diversity of the low
educated does not contribute to earnings and rents. At the same
time, everyone experiences the benefits of a diverse high education
group, suggesting human capital type spillovers.
1
Introduction
With the increase in immigration rates during the latter half of the twentieth century, many
cities in North America, Europe, and Australia have experienced dramatic changes in the
ethnic landscape of its populations. These cities have rapidly evolved into multiethnic and
diverse urban communities within a relatively short period of time. With the undergoing of
such profound demographic change, it is no wonder that immigration is of great importance
for many today. One central issue that remains controversial is the effect immigrant inflows
have on the well being of the native-born. While some see great value and benefit from
increased cultural diversity, others fear that increased immigrant presence comes only at the
expense of the native-born (Borjas 1994a). In addition to the social and cultural
implications of immigration, its economic impacts form a critical part of the public
discourse, connecting issues of ethnic diversity and immigration policy with labour market
outcomes and economic growth. As such, many economists have taken an interest in better
understanding the consequences of the growing cultural diversity brought about by
immigrant inflows (for example, see Borjas [1990, 1992, 1994a], Card [2001], Peri [2007]
among others).
This paper examines the economic impacts of cultural diversity in a Canadian
context, identifying the dominant utility or productivity effect that diversity brings to the
native-born. It has been suggested, both in the economics and broader social science
literature, that diversity has both consumption and production value (Glaeser et. al 2001).
In the case of consumption, cultural diversity can lead to a wider availability of
consumption goods (e.g. ethnic restaurants, arts and entertainment events), positively
affecting individuals with love of variety preferences.1 I call this the consumption variety
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
This term is borrowed from Dixit and Stiglitz (1977), who incorporate a taste for greater product variety through
constant elasticity of substitution (CES) preferences.
1
2
hypothesis. Diversity can also have a productivity value. Workers from different cultural
backgrounds may bring skills, abilities, and creativity that are complementary to each other
in the production process (Lazear 1999). By complementing each other’s skills, workers from
different backgrounds may have a positive externality effect on one another that increases
productivity. I refer to this as the complementary skillset hypothesis.
While these two positive effects of diversity have been identified, a priori, one cannot
conclude that a positive diversity effect certainly exists. Negative effects of diversity on
utility and productivity may offset these benefits, either partially or entirely. In the case of
utility, some individuals may have distaste for living in diverse environments. This can arise
if one’s cultural values feel threatened by the presence of foreign born. This view may be
reinforced by the popular media and prevailing cultural attitudes, which may or may not be
well founded. In addition, intergroup conflict may become problematic and further
exacerbate the issue. Productivity may also be negatively affected by diversity if the
skillsets of diverse workers are actually non-complementary or conflictual (O’Reilly et. al
1997). For instance, differences in language, communication, or work ethic may offset any
positive benefit from diversity in the production process.
As such, I conjecture that cultural diversity may be a relevant determinant in
consumption and production, though the direction of this effect is not immediately obvious.
While some studies have addressed this question in the case of the United States (Ottaviano
and Peri 2006; Peri 2007) and Europe (Bellini et. al 2008), little work has been done on
identifying overall effects of cultural diversity in Canada. One of the goals of this paper is to
quantify this effect in a Canadian context. There are several reasons why examining
immigration from a Canadian perspective is worthwhile and meaningful. First, the
composition of immigrants is significantly different in Canada than in the United States.
3
Whereas Mexico and Central America account for a large proportion of immigrants in the
U.S., Canada’s immigrants tend to come from a broader range of countries (though
concentrated from China, Hong Kong, Southeast Asia, and India). Immigrants from
different countries of birth presumably bring a different set of values and skills (“ethnic
capital”) that play a role in the overall diversity effect. Second, undocumented immigration
is a much greater concern in the United States than in Canada. It has been suggested that
up to 40% of new arrivals in the United States enter through illegitimate means, often with
little education and no English proficiency (Passel 2005). Not only does this create an
unfavourable attitude toward immigrants as a whole in the U.S., but it also brings into
question the accuracy of data since illegal immigrants may be underreported in data
gathering processes. Lastly, a comparison of the results between the U.S. and Canada can
help us better understand the role of institutional framework and governmental policy in
determining diversity effects.
One other contribution of this paper is in examining the role education plays in the
diversity effect. The way diversity affects utility and production could depend on education
level. This is especially true for the complementary skillset hypothesis, where certain
industries and education groups may be better suited to benefit from the varied skills and
creativity in diverse populations (Florida 2002). For example, industries with highly
educated workers that emphasize creativity and originality (e.g. software development and
IT) may be better suited to benefit from diversity than sectors that do not emphasize
originality or independent thought. As to whether or not education is relevant in diversity’s
utility effect (e.g. instilling tolerance and preference for ethnic diversity) is less obvious—
4
and perhaps more controversial.2 Regardless, education seems to be a significant factor when
looking at the impacts of cultural diversity. In this paper, I explicitly incorporate education
into my analysis by segmenting diversity effects into both within and out-of group
interactions. This method for controlling for education is an innovative feature of this paper
and is found to be extremely relevant to our study of diversity.
The empirical analysis focuses on identifying the relationship between cultural
diversity and average earnings and rents of the native-born in metropolitan areas. Building
upon a simple model of mobile workers and competitive firms originally developed by
Roback (1982), earnings and rents are estimated together to identify the dominant utility or
productivity effect of diversity. Using an index of fractionalization as a measure for
diversity, I control for relevant explanatory variables and city and time fixed effects to
determine any residual correlation between diversity and the earnings and rents of the
native-born. Relationships within the whole population are examined first, followed by a
decomposition of diversity, earnings, and rents according to education group. The main
result I show is as follows: cultural diversity is positively related with native-born earnings
and rents, though this relationship is driven only by the diversity of the highly educated.
This second qualification is, to my knowledge, a new contribution to the understanding of
how diversity affects economic outcomes. This diversity correlation is at least partially
causal in nature and consistent with a dominant positive productivity effect stemming from
diversity.
The remainder of the paper is organized as follows: section 2 provides a summary of
the existing economics literature that addresses the relationship between immigration,
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
The relationship between education and attitudes towards racial diversity has been more extensively studied by
scholars in the fields of sociology and psychology of education. See Troyna (1990) and Smolicz (1996) for a more
detailed discussion.
2
5
cultural diversity, and economic outcomes. Section 3 briefly introduces Roback’s theoretical
model that is used to develop a consistent estimation procedure for the diversity effect on
mean earnings and rents. Section 4 describes the data sources, key summary statistics, and
stylized facts of my research. I present and discuss my key estimation results in section 5,
including variations on the basic specifications, robustness checks, and addressing causality
and endogeneity. A conclusion of my findings is summarized in section 6, along with a
discussion of the limitations of this study and directions for future research.
2
Literature Review
Economists have been interested in understanding the economic implications of cultural
diversity for as long as immigration has been occurring. Its relevance has only increased as
technological advancements and political reform have ushered in an era of unprecedented
migration previously not possible. Initially, the economics literature focused on examining
the short run effects of immigrant worker inflows on the labour market outcomes of the
native-born. Much of this early work was likely motivated by a concern that rising
immigration would bring down the employment and earnings of the native-born. However,
little economic evidence supports this hypothesis. In a survey of the economic literature on
immigration, Borjas (1994) finds no evidence for any significant negative effect of
immigration on native outcomes. The methodology employed by early empirical work
focused on correlating changes in immigration rates with changes in key labour market
indicators (earnings, employment rate), studying short run changes within localized
geographic regions. This so-called area-analysis approach found only very modest effects,
with estimates for the elasticity of native wages with respect to the number of immigrants
converging around -.02 (Grossman 1982, Borjas 1990). Estimates for the elasticity of native
6
employment with respect to immigration have been even less significant (Borjas 1990,
Altonji and Card 1991). A similar result was confirmed by Card (1990), who exploited an
exogenous change in the immigrant labour supply in Miami caused by Cuban immigrants
from the Mariel Boatlift of 1980. Using this natural experiment, Card found only a
negligible effect on the employment outcomes of native-born low-skilled workers, consistent
with the results of the area-analysis methodology previously employed. Overall, there
appears to be minimal empirical support for a substantial adverse effect of immigration on
the labour market performance of natives.
This area-analysis approach, though originally helpful, is limited in scope. One
weakness is that it fails to take into account worker movement in response to immigration.
For example, it assumes closed geographic regions, whereas in reality workers are mobile
across regions. Migration flows are also rarely exogenous and workers and immigrants
presumably self-select into areas with the best opportunities. In addition, local workers may
out-migrate from immigrant-receiving cities, such that the actual effects of immigration are
understated (Frey 1996). Card (2001) attempts to address this issue of worker mobility by
looking only at how much immigration drives out similarly skilled workers in U.S. cities. He
finds little evidence for out-migration of workers in response to immigrant inflows, and in
fact finds weak support for the opposite: that increases in immigration actually encourage a
net inflow of similarly skilled workers. These results, along with previous area-analysis
studies, seem to confirm that the arrival of immigrants has a negligible effect on the labour
market outcomes of natives.
While there have been numerous economic studies on immigration, there has been
far less study of cultural diversity, an important consequence of immigration. The two,
though closely related, are distinct concepts. Diversity emphasizes the cultural richness and
7
variety that immigrants bring to society, and not just the presence of foreign-born or their
measurable skill level or wealth. For example, an area with many immigrants from the same
place of origin may have a large immigrant share yet low diversity. Most economic studies
have focused on immigrants as a single, homogenous group. As such, they have little to say
about how diversity affects individuals living in multiethnic communities. Social scientists
have long hypothesized that cultural diversity may play a significant role in affecting
productivity and consumption values. Much of the study of diversity has been confined to
cities, where the effects of diversity are most discernible. Quigley (1998) identifies diverse
populations within cities as one of the driving forces behind their economic growth and
success. Through the increased variety in the availability of goods and services, Quigley
argues diverse cities are able to draw upon shared inputs in production and consumption,
taking advantage of economies of scale. This, he argues, results in more innovative and
productive firms and workers in diverse cities. He also finds that heterogeneous labour
markets have greater availability of workers with different skills, resulting in more efficient
matching of workers and jobs (cf. the complementary skillset hypothesis). In a similar vein,
sociologist Florida (2002) identifies diversity as one of the key features of cities that attract
skilled and creative individuals. This concentration of the so-called “creative class” in cities
creates environments conducive to innovation, productivity, and economic growth.
Economists and psychologists have also long conjectured a love-of-variety in
preferences that yields positive utility effects when more choices are available (cf. the
consumption variety hypothesis). Applications of tastes for variety have been widely used in
the international trade literature, where some of the gains from trade arise from the
increased variety of consumption goods (for example, see Krugman 1980 and Broda and
Weinstein 2006).
8
Research in human capital externalities and peer effects are also relevant to our
study of diversity. Although the native-born do not contribute to cultural diversity, they
may still experience the effects of immigrant diversity. Furthermore, I hypothesize that
diversity effects are closely related with education. If so, a question to consider is how
different education groups contribute and respond to changes in diversity. Just as education
is thought to have a positive externality that exceeds private return (Moretti 2003, 2004),
diversity within certain groups may very well have spillover qualities that benefit society as
a whole. Peer effects have also been identified in ethnic capital, the unique social and
cultural experiences and upbringings that ethnic groups possess (Borjas 1994b). Borjas finds
that a person’s ethnicity plays an important role in the formation of human capital, where
one’s productivity and skill is related with their ethnic group’s average skill level.
This is not to say that the effects of increased cultural diversity are entirely
beneficial. Easterly and Levine (1997) find societal fragmentation to be a key factor
explaining cross-country differences in growth rates. The authors examine the case of
countries in Sub-Saharan Africa, using an index of fractionalization based on ethnolinguistic
groups to proxy for ethnic diversity. Using cross-country panel regressions over three
decades, they find ethnic diversity to be negatively correlated with per capita GDP growth.
In addition, they find that regions of higher ethnic diversity are associated with low
schooling, political instability, and lack of public infrastructure. The paper argues that more
diverse societies promote rent-seeking and predatory behavior that impedes economic
growth. Similarly, Alesina and La Ferrara (2005) look into the effects of ethnic diversity on
economic performance and policies at the country, county, and small village levels. Indeed,
their findings corroborate those of Easterly and Levine: they find that ethnic diversity is
negatively correlated with growth rates, even after controlling for region variables. They
9
point to difficulties in communication and incompatibility of skills and preferences across
cultural groups as factors that outweigh the benefits of diversity.
It should be noted that these studies have disproportionately focused on developing
countries with inherently weak and underdeveloped political institutions. Despite these
findings, political scientist Paul Collier (2001) suggests that ethnic diversity, when framed
within well functioning political institutions and processes, is not necessarily harmful to
economic growth. Collier argues that the drawbacks of societal fractionalization can be
overcome with adequate democratic institutions, even allowing the private sector to flourish
in diverse societies. This suggests that strong institutions are necessary for diversity to
benefit society and resolves seemingly inconsistent findings about diversity in different
areas.
Most similar to this paper’s study of diversity is the work of Ottaviano and Peri
(2006), who model cultural diversity as a city amenity affecting utility and production
functions. They build upon a simple model to identify long run equilibrium effects of
diversity on the average wages and rents of U.S. born residents after allowing for free
mobility of residents and firms. Using Census data from 1970 and 1990, the authors
calculate an index of diversity based on country of birth as a key explanatory variable to
explain differences in mean wages and rents across cities over the two time periods. Their
findings suggest a dominant positive productivity effect of diversity, where wages and rents
are positively and significantly correlated with diversity. Their results are robust to the
inclusion of additional control variables and instrumental variable estimation is used to
confirm a causal effect.
The work of Ottaviano and Peri is thought to be one of the first to look at the long
run equilibrium effects of diversity. Bellini et al. (2008) have replicated this study for areas
10
in the European Union, finding results that are similar to the United States. Peri (2007)
also repeated the original study for the case of California, finding a similar positive effect of
immigrant diversity on the native-born in California. This paper builds upon this same
approach with an extension to Canadian cities. A modification to the methodology is made
to address education as a mechanism through which diversity affects the economy, a
question previously left unanswered.
3
Underlying Economic Framework
This section presents a model to formalize how diversity affects earnings and rents, resulting
in a strategy to estimate the effect of diversity on equilibrium earnings and rents. The
model is an adaptation of a well-cited equilibrium framework proposed by Roback (1982),
which focuses on the role of city amenities in allocating workers across cities based on wages
and rents. This model has been extensively used in the social sciences to measure the value
of unique city qualities. Closely following the work of Ottaviano and Peri (2006), I modify
Roback’s general framework to model cultural diversity as a city-specific feature that enters
into the utility and production functions. If diversity is an amenity (disamenity), then
workers in equilibrium would be willing to pay higher (lower) rents in cities with a high
level of diversity. Similarly, if diversity has a positive (negative) productivity value, workers
in equilibrium would receive higher (lower) earnings to reflect their marginal productivity.
By estimating earnings and rents together in relation to diversity, a dominant utility or
productivity effect of diversity can be determined.
11
A concrete example confirms this intuition and provides a specific empirical
identification strategy. 3 Suppose there are a given number of distinct cities with freely
mobile households and competitive firms. Cities are distinct in the sense that they do no
overlap and do not contain residents working in another city (or have firms employing
workers residing in a different city). Free mobility assumes that in the long run individuals
and firms are able to freely choose their location. Preferences of individuals are Cobb
Douglas in the consumption of land ! and a homogenous composite good !, with diversity
affecting a shift factor !. The utility function for individuals living city ! is defined as
!!!
!! ! ! !"#! ! !!
!
(1)
!!
where ! ! ! ! !. If diversity is an amenity, then !" !"#$ is positive. Utility maximization
yields an indirect utility function based on the rent !! , wage !! , and diversity !"#! of city !.
The free mobility assumption implies that in the long run, all workers in each city receive
the same level of utility, such that no worker desires to change cities. That is,
!! !! ! !! ! !"#! ! ! !"#! ! ! ! !
!!! !
!
!!
!!! !
!!
!!
! !!!!!!!!!!!!!
(2)
where !! !!! is the indirect utility function, !! is the expenditure of an individual, and ! is
the equilibrium level of utility. Wages and rents adjust to equalize utility in all locations.
The free mobility condition implies that an exogenous increase in wages or rents is matched
with a similar increase the in the price of the other factor. A graphical representation of
equation (2) is shown in figure 1, denoted by an upward sloping curve with diversity acting
as a shift factor in the wage-rent graph space.
The case for firms is parallel. The production function for the composite good ! is
Cobb Douglas with two factors of production, land ! and labour ! . Diversity affects
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
The example I use is analogous to the model introduced by Ottaviano and Peri (2006). For brevity, only the key
elements are presented in this paper. For a more detailed exposition and discussion of the model, see Roback (1982)
and Ottaviano and Peri (2006).
3
12
production through a productivity shift factor !. A firm in city ! operates according to the
production function
(3)
!
!! ! !!!"#! ! ! !!!!
! !!
where ! ! ! ! ! . Again, if diversity is productive, !" !"#$ is positive. In perfect
competition, firms profit maximize and set a price equal to marginal cost. When the good is
freely traded between cities the zero-profit equilibrium condition implies
(4)
!! ! !!!!! !!! !! ! !!!!! ! !! !!!!"#!!!! ! !!!!!!!!!!!!!
Again, wages and rents adjust to equalize the price of the good across cities, depending on
diversity levels in each city. In equilibrium, the condition requires that an increase in the
cost of one input to be offset by a decrease in the other, such that all firms earn zero profit.
The downward sloping curve in figure 1 is a graphical representation of equation (4) in the
wage-rent space. Again, diversity acts as a shift factor—if diversity is productive, it shifts
the curve upwards to reflect higher factor prices.
Figure 1 shows the spatial equilibrium of our model. A pair of wages and rents exists
such that the free mobility and zero profit conditions are met. As diversity changes, the two
curves shift to form a new equilibrium. Observing how the equilibrium wages and rents shift
in response to changes in diversity forms the basis for the identification procedure. Solving
equations (2) and (4) give explicit expressions for equilibrium rents and wages in each city:
!" !! !
!
!!!"
!" ! ! !
!!!
! ! ! ! !" ! ! !
!
!" !! ! !!!"
! ! ! ! !" ! ! !
!!!
!!! !
! ! !! ! !" ! !"# ! !!!"#!
! ! ! ! ! ! !" ! ! !
!!! !
! ! !! ! !"
! !"# !!!
!
! !"# !!!
(5)
(6)
These two equations make it clear that observing only wages or rents in isolation cannot
identify the diversity effect. For example, a positive correlation between rents and diversity
is consistent with both a positive utility or productivity effect. At the same time, a positive
correlation between wages and diversity is consistent with either a negative utility effect or
13
a positive productivity effect. However, jointly estimating equations (5) and (6) together
yields the following identification procedure:
!"
!"#$
Positive
Positive
Negative
Negative
!"
Dominant Effect
!"#$
Positive
Negative
Negative
Positive
Positive productivity effect
Positive utility effect
Negative productivity effect
Negative utility effect
Thus by observing the signs of the diversity coefficient in each of the earnings and rent
regressions a dominant effect of diversity can be identified. Most relevant to this study is
the dominant positive productivity effect, which is associated with a positive correlation
between diversity, earnings, and rents.
4
Data Description and Summary Statistics
4.1
Data Source and Diversity Index
The primary unit of observation used for this study is at the Census Metropolitan Area
(CMA) level, defined by Statistics Canada as closely integrated urban areas with
populations greater than 100,000. The use of CMA level data is useful for several reasons.
Theoretically, the economics literature suggests that the hypothesized utility and
productivity effects from diversity are most prevalent in the context of urban
agglomerations. CMAs also tend to exhibit higher levels of variation in cultural diversity
than in non-urban areas, which tend to have lower levels of diversity with little change over
time. On a more practical note, the primary data source used to calculate city level
variables is the Statistics Canada Public Use Microdata File (PUMF), which restricts
geographic information to the CMA level. For these reasons, using aggregated CMA level
data is both theoretically appropriate and practically feasible.
14
This study covers the time period 1981-2006, with data collected in five-year
intervals for a total of six time period observations. Over this time span, Statistics Canada
has added new CMAs as more urban areas reach the 100,000-population threshold, thereby
increasing the number of unique CMA identifiers from 13 in 1981 to 23 in 2006. Use of an
unbalanced panel dataset is not problematic in this case, as the population criteria is
consistently applied to all urban areas and should not be correlated with the errors.4 In
total, there are 99 unique city-year observations included in the time series panel data set.
One peculiarity worth noting is the merging of certain urban areas in the PUMF as a
single CMA. This is done to ensure anonymity of results; however, it also presents a data
quality issue. Although several city combinations are geographically and culturally justified
(e.g. Ottawa-Gatineau5, St. Catherine’s-Niagara), many others share little commonality (e.g.
Regina-Saskatoon, Sudbury-Thunder Bay, Sherbrooke-Trois Rivières). The merging of these
cities’ data violates the assumption of non-overlapping and distinct cities and could impact
the way diversity affects earnings and rents. However as a practical consideration, in order
to maximize the number of observations all merged CMAs have been treated as single urban
areas. Robustness checks are performed to examine the sensitivity of the estimation results
to the inclusion of these mismatched CMAs.
The key variable of interest is the measure of cultural diversity. I use an “index of
fractionalization,” an index commonly used in demographic research to quantify levels of
variation in categorical data (see Ottaviano and Peri [2006] Easterly and Levine [1999] for
previous economic studies using this measure). As we are interested in measuring cultural
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
That is, the only factor deciding whether or not a city is included is whether it meets the population threshold.
Since the scope of this study is already limited to urban areas, this restriction does not pose any systematic selection
bias.
5
For the purposes of this study, the Ottawa-Gatineau CMA is considered to be in the province of Ontario, even
though Gatineau is a part of Quebec.
4
15
diversity, the place of birth response variable on the PUMF is used as a proxy for cultural
background. Place of birth is likely to capture differences in language, educational
background, social values and attitudes, religion, and other aspects that contribute to
overall cultural diversity. An advantage of using this variable is that it is easily identified
and consistently measured in each time period. Calculated for every CMA in each year, the
diversity index is defined as
!"#!!! ! ! !
!
! !
!!!!!"#! !!
(7)
where !!"#!! !!! is the square of the share of people from place of birth ! amongst residents of
city ! in year ! . Individual countries have been aggregated into groups such that the
diversity index is always calculated with the same categories of origin (see the data
appendix for a detailed list of country groupings). The index quantifies diversity by
approximating the probability that two randomly chosen individuals were born in the same
country. A perfectly homogenous city would have an index of zero, whereas a perfectly
heterogeneous city where no individual is born in the same place would have an index
approaching a value of one.6
Table 1 reports summary statistics for the mean diversity indices by education group
for each CMA in descending order, from most to least diverse. There is remarkable amount
of variation in diversity across the cities. The most diversified cities, Toronto and
Vancouver, have indices of .632 and .540, whereas the Sherbrooke-Trois Rivières and
Québec CMAs show a much higher degree of homogeneity with values of .049 and .046.
A further decomposition of diversity has been made according to education group.
Diversity indices for the high and low education groups have been calculated using equation
(7), except restricting the sample population to those with at least some college education
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
For a given number of places of birth N, the maximum value of the index is 1-N-1. This value approaches one as N
becomes large.
6
16
(high education group) and those with at most high school completion (low education
group). Breaking down diversity this way reveals several facts. Naturally, there is a strong
correlation between the diversity of the entire population and the diversity of each
education group (i.e. the more diverse the entire population, the more diverse each
education group). This correlation holds stronger for the low education group (correlation
coefficient of .993) than for the high education group (correlation coefficient of .892).
Diversity is also found to be significantly different between the two education groups, even
within the same city. Low education groups tend to be significantly more diverse than their
highly educated counterparts. In some cities, the difference is as large as 50%, suggesting
that immigrants tend to be less educated than those of the native-born, thus contributing to
the higher diversity of the low education group.
4.2
Mean Earnings and Rents
Table 2 presents descriptive statistics for the key dependent variables, mean earnings and
rents of the native-born. Again, each variable has been calculated for the entire population,
as well as high and low education groups. Because we are interested in utility and
productivity effects stemming from diversity itself (and not just changes to the population
due to immigrants), the sample for calculating mean earnings and rents is limited to nativeborn non-immigrants. I also control the sample for other demographic variables that could
affect earnings and rents that are unrelated to diversity. Mean earnings have been
calculated from the wages and salary income response on the PUMF for Canadian-born,
full-time employed males aged 30-50 for each CMA in each time period. To account for
inflationary growth in earnings, nominal values have been deflated into 2002 real dollars
using the Canadian Consumer Price Index (2001 basket content). A similar calculation was
17
used for obtaining the mean monthly rent per room (gross rent divided by number of rooms
response on the PUMF) for each CMA. For the rent calculations, the sample population
was controlled for native-born males aged 16-65 for each CMA in each time period, again
expressed in 2002 dollars.
Using the same order of descending diversity in table 1, table 2 summarizes the
average mean earnings and rents for each city during the time period, with further
distinctions made according to education group. The summary statistics confirm our
intuition on the relationship between earnings and rents. Again, there is a positive
correlation between mean earnings and rents (correlation coefficient .6346): non-immigrants
living in cities with higher average earnings also tend to pay higher rents. As expected, the
highly educated receive higher earnings than those with less education. The earnings
premium for the highly educated ranges from 40 to 60 percent. Lastly I note that in
addition to receiving higher earnings, those with more education also tend to pay higher
rents.
Table 3 presents summary statistics for the main control variables used in the
regression framework. The mean, standard deviation, minimum, and maximum values for
each variable are reported. Variables for population and employment rate were obtained
from the Statistics Canada CANSIM database. For combined CMAs, population and
employment rate were calculated as the weighted average of each city’s specific value. The
shares of high and low education groups are defined as earlier and are used as a control for
a city’s education level.
18
4.3
Stylized Facts
Economic theory suggests there is the possibility of a causal relationship stemming from
diversity to mean earnings and rents. Before using formal statistical methods to test this
hypothesis, a preliminary graphical representation helps to reinforce our finding of a positive
relationship. Figures 2a and 3a show the scatterplots of correlations between change in
diversity (entire population) and percentage change in mean earnings and rents of the
native-born from 1991 to 2006. The positively sloped best-fit line suggests a positive
relationship between diversity and mean earnings and rents. The slope coefficients estimate
a .1 increase in diversity to be associated with an 11 and 17 percent growth in earnings and
rents, respectively. Moreover, the positive relationship does not seem to be driven by any
particular city. A notable outlier in figure 2a is Calgary, which experienced very high
growth in earnings that exceeds the trend. This is perhaps explained by an exogenous boom
to Alberta’s energy sector, raising wages above normal. All in all, however, these initial
figures offer evidence for an underlying positive diversity effect on earnings and rents.
A further distinction of changes in diversity within education groups further confirms
our hypothesis that education matters in determining the diversity effect. Figures 2b and 3b
show the scatterplots for changes in diversity amongst the highly educated with mean
earnings and rents of the native-born. The positive relationship is clearly strengthened with
a better fit and lower dispersion around the regression line. This suggests that the diversity
effect is stronger for the high education group. On the contrary, when controlling for
diversity amongst the low education group in figures 2c and 3c, the relationship between
diversity, earnings, and rents is lost. The regression line has poor fit, a low R-squared value,
and is no longer significant. This indicates that the low educated group may contribute very
little to the overall diversity effect.
19
Although these simple scatterplots do not take into account relevant controls or
fixed effects estimation, they do provide visual evidence that reinforces this paper’s key
findings. Indeed, they support the proposition that cities with the highest growth in
diversity also experienced highest growth in earnings and rents for the native-born.
Furthermore, the stronger fit when controlling for diversity amongst the high education
group emphasizes the strength of high education diversity in driving the overall correlation.
5
Estimation Results
5.1
Basic Earnings and Rent Regressions
The theoretical model requires the simultaneous estimation of both earnings and rent
regressions for each city to identify dominant utility or productivity effects of diversity. To
estimate mean earnings for each CMA in each time period, the following OLS regression is
used as an implementation of equation (6):
!" !"#$%$&'!!! ! !! ! !! !"#!!!!! ! !!! ! !! ! !! ! !
(7)
The dependent variable, !"!!!"#$%$&'!!! ! is the natural logarithm of the mean real annual
earnings for native-born, full time employed males aged 30-50 in CMA ! at time !. The
variable of interest, !"#!!!!! is the diversity index calculated amongst the population of city !
in education group ! at time !. Education groups are divided into two broad categories: low
education (high school or less) and high education (some college or greater). An overall
diversity index that is not specific to a particular education group is also used to measure
overall diversity of the entire population. ! is a row vector of coefficients for !!, a column
vector of control regressors. A measure to control for a city’s education level is included in
every regression. City fixed effects !! and year fixed effects !! are included, as is an
20
unobserved residual ! with zero conditional mean.7 In our discussion !! is the relevant slope
that captures the equilibrium effect of diversity on earnings.
The identification procedure also requires the simultaneous estimation of a parallel
regression for mean rents. Analogous to the earnings regression, the following OLS
regression is used as an application of equation (5):
!" !"#$!!! ! !! ! !! !"#!!!!! ! !!! ! !! ! !! ! !
(8)
The dependent variable, !"!!!"#$!!! !, is the natural logarithm of mean real monthly rent per
room for native-born males aged 16-65 in CMA ! at time !. The measure of diversity,
!"#!!!!! is the same as used in the earnings regression, and ! is also a row vector of
coefficients for !!, a column vector of control regressors. In addition, the regression includes
city fixed effects !! and time fixed effects !! . Again, ! is an unobserved residual with zero
conditional mean. !! is the coefficient of interest that captures the equilibrium effect of
diversity on rents.
The OLS regression estimates for the basic earnings and rent equations (7) and (8)
are reported in tables 4 and 5. Specifications I and II in table 4 show that diversity amongst
the entire population has a positive, but not significant, correlation with earnings. The point
estimate suggests that a .1 increase in diversity is associated with a 4% increase in earnings
of the native-born. The positive correlation I find is consistent with Ottaviano and Peri
(2006), though at a smaller magnitude. Specifications III-VI break down diversity according
to education group. This decomposition confirms that education does matter in the
diversity-earnings correlation: diversity amongst the highly educated is strongly significant,
whereas diversity amongst the low educated group is a much weaker and insignificant
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
This zero conditional mean assumption is required for the OLS estimation to be unbiased. In section 5.4, I test for
robustness to omitted variables to check if the basic regression gives a consistent estimate for the real parameter
value.
7
21
predictor of earnings. The point estimate for the coefficient for diversity amongst the highly
educated is approximately 1.22 (more than eight times greater than the estimate for the low
educated diversity), suggesting a .1 increase in diversity to predict a 12% increase in
average earnings. All specifications have been tested both with and without a control for
employment rate, which is partially endogenously determined in our model, but could also
have effects on productivity (Ciccone and Hall 1996). Whether or not a control for
employment is included, however, does not significantly change the estimates for diversity.
One important control for the earnings regressions is education. While some similar
studies have used average years of schooling as an education control, there is not enough
variation is average years of schooling across cities in our sample to obtain a reliable and
statistically meaningful control variable.8 Instead, the share of highly educated within a city
is used as a measure for education level. Not only does this control for the educational
composition of a city’s workforce, but also takes into account human capital spillover effects
of the high education class that have been hypothesized by several economists (notable
examples are Moretti [2004a, 2004b], Shapiro [2005]). The results of the basic earnings
specifications show a positive, though insignificant, estimate for the share highly educated.
Here the insignificance of these estimates may be due to the relatively low variation in this
variable over time. Therefore, this measure still may not accurately control for education’s
actual effects on earnings. However, because education is an important factor to include, I
return to this question of education in section 5.2 where earnings, rents, and diversity are
decomposed by education level.
The parallel rent regressions are reported in table 5. The results are akin to those of
the wage regressions, although the standard errors are generally larger than those in table 4.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
This control for education is used as a robustness check in specification XIII of table 8. Using this alternate measure
does not meaningfully change the results.
8
22
The initial estimate in specification I for the coefficient on diversity is .391 which, like the
case of the earnings regression, is positive but insignificant. Breaking diversity down into
the two education groups yields very similar results. Specifications III and IV show that
diversity amongst the highly educated is, again, a highly significant predictor of rents with
an even larger magnitude of 1.6 to 1.7. This suggests that a .1 increase in diversity is
matched with a 16 to 17% increase in rents, which is slightly larger in magnitude than for
earnings. In specification V, the coefficient for diversity is virtually zero, suggesting that
diversity amongst the low education group is uncorrelated with rents. All specifications have
been tested with and without a control for population, which is partly endogenously
determined in the model through the free migration condition. Although the inclusion of
this partially endogenous variable may bias the estimates, in this case it does not
qualitatively change the results. This set of rent regressions shows that the diversity of the
highly educated is a consistently strong predictor of rents, whereas the diversity amongst
the low educated group remains insignificant.
As a whole, the basic earnings and rent regressions suggest that diversity of the
entire population is weakly positively related with wages and rents ( !! ! !! !! ! ! in
specifications I of tables 4 and 5). This is consistent with previous studies, which have
shown a positive correlation between diversity, earnings, and rents (Card 2009). The results,
however, are not statistically significant for the diversity variable of the overall population.
Nonetheless, we find that diversity amongst the highly educated is a consistently strong
predictor of earnings and rents with a very low p-value in every case. As the highly
educated group is a subset of the entire population, the decomposed regressions indicate
that the high education group drives the overall positive diversity effect. Diversity of the
23
low education group is never significant and seems to contribute only very weakly (or even
negatively) to the overall diversity effect.
5.2
Role of Education
In this section I consider a refinement to the estimation strategy of the previous section to
better understand the role of education in determining the diversity effect. In addition to
decomposing diversity by education, mean earnings and rents are also calculated for each
education group. This shows whether diversity effects are localized within specific groups or
spillover to other groups. It allows us to answer the questions: do those with low education
benefit from a more diverse highly educated workforce? Are diversity effects confined to
particular education groups, or are its benefits experienced by all? Previous studies have
shown that these spillovers exist for human capital (Moretti 2004); this section will examine
if such spillover effects also apply to diversity.
The estimates for the coefficients on the diversity indices for the mean earnings and
rents for each education group are reported in table 6.9 Each entry represents a separate
regression, each controlled for city and year fixed effects. The diagonals represent within
group effects, whereas the off-diagonal entries represent spillover effects into other groups.
The most obvious result is that diversity of the highly educated is positively and
significantly correlated with the earnings and rents of every education group. This reinforces
the finding of section 5.1, which showed that the diversity effect is almost entirely driven by
the high education group. Diversity effects should also be strongest within group and weaker
in others. Indeed, the within-group estimates (1.444 and 1.807 for earnings and rents,
respectively) are larger in magnitude than the out-of-group estimates (.817 and 1.626,
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
The last columns for earnings and rents correspond to the estimates found in tables 4 and 5, and are included as a
point of reference.
9
24
respectively). A diverse high education group benefits the highly educated the most, though
also benefits others to a lesser extent. The coefficients are always positive and statistically
significant. Using the model’s identification strategy, this is consistent with a dominant
positive productivity effect of a diverse high education group (!! ! !! !! ! !!.
On the other hand, diversity of the low education group is never significant, not even
affecting own-group earnings or rents. Although the standard errors are comparable to the
estimates for the high education diversity, the point estimates are considerably lower. As
such, there is little evidence for any diversity effect coming from the low education group.
This is in contrast to the diversity of the high education group, which is consistently
significant. One possible explanation for the difference between the two groups is nature of
certain industries, where some sectors benefit more from diversity than others. For instance,
the high-tech sector may be more adept to exploit diversity productivity effects, whereas a
labour intensive industry (e.g. construction, manufacturing) may be less suited to
experiencing the benefits of a diverse workforce. Whatever the reason, diversity seems to
affect outcomes only through the highly educated class, whether it is from the nature of
highly educated industries or ethnic capital spillovers (Borjas 1992, 1994b).
5.3
Long Run Equilibrium Effects
Lastly, employment and population are considered as endogenous variables in the model.
The theoretical model suggests that in long run equilibrium, workers are mobile and sort
themselves based on the distribution of wages and rents across cities. Earlier, employment
and population were described as partly endogenously determined in the model. Table 7
explicitly tests for this by regressing employment and population against diversity.
25
Specifications I-III show that diversity and employment are uncorrelated. This
confirms the hypothesis that in the long run, under free migration conditions, workers sort
themselves across cities to an equilibrium such that there is no incentive to move.
Employment rate, being related to job opportunities and prospects for employment, may act
as a draw factor for prospective workers. An unusually high employment rate would attract
an inflow of workers until the rate returns to normal, at which point a new equilibrium is
reached. The insignificant relationship between diversity and employment rate verify this
equilibrium effect.
Specifications IV-VI show that diversity is positively correlated with population.
This reinforces the expectation that more diverse cities also tend to be the most populous,
as immigrants tend to settle in larger metropolitan areas with larger preexisting immigrant
communities (Edin et. al 2003, Stark 1991). It also provides counter-evidence for the outmigration hypothesis. The positive correlation between population size and diversity
indicates that immigrant-receiving cities have net growth in population. Thus, it is unlikely
that the outflow of natives in response to immigration is significantly large. In addition,
cities seem to be able to absorb the increase in population with new job opportunities,
ultimately leaving the employment rate unchanged. If immigrant inflows really led to
substantial internal migration of natives, we would expect to see fluctuations in employment
rates as workers adjusted themselves across cities. These additional tests on the endogenous
variables of the model reinforce our understanding of the spatial equilibrium and lend
support for the dominant positive productivity effect.
26
5.4
Robustness Checks
The basic earnings and rent regressions reported in tables 4 and 5 may omit controls that
could, in principle, affect the results. Not including these variables may introduce estimation
bias. I also have not shown the extent to which these results depend on a small number of
particularly diverse cities, outliers, or population size. Here, I test for the robustness of the
results by adjusting the basic regression to accommodate additional controls and sample
variations.
Table 8 gives a summary of the robustness checks. The dependent variables are the
mean earnings and rents for the entire population; coefficients on the diversity indices for
each education group are reported. Each coefficient represents a separate regression—only
one measure of diversity is included for each regression. Specification I is included as a
baseline for comparison purposes. These robustness tests overwhelmingly attest to the
strength of the high education diversity variable as a predictor of earnings and rents,
remaining significant in every test.
First I test for how much specific cities shape the result. Specification II
underemphasizes small cities by weighing each observation by population. The estimated
coefficients for diversity of the highly educated fall slightly but still remain significant.
Specification III excludes two highly diverse cities, Toronto and Vancouver, to see how
much the results are driven by these unusually diverse cities. Reassuringly, diversity
amongst the highly educated is still significant—even increasing in magnitude. Taken
together, these two specifications suggest that the diversity effect is independent of city size
or level of diversity. During the time period of interest, the Albertan economy experienced a
boom in the natural resource and oil sector, leading to dramatic increases in wages (hence
the outlier illustrated in figure 2a). Specification IV omits observations from Alberta to
27
remove this abnormal growth in earnings. This results in lower coefficients but still
maintains positive significance. As earlier mentioned, the PUMF combines some cities into
single CMA codes, leading to odd combinations of otherwise unrelated urban areas.
Omitting these combined CMAs, however, has little effect on the estimates (specification
V). Specifications II-V show, then, that the main results are not sensitive to any particular
city.
It is not clear at which institutional level diversity affects the economy. Although
this study has focused on controlling for city level characteristics through city fixed effects
estimation, I check if provincial level controls are a better alternative. Specification VI uses
province fixed effects instead of city fixed effects. This reduces the slope estimates for the
high education diversity and causes the low education diversity to become significant in the
earnings regression. One explanation is that when city fixed effects are not controlled for,
the diversity indices capture other unique city attributes that are usually absorbed by fixed
effects. The OLS estimates for diversity become significant because diversity is capturing
unrelated factors. This confirms that city specific characteristics probably play an important
role and should be included.
As a more rigorous test for time series data, specifications VII-IX check for how well
diversity can predict off trend changes in earnings and rents. Particularly in the case of
earnings, there is an observed steady growth over time. This is usually attributed to growth
in productivity and technology, not necessarily diversity. Specification VII includes a quartic
time trend variable to allow for non-linear changes in earnings and rents over time. The
diversity of the highly educated, however, remains significant and robust to this time trend.
Specification VIII allows for a linear time trend to vary across cities. Again, diversity of the
highly educated remains very stable and significant in both sets of regressions. The most
28
rigorous test is specification IX, which includes city specific time trends as well as time fixed
effects. Here the challenge is not only in estimating a large number of parameters with few
observations, but also that most of the variation in the dependent variable is taken away by
time trends and fixed effects. Surprisingly, although standard errors are large, diversity
amongst the highly educated is still significant and proves to be a strong predictor of offtrend earnings and rents.10 The fact that the high education diversity survives such rigorous
time trend estimation shows the remarkable predictive power of this variable.
One source of spurious correlation could be other productivity or amenity shocks
that attract immigrants, thus increasing diversity alongside earnings and rents without any
causal relationship. Ottaviano and Peri (2006) note that “the share of … citizens in each city
[coming from another city] should be correlated with the same local productivity and
amenity shocks that attract foreigners.” One way of addressing this endogeneity is to control
for local productivity and amenity shocks.11 Specification X includes a control for the share
of population who moved in the previous five years as a proxy for these exogenous shocks.
This mobility control is positive and highly significant in the rent regression (p-value .0000,
not reported), though not significant in the earnings regression (p-value .6421, not
reported). In both cases, though, the estimates for the diversity indices remains unchanged
when including this control.
Specification XI tests if place of birth is a relevant proxy for cultural identity. The
share of the population identifying as a minority is another measure closely related to
diversity. This may share may include second-generation immigrants, but exclude
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
One anomaly is that diversity of the low education group, which is nearly never significant in any previous test,
statistically significant when including city specific time trends. This suggests that diversity, regardless of education
group, may be useful in predicting off trend changes. However, the low education diversity has little predictive power
in every other specification.
11
The issue of endogeneity will be addressed more completely in section 5.5.
10
29
immigrants from non-minority backgrounds. Even when minority share is included, the
estimates for diversity by place of birth remain significant. As such, it seems that place of
birth captures unique ethnic qualities otherwise not measured by association with a
minority group.
I also consider variations in the basic earnings regression. Since employment and
unemployment rates are not perfectly related, specification XII includes a control for the
unemployment rate instead of employment rate. The sign on the unemployment variable is
opposite (not reported), but the estimate for the diversity indices remains relatively
unaffected. Specification VIII uses a quartic polynomial in average years of schooling as a
different control for education level. This alternate method of controlling for education does
not affect the estimates for the diversity indices, with the diversity of the high education
group remaining positive and significant.
Lastly, in the rent regression I also control for average income per capita
(specification XIV). Of course, income per capita is heavily related to wages and is
endogenously determined in our model, but may also affect the level of rents in a city.
Including this potentially endogenous regressor, however, does not seem to have much effect
on the results of the base specification.
In summary, the significance of high education diversity is remarkably robust to
variations in the basic regression. In every specification the coefficient is significant, ranging
from .580 to 1.541 for earnings and .621 to 2.364 for rents. On the whole, the base
specification point estimates seem to provide an accurate estimate for the true parameter
value: a .1 increase in diversity of the highly educated associated with a 12% increase in
earnings and 17% increase in rents. Reassuringly, these results are not driven by any
particular city and no specification contradicts our hypothesis of a dominant positive
30
productivity effect. At the same time, the coefficients on the diversity index of the low
education group and entire population are also almost uniformly insignificant, suggesting
that the low educated do not contribute to any diversity effect.
5.5
Endogeneity
The previous results have shown a strong and robust correlation between diversity of the
highly educated and the earnings and rents of the native-born. However, there is an
endogeneity issue that complicates the interpretation of the correlations. While there may
be a causal link from diversity to earnings and rents (i.e. the diversity effect, as argued in
this paper), there is likely a reverse causal effect also at work: cities may experience
exogenous productivity or amenity shocks that attract immigrants, thereby increasing
diversity. In this case, increases in diversity are observed with increases in earnings and rent
with causality running from earnings and rents to diversity, instead of the other way
around. Although in reality causality probably runs in both directions, there are at least
four reasons to suggest that there exists at least some causal effect stemming from diversity.
First, specification X in table 8 controls for mobility of residents as a proxy for
exogenous shocks. Assuming that shocks to a city attract the native-born and immigrants
alike, this control should be correlated with city specific productivity or amenity shocks.
Including this control reduces the estimate for high education diversity in the rent
regression, suggesting that mobility does capture at least some of the amenity shocks that
attract immigrants (also, the mobility variable is highly significant in the rent regressions).
Regardless, even with the inclusion of this control, the coefficient on the diversity index of
the highly educated remains positive and statistically significant. In the earnings regressions,
31
however, mobility appears to be a weaker proxy for productivity shocks: its slope estimate is
no longer significant and leaves the coefficient on diversity virtually unchanged.
The robustness of the diversity variables in the time trend estimation also lends
support for the diversity effect hypothesis. By controlling for preexisting trends in earnings
and rents, time trend estimation ensures that the relationship is not merely spurious in
nature. The rigorousness of time trend estimation verifies the strength and robustness of the
high education diversity variable. Its ability to predict off trend changes reinforces our
understanding of a causal relationship and not merely spurious correlation.
A third way of addressing the endogeneity issue is to look at the differences between
diversities of the two education groups. If the correlation were purely driven by self-selection
of immigrants, we would expect all immigrants to be equally attracted to boom cities
regardless of education level. In other words, an individual’s education level should not
affect how she responds to city specific shocks—both the high and low education groups
should respond similarly. Hence, education should not be a relevant control for diversity if
the correlation is entirely driven by self-selection of immigrants. However, the estimation
results have shown a clear distinction between the diversity indices of the high and low
education groups. While diversity of the highly educated is a strong predictor of earnings
and rents, the diversity of the low educated is never significant and often in the opposite
sign. Clearly, the diversity indices of the two education groups behave differently. This
distinction shows that the correlation is not entirely driven by self-selection of immigrants
and gives evidence for a positive productivity effect from diversity.
Lastly, I turn to instrumental variable estimation to quantitatively isolate the
diversity effect. One potential issue is that limits the usefulness of IV estimation is the
nature of the data source. The PUMF limits geographic data to just 13 CMAs in 1981, thus
32
limiting our sample to that number of observations. These CMAs are the only ones that can
be consistently measured in both 1981 and 2006. This is hardly an ideal sample to use and
will almost certainly be affected by imprecise estimates. However, although these IV results
should not be considered definitive, they still lend support for the dominant positivity effect
hypothesis.
Following the shift-share methodology developed by Card (2001)12, the instrument is
a ‘predicted’ diversity index for each city based on initial levels of diversity in 1981 and
overall population growth rates for each immigrant group. More specifically, the instrument
is calculated as
!"#!!! ! ! ! !!"#!!!!!!"#! ! !! !!
(10)
where !"#!!!!!!"#! is the share of residents in city ! in education group ! born in place of
birth ! in 1981 and !! is the overall national growth rate of immigrants from place of birth !
from 1981 to 2006. This instrument should be correlated with the actual diversity index of
the city in 2006, but uncorrelated with any city specific shocks during the time period. The
first stage regression of the actual change in the diversity index against the predicted change
in the diversity index using the shift-share instrument is reported in table 9. The imputed
diversity instrument is strong, with highly significant F-statistics and high R-squared values
in each case.
Specifications I-III report the OLS and IV estimates for the change in diversity
indices for each education group in the earnings regressions. The first column is for the
diversity of the entire population, with the second and third columns for the high and low
education groups. The OLS estimates are consistent, both in magnitude and sign, with the
original earnings specifications. Although the large standard errors for the IV estimates
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
12
Use of this method has been replicated by Saiz (2003) and Ottaviano and Peri (2006) among others.
33
make it difficult to interpret with any degree of accuracy, the diversity of the highly
educated remains significant in specification II. This indicates an underlying effect from the
diversity of the highly educated to earnings.
Specifications IV-VI report the OLS and IV estimates for the change in diversity
indices for each education group in the rent regressions. Again, the OLS estimates match up
very well with the original rent specifications. However, it becomes obvious in the IV
estimates that the data is being pushed to its limit here—standard errors are very large and
point estimates correspond little with previous results. The large standard errors are likely a
consequence of the small sample size and not weak instruments. Fortunately, the sign of the
IV estimate on the high education diversity is positive (though not significant) and does not
contradict the previous findings.
In short, use of IV estimation—despite its limitations—does not give any result that
seriously challenges our initial hypothesis of a positive diversity effect on productivity. The
shift-share imputed diversity is an effective instrument, though is compromised from the
small sample size. However, the diversity of the high-educated group is robust to IV
estimation in the earnings regression and substantiates a positive effect from diversity onto
earnings. Although endogeneity remains a complicated issue to address, based on the earlier
discussion and IV estimation results, it is reasonable to conclude that a positive
productivity effect of high education diversity does exist.
6
Conclusion and Discussion
The main contributions of this paper are twofold, both verifying the results of previous
studies on diversity as well as introducing education as a relevant determinant of the
diversity effect. First, it has extended the results of previous studies concerning diversity to
34
a Canadian context, finding similar positive productivity effects expressed through higher
earnings and rents. I find that a more culturally diverse environment makes Canadian-born
workers more productive. This result is consistent with previous studies on diversity and
further corroborates that diversity can have desirable economic consequences on the nativeborn. A second, and perhaps more interesting, contribution of this paper has been showing
the way education determines this diversity effect: only the diversity of the highly educated
matters when it comes to the productivity effect. A diverse low educated group does not
have any effect on the earnings and rents of the native-born. Nevertheless, even though only
the diversity of the highly educated class is significant, everyone—regardless of education
level—enjoys the benefits. This suggests a positive externality effect of having a diverse
educated class, not unlike human capital spillover effects.
Although these findings deepen our understanding of the diversity effect and uncover
an important link between cultural diversity and education, they open the door to several
unanswered questions. The reason why the diversity effect is driven by the highly educated
class remains uncertain. One the one hand, it could be because schooling augments the
benefits of diversity (e.g. encouraging innovative thought and collaboration with others)
whilst minimizing the costs (e.g. reducing discrimination and inspiring tolerance). On the
other hand, education may only be representative of certain industries that require more
educated workers (e.g. the high-tech versus manual labour sectors). If so, it is the nature of
the industries themselves, and not education per-say, that exploit the benefits associated
with diversity. Looking at the way different industries respond to diversity is one potentially
interesting area for further discussion.
In addition, this paper has not looked into the role institutions play in determining
the diversity effect. The contrast between our findings (as well as for the case of the United
35
States and Europe) of a positive diversity effect and the harmful diversity effect found in
developing countries implies that political frameworks matter. Better understanding how
institutions and immigration policy can support diversity may be useful in truly maximizing
the benefits diversity offers.
Lastly, this paper has not examined the specific complementaries between ethnic
groups. Whether or not ethnic capital from different regions are better matched with
specific groups or industries is unclear. This paper has only used an aggregate measure of
diversity that does not distinguish between the particular ethnic makeups of diversity. A
more detailed look into the way specific ethnic groups contribute to the diversity effect is
another promising avenue for further research.
36
7
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Peri, Giovanni. “Immigrants’ Complementarities and Native Wages: Evidence from
California.” NBER Working Paper No. 12956 (2007).
Quigley, John. “Urban Diversity and Economic Growth.” Journal of Economic Perspectives,
12 (1998), 127-138.
Roback, Jennifer. “Wages, Rents, and the Quality of Life.” Journal of Political Economy, 90
(1982), 1257-1278.
Shapiro, Jesse. “Smart Cities: Quality of Life, Productivity, and Growth Effects of Human
Capital.” Review of Economics and Statistics, 88 (2005), 324-335.
Smolicz, Jerzy. “Education and Cultural Democracy,” in Margaret Secombe and Joseph
Zajda, eds., J.J. Smolicz on Education and Culture. Albert Park, Australia: James
Nicholas Publishers (1999).
Stark, Oded. The Migration of Labor. Cambridge, MA: Blackwell (1991).
Troyna, Barry and Bruce Carrington. Education, Racism, and Reform. New York, NY:
Routledge (1990).
38
Appendix Figure 1: Change in real earnings over time across CMAs, 1981-2006
39
Appendix Figure 2: Change in real rents over time across CMAs, 1981-2006
40
Data Appendix: Names and provinces of Canadian Metropolitan Areas used
Brantford, ON (2006)
Sherbrooke-Trois Rivières, PQ (1986)
Calgary, AB (1981)
St. Catherine’s-Niagara, ON (1981)
Edmonton, AB (1981)
Sudbury-Thunder Bay, ON (1991)
Halifax, NS (1981)
Toronto, ON (1981)
Hamilton, ON (1981)
Vancouver, BC (1981)
Kelowna-Abbotsford, BC (2006)
Victoria, BC (1991)
Kingston, ON (2006)
Windsor, ON (1991)
Kitchener, ON (1981)
Winnipeg, MB (1981)
London, ON (1981)
Moncton, NB (2006)
Montreal, PQ (1981)
Oshawa, ON (1991)
Ottawa-Gatineau, ON-PQ (1981)
Québec, PQ (1981)
Regina-Saskatoon, SK (1986)
Note: First observation year in parentheses. The Ottawa-Gatineau CMA is categorized within the province of Ontario.
41
Data Appendix: Groupings by country of birth
The diversity indices in each time period are constructed using eight places of origin for immigrants. Individual countries were grouped by continent to ensure a consistent set of places
of origin.
The regions were grouped as follows: Canada, United States, United Kingdom, Europe (Albania, Andorra, Austria, Belarus, Belgium, Bulgaria, Croatia, Czech Republic, Czechoslovakia, Denmark, Estonia, Finland, France, Germany, Gibraltar, Greece, Hungary, Iceland, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia, Malta, Monaco, Netherlands,
Norway, Poland, Portugal, Republic of Ireland, Republic of Moldova, Romania, Russian
Federation, San Marino, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, Vatican
City State, Yugoslavia), Africa (Algeria, Angola, Benin, Botswana, Burkina Faso, Burundi,
Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Côte d’Ivoire, Djibouti,
Egypt, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius,
Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Republic of South Africa, Republic of the Congo, Rwanda, Réunion, Saint Helena, Sao Tome and Principe, Senegal,
Seychelles, Sierra Leone, Somalia, Sudan, Swaziland, The Democratic Republic of Congo,
Togo, Tunisia, Uganda, United Republic of Tanzania, Western Sahara, Zambia, Zimbabwe),
Asia (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei, Cambodia, China, Cyprus, Darussalam, East Timor, Georgia, Hong Kong, Indonesia, Iran, Iraq,
Israel, Japan, Jordan, Kazakhstan, Kuwait, Kyrgyzstan, Laos, Lebanon, Macau, Malaysia,
Maldives, Mongolia, Myanmar, Nepal, North Korea, Oman, Pakistan, Palestine/West Bank/
Gaza Strip, Philippines, Qatar, Saudi Arabia, Singapore, South Korea, Sri Lanka, Syria,
Taiwan, Tajikistan, Thailand, Turkey, Turkmenistan, United Arab Emirates, Uzbekistan,
Vietnam, Yemen), Central & South America (Anguilla, Antigua and Barbuda, Argentina,
Aruba, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazil, British Virgin Islands, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador,
El Salvador, Falkland Islands Malvinas , French Guiana, Grenada, Guadeloupe, Guatemala,
Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Kitts and Nevis, Saint Lucia,
Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Turks and Caicos Islands, US Virgin Islands, Uruguay, Venezuela), Oceania & Others (American Samoa, Australia, Cook Islands, Federated States of Micronesia, Fiji, French Polynesia, Futuna, Guam,
Kiribati, Marshall Islands, Nauru, New Caledonia, New Zealand, Palau, Papua New Guinea,
Pitcairn, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, Wallis).
42
rents r
Figure 1: The spatial equilibrium
free migration
r*
zero profit
w*
43
wages w
Figure 2a: Correlation between change in real earnings of native-born
and entire population diversity index
%Δearnings = .151 + 1.766Δdivall
Adj. R2 = .0809
Figure 2b: Correlation between change in real earnings of native-born
and high education diversity index
%Δearnings = -.497 + 2.672Δdivheduc
Adj. R2 = .5571
Figure 2c: Correlation between change in real earnings of native-born
and low education diversity index
%Δearnings = .189 + .793Δdivleduc
Adj. R2 = -.0265
44
Figure 3a: Correlation between change in real rents of native-born
and entire population diversity index
%Δrent = .0747 + 1.182Δdivall
Adj. R2 = .1448
Figure 3b: Correlation between change in real rents of native-born
and high education diversity index
%Δrent = -.0324 + 1.481Δdivheduc
Adj. R2 = .5524
Figure 3c: Correlation between change in real rents of native-born
and low education diversity index
%Δrent = .101 + .646Δdivleduc
Adj. R2 = .0146
45
Table 1: Descriptive statistics for diversity indices by education groups over CMAs, 1981-2006
Entire Population
46
Location:
Toronto
Vancouver
Hamilton
Calgary
Kitchener
Windsor
Edmonton
London
St. Catherine's/Niagara
Montreal
Victoria
Winnipeg
Ottawa/Gatineau
Oshawa
Sudbury/Thunder Bay
Regina/Saskatoon
Halifax
Sherbrooke/Trois Rivieres
Quebec
Diversity
Index
.632
.540
.404
.381
.368
.348
.328
.325
.323
.316
.309
.305
.276
.275
.168
.144
.118
.049
.046
Standard
Deviation
.043
.055
.036
.018
.035
.052
.025
.038
.056
.022
.053
.036
.025
.042
.043
.041
.029
.012
.007
Minimum
.582
.484
.349
.366
.306
.272
.281
.258
.232
.295
.230
.246
.249
.214
.105
.078
.075
.033
.037
High Education
Maximum
.687
.602
.451
.417
.388
.386
.351
.345
.385
.035
.345
.347
.314
.304
.20
.188
.136
.060
.056
Diversity
Index
.460
.436
.237
.306
.238
.257
.240
.215
.172
.256
.256
.20
.259
.143
.097
.117
.131
.065
.060
Standard
Deviation
.097
.10
.026
.054
.031
.053
.010
.026
.017
.048
.038
.007
.033
.016
.033
.022
.022
.012
.009
Low Education
Diversity
Index
.745
.620
.521
.426
.441
.441
.398
.412
.420
.396
.340
.375
.291
.324
.254
.170
.095
.042
.040
Standard
Deviation
.041
.058
.036
.055
.046
.054
.039
.045
.052
.026
.065
.044
.025
.061
.045
.051
.036
.011
.004
Note: Cenus Metropolitan Areas appear in descending order by diversity index of entire population. High education defined as some college or more; low education defined
as high school completion and under. Data is collected in five-year intervals from 1981-2006, where available. See text for more information.
Table 2: Descriptive statistics for mean earnings and rents by education groups over CMAs, 1981-2006
47
Location:
Toronto
Vancouver
Hamilton
Calgary
Kitchener
Windsor
Edmonton
London
St. Catherine's/Niagara
Montreal
Victoria
Winnipeg
Ottawa/Gatineau
Oshawa
Sudbury/Thunder Bay
Regina/Saskatoon
Halifax
Sherbrooke/Trois-Rivieres
Quebec
All
46,323
43,319
49,544
39,113
39,010
40,439
49,174
41,843
44,276
39,446
44,030
41,832
40,467
44,179
42,031
37,716
36,924
34,969
36,923
Mean earnings
High
Low
Education Education
56,477
37,659
54,069
36,341
63,227
35,636
48,689
31,893
49,449
31,895
50,879
31,930
66,298
35,629
53,231
33,669
56,438
34,472
52,380
28,924
53,625
35,280
50,649
37,216
51,997
35,846
55,963
30,626
49,818
29,039
48,032
30,620
46,131
28,549
45,815
24,221
48,907
27,124
All
137.93
133.86
168.27
112.94
112.31
120.40
143.13
126.89
128.65
119.42
170.69
116.75
116.08
137.73
136.39
119.98
132.03
97.17
112.24
Mean rents
High
Education
144.08
146.36
192.40
119.78
123.24
134.71
151.07
138.35
139.30
132.10
187.65
127.07
123.64
156.45
147.36
130.68
151.48
103.59
121.72
Low
Education
133.66
129.06
149.71
109.92
109.95
113.32
136.99
120.99
121.47
112.62
159.10
112.70
112.26
123.83
125.65
112.92
118.55
92.97
104.58
Note: Census Metropolitan Areas appear in descending order by diversity index of entire population (see table 1). High education is
defined as some college or more; low education is defined as high school completion or under. Mean earnings is yearly employment
income of native-born, full time employed males aged 30-50. Mean rent is monthly gross rent per room for native-born males aged 16-65.
All figures expressed in 2002 dollars. Data is collected in give-year intervals from 1981-2006, where available. See text for more
information.
Table 3: Summary statistics for earnings, rents, and control variables
Real yearly earnings
All
High education
Low education
Real monthly rent per room
All
High education
Low education
Controls
Population
Employment rate
Share of high education
Share of low education
Mean
Standard
Deviation
Minimum
Maximum
41,950
52,922
32,653
5,428
6,700
4,178
33,851
42,633
22,968
66,136
83,602
45,724
130.84
142.97
123.18
21.0
23.9
18.3
89.42
96.01
85.69
201.15
222.99
168.79
948,051
62.8
.342
.265
1,136,745
4.0
.037
.068
126,424
54.6
.274
.130
5,423,955
73.8
.450
.424
Note: Mean earnings is yearly employment income of native-born, full time employed males aged 3050. Mean rent is monthly gross rent per romo for native-born males aged 16-65. All figures expressed
in 2002 dollars. Data is collected in five-year intervals, where available. See text for more information.
48
Table 4: Basic earnings specifications
Dependent variable: natural logarithm of mean earnings of native-born
Independent variable:
Diversity index
All
High education
(I)
(II)
(III)
(IV)
.397
(.292)
.404
(.295)
1.222***
(.243)
1.227***
(.242)
.143
(.128)
10.411***
(.10)
99
-.071
(.364)
.147
(.127)
10.701***
(1.506)
99
.080
(.108)
10.301***
(.061)
99
.071
(.256)
.076
(.106)
9.929***
(1.057)
99
.804
.802
.870
.868
Low education
49
ln(employment)
Share highly educated
Constant
No. of obs.
Adj. R 2
(V)
(VI)
.087
(.231)
.170
(.133)
10.498***
(.088)
99
.090
(.234)
-.047
(.373)
.172
(.132)
10.691***
(1.540)
99
.799
.796
Note: Heteroskedasticity robust standard errors in parentheses. Dependent variable is the natural logarithm of mean real yearly earnings for
native born, full time employed males aged 30-50, expressed in 2002 dollars. All regressions include city and year fixed effects. Significance
at 1% level denoted by ***, significance at 5% level denoted by **, significance at 10% level denoted by *.
Table 5: Basic rent specifications
Dependent variable: natural logarithm of mean rent of native-born
Independent variable:
Diversity index
All
High education
(I)
(II)
50
(III)
(IV)
.391
(.590)
-.125
(.661)
1.639***
(.309)
1.736***
(.511)
4.700***
(.179)
99
.396
(.219)
-.292
(2.734)
99
4.485***
(.065)
99
-.067
(.218)
5.333*
(2.776)
99
.763
.779
.834
.832
Low education
ln(population)
Constant
No. of obs.
Adj. R 2
(V)
(VI)
-.00956
(.481)
4.825***
(.163)
99
-.428
(.443)
.450**
(.193)
-.892
(2.457)
99
.760
.784
Note: Heteroskedasticity robust standard errors in parentheses. Dependent variable is the natural logarithm of mean real monthly rent per
room for native born males aged 16-65, expressed in 2002 dollars. All regressions include city and year fixed effects. Significance at 1% level
denoted by ***, significance at 5% level denoted by **, significance at 10% level denoted by *.
Table 6: Coefficients on diversity indices across education groups
Dependent variable:
Mean earnings
Coefficient:
Diversity index
High education
51
Low education
All
High
Education
Low
Education
1.444***
(.289)
.251
(.290)
.578
(.367)
.817***
(.208)
.041
(.202)
.346
(.234)
a
Mean rentb
All
1.227***
(.242)
.090
(.234)
.404
(.295)
High
Education
Low
Education
All
1.807***
(.479)
-.692
(.486)
-.408
(.718)
1.626***
(.522)
-.413
(.416)
-.118
(.630)
1.736***
(.511)
-.428
(.443)
-.125
(.661)
Note: Each entry represents a separate regression. Heteroskedasticity robust standard errors in parentheses. All regressions include
city and year fixed effects. Total number of observations: 99. Significance at 1% level denoted by ***, significance at 5% level denoted
by **, significance at 10% level denoted by *.
Dependent variable is natural logarithim of mean real yearly earnings for native born, full time employed males aged 30-50, expressed
in 2002 dollars. Regressions include natural logarithm of employment and share of highly educated as explanatory variables.
a
Dependent variable is natural logarithim of mean monthly rent per room for native born males aged 16-65. Regressions include
natural lograthim of population as an explanatory variable.
b
Table 7: Correlation between diversity and employment/population
Dependent variable:
ln(employment)
Coefficient:
Diversity index
All
52
High education
(I)
.112
(.128)
Low education
No. of obs.
Adj. R 2
(II)
-.058
(.130)
ln(population)
(III)
(IV)
1.304***
(.399)
99
99
.071
(.104)
99
.863
.862
.863
(V)
1.459***
(.205)
(VI)
99
99
.931**
(.364)
99
.997
.997
.997
Note: Heteroskedasticity robust standard errors in parentheses. All regressions include city and year fixed effects. Significance at 1%
level denoted by ***, significance at 5% level denoted by **, significance at 10% level denoted by *.
Table 8: Robustness checks: earnings and rent regressions
Dependent variable:
Mean earnings
Coefficient on diversity index:
Variations of base specification:
(I) Base
(II) Population weighted
(III) Excluding Toronto, Vancouver
(IV) Excluding Alberta
(V) Excluding combined CMAs
(VI) With province fixed effects
(VII) With city fixed effects, time trend
(VIII) With city trend
(IX) With city trend, time fixed effects
Additional control regressors:
(X) Including mobility
(XI) Including share visible minority
(XII) Including ln(unemployment)
(XIII) Including quartic in education
(XIV) Including ln(income)
High
Education
Low
Education
1.227***
(.243)
1.098***
(.251)
1.541***
(.332)
1.077***
(.240)
1.176***
(.286)
.580***
(.095)
1.169***
(.236)
1.175***
(.335)
1.221***
(.378)
1.227***
(.241)
1.511***
(.282)
1.303***
(.249)
1.412***
(.277)
a
Mean rentb
All
High
Education
Low
Education
All
.090
(.234)
.082
(.237)
-.285
(.251)
.128
(.222)
.026
(.218)
.348***
(.073)
.093
(.228)
-.587**
(.248)
-.697***
(.170)
.404
(.295)
.293
(.303)
.036
(.374)
.326
(.269)
.252
(.285)
.418***
(.082)
.406
(.289)
.096
(.491)
-.491
(.313)
1.736***
(.511)
1.493***
(.501)
2.163***
(.568)
.891**
(.349)
1.623***
(.566)
.621***
(.230)
1.494***
(.516)
1.553***
(.452)
1.352**
(.580)
-.428
(.443)
-.198
(.484)
-.689
(.467)
-.659**
(.338)
-.523
(.401)
-.028
(.124)
-.402
(.402)
-.698**
(.265)
-.882***
(.242)
-.125
(.661)
-.012
(.665)
-.366
(.720)
-.750
(.465)
-.315
(.614)
.077
(.160)
-.128
(.602)
.271
(.751)
-.855**
(.400)
.132
(.238)
-.451*
(.251)
.098
(.237)
.086
(.231)
.468
(.285)
-.174
(.415)
.407
(.301)
.693*
(.419)
1.129***
(.260)
2.364***
(.581)
-.229
(.242)
-.540
(.511)
-.058
(.338)
-.089
(.930)
1.725***
(.514)
-.421
(.453)
-.116
(.674)
Note: Each entry is a separate regression. Heteroskedasticity robust standard errors in parentheses.All regressions include city and year fixed effects
(except specifications VI-IX). Significance at 1% level denoted by ***, significance at 5% level denoted by **, significance at 10% level denoted by *.
Dependent variable is natural logarithim of mean real yearly earnings for native born, full time employed males aged 30-50, expressed in 2002 dollars.
Regressions include natural logarithm of employment and share of highly educated as explanatory variables.
a
Dependent variable is natural logarithim of mean monthly rent per room for native born males aged 16-65. Regressions include natural lograthim of
population as an explanatory variable.
b
53
Table 9: Instrumental variable estimation; instrument: shift-share imputed diversity
Dependent variable: Δln(earnings)
(I)
Coefficient:
ΔDiversity
All
(II)
OLS
IV
.778
(.569)
2.779
(2.132)
High education
(III)
OLS
IV
1.668***
(.454)
1.640**
(.685)
Low education
OLS
IV
.561
(.481)
2.059
(1.828)
Dependent variable: Δln(rent)
(IV)
Coefficient:
ΔDiversity
All
High education
(V)
OLS
IV
.761
(.856)
.256
(1.559)
Low education
Shift-share imputed diversity
R2
F-statistic
n.a.
(VI)
OLS
IV
1.468**
(.577)
.239
(.970)
First Stage Regression
n.a.
1.215***
(.123)
.827
52.43
2.902***
(.406)
.851
62.77
OLS
IV
.525
(.714)
-.666
(1.256)
n.a.
1.08***
(.116)
.798
43.32
Note: Heteroskedasticity robust standard errors in parentheses. Significance at 1% level denoted by ***, significance at 5% level denoted by
**, significance at 10% level denoted by *.
Dependent variable: Δln(wage) is the change from 1981 to 2006 in the natural logarithm of mean real yearly employment income for native
born, full time employed males aged 30-50, expressed in 2002 dollars. Δln(rent) is the change from 1981 to 2006 in the natural logarithm of
mean monthly rent per room for native born males aged 16-65. Intrumental variable: imputed change in diversity index using the shiftshare method described in text. No. of obs.: 13.
54