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Federal Reserve Bank of Chicago

Evidence on the within-industry
agglomeration of R&D, production,
and administrative occupations
Benjamin Goldman, Thomas Klier, and
Thomas Walstrum

November 2016
WP 2016-20

Evidence on the within-industry agglomeration of
R&D, production, and administrative occupations
Benjamin Goldman1, Thomas Klier2, and Thomas Walstrum3
November 30, 2016
Abstract
To date, most empirical studies of industrial agglomeration rely on data where observations are
assigned an industry code based on classification systems such as NAICS in North America and
NACE in Europe. This study combines industry data with occupation data to show that there are
important differences in the spatial patterns of occupation groups within the widely used industry
definitions. We focus on workers in manufacturing industries, whose occupations almost always
fit into three groups: production, administrative, or R&D. We then employ two approaches to
document the spatial distributions of each group within an industry. First, we calculate the
distribution of employment shares across local labor markets and second, we calculate a version
of the Duranton and Overman (2005) agglomeration index. Both approaches reveal appreciable
differences in the spatial distribution of occupation groups within most manufacturing
industries. These differences have important implications for our understanding of the sources
of industrial agglomeration, the spatial agglomeration of innovation, the effectiveness of local
economic development initiatives, and the spatial properties of particular industries.

Stanford Institute for Economic Policy Research
Federal Reserve Bank of Chicago
3 Federal Reserve Bank of Chicago
1
2

Introduction

The geographic concentration of industries – such as high-tech in Silicon Valley and autos in
Detroit – has fascinated researchers and practitioners going back at least to Alfred Marshall
(1890). This is because industrial agglomeration plays an important role in a variety of research
and practical fields, including economic growth, industrial organization, international trade,
business strategy, local economic development, local public finance, and urban planning. While
theories of industrial agglomeration have been well developed for some time, empirical studies
that test the theories have been published only somewhat recently. To date, these studies have
primarily relied on data where observations are assigned an industry code base on classification
systems such as NAICS in North America and NACE in Europe.1 This study shows that there
are notable differences in the spatial patterns of occupation groups within these industry
definitions, which can have important implications for our understanding of the nature and
effects of industrial agglomeration.
We are able to look within the black box of industry definitions because we use worker-level
data from the American Community Survey (ACS) rather than establishment-level data, such as
County Business Patterns. The ACS asks workers to specify their place of work, industry, and
occupation. We sort the Census occupation codes into twelve large groups based on the
similarity of the tasks the occupation titles describe. Following the earlier literature on
industrial agglomeration, and to simplify our analysis, we focus on manufacturing industries.
In that sector, production functions are similar to the extent that over 80% of workers fall into
either the production, administrative, or R&D2 occupation groups.
We use two approaches to document the differences in the spatial distributions of occupation
groups within an industry. First, for each group within an industry, we calculate the
distribution of employment shares across local labor markets. The idea behind this approach is
that since the industry classification systems delineate industries based on the similarity of
establishments’ production functions, we would expect the share of employment for an
occupation group in any given local labor market to be close to that of the industry as a whole.
That is, if 50% of all the workers in paper mills in the US are production workers, then we
would expect to find that about 50% of the workers in paper mills in the Lake Winnebago,
Wisconsin region are production workers. Overall, we find little evidence for that type of
relationship. Instead, we find notable variation in employment shares for most occupation
groups across local labor markets in most industries, with the largest variation typically for
R&D shares.

These classification systems generally seek to delineate industries based on the similarity of the
production functions of establishments, though some industry definitions are based on the similarity of
the product produced (Economic Classification Policy Committee 1993).
2 Note that because we are using occupation titles to identify R&D workers, our definition of R&D
activity necessarily differs from the literature on R&D labs.
1

Our second approach is to calculate a worker-level (rather than establishment-level) version of
the Duranton and Overman (2005) agglomeration index for each occupation group within an
industry. The idea behind this approach is that if occupation groups have different
agglomeration index values, their spatial distributions must be different. Here too, we find
notable differences in occupation groups’ index values. In particular, we find that in most
industries, R&D employment is the most concentrated, followed by administrative
employment, and then production employment.
The presence of agglomeration by occupation groups within industries is relevant to the many
research fields where industrial agglomeration plays an important role. The most direct
application is to the empirical literature on the sources of industrial agglomeration, which has
made tangible progress in the past two decades (Combes and Gobillon 2015). The aim of this
literature is to quantify the relative importance of the theories put forth by Marshall (1890) for
what causes agglomeration economies. Marshall suggested that industries agglomerate and
coagglomerate to reduce the costs of transporting goods, people, and ideas. Thus firms in the
same industry cluster near customers or suppliers, cluster to share in the same local labor force,
or to take advantage of intellectual spillovers. An additional explanation that has developed
since Marshall is that industries cluster in areas with natural advantages, such as the wine
industry in Napa Valley, where the soil and climate are particularly well suited to wine
production. To distinguish between these explanations, empirical studies have constructed
quantitative measures that aim to proxy for the possible sources of agglomeration economies
and used these measures to try to explain the variation in the agglomeration or coagglomeration
indexes of industries. Because it is likely that within-industry occupation groups cluster for
different reasons, studies that incorporate such information may be able to obtain further
precision in their estimates beyond what is in the current literature.
Our study also relates to the literature on the spatial agglomeration of innovation. This line of
research documents the highly concentrated nature of innovation in space – which is consistent
with our finding that R&D workers are the most concentrated within industries – and seeks to
understand why this is the case (Carlino and Kerr 2015). The primary sources of data for the
literature are the locations of R&D labs, R&D spending, and patents and citations. The results of
our study suggest that the location of R&D workers could be an additional useful source of
information on where innovation happens.
The results of this study are relevant for the literature on local economic development and
place-based policies (Bartik 2012, Neumark and Simpson 2015). To the extent that such policies
seek to support or develop an industrial cluster, it could be very important to account for the
within-industry characteristics of the workforce needed for the cluster. For example, should a
partnership between the firms in an industrial cluster and local educational institutions focus on
developing engineers with 4-year degrees or developing skilled machine operators?

Finally, the results of our study can also be useful for the study of particular industries. For
example, the declining share of motor vehicle production in the traditional Midwestern
locations and the shift of the industry southward is well documented (see e.g. Klier and
McMillen, 2008). Yet, this development does not seem to have affected the viability of the
automotive R&D cluster, which remains centered in Detroit, and seems as strong today as it has
been for quite some time (Hannigan, Cano-Kollmann and Mudambi 2015, Klier, Testa and
Walstrum 2014, Walstrum and Testa 2013).

Data description

Most studies of industrial agglomeration in the US use establishment-level data such as the
Annual Survey of Manufactures or County Business Patterns. Unfortunately, these sources
provide little information on the types of work that takes place in the establishments, which we
show can vary widely for manufacturers. For example, a firm can have separate R&D labs and
production plants that are separate establishments, but that are treated as identical in
establishment-based datasets. To look within the black box of the industry definitions that the
establishment data rely on, we use worker-level data from the public 2010-14 ACS, as provided
by IPUMS (Ruggles, et al. 2015). ACS respondents report their age, employment status, work
location, industry, and, importantly, their occupation. We include in our analysis anyone over
age 16 who reports being employed.
To delineate local labor markets, we use commuting zone definitions (year 2000 version) created
by the US Department of Agriculture’s Economic Research Service. Commuting zones (CZs)
cover the entire United States, which is an advantage over the US Office of Management and
Budget’s Metropolitan Statistical Areas (MSAs), which cover only urban areas. Like MSAs, CZs
are a set of adjacent counties. The ACS identifies a respondent’s place of work as within the
boundaries of a Public Use Microdata Area (PUMA), which are drawn by the Census Bureau to
contain roughly 100,000 people. Thus PUMAs in urban areas can cover very small land areas,
while PUMAs in rural areas can cover multiple counties. Because PUMA boundaries are not
county-based, occasionally, a PUMA overlaps more than one commuting zone. In this case, we
use a crosswalk between counties and PUMAs generated by the Missouri Census Data Center’s
MABLE/Geocorr14 geographic correspondence engine. We multiply the crosswalk’s population
weights by the individual sampling weights in the ACS, so that the observations for individuals
in PUMAs that fall in more than on CZ are split across the CZ based on the share of the
PUMA’s population in each county.
Industry is defined by the 2012 Census industry classification system, which is based on the
NAICS and at roughly the same aggregation level as the 4-digit NAICS.
We use the occupation codex created by IPUMS called OCC1990 that is based on Census
occupation codes, but consolidated so that they are consistent across Census years from the
present back to 1950. We then sort workers into one of 12 occupation groups based on their
occupation titles. Appendix table A1 lists the occupation codes, occupation titles, and the
4

groups we assign to them. Table 1 shows the distribution of workers by occupation for all
industries and for manufacturing industries. Across all industries, administrative workers are
the largest group (25.7%), followed by production (12.5%). The ranking switches for
manufacturing industries, with 45.8% of workers in the production group and 22.8% in the
administrative group. R&D workers are the next largest group in manufacturing, representing
12.6% of workers. This share is much larger than for all industries together, where only 6.4% of
workers are in R&D occupations. Figure 1 shows the distribution of occupation group shares
across manufacturing industries. Production shares vary the most, while administrative and
R&D shares are more uniform.
Table 1 also shows that there are a number of occupation types that are closely tied to particular
non-manufacturing industries. For example, educators largely serve in the education industry
and farmers work almost exclusively in the farming industry. However, some ACS survey
respondents say they are farmers working in a manufacturing industry. While it is possible that
there are some food growers employed by manufacturing firms, it is likely that Census Bureau
misclassified such workers’ industries or occupations. That said, it appears that the number of
misclassifications is small. Moreover, our subsequent analysis focuses on production,
administrative, and R&D workers. It is possible that some of these workers’ occupations are
misclassified, but the measurement error is also likely to be small.

Methodology

We use two approaches to document the differences in the spatial distributions of occupation
groups within a manufacturing industry. First, we calculate the distribution of employment
shares by occupation group across local labor markets as defined by CZs. For example, for
workers in the dairy product manufacturing industry, for each local labor market, we calculate
the share of workers who are in the production group. We then compare the distribution of
local labor market shares to the overall share of production workers in dairy product
manufacturing. If there is a large variance in the distribution of shares, we can conclude that the
spatial distributions of within-industry occupation group are unique. To assess the size of the
variance, we calculate the absolute percentage distance of each CZ’s occupation share from the
overall industry share:

𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 100 ∙ |

𝑂𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 𝑆ℎ𝑎𝑟𝑒𝐶𝑍
− 1|.
𝑂𝑐𝑐𝑢𝑝𝑎𝑡𝑖𝑜𝑛 𝑆ℎ𝑎𝑟𝑒𝐼𝑛𝑑

We then calculate for each occupation group the share of an industry’s workers who live in
commuting zones where the absolute percentage distance is greater than 25% and 50%. We
focus on the 53 of 80 manufacturing industries that have at least 20 commuting zones with more
than 50 observations.

To measure the agglomeration of the occupation groups, we use the index created by Duranton
and Overman (Duranton and Overman 2005). The DO index was designed to be calculated at
the establishment level and is based on a nonparametric estimate of the probability density
function of the pairwise distances between establishments. Thus the distribution’s mean is the
mean distance between establishments. Our data are at the worker level and in calculating the
index we treat each worker as an “establishment”.
Calculating an agglomeration index requires a counterfactual distribution. The most common
counterfactuals used in the literature to date are the distribution of overall employment and the
distribution of overall manufacturing employment. Because we focus on manufacturing
industries, we use the distribution of overall manufacturing employment as our counterfactual.
An industry or occupation group exhibits agglomeration if the distribution of distances between
its workers is more concentrated at smaller distances than the overall distribution of the
distances between manufacturing workers.
A nonparametric estimate of the distribution of pairwise distances between 𝑛 establishments is
the summation of

𝑛(𝑛−1)
2

Gaussian kernel functions, giving a kernel density function of:
𝑛−1

𝑛

2
𝑑 − 𝑑𝑟𝑠
̂=
𝐾
∑ ∑ 𝑓(
),
𝑛(𝑛 − 1)ℎ
ℎ
𝑟=1 𝑠=𝑟+1

where 𝑑𝑟,𝑠 is the distance between establishments 𝑟 and 𝑠 and 𝑓(∙) is a Gaussian kernel function
with bandwidth ℎ.3
To calculate distances, we would ideally have the exact address of a worker’s place of work. In
this case, we only know that the place of work is somewhere within a PUMA, so we use the
Euclidian distances between PUMA centroids as the measure of the distance between workers.
Because PUMAs contain multiple workers (“establishments”) with identical distance profiles,
̂ at the PUMA level, weighting by employment levels in the PUMA:
we calculate 𝐾
𝑛

̂=
𝐾

𝑛

2
𝑑 − 𝑑𝑟𝑠
∑ ∑ 𝑤𝑟𝑠 𝑓 (
),
𝑛
𝑛
∑𝑟=1 ∑𝑠=𝑟 𝑤𝑟𝑠
ℎ
𝑟=1 𝑠=𝑟

where
𝑤𝑟𝑠 = 𝑒𝑚𝑝𝑟 𝑒𝑚𝑝𝑠 𝑖𝑓 𝑟 ≠ 𝑠
𝑒𝑚𝑝𝑟
𝑤𝑟𝑠 = (
) 𝑖𝑓 𝑟 = 𝑠.
2

For the bandwidth, we follow the standard approach in the literature, which is to use the optimal
bandwidth derived by Silverman (1986), 1.06𝑠𝑛−0.2 , where 𝑠 is the standard deviation of the 𝑛(𝑛 − 1)
distance.
3

̂ does not have any positive density over negative
We make a final adjustment so that 𝐾
distances. This problem arises because the kernels are symmetrical so that when individuals
have very short distances between them, some of the affiliated kernel will be in negative
territory. One solution, proposed by Silverman (1986), is to reflect the density for negative
distances over the zero line. For example, any density at 𝑑 = −3 is added to the density at 𝑑 =
3. This adjustment results in the kernel density function
𝑛

̂=
𝐾

𝑛

2
𝑑 − 𝑑𝑟𝑠
−𝑑 − 𝑑𝑟𝑠
∑ ∑ 𝑤𝑟𝑠 [𝑓 (
)+𝑓(
)] 𝑓𝑜𝑟 𝑑 > 0,
𝑛
𝑛
∑𝑟=1 ∑𝑠=𝑟 𝑤𝑟𝑠
ℎ
ℎ
𝑟=1 𝑠=𝑟

̂ = 0 𝑓𝑜𝑟 𝑑 ≤ 0.
𝐾
The DO agglomeration index is the sum of the differences in densities from zero miles up to a
selected threshold. This is equivalent to the difference in the CDFs of the kernel density
functions at a given distance. Formally, the index is:
𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑

̂𝑖𝑛𝑑 (𝑑) − 𝐾
̂𝑚𝑓𝑔 (𝑑) 𝑑𝑑
𝐾

Γ𝑖𝑛𝑑 = ∫
0

We also calculate an alternate version of the index, which is the ratio of the CDFs of the kernel
density functions at a given distance. We believe this version is easier to interpret in the context
of this paper as it tells us how many times greater the share of pairwise distances under a
certain threshold is for an industry or occupation group. Formally, the alternative “ratio”
version of the index is:
𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑

Ρ𝑖𝑛𝑑 = ∫
0

̂𝑖𝑛𝑑 (𝑑)
𝐾
𝑑𝑑
̂𝑚𝑓𝑔 (𝑑)
𝐾

The value of the index hinges critically on the choice of threshold. The literature provides some
guidance on the choice. Duranton and Overman (2005) use the median establishment-toestablishment distance in their UK-based data, which is 112 miles (180 kilometers). Ellison,
Glaeser, and Kerr (2010) estimate a median plant distance of around 1,000 miles in their USbased data, calculate the index using thresholds ranging from 100 to 1,000 miles, and use the
250-mile threshold for their primary results. Ellison, Glaeser, and Kerr (2010) note that the large
geographic area of the lower 48 states in US makes it harder to pick a threshold and that to date
there is no theory to dictate the threshold.
In this paper we report index values at the 100 and 250 mile threshold, and our preferred
threshold is 100 miles. Our logic for the 100 mile preference is that a 100 mile diameter circle
covers most metropolitan areas, but goes no further, so that the measure is not diluted by the
relatively sparsely populated areas between metropolitan areas. Thus one way to think of the
agglomeration index at this threshold is it that counts the number of metropolitan areas with a
significant cluster: the lower the number, the greater the index. At 250 miles and greater

distances, the index has the advantage of capturing possible linkages between neighboring
metropolitan areas, but at the expense of additional noise.

A case study of the auto industry

The auto industry is an archetype of the clustering of occupation groups within an industry and
thus serves as a useful case study. In this section, we document the spatial distribution of the
auto industry’s R&D, production, and administrative occupation groups and show how their
distributions differ using the methodology detailed in section 3.
Figure 2 shows a dot distribution of all auto workers in the continental US from the 2014 ACS,
where there are 2,500 dots and one dot equals 0.04% of workers. While there is some
employment in the heavily populated coastal areas, the auto industry is clearly concentrated in
the middle of the country, starting in the Detroit area and extending south along what is known
as Auto Alley. Figure 3 provides a picture inside the overall spatial distribution of auto workers,
with a separate map for production, administrative, and R&D workers. While all types of
workers are concentrated in the Detroit area, production workers appear to be the most spread
out, with greater density in Auto Alley and the Appalachians. Administrative and R&D
workers also appear to be more concentrated in urban areas.
Our first approach to quantitatively documenting these visual differences is to examine the
distribution of employment shares by occupation group across local labor markets. The idea of
this approach is that if the establishments within an industry have similar production functions,
the employment shares of the occupation groups should be similar across local labor markets.
Figure 4 shows maps for each of the occupation groups where we color-coded CZs based on
how close they are to the occupation group’s countrywide share of industry employment. For
example, panel C shows R&D worker shares across CZs. R&D workers make up about 12% of
all autoworkers, and we assign yellow to commuting zones whose shares are plus or minus 25%
percent of 12% (i.e., shares that range from 9% to 15%). In red commuting zones, the share is
more than 50% of the overall industry share (i.e., greater than 18%) and in dark green
commuting zones, the share is less than 50% of the overall industry share (i.e., less than 6%). Of
particular importance in the case of the auto industry, the Detroit CZ (where about 16% of all
autoworkers work) is red. About 28% of autoworkers in the Detroit CZ are R&D workers,
which is over twice the share of the auto industry as a whole.
We summarize the maps from figure 4 in figures 5 and 6. Figure 5 shows the distribution of
employment shares by occupation groups across CZs as a histogram. Returning to the R&D
distribution, panel C shows that around 75% of commuting zones have an R&D employment
share that is less than the industrywide share and that around 60% of CZs have an R&D
employment share that is 25% less than the industrywide share. This is strong evidence that
auto R&D workers are not evenly distributed across local labor markets.

We also want to take into account that auto employment is not evenly distributed across the
CZs that have auto employment. As we noted earlier, 16% of all autoworkers work in one CZ,
Detroit. For this reason, we also calculate the distributions of occupation group shares across
CZs weighted by the CZ’s total auto industry employment. Again turning to the R&D group,
figure 6 panel C shows that around 40% of all autoworkers work in a CZ whose share of R&D
workers is at least 25% percent less than the overall industry share. In addition, 26% of
autoworkers work in a CZ whose share of R&D workers is at least 25% more than the overall
industry share (70% of those workers work in the Detroit CZ). This too is strong evidence that
auto R&D workers are not evenly distributed across local labor markets.
We now apply our second approach for documenting differences in the spatial patterns of
occupation groups within industries to the auto industry. We use the DO agglomeration index,
which is based on the kernel density function (KDF) of pairwise distances between
establishments, or in our case, workers. Figure 7 shows the KDFs for the occupation groups in
the auto industry compared to the KDF for all manufacturing workers. All three occupation
groups are clearly more concentrated than manufacturing as a whole (that is, they have a much
larger share of pairwise distances at low mileages), but it is also clear that there are differences
between the groups. R&D is far more concentrated than the other groups, and the distributions
for administrative and R&D workers are slightly bimodal.
Table 2 shows values of the DO index for the auto industry as a whole and the occupation
groups within it when calculated using either a 100- or 250-mile threshold. At the 100-mile
threshold, the standard (0.176) and ratio (7.2) versions of the index confirm that R&D
employment is the most concentrated. The ratio version of the index indicates that the share of
pairwise distances that are less than 100 miles is 7.2 times larger for R&D autoworkers than for
all manufacturing workers. Administrative and production autoworkers are still quite
concentrated (with ratios of 3.6 and 2.5), but notably less so than R&D autoworkers. At the 250mile threshold, R&D autoworkers are still much more concentrated than other autoworkers, but
there is no difference in the concentration of administrative and production workers.

Results for all manufacturing industries

While the auto industry is an archetype for within-industry agglomeration by occupation
group, we find that occupation groups have agglomerate within most other manufacturing
industries as well. We first document the extent to which occupation group shares in CZs differ
from their industry’s overall share.
Figure 8 shows the distribution across industries and occupation groups of the share of workers
living in CZs where the absolute percentage distance is greater than 25%. Summary statistics for
the distributions are given in table 3. As in the auto industry, the variance of R&D shares across
CZs is the largest for most industries. For any manufacturing industry, at least 24% of workers
live in a CZ where the absolute percentage difference is greater than 25%. The average is 62%.

Figure 9 shows the distribution across industries and occupation groups of the share of workers
living in CZs where the absolute percentage distance is greater than 50%. At this threshold, far
fewer workers qualify, particularly in terms of production shares, where for nine industries,
zero workers qualify. However, there are many industries where a large share of workers
qualify in terms of R&D shares, where the average share of workers is 34% and goes as high as
78%.
We now turn to evidence on within-industry clustering of occupation groups based on DO
agglomeration indexes. Table 4 shows summary statistics for the four versions of the index we
calculate for across industries and occupation groups. (Note that the standard and ratio
versions of the index inherently tell the same story because their formulas are closely related.)
In line with the literature, the agglomeration indexes we calculate indicate that most
manufacturing industries agglomerate, with an average standard version index value of 0.02 at
the 100-mile threshold and 0.04 at the 250-mile threshold. The ratio versions of the indexes
indicate that the average industry is 1.89 times more concentrated than manufacturing
employment as a whole at the 100-mile threshold and 1.42 times more concentrated at the 250mile threshold. Once again in line with the literature, the indexes we calculate indicate that
there is a lot of variation across industries in their degree of concentration, with some industries
not concentrated at all (standard version index values of less than zero, ratio version index
values of less than 1) and some industries highly concentrated.
The agglomeration patterns of occupation groups within industries are quite similar to those of
the auto industry. Administrative employment tends to be more concentrated than production
employment at the 100-mile threshold, but not at the 250-mile threshold. R&D employment is
the most concentrated, with an average 100-mile ratio index of 2.8, compared to an average
production index of 1.9, and an average administrative index of 2.1. Figure 10 shows the full
distributions of the 100-mile threshold ratio version index across industries and occupation
groups. While index values for most industries fall between 1 and 2 for all occupation groups, it
is clear that the R&D distribution is the widest and most skewed away from 1, followed by the
administrative distribution, and, finally, the production distribution.
We explicitly compare the occupation group index values for all versions of the index in table 5.
For the standard indexes, we calculate the difference between the R&D index and either the
production or administrative index. For the ratio indexes, we calculate the ratio of the R&D
index to either the production or administrative index. On average, at the 100-mile threshold,
R&D employment is 56% more concentrated than production employment and 33% more
concentrated than administrative employment. At the 250-mile threshold, R&D employment is
about 20% more concentrated on average than both production and administrative
employment.
It is also worth noting that there is some variation across industries in how different their
occupation group indexes are. For a small minority of industries, R&D employment is less
concentrated than production or administrative employment. There are also a handful of
10

industries where R&D is substantially more concentrated. Figure 11 presents the full
distribution of the ratio of the R&D index to either the production or administrative index for
the 100-mile ratio version of the index. Most of the ratios are greater than 1.25 and many are
greater than 1.5, values that represent a notable difference in the degree of concentration. Figure
11 makes clear, then, that for most manufacturing industries, occupation groups have different
spatial footprints.

Conclusion

This paper provides evidence that occupation groups within industries have unique spatial
patterns. We show this using two approaches. First, we document variation across local labor
markets in occupation groups’ shares of employment. We find that for most manufacturing
industries and most occupation groups, there are many local labor markets where an
occupation group’s share is much larger or smaller than its overall industry share. Our second
approach is to calculate agglomeration indexes for occupation groups. This approach reveals
notable differences for most manufacturing industries in the degree of concentration of
occupation groups, particularly when comparing R&D to production occupations. These
differences provide strong evidence for a broad-based pattern of different spatial footprints for
occupation groups within manufacturing industries.
We hope that our finding will filter into the wide array of research topics where industrial
agglomeration plays a role, including the literatures on the sources of industrial agglomeration,
the spatial agglomeration of innovation, and local economic development. For example, R&D
occupations likely cluster to take advantage of knowledge spillovers and labor market pooling,
while production occupations likely cluster to take advantage of supplier linkages and
proximity to customers. Because there are likely different forces behind the agglomeration of
R&D and production occupation groups, the variation in their spatial patterns could help
further clarify the relative importance of the sources of industrial agglomeration. The literature
on the spatial agglomeration of innovation may benefit from the ability to identify the location
of R&D workers within industries and possible knowledge sharing linkages across industries.
Finally, the literature on local economic development could evaluate, for example, the payoff to
subsidies to certain industrial clusters depends on the occupational composition of the cluster.

Tables and Figures

Table 1. Distribution of Occupation Types, 2010-14 ACS

Total
Production
Administrative
R&D
Business Services
Transportation
Sales
Personal Services
Education
Entertainment
Farming
Government
Health

Employment (1000s)
All Industries Manufacturing
144,377
14,967
18,113
6,854
37,164
3,407
9,188
1,887
9,047
916
5,982
683
15,718
637
15,339
355
13,336
< 100
6,618
< 100
1,778
< 100
10,022
< 100
2,073
< 100

Share
All Industries Manufacturing
100.0
100.0
12.5
45.8
25.7
22.8
6.4
12.6
6.3
6.1
4.1
4.6
10.9
4.3
10.6
2.4
9.2
<1
4.6
<1
1.2
<1
6.9
<1
1.4
<1

Table 2. Duranton and Overman Indexes for the Auto Industry
Standard
Ratio
100-mile
250-mile
100-mile
250-mile
All Workers
0.054
0.171
2.9
2.7
Production
0.042
0.167
2.5
2.7
Administrative
0.074
0.169
3.6
2.7
R&D
0.176
0.296
7.2
4.0
Note: The standard version of the index is the share of pairwise
distances under a certain mileage threshold for a given group minus
the share of pairwise distance under the threshold for all
manufacturing workers. The ratio version of the index is the share of
a given group divided by the share for all manufacturing workers.

Table 3. Summary statistics of distribution across industries of
share of workers in commuting zones with absolute percentage
distance from overall industry share greater than 25% or 50%
Mean
Std. Dev. Minimum
Maximum
Share of workers > 25%
Production
30
21
1
79
Administrative
41
17
13
82
R&D
62
17
24
100
Share of workers > 50%
Production
7
8
0
31
Administrative
14
12
0
62
R&D
34
18
2
78
Note: Commuting zones with fewer than 50 observations are excluded.
Industries with fewer than 20 commuting zones with fewer than 50
observations are excluded. Fifty-three of 80 manufacturing industries
meet this requirement.

Table 4. Summary statistics of the Duranton and Overman
agglomeration indexes across industries, by version, mileage
threshold, and occupation group
Mean Std. Dev.
Minimum
Maximum
Standard, 100-mile
Industry
0.02
0.05
-0.01
0.36
Production
0.02
0.05
-0.01
0.37
Administrative
0.03
0.05
0.00
0.37
R&D
0.05
0.06
0.00
0.36
Standard, 250-mile
Industry
0.04
0.08
-0.02
0.49
Production
0.04
0.08
-0.02
0.52
Administrative
0.04
0.07
-0.01
0.44
R&D
0.07
0.10
-0.02
0.53
Ratio, 100-mile
Industry
1.89
1.65
0.77
13.67
Production
1.85
1.73
0.79
14.11
Administrative
2.10
1.72
0.84
14.08
R&D
2.77
2.05
1.07
13.88
Ratio, 250-mile
Industry
1.42
0.77
0.83
5.87
Production
1.43
0.83
0.82
6.23
Administrative
1.41
0.66
0.88
5.36
R&D
1.73
1.00
0.82
6.27
Note: There are 80 manufacturing industries. The standard version
of the index is the share of pairwise distances under a certain
mileage threshold for a given group minus the share of pairwise
distance under the threshold for all manufacturing workers. The
ratio version of the index is the share of a given group divided by
the share for all manufacturing workers.

Table 5. Summary statistics of the differences between or ratios of the
standard or ratio versions of the Duranton and Overman agglomeration
index for occupation groups, by mileage threshold
Mean Std. Dev. Minimum Maximum
Standard, 100-mile
R&D minus Production
0.03
0.03
-0.01
0.13
R&D minus Administrative
0.02
0.03
-0.05
0.16
Standard, 250-mile
R&D minus Production
0.03
0.04
-0.02
0.14
R&D minus Administrative
0.03
0.05
-0.01
0.28
Ratio, 100-mile
R&D divided by Production
1.56
0.52
0.79
3.70
R&D divided by Administrative
1.33
0.38
0.74
3.19
Ratio, 250-mile
R&D divided by Production
1.21
0.25
0.80
1.89
R&D divided by Administrative
1.20
0.22
0.88
1.92
Note: There are 80 manufacturing industries. The standard version of the index
is the share of pairwise distances under a certain mileage threshold for a given
group minus the share of pairwise distance under the threshold for all
manufacturing workers. The ratio version of the index is the share of a given
group divided by the share for all manufacturing workers.

Figure 2. Distribution of auto employment, all occupation groups, 2014 ACS
(2500 dots, 1 dot = 0.04% of workers)

Figure 3. Distribution of auto employment by occupation group, 2014 ACS
A.

Auto production workers (2500 dots, 1 dot = 0.04% of workers)

Auto administrative workers (2500 dots, 1 dot = 0.04% of workers)

Auto R&D workers (2500 dots, 1 dot = 0.04% of workers)

Figure 4. Distribution of auto employment shares by occupation group and commuting zone, 2010-14
ACS
A.

Auto production workers

Auto administrative workers

Auto R&D workers

Figure 5. Distribution of auto employment shares by occupation group across commuting zones (solid
red line is the industrywide share; dashed red lines are ±25% of the industrywide share), 2010-14 ACS
A.

Auto production workers

Auto administrative workers

Auto R&D workers

Figure 6. Distribution of auto employment shares by occupation group over commuting zones,
weighted by employment
A.

Auto production workers

Auto administrative workers

Auto R&D workers

Figure 7. Kernel density function of pairwise distances between workers by occupation group
(smoothed using a 25-mile lead, 25-mile lag moving average)
A.

Production

Administrative

R&D

References

Bartik, Timothy J. "The future of state and local economic development policy: what research is
needed." Growth and Change 43, no. 4 (December 2012): 545-562.
Carlino, Gerald, and William R. Kerr. Agglomeration and Innovation. Vol. 5A, in Handbook of
Regional and Urban Economics, edited by Gilles Duranton, J. Vernon Henderson and
William C. Strange, 349-404. Elsevier, 2015.
Combes, Pierre-Philippe, and Laurent Gobillon. The Empirics of Agglomeration Economies. Vol.
5A, in Handbook of Regional and Urban Economics, edited by Gilles Duranton, J. Vernon
Henderson and William C. Strange, 247-348. Elsevier, 2015.
Duranton, Gilles, and Henry G. Overman. "Testing for localization using microgeographic
data." Review of Economic Studies 72 (2005): 1077-1106.
Economic Classification Policy Committee. "Issue Paper No. 1." US Census Bureau. 1993.
http://www.census.gov/eos/www/naics/history/docs/issue_paper_1.pdf (accessed
November 30, 2016).
Ellison, Glenn, Edward L. Glaeser, and William R. Kerr. "What causes industry agglomeration?
Evidence from coagglomeration patterns." American Economic Review 100, no. June 2010
(2010): 1195-1213.
Hannigan, Thomas J., Marcelo Cano-Kollmann, and Ram Mudambi. "Thriving innovation
amidst manufacturing decline: the Detroit auto cluster and the resilience of local
knowledge production." Industrial and Corporate Change 24, no. 3 (2015): 613-634.
Klier, Thomas, and Daniel P. McMillen. "Evolving Agglomeration in the US Auto Supplier
Industry." Journal of Regional Science 48, no. 1 (2008): 245-267.
Klier, Thomas, William Testa, and Thomas Walstrum. "Michigan's Automotive R&D Part II."
Midwest Economy Blog. March 17, 2014. http://midwest.chicagofedblogs.org/?p=2044
(accessed November 30, 2016).
Marshall, Alfred. Principles of Economics. London: MacMillan, 1890.
Neumark, David, and Helen Simpson. Place-based Policies. Vol. 5B, in Handbook of Regional and
Urban Economics, edited by Gilles Duranton, J Vernon Henderson and William C.
Strange, 1197-1287. Elsevier, 2015.
Ruggles, Steven, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated
Public Use Microdata Series: Version 6.0 [Machine-readable database]. Minneapolis:
University of Minnesota, 2015.

Silverman, Bernard W. Density Estimation for Statistics and Data Analysis. London: Chapman and
Hall, 1986.
Walstrum, Thomas, and William Testa. "Michigan Automotive, More than Production." Midwest
Economy Blog. November 12, 2013. http://midwest.chicagofedblogs.org/?p=2034
(accessed November 30, 2016).

Appendix
Table A1. Occupational Classifications
Code
Title
MANAGERIAL AND PROFESSIONAL SPECIALTY OCCUPATIONS
Executive, Administrative, and Managerial Occupations:
3 Legislators
4 Chief executives and public administrators
7 Financial managers
8 Human resources and labor relations managers
13 Managers and specialists in marketing, advertising, and public relations
14 Managers in Education and related fields
15 Managers of medicine and health occupations
16 Postmasters and mail superintendents
17 Managers of food-serving and lodging establishments
18 Managers of properties and real estate
19 Funeral directors
21 Managers of service organizations, n.e.c.
22 Managers and administrators, n.e.c.
Management Related Occupations:
23 Accountants and auditors
24 Insurance underwriters
25 Other financial specialists
26 Management analysts
27 Personnel, HR, training, and labor relations specialists
28 Purchasing agents and buyers, of farm products
29 Buyers, wholesale and retail trade
33 Purchasing managers, agents and buyers, n.e.c.
34 Business and promotion agents
35 Construction inspectors
36 Inspectors and compliance Administrators, outside construction
37 Management support occupations
Professional Specialty Occupations
Engineers, Architects, and Surveyors:
43 Architects
Engineers:
44 Aerospace engineer
45 Metallurgical and materials engineers, variously phrased
47 Petroleum, mining, and geological engineers
48 Chemical engineers
53 Civil engineers
55 Electrical engineer
56 Industrial engineers
57 Mechanical engineers
59 Not-elsewhere-classified engineers
Mathematical and Computer Scientists:
64 Computer systems analysts and computer scientists
65 Operations and systems researchers and analysts
66 Actuaries
67 Statisticians
68 Mathematicians and mathematical scientists
Natural Scientists:
69 Physicists and astronomers
73 Chemists
74 Atmospheric and space scientists
75 Geologists
76 Physical scientists, n.e.c.
77 Agricultural and food scientists
78 Biological scientists

Category

Farm
Farm
Farm
Farm

Farm
Farm
Farm
Farm
Farm
Professional Services
Farm
Farm
Farm
Farm
Farm

Business Services

Professional Services
Business Services
Business Services
Business Services
Professional Services
Business Services
Business Services
Business Services
Business Services
Business Services
Professional Services
Business Services
Business Services
Professional Services
Business Services
Business Services
Business Services
Business Services
Business Services
Business Services
Business Services

Production
Production
Production
Production

Table A1. Occupational Classifications
Code

Title
575 Electricians
577 Electric power installers and repairers
579 Painters, construction and maintenance
583 Paperhangers
584 Plasterers
585 Plumbers, pipe fitters, and steamfitters
588 Concrete and cement workers
589 Glaziers
593 Insulation workers
594 Paving, surfacing, and tamping equipment operators
595 Roofers and slaters
596 Sheet metal duct installers
597 Structural metal workers
598 Drillers of earth
599 Construction trades, n.e.c.
Extractive Occupations:
614 Drillers of oil wells
615 Explosives workers
616 Miners
617 Other mining occupations
Precision Production Occupations:
628 Production supervisors or foremen
Precision Metal Working Occupations:
634 Tool and die makers and die setters
637 Machinists
643 Boilermakers
644 Precision grinders and filers
645 Patternmakers and model makers
646 Lay-out workers
649 Engravers
653 Tinsmiths, coppersmiths, and sheet metal workers
Precision Woodworking Occupations:
657 Cabinetmakers and bench carpenters
658 Furniture and wood finishers
659 Other precision woodworkers
Precision Textile, Apparel, and Furnishings Machine Workers:
666 Dressmakers and seamstresses
667 Tailors
668 Upholsterers
669 Shoe repairers
674 Other precision apparel and fabric workers
Precision Workers, Assorted Materials:
675 Hand molders and shapers, except jewelers
677 Optical goods workers
678 Dental laboratory and medical appliance technicians
679 Bookbinders
684 Other precision and craft workers
Precision Food Production Occupations:
686 Butchers and meat cutters
687 Bakers
688 Batch food makers
Precision Inspectors, Testers, and Related Workers:
693 Adjusters and calibrators
Plant and System Operators:
694 Water and sewage treatment plant operators
695 Power plant operators
696 Plant and system operators, stationary engineers

Category
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Professional Services
Production
Production
Production
Health
Production
Production
Professional Services
Professional Services
Professional Services
Production
Governmental
Production
Production

Table A1. Occupational Classifications
Code

Title
699 Other plant and system operators
OPERATORS, FABRICATORS, AND LABORERS
Machine Operators, Assemblers, and Inspectors
Machine Operators and Tenders, Except Precision
Metal Working and Plastic Working Machine Operators:
703 Lathe, milling, and turning machine operatives
706 Punching and stamping press operatives
707 Rollers, roll hands, and finishers of metal
708 Drilling and boring machine operators
709 Grinding, abrading, buffing, and polishing workers
713 Forge and hammer operators
717 Fabricating machine operators, n.e.c.
Metal and Plastic Processing Machine Operators:
719 Molders, and casting machine operators
723 Metal platers
724 Heat treating equipment operators
Woodworking Machine Operators:
726 Wood lathe, routing, and planing machine operators
727 Sawing machine operators and sawyers
728 Shaping and joining machine operator (woodworking)
729 Nail and tacking machine operators (woodworking)
733 Other woodworking machine operators
Printing Machine Operators:
734 Printing machine operators, n.e.c.
735 Photoengravers and lithographers
736 Typesetters and compositors
Textile, Apparel, and Furnishings Machine Operators:
738 Winding and twisting textile/apparel operatives
739 Knitters, loopers, and toppers textile operatives
743 Textile cutting machine operators
744 Textile sewing machine operators
745 Shoemaking machine operators
747 Pressing machine operators (clothing)
748 Laundry workers
749 Misc. textile machine operators
Machine Operators, Assorted Materials:
753 Cementing and gluing machine operators
754 Packers, fillers, and wrappers
755 Extruding and forming machine operators
756 Mixing and blending machine operatives
757 Separating, filtering, and clarifying machine operators
759 Painting machine operators
763 Roasting and baking machine operators (food)
764 Washing, cleaning, and pickling machine operators
765 Paper folding machine operators
766 Furnace, kiln, and oven operators, apart from food
768 Crushing and grinding machine operators
769 Slicing and cutting machine operators
773 Motion picture projectionists
774 Photographic process workers
779 Machine operators, n.e.c.
Fabricators, Assemblers, and Hand Working Occupations:
783 Welders and metal cutters
784 Solderers
785 Assemblers of electrical equipment
789 Hand painting, coating, and decorating occupations
Production Inspectors, Testers, Samplers, and Weighers:

Category
Production

Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Professional Services
Production
Production
Production
Production
Production
Production
Production
Professional Services
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production
Production

Table A1. Occupational Classifications
Code

Title
796 Production checkers and inspectors
799 Graders and sorters in manufacturing
Transportation and Material Moving Occupations
Motor Vehicle Operators:
803 Supervisors of motor vehicle transportation
804 Truck, delivery, and tractor drivers
808 Bus drivers
809 Taxi cab drivers and chauffeurs
813 Parking lot attendants
815 Transport equipment operatives--allocated (1990 internal census)
Transportation Occupations, Except Motor Vehicles
Rail Transportation Occupations:
823 Railroad conductors and yardmasters
824 Locomotive operators (engineers and firemen)
825 Railroad brake, coupler, and switch operators
Water Transportation Occupations:
829 Ship crews and marine engineers
834 Water transport infrastructure tenders and crossing guards
Material Moving Equipment Operators:
844 Operating engineers of construction equipment
848 Crane, derrick, winch, and hoist operators
853 Excavating and loading machine operators
859 Misc. material moving occupations
Helpers, Construction and Extractive Occupations:
865 Helpers, constructions
866 Helpers, surveyors
869 Construction laborers
874 Production helpers
Freight, Stock, and Material Handlers:
875 Garbage and recyclable material collectors
876 Materials movers: stevedores and longshore workers
877 Stock handlers
878 Machine feeders and offbearers
883 Freight, stock, and materials handlers
885 Garage and service station related occupations
887 Vehicle washers and equipment cleaners
888 Packers and packagers by hand
889 Laborers outside construction
890 Laborers, except farm--allocated (1990 internal census)
MILITARY OCCUPATIONS
905 Military

Category
Production
Production

Transportation
Transportation
Transportation
Transportation
Transportation
Transportation

Transportation
Transportation
Transportation
Transportation
Transportation
Production
Production
Production
Production
Production
Production
Production
Production
Governmental
Transportation
Transportation
Production
Transportation
Professional Services
Business Services
Production
Production
Production
Governmental

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Nathaniel Baum-Snow and Daniel Hartley

WP-16-09

The Effect of the Patient Protection and Affordable Care Act Medicaid Expansions
on Financial Wellbeing
Luojia Hu, Robert Kaestner, Bhashkar Mazumder, Sarah Miller, and Ashley Wong

WP-16-10

The Interplay Between Financial Conditions and Monetary Policy Shock
Marco Bassetto, Luca Benzoni, and Trevor Serrao

WP-16-11

Tax Credits and the Debt Position of US Households
Leslie McGranahan

WP-16-12

The Global Diffusion of Ideas
Francisco J. Buera and Ezra Oberfield

WP-16-13

Signaling Effects of Monetary Policy
Leonardo Melosi

WP-16-14

Constrained Discretion and Central Bank Transparency
Francesco Bianchi and Leonardo Melosi

WP-16-15

Escaping the Great Recession
Francesco Bianchi and Leonardo Melosi

WP-16-16

The Role of Selective High Schools in Equalizing Educational Outcomes:
Heterogeneous Effects by Neighborhood Socioeconomic Status
Lisa Barrow, Lauren Sartain, and Marisa de la Torre
Monetary Policy and Durable Goods
Robert B. Barsky, Christoph E. Boehm, Christopher L. House, and Miles S. Kimball

WP-16-17

WP-16-18

Interest Rates or Haircuts?
Prices Versus Quantities in the Market for Collateralized Risky Loans
Robert Barsky, Theodore Bogusz, and Matthew Easton

WP-16-19

Evidence on the within-industry agglomeration of R&D,
production, and administrative occupations
Benjamin Goldman, Thomas Klier, and Thomas Walstrum

WP-16-20

Full text of Working Papers (Federal Reserve Bank of Chicago) : Evidence on the Within-Industry Agglomeration of R&D, Production, and Administrative Occupations, Working Paper 2016-20

FRASER