Understanding the Urban Earnings GrowthPremium*Guangbin Hong†
University of Toronto

March 22, 2024
Preliminary Draft
Abstract
It has been documented that larger cities foster faster earnings growth, which is an
important driver for spatial earnings inequality. In this paper, I empirically investigate
the sources of the urban earnings growth premium. I find that the between-firm andwithin-firm growth components each explain 66% and 34% of the greater returns to big
city experience, respectively. Workers do not move between jobs more frequently but
enjoy a steeper job ladder in larger cities. Faster within-firm learning in larger cities
is mostly explained by better learning environments at the firm level. The empiricalresults highlight the important role of firm heterogeneity across cities in explaining the
dynamic gains from working in bigger cities.
Keywords: Urban earnings growth premium, job ladder, learning, firm heterogeneity.
JEL codes: R12, R13.
* This is the second chapter of Guangbin Hong’s PhD dissertation. I am deeply indebted to Joseph B.
Steinberg, Nathaniel Baum-Snow, Kevin Lim, Serdar Ozkan, and Kory Kroft for their continuous guidanceand support. The empirical analysis of this paper is conducted in the Statistics Canada Research Data Center(RDC). The results and views expressed in this paper are those of the author and do not represent Statistics
Canada. All remaining errors are on my own.
† Department of Economics, University of Toronto. Email: g.hong@mail.utoronto.ca.

1Introduction
Workers enjoy greater returns to work experience in big cities, which account for at least

half of the urban earnings premium (Baum-Snow and Pavan, 2012; De La Roca and Puga,2017). Therefore, uncovering the drivers of urban earnings growth premium (henceforth
UEGP) is important to understand the spatial earnings structure. Previous studies havefound that knowledge spillovers allow workers to accumulate human capital faster in big
cities (Ellison et al., 2010; Atkin et al., 2022; Baum-Snow et al., 2023). Meanwhile, otherstudies have emphasized the matching benefits in big cities, helping workers move up thejob ladder and earn more (Petrongolo and Pissarides, 2006; Bleakley and Lin, 2012; Eckert
et al., 2022). However, little empirical evidence exists on the relative importance of the twochannels in their contribution to the UEGP. Such evidence can not only help shed light on
the nature of agglomeration economies but also have significant implications for aggregate
outcomes.1
In this paper, I empirically investigate the sources of faster earnings growth in big
cities. Using panel data recording the job and location history of the universe of Canadianworkers, I examine earnings growth across different cities and decompose it into growthcomponents from between-firm mobility and within-firm learning. I find that doubling
the city population is associated with a 0.11 percentage point increase in annual earnings
growth. Earnings growth from between-firm mobility and within-firm learning eachaccount for 65.6% and 34.4% of the UEGP, respectively. Workers do not move between
jobs more frequently in larger cities, yet the average gain from each job-to-job movement
is much higher, implying a steeper job ladder. Working for larger and high-paying firms
and with more skilled co-workers accounts almost entirely for faster within-job learning inlarger cities.
My empirical analysis underscores the important role of spatial firm sorting in shaping
the UEGP. While prior studies have emphasized the significance of matching with highpaying firms in larger cities for the urban earnings premium, I extend this understandingby showing that more productive firms in larger cities also facilitate faster earnings growth.
1See Duranton and Puga (2004) for the anatomy of the micro-foundations of the agglomeration spillovers,
i.e. sharing, matching, and learning. And see Martellini (2019), Davis and Dingel (2019), Crews (2023) andDuranton and Puga (2023) for discussions on how local knowledge spillovers affect aggregate growth of theeconomy.

1

They not only increase the steepness of local job ladders but also cultivate faster on-the-job
learning. It has been shown that heterogeneity in firms’ promotion of human capital(Gregory, 2020; Ma et al., 2021) and learning benefits from co-workers (Herkenhoff et al.,2018; Jarosch et al., 2021) are crucial determinants of workers’ lifetime earnings. Applying
such insight into the spatial context, this paper contributes to the urban economics literatureby documenting the importance of firm heterogeneity across cities in generating different
local learning environments. With two-sided sorting of workers and firms across cities(Diamond, 2016; Gaubert, 2018) and greater extents of assortative matching within larger
cities (Dauth et al., 2022), workers in urban areas benefit from better learning environments
by working for higher-productivity firms and with higher-skilled peers.
The contributions of between-firm mobility and within-firm learning vary between
workers of different skills and evolve with different stages over the life cycle. High-skilledworkers benefit more from the working experience in big cities, suggesting skill-biased
dynamic agglomeration benefits. More interestingly, the importance of between-firm
mobility and within-firm learning varies monotonically with worker skill. Higher-skilledworkers enjoy faster learning in big cities, whereas lower-skilled workers benefit more fromthe job ladder. In addition, the UEGP decreases over the life cycle. Workers in larger cities
benefit from significantly faster learning before age 30. The steeper job ladder benefit plays
a more important role in the latter stages of the life cycle. It suggests that larger cities help
workers accumulate human capital faster at the start of their careers and help them findbetter matches to fully utilize their skills later on. Baum-Snow and Pavan (2012) documenta migration pattern that college-educated workers are more likely to migrate to big cities
when young and leave big cities when older. My empirical findings of skill-biased urban
learning premium and the decreasing importance of human capital accumulation over thelife cycle provide a rationale for such a pattern.
I also examine the earnings growth for between-city migrants. Compared to workers
who do not move in the sample period, there is a stronger relationship between earnings
growth and city population for between-cities migrants. Specifically, the city-size elasticity
of earnings growth for migrants in their destination city is 0.46, about 3 times as large as theelasticity of 0.17 for the never-movers. The migrants not only learn faster within each firm
where they are employed but also enjoy a steeper job ladder. This suggests that workers

2

moving to bigger cities benefit more from the urban environment. One explanation forsuch difference is that individuals with better people skills and self-confidence tend tomove to larger cities, as found in Bacolod et al. (2009) and De la Roca et al. (2023). Suchskills complement the big-city environment by facilitating more social interactions andmore progressive job movements.
The empirical results should not be interpreted as causal estimates of the treatment
effect of the urban environment. For example, workers who self-select in larger cities may
possess more specialized skills, which makes them benefit more from having access to alarger and more diverse set of firms. Nonetheless, the empirical finding of greater returnsof big-city experiences for migrants provides a cautionary note for studies applying the
between-city movers design to study the causal static and dynamic gains of working inlarge cities (e.g. De La Roca and Puga (2017) and Porcher et al. (2023)).2 If the movers
expect greater long-term returns from large city experiences, they may trade off in the
short term by moving to below-average firms when they migrate for greater long-run
benefits.3 As a result, the earnings changes associated with the between-city movement
could understate the static earnings gain. Moreover, movers and stayers exhibit very
different earnings-experience profiles. Empirical estimates that do not distinguish between
the two may mask significant underlying heterogeneity in the returns to big-city experience,
potentially stemming from multidimensional worker heterogeneity, e.g. ambition, learning
abilities, and social skills.
I contribute to the literature that studies the greater returns to big city experiences,pioneered by Glaeser and Mare (2001) and then studied by D’Costa and Overman (2014),
Baum-Snow and Pavan (2012), De La Roca and Puga (2017) and Eckert et al. (2022). The
most relevant work to this paper is Baum-Snow and Pavan (2012), who use geocoded NLSY
data to compare earnings growth from job-to-job transition and on-the-job learning inlarge, medium, and small cities in the US. Their empirical analysis relies on a small sample
and focuses on the initial 15 years of work experience. By leveraging administrative data
covering the universe of Canadian workers, this paper provides a holistic view of the life
2Many studies in this vein use an event-study figure to test the parallel pre-trend of individuals moving todifferent (groups of) cities and non-migrants, following Card et al. (2013). This visualization helps illustratethe (lack of) existence and significance of time-varying earnings shocks that are correlated with spatial
movements, i.e. the underlying sources of endogenous mobility.
3See Card et al. (2023) for the empirical evidence on this. They find that migrants tend to move to
below-average firms in their destination cities.

3

cycle earnings growth differences across cities. The decomposition confirms the importanceof both the steeper job ladder and faster human capital accumulation on the UEGP, and it
sheds light on the crucial role of spatial firm heterogeneity. Although building a modelto account for these facts is beyond the scope of this paper, future structural work on thespatial wage structure and its aggregate implications should take these into consideration.
The rest of the paper is organized as follows. I describe the data for the empirical
exercise in Section 2. I then present three empirical facts on UEGP in Section 3, followed bydiscussions on the heterogeneity of UEGP across skill, age, and migration status in Section
4. Finally, I conclude in Section 5.

2

Data and Sample Construction

2.1

Data

The main data sets I use are the administrative T1 Personal Master File and T4 Statementof Remuneration Paid File. These administrative files cover the universe of tax-filingindividuals in Canada, and I use the files from 2010 to 2017. The T1 file records individuallevel demographic characteristics and location information. The T4 file records annual
job-level information including worker and firm identifiers, annual earnings, and industry.
A worker can have more than one T4 slips in a year if she works for more than one firms.
For multiple job holders, I keep the job that offers the largest earnings of the year andcall it the main job.4 Nominal annual earnings are converted to 2002 Canadian dollars. I
merge these two administrative files using anonymized individual tax identifiers to form
an annual worker panel.
A city is defined as a Census Metropolitan Area (CMA) or a Census Agglomerate (CA)delineated in the 2016 Census of Population. The concept of CMA and CAs resembles theone of commuting zones in the U.S. – they are formed by combining a population centerand adjacent municipalities with a high degree of integration with the center measured bycommuting flows. To only keep those with significant sizes and sufficient labor market
integration, I keep CMAs and CAs with no fewer than 15,000 full-time working individuals
in 2002. I further drop one small outlier city that has average earnings greater than 150%
4I also compute the total annual earnings by summing up earnings from all T4 files.

4

of the national average. This selection process leaves me with 66 cities.

2.2

Sample construction

I restrict the baseline sample to full-time working individuals between the ages of 25 and60 who live in a city. The T4 files do not include information on hours worked. Following
Guvenen et al. (2021), I only include workers with annual earnings from the main job
no less than the equivalent of working 20 hours per week for 13 weeks at the minimum
hourly wage.5 I exclude workers employed in agriculture (NAICS 11), mining (NAICS 21),
utilities (NAICS 22), education (NAICS 61), hospitals (NAICS 62), non-profit organizations(NAICS 813), and public administrations (NAICS 92). Furthermore, the baseline sample only
includes individuals who have earnings above the minimum threshold for two consecutive
years to compute earnings growth and excludes the observations of the first year when
an individual moves to a new city. This later exclusion is because this paper focuses on
the reasons behind the difference in earnings growth within cities of different sizes, rather
than the earnings changes caused by moving across cities.
To compare the earnings growth of between-city movers and within-city stayers, I
construct a movers sample and a stayers sample. The movers sample includes individuals who
have migrated across cities only once in the sample period. Workers who migrate morethan once in a seven-year sample may have low local labor market attachment and different
human capital accumulation processes. I keep the first five years since the movers are in
the destination city.6 The stayers sample includes individuals who have stayed in the samecity throughout the sample period.
In Table A.1, I show the summary statistics of the three samples. I split the baseline sample
into one of the largest three cities and one of the smaller cities for a first-pass comparison. I
multiply the changes in log earnings by 100 to express them as percentage point changes.
We can have several immediate observations from the summary statistics table. First, on
average a Canadian worker experiences 1.05% earnings growth annually. The average
earnings growth in large cities (1.42%) is more than twice the growth in smaller ones

5Canadian provinces set their own minimum wage standards. I take the lowest of these provincial
standards as the national minimum wage.
6I do not include the observations of the movement years. The earnings changes in these movement
years reflect the static benefits of migration, which are out of the scope of this paper.

5

(0.68%), showing the UEGP. Second, the standard deviation of annual earnings growth is47.85% for all Canadian workers.7 Individuals in larger cities also exhibit a slightly larger
standard deviation of earnings growth, suggesting greater earnings risks. The two firstobservations are similar to the cross-sectional spatial earnings structure in that larger cities
have both higher average earnings and greater earnings dispersions (e.g. Baum-Snow andPavan (2013) and Dauth et al. (2022)). Third, a larger share of workers in big cities work
for large firms and in high-wage industries, such as the tradeable services sector, thanin small cities. This suggests that the heterogeneity in firm composition and industrial
structure may play crucial roles in explaining the spatial earnings growth difference.
Fourth, between-city migrants tend to be younger and experience faster earnings growththan within-city stayers. It is well-known that earnings growth is faster when workers areyoung. Later, I will show that this migrants-stayers difference in earnings growth is robust
to controlling for the life-cycle earnings growth profile.

3

Empirical Facts
In this section, I present three empirical facts on earnings growth across cities. First, I

compare average earnings growth between larger and smaller cities, establishing the UEGP.
Second, I decompose city-level average earnings growth into between-firm and withinfirm growth components and compare these two components across cities with different
populations. Third, I assess the extent to which spatial variations in firm composition
and industrial structure can explain the differences in within-firm earnings growth acrosscities.

3.1

Urban earnings growth premium

I begin by associating city-level average earnings growth, denoted as E𝑐 [Δ𝑤 𝑖𝑐𝑡 ], withcity size, which is shown in Figure 1. The literature has documented a robust empiricalpattern of a hump-shaped age-earnings profile.8 This pattern entails low earnings foryoung workers upon entering the labor market, followed by an increase with age, levelingoff at a certain point, and eventually declining after reaching peak earnings age. To address
7This is close to the standard deviation of 51% as reported in Guvenen et al. (2021) the Social Security
Administration data.
8See Sanders and Taber (2012) for a review on literature studying the life-cycle wage growth.

6

Figure 1: The relationship between earnings growth and city population

Average earnings growth

0.5

0.0

−0.5
Slope = 0.157 (0.044)
−1.0

−1.5

30k

100 k

300 k

1m

City Population

Notes: Individual earnings growth used to compute the city-level growth terms have been residualizedof a third-order age polynomial and year dummies. The point estimate with the robust standard error
regression, which is weighted by the number of worker-year observations of each city, is reported in the
figure.

the age effect, I residualize individual earnings changes by controlling for a third-order age
polynomial. I follow Card et al. (2013) to assume that the lifetime earnings profile is flat atage 40. In addition, I include a set of year dummies to account for yearly macroeconomic
shocks. I construct E𝑐 [Δ𝑤 𝑖𝑐𝑡 ] by taking the average of the residualized individual earnings
growth for each city and multiplying it by 100 to represent percentage point changes.
Figure 1 reveals a significant positive relationship between earnings growth and city
population, with an earnings-growth-to-population elasticity estimated as 0.157.9 Thistranslates to an additional 1.1 percent increase in earnings after 10 years of experience
working in a city twice as large, which is similar to the estimates of extra earnings gains fromworking in big cities reported in De La Roca and Puga (2017) and Porcher et al. (2023) using
Spanish administrative data.10 As reported in Hong (2024), doubling the city population isassociated with a 1.6% increase in annual earnings in Canada. This cross-sectional city-size
9Figure 1 uses workers’ earnings growth from the main job. I also examine the relationship using workers’
total earnings growth and find a similar result.
10For example, Porcher et al. (2023) find that workers benefit from an additional 1.23 percent in earnings
after 9.4 years of working experience in a city twice as large.

7

earnings premium corresponds to 21 years of accumulated earnings gap at an annual
growth rate difference of 0.109 percentage point, if the workers start their careers withidentical earnings.
Two points are noteworthy for the result. First, as mentioned earlier, only workers
who have stayed in the same city for two consecutive years are included in the sample.
Earnings changes associated with migration are informative of the static gains of moving toa city, which is beyond the scope of this paper. De La Roca and Puga (2017) find the initial
earnings premium of big cities to be about 40% of the medium-term premium associatedwith 7.7 years of local experience. Second, it does not speak to differences in unemployment
risks across cities, as workers who report annual earnings below a minimum thresholdare excluded from the sample. By examining the earnings dynamics of U.S. workers,
Guvenen et al. (2021) find that job loss risks constitute an important source of earnings
risks, especially for low-income workers. In Figure A.1, I plot the unemployment rate
against the city population. The figure reveals considerable geographical variation in local
unemployment rates, yet no systematic relationship between unemployment rates andcity population.11 This shows that larger cities do not necessarily mitigate or exacerbate
unemployment risks.
I also compare the urban earnings growth premium by sector in Table A.2. The results
reveal a moderate extent of heterogeneity across sectors. On the one hand, workers in
the transportation sector benefit the most from working in bigger cities, with a growthpopulation elasticity estimated as 0.239. On the other hand, the returns from big city
experience for the workers in the retail and wholesale trade sector is only about one-third
compared to transportation workers.

3.2

Decomposing the urban earnings growth premium

The greater earnings growth in large cities may be because workers take advantage of asteeper job ladder and because workers learn faster within each job. To understand how
job mobility and on-the-job learning contribute to spatial disparities in earnings growth, I

11See Kline and Moretti (2013) and Bilal (2023) for more detailed discussions on the spatial variation in
unemployment rates.

8

Table 1: Regression of earnings growth components on city population
Components:

Total

Between-firm

Within-firm

Total

𝑚𝑐

E𝑚
𝑐 [Δ𝑤 𝑖𝑐𝑡 ]

Total

𝑠𝑐

E𝑠𝑐 [Δ𝑤 𝑖𝑐𝑡 ]

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Log Pop.

0.157∗∗∗(0.044)0.103∗∗∗(0.014)0.001(0.001)0.713∗∗∗(0.110)0.054∗(0.032)−0.001(0.001)

0.062(0.038)Constant−2.089∗∗∗ −0.973∗∗∗ 0.136∗∗∗(0.580)(0.187) (0.019)−6.667∗∗∗(1.456)−1.115∗∗∗ 0.864∗∗∗(0.416) (0.019)−1.308∗∗(0.507)

Observations
R2

66
0.165

66
0.449

66
0.003

66
0.394

66
0.043

66
0.003

66
0.040

Notes: The dependent variables are constructed according to equation (1). Individual earnings growth
used to compute the city-level growth terms have been residualized of a third-order age polynomial andyear dummies. All regressions are weighted by the number of worker-year observations in each city.
Robust standard error reported. Significance level: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

decompose city-level average earnings growth below:𝑠E𝑐 [Δ𝑤 𝑖𝑐𝑡 ] = 𝑚 𝑐 × E𝑚𝑐 [Δ𝑤 𝑖𝑐𝑡 ] + 𝑠 𝑐 × E𝑐 [Δ𝑤 𝑖𝑐𝑡 ]| {z } |
{z
}
{z
} |
total

between-firm(1)within-firm

where 𝑖 indexes a worker, 𝑐 indexes a city, and 𝑡 indexes a year, E𝑐 [Δ𝑤 𝑖𝑐𝑡 ] is city 𝑐’s𝑠average earnings growth, E𝑚𝑐 [Δ𝑤 𝑖𝑐𝑡 ] and E𝑐 [Δ𝑤 𝑖𝑐𝑡 ] are city 𝑐’s average earnings growthfor between-firm movers and within-firm stayers, 𝑚 𝑐 is the between-firm mobility rate incity 𝑐, and 𝑠 𝑐 := 1 − 𝑚 𝑐 is the staying rate in city 𝑐. Denote 𝑗(𝑖, 𝑡) as the firm 𝑗 worker 𝑖 is
employed in year 𝑡, I defineE𝑚𝑐 [Δ𝑤 𝑖𝑐𝑡 ] = E𝑐 [𝑤 𝑖𝑐,𝑡 − 𝑤 𝑖𝑐,𝑡+1 | 𝑗(𝑖, 𝑡 + 1) ≠ 𝑗(𝑖, 𝑡), 𝑐(𝑖, 𝑡 + 1) = 𝑐(𝑖, 𝑡)]
E𝑠𝑐 [Δ𝑤 𝑖𝑐𝑡 ] = E𝑐 [𝑤 𝑖𝑐,𝑡 − 𝑤 𝑖𝑐,𝑡+1 | 𝑗(𝑖, 𝑡 + 1) = 𝑗(𝑖, 𝑡), 𝑐(𝑖, 𝑡 + 1) = 𝑐(𝑖, 𝑡)]and
1{𝑗(𝑖, 𝑡 + 1) ≠ 𝑗(𝑖, 𝑡), 𝑐(𝑖, 𝑡 + 1) = 𝑐(𝑖, 𝑡)}Í
1{𝑐(𝑖, 𝑡 + 1) = 𝑐(𝑖, 𝑡)}Í
1{𝑗(𝑖, 𝑡 + 1) = 𝑗(𝑖, 𝑡), 𝑐(𝑖, 𝑡 + 1) = 𝑐(𝑖, 𝑡)}Í𝑠𝑐 =.
1{𝑐(𝑖, 𝑡 + 1) = 𝑐(𝑖, 𝑡)}

𝑚𝑐 =

Í

9

From equation (1), the total earnings growth is higher in a city if 1) holding the job
mobility rate fixed, workers benefit more from each job movement and each additional yearof experience staying in the same firm, and 2) the job mobility rate is higher if E𝑚𝑐 [Δ𝑤 𝑖𝑐𝑡 ]>𝑠
E𝑠𝑐 [Δ𝑤 𝑖𝑐𝑡 ] or lower if E𝑚𝑐 [Δ𝑤 𝑖𝑐𝑡 ] < E𝑐 [Δ𝑤 𝑖𝑐𝑡 ]. This analysis is akin to the one conducted in

Ozkan et al. (2023), who studied the role of job ladder risks and human capital accumulationin shaping lifetime earnings inequality across U.S. workers.
In Table 1, I show the regressions of all terms in equation (1) on log city population. The
results suggest the following points. First, comparing the slope coefficients of Columns (2)and (5) to the one of Column (1) indicates that the between-firm component and the withinfirm component account for 65.6% and 34.4% of UEGP, respectively. This decomposition
result should not be interpreted as a comparison of the levels of the between-firm andwithin-firm components. Rather, it speaks to the gradient of the two components withrespect to city population and their contributions to the total earnings-growth to population
gradient. Second, workers in larger cities do not move between jobs more or less frequently
than workers in smaller cities, as shown by the small and insignificant slope estimate in
Column (3). Using NLSY79 data, Martellini (2019) also finds that job-to-job mobility rates
are similar in U.S. cities with different sizes. Third, the city-size gradients of between-firm
and within-firm components are mostly accounted for by differences in earnings growthwhen moving between firms and when staying in a firm. Coefficients in Columns (4)and (7) imply that doubling city population is associated with a 0.50 percentage point
higher earnings growth from each job-to-job movement and a 0.04 percentage point higher
earnings growth from one additional year of experience staying in the same firm.
The positive correlation between city population and the gains from job-to-job movement
yet population-invariant job mobility rates can be better understood in the context of alabor search model (e.g. Petrongolo and Pissarides (2006)). In larger cities, workers receive
more frequent offers due to lower search frictions, and the offers are drawn from a betterwage offer distribution. However, the wage distribution of employed workers is also betterin large cities. Therefore, conditional on receiving an offer, the probability of accepting theoffer is lower in larger cities, as workers are willing to wait longer until they receive anoffer to make a favorable move. This can explain the greater gains from each job-to-job
movement but not more frequent movements in larger cities.

10

3.3

Understanding the spatial variation in within-firm earnings growth

In this section, I investigate the role of firm heterogeneity in fostering faster earnings
growth in larger cities. Workers could learn faster in larger cities due to the spatial sortingof firms and more productive firms being better learning environments (Gregory, 2020).
They could also benefit more from knowledge spillovers in the denser urban environment(Atkin et al. (2022)), which is an important micro-foundation of the agglomeration economy(Duranton and Puga, 2004).12
To disentangle the two, I use a sample of within-firm stayers to regress individual
earnings growth on log city populations while controlling for a set of industry dummies
and firm characteristics. The set of firm-level controls includes log firm size, firm FE, andmean worker FE. The fixed effects are estimated from the empirical two-way fixed effects
AKM equation Abowd et al. (1999), and I follow Bonhomme et al. (2019) to group firms into
10 clusters. Controlling mean worker FE allows examining the learning from co-workers
mechanism explored in Jarosch et al. (2021). I also include interactions of firm characteristics
and log city population to test the complementarity between city-level and firm-level
environments. For example, workers may learn more from higher-skilled coworkers. Better
urban amenities are likely to strengthen the learning benefits by encouraging more social
interactions outside the workplace.
The results are shown in Table 2. First, I find that controlling industry dummies does not
significantly affect the slope estimate, suggesting a minor role played by industrial structure.
Second, all three firm characteristics included in Columns (3), (5), and (7) are significantly
and positively correlated with earnings growth. At the same time, including these firm
controls dramatically decreases the slope estimates of the city population. Controlling
only log firm size decreases the city-size gradient almost to zero. This suggests a central
role of firm heterogeneity in driving human capital accumulation differences across cities.
Lastly, I find evidence of complementarity between firms and cities in facilitating humancapital accumulation. This can be seen from the significant and positive estimates of theinteraction terms of firm characteristics and city population in Columns (4), (6), and (8). I
include all three firm controls in Column (9) and their interactions with the city population
12Another possibility is that employed workers in larger cities receive more competing outside offers
which they leverage to bargain for higher wages with their current employer (Postel-Vinay and Robin, 2002).
It can be viewed as a city-wide benefit not restricted to the firm by which a worker is employed.

11

Table 2: The role of firm for within-firm stayers’ earnings growth across cities
Dep. Var.:

Log Pop

Earnings growth
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

0.06
(0.04)

0.05(0.04)

0.01(0.03)−0.08(0.05)

0.04(0.04)−0.20∗∗∗(0.04)

0.02(0.04)

0.13∗∗(0.06)-0.02(0.03)-0.05(0.09)

0.13∗∗∗(0.02)−0.19(0.14)

0.09∗∗∗(0.02)-0.08(0.15)Log Firm Size

0.02∗∗(0.01)

× Log Pop

0.01
(0.01)
2.24∗∗∗(0.32)

Firm FE−3.43∗∗∗(1.26)−3.43∗∗∗(0.28)

0.43∗∗∗(0.10)

× Log Pop

0.09
(0.12)
2.16∗∗∗(0.16)

Avg. Worker FE−0.19(0.73)

3.55∗∗∗(0.15)

0.18∗∗∗(0.06)

× Log Pop

Industry FE

N

Y

Y

Y

Y

Observations

Y−4.62∗∗∗(1.48)

YY

2.24∗∗∗(0.92)
0.10(0.07)YY

27,859,100

Notes: All regressions in this table use the sample of within-firm stayers. Firm size refers to the number ofworkers in the firm each year. Worker and firm FEs are estimated from a two-way FE earnings equation.
I follow Bonhomme et al. (2019) and group firms into 10 clusters based on firms’ empirical earnings
distribution. Average worker FE refers to the average fixed effects of all workers employed by the firmeach year. Robust standard error clustered at the city level is reported. Significance level: ∗ p<0.1; ∗∗ p<0.05;∗∗∗ p<0.01.

in Column (10). Average worker FE is the only positive and significant in Column (10),indicating the predominant importance of learning from co-workers.

4

Heterogeneity analysis

4.1

Urban earnings growth premium for different skilled workers

It has been found that higher-skilled workers benefit more from big city experience
than lower-skilled workers (e.g. De La Roca and Puga (2017) and Baum-Snow et al. (2018)).
I investigate this skill-bias pattern in Table 3. The administrative T1 and T4 data sets do notcontain education information. I use the worker fixed effect from the two-way fixed effect
AKM equation to measure worker skill and I group them into four worker FE quartile
groups. I regress the terms in equation (1) on log city population separately for each12

Table 3: Urban earnings growth premium for different skilled workers
Total

BetweenWithinPanel A: First quartile

Total

BetweenWithinPanel B: Second quartile

Log Pop

0.103(0.079)0.208∗∗∗(0.031)-0.105∗(0.053)Log Pop0.137∗∗∗(0.051)0.117∗∗∗(0.015)0.020(0.041)

Constant−3.065∗∗∗(1.039)−1.982∗∗∗(0.414)-1.083(0.697)

Constant−1.640∗∗(0.672)−1.074∗∗∗(0.192)-0.566(0.543)

0.026

0.406

0.058

R2

0.102

0.502

0.004

0.134∗∗∗

0.077∗∗∗

0.057∗(0.037)(0.009)(0.032)0.171∗∗∗(0.036)0.028∗∗∗(0.008)0.142∗∗∗(0.032)−1.416∗∗∗(0.490)−0.748∗∗∗(0.124)-0.668(0.421)

Constant −1.294∗∗∗(0.484)-0.297∗∗∗(0.102)−0.998∗∗∗(0.420)

0.079

0.297

0.005

R2

0.173

0.241

R2

Panel C: Third quartile
Log Pop
Constant
R2

Panel D: Fourth quartile
Log Pop

0.254

Notes: Workers are binned into four quartile groups based on worker FE estimates from the AKM
equation. The regressions are weighted by the number of worker-year observations in each city-skill
group bin. Robust standard error clustered at the city level is reported. Significance level: ∗ p<0.1;∗∗ p<0.05; ∗∗∗ p<0.01.

worker group.
Table 3 confirms that high-skilled workers benefit more from big-city experiences thanlow-skilled workers, although the slope estimates are not statistically different between
different groups. Zooming into the two earnings components, I find that the big city
benefits heterogeneous-skilled workers in different ways. Higher-skilled workers benefit
more from faster within-firm learning, whereas low-skilled workers benefit more fromthe steeper job ladder. Related to the results in Table 2, this cross-skill difference in therelative between-firm and within-firm contributions is closely related to the higher degreeof worker-firm assortative matching in larger cities. Higher-skilled workers are much
more likely to match with high-productivity firms in a larger city, which fosters greater
within-firm learning and less gains from moving between firms. On the other hand,lower-skilled workers start from lower-rank firms in bigger cities and benefit the most fromclimbing up the steeper job ladder.
A limitation of the fixed effect approach is that the worker fixed effect contains both13

Table 4: Urban earnings growth premium over the life-cycle
Total

BetweenWithinPanel A: Age ≤ 30

Total

BetweenWithinPanel B: Age 31–40

0.428∗∗∗(0.091)0.224∗∗∗(0.041)0.203∗∗∗(0.054)

Log Pop0.104∗∗(0.046)0.126∗∗∗(0.017)−0.022(0.033)

Constant −5.781∗∗∗(1.196)−1.740∗∗∗(0.535)-4.042∗∗∗(0.716)

Constant−1.407∗∗(0.610)−1.029∗∗∗(0.226)0.378(0.440)

0.322

0.179

R2

0.074

0.466

0.007

Log PopR2

0.257

Panel C: Age 41–50

Panel D: Age >50

0.076∗∗

0.062∗∗∗(0.032)
Constant
R2

Log Pop

Log Pop(0.012)0.014(0.025)0.188∗∗∗(0.056)

0.067∗∗∗(0.014)0.122∗∗∗(0.044)−1.055∗∗(0.427)−0.594∗∗∗(0.157)-0.461(0.331)

Constant −2.414∗∗∗(0.740)-0.915∗∗∗(0.179)−1.498∗∗∗(0.580)

0.079

0.297

0.005

R2

0.271

0.106

0.152

Notes: The regressions are weighted by the number of worker-year observations in each city-age groupbin. Robust standard error clustered at the city level is reported. Significance level: ∗ p<0.1; ∗∗ p<0.05;∗∗∗ p<0.01.

innate ability and accumulated human capital. For example, a worker with low innate
ability and more years of experience in a large city may be identified as a high-skilledworker. Hence, the fourth quartile group constructed by ranking worker FEs includes
workers with lower innate abilities in large cities and excludes workers with higher innate
abilities in smaller cities. If the UEGP positively correlates with workers’ innate abilities,
then the results in Table 3 would understate such a skill-biased effect.

4.2

Urban earnings growth premium over the life-cyclein Table 4, I examine the UEGP in different stages during the life cycle. I find that workers
benefit more from working in big cities when young.13 This aligns with Baum-Snow andPavan (2012)’s findings that workers tend to move into bigger cities when young and leavefor smaller cities later. This migration pattern maximizes workers’ lifetime real income bytaking advantage of faster learning in bigger cities when younger and cheaper housing
13Recall that I already control for the average earnings-age profile when I construct the city average
earnings growth terms.

14

Table 5: Urban earnings growth premium for between-city migrants and within-city stayers
Components:

Total

Between-firm(1)Within-firm

Total

𝑚𝑐

E𝑚
𝑐 [Δ𝑤 𝑖𝑐𝑡 ]

Total

𝑠𝑐

E𝑠𝑐 [Δ𝑤 𝑖𝑐𝑡 ]

(2)

(3)

(4)

(5)

(6)

(7)

Panel A: Between-city migrants
Log Pop0.459∗∗∗(0.105)0.271∗∗∗(0.043)0.004(0.003)1.210∗∗∗(0.197)0.188∗∗(0.078)−0.004(0.003)0.227∗∗(0.098)Constant−3.894∗∗∗(1.336)−2.152∗∗∗(0.545)0.160∗∗∗(0.040)−9.317∗∗∗(2.495)−1.742∗(0.985)0.840∗∗∗(0.040)−2.046(1.248)

0.229

0.384

0.026

0.372

0.084

0.026

0.077

R2

Panel B: Within-city stayers
Log Pop0.170∗∗∗(0.044)

0.108∗∗∗(0.014)0.001(0.001)0.759∗∗∗(0.108)0.061∗(0.032)−0.001(0.001)0.071∗(0.038)Constant−2.314∗∗∗(0.575)−1.067∗∗∗(0.181)0.123∗∗∗(0.018)−7.391∗∗∗(1.431)−1.246∗∗∗(0.419)0.877∗∗∗(0.018)−1.449∗∗∗(0.508)

0.191

0.493

0.015

0.434

0.055

0.015

0.050

R2

Notes: Panel A uses the movers sample and Panel B uses the stayers sample. The regressions are weighted
by the number of worker-year observations in each city. Robust standard error clustered at the city levelis reported. Significance level: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

in smaller cities when older. Moreover, the city-size premium of within-firm growthdiminishes over the life-cycle, albeit resurging after the age of 50. Given the importance offirms in explaining within-firm growth, this result implies that the match quality of theinitial employer has persistent impacts on a worker’s life-cycle earnings trajectory. The
resurgence after 50 may reflect slower decreases in hours worked as workers approachretirement in larger cities.

4.3

Urban earnings growth premium for between-city migrants

Lastly, I compare the urban earnings growth premium of between-city movers andwithin-city stayers. As described in Section 2, I construct a movers sample keeping workers
who move only once in the sample period and the five first years after a worker moves
to a new city; I construct a stayers sample using workers who have stayed in the same city
throughout the sample period. I regress the terms in equation (1) on log city population15

separately for the two samples.
A comparison between results in Table 5 suggests that between-city migrants benefit
more from an additional year of big-city experience. The elasticity estimate in Column (1)for migrants is 2.7 times as high as the one for stayers. This is driven by being able to moveto higher-paying firms, shown in Column (4), and learning faster within a job, shown inColumn (7). The elasticity of the job-to-job mobility rate to the city population is also small
and insignificant, showing that the migrants do not change jobs more frequently in big
cities.
Many empirical studies have exploited between-city movers for causal estimates ofthe static and dynamic urban premium. The empirical finding of greater returns of
big-city experiences for migrants provides a cautionary note for using the movers design.
If the movers expect greater long-term returns from large city experiences, they may
trade-off in the short term by moving to below-average firms when they migrate. Then, theearnings changes associated with the between-city movement could understate the static
earnings gain. Moreover, movers and stayers exhibit very different earnings-experience
profiles. Empirical estimates that do not distinguish between these two may mask massive
underlying heterogeneity in the returns to big-city experience, potentially stemming fromworker heterogeneity beyond a uni-dimensional vertical skill difference.

5

Conclusion
In this paper, I utilize panel data covering the universe of Canadian workers to investigate

the source of the urban earnings growth premium. Specifically, I find the between-firm andwithin-city components each account for 65.6% and 34.4% of the greater returns to big-cityexperience, respectively. Greater job mobility benefits in big cities are due to greater average
gains from every job-to-job movement, but not higher job-to-job mobility rates. Fasterwithin-firm learning in big cities is mostly explained by spatial firm heterogeneity. Workers
have greater earnings growth in high-wage, larger firms, and such firms systematically sort
into larger cities. The returns to big city experience also vary by worker skill and different
stages in the life cycle. Migrants enjoy greater growth benefits from moving to big cities
than the stayers.
The findings that a worker’s earnings growth depends on her own skill and the16characteristics of her employer also echo the findings of Hong (2024). Both higher earnings
and faster earnings growth in larger cities are tightly related to the systematic sorting ofworkers and firms across space. Therefore, it is crucial to account for worker and firm
heterogeneity when studying both static and dynamic agglomeration effects.
The distinction between the two growth components is important for macroeconomic
studies that investigate the aggregate implications of local knowledge spillovers and humancapital accumulation. Such studies typically measure human capital growth differences
across cities using city-level average earnings growth (e.g. Crews (2023) and Duranton andPuga (2023)). My findings suggest that neglecting the job ladder benefits of big cities willoverstate the aggregate gains from local knowledge spillovers.
The empirical result highlighting the central role of firm heterogeneity in the spatial
earnings growth differential has important policy implications. The local compositionof firms not only matters for the earnings distribution of a city at any point in time but
more importantly shapes the evolution of the distribution in the longer run. Therefore,
policymakers should consider the long-term gains, in addition to the short-term gains
from job creation and wage increases, when designing place-based incentives to attract
high-productivity firms to economically distressed regions.

17

References
Abowd, J. M., F. Kramarz, and D. N. Margolis (1999): “High Wage Workers and High
Wage Firms,” Econometrica, 67, 251–333.
Atkin, D., M. K. Chen, and A. Popov (2022): “The Returns to Face-to-Face Interactions:
Knowledge Spillovers in Silicon Valley,” Working Paper 30147, National Bureau of
Economic Research.
Bacolod, M., B. S. Blum, and W. C. Strange (2009): “Skills in the city,” Journal of Urban
Economics, 65, 136–153.
Baum-Snow, N., M. Freedman, and R. Pavan (2018): “Why has urban inequality increased?”
American Economic Journal: Applied Economics, 10, 1–42.
Baum-Snow, N., N. Gendron-Carrier, and R. Pavan (2023): “Local productivity spillovers,”
Working paper.
Baum-Snow, N. and R. Pavan (2012): “Understanding the City Size Wage Gap,” Review of
Economic Studies, 79, 88–127.
——— (2013): “Inequality and City Size,” Review of Economics and Statistics, 95, 1535–1548.
Bilal, A. (2023): “The Geography of Unemployment,” Quarterly Journal of Economics, 138,
1507–1576.
Bleakley, H. and J. Lin (2012): “Thick-market effects and churning in the labor market:
Evidence from US cities,” Journal of urban economics, 72, 87–103.
Bonhomme, S., T. Lamadon, and E. Manresa (2019): “A Distributional Framework for
Matched Employer Employee Data,” Econometrica, 87, 699–739.
Card, D., J. Heining, and P. Kline (2013): “Workplace Heterogeneity and the Rise of West
German Wage Inequality,” Quarterly Journal of Economics, 128, 967–1015.
Card, D., J. Rothstein, and M. Yi (2023): “Location, Location, Location,” Working Paper
31587, National Bureau of Economic Research.
Crews, L. G. (2023): “A dynamic spatial knowledge economy,” .
18Dauth, W., S. Findeisen, E. Moretti, and J. Suedekum (2022): “Matching in Cities,” Journalof the European Economic Association, 20, 1478–1521.
Davis, D. R. and J. I. Dingel (2019): “A Spatial Knowledge Economy,” American Economic
Review, 109, 153–70.
D’Costa, S. and H. G. Overman (2014): “The urban wage growth premium: Sorting or
learning?” Regional Science and Urban Economics, 48, 168–179.
De la Roca, J., G. I. Ottaviano, and D. Puga (2023): “City of dreams,” Journal of the European
Economic Association, 21, 690–726.
De La Roca, J. and D. Puga (2017): “Learning by Working in Big Cities,” Review of Economic
Studies, 84, 106–142.
Diamond, R. (2016): “The Determinants and Welfare Implications of US Workers’ Diverging
Location Choices by Skill: 1980-2000,” American Economic Review, 106, 479–524.
Duranton, G. and D. Puga (2004): “Micro-Foundations of Urban Agglomeration
Economies,” Handbook of Regional and Urban Economics, 4, 2063–2117.
——— (2023): “Urban growth and its aggregate implications,” Econometrica, 91, 2219–2259.
Eckert, F., M. Hejlesen, and C. Walsh (2022): “The return to big-city experience: Evidence
from refugees in Denmark,” Journal of Urban Economics, 130, 103454.
Ellison, G., E. L. Glaeser, and W. R. Kerr (2010): “What causes industry agglomeration?
Evidence from coagglomeration patterns,” American Economic Review, 100, 1195–1213.
Gaubert, C. (2018): “Firm Sorting and Agglomeration,” American Economic Review, 108,
3117–53.
Glaeser, E. L. and D. C. Mare (2001): “Cities and skills,” Journal of Labor Economics, 19,
316–342.
Gregory, V. (2020): “Firms as learning environments: Implications for earnings dynamics
and job search,” FRB St. Louis Working Paper.

19

Guvenen, F., F. Karahan, S. Ozkan, and J. Song (2021): “What Do Data on Millions of US
Workers Reveal about Lifecycle Earnings Dynamics?” Econometrica, 89, 2303–2339.
Herkenhoff, K., J. Lise, G. Menzio, and G. M. Phillips (2018): “Production and learning inteams,” Tech. rep., National Bureau of Economic Research.
Hong, G. (2024): “Two-Sided Sorting of Workers and Firms: Implications for Spatial
Inequality and Welfare,” Tech. rep., Working Paper.
Jarosch, G., E. Oberfield, and E. Rossi-Hansberg (2021): “Learning from coworkers,”
Econometrica, 89, 647–676.
Kline, P. and E. Moretti (2013): “Place Based Policies with Unemployment,” American
Economic Review, 103, 238–43.
Ma, X., A. Nakab, D. Vidart, et al. (2021): Human capital investment and development: The
role of on-the-job training, University of Connecticut, Department of Economics.
Martellini, P. (2019): “The City-Size Wage Premium: Origins and Aggregate Implications,”
Tech. rep., Working Paper.
Ozkan, S., J. Song, and F. Karahan (2023): “Anatomy of lifetime earnings inequality:
Heterogeneity in job-ladder risk versus human capital,” Journal of Political Economy
Macroeconomics, 1, 506–550.
Petrongolo, B. and C. Pissarides (2006): “Scale effects in markets with search,” The
Economic Journal, 116, 21–44.
Porcher, C., H. Rubinton, and C. Santamaría (2023): “JUE insight: The Role of Establishment Size in the City-Size Earnings Premium,” Journal of Urban Economics, 136,
103556.
Postel-Vinay, F. and J.-M. Robin (2002): “Equilibrium wage dispersion with worker andemployer heterogeneity,” Econometrica, 70, 2295–2350.
Sanders, C. and C. Taber (2012): “Life-cycle wage growth and heterogeneous humancapital,” Annu. Rev. Econ., 4, 399–425.

20Appendix A

Additional Tables and Figures
Table A.1: Summary statistics

Sample:Baseline

Mover

StayerAll

Big City

Small City

Mean change log earnings (×100)
Std. change log earnings (×100)
Mean log earnings
Std. log earnings
Mean age
Std. age

1.05
47.59
10.47
0.82
42.50
9.61

1.42
47.85
10.48
0.83
42.47
9.51

0.68
47.31
10.46
0.82
42.53
9.72

3.93
54.30
10.56
0.84
38.49
8.81

0.95
47.30
10.47
0.82
42.68
9.60

Share in Firm Emp >50

47.5%

54.4%

40.6%

45.6%

47.7%

Employment share by sector:
Construction
Manufacturing
Retail and wholesale
Transportation
Tradable services
Admin, education, healthcare
Entertainment and hospitality

8.9%
16.2%
19.8%
6.1%
32.0%
6.6%
10.4%

6.9%
15.9%
19.7%
6.4%
34.5%
7.0%
9.6%

10.8%
16.5%
19.9%
5.8%
29.6%
6.3%
11.2%

8.5%
13.6%
18.4%
5.8%
37.6%
6.4%
9.6%

8.9%
16.3%
19.9%
6.1%
31.8%
6.6%
10.4%

32563000
7640000

16279000
3799000

16284000
3940000

54800022400031280000
7304000

Number of worker-year observations
Number of workers

Notes: See Section 2 for a description of the sample construction procedure. Big city refers to the largest
three cities in Canada, and small city refers to the remaining ones.

21

Table A.2: Urban earnings growth premium by sector
Sector

Total

BetweenWithinConstruction

0.166∗(0.094)0.133∗∗(0.058)0.074∗∗(0.035)0.259∗∗∗(0.074)0.163∗∗∗(0.035)0.179∗∗(0.068)0.121∗∗(0.060)0.078∗(0.046)0.080∗∗∗(0.027)0.122∗∗∗(0.020)0.092∗∗(0.038)0.095∗∗∗(0.008)0.050(0.053)0.103∗∗∗(0.026)0.088(0.056)0.053(0.047)−0.048∗∗(0.022)0.166∗∗∗(0.056)
0.068∗∗(0.034)0.129∗∗(0.052)0.018(0.042)

Manufacturing
Retail and wholesale
Transportation
Tradeable services
Admin, education and healthEntertainment and hospitality

Notes: The regressions are weighted by the number of worker-year observations in each city-sector bin.
Robust standard error clustered at the city-sector level. Significance level: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

22

Figure A.1: Unemployment rate and city population

Slope = 0.194 (0.157)

Unemployment Rate

9

8

7

6

5

100 k

300 k

1m

3m

City Population

Notes: The figure plots the unemployment rate against the city population for 33 Census Metropolitan
Areas of 2016. Data source: StatsCan Table 14-10-0096-01. The point estimate with its standard error of
the population-weighted OLS regression is reported in the figure.

23