The Comparative Advantage of Cities∗
Donald R. Davis†
Columbia University and NBER

Jonathan I. Dingel‡
Columbia University

June 2013
Preliminary and Incomplete

Abstract
What determines the distributions of skills, occupations, and industries acrosscities? We develop a theory to jointly address these fundamental questions about
the spatial organization of economies. Our model incorporates a system of cities, theirinternal urban structures, and a high-dimensional theory of factor-driven comparativeadvantage. It predicts that larger cities will be skill abundant and specialize in skillintensive activities according to the monotone likelihood ratio property. We test themodel using data on 270 US metropolitan areas, 3 to 9 educational categories, 22 occupations, and 21 manufacturing industries. The results provide support for our theory’s
predictions.

∗

We thank Corinne Low, Joan Monras, Keeyoung Rhee, Bernard Salanie, Daniel Sturm, and attendeesof the Columbia international trade colloquium and international economics workshop for helpful comments.
We thank Yuxiao Huang and especially Antonio Miscio for research assistance. Dingel acknowledges financial support from the Institute for Humane Studies and the Program for Economic Research at Columbia
University.
†
drdavis@columbia.edu‡
jid2106@columbia.edu

11Introduction

The distributions of skills, occupations, and industries vary substantially and systematically
across US cities. Figures 1 through 3 illustrate this with three selected examples for each.
• Figure 1 plots the population of three educational attainment categories against total
metropolitan area population.1 The left panel plots the data; the right panel plots a
locally weighted regression for each category. While each educational category’s population rises with metropolitan population, the relative levels also exhibit a systematic
relationship with city size. Comparing elasticities, the population with a bachelor’s
degree rises with city size faster than the population of college dropouts, which in turnrises faster than the population of high-school graduates.

5
Log skill population (deviation from mean)
-1
0
1
2
3
4
-2

-2

Log skill population (deviation from mean)
-1
0
1
2
3
4

5

Figure 1: Populations of three educational groups across US metropolitan areas

10

12
14
16
MSA log population

18

10

12
14
16
MSA log population

High school graduate

High school graduate

College dropout

College dropout

Bachelor's degree

Bachelor's degree

18

Data source: 2000 Census of Population microdata via IPUMS-USA• Figure 2 plots metropolitan area employment in three occupational categories.2 Computer and mathematical employment rises with city size faster than office and administrative employment, which in turn rises faster than installation, maintenance andrepair employment. These sectors also differ in their employee characteristics. Nationally, the average individual in computer and mathematical occupations has about
two more years of schooling than the average individual in office and administrative
support and three more years than those in installation, maintenance, and repair.
1

We use the terms cities and metropolitan areas interchangeably, as is customary in the literature. These
three educational groups comprise about 70 percent of the employed metropolitan population (see Table 1).
2
The occupations are SOC 49, 43, and 15 in the 2000 Occupational Employment Statistics data.

2

6
Log employment (deviation from mean)
-2
-1
0
1
2
3
4
5
-3
-4

-4

-3

Log employment (deviation from mean)
-2
-1
0
1
2
3
4
5

6

Figure 2: Employment in three occupations across US metropolitan areas

10

12
14
16
MSA log population

18

10

12

14
16
MSA log population

18

Installation, maintenance, and repair

Installation, maintenance, and repair

Office and administrative support

Office and administrative support

Computer and mathematicalComputer and mathematical

Data source: Occupational Employment Statistics 2000

• Figure 3 plots employment in three manufacturing industries.3 Employment in computer and electronic products rises with city size faster than machinery, which in turnrises faster than wood products. On average, computer and electronic employees haveabout two more years of education than wood products employees.

65
Log employment (deviation from mean)-5 -4 -3 -2 -1 0 1 2 3 4
-6
-7

-7

-6

Log employment (deviation from mean)-5 -4 -3 -2 -1 0 1 2 3 4

5

6

Figure 3: Employment in three manufacturing industries across US metropolitan areas

10

12
14
16
MSA log population

18

10

12

14
16
MSA log population

18

Wood products

Wood products

Machinery

MachineryComputer and electronic products

Computer and electronic products

Data source: County Business Patterns 2000
3

The industries are NAICS 321, 333, and 334 in the 2000 County Business Patterns data. Employment
levels cluster at particular values due to censored observations. See appendix C describing the data.

3

Together, these three figures suggest that larger cities are skill abundant and specialize inskill-intensive activities. Explaining these patterns involves fundamental questions about the
spatial organization of economic activity. What determines the distribution of skills acrosscities? What determines the distribution of occupations and industries across cities? How
are these two phenomena interrelated? In this paper, we develop a theory describing thecomparative advantage of cities that predicts such a pattern of skills and sectors in a manner
amenable to empirical investigation.
As we describe in section 2, prior theories describing cities’ sectoral composition haveoverwhelmingly focused on the polar cases in which cities are either completely specialized“industry towns” or perfectly diversified hosts of all economic activities (Helsley and Strange,2012). Yet Figures 2 and 3 make clear both that reality falls between these poles and that sectoral employment shares are systematically related to cities’ sizes. In this paper, we integrate
modern trade theory with urban economics by introducing a spatial-equilibrium model inwhich the comparative advantage of cities is jointly governed by the comparative advantage
of individuals and their locational choices. Our theory both describes the intermediate casein which cities are incompletely specialized and relates the pattern of specialization to cities’observable characteristics. It makes strong, testable predictions about the distributions ofskills and sectors across cities that we take to the data.
Section 3 introduces our model of a system of cities with heterogeneous internal geographies. Cities are ex ante homogeneous, so cross-city heterogeneity is an emergent outcomeof the choices made by freely mobile individuals. Agglomeration economies make cities withlarger, more skilled populations exhibit higher total factor productivity (TFP). Locations
within cities are heterogeneous and more desirable locations are relatively scarce, as is customary in land-use models (Fujita and Thisse, 2002, Ch 3). These cities are populated byheterogeneous individuals with a continuum of skill types, and these individuals may be
employed in a continuum of sectors. Comparative advantage causes more skilled individuals
to work in more skill-intensive sectors, as in Sattinger (1975), Costinot (2009), and Costinot
and Vogel (2010). There is a complementarity between individual income and locational attractiveness, so more skilled individuals are more willing to pay for more attractive locations
and occupy these locations in equilibrium, as in the differential rents model of Sattinger(1979).
In equilibrium, agglomeration, individuals’ comparative advantage, and heterogeneity
across internal locations within cities combine to deliver a rich set of novel predictions.
Agglomeration causes larger cities to have higher TFP, which makes a location within a larger
city more attractive than a location of the same innate desirability within a smaller city. For
example, the best location within a larger city is more attractive than the best location
within a smaller city due to the difference in TFP. Since more skilled individuals occupy
more attractive locations, larger cities are skill abundant. The most skilled individuals in
the population live only in the largest city and more skilled individuals are more prevalentthan less skilled individuals in larger cities, consistent with the pattern shown in Figure 1. By
individuals’ comparative advantage, the most skill-intensive sectors are located exclusively
in the largest cities and larger cities specialize in the production of skill-intensive output.
More skill-intensive sectors exhibit higher population elasticities of sectoral employment, assuggested in Figures 2 and 3. Our model therefore predicts an urban hierarchy of skills andsectors.
4

We examine the model’s predictions about the spatial distribution of skills and sectors
across US cities using data from the 2000 Census of Population, County Business Patterns,
and Occupational Employment Statistics described in section 4. We use two empirical approaches. The first involves regression estimates of the population elasticities of educational,
occupational, and industrial populations akin to those shown in Figures 1 through 3. The
second involves pairwise comparisons governed by the monotone likelihood ratio property,
as per Costinot (2009).4
Section 5 reports the results, which provide support for our model’s predictions about the
spatial pattern of skills and sectors. Characterizing skills in terms of three or nine educationalgroups, we find that larger cities are skill abundant. Among US-born individuals, cities’skill distributions typically exhibit the monotone likelihood ratio property.5 Characterizing
sectors in terms of 21 manufacturing industries or 22 occupational categories, we find thatlarger cities specialize in skill-intensive sectors. While sectors do not exhibit the monotone
likelihood ratio property as reliably as skills, there is systematic variation in cities’ sectoral
distributions that is consistent with the novel predictions of our theory.
In short, when mobile individuals optimally choose locations and sectors, larger cities
will have more skilled populations and thereby comparative advantage in skilled activities.
These features are consistent with US data.

2

Related literature

Our contributions are related to a diverse body of prior work. Our focus on high-dimensional
labor heterogeneity is related to recent developments in labor and urban economics. Our theoretical approach integrates elements from the systems-of-cities literature, land-use theory,and international trade. Our model yields estimating equations and pairwise inequalities describing the comparative advantage of cities that are related to prior reduced-form empirical
work in urban economics, despite a contrast in theoretical underpinnings.
Our theory describes a continuum of heterogeneous individuals. A large share of systemsof-cities theories describe a homogeneous population (Abdel-Rahman and Anas, 2004). Most
previous examinations of heterogeneous labor have only described two skill levels, typicallylabeled skilled and unskilled.6 To describe greater heterogeneity, we assume a continuumof skills, like Behrens, Duranton, and Robert-Nicoud (2010) and Davis and Dingel (2012).7
Understanding the distribution of skills across cities with more than two types is valuable
for at least three reasons. First, a large literature in labor economics has described impor4

The distributions fc (σ) and fc0 (σ) exhibit the monotone likelihood ratio property if, for any σ > σ 0 ,fc (σ)
fc (σ 0 )fc0 (σ) ≥ fc0 (σ 0 ) .
5Relative to our theory, foreign-born individuals with less than a high-school education tend to disproportionately locate in large US cities. Data from 1980, when foreign-born individuals were a substantiallysmaller share of the US population, suggest this reflects particular advantages that large cities offer foreignborn individuals rather than a general tendency for the unskilled to locate in large cities. See section 5.1.2.
6
We focus on theories in which labor is heterogeneous in some asymmetric sense (e.g. more skilledindividuals have absolute advantage in tasks or more skilled individuals generate greater human-capital
spillovers). There are also models describing matching problems, such as Helsley and Strange (1990) andDuranton and Puga (2001), in which labor is heterogeneous in a horizontal characteristic.
7
Eeckhout, Pinheiro, and Schmidheiny (2011) describe a model with three skill types.

5tant empirical developments such as wage polarization, job polarization, and simultaneouschanges in between- and within-group inequality that cannot be explained by a model with
two homogeneous skill groups (Acemoglu and Autor, 2011). Second, these developments
have counterparts in cross-city variation in inequality and skill premia (Baum-Snow andPavan, 2011; Davis and Dingel, 2012). Third, we document systematic patterns in the crosscity distribution of skills at high levels of disaggregation, which suggests that individuals
within broad skill categories are imperfect substitutes.8
Our model is a novel integration of systems-of-cities theory with land-use theory. The
Alonso-Muth-Mills model of a single city describes a homogeneous population of residents
commuting to a central business district (Brueckner, 1987). In that model, higher rents
for locations with shorter commutes equalize utility across locations in equilibrium. When
individuals are heterogeneous and value the rent-distance tradeoff differently, the single
city’s equilibrium rent schedule is the upper envelope of individuals’ bid-rent functions (vonThünen, 1826; LeRoy and Sonstelie, 1983; Fujita and Thisse, 2002, Ch 3). Models of asystem of cities have incorporated the Alonso-Muth-Mills urban structure in which all individuals are indifferent across all locations within a city as a city-level congestion mechanism(Abdel-Rahman and Anas, 2004; Behrens, Duranton, and Robert-Nicoud, 2010). Our novel
contribution is to describe multiple cities with internal geographies when individuals are
not spatially indifferent across all locations.9 The essential idea is that individuals choosing
between living in Chicago or Des Moines simultaneously consider in what parts of Chicago
and what parts of Des Moines they might locate. Though these tradeoffs appear obvious,
we are not aware of a prior formal analysis. Considering both dimensions simultaneously ismore realistic in both the description of the economic problem and the resulting predictedcross-city skill distributions. Since we have a continuum of heterogeneous individuals, we
obtain equilibrium rent schedules that are integrals rather than upper envelopes of a discrete
number of bid-rent functions.10
Our model belongs to a long theoretical tradition describing factor-supply-driven comparative advantage, dating from the Heckscher-Ohlin theory formalized by Samuelson (1948). In
international contexts, theorists have typically taken locations’ factor supplies as exogenously
endowed. Since individuals are mobile across cities, our theory endogenizes cities’ factor supplies while describing how the composition of output is governed by comparative advantage.
Our approach to comparative advantage with a continuum of factors and a continuum ofsectors follows a large assignment literature and is most closely related to the recent workof Costinot (2009) and Costinot and Vogel (2010).11 While these recent papers assume thatcountries’ factor endowments exhibit the monotone likelihood ratio property, we obtain the
result that cities’ skill distributions exhibit this property as an equilibrium outcome. Thus,
from a theoretical perspective, cities within a country constitute a natural setting to examine
these theories of comparative advantage. Moreover, the assumption of a common production
8
A long line of empirical work describes cross-city variation in skill distributions in terms of the share ofresidents who have a college degree (Glaeser, 2008). Most closely related to our work is Hendricks (2011),
who finds a weak relationship between cities’ industries and college shares.
9
In order to tractably characterize multiple cities with internal geographies and heterogeneous agents, we
neglect the business-vs-residential land-use problem studied by Lucas and Rossi-Hansberg (2002)10
Our continuum-by-continuum approach to a differential rents model is in the spirit of Sattinger (1979).
11
Sattinger (1993) surveys the assignment literature.

6technology is likely more appropriate within than between economies, and data from a single
economy are likely more consistent and comparable than data combined across countries.
The Heckscher-Ohlin model has been the subject of extensive empirical investigation ininternational economics. A pair of papers describe regional outputs using this framework.
Davis and Weinstein (1999) run regressions of regional outputs on regional endowments, employing the framework of Leamer (1984), but they abstract from the issue of labor mobility
across regions. Bernstein and Weinstein (2002) consider the two-way links between endowments and outputs, concluding that if we know regions’ outputs, we know with considerable
precision the inputs used, but not vice versa. For these reasons, traditional Heckscher-Ohlinmodels did not appear a promising way to explain regional differences in sectoral composition.
Our theory predicts systematic variation in sectoral composition in the form of an urban
hierarchy of sectors. Prior systems-of-cities theories have overwhelmingly described polarized
sectoral composition: specialized cities that have only one tradable sector and perfectly
diversified cities that have all the tradable sectors (Abdel-Rahman and Anas, 2004; Helsley
and Strange, 2012). A recent exception is Helsley and Strange (2012), who examine whether
the equilibrium level of coagglomeration is efficient. While Helsley and Strange (2012) makeminimal assumptions in order to demonstrate that Nash equilibria are generically inefficientwhen there are interindustry spillovers, we make strong assumptions that yield testable
implications about the distribution of sectoral activity across cities.
Our model’s equilibrium exhibits a hierarchy of cities and sectors, as larger cities produce
a superset of the goods produced in smaller cities. Models in central place theory, dating fromChristaller (1933) through Hsu, Holmes, and Morgan (2013), have attributed this hierarchy
property to the interaction of industry-specific scale economies and geographic market accessbased on the distance between firms located in distinct city centers. It is interesting that ourmodel yields the hierarchy property in the absence of both. Our theory links the hierarchyof sectors to a hierarchy of skills shaped by the internal geography of cities, neither of whichhave been considered in central place theory.
A recent empirical literature has demonstrated significant agglomeration and coagglomeration of industries relative to the null hypothesis of locations being (uniformly) randomly
assigned in proportion to local population (Ellison and Glaeser, 1997; Duranton and Overman, 2005; Ellison, Glaeser, and Kerr, 2010). Our model’s predictions are consistent withthese findings. Since our theory says that sectors are ranked in terms of their relative employment levels, at most one sector could exhibit employment proportionate to total population.
All other sectors will exhibit geographic concentration. Similarly, since sectors more similarin skill intensity will exhibit more similar relative employment levels, the cross-city distribution of sectoral employment will be consistent with skill-related coagglomeration. We obtainthese results in the absence of industry-specific scale economies and industry-pair-specificinteractions or spillovers.
Our empirical work follows directly from our model’s predictions about the cross-city
distribution of sectoral activity relating cities’ and sectors’ characteristics. There is a small
empirical literature describing variation in cities’ sectoral composition, but this work has notbeen tightly tied to theory. This is likely because theories describing specialized or perfectly
diversified cities provide limited guidance to empirical investigations of data that fall between the extremes. Holmes and Stevens (2004) survey the spatial distribution of economic
7

activities in North America. In examining the empirical pattern of specialization, they showthat agriculture, mining, and manufacturing are disproportionately in smaller cities, while
finance, insurance, real estate, professional, and management activities are disproportionately in larger cities. However, they do not reference a model or theoretical mechanismthat predicts this pattern to be the equilibrium outcome. Seminal work by Vernon Henderson explores theoretically and empirically the relationship between city size and industrial
composition (Henderson, 1991). Henderson (1974) theoretically describes the polar cases of
specialized and perfectly diversified cities (Helsley and Strange, 2012), while our model predicts incomplete industrial specialization. Henderson has argued that localization economies
link cities’ and industries’ sizes, while our theory relies on urbanization economies and individuals’ comparative advantage.12 Despite these contrasts, our theory yields estimating
equations for the population elasticities of sectoral employment that are closely related to
the reduced-form regressions of employment shares on population that Henderson (1983) estimated for a few select industries. Our theory provides an explicit microfoundation for these
regressions for an arbitrary number of sectors. Moreover, it predicts that we can order theseelasticities by skill intensity. It also describes how to compare the sectoral composition ofgroups of cities ordered by size, nesting the comparison of large and medium-size cities madeby Henderson (1997). While our urbanization-based theory abstracts from the localization
economies emphasized by Henderson, we believe future work should seek to integrate these
distinct approaches.

3

Model

We develop a general-equilibrium model in which L heterogeneous individuals choose a city,
a location within that city, and a sector in which to produce. There are C discrete cities(c ∈ C = {1, . . . , C}), a continuum of skills, and a continuum of sectors. We study theconsequences for city total factor productivity and the cross-city distributions of skills andsectors.

3.1

Preferences, production, and places

Individuals consume a freely traded final good. This final good is the numeraire and producedby combining a continuum of freely traded, labor-produced intermediate goods indexed byσ ∈ Σ. These have prices p(σ) that are independent of location because trade costs are zero.
Locations are characterized by their city c and their (inverse) desirability δ ∈ ∆, so they
have rental prices r(c, δ).
Final-goods producers have a CES production function
Q=

n

−1

B(σ)[Q(σ)]  dσ


o −1

,

(1)

σ∈Σ
12

The literature traditionally distinguishes two types of external economies of scale (Henderson, 1987,
p.929). Localization economies are within-industry, reflecting the scale of activity in that industry in thatlocation. Urbanization economies are general, reflecting the scale of all economic activity in a location.
Beyond scale, Lucas (1988) has stressed the composition of a location’s human capital. The agglomeration
process generating city-level productivities in our theory incorporates both scale and composition effects.

8where the quantity of intermediate good σ is Q(σ),  > 0 is the elasticity of substitution
between intermediates, and B(σ) is an exogenous technological parameter. The profits offinal-goods producers are given byp(σ)Q(σ)dσ.

Π=Q−

(2)

σ∈Σ

Heterogeneous individuals use their labor to produce intermediate goods. There is a massof L heterogeneous individuals with skills ω that have the cumulative distribution function
F (ω) and density f (ω) on support Ω ≡ [ω, ω̄]. The productivity of an individual of skill ωin sector σ at location δ in city c isq(c, δ, σ; ω) = A(c)D(δ)H(ω, σ).

(3)A(c) denotes city-level total factor productivity, which results from agglomeration and istaken as given by individuals. D (δ) reflects the productivity effects of location within thecity, which in a canonical case is the cost of commuting to the central business district.13
We assume that D(δ) is continuously differentiable and D0 (δ) < 0, which is just a normalization that higher-δ locations are less desirable. We assume that H(ω, σ) is strictly
log-supermodular in ω and σ and strictly increasing in ω.14 The former governs comparativeadvantage, so that higher-ω individuals are relatively more productive in higher-σ sectors.15
The latter says that absolute advantage is indexed by ω, so that higher-ω individuals are
more productive than lower-ω individuals in all sectors. Each individual inelastically supplies
one unit of labor, so her income is her productivity times the price of the output produced,
q(c, δ, σ; ω)p(σ).
Locations within each city are heterogeneous, with the desirability of a location indexedby δ ≥ 0. The most desirable location is denoted δ = 0, so higher values of δ denote greater
distance from the ideal location. The supply of locations with desirability greater than δ isS(δ).16 This is a strictly increasing function, since the supply of available locations increases
as one lowers one’s minimum standard of desirability. S(0) = 0, since there are no locations
better than the ideal. We assume S(δ) is twice continuously differentiable. Locations are
owned by absentee landlords who spend their rental income on the final good. The city has
sufficient land capacity that everyone can reside in the city and the least desirable locations
are unoccupied. We normalize the reservation value of unoccupied locations to zero, sor(c, δ) ≥ 0.
Individuals choose their city c, location δ, and sector σ to maximize utility. An individual’s utility depends on their consumption of the numeraire final good, which is their income
13

As written, D(δ) indexes the desirability of the location for its productive advantages, but a closely
related specification makes D(δ) describe a location’s desirability for its consumption value. The production
and consumption interpretations yield very similar results but differ slightly in functional form. For expositional clarity, we use the production interpretation given by equations (3) and (4) in describing the modelin the main text and present the consumption interpretation in appendix A.
14
In R2 , a function H(ω, σ) is strictly log-supermodular if ω > ω 0 , σ > σ 0 ⇒ H(ω, σ)H(ω 0 , σ 0 ) >H(ω, σ 0 )H(ω 0 , σ).
15
We refer to higher-ω individuals as more skilled and higher-σ sectors as more skill-intensive.
16
In the special case of the classical von Thünen model, δ describes physical distance from the central
business district and the supply is S(δ) = πδ 2 .

9

after paying their locational cost:U (ω, c, δ, σ) = A(c)D(δ)H(ω, σ)p(σ) − r(c, δ).

(4)

Denote the endogenous quantity of individuals of skill ω residing in city c at location δ andworking in sector σ by L × f (ω, c, δ, σ).
City-level TFP, A(c), reflects agglomeration gains derived from both population size andcomposition. A(c) is higher when a city contains a larger and more skilled population.
Denote the endogenous quantity of individuals of skill ω residing in city c by L × f (ω, c) =f (ω, c, δ, σ)dδdσ. Total factor productivity isσ∈Σ δ∈∆ A(c) = J Lj(ω)f (ω, c)dω ,

(5)

ω∈Ω

where J(·) is a positive, strictly increasing function and j(ω) is a positive, non-decreasing
function.

3.2

Equilibrium

In a competitive equilibrium, individuals maximize utility, final-good producers and landowners maximize profits, and markets clear. Individual maximize their utility by their choicesof city, location, and sector such thatf (ω, c, δ, σ) > 0 ⇐⇒ {c, δ, σ} ∈ arg max U (ω, c, δ, σ).

(6)Profit maximization by final-good producers yields demands for intermediates
Q(σ) = I p(σ) −
B(σ)

,

(7)

P   where I ≡ L c σ ω δ q(ω, c, δ, σ)p(σ)f (ω, c, δ, σ)dδdωdσ denotes total income and theseproducers’ profits are zero. Profit maximization by absentee landlords engaged in Bertrand
competition causes unoccupied locations to have rental prices of zero,0r(c, δ) × S (δ) − Lf (ω, c, δ, σ)dωdσσ∈Σ= 0 ∀c ∀δ.

(8)

ω∈Ω

Market clearing requires the endogenous quantity of individuals of skill ω residing in city
c at location δ and working in sector σ, L × f (ω, c, δ, σ), to be such that the supply of alocation type is greater than or equal to its demand, the demand and supply of intermediate
goods are equal, and every individual lives somewhere.
0

S (δ) ≥ L
Q(σ) =Xc∈C

Q(σ, c) = L

f (ω, c, δ, σ)dσdω
ω∈Ω

σ∈Σ

c∈C

ω∈Ω

X

(9)

∀c ∀δ



q(c, δ, σ; ω)f (ω, c, δ, σ)dωdδ
δ∈∆

10

∀σ

(10)

f (ω) =X

f (ω, c) =

c∈C



X
c∈C

f (ω, c, δ, σ)dδdσ
σ∈Σ

∀ω

(11)

δ∈∆

A competitive equilibrium is a set of functions Q : Σ → R+ , f : Σ × C × ∆ × Ω → R+ ,A : C → R+ , r : C × ∆ → R+ , and p : Σ → R+ such that conditions (6) through (11) hold.

3.3

An autarkic city

We begin by considering a single city, denoted c, with exogenous population L(c) and skill
distribution F (ω). With fixed population, autarky TFP is fixed by equation (5). We describe
individuals’ choices of sectors and locations to solve for the autarkic equilibrium.
To solve, we exploit the fact that locational and sectoral argument enters individuals’
utility functions separably. Individuals’ choices of their sectors are independent of theirlocational decisions:arg max A(c)D(δ)H(ω, σ)p(σ) − r(c, δ) = arg max H(ω, σ)p(σ)σσ

Taking prices p(σ) as given, define the assignment function M (ω) = arg maxσ H(ω, σ)p(σ)
so that we can write G(ω) ≡ H(ω, M (ω))p(M (ω)).17 By absolute advantage, more skilledindividuals earn higher nominal incomes and G(ω) is a strictly increasing function.18
Individuals’ choices of their locations are related to their sectoral decisions in the sensethat willingness to pay for more desirable locations depends on the skill component of income
G(ω). Within the city, individual choose their optimal location:
max A(c)D(δ)G(ω) − r(c, δ)δCompetition among landlords ensures that the most desirable locations are those occupied,so the least desirable occupied site δ̄(c) ≡ maxδ {δ : f (ω, c, δ, σ) > 0} in a city of population
L(c) is defined by L(c) = S(δ̄(c)). More desirable locations have higher rental prices.
δLemma 1 (Populated locations). In equilibrium, S(δ) = L 0 σ∈Σ ω∈Ω f (ω, c, x, σ)dωdσdx ∀δ ≤
δ̄(c), r(c, δ) is strictly decreasing in δ ∀δ < δ̄(c), and r(c, δ̄(c)) = 0.
Individuals of higher skill have greater willingness to pay for more desirable locations.
∂2
A(c)D(δ)G(ω) < 0 because locational advantages complement individual proThat is, ∂δ∂ωductivity. As a result, in equilibrium higher-ω individuals occupy lower-δ locations.
Lemma 2 (Autarky locational assignments). In autarkic equilibrium, there exists a continuous and strictly decreasing locational assignment function N : ∆ → Ω such that f (ω, c, δ, σ) >0 ⇐⇒ N (δ) = ω, N (0) = ω̄ and N (δ̄(c)) = ω.
17

Lemma 1 of Costinot and Vogel (2010) shows that strict log-supermodularity of H(ω, σ) is sufficient for
M (ω) to be continuous and strictly increasing for any price schedule p(σ).
18Absolute advantage across all sectors is far from necessary. The weaker condition that productivity is∂
increasing in skill at the equilibrium assignments, ∂ωH(ω, M (ω)) > 0, is sufficient.

11

This assignment function is obtained by equating supply and demand of locations: δS(δ) = Lf (ω, c, x, σ)dωdσdx
L(c) − S(δ)−1
⇒ N (δ) = F
L(c)
0

σ∈Σ

ω∈Ω

Given individuals’ equilibrium locations within the city, the schedule of locational rental
prices supporting these assignments comes from combining individuals’ utility-maximizing
decisions and the boundary condition r(c, δ̄(c)) = 0.
Lemma 3 (Autarky locational prices). In autarkic equilibrium, r(c, δ) is continuously dif δ̄(c)ferentiable on δ ≥ 0 and given by r(c, δ) = −A(c) δ D0 (t)G(N (t))dt for δ ≤ δ̄(c).
The properties of interest in a competitive equilibrium are characterized by the assignment functions M and N . In the autarkic equilibrium, more skilled individuals work in moreskill-intensive sectors and live in more desirable locations.
Figure 4 illustrates the familiar von Thünen disc example, which captures many of theimportant properties of the model. The center of the disc is the δ = 0 location and the
supply of locations at least as good as δ is S(δ) = πδ 2 . The radius of the disc, δ̄(c) or the“city limit”, is determined by the total population size such that L(c) = π δ̄(c)2 . The best
available location, δ = 0, is occupied by the most skilled individual, ω̄. The least desirable
occupied location, the edge of the disc, is occupied by the least skilled individual, ω. Rental
prices at the city limit are zero, and rental prices of locations closer to the center of the discare given by integrating the marginal benefit of moving closer to the center as valued by theindividual occupying each location.
Figure 4: Autarky equilibrium for the von Thünen disc
δ̄(c)
ω̄
δ=0

ω
r(c, δ̄(c)) = 0

L(c) = π δ̄(c)2 δ̄(c)r(c, δ) = −A(c)D0 (t)G(N (t))dtδ

3.4

A system of cities

The previous section described a single city with an exogenous population. We now describea system of cities in which these populations are endogenously determined in spatial equilibrium. Take cities’ TFPs, which will be endogenously determined in equilibrium, as given
12

for now and order the cities so that A(C) ≥ A(C − 1) ≥ · · · ≥ A(2) ≥ A(1).19 In autarky, δ
was a sufficient statistic for the attractiveness of a location. Now a location’s attractiveness,
which we denote by γ, depends both on city-level TFP and its innate desirability within thecity.
Definition 1. The attractiveness of a location in city c of desirability δ is γ = A(c)D(δ).
Cities with higher TFP have larger populations. Consider two cities, c and c0 , that differ
in productivity, with A(c) > A(c0 ). The city with greater TFP will have greater population,
L(c) > L(c0 ). If it did not, the least desirable occupied location in city c would be moredesirable than the least desirable occupied location in city c0 , δ̄(c) ≤ δ̄(c0 ), since the supply of
locations, S(δ), is common across cities. Since TFP is also higher in c, this would make theleast attractive occupied location in city c more attractive than the least attractive occupied
location in city c0 , A(c)D(δ̄(c)) > A(c0 )D(δ̄(c0 )). In equilibrium, the least desirable occupied
location in each city has a price of zero, r(c, δ̄(c)) = r(c0 , δ̄(c0 )) = 0, by lemma 1. In thatcase, every individual would agree that living in c at δ̄(c) is strictly better than living in c0
at δ̄(c0 ) (because A(c)D(δ̄(c))G(ω) > A(c0 )D(δ̄(c0 ))G(ω)), which contradicts the definitionof δ̄(c0 ) as an occupied location. So the city with higher TFP must have a larger population.
A smaller city’s locations are a subset of those in a larger city in terms of attractiveness.
For every location in the less populous city, there is a location in the more populous city thatis equally attractive. The location in city c0 of desirability δ 0 is equivalent to a location δ incity c, given by A(c)D(δ) = A(c0 )D(δ 0 ). The equally attractive location in the larger city has
higher TFP but lower innate desirability. That is, an individual who is indifferent between
Chicago and Des Moines lives closer to the most
 location in Des Moines than the desirable
A(c)
0
−1
D(δ) < δ. The more populous city also
most desirable location in Chicago, δ = D
A(c0 )
has locations that are strictly more attractive than the best location in the less populous
city; locations of attractiveness γ > A(c0 )D(0) are found in c and not in c0 . In equilibrium,
two locations of equal attractiveness must have the same price, so we can describe the rental
price of a location of attractiveness γ as rΓ (γ).
To characterize locational assignments and prices in the system of cities, we first characterize assignments and prices in terms of γ. The solution is analogous to that derived in theautarkic case. We then translate these assignments and prices into functions of c and δ.
Individuals of higher skill have greater willingness to pay for more attractive locations,so in equilibrium higher-ω individuals live in higher-γ locations.
Lemma 4 (Locational assignments). In equilibrium, there exists a continuous and strictlyincreasing locational assignment function K : Γ → Ω such that f (ω, c, δ, σ) > 0 ⇐⇒A(c)D(δ) = γ and K(γ) = ω, where K(γ) = ω, and K(γ̄) = ω̄.
To obtain an explicit expression for K : Γ → Ω, we can denote the supply of locations
offering benefits γ or greater as SΓ (γ). The supply function is



X
γ
−1
SΓ (γ) =S D
A(c)c:A(c)D(0)≥γ19Individuals take these TFPs as given. For now, we can assume these differences in total factor productivity are exogenously given. We describe their endogenous determination in section 3.6.

13

By definition SΓ (γ̄) = 0 and by the fact that the best locations are populated SΓ (γ) = γ̄
L. Lemmas 1 and 4 allow us to say that SΓ (γ) = L γ f (K(x))K 0 (x)dx, so K(γ) =−1 L−SΓ (γ)
F. These locational assignments yield an expression for equilibrium locationalL
prices.
Lemma 5 (Locational prices). In equilibrium, rΓ (γ) is continuously differentiable on [γ, γ̄]γ
and given by rΓ (γ) = γ G(K(x))dx.
Therefore, the determination of locational assignments and prices within the system ofcities is analogous to determining these locational assignments and prices for an autarkic
city with a supply of locations that is the sum of locations across the system of cities. The
task that remains is to translate these assignments and prices from γ back to c, δ.
γThe city limit δ̄(c) = D−1 A(c) . Since we are not focused on rental prices, a sufficientcharacterization is r(c, δ) = rΓ (A(c)D(δ)). For locational assignments, we obtain an explicit
expression that characterizes cities’ skill distributions in terms of ω and c.
Lemma 6 (A city’s skill distribution). The population of individuals of skill ω in city c is 
 −1 
( −10−K(ω) −10 K −1 (ω)
0−1 K (ω)
DS
D
if A(c)D(0) ≥ K −1 (ω)
A(c)L
A(c)
A(c)
.
f (ω, c) =0otherwise
The relative populations of individuals of skill ω depends on the relative supply of locations of attractiveness K −1 (ω). Since higher-ω individuals live in more attractive locations
and the most attractive locations are found exclusively in the larger city, there is an intervalof high-ω individuals who reside exclusively in the larger city. Individuals of abilities below
this interval are found in both cities, and individuals of equal skill reside in equally attractive
locations that have equal rental prices.
Figure 5 illustrates an example in which the system consists of two cities that are vonThünen discs with A(c) > A(c0 ). The sum of the areas of the two discs equals the total
population, and the higher-TFP city has a city limit that is more distant from its center.
The most attractive location, δ = 0 in city c, is occupied by the most skilled individual, ω̄.
The least attractive occupied locations, the edges of the two discs, are occupied by the least
skilled individuals, ω. Denote the individual living at the most attractive location withincity c0 by ω̃. Individuals of this skill type also live in the higher-TFP city at distance δ̃.
Individuals of skill ω > ω̃ live only in city c, in locations less than δ̃ from the center of thedisc. Individuals of skill ω ≤ ω̃ live in both cities. Rental prices of equally attractive location
are equal across the two cities.

14

Figure 5: Two-city equilibrium with von Thünen discs
δ̄(c)

!

δ̄(c )
ω̃

ω

ω̃

δ=0

r(c! , δ̄(c! )) = 0

ω̄

δ̃

ω

r(c, δ̄(c)) = 0
L = π δ̄(c0 )2 + π δ̄(c)2γ̃ = A(c0 )D(0) = A(c)D(δ̃)
r(c0 , 0) = r(c, δ̃)

3.5

Cities’ populations and sectors

When more desirable locations within cities are scarcer, our model implies that larger cities
will exhibit relatively more skilled populations and produce relatively more in skill-intensive
sectors. The first result comes from large cities offering more attractive locations, which areoccupied by higher-income individuals in equilibrium. Higher-income individuals are moreskilled due to our assumption of absolute advantage. The second result comes from larger
cities’ TFP advantages being sector-neutral, so that sectoral composition is governed by skill
composition.
Larger cities have relatively more skilled populations. We have already shown that themost skilled individuals live exclusively in the largest city because it offers the most attractive
locations. What about individuals of skill levels found in multiple cities? The distributionof skills is governed by the interaction of city TFPs, A(c), and the supply of locations withincities yielding at least D(δ) = z, which is S(D−1 (z)). We say that the latter is well-behaved
when more desirable locations are scarcer and their distribution satisfies a common regularity
condition.20∂
S(D−1 (z))
Definition 2. The supply of locations within cities is well-behaved if the density − ∂zis decreasing and log-concave.

When the supply of locations within cities is well-behaved, more skilled individuals are
relatively more prevalent in larger cities throughout the skill distribution. Consider a locationin Des Moines, which has attractiveness γ 0 . Higher-TFP Chicago has more locations of
attractiveness γ ≥ γ 0 , since S(δ) is increasing. If S(D−1 (z)) is well-behaved, then Chicago’s
20

Log-concavity is most commonly used in economics in the context of probability distributions (Bagnoli
and Bergstrom, 2005).

15

supply of γ 0 locations relative to γ 00 locations, for any γ 0 > γ 00 , is greater than that of Des
Moines. Since higher-ω individuals live in higher-γ locations, this means the population ofChicago is relatively more skilled when comparing any two skill types. Proposition 1 states
this result for a system of cities; its proof is in appendix B.
Proposition 1 (Skill abundance). If the supply of locations within cities is well-behaved,
then f (ω, c) is log-supermodular.
Figure 6: Two-city illustration of Proposition 1 result

δ̄

!

δ!

!

δ + dδδ!

δ̃

δ + dδ

δ̄

A(c0 )D(δ 0 ) = A(c)D(δ)
A(c0 )D(δ 0 + dδ 0 ) = A(c)D(δ + dδ)To illustrate the logic of Proposition 1, we consider the special case of two circular von
Thünen cities and linear commuting costs.21 The two cities differ in TFP, with A(c) > A(c0 ),so the radius of city c is larger than that of city c0 , δ̄(c) > δ̄(c0 ), as depicted in Figure 6.
Consider two locations δ and δ 0 that are equally attractive, such that A(c)D(δ) = A(c0 )D(δ 0 ).
These locations are the blue circles in Figure 6, with δ > δ 0 . Now consider an arbitrary, lessdesirable location at distance δ + dδ. The equivalent location in city c0 , denoted by δ 0 + dδ 0 ,can be identified using A(c)D(δ + dδ) = A(c0 )D(δ 0 + dδ 0 ). These locations are the red circles
in Figure 6. Each city’s relative supply of red locations to blue locations is the ratio of their00and δ +dδ
, respectively. The supply of the worse location is relatively
circumferences: δ+dδδδ0
A(c0 )dδ
greater in the smaller city. By the linearity of D(·), we know that dδ
0 = A(c) < 1. Therefore
δ 0 +dδ 0
δ00= 1 + dδδ0 > 1 + dδδ = δ+dδ
, since δ > δ 0 and dδ 0 > dδ. More attractive locations areδrelatively more abundant in the larger city. Since equally attractive locations are occupied
by individuals of equal skill, the larger city’s population is relatively skilled.
Our next set of results concern the sectoral output of cities. Since individuals’ choices ofsectors are independent of their cities and locations, the cross-city skill distribution governs
the cross-city output distribution. Larger cities are relatively skill abundant and more skilled
21∂
When S(δ) = πδ 2 and D(δ) = d1 − d2 δ, − ∂z(d1 − z) is decreasing and log-concave.
S(D−1 (z)) = 2π
d2
2

16

individuals work in more skill-intensive sectors, so larger cities produce relatively more inskill-intensive sectors.
These patterns of specialization and trade are closely related to the high-dimensionalmodel of endowment-driven comparative advantage introduced by Costinot (2009), but in our
setting cities’ populations are endogenously determined.22 Since at equilibrium larger cities’
productivity advantages are sector-neutral differences in total factor productivity, f (ω, c)
is log-supermodular, and H(ω, σ) is log-supermodular, our economy’s equilibrium satisfies
Definition 4, Assumption 2, and Assumption 3 of Costinot (2009).23 The result is that Q(σ, c)
is log-supermodular. As shown by Corollaries 2 and 3 in Costinot (2009), we can therefore
rank the relative output (Q(σ, c)), employment (f (σ, c) ≡ δ∈∆ ω∈Ω f (ω, c, δ, σ)dωdδ), andrevenue (R(σ, c) ≡ p(σ)Q(σ, c)) of any two sectors in any two cities.
Proposition 2 (Comparative Advantage). If f (ω, c) is log-supermodular, then Q(σ, c),f (σ, c), and R(σ, c) are log-supermodular, so that for c ≥ c0 and σ ≥ σ 0 , the following
inequalities hold trueQ(σ, c)Q(σ 0 , c0 ) ≥ Q(σ, c0 )Q(σ 0 , c)f (σ, c)f (σ 0 , c0 ) ≥ f (σ, c0 )f (σ 0 , c)R(σ, c)R(σ 0 , c0 ) ≥ R(σ, c0 )R(σ 0 , c)

(12)These inequalities characterize the pattern of comparative advantage across cities.24

3.6

Endogenizing cities’ total factor productivities

Our exposition of equilibrium in sections 3.4 and 3.5 took cities’ total factor productivities as
exogenously given. When the conditions of Proposition 1 are satisfied, a city that has higher
total factor productivity A(c) is larger and has a skill distribution f (ω, c) that likelihood ratio
dominates those of cities with lower TFPs. Thus, this spatial pattern can be supported byendogenous productivity processes that make the city-level characteristic A(c) higher when
the city contains a larger and more skilled population, such as the class of agglomeration
functions described by equation (5). Numerous agglomeration processes may generate suchproductivity benefits, and we do not attempt to distinguish between them here.
22

Assumption 2 in Costinot (2009)’s factor endowment model is that countries’ exogenous endowments
are such that countries can be ranked according to the monotone likelihood ratio property. Proposition 1
identifies sufficient conditions for cities’ equilibrium skill distributions to exhibit this property.
23
Definition 4 of Costinot (2009) requires that factor productivity vary across countries (cities) in a Hicksneutral fashion. Since productivity A(c)D(δ) varies both across and within cities, our production function
q(c, δ, σ; ω) does not satisfy this requirement for arbitrary locational assignments. However, in equilibrium,
our economy does exhibit this property. In the production interpretation of D(δ), equilibrium productivityq(c, δ, σ; ω) = K −1 (ω)H(ω, σ) does not vary across ω-occupied locations and is log-supermodular in ω andσ. In the notation of equation (6) in Costinot (2009), a(γ) = 1 and h(ω, σ) = K −1 (ω)H(ω, σ), satisfying
Definition 4 and Assumption 3.
24
A traditional definition of comparative advantage refers to countries’ autarkic prices. In our setting,autarky means prohibiting both trade of intermediate goods and migration between cities. Since individuals are spatially mobile, cities do not have “factor endowments”, and we must specify the autarkic skill
distributions. If we consider an autarkic equilibrium with the skill distributions from the system-of-cities
equilibrium, then larger cities have lower relative autarkic prices for higher-σ goods because they are skill
abundant in the MLRP sense, as shown by Costinot and Vogel (2010, p. 782).

17

4

Empirical approach and data description

We examine the predictions of Propositions 1 and 2 using two approaches. The first involves
regression estimates of the population elasticities of educational, occupational, and industrial
populations. The second involves pairwise comparisons governed by the monotone likelihood
ratio property.
Empirically testing our model requires data on cities’ skill distributions, sectors’ skill
intensities, and cities’ sectoral employment. We use public-use microdata from the US Census
of Population to identify the first two. The latter is described by data from County Business
Patterns and Occupational Employment Statistics. The Census of Population describes
individuals’ educational attainments, geographic locations, places of birth, occupations, andindustries. County Business Patterns describes cities’ industrial employment. Occupational
Employment Statistics describes cities’ occupational employment.
See appendix C for
details of the geographies covered by these data sources.

4.1

Empirical tests

Propositions 1 and 2 say that the distribution of skills across cities, f (ω, c), and the distribution of sectoral employment across cities, f (σ, c), are log-supermodular functions. Logsupermodularity has many implications; we focus on two that are amenable to empiricaltesting. If the function f (ν, c) is log-supermodular, then
• a linear regression ln f (ν, c) = αν + βν ln L(c) + ν,c in which αν are fixed effects andL(c) is city population yields βν ≥ βν 0 ⇐⇒ ν ≥ ν 0 ;
0
• if
and
than C 0 (inf c∈C L(c) > supc0 ∈C 0 L(c0 )), then
PC and C are
P distinct 0sets
PC is greater
P
0
0
0
0
c∈C f (ν, c)
c0 ∈C 0 f (ν , c ) ≥c∈C f (ν , c)c0 ∈C 0 f (ν, c ) ∀ν > ν .

The first implication, which we will refer to as the “elasticity test,” says that the citypopulation elasticity of the population of a skill type in a city f (ω, c) is increasing in skill
ω.25 Similarly, the population elasticity of sectoral employment f (σ, c) is increasing in skill
intensity σ. The elasticity test examines the patterns suggested by Figures 1 through 3,
where steeper slopes correspond to higher elasticities. Our theory thus provides a structure
to interpret previous work describing the population elasticities of sectoral employment, suchas Henderson (1983) and Holmes and Stevens (2004).26 The second implication, which wewill refer to as the “pairwise comparisons test”, says that if cities are divided into bins ordered
The linear regression may understood as a first-order Taylor approximation: ln f (ν, c) ≈ ln f (ν, c∗ ) +is increasing in ν by logsupermodularity of f (ν, c).
26Henderson (1983) regresses employment shares on population levels, but reports “percent ∆ share /percent ∆ population”, which is equal to βσ −1 in our notation. Similarly, Holmes and Stevens (2004) describe
how location quotients, a city’s share of industry employment divided by its
P share of total employment, varyf (σ,c)/ c0 f (σ,c0 )with city size. In our notation, a location quotient is LQ(σ, c) =, so the L(c)-elasticity ofL(c)/L
LQ(σ, c) is βσ − 1.
25

∂ ln f (ν,c∗ )∂ ln f (ν,c∗ )∗∂ ln L(c) (ln L(c) − ln L(c )) +  = αν + βν ln L(c) + ν,c , where βν = ∂ ln L(c)18by population sizes, then in any pairwise comparison of two bins and two skills/sectors, thebin containing more populous cities will have relatively more of the more skilled type.27

4.2

SkillsFollowing a large literature, we use observed educational attainment as a proxy for individuals
skills.28 Educational attainment is a coarse measure, but it is the best measure available indata describing a large number of people across detailed geographic locations. To describe
cities’ skill distributions, we aggregate individual-level microdata to the level of metropolitan
statistical areas (MSAs). A large literature in urban economics describes variation in terms of
two skill groups, typically college and non-college workers. Following Acemoglu and Autor(2011), we use a minimum of three skill groups. The Census 2000 microdata identify 16
levels of educational attainment, from “no schooling completed” to “doctoral degree”. We
define three skill groups of approximately equal size among the working population: highschool degree or less; some college or associate’s degree; and bachelor’s degree or more. In a
more ambitious approach, we also consider nine skill groups, ranging from individuals who
never reached high school (3 percent of the population) to those with doctoral degrees (1percent).29 Table 1 shows the population shares of each of these skill groups in 2000.
Table 1: Skill groups by educational attainment
Skill (3 groups)
Population share Skill (9 groups)
Population share
High school or less
0.35
Less than high school
0.03
High school dropout
0.07
High school graduate
0.24
Some college
0.32
College dropout
0.24
Associate’s degree
0.08
BA or more
0.33
Bachelor’s degree
0.21
Master’s degree
0.08Professional degree
0.03
Doctoral degree
0.01
Population shares are percentages of full-time, full-year prime-age workers.
Source: Census 2000 microdata via IPUMS-USA

4.3

Sectors

In our model, workers produce freely traded sectoral outputs indexed by σ that are used
to produce the final good. In the international trade literature, it is common to interpret
The pairwise comparisons test follows from taking sums twice of each side of f (ν, c)f (ν 0 , c0 ) ≥f (ν , c)f (ν, c0 ) given c > c0 ∀c ∈ C ∀c0 ∈ C 0 ∀ν > ν 0 .
28
Costinot and Vogel (2010) show that log-supermodularity of factor supplies in an observed characteristic
and unobserved skill ω is sufficient for mapping a theory with a continuum of skills to data with discrete
characteristics.
29
Individuals with doctorates typically earn less than individuals with professional degrees, so it may beinappropriate to treat PhDs as higher-ω individuals than professionals.
27

0

19

sectors in models of comparative advantage as industries. Recent work in both international
and labor economics has emphasized a perspective focused on workers completing tasks,
which empirical work has frequently operationalized as occupations (Grossman and RossiHansberg, 2008; Acemoglu and Autor, 2011). We will implement empirical tests using each.
We define sectors to be the 21 manufacturing industries in the three-digit stratum of theNorth American Industry Classification System (NAICS) or the 22 occupational categories
in the two-digit stratum of the Standard Occupational Classification (SOC). We suspect thatthe assignment of workers to sectors is better characterized as assignments to occupations
than assignments to industries, since virtually all industries employ both skilled and unskilledworkers. Our measures of cross-sectoral variation in skill intensities in the following section
are consistent with this conjecture.
We measure industrial employment in a metropolitan area using data from the 2000
County Business Patterns. We measure occupational employment in a metropolitan area
using estimates from the 2000 BLS Occupational Employment Statistics. See appendix C
for details.

4.4

Skill intensities

Our theory makes the strong assumption that H(ω, σ) is strictly log-supermodular so that
sectors are ordered with respect to their skill intensities. In our empirical work, we infersectors’ skill intensities from the data using the observable characteristics of the workers
employed in them. We use microdata from the 2000 Census of Population, which contains
information about workers’ educational attainments, industries, and occupations. We use
the average years of schooling of workers employed in a sector as a measure of its skill intensity.30 In doing so, we control for spatial differences by regressing years of schooling on
both sectoral and city fixed effects, but we have found that omitting the city fixed effects
has little effect on the estimated skill intensities. Table 2 reports the five least skill-intensive
and five most skill-intensive sectors among both the 21 manufacturing industries and the 22
occupational categories. There is considerably greater variation in average years of schoolingacross occupational categories than across industries.31 This may suggest that the “assignment to occupations” interpretation of our model will be a more apt description of the data
than the “assignments to industries” interpretation.

30

Autor and Dorn (2012) rank occupations by their skill level according to their mean log wage. Our
assumption of absolute advantage is consistent with such an approach. Using average log wages as ourmeasure of skill intensity yields empirical success rates comparable to and slightly higher on average thanthose reported in section 5. We use years of schooling rather than wages as our measure of sectoral skill
intensities since nominal wages may also reflect compensating differentials or local amenities.
31
The standard deviations of average years of schooling across occupational categories, industries, andmanufacturing industries are 2.2, 1.0, and 0.9, respectively.

20

Table 2: Sectoral skill intensities
SOC
45
37
35
47
51
29
21
25
19
23

Skill
Occupational category
intensity NAICS Manufacturing industry
Farming, Fishing, and Forestry
9.3
315
ApparelBuilding & Grounds Cleaning
10.9
314
Textile Product Mills
Food Preparation and Serving
11.4
316
Leather and Allied ProductConstruction and Extraction
11.5
313
Textile Mills
Production
11.6
337
Furniture and Related Products
Healthcare Practitioners and Technical
15.6
312
Beverage and Tobacco Products
Community and Social Services
15.8
336
Transportation Equipment
Education, Training, and Library
16.5
324
Petroleum and Coal Products
Life, Physical, and Social Science
17.1
334
Computer & Electronic Products
Legal
17.3
325
Chemical
Source: Census 2000 microdata via IPUMS-USA.

4.5

Pairwise weights

The most disaggregate implications of Propositions 1 and 2 are inequalities describing thenumber of individuals residing (employed) in two cities and two skill groups (sectors). Empirically testing these pairwise predictions involves evaluating as many as ten million of theseinequalities and summarizing the results. An important question is whether each of thesecomparisons should be considered equally informative.
An unweighted summary statistic assigns equal weight to correctly predicting that Chicago(population 9 million) is relatively more skilled than Des Moines (population 456 thousand)and correctly predicting that Des Moines is relatively more skilled than Kalamazoo (population 453 thousand). Given the numerous idiosyncratic features of the real world omitted
from our parsimonious theory, the former comparison seems much more informative about
the relevance of our theory than the latter. Similarly, an unweighted summary statistic treats
comparisons involving high school graduates (24 percent of the workforce) and comparisonsinvolving PhDs (1 percent of the workforce) equally, while these differ in their economic
import.
Following Trefler (1995), we report weighted averages of success rates in addition to unweighted statistics. In describing skill distributions, we weight each pairwise comparison by
the two cities’ difference in log population. When we consider nine skill groups, we also
report a case where we weight by the product of the two skill groups’ population shares.
Figure 7 shows the distribution of differences in log population across city pairs. Since themajority of city pairs have quite small differences in log population, the unweighted andweighted statistics may yield substantially different results. In describing sectoral distributions, we weight pairwise comparisons by the two cities’ difference in log population, the two
sectors’ difference in skill intensity, or the product of these. Figure 8 shows the distributionof differences in skill intensity across occupational pairs. While not as right-skewed as the
distribution of differences in log population, this distribution may cause the unweighted andweighted statistics to differ. Figure 9 shows the distribution of differences in skill intensity
across industries. The median difference between occupations is 2.3 years while the mediandifference between manufacturing industries is only 0.9 years. This relative compression in
21

Skill
intensity
10.7
11.4
11.7
11.7
11.7
13.1
13.2
13.5
14.1
14.1

skill differences in the industrial data suggests that it may prove harder to make strong
statements about differences across cities in industries than in occupations. Figures 8 and 9
underscore the importance of looking at weighted comparisons.
.2ifference
Density
.6
.4
.2
0
2
4
D
6
Difference(2000
Population
CMSA)
in logdifferences
populations

Population differences

0.2

Density.4.6

Figure 7: Differences in(2000
population
across city pairs
CMSA)

0

2
4
Difference in log populations

6

Data source: Census 2000 PHC-T-3

0

.05

.1

Density
.15

.2

.25

Figure 8: Differences in skill intensities across occupational pairs

024
Difference in schooling68

231 pairwise comparisons between 22 2-digit SOC occupational categories.
Data source: 2000 Census of Population microdata via IPUMS-USA.

22

0

.2

Density.4.6.8

Figure 9: Difference
Differences.2
Density
.8
.6
.4
.2
0
1
2
3
D
4
ifference in schooling in skill intensities across industrial pairs012
Difference in schooling3

4

210 pairwise comparisons between 21 3-digit NAICS manufacturing industries.
Data source: 2000 Census of Population microdata via IPUMS-USA.

5

Empirical results

In this section, we test our predictions relating cities’ sizes to their distributions of skill, occupational employment, and industrial employment. First, we examine whether populations
are log-supermodular in educational attainment and city size. This prediction is a muchstronger characterization of cities’ skill distributions than the well known fact that larger
cities typically have a greater share of college graduates. Second, we examine whether the
spatial pattern of sectoral employment is governed by this spatial pattern of skills. Our theory’s predictions are more realistic than completely specialized or perfectly diversified cities
and more specific than theories allowing arbitrary patterns of interindustry spillovers.
The data are broadly consistent with both of our novel predictions. Skill distributions
regularly exhibit the monotone likelihood ratio property, though international migration
plays an important role in the largest US cities that is omitted from our model. More skillintensive sectors are relatively larger in more populous cities, on average. However, cities’
sectoral distributions do not exhibit the monotone likelihood ratio property as often as
cities’ skill distributions do. One interpretation of this result is that skill-driven comparativeadvantage plays an important role in determining the spatial pattern of production, but
localization and coagglomeration economies may also play a role.32 We show that there are
not systematic violations of our predicted pattern of comparative advantage.

5.1

Larger cities are relatively more skilled

This subsection tests our prediction that larger cities have relatively more skilled populations.
We empirically describe skill abundance using the two tests described in section 4.1. We first
do these exercises using three skill groups defined by educational attainment levels and thenrepeat them using nine very disaggregated skill groups.
32

Since the employment data do not distinguish employees by birthplace, another possibility is that thedisproportionate presence of low-skill foreign-born individuals in larger cities influences sectoral composition
in a manner not described by our theory.

23

5.1.1Three skill groups

The elasticity test applied to the three skill groups across 270 metropolitan areas is reported in Table 3. The results match our theory’s prediction that larger cities will haverelatively more people from higher skill groups. The population elasticities are monotonically increasing in educational attainment and the elasticities differ significantly from eachother. In anticipation of issues related to international immigration that arise when we examine nine skill groups, the second column of the table reports the population elasticities
of US-born individuals for these three educational categories. The estimated elasticities areslightly lower, since foreign-born individuals are more concentrated in larger cities, but thedifferences between the elasticities are very similar.
Table 3: Population elasticities of educational groups
Dep var: ln f (ω, c)AllUS-bornβω1 HS or less
0.96
0.90
× log population (0.011)(0.016)βω2 Some college
1.00
0.97
× log population (0.010)(0.012)βω3 BA or more
1.10
1.07
× log population (0.015)(0.017)
Standard errors, clustered by MSA, in parentheses.
Sample is all full-time, full-year employees residing in 270 metropolitan areas.

The pairwise comparison test examines ordered groups of cities to see if the relative
population of the more skilled is greater in larger cities. Implementing this test involves
defining groups of cities. Ordering cities by population, we partition the 270 metropolitan
areas in our data into 2, 3, 5, 10, 30, 90, and 270 sets of cities. Making pairwise comparisons
between three skill groups and as many as 270 metropolitan areas involves computing up
to 108,945 inequalities.33 Note that prior work typically describes a contrast between large
and small cities for high and low skills, whereas our most aggregated comparison is between
large and small cities for three skill groups.
Figure 10 and Appendix Table 6 summarize the results of these tests using various sets ofcities, weights, and birthplaces. In the unweighted comparisons, the success rate ranges from60 percent when comparing individual cities to 97 percent when comparing five groups ofcities to 100 percent for the standard case of two groups of cities. Weighting the comparisonsby the population difference generally yields a higher success rate.34 When we weight by
population differences, the success rate is 67 percent when comparing individual cities, 98
percent for five groups of cities, and 100 percent for the simple comparison of large versus
small cities.35m(m−1)
3×2
With n city groups and m skill groups, we make n(n−1)
comparisons. For example, 270×269
2
2
2
2 =
108, 945.
34
Despite the fact that the success rate of the Des-Moines-Kalamazoo comparisons is actually higher thanthe Chicago-Des-Moines comparisons.
35
Our comparisons of two or five groups of cities are analogous to the empirical exercises presented in
Eeckhout, Pinheiro, and Schmidheiny (2011) and Bacolod, Blum, and Strange (2009).
33

24

0.5

0.6
0.7
0.8
0.9
1.0
Pairwise comparison success rate

Figure 10: Pairwise comparisons of three skill groups

270
108,94590
12,015

30
1305

10
135

Unweighted, all
Pop-difference weighted, US born

5.1.2

5
30

3
9

2 Bins3 ComparisonsPop-difference weighted, all

Nine skill groups

We next examine our tests for the case with nine skill groups. Starting with the elasticity
test, Table 4 shows, contrary to our model’s predictions, that those not completing highschool are highly prevalent in larger cities. The second column reveals that this result isdue to the presence of foreign-born individuals with low educational attainment in larger
cities. If we restrict attention to US-born individuals, we can only reject the hypothesis thatβω ≥ βω0 ⇐⇒ ω ≥ ω 0 in one of thirty-six comparisons, the case where βω2 = 0.94 > 0.90 =βω3 .36 In short, the elasticity test provides strong support for our theory when we examine
the US-born population.37
How should we interpret the difference between the spatial distribution of skills among the
population as a whole and among US-born individuals? One possibility is that immigrants
strongly prefer larger cities for reasons omitted from our model, causing less-skilled foreignborn individuals to disproportionately locate in larger cities. This would be consistent withan established literature that describes agglomeration benefits particular to unskilled foreignborn individuals, such as linguistic enclaves (Edin, Fredriksson, and Aslund, 2003; Bauer,
Epstein, and Gang, 2005).38
36

The estimated elasticities for the tails of the skill distribution have larger standard errors. This likely
reflects greater sampling noise for scarce educational categories; for example, the median (C)MSA had 34
observations of full-time, full-year employees with a PhD in the 5 percent public-use 2000 Census microdata.
37
Interestingly, among US-born individuals, the nine estimated elasticities naturally break into the three
more aggregate educational attainment categories that we used above: βω1 , βω2 , βω3 ∈ (0.91, 0.94); βω4 , βω5 ∈(0.96, 0.98); βω6 , βω7 , βω8 , βω9 ∈ (1.06, 1.09).
38
Another potential mechanism is that immigrants may find larger cities’ combination of higher nominal
wages and higher housing prices more attractive than natives (Diamond, 2012), possibly because they remit25Table 4: Population elasticities of educational groups, 2000
Dep var: ln f (ω, c)AllUS-bornβω1 Less than HS
1.17
0.91
× log population(0.039)(0.028)βω2 High school dropout
1.03
0.94
× log population(0.017)(0.020)βω3 High school graduate
0.93
0.90
× log population(0.013)(0.016)βω4 College dropout
1.00
0.98
× log population(0.011)(0.013)βω5 Associate’s degree
1.00
0.96
× log population(0.014)(0.016)βω6 Bachelor’s degree
1.10
1.07
× log population(0.015)(0.017)βω7 Master’s degree
1.12
1.09
× log population(0.018)(0.019)βω8 Professional degree
1.12
1.09
× log population(0.018)(0.019)βω9 PhD
1.11
1.06
× log population(0.035)(0.033)Standard errors, clustered by MSA, in parentheses.
Sample is all full-time, full-year employees residing in 270 metropolitan areas.

Eeckhout, Pinheiro, and Schmidheiny (2011) articulate another possibility, in which an
economic mechanism they term “extreme-skill complementarity” causes less skilled individuals, foreign-born or US-born, to disproportionately reside in larger cities. Larger cities’
benefits for immigrants serve as a “tie breaker” that causes the foreign-born to choose larger
cities in equilibrium. This theory predicts that in the absence of foreign-born low-skilledindividuals, US-born low-skilled individuals would disproportionately locate in larger cities.
We attempt to distinguish between these hypotheses by looking at the skill distributionsof US cities two decades earlier. In 2000, foreign-born individuals were 11 percent of the
US population, while in 1980 they constituted about 6 percent. More importantly, in 2000,
foreign-born individuals constituted nearly 80 percent of the lowest skill group, while in1980 they were less than one third of the lowest skill group. If our hypothesis that foreignborn individuals are particularly attracted to larger cities is correct, then the population
elasticity of less-skilled types should be lower when foreign-born shares are lower. Table 5
demonstrates that this is the case in 1980. It does not provide any evidence that the least
skilled were overrepresented in larger cities in 1980, among either the population as a wholeor US-born individuals.39 Reconciling these results with the model of Eeckhout, Pinheiro,
and Schmidheiny (2011) would require that the production function changed from top-skill
their nominal incomes abroad or demand less housing than US-born individuals.
39
The educational categories in Table 5 differ from prior tables because Census microdata collected prior
to 1990 identify coarser levels of educational attainment in terms of years of schooling rather than highest
degree attained.

26

Table 5: Population elasticities of educational groups, 1980
Population
share
All
US-bornβω1 Less than grade 9
0.06
0.99
0.89
× log population(0.028)(0.030)βω2 Grades 9-11
0.11
1.00
0.98
× log population(0.019)(0.021)βω3 Grade 12
0.33
0.97
0.95
× log population(0.013)(0.015)βω4 1 year college
0.08
1.04
1.03
× log population(0.018)(0.018)βω5 2-3 years college
0.13
1.09
1.07
× log population(0.018)(0.018)βω6 4 years college
0.13
1.10
1.08
× log population(0.018)(0.018)βω7 5+ years college
0.13
1.13
1.11
× log population(0.022)(0.022)Standard errors, clustered by MSA, in parentheses
Sample is all full-time, full-year employees residing in 253 metropolitan areas.

complementarity in 1980 to extreme-skill complementarity in 2000.
We now turn to the pairwise comparisons for the case with nine skill groups in 2000,
summarized in Figure 11 and Appendix Table 7. These test inequalities for 36,315 individual
city pairs for each pairing of the nine skill groups. In both the unweighted and weighted
comparisons, our theory does best in predicting comparisons of skill groups that have a highschool degree or higher attainment. Fewer than 50 percent of the comparisons yield thecorrect inequality when the “less than high school” skill group is involved in the comparison.
As would be expected from the previous results, these comparisons are considerably more
successful when restricted to the US-born population. When these are also weighted bypopulation differences and education shares, the overall success rate in comparing individual
cities rises to 64 percent.
Figure 11 and Appendix Table 8 show how the pairwise comparison success varies when
we group cities by size. When we restrict attention to the US-born, the unweighted successrate respectively for individual cities, five and two groups of cities are 56 percent, 71 percent,and 81 percent. If, in addition, we weight successes by education shares and populationdifferences, the success rates for individual cities, five and two groups of cities are 64 percent,87 percent, and 88 percent, respectively. In short, for the case of nine skill groups, the raw
comparisons for individual cities including the foreign born show very modest success. As
in the elasticities test, restricting attention to the US-born population yields significant
improvement. Likewise, there is considerably greater success as we group cities and as weweight them by the overall prevalence of the education group in the labor force. Overall, we
consider this solid support for our theory.

27

0.5

0.6
0.7
0.8
0.9
1.0
Pairwise comparison success rate

Figure 11: Pairwise comparisons of nine skill groups

270
1,307,340

90
144,18030
15,660101620

Unweighted, all
Pop-diff-edu-share-weighted, US born

5.2

5
360

3
108

2 Bins36 Comparisons

Unweighted, US born

Larger cities specialize in skill-intensive sectors

This section examines the spatial pattern of sectoral employment. In our theory, larger cities
are relatively more skilled, cities’ equilibrium productivity differences are Hicks-neutral, andsectors can be ordered by their skill intensity, so larger cities employ relatively more labor inskill-intensive sectors. We established that larger cities are relatively more skilled in section
5.1. We now examine whether larger cities are relatively specialized in skill-intensive sectors.
Since employment levels in both industries and occupations are readily available in the data,
we test the employment implications of Proposition 2.40
5.2.1

The spatial distribution of occupations

We first implement the elasticities test and the pairwise comparisons test interpreting sectors
as occupations. We begin with a visualization of the elasticity data. Figure 12 plots the
22 occupational categories’ estimated population elasticities of employment against theirskill intensities, measured as the average years of schooling of individuals employed in thatoccupation.41 There is a clear positive relationship. Outliers in the figure include close-tounitary elasticities for the relatively skilled occupations in education, healthcare, and social
services, which may reflect non-traded status. On the other side, computer and mathematicaloccupations have an elasticity that is quite high relative to their average schooling.
40

Section 5.1 showed that US-born individuals better match our model’s predictions about the distributionof skills. Unfortunately, the County Business Patterns and Occupational Employment Statistics data describe
employment counts, not individual employees’ characteristics, so we cannot address the birthplace issues inthis section.
41
These elasticities are estimated without including zero-employment observations. The results obtained
when including those observations are similar.

28

1.4

Farming,
Building
Food
Construction
Production
Transportation
Installation,
Personal
OfficeProtective
Sales
Management
Arts,
Business
Architecture
Computer
Healthcare
Community
Education,
Life,
Legal
1
.8
1.4
1.2
Population
10
12
14
16
18
Skill
.2Physical,
Preparation
Design,andOccupations
andintensity
andCareFishing,andRelatedandAdministrative
Service
Training,
SupportPractitioners
Occupations
andMaintenance,
Grounds
andoccupations
andFinancialEntertainment,
andMathematicalandSocialandEngineering
andExtraction
elasticity
Occupations
Service
SocialOccupations
Material
Occupations
andCleaning
Forestry
Serving(employees'
Operations
ServicesandLibrary
ScienceandSupportOccupations
MovingOccupations
Technical
Sports,Occupations
Occupations
RepairandOccupations
Occupations
Occupations
OccupationsofOccupations
Occupations
MaintenanceOccupations
andWorkers
employment
Occupations
Media
averageOccupations
Occupations
years of schooling)
Figure12:
Occupations’population
elasticities and skill intensities.8

Population elasticity of employment
1
1.2

Computer and Mathematical Occupations

Architectureand Engineering
Occupations
Business
and Financial
Operations Legal
Occupations
Occupations
Life, Physical, and Social Science Occupations
Arts, Design, Entertainment, Sports, and Media Occupations
Protective Service Occupations
Management
occupations
Office and Administrative
Support Occupations
Personaland Moving
Service Occupations
Occupations
Transportation
andCareMaterial
Production
Building and
GroundsOccupations
Cleaning
andSalesMaintenanceOccupations
Construction
and Extraction
Occupations
and Related
Occupations
Installation, Maintenance, and Repair Workers
Food Preparationand Serving
Occupations
Healthcare
SupportOccupations

Education, Training, and Library Occupations
Healthcare Practitioners and Technical Occupations
Community and Social Services Occupations

Farming, Fishing, and Forestry Occupations

10

12
14
16
Skill intensity (employees' average years of schooling)18

We can also look at this more formally. With the population elasticities of occupationsin hand, the hypothesis that βσ ≥ βσ0 ⇐⇒ σ ≥ σ 0 involves 231 (= 22 × 21/2) comparisonsof the estimated coefficients.42 This hypothesis is rejected at the five-percent significancelevel in 46 comparisons, so the success rate is 80 percent.
The results for pairwise comparisons for occupations appear in Figure 13 and Appendix
Table 10. When we do this for 276 cities and 22 occupations, we have a total of 8,766,450
pairwise comparisons, of which 53 percent are correct. This is low compared to our results
for skills. When we stay with individual cities but weight by population and skill differences,
this rises above 58 percent. We can maintain the weighting and consider it for cities groupedby size into, for example, 30, 5, or 2 groups. The corresponding proportion of successes rises
respectively to 66, 73, and 78 percent. While the results for occupations are not as strongas the results for skills, there are nonetheless quite informative patterns – even when wegroup cities into five size-based bins, we get nearly three-fourths of the pairwise comparisonscorrect across the 22 occupational categories.

42

The elasticity estimates appear in Appendix Table 9.

29

0.5

0.6
0.7
0.8
Pairwise comparison success rate

0.9

Figure 13: Pairwise comparisons of 22 occupational categories

270
8,766,450

90
925,15530
100,485

Unweighted

5.2.2

10
10,39552,310

3
2 Bins693 231 ComparisonsPop-diff-skill-diff weighted

The spatial distribution of industries

We now implement the elasticities test and the pairwise comparisons test interpreting sectors
as manufacturing industries.43 A visualization of the elasticity test appears in Figure 14.
Again, as predicted by our theory, there is a clear positive relationship so that the population
elasticity of industry employment is rising with the skill intensity of the industry. The
apparel industry is an outlier, with low average education and a high population elasticity
of employment. This may reflect the share of apparel industry employees who are lessskilled foreign-born individuals, consistent with our previous discussion of skills. Testing the
hypothesis that βσ ≥ βσ0 ⇐⇒ σ ≥ σ 0 for the 21 manufacturing industries involves 210(= 21 × 20/2) comparisons of these estimated elasticities.44 This hypothesis is rejected in
26 comparisons, so the elasticity implication holds true for manufacturing industries about
87 percent of the time.45 This success rate is higher than the corresponding statistic for
occupational elasticities.
The pairwise comparisons results for industries appear in Figure 15 and Appendix Table
12. When we do this for 276 individual cities and 21 industries, we have a total of nearly
8 million pairwise comparisons, of which just half are correct. Weighting this by skill andpopulation differences raises this to 54 percent, again low compared to our results for pairwise
comparisons of skills. We can maintain the weighting and consider this for cities grouped by
43
We focus on manufacturing industries since we believe they have the lowest trade costs, but we havefound broadly similar results when using all industries.
44
The elasticity estimates appear in Appendix Table 11.
45
If we restrict the data to uncensored observations, which reduces the sample considerably, this hypothesis
is rejected in 32 comparisons, for an 85 percent success rate. See appendix C for a discussion of censoring inCounty Business Patterns data.

30

.8

Population elasticity of employment
1
1.2
1.4

1.6

Figure14:
Industries’
population
elasticities and skill intensities
ApparelLeather
TextileFurniture
Wood
Food
Plastics
FabricatedNonmetallic
Primary
Paper
Printing
Electrical
MachineryMiscellaneous
BeverageTransportation
Petroleum
Computer
Chemical
1
.8
1.6
1.4
1.2
Population
11
12
13
14
Skill
.2 Manufacturing
Manufacturing
Product
intensity
Product
Mills
Metal
and
Manufacturing
Manufacturing
Equipment,
and
and
and
Manufacturing
and
Metal
MineralRubberRelatedAlliedRelatedManufacturing
TobaccoElectronic
Manufacturing
Coal
Equipment
Mills
Product
elasticity
Product
Product
Products
Support
Appliance,
Product(employees'
Product
Product
Manufacturing
Manufacturing
Manufacturing
Manufacturing
Activities
Manufacturing
Manufacturing
Manufacturing
andofManufacturing
Component
employment
average
Manufacturing
years of schooling)

Computer and Electronic Product Manufacturing

Chemical Manufacturing
Transportation Equipment Manufacturing
Miscellaneous Manufacturing
Printing and Related Support Activities

Apparel ManufacturingandAppliance,
Tobacco Product
Manufacturing
ElectricalBeverage
Equipment,
and Component
Manufacturing
Textile ProductMills
Textile Mills
Fabricated Metal Product Manufacturing
Food Manufacturing
Machinery Manufacturing
Primary Metal Manufacturing
Nonmetallic Mineral Product Manufacturing
Plastics and Rubber Products Manufacturing
Petroleum and Coal Products Manufacturing
Paper Manufacturing
Wood Product Manufacturing
Furniture and Related Product Manufacturing
Leather and Allied Product Manufacturing

11
12
13
14
Skill intensity (employees' average years of schooling)

size into 30, 5, or 2 groups. The corresponding proportion of successes rises respectively to
58, 66, and 71 percent. These are low relative to the prior results on occupations and evenmore so relative to the results on skills. Nonetheless, they do show that there is systematic
variation across cities of different sizes in the composition of manufacturing.46 Note thatprior work contrasting large and medium-size cities, Henderson (1997), is analogous to ourcomparisons of two or three groups of cities ordered by population.

46
These results are not driven solely by the largest metropolitan areas; excluding the ten largest cities
from pairwise comparisons of occupations and industries yields similar success rates.

31

0.5

0.6
0.7
0.8
Pairwise comparison success rate

0.9

Figure 15: Pairwise comparisons of 21 manufacturing industries

270
7,969,500

90
841,05030
91,350

Unweighted

5.3

10
9,450

5
2,100

3
2 Bins630 210 ComparisonsPop-diff-skill-diff weighted

Testing for systematic failures of comparative advantage

Our results for the cross-city distributions of skills, industries, and occupations demonstrate
systematic patterns in line with our theory’s predictions. While demonstrating predictive
power, the pairwise comparisons also reveal that we fall well short of 100 percent success.
This is not surprising, given that our model’s parsimony stems from making strong assumptions that omit various features that influence the real world. An important question iswhether our theory’s unsuccessful pairwise predictions are merely idiosyncratic deviations
from the pattern of comparative advantage or are systematic violations of our predicted
pattern.
Sattinger (1978) develops an approach to test for such systematic violations in the formof systematic intransitivity in the pattern of comparative advantage.
It is possiblefor 0 thef (σ,c0 )f (σ 0 ,c0 )(σ ,c00 )000
000 f (σ,c)
data to exhibit, for c > c > c and σ > σ > σ , f (σ0 ,c) ≥ f (σ0 ,c0 ) and f (σ00 ,c0 ) ≥ ff(σ00 ,c00 )00

f (σ,c )without exhibiting ff(σ(σ,c)
00 ,c) ≥ f (σ 00 ,c00 ) . With hundreds of metropolitan areas and dozens ofsectors, it is easy to find three cities and three sectors in the data exhibiting such intransitivity. But does intransitive comparative advantage arise systematically? Sattinger (1978)shows that if ln f (σ, c) is a polynomial function of β̂σ and ln populationc , then there can besystematic intransitivity only if ln f (σ, c) is a function of higher-order interactions of β̂σ andln populationc . We therefore added quadratic terms and their interactions to our elasticity
regressions. These did little to improve the regression’s adjusted R2 , and F-tests yieldedp-values that did not come close to rejecting the null that these additional terms were uninformative. There is no evidence of systematic intransitivity in comparative advantage.
While our theory’s predictive successes are systematic, the empirical departures from ourtheory appear to be idiosyncratic.

32

6

Discussion and conclusions

In this paper, we introduce a model that simultaneously characterizes the distribution ofskills and sectors across cities. We describe a high-dimensional economic environment thatis a system of cities in which cities’ internal geographies exhibit substantive heterogeneity andindividuals’ comparative advantage governs the distribution of sectoral employment. Ourmodel achieves two aims. First, we obtain “smooth” predictions, in the sense that cities’skill and sectoral distributions will be highly overlapping. These are more realistic thanprior theories describing cities that are perfectly sorted along skills or polarized in termsof sectoral composition. Second, we obtain “strong” predictions, in the sense that cities’skill and sectoral distributions will exhibit systematic variation according to the monotone
likelihood ratio property. These are more precise than the predictions of many prior theoriesof the spatial organization of economy activity and guide our empirical investigation.
Examining data on US metropolitan areas’ populations, occupations, and industries inthe year 2000 reveals systematic variation in the cross-city distribution of skills and sectorsthat is consistent with our theory. Larger cities are skill abundant. Our results using threeequal-sized categories of educational attainment are quite strong. Even disaggregated to nine
educational categories, the cross-city distribution of US-born individuals is well described byour theory.
Empirically, we find that larger cities specialize in skill-intensive activities. More skillintensive occupations and industries tend to have higher population elasticities of employment. In making pairwise comparisons, our model does better in describing the pattern ofoccupational employment than industrial employment. This is consistent with a recent emphasis in the literature on workers performing tasks. Our results demonstrate that metropolitan skill distributions shape the comparative advantage of cities, though the fact that our
sectoral-employment predictions are not as strong as our skill-distribution predictions is consistent with the idea that localization or coagglomeration economies omitted from our model
may also be important.
We believe that our framework is amenable to both theoretical and empirical applications
and extensions. The “smoothness” resulting from the simultaneous consideration of crossand within-city heterogeneity in a continuum-by-continuum environment would make ourmodel amenable to theoretical analyses of the consequences of commuting costs, globalization, and skill-biased technical change. The “strong” character of our predictions and theirdemonstrated relevance for describing US cities in 2000 suggest that their examination inother settings, such as economies at different stages of development or in different historicalperiods, would be interesting.

33

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A

Appendix: Consumption interpretation

The production and consumption interpretations yield very similar results but differ slightly
in notation. In the consumption interpretation, an individual’s productivity and utility areq(c, δ, σ; ω) = A(c)H(ω, σ)U (ω, c, δ, σ) = D(δ) [A(c)H(ω, σ)p(σ) − r(c, δ)]

(13)
(14)

where D(δ) determines the value of the individual’s disposable income after paying his or her
locational price.47 In this interpretation, preferences are non-homothetic in a manner akinto that of Fajgelbaum, Grossman, and Helpman (2011). Higher-income individuals are more
willing to pay for higher-quality locations because a more desirable location complements
their higher consumption of tradables.
In this case, instead of γ = A(c)D(δ) = A(c0 )D(δ 0 ) ⇐⇒ r(c, δ) = r(c0 , δ 0 ) = rΓ (γ),
the appropriate equivalence between two locations is their “amenity-inclusive price”, whichis D(δ)r(c, δ). So the equivalence statement is now γ = A(c)D(δ) = A(c0 )D(δ 0 ) ⇐⇒D(δ)r(c, δ) = D(δ 0 )r(c0 , δ 0 ) = rΓ (γ). The results in lemma 1 are unaltered, though the proof
is modified to use the relevant U (ω, c, δ, σ). The expressions for K : Γ → Ω, γ̄, and γ are
unaltered. This leaves the conclusions of lemmas 4, 5, and 6 intact. The locational price.
schedule is given by r(c, δ) = rΓ (A(c)D(δ))D(δ)
These locational prices do not appear in the proof of Proposition 1 nor the endogenous definition of A(c). When evaluated at equilibrium, occupied locations’ productivities q(c, δ, σ; ω) = A(c)H(ω, σ) differ across cities in a Hicks-neutral fashion that satisfies
Costinot’s Definition 4 (see footnote 23), so Proposition 2 applies. As a result, the predictions about cities’ population, sectors, and productivities described in sections 3.5 and 3.6 are
unaltered by interpreting D(δ) as describing consumption benefits rather than production
benefits.

B

Appendix: Proofs

Proof of Lemma 1:

 δ0 



Proof. Suppose that ∃δ 0 < δ̄(c) : S(δ 0 ) > L 0 σ∈Σ ω∈Ω f (ω, c, x)dωdσdx. Then ∃δ ≤
δ 0 : S 0 (δ) > L σ∈Σ ω∈Ω f (ω, c, δ)dωdσ. Then r(c, δ)) = 0 ≤ r(c, δ̄(c)), so U (ω, c, δ, σ) >U (ω, c, δ̄(c), σ) ∀ω∀σ since D(δ) is strictly decreasing. This contradicts the definition ofδ̄(c), since δ̄(c) is a location that maximizes utility for some individual. Therefore S(δ) =δL 0 σ∈Σ ω∈Ω f (ω, c, x)dωdσdx ∀δ ≤ δ̄(c).
47

Recall that the final good is the numeraire.

37

Suppose that ∃δ 0 , δ 00 : δ 0 < δ 00 ≤ δ̄(c) and r(c, δ 0 ) ≤ r(c, δ 00 ). Then U (ω, c, δ 0 , σ) >U (ω, c, δ 00 , σ) ∀ω∀σ since D(δ) is strictly decreasing. This contradicts the result that δ 00
maximizes utility for some individual. Therefore r(c, δ) is strictly decreasing in δ ∀δ ≤ δ̄(c).
Suppose r(c, δ̄(c)) > 0. Then by its definition as a populated location, ∃ω : A(c)D(δ̄(c))G(ω)−
r(c, δ̄(c)) ≥ A(c)D(δ̄(c) + )G(ω) ∀ > 0. This inequality is false for all ω for sufficiently
small , by the continuity of D(δ). Therefore r(c, δ̄(c)) = 0.
Proof of Lemma 2:
Proof. Our argument
follows the proof of Lemma 1 in Costinot and Vogel (2010). Definef (ω, c, δ) ≡ σ∈Σ f (ω, c, δ, σ)dσ. Define Ω(δ) ≡ {δ ∈ ∆|f (ω, c, δ) > 0} and ∆(ω) ≡ {ω ∈Ω|f (ω, c, δ) > 0}.
1. ∆(ω) 6= ∅ by equation (11) and f (ω) > 0. Ω(δ) 6= ∅ ∀δ ≤ δ̄(c) by lemma 1.
2. Ω(δ) is a non-empty interval. For δ < δ 0 , if ω ∈ Ω(δ) and ω 0 ∈ Ω(δ 0 ) , then ω ≥ ω 0 .
/ Ω(δ). ∃δ 0 : ω 0 ∈ Ω(δ 0 ). Suppose
Suppose ω < ω 0 < ω 00 with ω, ω 00 ∈ Ω(δ) and ω 0 ∈δ 0 > δ. By utility maximization
A(c)D(δ 0 )G(ω 0 ) − r(c, δ 0 ) ≥ A(c)D(δ)G(ω 0 ) − r(c, δ)A(c)D(δ)G(ω) − r(c, δ) ≥ A(c)D(δ 0 )G(ω) − r(c, δ 0 )These jointly imply (D(δ 0 ) − D(δ))(G(ω 0 ) − G(ω)) ≥ 0, contrary to δ 0 > δ and ω 0 > ω.
The δ 0 < δ case is analogous, using ω 0 and ω 00 . Therefore Ω(δ) is a non-empty interval.
The same pair of inequalities proves that for δ < δ 0 , if ω ∈ Ω(δ) and ω 0 ∈ Ω(δ 0 ) , thenω ≥ ω0.
3. Ω(δ) is singleton for all but a countable subset of ∆. Follow Costinot and Vogel (2010).
4. ∆(ω) is singleton for all but a countable subset of Ω. Follow Costinot and Vogel (2010).
5. Ω(δ) is singleton. Follow Costinot and Vogel (2010).
Step 5 means there is a function N : ∆ → Ω such that f (ω, c, δ) > 0 ⇐⇒ N (δ) = ω. Step
2 says N is weakly decreasing. Since Ω(δ) 6= ∅, N is continuous and satisfies N (0) = ω̄ andN (δ̄(c)) = ω. Step 4 means that N is strictly decreasing.
Proof of the explicit expression of N (δ) that follows Lemma 2: δS(δ) = Lf (ω, c, x, σ)dωdσdx
0

 δ

σ∈Σ

0

 δ

ω∈Ω

0

δ0

f (ω)δ Dirac [x − N −1 (ω)]dωdx

=L
=L
 δ

ω∈Ω

f (N (δ 0 ))δ Dirac [x − δ 0 ]N 0 (δ 0 )dδ 0 dx

f (N (x))N 0 (x)dx = L(1 − F (N (δ)))
0
L − S(δ)−1⇒ N (δ) = F
L
= −L

38

Proof of Lemma 3:
Proof. By utility maximization
A(c)D(δ)G(N (δ)) − r(c, δ) ≥ A(c)D(δ + dδ)G(N (δ)) − r(c, δ + dδ)A(c)D(δ + dδ)G(N (δ + dδ)) − r(c, δ + dδ) ≥ A(c)D(δ)G(N (δ + dδ)) − r(c, δ)Together, these inequalities imply
A(c)D(δ + dδ)G(N (δ)) − A(c)D(δ)G(N (δ))
r(c, δ + dδ) − r(c, δ)≤dδdδA(c)D(δ + dδ)G(N (δ + dδ)) − A(c)D(δ)G(N (δ + dδ))≤dδ
Taking the limit as dδ → 0, we obtain ∂r(c,δ)= A(c)D0 (δ)G(N (δ)). Integrating from δ to∂δ δ̄(c)δ̄(c) and using the boundary condition r(c, δ̄(c)) = 0 yields r(c, δ) = −A(c) δ

D0 (t)G(N (t))dt.

Proof of Lemma 4:[This proof is analogous to the proof of lemma 2.]
Proof of Lemma 5:
Proof. By utility maximization
γG(K(γ)) − rΓ (γ) ≥(γ + dγ)G(K(γ)) − rΓ (γ + dγ)(γ + dγ)G(K(γ + dγ)) − rΓ (γ + dγ) ≥γG(K(γ + dγ)) − rΓ (γ)
Together, these inequalities imply(γ + dγ)G(K(γ)) − γG(K(γ))(γ + dγ)G(K(γ + dγ)) − γG(K(γ + dγ)) rΓ (γ + dγ) − rΓ (γ)
≥
≥
dγ
dγ
dγ
Γ (γ)
Taking the limit as dγ → 0, we obtain ∂r∂γ
= G(K(γ)). Integrating from γ to γ andγusing the boundary condition rΓ (γ) = 0 yields rΓ (γ) = γ G(K(x))dx.

Proof of Lemma 6:
Proof. In city c, the population of individuals with skills between ω and ω + dω is

 −1


 −1

 ω+dωK (ω)
K (ω + dω)−1−1Lf (x, c)dx = S D
−S D
A(c)
A(c)
ω
Taking the derivative with respect to dω and then taking the limit as dω → 0 yields the
population of ω in c.
In the course of proving Proposition 1, we use the following lemma.
Lemma 7. Let f (z), g(x), h(y) : R+ → R++ be C 1functions
with g 0 (x) > 0 ∀x and h0 (y) >g(x)0 ∀y. If f (z)is decreasing and log-concave, then f h(y)is log-supermodular in x and y.
39

Proof. f



g(x)
h(y)



is log-supermodular in x and y if and only if∂ 2 ln f (g(x)/h(y))
g 0 h0
= 2
∂x∂y
h




∂
∂ ln f (z)
−
z
≥ 0.
∂z
∂z
z=g(x)/h(y)




g(x)
∂
z ∂ ln∂zf (z) ≤ 0 ∀z ≥ 0, so f h(y)is logIf f (z) is decreasing and log-concave, then ∂z
supermodular.
Proof of Proposition 1:
Proof. The population of skill ω in city c can be written as
 −1





0

K (ω)
K −1 (ω)
∂
1 ∂
−1
−1
S D
=
− S D (z)f (ω, c) = −K −1 (ω)
L ∂ω
A(c)
A(c)L
∂z
z= A(c)−10(ω)
Since KA(c)Lis multiplicatively separable in ω and c, f (ω, c) is log-supermodular if andonly if the term in brackets is log-supermodular. Since K −1 (ω) and A(c) are increasing∂
S(D−1 (z)) is a decreasing and log-concave function, then f (ω, c) is logfunctions, if − ∂zsupermodular by lemma 7.

CAppendix: Data

Data sources: Our metropolitan population data are from the US Census website (2000).
Our data on individuals’ demographics, educational attainments, geographic locations, andsectors of employment come from the 5 percent sample of the 2000 US Census and the
1 percent metro sample of the 1980 US Census made available by IPUMS-USA (Ruggles,
Alexander, Genadek, Goeken, Schroeder, and Sobek, 2010). Our data on industrial employment come from the 2000 County Business Patterns, available from the US Census Bureau
website. Our data on occupational employment come from the 2000 Occupational Employment Statistics, available from the Burea of Labor Statistics website.
Geography: We use (consolidated) metropolitan statistical areas as defined by the OMB
as our unit of analysis.
The smallest geographic unit in the IPUMS-USA microdata is the public-use microdata
area (PUMA), which has a minimum of 100,000 residents. We map the PUMAs to metropolitan statistical areas (MSAs) using the MABLE Geocorr2K geographic correspondence engine
from the Missouri Census Data Center. In some sparsely populated areas, a PUMA is larger
than a metropolitan area. We drop six MSAs in which fewer than half of the residents of
the only relevant PUMA live within the metropolitan area. As a result, there are 270 MSAs
when we use Census of Population data.
The 1980 Census of Population IPUMS-USA microdata do not identify PUMAs, so we
use the “metarea” variable describing 253 consolidated MSAs for the regressions in Table 5.
The County Business Patterns data describe 318 metropolitan statistical areas. These
correspond to a mix of OMB-defined primary and consolidated metropolitan statistical areas outside New England and New England county metropolitan areas (NECMAs). We
40

aggregate these into OMB-defined (consolidated) metropolitan statistical areas to obtain
276 MSAs.
The Occupational Employment Statistics data describe 331 (primary) metropolitan statistical areas. We aggregate these into OMB-defined (consolidated) metropolitan statisticalareas to obtain observations for 276 MSAs.
Skill distribution: Our sample of individuals includes all full-time, full-year prime-age
workers, defined as individuals 25 to 55 years of age who reported working at least 35 hour
per week and 40 weeks in the previous year. Using the “educd” variable from IPUMS, weconstruct nine levels of educational attainment: less than high school, high school dropout,high school graduate, some college, associate’s degree, bachelor’s degree, master’s degree,
professional degree, and doctorate. There is at least one observation in every educational
category in every metropolitan area.
Sectoral skill intensity: Using the same sample of full-time, full-year prime-age workers, we measure a sector’s skill intensity by calculating the average years of schooling of its
employees after controlling for spatial differences in average schooling. We calculate years ofschooling using the educational attainment “educd” variable from IPUMS at its finest levelof disaggregation. For instance, this means that we distinguish between those whose highest
educational attainment is sixth grade or eighth grade. We use the “indnaics” and “occsoc”
variables to assign individuals to their 3-digit NAICS and 2-digit SOC sectors of employment. Aggregating observations to the MSA-sector level, weighted by the IPUMS-provided
person weights, we regress the average years of schooling on MSA and sectoral dummies.
The sectoral dummy coefficients are our measure of skill intensities.
Industrial employment: There 96 3-digit NAICS industries, of which 21 are manufacturing industries. 75 of these industries, including all 21 manufacturing industries, appear
in both the Census of Population microdata and the County Business Patterns data. The
County Business Patterns data are an almost exhaustive account of US employer establishments. When necessary to protect the confidentiality of individual establishments, employment in an industry in a location is reported as falling within an interval rather than its exact
number. In our empirical work, we use the midpoints of these intervals as the level of employment. There are 390 (C)MSA-manufacturing-industry pairs, out of 5796 = 21 × 276, in
which there are zero establishments. The County Business Patterns data omit self-employedindividuals and employees of private households, railroads, agriculture production, the postal
service, and public administrations. See the CBP methodology webpage for details.
Occupational employment: There are 22 2-digit SOC occupations. Across 331 (P)MSAs,
there should be 7282 metropolitan-occupation observations. The 2000 BLS Occupational
Employment Statistics contain employment estimates for 7129 metropolitan-occupation observations, none of which are zero. The 153 omitted observations “may be withheld frompublication for a number of reasons, including failure to meet BLS quality standards or theneed to protect the confidentiality of [BLS] survey respondents.” 31 of these observations arefor “farming occupations”, and the vast majority of them are for less populous metropolitan
areas. We assign these observations values of zero when they are included in our calculations.

D

Appendix: Tables
41

Table 6: Pairwise comparisons of three skill groups
Bins

2
2
2
2
3
3
3
3
5
5
5
5
10
10
10
10
30
30
30
30
90
90
90
90
270
270
270
270

Weights
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff
Unweighted
Pop diff

BirthplaceAll
All
US-born
US-bornAll
All
US-born
US-bornAll
All
US-born
US-bornAll
All
US-born
US-bornAll
All
US-born
US-bornAll
All
US-born
US-bornAll
All
US-born
US-born

College vssome college
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
.800
.914
.800
.914
.763
.875
.775
.886
.671
.788
.672
.790
.629
.717
.624
.712

College vs
HS or less
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
.844
.935
.867
.951
.717
.845
.759
.878
.666
.774
.686
.800
.616
.695
.635
.726

42

Some college
vs HS or less
1.00
1.00
1.00
1.00
.667
.853
1.00
1.00
.900
.959
1.00
1.00
.756
.875
.756
.875
.625
.699
.699
.817
.587
.634
.639
.727
.556
.588
.589
.647

Total
comparisons
3
3
3
3
9
9
9
9
30
30
30
30
135
135
135
135
1,305
1,305
1,305
1,305
12,01512,01512,01512,015
108,945
108,945
108,945
108,945

Average
1.00
1.00
1.00
1.00
.889
.951
1.00
1.00
.967
.986
1.00
1.00
.8
.908
.808
.913
.702
.806
.744
.86
.641
.732
.666
.772
.6
.667
.616
.695

Table 7: Pairwise comparisons of nine skill groups with one city per bin

Unweighted comparisons
LHS HSD HS CD AA BA MA
Pro
2 HSD .423
3 HS.399 .413
4 CD.428 .486 .587
5 AA
.43 .483 .571 .483
6 BA
.476 .555 .644 .619 .602
7 MA .484 .558 .643 .614 .615 .528
8 Pro.484 .57 .645 .617 .604 .524 .499
9 PhD .49 .548 .598 .576 .577 .521 .501
.511
Population-difference weighted comparisons of US-born population
LHS HSD HS CD AA BA MA
Pro
2 HSD .568
3 HS
.488 .435
4 CD.583 .569 .649
5 AA.552 .53 .616 .453
6 BA.644 .65 .738 .695 .682
7 MA .648 .651 .738 .686 .695 .544
8 Pro.654 .654 .73 .676 .676 .533 .493
9 PhD .611 .605 .651 .605 .617 .502 .476
.497

43

Table 8: Pairwise comparisons of nine skill groups
Total
All individuals
US-born
Bins Weights
comparisons
success rate
success rate
2
Unweighted
36
.556
.806
2
Population differences
36
.556
.806
2
Educational shares
36
.754
.883
2
Pop diff × edu shares
36
.754
.883
3
Unweighted
108
.5
.685
3
Population differences
108
.54
.744
3
Educational shares
108
.707
.836
3
Pop diff × edu shares
108
.736
.866
5
Unweighted
360
.536
.714
5
Population differences
360
.55
.767
5
Educational shares
360
.747
.844
5
Pop diff × edu shares
360
.756
.873
10 Unweighted
1620
.528
.617
10 Population differences
1620
.551
.696
10 Educational shares
1620
.67
.715
10 Pop diff × edu shares
1620
.725
.802
30 Unweighted
15,660.521.59
30 Population differences
15,660.545.656
30 Educational shares
15,660.632.666
30 Pop diff × edu shares
15,660.699.754
90 Unweighted
144,180.527.568
90 Population differences
144,180.555.626
90 Educational shares
144,180.594.614
90 Pop diff × edu shares
144,180.657.694
270 Unweighted
1,307,340.536.559
270 Population differences
1,307,340.564.605
270 Educational shares
1,307,340.567.579
270 Pop diff × edu shares
1,307,340
.612
.635

44

Table 9: Occupational employment population elasticities

βσ1 Farming, Fishing, and Forestry Occupations
0.803 βσ12 Sales and Related Occupations
× log population(0.048)× log population
βσ2 Building and Grounds Cleaning and Maintenance 1.039 βσ13 Management occupations
× log population(0.011)× log population
βσ3 Food Preparation and Serving Occupations
0.985 βσ14 Arts, Design, Entertainment, Sports, and Media
× log population(0.011)× log population
βσ4 Construction and Extraction Occupations
1.037 βσ15 Business and Financial Operations Occupations
× log population(0.014)× log population
βσ5 Production Occupations
1.045 βσ16 Architecture and Engineering Occupations
× log population(0.025)× log population
βσ6 Transportation and Material Moving Occupations 1.061 βσ17 Computer and Mathematical Occupations
× log population(0.014)× log population
βσ7 Installation, Maintenance, and Repair Workers
1.015 βσ18 Healthcare Practitioners and Technical Occupations
× log population(0.011)× log population
βσ8 Healthcare Support Occupations
0.980 βσ19 Community and Social Services Occupations
× log population(0.013)× log population
βσ9 Personal Care and Service Occupations
1.065 βσ20 Education, Training, and Library Occupations
× log population(0.017)× log population
βσ10 Office and Administrative Support Occupations
1.081 βσ21 Life, Physical, and Social Science Occupations
× log population(0.010)× log population
βσ11 Protective Service Occupations
1.123 βσ22 Legal Occupations
× log population(0.014)× log population
Observations
5943 Observations
R-squared
0.931 R-squared
Occupation FE
Yes
Occupation FE
Standard errors, clustered by MSA, in parentheses

Table 10: Pairwise comparisons of occupations
Bins
Weights
Comparisons Success rate
2
Unweighted
231
0.723
2
popdif*skilldif
231
0.779
3
Unweighted
693
0.693
3
popdif*skilldif
693
0.772
5
Unweighted
2,310
0.652
5
popdif*skilldif
2,310
0.728
10
Unweighted
10,3950.606
10 popdif*skilldif
10,3950.688
30
Unweighted
100,4850.579
30 popdif*skilldif
100,4850.659
90Unweighted
925,155
0.561
90 popdif*skilldif
925,155
0.634
276
Unweighted
8,766,450
0.533
276 popdif*skilldif
8,766,450
0.583

45

1.037
(0.010)1.082(0.015)1.158(0.019)1.204(0.018)1.209(0.026)1.395(0.034)1.001(0.014)0.986(0.020)1.011(0.017)1.170(0.030)1.200(0.022)

5943
0.931
Yes

Table 11: Industrial employment population elasticities

βσ1 Apparel Manufacturing
× log population
βσ2 Textile Product Mills
× log population
βσ3 Leather and Allied Product Manufacturing
× log population
βσ4 Furniture and Related Product Manufacturing
× log population
βσ5 Textile Mills
× log population
βσ6 Wood Product Manufacturing
× log population
βσ7 Fabricated Metal Product Manufacturing
× log population
βσ8 Food Manufacturing
× log population
βσ9 Plastics and Rubber Products Manufacturing
× log population
βσ10 Primary Metal Manufacturing
× log population
Observations
R-squared
Industry FE
Only uncensored obs

1.237

1.024

1.018

0.955

(0.070)(0.148)

βσ11 Nonmetallic Mineral Product Manufacturing
× log population
1.125 0.905 βσ12 Paper Manufacturing(0.056) (0.135) × log population
0.743 0.147 βσ13 Printing and Related Support Activities(0.099) (0.284) × log population
1.120 1.000 βσ14 Electrical Equipment, Appliance & Component(0.050) (0.076) × log population
0.823 0.352 βσ15 Machinery Manufacturing(0.105) (0.208) × log population
0.848 0.608 βσ16 Miscellaneous Manufacturing(0.055) (0.085) × log population
1.094 1.036 βσ17 Beverage and Tobacco Product Manufacturing(0.048) (0.050) × log population
0.953 0.864 βσ18 Transportation Equipment Manufacturing(0.050) (0.067) × log population
1.105 0.975 βσ19 Petroleum and Coal Products Manufacturing(0.056) (0.070) × log population
0.997 0.449 βσ20 Computer and Electronic Product Manufacturing(0.078) (0.107) × log population
βσ21 Chemical Manufacturing
× log population
5406
2130 Observations
0.564 0.541 R-squared
Yes
Yes
Industry FE
Yes
Only uncensored obs
Standard errors, clustered by MSA, in parentheses(0.036)(0.042)

0.901

0.539

(0.063)(0.104)

1.202

1.122

(0.036)(0.047)

1.159

0.813

(0.074)(0.111)

1.071

0.960

(0.055)(0.069)

1.224

1.208

(0.044)(0.059)

1.168

1.010

(0.065)(0.147)

1.254

0.940

(0.075)(0.101)

0.951

0.393

(0.074)(0.308)

1.453

1.254

(0.075)(0.108)

1.325

0.992

(0.065)(0.098)

5406
0.564
Yes

2130
0.541
Yes
Yes

Table 12: Pairwise comparisons of manufacturing industries
Bins
Weights
Comparisons Success rate
2
Unweighted
210
.638
2
popdif*skilldif
210
.675
3
Unweighted
630
.640
3
popdif*skilldif
630
.665
5
Unweighted
2,100
.606
5
popdif*skilldif
2,100
.633
10
Unweighted
9,450
.561
10 popdif*skilldif
9,450
.597
30
Unweighted
91,350.543
30 popdif*skilldif
91,350.573
90Unweighted
841,050.522
90 popdif*skilldif
841,050.549
276
Unweighted
7,969,500.495
276 popdif*skilldif
7,969,500.531
Note: The number of cities per “bin” may differ by one, due to the integer constraint.

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