Inshoring: the Geographic Fragmentation of Production andInequality
Wen-Chi Liao1
National University of Singapore
March 2010

1
Department of Real Estate, National University of Singapore. 4 Architecture Drive, 117566 Singapore. Email: wliao@nus.edu.sg; Fax: +65-6774-8684. I am grateful to Thomas Holmes for his advice and encouragement.
I thank David Albouy, Marcus Berliant, Grace Bucchianeri, James Foster, Yuming Fu, Samuel Kortum, Fan-Chin
Kung, Andrea Moro, Jordan Rappaport, Paul Rothstein, Esteban Rossi-Hansberg and Albert Saiz for comments
and suggestions. All errors are mine.

Abstract
The advent of information technology facilitates geographic separation of production. Nevertheless, theresearch on inshoring has been limited, even though the literature on oﬀshoring has flourished. This
paper examines inshoring on both empirical and theoretical fronts. Empirically, it shows that businesssupport services are increasingly sent to small localities for cost savings and being separated from theirdownstream industries. Theoretically, it analyzes welfare impact of inshoring. Contrary to predictionsin the oﬀshoring literature, support workers can be better oﬀ, primarily because they can benefit fromhigher urban productivity without bearing urban costs.
JEL: R11; R12; R13; R23
Key words: Inshoring; geographic fragmentation of production; welfare inequality; system-of-cities

1

Introduction

The advent of information technology, including the Internet, has allowed geographic separation of certain
production tasks. For instance, lawyers and legal typists used to work in the same building, but now,
typists can type a lawyer’s dictation miles away from where the lawyer works. In other words, thetyping services can be sourced from remote locations, and the consequence is geographic fragmentationof production.
To date, much of academic discussion of the above sourcing practice is on oﬀshoring, which applies
to an international context where businesses relocate some of their production tasks to foreign countries.
Nevertheless, this sourcing practice can also take place domestically. For instance, a law firm in New
York City may send its typing jobs to North Dakota but India. In the press, such domestic sourcing isoften termed inshoring 1 .
Although the literature on oﬀshoring has flourished2 , inshoring remains an understudied area. However, inshoring deserves research attention. This is because, from a domestic perspective, labor is mobile,and therefore, inshoring may have a diﬀerent impact from oﬀshoring, which is in an environment withimmobile labor.
This paper studies, both empirically and theoretically, the inshoring of business support services,such as customer services and telephone call centers, which is most influenced by progress of information
technology. There are four main empirical findings. First, the paper provides clear evidences thatbusiness support service industries have been growing substantially faster in smaller localities, and as
the result, geographic concentration of these industries has been shifted away from big cities and toward
small cities and rural areas. Second, downstream industries of the business support services are identified
through an examination of input-output flows. It is found that geographic distribution of the downstream
industries has remained stable and has been more concentrated in big cities. Third, further examinationsuggests a growing trend toward geographic fragmentation of production, that is the separation between
the business support services and their downstream industries in diﬀerent locations. Also, there is anincreased tendency for business support workers to be geographically separated from others. Lastly, thestudy shows that low wages in small localities are the primary reason that attracts the business support
1 Inshoring includes both outsourcing and in-housing sourcing. The former is involved with procuring goods and services

from outside contractors, while the later is not. Although this paper follows the convention and use inshoring this term
to avoid possible confusion, one may find that inshoring and outsourcing are really synonymous when he or she considers
establishments as firms, which is how economists often think in doing empirical research given the design of U.S. Economic
Censuses. In a companion paper of mine (Liao, forthcoming), the geographic fragmentation of production is termed
outsourcing.
2 Researchers, including, but not limited to, Pol Antras, Rechard Baldwin, Luis Garicano, Gene Grossman, Elhanam
Helpman and Esteban Rossi-Hansberg have made significant contributions to this literature.

1

service industries.
Theoretically, a simple general equilibrium model of a system of cities (see Abdel-Rahman and Anas,
2004, for a thorough review and discussion on this type of models) is formulated, and the construction of
the model is guided by the above empirical findings. The model comprises large localities that are called
cities and small localities that are called rural areas, and it consists of skilled managers who execute
managerial work and unskilled clerks who perform support work. The agglomeration of managers incities creates knowledge spillovers, which attract firms to cities. However, firms face a trade-oﬀ between
labor costs and remote communication friction, and this is a key element of the model. On the one hand,clerks in rural areas demand less wage compensation, because commuting costs and housing prices arelow. On the other hand, these clerks have lower marginal productivity than their urban counterparts,
due to the friction caused by remote communication with them. New technologies that lower the friction
can increase clerks’ marginal productivity in rural areas. As the consequence, support jobs are sent torural areas by firms through inshoring and separate from managerial jobs. Since labor is mobile, clerks
relocate in response to the labor demand shift, and this leads to segregation.
The most surprising theoretical finding concerns welfare impact. The paper shows that the reduction
in communication friction leading to inshoring necessarily benefits unskilled clerks but has an ambiguous
welfare impact on skilled managers. Overall, the inshoring unambiguously decreases welfare inequality
between the two types of workers. While details will be explained later, the basic reason is that theinshoring allows clerks to benefit from higher productivity without bearing urban costs. The above
implication is in sharp contrast to the findings in the international trade literature about the impact ofoﬀshoring. For instance, Antras, Garicano and Rossi-Hansberg (2004) and Kremer and Maskin (2006)
both show that unskilled workers are worse oﬀ relative to skilled workers in the origin country in whichfirms adopt oﬀshoring. This distinction is due to the diﬀerent model assumptions on labor mobility—
workers are mobile domestically but immobile internationally.
On the theoretical front, the paper closest to mine is that of Duranton and Puga (2005) who also
analyze geographic fragmentation of production. When communication friction is high, firms integratemanagerial and manufacturing functions in the same city, and cities feature sectoral specialization in
equilibrium. When communication friction is low, firms separate the two functions in diﬀerent cities, andcities feature functional specialization in equilibrium. My paper is diﬀerent. Whereas their paper assumes
multiple sectors and homogeneous workers and focuses on change in cities’ industrial structure, this paper
assumes heterogeneous workers and one single sector and focuses on welfare impact. Additionally, thispaper is also related to Liao (forthcoming), but the diﬀerence is that this paper focuses on an empirical2

examination of inshoring and a theoretical analysis of its welfare impact, while that paper introduces aricher model which provides interesting implications for how firms optimize the variety of tasks to be"inshored" and how the inshoring interacts with various local labor and housing markets.
On the empirical front, there have been a few papers on domestic outsourcing, i.e., the use of domestic
outside contractors. Abraham and Taylor (1996) show that domestic outsourcing is significant at thenational level, and Ono (2007) shows that firms in big cities are more likely to adopt outsourcing. My
paper is diﬀerent from theirs in two aspects. First, this paper analyzes inshoring that includes bothoutsourcing and in-house sourcing, though it does not attempt to make a distinction between the two
subtypes of sourcing. Second, this paper focuses on the process of geographic fragmentation of production,
which is not touched on in their papers.
The remainder of this paper is organized as follows. Section 2 presents the empirical findings. Section
3 introduces the model and provides the theoretical analysis. Section 4 concludes.

2

Empirical Findings

This section presents empirical findings using data from County Business Patterns (CBP), Input-Output
Accounts (I-O Accounts), and Integrated Public Use Microdata Series (IPUMS). The presentation isorganized into five parts. The first part includes a discussion on the data sources and data construction.
The second part is an examination of growth and geographic concentration of the industry performing
inshored business support services. The third part identifies downstream industries of the business support
services and studies where they are. The fourth part includes assessments on geographic separation
between the business support service industry and the downstream industries and between businesssupport workers and other types of workers, and the fifth part concerns why small cities attract thebusiness support service industry.

2.1

Data Sources

The CBP is an annual establishment-level microdata in the form of cell counts by employment-size class,industry, and location. The publicly released information includes establishment and employment counts
as well as payroll. However, CBP routinely withholds data regarding employment and payroll to meet
nondisclosure requirements, and nondisclosure is common for large employment-size classes and small
localities. Thus, one would need estimates of employment if the research question concerns employment
in a particular industry at a particular location. I follow the approach of Holmes and Stevens (2002)3

to estimate mean employment by employment-size class. It is assumed that establishment employment
follows a log-normal distribution, and a procedure of the generalized method-of-moments is applied toestimate the parameters of this distribution. Holmes and Stevens (2004) provide more information on
details and quality of this procedure and the CBP data.
The I-O Accounts, published by the Bureau of Economic Analysis, provide the make and use ofcommodities by each industry. With detailed information on flows of goods and services in production
processes of domestic industries, the I-O Accounts facilitate research on interindustry relationships within
the country. This paper makes use of 1997 Benchmark I-O Accounts for a purpose that will be explainedlater.
The IPUMS is an integrated data source on the American population. In addition to the U.S.
Decennial Census of Population and Housing, IPUMS also consists of the American Community Survey(ACS), which is an annual census first initiated in the year 2000. Since IPUMS is not a panel dataset,
one needs to take care of or be aware of comparability issues if research is to examine changes acrosscensus years.
This research makes use of 1998 and 2006 CBP data, as well as data from the 2000 Census and 2006
ACS. To facilitate comparisons, these data are aggregated to a metropolitan level, at which metropolitan
boundaries follow the current metropolitan-area definition, the 2003 Core Based Statistical Areas (CBSA).
By this definition, each metropolitan area is a union of a set of counties. This is advantageous as comparedto the previous MSA/CMSA definitions. Aggregating CBP data to the CBSA level is simple because
county-level information is available. Aggregating IPUMS data is harder, because IPUMS does nothave county information and the aggregation is involved with mapping metropolitan areas onto PUMA
geographic units. Jaeger et al. (1998) discuss issues and quality of this mapping strategy.

2.2

Growth and Geographic Concentration of Business Support Services

The industry of interest here is the Business Support Services (BSS), NAICS 5614, which is defined in the
North America Industrial Classification System as an industrial group that “comprises establishments
engaged in performing activities that are ongoing routine, business support functions that businesses andorganizations traditionally do for themselves” and is selected for the following reason. A main theme ofthis paper is the inshoring of day-to-day routines of businesses, and therefore, NAICS 56 is potentially thepool of industries that can be looked at. However, to reflect the inshoring most influenced by the adventof IT and the interest or concern of the American public on oﬀshoring versus inshoring of lower skilledoﬃce occupations, the study would not cover industries like logistics, protection services, cleaning, etc.,4that have a local context and industries like financial planning that are rather skilled. In addition, thestudy would not cover industries like travel arrangement and personal services that also largely engaged
in serving private households. Taking all these into consideration, the most appropriate industry for the
purpose of the study is the BSS.
The BSS consists of various sub-industries including Document Preparation Services, Telephone Call
Centers, Business Service Centers, Collection Agencies, Credit Bureaus, etc. Since many U.S. industries
rely on these services, BSS has been an important industry with about 800 thousand employees. Furthermore, BSS is fast growing. It grew by 21% between 1998 and 2006, while U.S. total employment only
grew by 11% during the period.
The BSS is a “low-skilled” industry, as its contents of work have a lower skill requirement. In 2000,
only 16 percent of BSS workers had a bachelor’s degree, while 23 percent of American workers had thedegree. The lower education profile of BSS is because the Oﬃce and Administrative Support occupations(the SOC definition), that generally do not require a bachelor’s degree, are the backbone of BSS. In 2000,
48 percent of BSS employees were in these occupations, but only 17 percent of American workers werein.
One might have a question on whether BSS data examined here are about inshoring or outsourcing.
The NAICS definition of BSS might sound like outsourcing, but the data studied here are, in fact, about
inshoring because of the design of Economic Census, the primary source of CBP’s industry classification.
The Economic Census is conducted on an establishment basis, and each establishment is assigned aseparate industry classification based on its primary activity and not on that of its parent company.
Thus, BSS establishments identified in CBP can include a customer service center directly owned by abank.
The maps in Figure 1 illustrate the employment growth in BSS between 1998 and 2006 for eachmetropolitan area, i.e., city3 . The year 1998 was the first year that CBP identified BSS, because BSS was
not defined in the industrial classification system until 1997. In the maps, cities filled with dots had agrowth rate below the mean growth rate during the eight-year period, while cities filled with backslashes
had a growth rate above the mean. Panel A highlights BSS growth for the ten biggest cities on a graybackground. Nine out of these ten cities had a growth rate below the average. The only exception was
Miami, Florida. Panel B highlights BSS growth for other cities with more than one million people on agray background. Most of these cities also had a growth rate below the mean. Thus, the maps seem tosuggest that the growth of BSS was highly concentrated in small cities.
3 All 360 metropolitan areas identified by the 2003 definition had positive employment in both 1998 and 2006.

5

[Insert Figure 1 here]
Table 1 presents the growth of BSS employment and the change in the BSS location quotient (LQ)
between 1998 and 2006 by city-size class4 . The LQ is a ratio measuring geographic concentration of
economic activity in the area of interest. Here, it is the location’s share of U.S. BSS employment relative
to the location’s share of U.S. total employment. The higher the ratio is, the higher the concentration.
If every location has a LQ that equals to 1, then the geographic distribution of the activity is even acrosslocations. As the table shows, overall growth of BSS employment was considerably higher for cities insmaller size classes. While the growth rates were only 4.8% for the ten biggest cities and 8.9% for other
cities with more than one million people, the growth rate was 39.6% for cities with a population between
a quarter million and one million. For the smallest class, which also includes rural areas in addition
to cities with less than a quarter million people, the growth rate was 41.2%. Since the two largest sizeclasses had faster growth in total employment, the geographic concentration of BSS greatly shifted frombig cities to small cities and rural areas during this eight-year period, as shown in the table. Particularly,
smaller cities with a population between a quarter and one million had already become the places withthe highest BSS concentration, and the 10 biggest cities had already featured a weak presence of BSS.
[Insert Table 1 here]

2.3

Downstream Industries of BSS: Who and Where

It is necessary to identify the downstream industries (DIs) of BSS and study where they are, since thispaper concerns geographic separation of production. Here, DIs are identified through examining inputoutput flows using 1997 Benchmark I-O Accounts. Then, geographic concentration of DIs and geographic
separation between DIs and BSS are studied.
Table 2 presents the use of BSS as intermediate inputs by industry. The industries are classified bytheir 2-digit NAICS codes, expect for two 3-digit industries that used BSS much more intensively than
did other industries with their superordinate 2-digit NAICS code. This can be viewed in column 7 which
reports a ratio on "BSS intensity." This intensity ratio is the share of BSS services used by an industry
divided by the share of total value added created by the industry. Those industries with a ratio greaterthan one used BSS more intensively. The first ten industries in the table are those with an intensity ratio
greater than one. They used 80% of the intermediate goods supplied by BSS but accounted for only 47%of the total value added. Thus, these ten industries are considered the DIs: the main users of BSS.
4 The result is not sensitive to the selection of size classes’ threshold values.

6[Insert Table 2 here]
To assess upstream-downstream ties between BSS and other industries, it may be more conventional
to calculate the value of an industry’s BSS inputs per dollar value of the industry’s output, as in Ellison
and Glaeser (1997). However, the intensity ratio defined above is instead used, because a multiple numberof downstream industries need to be identified. The intensity ratio is an advantageous measure in thissituation, as it provides an objective cutoﬀ above which the upstream-downstream relationship is strong.
Actually, the more conventional measure and the intensity ratio were both calculated for each industry
listed in Table 2, and it was found that the correlation between the two measures is above 98%.
Table 3 presents DIs’ growth of employment and change in LQ between 1998 and 2006 by city-sizeclass. Unlike BSS, DIs were always more concentrated in larger cities, and the geographic distribution of
DIs remained quite constant over the period. These were in stark contrast to BSS’s change in geographic
concentration.
[Insert Table 3 here]

2.4

Geographic Separation of Production and Workers

Tables 1 and 3 jointly seem to indicate a growing tendency of geographic fragmentation of production,
as the upstream BSS increasingly locate in smaller localities and the downstream DIs remain in larger
localities. This indication is supported by an examination using Ellison Glaeser Coagglomeration Index(Ellison and Glaeser, 1997), which measures overall degree of geographic clustering or separation for plants
in diﬀerent industries. Ellison, Glaeser and Kerr (2007) prove that, when studies concern coagglomerationof two industries, the Index’s formula can be reduced to a special form. In the context of this research,
the special form can be written as:



 =

X


( −  ) ( −  )
1−

X

2

(1)



where   is the index value, and  ,  , and  denote location ’s share of U.S. BSS, DI, andnational employment, respectively. The denominator of the index eliminates the sensitivity to choices ofgeographic fineness, and the value of the coagglomeration index crucially depends upon the covariance ofthe locations’ employment shares in the two industries. If the index value is greater than zero, then there
is more agglomeration of plants among the two industries than what would be expected if each industry

7was as agglomerated as it is, but each industry’s agglomeration locations were independent of the otherindustry’s . Simply put, plants in the two diﬀerent industries tend to co-locate in the same place. If
the value equals to zero, there is no coagglomeration. If the value is negative, then plants in diﬀerent
industries tend to locate in diﬀerent places.
Table 4 presents values of the coagglomeration index in 1998 and 2006. The first column concerns"coagglomeration" of BSS and DIs in the four city-size classes. Coagglomeration, in fact, may not bea most suitable term here, as localities in the same size class are not adjacent. Rather, the reported
values give a sense on whether BSS and DIs are situated in diﬀerent sizes of cities. Eq. (1) makes it clearthat the values are closely related to the information presented in Tables 3 and 4, as those two tables
report BSS LQ (  ) and DI LQ (  ) which are highly correlated with  −  and −  , respectively. As the result, it is not surprising to see a substantial decrease in the index, andthe decrease indicates a growing tendency for BSS and DIs to locate in diﬀerent sizes of cities over theperiod.
[Insert Table 4 here]
The second column in Table 4 presents the overall level of coagglomeration between BSS and DIs,
using metropolitan areas and rural counties’ data. The reported numbers are not much diﬀerent fromzero, possibly for two reasons. First, the index’s absolute value tends to be small, by construction5 .
Second, the variance of  −  is small, as DIs are broadly classified. Thus, the important pieces of
information here are rather on the sign and change. The index values were negative for both years, but thevalue in 2006 was substantially lower, indicating that BSS and DIs already tended to locate in diﬀerent
places in 1998, and the tendency had considerably increased since then. Such pattern of decreasing levelof coagglomeration could be due to progress of IT that dramatically reduced data transmission costs
and allowed the separation of BSS and DIs, since Marshall’s theory suggests that saving trade costs is a
crucial reason for the coagglomeration of industries with input-output dependency.
Does inshoring result in geographic separation of oﬃce and administrative support workers (hereafterreferred to as the support workers) from others? Table 4, which suggests geographic fragmentation of
production, cannot answer this question, because establishments in BSS not only employ the support
workers, and plants in DIs do hire this type of employees. Since CBP does not have occupational
information, IPUMS data are used, to calculate support workers’ LQ by city size class for getting a senseof the relationship between their concentration and location size and to compute two segregation indexes
5 See Ellison and Glaeser (1997), and Ellison, Glaeser, and Kerr (2007).

8

for measuring overall degree of separation between the support workers and other types of workers.
Table 5 presents LQ of the support workers in BSS and DIs put together. This is the location’s shareof BSS and DIs’ support workers relative to the location’s share of BSS and DIs’ total employment. The
table reports this LQ by city-size class by census year and indicates a shift in geographic concentrationof the support workers. In 2000, these workers were almost evenly distributed across the four classes, but
in 2006, they were more concentrated in smaller locations. Also presented are percentage changes in LQ
during the period. Bigger size classes consistently had a smaller percentage change.
[Insert Table 5 here]
To assess the overall degree to which the BSS and DIs’ support workers are separated from othertypes of workers, two measures are used. The first one is the dissimilarity index: =¯¯
1 X ¯¯  ¯¯−
2  ¯¯

where  and  refer to the number of the BSS and DIs’ support workers in location  and the country,
respectively, and  and  refer to the number of non-support workers in location  and the country,
respectively. This index indicates the share of the support workers who would have to relocate in orderto achieve an even distribution across cities and rural areas.
The second measure is a correlation index as proposed by Kremer and Maskin (1996). Denoted by ,
the index is defined as:=X (Π − Π)2 Π (1 − Π)

where  and  refer to BSS and DIs’ total employment in location  and the country, respectively, andΠ and Π refer to support workers’ share of  and  , respectively. In addition, the 95% confidence
interval for this index is ( −   − 1)0025 ( −   − 1)0975≤≤1− ( −   − 1)0025 + 
 ( −   − 1)0975 + 1−where  is the total number of locations. This index normalizes the variance of support workers’ shareof employment across locations by dividing by the variance of worker status (either a support or a nonsupport worker) of the country. An index of zero indicates that all locations have the same mix of support
and non-support workers, and an index of one indicates complete separation.
Table 6 presents time paths of the dissimilarity and correlation indexes. The first two columns
9are essential and are concerned with geographic separation between the support workers and all otherworkers in BSS and DIs, put together. The indexes’ values reported in these two columns suggest that,
although the support workers were still distributed quite evenly across locations, there was a significant
tendency toward increasing separation of the support workers from others between 2000 and 2006, since
the dissimilarity index increased from 0.050 to 0.064, and the correlation index increased from 0.0034[0.0031, 0.0038] to 0.0055 [0.0049, 0.0061]. Furthermore, as columns 3 and 4 reveal, the tendency of theseparation is stronger, when index calculation only includes oﬃce workers in BSS and DIs6 . Additionally,columns 5 and 6 also suggest the separation among oﬃce workers in all industries.
[Insert Table 6 here]

2.5

What Attracts BSS to Small Localities

This section examines why BSS employment grows substantially faster in smaller localities, using theapproach below. Firstly, a bivariate regression that indicates a negative relationship between BSS growthand location population is performed, and then, a process of adding explanatory variables is applied to
identify the underlying reason for small localities to attract BSS. The logic behind this approach is thefollowing. A researcher can definitely argue that the estimated negative relationship from the bivariate
regression is subject to omitted variable bias; there may be an omitted variable, say the wage rate, thatis both correlated with BSS growth and location population. Because of the omission, the influence on
BSS growth that should be rightly attributed to the wage rate is picked up by location population. If
the inclusion of this previously omitted variable does eliminate the impact of location population on BSS
growth from the regression, then the researcher can argue that it is the wage rate, which is positively
correlated with location population, that accounts for BSS growth, but not location population itself. In
other words, the lower wage rate is the underlying reason for smaller localities to attract BSS.
To identify the underlying reason for small localities to attract BSS, three sets of control variables areincluded to assess three competing hypothesis. The first set of variables, which include the percentage
of location employment in DIs in 1998 and the logarithmic change of DI employment between 1998 and2006, examines whether BSS growth is tied to the proximity of DIs, which use BSS more intensively thando other industries7 . Such an examination is needed, as one would naturally suspect that the reason for
BSS to grow faster in small localities is simply because DIs are there or are growing faster there. Thishypothesis is sound, if the inshoring requires physical proximity of BSS to DIs: If DIs must use local BSS,
6 The oﬃce workers include those with a managerial, professional specialty, technical, sales, or administrative support
occupation.
7 The percentage of city employment in DIs is equivalent to the LQ of DIs up to a scale.

10

then a location with a higher geographic concentration of DIs or a faster DI growth should also have afaster BSS growth during a wave of inshoring of business support services.
The second set of variables is on localization and urbanization economies. There are appealing theories
that attribute industrial growth to these external economies, whereby a firm can benefit from innovations
and improvements that occur outside the firm but within the locality. Glaeser et al. (1992) test these
theories, using CBP data and examining growth determinants for industries in 170 of the largest U.S.
metropolitan areas. This paper follows their approach to take into account the influence of several sourcesof external economies on BSS growth in regression analysis. However, the main purpose here is not on
growth determinants of BSS, but on whether taking into consideration these sources of external economies
can explain away the negative relationship between location population and BSS growth and thus identify
a reason for BSS to grow faster in small localities.
Three variables on external economies are included. The first variable is about specialization, theMAR externality. Marshall (1890), Arrow (1962) and Romer (1986) discuss how knowledge spillovers ina specialized, geographically concentrated industry can lead to faster growth for that industry. The second
variable is about diversity. Jacobs (1969) believes that the diversification of geographically proximate
industries can stimulate innovation and growth for local industries. The third is local competition (Porter,1990) which stimulates the pursuit of innovations and improvements as opposed to monopoly.
Here, specialization in a location is measured by the percentage of the location’s employment in BSS,
and diversity in location  is measured by the formula below: = −

X

2

6=

where  indicates industry ’s share of location ’s total employment. This measure is the sum of thesquares of the location’s employment shares for all industries except BSS multiplied by negative one. A
higher value of this measure indicates greater diversity. Lastly, local competition of a location’s BSS
is the number of establishments per worker in BSS in the location divided by ditto in the country. If
this measure is greater than one, the degree of competition in the location is stronger than the national
average.
The third set of variables is about local labor market conditions. The variables include wages andmeasures on labor suitability, and the data are from the 2000 Census. Documented in various sources,
the primary reason for oﬀshoring is to access low-cost labor. Possibly, this is also a key motive behindinshoring. Thus, locations’ average wage rates are considered. Specifically, these are the average hourly

11wages of workers in the Oﬃce and Administrative Support occupations, the backbone of BSS8 .
A more suitable labor force composition may attract BSS. Following Glaeser and Kerr (2008), twomeasures of labor suitability are used. Classifying workers’ educational attainment into 14 categories
according to the groupings of the 2000 Census, the first measure is defined as = −

X¯
¯
¯
¯
− 



(2)



where the subscript  indicates the category of educational attainment and the subscript  indicates thelocation. is the percentage of BSS national employment in category , and  is the percentage
of location ’s total employment in category . Multiplying the summation by negative one, a higher
value indicates that, in terms of educational attainment, the location’s labor-force composition is moresuitable for BSS. The second measure considers 476 occupations as classified by SOC, and its constructionuses the same formula as (2) except that the subscript  instead indicates occupation.
Table 7 provides summary statistics for the above variables used in the regression analysis, and Table
8 presents the correlation matrix. From the matrix, one can see that some variables, such as the averagehourly wages, are indeed considerably correlated with both the BSS employment growth and location
population. This potentially creates an opportunity for omitted variable bias to apply.
[Insert Table 7 here][Insert Table 8 here]
Table 9 presents regression results. The method of estimation is weighted least squares (WLS) as theBreusch-Pagan test constantly rejects the homoscedasticity assumption of OLS. The regressions include
state fixed eﬀects that control for unobservable characteristics like state policies and regional environment9 . The regressions only consider metropolitan areas with more than 250,000 people, due to two
limitations of CBP and IPUMS data. First, because of nondisclosure, information from CBP is of limited
value for narrowly defined industries in small geographic areas (Holmes and Stevens, 2004). Specifically,
industrial employment information used here is derived from the estimates of a log-normal distribution
when CBP withholds actual information. Because nondisclosure is common for less populated areas,measurement error may be a problem if the regressions also included rural areas and those rather small
metropolitan areas. The second limitation arises from the design of PUMA geographic areas that are thebasic geographic units of 2000 Census and 2006 ACS. The PUMAs must contain at least 100,000 people
8 Using the average hourly wages of all BSS employees or all wage and salary earners yields a similar result.
9 Among the 166 metropolitan areas with more than 250,000 people, 22 of them (not necessarily big cities) cross statelines. I consider these 22 metropolitan areas as being in the state in which their central city is located.

12

but can cross county lines. Thus, census information can be coarse for smaller metropolitan areas that
follow a county-based definition, like CBSA. Taking the two limitations into consideration, it is decided
to include only those metropolitan areas with more than 250,000 people. The results in Table 9 still shedlight overall, given the data, presented in any other tables of this paper, all change monotonically in thedimension of location size. Nevertheless, the regressions are repeated and metropolitan areas with morethan 100,000 people are included. The results are still similar to those in Table 9 and are reported inTable A1 in the appendix.
[Insert Table 5 here]
The first column of Table 9 shows a significant negative relationship between BSS employment growthand city size. As explained, this could be due to omitted variable bias, and including the three sets ofcontrol variables introduced earlier into the regressions allows us to identify which one of the threehypotheses is most plausible in explaining this negative relationship.
The results from Table 9 suggest that lower wages may be the main reason for small cities’ successin attracting BSS. As shown in columns 2 and 3, including DI concentration and growth makes little
diﬀerence to the coeﬃcient of log city size, and including sources of external economies only modestly
reduces the coeﬃcient. However, column 4 shows that adding local labor market conditions eliminates
the impact of city size on BSS growth almost entirely, and the remaining impact is insignificant. This may
be primarily due to the inclusion of the wage rate variable, as it is indicated that a one dollar decrease in
the average hourly wage is significantly associated with an extra 16 percent growth of BSS employment,
while the eﬀects of the two labor suitability measures are both insignificant.
Column 4 also indicates that the DIs’ concentration has insignificant eﬀect on BSS growth. This is not
surprising, given tremendous progress in IT and dramatic decrease in communication costs have made agreat many business support functions “virtual.” Since DIs can inshore the support functions to remoteplaces, a location with a strong presence of DIs need not necessarily have a faster BSS growth during awave of BSS inshoring. Nevertheless, DIs’ growth has a somewhat significant (a 0.10 p-value) positiveeﬀect on BSS growth, perhaps because some BSS functions, like copy centers, still more or less have a
local context. Thus, a place that atracts DIs might also attract some BSS.
How do localization and urbanization economies aﬀect BSS growth? Glaeser et al. (1992) show thatthe initial concentration of an industry has a significant negative eﬀect on the industry’s growth, whileurban diversity and local competition have significant positive influence over the growth. The estimatesin column 4 are consistent with their findings.

13

In summary, the results in Table 9 suggest that BSS grows faster in small cities mainly because
support workers are less expensive there. This finding is consistent with the common view that the
primary purpose of inshoring/oﬀshoring/outsourcing is to reduce costs.

3

Theoretical Analysis

This section examines impact of inshoring using a simple system-of-cities model in which large institutions
called city developers organize cities. This modeling approach is introduced by Henderson (1974) and is
widely adopted in the literature. Henderson, as well as other researchers, has argued that the existence ofcity developers is not an unrealistic assumption, at least for developed countries. With a key element beingthat firms face a trade-oﬀ between labor costs and remote communication friction, the model presented inthis section delivers the paper’s empirical findings and has implication on welfare inequality. Nevertheless,the implication can also be generated by other modeling approaches such as self-organization. This point
will be discussed later.

3.1

Model

For simplicity, assume a large economy that can facilitate a continuum of cities. The model economy
has one unit of workers;  units of them are managers, and the rest are clerks. The workers first choose
where to live and then inelastically demand one unit of residential land and participate in the local labormarket. Their preferences are linear in consumption of a numeraire.
The producers of the numeraire are in cities. They use a Cobb-Douglas production technology whichis constant return to scale in two inputs: managers and clerks. Assuming perfect competition, each city
has a representative producer with the production function: =   ( +   )1−

where  and  denote the numbers of managers (skilled workers) and clerks (unskilled workers) in thecity, respectively, and  denotes the number of clerks who are outside the city and perform the inshoredclerical work for the producer. Inshoring is subject to an iceberg cost due to communication friction:
Only a  proportion of the work can be delivered. The larger the proportion is, the better the technology.
The capital letter , in the above production function, denotes the level of knowledge spillovers which14

is a function of the total number of managers in the city: = 

where  is a parameter on the curvature of this function. It is assumed that only managers—the skilledworkers—can contribute to the level of the spillovers. The empirical literature supports this assumption(see Rosenthal and Strange, 2004). It is also assumed that the spillovers only exist locally. Although one
might suspect that the advent of information technology could jeopardize the validity of this assumption,
empirical literature such as Gaspar and Glaeser (1998) finds that the local context of knowledge spillovers
may remain important, because IT and face-to-face communication are not substitutes but complements.
Regarding land use, there is an infinite supply of land on a real line on which monocentric cities canbe set up. Each city has a central business district (CBD) in which the production takes place, andresidential land is on both sides of the CBD. Workers living in the city pay rent for the one unit of landthat they occupy, and they commute between home and the CBD. Let  denote the commuting cost, in
terms of the numeraire, per unit of land for a round trip. Let  denote the city population. The city
edges are 2 units away from the CBD, and the total commuting cost in the city is:  = 2Z 20 =

 2
4

where  indicates a home location. Thus,  is the commuting cost for the worker who lives  units awayfrom the CBD. The total commuting cost (i.e., city’s total congestion cost) is increasing in city size.
Rural areas are land outside the cities. Because rural areas are inexhaustible and their size is ofmeasure zero, workers living there pay zero rent and zero commuting cost.
Competitive developers set up cities. The sunk cost is zero because rural rent is zero. The representative developer owns city’s land and earns rent. The developer has to decide the numbers of managers andclerks that it wants to include in its city. It guarantees utility levels and factor prices (wages) to attract
workers and numeraire producers, respectively. It oﬀers a transfer denoted by  to attract managers who
contribute to the city’s knowledge spillovers, due to competition with other developers for managers10 .
The developer’s profit equals its total revenue from rent minus its total expenditure on transfers. Later,
this profit maximization problem will be explained in more details.
One limitation of system-of-cities models is that they work only in a range of parameter values, and
1 0 The city developer could also oﬀer a transfer to the clerks. However, the equilibrium value of this transfer will equal
zero because clerks do not contribute any spillovers to the city. Thus, I abstract this option from the model.

15

virtually all researchers focus on symmetric equilibria. To focus on the interesting case, the model of¡¢this paper makes three assumptions about parameter values: (i)   13 , (ii)  ∈ 1−
2   and (iii)  2−(1−). These assumptions ensure that the equilibrium is unique and symmetric and has a finite
2
city size and positive welfare for both types of workers. Secondarily, if   13 and   −
1− are additionallyassumed, the welfare of managers will be higher than that of clerks.
Let  ,  and  denote the wages of city managers, city clerks and rural clerks, respectively, andlet  denote the total number of cities endogenously formed. Then, the equilibrium and the types of
equilibrium outcomes can be defined as follows:
Definition 1 The equilibrium consists of an allocation (     ), a price vector (     ) anda transfer  and satisfies the following conditions: (i) workers maximize utility, (ii) producers
maximize profit, (iii) city developers maximize profit, and (iv) the below market clearing conditions
hold11=  +

(3)

 = 
1 −  =  +  
Definition 2 The economy is completely integrated if all clerks live in cities and are with managers. In
this case,  = 0. The economy is completely segregated if all clerks live in rural areas and areaway from managers. In this case,  = 0. The economy is partially segregated if clerks are in cities
as well as rural areas.
In this model, the equilibrium maximizes social welfare which is equal to aggregate output minus
aggregate commuting cost. The equilibrium is optimal because city developers internalize the knowledge
spillovers—the externality—through transfer payments. This is a standard feature of system-of-cities model.

3.2

Equilibrium Analysis

This section studies the equilibrium and includes an analysis on how decreased communication friction
leads to geographic fragmentation of production and aﬀects welfare. A quick comparison between two
1 1 The second condition implies that managers all live in cities because managerial jobs are only available in cities. The
third condition implies that clerks who perform inshored work all live in rural areas because they want to save on commuting
costs and rent.

16

limiting cases,  = 1 and  = 0, facilitates discussion on the intuition of key results, while the solution
and analysis for the general case,  ∈ (0 1), is also included.
3.2.1

Compare Two Limiting Cases

In the equilibrium, workers must be indiﬀerent as to where to live. Therefore, the urban cost of living,
the sum of commuting costs and rent, must be the same everywhere within the city. Given a population
 , the urban cost of living is
Urban cost of living =2since the worker living at the city edge pays zero rent and 
2 commuting costs. By the indiﬀerence¡¢
condition, the rent is  2 −  for land  units away from the CBD. Thus, the total rent earned by thecity developer is

Total rent = 2¶
Z 2 µ 2−   =2
40

τ = 1 In this case, inshoring is frictionless. All clerks strictly prefer living in rural areas, because theycan earn as much as if they were in cities and they need not pay the urban cost of living. Thus,  = 0
and  =  . The city developer’s profit maximization problem is { }



 2
− 
4
 =  +  −2

 =  +−1 1− = (1 − )  + −
where  is the indirect utility of managers. (The indirect utility of clerks is  =  in this case.) The
three constraints are "no-incentive-to-leave constraints" solved from the workers’ and firms’ optimization
problems. The intuition of these constraints is as follows: The developer must guarantee managers autility level, such that managers will not do better if they live elsewhere. Moreover, the developer must
guarantee wage rates, which have to be low enough so that the producer is willing to locate in the city andhave to be high enough so that workers are willing to work for the city’s producer. Through competition,
the guaranteed levels are equal among developers in diﬀerent cities, and the developers simply take theselevels as given.

17

Solving the first order conditions of the developer’s problem and using  = 0 and (3), we have

 = 



µ

1−


¶1−

(4)

This rate of transfer internalizes the externality of knowledge spillovers, because it equals the diﬀerence
between the social and private marginal productivity. Thus, the First Welfare Theorem holds. In addition,
the developer earns zero profit in the equilibrium because of free entry. This zero profit condition and(4) together imply that
=

µ

4


1
µ
¶ 1−

1−


¶ 1−
1−

(5)

Then, (3), (5) and  = 0 determine the equilibrium allocation which, in turn, determines the equilibrium
wages and transfer.
τ = 0 Inshoring clerical work is impossible in this case. All clerks must live in cities, and  = 0. The
developer’s profit maximization problem is2 ( + )− 
4 ( + ) =  +  −2
 ( + ) =  −2 {} =  +−1 1− = (1 − )  + −Solving the equilibrium is analogous to the case of  = 1. After deriving

 =

2
1−

µ

4


1
µ
¶ 1−

1−


¶ 1−
1−

(6)

the rest of the solution can be determined.
Comparison To facilitate comparison, I attach a subscript 1 to the equilibrium elements derived fromthe  = 1 case and a subscript 0 to those from the  = 0 case. A new technology that increases
the proportion of deliverable inshored services from 0 to 1 not only leads to geographic separation of
production tasks, but also has eﬀects on segregation, city size, average rent, output and welfare.
Increasing  from 0 to 1 changes the economy from complete integration to complete segregation.

18

This is because when  = 1, rural clerks can earn as much as if they were in cities, but they need not
pay commuting costs and rent. Thus, clerks strictly prefer rural areas to cities.
The city size gets bigger when  increases from 0 to 1, because
1+1 = 1   1− 1 =1
0 = 0

given  ∈ (0 1). Since the First Welfare Theorem holds, we can use the social planner’s view to explain
why city size increases. The intuition is as follows: Adding a manager into the city when a clerk leaves,the city size does not change, and the total commuting cost stays the same. However, there are more
knowledge spillovers in the city, because the population of managers has increased. As the result, themarginal benefit of agglomeration increases and outweighs the marginal cost of congestion at the originalcity size. Thus, city size should increase by adding even more managers. The bigger city size then implies
a higher average rent in the city and higher aggregate output of the economy.
As  increases from 0 to 1, clerks are strictly better oﬀ because
1 = 1 (1 − )

µ

1−


¶−

 0 (1 − )

µ

1−


¶−

−

0
= 0
2

In the new equilibrium, clerks not only save on commuting costs and rent by living in rural areas, but alsoearn higher wages, because the higher level of knowledge spillovers makes producers more productive.
However, managers may be either better or worse oﬀ. Although managers earn higher wages at  = 1,
they also have to pay a higher urban cost of living. In a numerical example with  = 066,  = 05 and = 02, the new technology makes managers worse oﬀ.
Nevertheless, increasing  from 0 to 1 unambiguously decreases welfare inequality, defined as the ratioof managers’ welfare to clerks’, because1 +  − 2 1 −  +  − 2
1−
0
=

=
1
1−

1 −  − 2 (1 − ) 0Here, 1− reflects how welfare inequality depends upon the economy’s relative supply of clerks to man+−2
agers, and the change in the ratio from 1−−2(1−)to +−2
reflects the impact on welfare inequality1−when  increases from 0 to 1.
To see this, let us consider the following facts. First, both types of workers earn a constant share of
economy’s aggregate output. This is  for managers and 1 −  for clerks. Second, aggregate urban costof living is a constant proportion, 2, of the aggregate output. Half of this cost is aggregate congestion19

(commuting costs), and the other half is aggregate rent which eventually becomes part of managers’
income through developers’ transfer payments.
Third, when the economy is completely integrated ( = 0), all workers share equally the aggregate
urban cost of living. Thus, a  proportion of this cost is paid by managers, and the rest 1 −  proportionis paid by clerks. However, when the economy is completely segregated ( = 1), the entire aggregate
urban cost of living is paid by managers, while clerks do not pay any. This is why the new technology
decreases welfare inequality.
In summary, the new technology that facilitates inshoring benefits clerks, because it allows them toaccess the high productivity in cities without paying the urban cost of living. Eliminating communication
friction makes the marginal productivity of rural clerks as high as their urban counterparts, and living inrural areas saves on urban living costs. This generates the sorting of clerks into rural areas and leads tocomplete segregation. Because accommodating clerks, who do not contribute to knowledge spillovers, incities is no longer a constraint, cities are able to increase in size and productivity (knowledge spillovers).
Through inshoring, rural clerks can further benefit from this additional higher productivity. On the other
hand, managers may not necessarily benefit from the sorting. Although they can obtain higher incomes,
they must also pay higher urban living costs. Finally, welfare inequality is mitigated, because there is
redistribution in who pays the aggregate congestion costs and who is the ultimate source of developers’
transfer payments; clerks do not pay these at all after the sorting.
One might suspect that the above welfare implication is driven by the functions of city developers.
With transfer payments, managers are seemingly landlords since they ultimately receive all rental revenues. Thus, one might suspect that welfare inequality is mitigated just because clerks no longer needto “subsidize” managers as  increases from 0 to 1. Below, I quickly show that this suspicion is notborne out by using an alternative modeling approach – self-organization, which is typically found in
the literature, e.g., Anas (1992) and Venables (2005). Under this approach, sorting is created throughatomistic decisions of workers and firms, but not actions of large institutions like city developers. Here,I briefly sketch a model of self-organization and discuss its solution.
Assume no city developers, distribute each city’s rental revenue to all of its residents equally (as is
assumed in many models), translate the utility function in the previous model by  units, and keep
everything else the same as in the previous model. Recall that the model economy is large and canfacilitate a large number of cities in equilibrium. This implies that, in equilibrium, cities are symmetricand managers and clerks must be indiﬀerent between established cities and a potential city with  oran infinitesimal number of managers (see Henderson, 1974, for a discussion of this fact). Therefore,20

we can use this indiﬀerence condition of managers to pin down the equilibrium number of managers inestablished cities. Then, the potential city’s ratio of managers to clerks can be adjusted to hold theindiﬀerence condition of clerks. It is not diﬃcult to verify that mangers’ indirect utility is  in both  = 0
and  = 1 cases, but the equilibrium number of managers in established cities increases when  increases
from 0 to 1. Then, it is readily seen that clerks’ indirect utility is increased and welfare inequality is
mitigated when  increases from 0 to 1 because, by living in rural areas, clerks benefit from not onlysaving the commuting costs but also the higher productivity of firms (due to more knowledge spillovers)in the new equilibrium with frictionless inshoring.
3.2.2

General Case

The solution for the general case in which  ∈ (0 1) and the propositions about how a marginal increasein  aﬀects the model economy are presented here for the sake of completeness. The intuition of the
propositions is fairly similar to that in the previous discussion.
For this general case, the developer’s problem can be written as: { } ( + )2− 
4
 ( + ) =  +  −2
 ( + ) =  −2
 =  +−1 ( +   )1− = (1 − )  + ( +   )− =  (1 − )  + ( +   )− ≥ 0;  ≥ 0;  ≥ 0

The non-negative constraints are not binding when the value of  is in the following range:
⎛

⎞
(1
−
)
−
2
(1
−
)
(1
−
)
³
´⎠
(  ̄ ) = ⎝
(1 − )(1 − ) + 2 1−When  ∈ (  ̄ ), the equilibrium is characterized by partial segregation: Clerks can be found in cities aswell as rural areas. The equilibrium is characterized by complete integration when  ≤  and complete
segregation when  ≥ ̄ .

21

Solving the developer’s problem, we have
µ
µ
¶¶1−
1−
 =   + 
−



and
=

µ

4


1
¶ 1−

(1 + )

−2
1−

µ
µ
¶¶ 1−
1−
1−
+
−


(7)

(8)

where  denotes the ratio of clerks to managers in the city. The equilibrium value of this ratio is adecreasing function in  :
=

 1−
1 −  − 2 1−


2 − 1 + ,∀ ∈ (  ̄ )(9)Together with (3) and the first order conditions of the producer’s profit maximization problem, the above
three equations, (7), (8) and (9), determine the equilibrium allocation, wages and transfer. The next two
propositions concern the impact of a marginal increase in  when  ∈ (  ̄ ).
Proposition 1 When  ∈ (  ̄ ), a marginal increase in  increases the number of clerks in rural areas.
In addition, city size and average rent increase.
Proof. Let  denote the total number of clerks in rural areas. We have  = 1 −  − . Since  0,
we have  0. Next,(1 + ) (1 − ) − (1 − ) (1 −  )

=


(1 − ) (1 −  ) ( −  )It is not diﬃcult to verify that ∀ ∈ (  ̄ ),   0. Since the city population increases in  , so will theaverage rent.
Proposition 2 When  ∈ (  ̄ ), a marginal increase in  decreases the welfare inequality.

Proof. For the aggregate urban cost of living, 1−proportion is paid by managers and the rest 1−−
1−

proportion is paid by urban clerks. Thus, the welfare inequality can be written as +  − 1−
2 1 − 

=
1−−

1 −  − 1− 2   0, since  0.
Clearly, 22

4Conclusion

This paper examines inshoring. Empirically, the paper shows that the industry performing inshoredbusiness support services grows substantially faster in small localities than it does in big cities. The
industry is increasingly separated from its downstream industries geographically, and there is also a
growing tendency toward geographic separation of the support workers from others. The examination of
growth determinants suggests that the primary reason for the small localities’ success may be the lessexpensive support workers there.
This paper presents a simple general equilibrium model that predicts the empirical findings. More
importantly, the model suggests that inshoring benefits support workers relative to managers and thus
mitigates welfare inequality. This implication is in stark contrast to the oﬀshoring literature, which often
predicts that oﬀshoring may make domestic support workers worse oﬀ. Despite certain stylization, themodel does shed light on how inshoring could aﬀect where people live and how well they live, and boththese results are obtainable under self-organization, an alternative modeling approach. The possibility
for support workers to work for urban firms without bearing urban costs is the key.
The result on welfare inequality relies on the assumption of mobile labor. If workers cannot move,urban support workers will get hurt when new technology shifts the demand for support workers to rural
areas. Then, the welfare impact will be in line with the oﬀshoring literature.
The model in this paper considers only one source of externality. If other sources of externality suchas neighborhood eﬀect are important, then moving to rural areas may not necessarily benefit support
workers as they may miss out on the positive interactions that are only available in the city, and, inthe long run, the segregation might have adverse impact on the economy’s productivity. Future research
could consider this possibility.

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23

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25

Table 1: Growth of BSS Employment and LQ
BSS employment (thousands)
Size class \ Year
1998
2006 % growth
10 biggest cities
188
197
4.8%
1,000k ~ 4,390k
223
243
8.9%
250k ~ 1,000k
136
190
39.6%
rural areas ~ 250k
90
127
41.2%
Total Employment (thousands)
Size class \ Year
1998
2006 % growth
10 biggest cities
29,64932,93411.1%
1,000k ~ 4,390k
31,50435,43512.5%250k ~ 1,000k
20,76422,932
10.4%
rural areas ~ 250k
24,46525,376
3.7%
BSS LQ
Size class \ Year
1998
2006 % change
10 biggest cities
1.06
0.92
‐12.9%
1,000k ~ 4,390k
1.18
1.06
‐10.6%
250k ~ 1,000k
1.10
1.28
16.6%
rural areas ~ 250k
0.61
0.77
25.7%

Table 2: Use of the BSS as Intermediate Inputs, 1997
Column 1

NAICS Industry

Column 2

Column 3

Percentage
Intermediate of the total
use of BSS intermediate(millions)use of BSS

Column 4

Column 5

Column 6

Column 7

Cummulative
Cummulative BSS intensity
percentage
Percentage percentage (column 2of the totalintermediate Value added of total value of total value divided byuse of BSS (millions)
added
added
column 5)

52

Finance and Insurance

5358

14.2

14

612350

8.8

9

1.6

42

Wholesale Trade

4715

12.5

27

503890

7.2

16

1.7

62

Health Care and Social Assistnace

4156

11.0

38

502908

7.2

23

1.5

54

Professional, Scientific, and Technical Services

4035

10.7

48

535786

7.7

31

1.4

44

Retail Trade

3773

10.0

58

442995

6.4

37

1.6

334

Computer and Electronic Product Manufacutring

2397

6.3

65

156565

2.2

39

2.8

51

Information

1970

5.2

70

351782

5.0

45

1.0

813

Religious, Grant Making, Civic, Professional and Similar

2082

5.5

75

57138

0.8

45

6.7

61
71

Educational Services
Arts, Entertainment and Recreation

936
689

2.5
1.8

78
80

59049
82714

0.8
1.2

46
47

2.9
1.5

33A

Manufacturing 33 except 334

1242

3.3

83

495550

7.1

54

0.5

53

Real Estate and Rental and Leasing

1139

3.0

86

599139

8.6

63

0.4

48

Transportation and Warehousing 48

783

2.1

88

198165

2.8

66

0.7

23

Construction

707

1.9

90

323862

4.6

71

0.4

32

Manufacturing 32

704

1.9

92

345308

5.0

76

0.4

81A

Other Services Except Public Administration and 813

477

1.3

93

194197

2.8

78

0.5

56

Adminstrative and Support and Waste Management…

651

1.7

95

245056

3.5

82

0.5

72

Accomodation and Food Services

518

1.4

96

220757

3.2

85

0.4

31

Manufacturing 31

481

1.3

97

200559

2.9

88

0.4

22

Utilities

403

1.1

98

154381

2.2

90

0.5

49

Transportation and Warehousing 49

266

0.7

99

95071

1.4

91

0.5

93

Owner-occupied Dwellings

203

0.5

100

516730

7.4

99

0.1

21
11

MiningAgriculture, Forestry, Fishing, and Hunting

111
3

0.3
0.0

100
100

72171
9477

1.0
0.1

100
100

0.3
0.1

Total intermediate use of BSS
Total value added

37799
6975600

Table 3: Growth of DI Employment and LQ
DI employment (thousands)
Size class \ Year
1998
2006 % growth
10 biggest cities
16,792
19,33515.1%
1,000k ~ 4,390k
16,805
19,54216.3%
250k ~ 1,000k
10,907
12,60015.5%
rural areas ~ 250k
12,097
13,0277.7%
DI LQ
Size class \ Year
1998
2006 % change
10 biggest cities
1.06
1.06
‐0.2%
1,000k ~ 4,390k
1.00
1.00
‐0.5%
250k ~ 1,000k
0.99
0.99
0.7%
rural areas ~ 250k
0.93
0.93
‐0.1%

Table 4: Coagglomeration of BSS and Dis
Ellison and Glaeser Coagglomeration Index
Coagglomeration: four city size class level
Coagglomeration: MAs and rural counties level1998
0.00233‐0.00007

2006
0.00042‐0.00013

Table 5: Support Workers LQ
Size class \ Year2000
10 biggest cities
0.99
1,000k ~ 4,390k
1.00
250k ~ 1,000k
1.01
rural areas ~ 250k
1.01

2006 % change
0.97
‐2.3%
0.99
‐1.0%
1.03
2.5%
1.03
2.7%

Table 6: Separation of the Support Workers from Others
Among all workers
Among office workers
in BSS and DIs
in BSS and DIs
2000
2006
2000
2006
Dissimilarity Index
0.050
0.064
0.064
0.081
Correlation Index
0.00340.00550.00630.01000.00310.00490.00570.009095% Confidence Interval
0.00380.00610.00700.0111of Correlation Index

Among office workers
in all industries
2000
2006
0.050
0.065
0.00410.00640.00370.00580.00450.0071

Table 7: Summary Statistics of Variables Used in the Regression

VariableMean

Log(BSS employment in 2006/ BSS employment in 1998)
Log(MSA population in 2000)
Percentage of MSA employment in the DI in 1998
Log(DI employment in 2006/ DI employment in 1998)
Percentage of MSA employment in the BSS in 1998
Diversity in 1998
Competition in 1998
Average hourly wages in 2000
Lobor suitability (2000 education)
Labor suitability (2000 occupation)

0.285
13.47952.8130.152
0.628‐0.1031.437
13.150‐29.776‐130.463

Standard
Minimum Maximum
Deviation
0.636
0.897
5.743
0.111
0.498
0.025
0.946
1.523
10.0366.598‐1.69212.43531.061‐0.1890.042‐0.2720.201
9.434‐59.249‐147.099

2.760
16.72466.0640.726
3.248‐0.0746.330
18.032‐11.869‐118.908

Table 8: Correlation Matrix of Variables Used in the Regression
Variable Y
X1
X2
X3
X4
X5
X6
X7
X8
X9
Y
1
X1
‐0.19
1
X2
‐0.03
0.16
1
X3
0.21
0.09
‐0.04
1
X4
‐0.28
0.14
0.07
0.06
1
X5
‐0.10
0.44
0.54
0.06
0.28
1
X6
0.34
‐0.29
‐0.14
0.18
‐0.60
‐0.38
1
X7
‐0.43
0.52
0.29
‐0.03
‐0.01
0.28
‐0.11
1
X8
0.05
‐0.19
‐0.32
‐0.14
0.11
‐0.05
‐0.06
‐0.42
1
X9
‐0.32
0.58
0.39
0.01
0.25
0.56
‐0.38
0.55
‐0.17
Y: Log(BSS employment in 2006/ BSS employment in 1998)
X1: Log(MSA population in 2000)
X2: Percentage of MSA employment in the DI in 1998
X3: Log(DI employment in 2006/ DI employment in 1998)
X4: Percentage of MSA employment in the BSS in 1998
X5: Diversity in 1998
X6: Competition in 1998
X7: Average hourly wages in 2000
X8: Lobor suitability (2000 education)
X9: Labor suitability (2000 occupation)

1

Table 9: Determinants of BSS Employment Growth: Results from WLS Regressions

Constant
Log(MSA population in 2000)

Dependent variable: Logarithmic growth of BSS
employment from 1998 to 2006
(1)
(2)
(3)
(4)
3.057**
3.151**
2.628
2.247(.798)(.914)(1.306)(2.335)‐.178**(.057)‐.188**(.056)‐.134**(.057)‐0.006(.062)

Percentage of MSA employment in
the DI in 1998‐.003(.010)‐.004(.012).004(.012)

Log(DI employment in 2006/ DI
employment in 1998)

1.323**(.538).910*(.462).620*(.374)

Percentage of MSA employment in
the BSS in 1998‐.340**(.138)‐.273**(.128)

Diversity in 1998

1.866
(2.917)5.731**(2.783)Competition in 1998

.135*(.080).157**(.073)

Average hourly wages in 2000‐.163**(.050)

Lobor suitability (2000 education)

‐.008
.005

Labor suitability (2000 occupation)

‐.007
.011

State fixed effects

Yes

Yes

Yes

Yes

R squareNumber of observation

.41
166

.43
166

.51
166

.57
166

Note: * and ** stand for significance at 10% and 5%, respectively.
Standard errors of parameter estimates are in parentheses beneath these estimates

Table A1: Determinants of BSS Employment Growth: Results from WLS Regressions for
Cities with 100k+ people

Constant
Log(MSA population in 2000)

Dependent variable: Logarithmic growth of BSS
employment from 1998 to 2006
(1)
(2)
(3)
(4)
2.165**
1.564
2.628
2.247
(1.029)(.973)(1.306)(2.335)‐.153*(.086)‐.186**(.079)‐.129**(.057)‐0.010(.056)

Percentage of MSA employment in
the DI in 1998

.015**(.007).017**(.008).010(.009)

Log(DI employment in 2006/ DI
employment in 1998)

1.431**(.499)

1.108**(.441).954**(.393)

Percentage of MSA employment in
the BSS in 1998‐.310**(.093)‐.313**(.091)

Diversity in 1998.067
(2.197)2.453(2.175)Competition in 1998

.110**(.047).105**(.045)

Average hourly wages in 2000‐.164**(.042)

Lobor suitability (2000 education)

‐.007
.005

Labor suitability (2000 occupation)

‐.001
.010

State fixed effects

Yes

Yes

Yes

Yes

R squareNumber of observation

.10
331

.15
331

.25
331

.34
329

Note: * and ** stand for significance at 10% and 5%, respectively.
Standard errors of parameter estimates are in parentheses beneath these estimates