Labor market pooling and occupational agglomeration

Abstract

This paper examines the micro-foundations of occupational agglomeration in U.S. metropolitan areas, with an emphasis on labor market pooling. Controlling for a wide range of occupational attributes, including proxies for the use of specialized machinery and for the importance of knowledge spillovers, we find that jobs characterized by a unique knowledge base exhibit higher levels of geographic concentration than do occupations with generic knowledge requirements. Further, by analyzing co-agglomeration patterns, we find that occupations with similar knowledge requirements tend to co-agglomerate. Both results provide new evidence on the importance of labor market pooling as a determinant of occupational agglomeration.

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Federal Reserve Bank of New York
Staff Reports
Labor Market Pooling and Occupational Agglomeration
Todd M. Gabe
Jaison R. Abel
Staff Report no. 393
September 2009
Revised January 2010
This paper presents preliminary findings and is being distributed to economists
and other interested readers solely to stimulate discussion and elicit comments.
The views expressed in the paper are those of the authors and are not necessarily
reflective of views at the Federal Reserve Bank of New York or the Federal
Reserve System. Any errors or omissions are the responsibility of the authors.

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Labor Market Pooling and Occupational Agglomeration
Todd M. Gabe and Jaison R. Abel
Federal Reserve Bank of New York Staff Reports, no. 392
September 2009; revised January 2010
JEL classification: R12, J24, R20
Abstract
This paper examines the micro-foundations of occupational agglomeration in U.S.
metropolitan areas, with an emphasis on labor market pooling. Controlling for a wide
range of occupational attributes, including proxies for the use of specialized machinery
and for the importance of knowledge spillovers, we find that jobs characterized by a
unique knowledge base exhibit higher levels of geographic concentration than do
occupations with generic knowledge requirements. Further, by analyzing co-
agglomeration patterns, we find that occupations with similar knowledge requirements
tend to co-agglomerate. Both results provide new evidence on the importance of labor
market pooling as a determinant of occupational agglomeration.
Key words: agglomeration, occupations, labor market pooling, knowledge
Gabe: University of Maine (e-mail: todd.gabe@umit.maine.edu). Abel: Federal Reserve Bank
of New York (e-mail: jaison.abel@ny.frb.org). The views expressed in this paper are those of the
authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or
the Federal Reserve System.

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I. INTRODUCTION
Alfred Marshall’s ideas provide a conceptual foundation for contemporary
research on the determinants of agglomeration: labor market pooling, sharing of
specialized inputs, and knowledge spillovers.1 One important aspect of labor market
pooling is that a high agglomeration of activity provides workers and businesses with a
wide range of options if they possess or require a unique skill set. In their study of co-
agglomeration patterns, Ellison, Glaeser and Kerr (2009) found that industries employing
the same types of workers tend to co-agglomerate. This behavior is advantageous to
workers and firms: people can move among employers without retooling and businesses
have access to a deep pool of labor with the skills they need.
This paper examines the micro-foundations of occupational agglomeration in U.S.
metropolitan areas. Here, we emphasize the importance of labor market pooling as
measured by the extent to which workers possess a specialized knowledge base covering
a wide range of topics. People in jobs with generic knowledge requirements are not
expected to benefit from labor market pooling, whereas individuals in occupations that
need a specialized base of knowledge are apt to seek out a place with a high
agglomeration of activity. With respect to co-agglomeration, we expect occupations with
similar knowledge profiles to co-locate. Such behavior facilitates movement among jobs
with similar types of knowledge and helps to ensure Marshall’s (1920) “constant market
for skill.”
Our analysis of the geographic concentration of occupations provides a new way
to look at the forces of agglomeration. Industry-centric studies focus on where similar

1 For surveys of the literature on agglomeration, see Duranton and Puga (2004) and Rosenthal and
Strange (2004).

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types of goods and services are made, since sectors are assigned based on a firm’s
primary output. In contrast, recent occupational-based approaches to urban and regional
analysis emphasize what people do in their jobs (Feser 2003; Markusen 2004; Florida,
Mellander and Stolarick 2008; Gabe 2009; Scott 2009; Bacolod, Blum and Strange
2009a, 2009b). For example, Markusen (2004, p. 254) suggests the use of occupational-
level data to examine the “skills and activities of those in a particular neighborhood.”
Here, we use occupations to understand the knowledge required to perform a job, as well
as a worker’s use of specialized equipment and the importance of keeping current with
new information and trends.
For at least two of Marshall’s (1920) micro-foundations of agglomeration, the
benefits of a geographic concentration of activity seem to be more relevant for
occupations (i.e., tasks and activities people perform in their jobs) than industries (i.e.,
goods and services provided). Agglomeration facilitates knowledge spillovers because it
allows individuals to share ideas and tacit knowledge (Kloosterman 2008; Ibrahim, Fallah
and Reilly 2009). A computer programmer, for example, presumably benefits more from
proximity to others involved in similar day-to-day activities (e.g., interacting with
computers, using technology) than he or she gains from working next to others in the
same industry (e.g., a software company’s receptionist, human resources specialist, or
chief executive).
Likewise, the basic idea behind labor market pooling—that agglomeration
provides a thick labor market for those who possess or require a particular skill set—
seems to apply more readily to occupations than industries. In an analysis of industry
agglomeration, Rosenthal and Strange (2001, p. 205) suggest that labor market pooling is

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the most problematic of the Marshallian micro-foundations to measure because “it is
difficult to identify industry characteristics that are related to the specialization of the
industry’s labor force.”2 This is not the case with occupations. Some jobs require a very
specific knowledge and skill set that is specialized to the task at hand, while other
occupations call for a more generic set of knowledge and skills.
II. AGGLOMERATION OF U.S. OCCUPATIONS
Following Krugman (1991) and Audretsch and Feldman (1996), we begin our
analysis using locational Gini coefficients to measure occupational agglomeration across
U.S. metropolitan areas. The locational Gini coefficient (LGINI) for U.S. Census
occupations, indexed by k, is calculated as (Kim, Barkley and Henry 2000):
(1)
LGINIk = Δ / 4 u,
where Δ = {1 / [n (n-1)]}
1
n
i=∑
1
n
j=∑| xi – xj |

i, j = U.S. metropolitan areas (i ≠ j)

u = mean of xi

xi(j) = (metro area i’s (j’s) share of employment in k /
metro area i’s (j’s) share of total employment)
and, n = 324, the number of U.S. metropolitan areas included in the analysis.
Locational Gini values close to zero suggest that employment in the occupation is widely
dispersed across U.S. metropolitan areas and spread out in a manner similar to the
distribution of overall employment. Values close to 0.5 suggest that workers in the

2 Rosenthal and Strange (2001) use three measures (e.g., net productivity, an indicator of “brains to
brawn,” and the percentage of workers with advanced degrees) as proxies for the importance of labor
market pooling. Overman and Puga (2009) point out the limitations of these indicators and, instead,
focus on the effects of idiosyncratic firm-level employment shocks on industry agglomeration. Their
results suggest establishments that expand while the overall industry declines (or vice versa) benefit
more from agglomeration than plants in sectors with homogeneous employment shocks.