Building Specialization, Anchor Tenants and Agglomeration Economies

Crocker H. Liu Robert A. Beck Professor of Hospitality Financial Management

School of Hotel Administration Cornell University

Phone: (607) 255-3739 chl62@cornell.edu

Stuart S. Rosenthal

Maxwell Advisory Board Professor of Economics Department of Economics and Center for Policy Research

Syracuse University Syracuse, New York, 13244-1020 Phone: (315) 443-3809

ssrosent@maxwell.syr.edu

William C. Strange SmartCentres Professor of Real Estate

Rotman School of Management University of Toronto, Toronto Ontario M5S 3E6, Canada

Phone: (416) 978-1949 wstrange@rotman.utoronto.ca

October 31, 2017

PRELIMINARY: NOT FOR QUOTATION OR CITATION

Dun and Bradstreet data were obtained using the Syracuse University site license. CoStar data were obtained with support from the Center for Real Estate and Finance at the School of Hotel Administration at Cornell. Strange acknowledges financial support from the Social Sciences and Humanities Research Council of Canada and the Centre for Real Estate at the Rotman School of Management. Boqian Jiang provided excellent research assistance. Errors are the responsibility of the three authors.

Abstract It is well-known that spatial clustering of industries occurs at regional, metropolitan, and neighborhood levels. This paper establishes that there exists specialization at the level of individual buildings. Specialization is robust to building and location characteristics, significantly exceeds patterns under random assignment of establishments, and occurs in part because of within-building agglomeration externalities. The presence of an anchor tenant (an externality generator) skews the building’s composition towards the anchor’s industry, while the presence of an anchor in an adjacent building does not. An analogous result holds for establishment sales, confirming the importance of buildings as spatial units.

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I. Introduction

A vast literature has confirmed that the spatial concentration of employment contributes

to productivity spillovers and helps to explain why cities are productive.1 Central to this literature

is the idea that proximity matters. This paper takes an entirely new approach to proximity by

analyzing agglomeration economies at the building level.

Our analysis of spillovers focuses on the downtowns of modern cities and the service

industries that dominate them. Much of the literature on agglomeration to date has instead

emphasized manufacturing industries, activities that are increasingly found in suburban and

nonurban locations. In this context, older papers typically examine how a metropolitan area’s

productivity depends on its scale and composition (Glaeser et al, 1992, and Henderson et al, 1994

are two notable examples). More recently, researchers have used distance-based measures, such

as activity within five miles, to look inside of metropolitan areas for evidence that agglomeration

processes have important local elements (Rosenthal and Strange, 2003, 2008). More recently

still, the focus has tightened further, allowing for consideration of agglomeration economies that

operate at a highly local level (Rosenthal and Strange, 2005; Arzaghi and Henderson, 2008). For

much of this literature, the guiding principle has been that interactions tend to take place more

frequently and produce greater benefits as agents become closer together.

While abstracting from the built environment makes sense in some contexts, the idea that

buildings matter is an old one, going back to Jacobs (1961), at least. Furthermore, the built

environment is especially relevant to commercial activity in city centers. Our focus on buildings

as spatial units mirrors the perceptions and decisions of business owners. Entrepreneurs lease

space in specific buildings and in that sense, it is the building that they choose when deciding

where to locate. Meeting with individuals outside of one’s own building is also a qualitatively

different experience than meeting with people inside of the building: the former entails walking

1 For reviews of the agglomeration literature, see Rosenthal and Strange (2004), Duranton and Puga (2004), Behrens and Robert-Nicoud (2015), and Combes and Gobillon (2015).

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down the hall or at most an elevator ride, while the latter may require dress for stormy weather in

addition to navigating city traffic. Individuals operating within the same building are also far

more likely to experience spontaneous interactions to the extent that they enter and exit through

the same doorway and ride the same elevators. For all of these reasons, it is likely that buildings

matter as spatial units and that productivity spillovers decline discretely upon stepping outside of

one’s building. Moreover, in contrast to other instances where externalities abound, building

owners have compelling incentives to manage their tenant mix to internalize spillovers. By doing

so, building managers not only enhance productivity and rent within the building, but also help to

ensure that cities are productive environments.

To motivate our empirical analysis, we develop a theoretical model of the equilibrium

determination of building employment composition in the presence of spillovers. In the model,

tenants are heterogeneous in two ways. First, tenants operate in different industries, which

impacts both the agglomeration economies that a tenant might enjoy in a particular location and

also the rivalry between businesses to serve particular customers. Second, some large tenants,

known in the commercial real estate industry as anchors, generate particularly valuable

externalities for other tenants in the building. In this context, and in the empirical work to follow,

anchor tenants will be defined as especially large establishments in both absolute and relative

terms. In addition to this differentiation of tenants, buildings also differ in their physical

attributes in ways that affect perceptions of building quality. A high quality building will have a

range of superior amenities, while a low quality building will not. Building quality affects tenant

profit and willingness to pay for occupancy.

The building owner’s objective is to select a mix of tenants that maximizes aggregate

rent, which depends on tenant profit. The key predictions of the model are that productivity

spillovers encourage building-level specialization and that the presence of an externality-

generating anchor in a building will lead the building to specialize in the anchor’s industry to a

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greater degree. See Brueckner (1993) and Konishi and Sandfort (2003) on the economics of

anchors in the specific setting of malls. See Gould et al (2005) for a related empirical analysis.

We estimate and test various features of our model using establishment-level data from

Dun and Bradstreet for the core areas of five large cities. The data allow us to determine the scale

and composition of employment in every commercial building in the cities of Manhattan (New

York County), Los Angeles (Los Angeles County), Chicago (Cook County), San Francisco (San

Francisco County), and Washington (District of Columbia). We test the model’s implications for

Finance (SIC 62 plus SIC 67), Law (SIC 81), and Advertising (SIC 7311, 7312, 7319), crucial

industries for cities where Marshallian externalities are likely to be present. We also estimate the

model for Retail (SIC 53-59), where shopping externalities are likely to dominate, and for

Manufacturing (SIC 20-39) for which exporting to markets beyond the city should be common.

The estimates support the model’s predictions. Even within narrowly defined

neighborhoods, we see substantial specialization of buildings. Monte Carlo methods that allow

for both building-level space constraints and the size distribution of establishments confirm that

this pattern is nonrandom. In order to assess the underlying mechanisms driving specialization,

we consider the collocation of anchors and other smaller tenants within buildings. The key idea

is that anchors generate substantial externalities, implying that they will collocate with other

tenants who benefit from the externalities.2 Controlling for the level and composition of nearby

employment within a roughly two-block area (within 0.1 miles), we see compelling evidence that

anchors collocate with smaller companies in their industry in the same building. This pattern

persists even after controlling for the physical quality of the building, both through direct

measures (e.g. Class A, B and C and Star Rating) and also through building fixed effects. The

presence of an anchor in the target industry is associated with 5 to 25 percent additional own-

industry employment in the building. Additional estimates indicate that spillovers are much

2See Ellison et al (2010) for a related analysis and O’Sullivan and Strange (2017) for a result providing foundations for this approach.

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smaller across industries within the same building and that the effect of an anchor in an adjacent

building on the same block face is also small.

Our findings confirm that within building spillovers are present between anchors and

smaller companies in the same industry, that these sorts of spillovers do not extend across the

industries defined in this study, and that these spillovers decline sharply upon leaving a building.

Buildings, therefore, are important spatial units. Because buildings are privately owned, building

managers have an incentive to internalize these externalities and should be cognizant of

opportunities to specialize.

The rest of the paper is organized as follows. Section II describes the theoretical model,

Section III discusses the data, Section IV presents evidence that buildings are specialized within

very narrowly defined neighborhoods, and Section V presents results from various regressions

that highlight underlying mechanisms. We conclude with Section VI.

II. Model A. Overview There are two classes of agents in this model, the owners of commercial buildings and the

tenants who locate in them. The tenants are differentiated by industry. They are also

differentiated according to their impacts on the building’s business environment. Those with

large impact are referred to as “anchors,” while ordinary tenants are referred to as “nonanchors.”

Building owners choose how to allocate a fixed amount of space among the tenants to maximize

aggregate rent subject to participation constraints that determine the rent that tenants are willing

to pay.

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B. Primitives We will work with a two-industry, partial equilibrium model. This is especially

transparent, and it allows graphical representation of the solution for the composition of a

building. We will discuss generalizations later.

There are two industries, denoted i = {1,2}. The model will consider how the

composition of a building, the space allocated to the two industries, the quality of the building,

and the possible presence of an anchor together impact profit.

Without loss of generality, we will suppose that the only possible anchor is type-1. Let

the total amount of space be normalized to one. Let a denote the space allocated to the type-1

anchor. Let s1 and s2 denote the space allocated to the nonanchor tenants in the two industries.

Anchor tenants produce positive externalities within the building. Because of this, they

are sought after by building owners. Let r0 denote the rent per square foot that an anchor

receives as a result of competition among building owners. This partial equilibrium approach

captures the discounts that anchors receive. In general equilibrium, the discount will depend on

the amount of the externalities.

We suppose that the profits of the nonanchor tenants depend on the composition of the

building as follows. A type-1 nonanchor earns profit

f(s1) + g(a) + q + – r. (II.1) The first term, f(s1), captures the impact of within-building agglomeration of type-1 tenants on a

type-1 firm's profits. As in the agglomeration literature, we suppose that f(-) is increasing for

low values of s1 and decreasing for high values of s1. The second term, g(a), captures the impact

of space allocated to the anchor on the profits of tenants in the same industry. We suppose that

g(-) is increasing and concave: allocating more space to the anchor raises the rents of the

nonanchors in the same industry, but at a decreasing rate. The third term, q, captures the quality

of the building. The fourth term, , captures neighborhood contributions to profit. The final

term, r, denotes rent.

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The profits of a type-2 tenant are given by

h(s2)– r. (II.2) h(-) is initially increasing in s2 and later decreasing, as with the parallel expression for the type-1

firms. We suppose that there is no impact of building quality or the building's neighborhood.

This allows us to consider the impact of changes in quality or neighborhood that positively

impact only the profits of type-1 tenants.

The building owner has a fixed amount of space to allocate. Normalizing the available

space to unity means that (II.2) can be rewritten as

h(1-a - s1)– r. (II.3) Solving for bid rent is straightforward. Setting profit equal to zero and solving gives rent as a

function of building composition for a nonanchor tenant for the two industries:

r1(s1, a, q,)= f(s1) + g(a) + q + (II.4a) r2(s2, a)= h(1- a - s1) (II.4b) The building owner chooses how to allocate a fixed amount of space between a type-1 anchor and

the nonanchor tenants in the two industries. The owner’s objective, aggregate rent, R, is defined

as

R = ar0 + s1 r1(s1, a, q,) + (1-a- s1) r2(1-a-s1). (II.5) C. Solution The first-order conditions for an interior solution to the building owner's choice problem

are:

r0 + s1 g'(a) - r2(1-a-s1) = 0. (II.6a) r1(s1, a, q,) - r2(1-a-s1) = 0. (II.6b) (II.6b) means that the shares of the building allocated to the two types of nonanchor tenant must

set bid rents equal for the two tenant types. (II.6a) means that the marginal value of adding more

space to the anchor, equal to the anchor's rent plus the externality created by the anchor, equal the

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rent paid by a type-2 nonanchor tenant. By (II.6b), the latter also equals the rent paid by a type-1

nonanchor.

The second order conditions are

g"(a) < 0 (II.7a) f'(s1) - h'(1-a-s1) < 0. (II.7b) g"(a)[f'(s1) - h'(1-a-s1)] - [g'(a)-h'(1-a-s1)]2 >0 (II.7c) (II.7a) holds by assumption. (II.7b) is familiar in the agglomeration literature as a "stability"

condition. It will be met if f'(s1) < 0 and h'(1-a-s1) > 0. f'(s1) < 0 means that a marginal nonanchor

of type-1 has profit that is decreasing in s1. Likewise, h'(1-a-s1) > 0 means that a type-2

nonanchor is in the same situation, since profit increasing in the other type means that profit is

decreasing in own type. This is a stability condition in the sense that if it were to fail, a

perturbation that added another type-1 tenant would increase type-1 profit. The first term of the

third (quadratic) condition is positive. This condition then requires that the second term, [g'(a)-

h'(1-a-s1)]2, be not too large. We assume that this condition holds.

There are two equilibrium relationships in (II.6a) and (II.6b). (II.6a) can be interpreted as

describing how a varies with s1. Implicitly differentiating gives

ds1/da = - [g'(a)-h'(1-a-s1)] /[s1g"(a) )-h'(1-a-s1)]. (II.8) If h'(1-a-s1) is small (as discussed above), then we have ds1/da > 0. A larger anchor makes sense

for the owner with many tenants, since the anchor raises nonanchor rents in its industry.

However, the space to be allocated is fixed, and the benefits of the anchor must be weighted

against the opportunity costs of having a large anchor.

Similarly, (II.6b) can be interpreted as describing how s1 varies with a. Implicitly

differentiating again gives

ds1/da = - [g'(a)- f'(s1) - h'(1-a-s1)] /[ f'(s1) - h'(1-a-s1)]. (II.9) With a small h'(1-a-s1) and additionally a small f'(s1), we also have ds1/da > 0. A larger anchor

raises the rents for the nonanchors, leading the owner to include more. This is depicted in Figure

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1. Moving right corresponds to a larger s1, while moving left corresponds to a larger s2. The

solid line shows r1(s1, a, q,), while the dashed line shows r2(1-a-s1). These are equal at the

owner’s solution for s1 and s2. A larger anchor raises rents from type-1 tenants (the dotted line),

leading to a larger s1.

In the conditions described above, both relationship between s1 and a are increasing. An

increase in s1 means that the externalities created by increasing a are especially valuable. An

increase in a makes the building more attractive to industry-1 tenants. We thus anticipate a

positive equilibrium relationship between a and s1.

There are other factors that impact the building composition decision. The following

comparative statics are worth some discussion:

∂s1/∂q = - g"(a)s1 / , (II.10a) ∂s1/∂ = - g"(a)s1 / (II.10b) (II.10a) means that as a building becomes more attractive to type-1 tenants, the share of type-1

nonanchors rises. (II.10b) means that as a neighborhood becomes more attractive to type-1

tenants, the share of type-1 nonanchors rises. We have not focused here on the a decision, since

the partial equilibrium framework does not relate the favorable rent that an anchor receives to its

environment.

There are, thus, three key predictions of the model:

(i) A large anchor results in a large share of tenants in the anchor's industry. (ii) A neighborhood that is favorable to a given industry will have buildings that host larger

shares of that industry. (iii) A building that is favorable to a given industry will host larger shares of that industry.

We test these predictions below.

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III. Data and summary statistics A. Data sources

The primary data for the analysis are from Dun and Bradstreet (D&B), obtained through

the Syracuse University library. Rosenthal and Strange (2001, 2003, 2005, 2008, and 2012) have

previously used D&B data in a series of papers in which the data were obtained aggregated to the

5-digit zipcode level. Syracuse University now has a site license with Dun and Bradstreet that

permits us (given Rosenthal’s Syracuse affiliation) to access the D&B data at the establishment

level. Approximately, the D&B database covers all establishments in metropolitan areas across

the U.S. The data provide detailed information on employment and sales at an establishment’s

site, the primary industry classification based on SIC code, and many other attributes of the

establishments.3

Critical to our work, the D&B data provide the complete street address for

establishments. By grouping establishments by street address, we are able to determine the level

of composition of employment in each building that contains at least one establishment in the

D&B database.4 In the empirical work to follow, we downloaded the full set of establishments

for the core counties of five major cities, including New York (New York County), Chicago

(Cook County), San Francisco (San Francisco County), Los Angeles (Los Angeles County), and

Washington, DC.

We enhance the geographic features of the data through two intensive applications of

geographic information system (GIS) software (using MapInfo and MapBasic). For each

3 We have also used establishment level D&B data in related work that examines the vertical location of establishments and vertical employment density gradients (Liu, Rosenthal and Strange, 2016a, 2016b). 4 The D&B data report street address as a single reference that includes street name, street number, zipcode and in many instances suite number within the building. To process the street address, we wrote extensive code that broad the street address reference into its constituent parts (e.g. street name, street number, zipcode). Zipcode is almost always reported in a manner that is straightforward to work with. For that reason, we first grouped all establishments by zipcode. Constructing code that matched establishments on the same street was more demanding. This is because a given street name can be spelled in different ways (e.g. 1st Avenue versus First Avenue). Programming was developed that matched many such alternate spellings. The final level of coding then matched establishments on the same street that are found at the same street number.

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building in all five cities, we determine the set of buildings located within 0.1 miles,

approximately two city blocks. This enables us to summarize the level and composition of

employment within 0.1 miles outside of each building, measures used in the regressions to

follow. In a second GIS exercise, for each building we identify the adjacent building on the same

side of the block (referred to as the same block face) when such a building is present. This allows

us to evaluate the influence of employment in the adjacent building on activity in the target

building. This comparison is a key part of our identification strategy, as will become apparent.

For each building, we also code whether an anchor tenant is present and if so, the primary

industry to which it belongs. To be coded an anchor, an establishment must satisfy three criteria:

(i) it must be the largest establishment in the building (based on employment at the site), (ii) it

must contain at least 20 percent of employment in the building, and (iii) it must reside in a

building with at least 50 workers present. Coded in this way, buildings with fewer than 50

workers do not have anchors as do larger buildings in which the largest establishment contains

less than 20 percent of a building’s employment.

In the empirical work to follow, we classify establishments into one of six industry

groupings. These include Retail (SIC 53-59), high level Finance (SIC 62 and 67), Advertising

(SIC 7311, 7312, and 7319), Law (SIC 81), Manufacturing (SIC 20-39), and all other industries

combined (Other Industry). See Appendix A-1 for further detail.

We augment the D&B data with information on building quality by matching D&B street

address with buildings drawn from CoStar. The CoStar data provide information on the number

of elevators in a building, distance in miles to the closest transit stop, building class (A, B, or C,

where Class A is the highest quality), and star rating (1, 2, 3, 4, or 5, where 5 is best). Building

class is commonly used by commercial real estate investors and is based on a mix of physical

attributes of the structure and also the perceived quality of the building’s immediate

neighborhood. Broadly, Class A buildings are “prestigious” and tend to be expensive relative to

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lower class buildings. The star rating of a building is different in that it is determined exclusively

based on the physical quality of the building.5

It is worth emphasizing that when characterizing the level and composition of

employment within 0.1 miles outside of a target building, all employment in all buildings in the

D&B database are used to produce that information; this includes nearby buildings with as few as

just one worker present. The estimating samples, however, are restricted to target buildings with

at least 10 workers present. In the simplest specifications that control only for target building

employment along with employment within 0.1 miles, the sample includes 48,662 buildings

spread across the five cities noted above. Controlling for adjacent building attributes reduces the

estimating sample to 33,702 since not all buildings have adjacent buildings on the same block

face. Adding the CoStar variables on building quality further reduces the estimating sample to

5,431 buildings as CoStar’s coverage of buildings is much less complete than that of Dun and

Bradstreet. As will become apparent, despite the differences in sample size, results are similar

across the models to follow.

B. Summary statistics for the regression samples

Tables 1a, 1b, and 1c present summary measures for the target buildings used in the

regression analysis. In Table 1a, Panel A, 30 percent of the buildings are in New York, 22

percent in Chicago, 20.5 percent in Los Angeles, and roughly 13.5 percent in each of San

Francisco and Washington, DC.

In Table 1a, Panel B, it is important to note that many buildings do not have any

establishments present in a given industry. The censoring issue is especially acute for

advertising, for which only 2.8% of buildings include one or more advertising establishments.

Retail is far more common and is present in 43% of the buildings in the sample. Finance and

5 See http://www.buildingratingsystem.com/ for details.

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Law both appear in a bit over 8 percent of buildings while manufacturing and the Other Industry

category are present in 13.6% and 76.3% of buildings. There is nothing surprising about the

nature of these patterns, but it does indicate that care must be given to address the large number

of zeros in the data when estimating the building employment share models to follow. For that

reason, except in one instance all of the models to be presented are based on Tobit specifications

that take censoring into account.

In Table 1a, Panel C, among those buildings for which CoStar details are available,

9.59% are Class A, 42.2% are Class B and 48%.2 are Class C. Very few buildings receive a five

star rating – just 0.65%. Roughly 9% of buildings are rated 4 stars, however, and roughly 45%

and 43% receive ratings of 3 and 2 stars, respectively. The remaining 1.9% of buildings are

designated one star. The average CoStar building is 3.8 miles from a transit stop.

Table 1b, Panel A, summarizes the average share of nearby employment by industry

grouping. The first column presents summary measures based on employment within 0.1 mile

outside of a target building. Most of the employment is in the residual “other industry” category,

with 67.9% of the employment share. 21.3% of employment is in Retail, 2.1% is in Finance,

0.8% in Advertising, 2.1% in Law, and 5.7% in Manufacturing. The second and third columns

present analogous measures for the adjacent buildings and target buildings, respectively. Here we

restrict the set of buildings to those with 100 or more workers. The employment shares are lower

for Retail in these taller buildings, roughly 12 to 13 percent compared to 21.3% for the

surrounding 0.1 mile area. On the other hand, employment shares are somewhat higher in tall

buildings for finance and law.

In Panel B of Table 1b, employment shares are reported separately for Class A, B, and C

buildings, all of which are restricted to have 100 or more workers. As might be expected, Class

A buildings are oriented towards sectors such as Law and Finance, where workers tend to be well

paid. Retail is 5.1% of employment in Class A buildings compared to 9.0% and 9.3% percent in

Class B and C buildings. Finance, in contrast, comprises 11.8% of employment in Class A

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buildings compared to just 2.3% in Class B and 1.4% in Class C. For law, the corresponding

employment shares for Class A, B, and C buildings are 7.8%, 4.0% and 1.9%, respectively.

Panel C of Table 1b reports analogous industry employment shares by the star rating of

the building. The patterns resemble the building class patterns in Panel B, but they are not

identical. Finance and Law are clearly more prevalent in higher quality buildings but so too is

manufacturing, which accounts for 13.7 percent of employment in 5-star buildings.

Recall that anchors must be of sufficient size, both in absolute terms and relative to the

level of employment in the building. Table 1c summarizes the share of buildings with an anchor

present by industry grouping. Also reported is the share of buildings with fewer than 50 workers

for which anchors are defined as not present. 74.0% of target buildings in column 1 are below

that threshold and 85.7% of adjacent buildings. Restricting the sample to buildings with 100 or

more workers present, the share of target buildings with a Retail anchor present is 8.0%; the share

with finance is 1.8%; Advertising is 0.8%; Law is 2.1%; Manufacturing is 4.9%, and the Other

Industry category is 51.6%. A similar pattern is present for adjacent buildings. Collectively,

these patterns illustrate that most buildings do not have an anchor present. At the same time, an

important share of all buildings, especially of larger buildings, do have anchors.

III. Building Specialization

In this section, we present evidence that downtown commercial buildings tend to be

specialized. We begin with some simple descriptive measures.

A. Employment composition

Table 2 reports correlations in industry employment shares for buildings and the adjacent

building on the same block face (when present) and also for buildings and employment within 0.1

miles (including the adjacent building but not the building itself). Panel A includes buildings

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with 10 or more workers while Panel B restricts the sample to buildings with more than 100

workers. Several striking patterns are apparent.

First, in the initial column for both panels, the correlation between target building

industry employment shares and industry-specific shares of employment within 0.1 miles is

sharply higher for larger buildings. For Retail, for example, the correlation is 26.7% for larger

buildings and just 6.4% for the full sample (which includes buildings with fewer than 100

workers). The same qualitative pattern is present for the other five industries, with law displaying

the highest correlation for both samples (22% and 34.8% in Panels A and B, respectively).

The middle columns in the two panels report correlations between the employment shares

in the target and adjacent buildings. Here there is little difference between the two panels,

indicating that correlations in employment shares between the target and adjacent buildings are

not particularly sensitive to the size of the target and adjacent buildings. More important, the

correlations between the target and adjacent buildings are all low ranging from 6% to 17.8% in

Panel A and 6.2% to 17.4% in Panel B. These patterns confirm that target buildings often include

a very different mix of employment from the adjacent building, consistent with specialization at

the building level.

B. Building specialization allowing for space constraints and the size distribution of

establishments

The patterns in Table 2 confirm that buildings tend to be specialized. This does not,

however establish that the observed patterns are different from what might arise if establishments

were randomly assigned to buildings. There are two reasons for this. One is that the degree to

which establishments are concentrated in a select set of buildings is sensitive to the size

distribution of companies within a given industry. An industry dominated by a small number of

very large establishments, for example, will appear spatially concentrated. This was emphasized

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by Ellison and Glaeser (1997) although in a very different context.6 Second, and new to this

paper, buildings have capacity constraints and, for that reason, the size distribution of buildings

also affects observed patterns of specialization. A neighborhood dominated by one massive

building, for example, will tend to exhibit a different degree of specialization at the building level

as compared to a similar size community that is filled with many smaller buildings.

We address both of these concerns using a novel approach that allows us to formally test

whether the observed level of specialization at the building level is non-random. Our method

relies on a Monte Carlo procedure that randomizes establishment locations across buildings in a

manner that respects the size distribution of companies within an industry as well as the size

distribution of buildings in a given neighborhood. As will become apparent, the randomization

procedure draws in part on techniques developed by Duranton and Overman (2005). Because our

approach is computationally intensive, we focus on two well-known neighborhoods, the area

close to Grand Central Station and the area close to the New York Stock Exchange, both of which

are in Manhattan.

Table 3a displays summary measures by industry for all buildings with more than 10

workers within 0.3 miles for each of the two target landmarks (Panel A for the NYSE and Panel

B for Grand Central). The table reports counts and shares of establishments and employment by

industry as well as the share of buildings in each area with employment present in the specified

industry. It is worth noting that both locations contain relatively similar mixes of employment

based on both the establishment and employment shares across industries. It is also evident that

each industry is present in only a relatively small subset of buildings.

The key statistic of interest in the table is in the far right column. In that column, we

report a measure that we refer to as a “spatial G.” This is calculated as Gj ≡ ∑i (Sij – Xi)2, where

Xi is the share of total employment in the area in building i (i = 1, …, I) and Sij is the share of

6 Ellison and Glaeser (1997) examined the spatial concentration of employment among manufacturing industries at the metropolitan level.

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industry j employment in the area located in building i. If all employment in industry j was

allocated across buildings in a manner that mirrors total employment, then Sij = Xi for all i and

Gj would equal 0. If instead, all employment in industry j was located in a single building, then

Gj would converge to 1.0 from below as the number of buildings becomes large (becausae Xi2

becomes close to zero for each i). This measure has been used by Krugman (1991) and

Audretsch and Feldman (1996) to summarize the degree of spatial correlation in industry-specific

activity across states. The measure addresses the tendency for activity in a given industry to be

more (or less) spatially concentrated relative to total employment.

Bearing the above interpretation in mind, in Panel A for the NYSE, the largest value for

G is for finance at 0.21 while the smallest is for the Other Industry category at 0.031. Comparing

the industry specific measures for G between the two panels, notice that Retail is more

concentrated near Grand Central (Panel B) with a spatial G of 0.26 compared to 0.10 close to the

NYSE (Panel A). Finance, in contrast, is more concentrated near the NYSE, with G equal to 0.21

compared to just 0.068 near Grand Central. Advertising also displays sharp differences: the

spatial G is 0.09 near NYSE but 0.215 near Grand Central.

These patterns confirm that there is considerable variation in spatial concentration across

industries and that for a given industry spatial concentration can differ sharply across

neighborhoods. The question remains as to whether these differences depart from what might

occur with a random allocation of establishments across buildings. Tables 3b and 3c address this

question.

In Tables 3b and 3c, we report results from a series of Monte Carlo experiments that

allow us to calculate the empirical distribution for each industry’s spatial G measure when

establishments are randomly reassigned across buildings. More precisely, for a given sample of

buildings (e.g. within 0.3 miles of the NYSE), we conducted 100 Monte Carlo simulations. In

each simulation, all establishments in the sample were randomly reassigned to buildings within

the target area. To allow for building specific space constraints, we assumed that each building

18

could accommodate up to 10% more employment than is actually observed in the data. We then

randomly drew a first establishment from among all establishments in the neighborhood. That

establishment is assigned to a randomly drawn building in the neighborhood from among the

subset of buildings with more capacity than the number of workers in the company. A second

establishment was next drawn and the exercise repeated, taking into account only the remaining

space available in each building. This procedure was repeated until all establishments were

assigned to a building. If an establishment is drawn for which no building has sufficient capacity

to accommodate the company’s employment, we split the company across up to four different

buildings as needed.7 For each randomized sample, we re-compute the spatial G measures for

each industry. Repeating this procedure 100 times yields an empirical distribution of simulated

values for G, with sample size of 100.

We conducted the exercise above twice for each of the target landmarks, once for a

sample based on all buildings with 10 or more workers within 0.3 miles, and once for a sample

that includes all such buildings within 1.0 mile. Results are in Tables 3b and 3c for the NYSE and

Grand Central Station, respectively, where Panel A in each table is for the sample within 0.3

miles of the target landmark, and Panel B is for the sample within 1.0 mile. In each panel and for

each industry, estimates are reported for the actual values of G, the mean and standard deviation

of the 100 simulated values, and the 5th, 10th, 90th and 95th percentile values from the empirical

distribution. In each instance, significance tests compare the actual value of G to the sample

mean of the empirical distribution, drawing on the principle that the sample mean converges in

distribution to a normal. Note that three stars adjacent to the actual value for G denote significant

departures at the 99% level and two stars at the 95% level.

7 In doing so, we first assign company employment to the building with the largest amount of open employment capacity. Any remaining employment in the company is then allocated to the next building with the largest amount of open capacity and so on up to four buildings. Employment remaining after spreading the company across four buildings was discarded but this rarely happens. We also experimented with discarding the entire establishment if it could not fit into a single building. This yielded results quite similar to the procedure just described.

19

Several broad patterns are evident in Tables 3a and 3b. First, in most cases, buildings are

significantly more specialized than would occur from random assignment of establishments. This

result holds even after allowing for the influence of the size distribution of buildings and

establishments. Second, the actual (and simulated) values for G are larger for the narrower level

of geography in Panel A for both target locations. This hints that tendencies to specialize may be

especially prevalent in narrowly defined geographic areas, consistent with evidence later in the

paper.

Focusing on the individual industries, in Table 3a (for the NYSE), finance is especially

concentrated. The actual value for G in Panel A (within 0.3 miles) is 0.21, far above the sample

mean value of 0.077 as well as the 95th percentile value of 0.11. This is consistent with folklore

that finance is highly concentrated in the core financial district of Manhattan. This pattern,

however, does not carry over to the neighborhood near Grand Central where the actual spatial G

statistic in Panel A is 0.068 and is only slightly above the sample mean value of 0.063 (although

significant at the 95% level). On the other hand, Advertising is very spatially concentrated in the

area within 0.3 miles of Grand Central Station (Panel A, Table 3c), with actual G equal to 0.215

compared to 0.185 for the sample mean (and significant at the 99% level). This echoes Arzaghi

and Henderson (2008) who document and assess spatial concentration of advertisers in Manhattan

in areas close to Grand Central.

Law is more spatially concentrated close to the NYSE in comparison to Grand Central,

but in both locations to a much lesser degree relative to the estimates noted above. Close to the

NYSE (in Panel A, Table 3b), manufacturing is no more spatially concentrated than would arise

from random assignment while the Other Industry category is significantly under-concentrated

relative to its sample mean (the actual G is 0.03 compared to a sample mean of 0.04). Retail is

highly concentrated within 0.3 miles (Panel A) of both target locations, but only modestly more

so than would arise from random assignment.

20

C. The distribution of establishments across buildings

Figures 2 (for Grand Central) and 3 (for the NYSE) provide a complementary way to

assess the degree to which industry employment is unusually spatially concentrated across

buildings within the two neighborhoods. In this case, we calculate the Lorenz curve associated

with the distribution of employment across individual buildings, both for the actual data and for

each of the 50 Monte Carlo experiments. For each sample, buildings are sorted by size along the

horizontal axis with the cumulative share of employment plotted vertically. If employment shares

are equal across buildings, then the Lorenz curve would lie along the 45 degree line which is

shown. Note also that the solid line in each plot corresponds to the actual distribution of

employment across buildings while the dotted convex lines are the 95 percent confidence bands

from the Monte Carlo runs.

As employment becomes more concentrated in a small number of buildings, the Lorenz

curve becomes more convex. This suggests that as the size distribution of buildings becomes

increasingly non-uniform, even in the absence of any behavioral tendencies for companies to

cluster together within a given industry, the Lorenz curve associated with the distribution of

employment across buildings will become increasingly convex. To highlight this and other

related points, Figures 2 and 3 present two sets of plots for each industry. The first ignores space

constraints when calculating the confidence bands while the second respects building space

constraints.8

In each of Figures 2 and 3, separate panels A through F, are provided for the six industry

groupings from before. Plots that ignore space constraints are on the left and those that control

for space constraints are on the right. Comparing plots across columns, it is apparent that

controlling for space constraints causes the confidence bands to become more convex. This is

8 Ignoring space constraints when doing the Monte Carlo re-assignments loads some buildings up with more employment than the building capacity would actually allow.

21

most evident for the Other Industry category in Panel F of both figures (which has the most

employment from among the industry groupings). The plots on the left indicate substantial and

strongly significant evidence of spatial concentration of employment in a select set of buildings.

Upon controlling for space constraints, however, the confidence bands for the plots on the right

are far more convex and evidence of departure from a random distribution disappears. These

patterns underscore the importance of taking space constraints across buildings into account when

assessing the determinants of spatial concentration in the commercial districts that dominate most

urban centers.

Additional patterns in Figures 2 and 3 include the following. First, in both Figures 2 and

3, Retail employment (in Panel A) is somewhat more spatially dispersed than would be expected

following a random assignment of establishments and allowing for space constraints. This can be

seen by the fact that the actual Lorenz curve is less convex than the confidence bands.

For Finance and Law the reverse patterns are present. For these industries, and allowing

for space constraints, employment is more spatially concentrated at the building level than would

be anticipated from a random allocation: this is evident from the fact that the actual Lorenz curves

are more convex than their associated confidence bands.

A final pattern of note in the figures is that the Lorenz curves and their confidence bands

are mostly right angles for Advertising and for Manufacturing. This reflects in part the small

number of advertising establishments and also the tendency for advertising and manufacturing

employment to be dominated by a small number of large companies.

IV. Drivers of building specialization

The previous section established that commercial buildings are often specialized beyond

what would be expected from a random allocation of establishments. This section examines why.

We begin with a parsimonious model that controls for the nearby level and spatial composition of

employment.

22

A, Spillovers between anchor and non-anchor establishments

Table 4a presents estimates from building-level regressions for each of the six industry

groupings. For each regression, the dependent variable is the building’s share of employment in

the specified industry. Control measures included fixed effects for the 5 cities along with

measures designed to summarize the level and composition of nearby employment. These

include the log of total employment within 0.1 miles outside of the target building, the log of

employment in the target building, and the composition of employment within 0.1 miles outside

of the target building. The latter is measured by the share of employment in each industry

treating Retail as the omitted category.

The main variables of interest are a set of dummy variables (1 if yes) that control for

whether an anchor establishment is present and the industry to which the anchor belongs. This

includes a dummy variable for whether the building has fewer than 50 workers so that the omitted

category is a building with more than 50 workers but for which an anchor establishment is not

present. The models are estimated by Tobit.9

Consider first the influence of the level of nearby employment outside of the building

relative to employment within the building. Estimates for these measures are in the first two sets

of rows of Table 4a. Retail employment share increases in the building with an increase in

nearby employment: the coefficient in the top row is 0.10 with a t-ratio of 34.14. Employment

shares also increase with nearby levels of employment for Finance, Advertising, Law and

Manufacturing but to a more muted degree. The relationship for the Other Industry category is

also relatively small and for this industry grouping negative. These patterns make clear that

service-oriented employment is more concentrated in heavily developed neighborhoods (based on

the area within 0.1 miles).

9 OLS results exhibit the same patterns.

23

Controlling for the concentration of nearby employment, the second coefficient in the

table captures the effect of building size on building employment share. Here a different pattern

is present. Reading across the row, it is clear that larger buildings disproportionately attract

additional employment in Finance, Advertising, Law and Manufacturing relative to Retail and the

Other Industry category.

Consider now the shaded “main diagonal” terms that display own-industry coefficients.

The upper set highlight the association between own-industry employment share within 0.1 miles

and employment share within the building outside of the anchor if an own-industry anchor is

present. For all six industries, these coefficients are all positive, highly significant, and range in

magnitude from 0.511 for Finance to 1.485 for Manufacturing.10 These estimates confirm that a

building’s share of a given industry rises when an anchor is present in the same industry. This is

as anticipated.

The lower bank of highlighted coefficients provides graphic evidence that in equilibrium,

anchor establishments tend to collocate at the building level with smaller companies in their same

industry. This pattern is present regardless of the industry to which the anchor belongs and is

large in magnitude. Moreover, the highlighted anchor coefficients are all highly significant and

range in magnitude from 12.86% for the Other Industry category to 45.63% for Retail.

Table 4b extends the model by adding controls for the presence and identity of an anchor

establishment in the adjacent building on the same block face. Because not all target buildings

have such an adjacent neighbor, the sample size for these regressions is reduced from 48,662

buildings to 33,702 buildings. Notice also that three sets of main diagonal coefficients are

highlighted in the table, including those for industry employment shares within 0.1 miles outside

10 For the retail regression (in column 1), all of the coefficients on industry employment shares within 0.1 miles are negative. This indicates that retail is less prevalent when other industries are increasingly present and regardless of which of the alternate industries one considers.

24

of the target building, the presence of an anchor in the target building, and the presence of an

anchor in the adjacent building.

The patterns in Table 4b are striking. Most important, the coefficients on nearby

employment composition and target building anchor are quite similar to estimates from the more

parsimonious specification in Table 4a. In addition, it is apparent that the coefficients on the

anchor variables in the adjacent building are much smaller in magnitude and mostly not

significant. The primary exceptions are for Retail and the Other Industry category for which the

adjacent building anchor coefficients are 7.86% and 5.07%, respectively, with t-ratios of 2.10 and

3.29.

The results in Table 4b suggest that spillover effects associated with the presence of an

own-industry anchor mostly do not extend to the adjacent building. Along with other patterns in

the table, the evidence suggests that business owners care about the neighborhood they are in (e.g.

the area within 0.1 miles or roughly two city blocks), and also the specific building within the

neighborhood. They are much less sensitive to activity in the adjacent building.

B. Building quality

The models in this section control for building quality. We do this in two ways. First by

merging in building specific attributes from the CoStar data. This allows us to test for structure-

specific effects directly but also reduces sample size from 33,702 in Table 4b to 5,431 in Table

4c. In a second approach, we estimate building fixed effect models that sweep away all building-

specific features. Identification in those models is possible by pooling data across industries and

drawing on within-building cross-industry variation. Each specification is discussed below.

In Table 4c, consider first the coefficients on building attributes at the bottom of the

table. For Finance, companies are clearly drawn to higher class buildings (based on Class A, B

and C) and also buildings with higher quality physical attributes (higher star ratings). The same

25

is true for Law while Manufacturing establishments tend to avoid higher-class buildings.11

Controls for the closest transit stop mostly have limited effect on employment shares.

Consider next the highlighted own-industry coefficients. The important result is that the

coefficients are quite similar in magnitude (and sign) to those in Table 4b and the qualitative

patterns are unchanged. The coefficients on the own-industry anchors in the target building are

all highly significant, positive and range in magnitude from a low of 5.10% for the Other Industry

category to a high of 26.57% for Law. Thus, while select industries are attracted to buildings of a

given quality, both high (as with finance) and low (as with manufacturing), the common

attraction to buildings of a given quality does not change the core results from before: Evidence

of spillovers that contribute to the collocation between anchors and other smaller companies in

the same industry remains compelling.

Table 4d explores these issues further using the building fixed effects specification. The

dependent variable remains as before and is an industry’s share of employment in the building not

including the anchor if the anchor is in the target industry. The control measures include the

own-industry share of employment within 0.1 miles outside of the target building, and the

presence of own-industry anchors in the target and adjacent building. Also included are constant

terms for each of the six industries.

An important disadvantage of this specification is that it restricts the coefficients on the

slope variables to be alike across all six industries. Based on the results in Tables 4b and 4c, this

is clearly an approximation. In addition, the presence of thousands of building fixed effects

preclude the use of a Tobit specification. Instead, the models are estimated by ordinary least

squares with building fixed effects. On the other hand, the specification in Table 4d also has

appealing advantages. We gain considerable power by treating the data in a panel context. This

11 For finance, the share of employment is 5.1% higher in a Class A building relative to a Class C building (with a t-ratio of 5.29), while the coefficients on 5-star and 4-star buildings are also positive and significant. Law also has a positive coefficient on 5-star buildings, equal to 5.48% and a t-ratio of 1.8.

26

allows us to obtain precise estimates when stratifying the model by building size as is shown in

the table.12 Most important, the building fixed effects go much further than the building quality

measures in Table 4c to control for the physical attributes of the building and any other building-

specific features that may present a common attraction to companies in a given industry.

Column 1 of Table 4d presents the core results for all buildings with 10 or more workers.

Column 2 restricts the sample to buildings with 10 to 100 workers. Column 3 includes only

buildings with 100 or more workers in the sample. Columns 4-6 repeat these specifications

omitting Retail from among the estimating sample.

The core results from the previous models prove to be highly robust. For the full sample

in column 1, estimates in the first row indicate that increasing the own-industry employment

share by 10 percent within 0.1 miles outside of the target building increases an industry’s within-

building employment share by 45 percent with a t-ratio of 45.90. The corresponding estimate is

32.76% for buildings over 100 workers in column 3 and also slightly lower when Retail is

removed from the sample in columns 4-6, but still always highly significant.

In column 1 (the full sample with all six industries), notice also that the coefficients on

own industry anchors in the target and adjacent buildings are 19.27% and 2.39%, respectively,

with t-ratios of 35.24 and 3.17. These patterns reaffirm our most important finding: at the

building level, anchors tend to co-locate in equilibrium with other companies in their industry.

The presence of an anchor in the adjacent building, however, has limited effect on target building

employment composition. These patterns are present for both smaller buildings (with 10 to 100

workers) and larger buildings (with 100 or more workers). They are also present when Retail is

excluded from the sample in which case the magnitude of the coefficients is slightly reduced.

12 We also stratified the models in Table 4b by building size, results from which are in the appendix. To provide a closer comparison to the estimates in Table 4d, the models in the appendix were estimated using ordinary least squares rather than the Tobit specification. The qualitative patterns in the appendix models are consistent with those in Table 4b and support the core relationships emphasized above.

27

C. Labor productivity and building specialization

Table 5 presents an additional set of regressions that provide corroborating evidence that

building-specific spillovers contribute to labor productivity. In this instance, we focus only on

Retail and Law for which separate models are estimated. In each of the regressions, the

dependent variable is the building level average sales per worker not including sales and

employment from an own-industry anchor if present. We focus on Retail and Law because these

industries tend to use sales as a common indicator of labor productivity. Sales are also well

measured at the establishment level for these industries in contrast to other industries where sales

for multi-site firms are commonly attached to a headquarter facility. As with Table 4a-4c, the

models in Table 5 are estimated using a Tobit model given the prevalence of zeros for many

buildings.

Before proceeding two cautionary notes are in order. If the physical attributes of the

building affect profit only as a productivity enhancing capital input, then we can make a case for

treating building quality as exogenous. On the other hand, if building quality is a workplace

amenity, then high performing companies may seek attractive office space in order to appease

their workers (which could translate into lower wage costs). In this case building quality could be

endogenous. Partly for that reason, we do not include building quality measures in the Table 5

regressions. This gives our models a reduced form interpretation.

It is also important to recognize that estimates in Table 5 may be driven in part by

endogenous sorting of productive non-anchor tenants into buildings in which anchors are present

(and that the same may be true in reverse). This would upward bias estimates of the causal effect

of spillovers arising from proximity to an anchor. Nevertheless, as with the location models in the

earlier tables, if endogenous sorting is driven by anticipated benefits from proximity to an anchor,

then evidence of a positive, significant association between sales per worker and proximity to an

anchor supports the view that productivity spillovers are present.

28

Bearing the above caveats in mind, observe that the patterns in Table 5 closely mirror

those for the employment share models in Table 4b. Most important, the coefficients on own-

industry anchors in the target building are positive and highly significant. For Retail the

coefficient is $26,990 with a t-ratio of 5.0; for Law the coefficient is $105,345 with a t-ratio of

10.35. The corresponding coefficients for anchors in adjacent building are much smaller and not

significant. These patterns are suggestive that sorting into specialized buildings increases labor

productivity for Retail and Law establishments. The pattern is especially strong for Law.13

V. Conclusions

This paper considers agglomeration in a novel way. Instead of working with city or

metropolitan level or distance-based spatial units, this paper uses individual buildings as the

spatial unit, focusing on the understudied commercial sector that dominates downtowns.

Drawing on establishment-level data from Dun and Bradstreet, we determine the scale

and composition of employment in every commercial building in the core counties of Manhattan,

Los Angeles, Chicago, San Francisco, and Washington. Even within narrowly defined

neighborhoods, we see substantial specialization across buildings. Monte Carlo methods confirm

that this pattern is nonrandom.

We develop a theoretical model to explain these patterns. In the model, building owners

choose the industrial composition of their buildings in order to maximize rents. Anchor tenants

generate particularly important externalities, implying that buildings with anchors will have a

larger share of the anchor’s industry.

13 When building quality measures are added to the models sales per worker among law offices are notably higher in nicer buildings based on class rating and star rating. The quality controls have relatively little effect on sales per worker among Retail establishments. However, the anchor coefficients are largely wiped out for Retail, reduced in magnitude and no longer significant. For Law, the own-industry anchor in the target building remains significant with a coefficient of $44, 569.

29

We test the model’s implications for Finance, Law, and Advertising, crucial industries for

cities where Marshallian externalities are likely to be present. We also estimate the model for

Retail, where shopping externalities are likely to dominate. The estimates support the model’s

predictions. Controlling for the level and composition of nearby employment up to the adjacent

building, our most robust models indicate that the presence of an anchor in the target industry is

associated with an increase in the within-building industry share of employment outside of the

anchor by 5 to 26 percent. The effect of a corresponding anchor in an adjacent building is much

smaller and often zero. Additional estimates confirm that sales per worker in Retail and Law –

two industries for which sales are accurately measured – increase with anticipated drivers of

agglomeration economies.

Our results demonstrate that commercial establishments sort into buildings in part to take

advantage of within-industry agglomeration economies. This contributes to nonrandom

specialization across buildings that increases productivity in city centers.

30

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32

Figure 1: Sorting Into Buildings

s1

r

s2

33

Figure 2: Distribution of Establishments Across Buildings Within 0.3 Miles of New York Stock Exchange

Panel A: Retail Ignoring Space Constraints Panel A: Retail Allowing for Space Constraints

Panel B: Finance (62, 67) Ignoring Space Constraints Panel B: Finance (62, 67) Allowing for Space Constraints

Panel C: Advertising Ignoring Space Constraints Panel C: Advertising Allowing for Space Constraints

34

Figure 2: Distribution of Establishments Across Buildings Within 0.3 Miles of New York Stock Exchange

Panel D: Law Ignoring Space Constraints Panel D: Law Allowing for Space Constraints

Panel E: Manufacturing Ignoring Space Constraints Panel E: Manufacturing Allowing for Space Constraints

Panel F: All Others Ignoring Space Constraints Panel F: All OthersAllowing for Space Constraints

35

Figure 3: Distribution of Establishments Across Buildings Within 0.3 Miles of Grand Central Station

Panel A: Retail Ignoring Space Constraints Panel A: Retail Allowing for Space Constraints

Panel B: Finance (62, 67) Ignoring Space Constraints Panel B: Finance (62, 67) Allowing for Space Constraints

Panel C: Advertising Ignoring Space Constraints Panel C: Advertising Allowing for Space Constraints

36

Figure 3: Distribution of Establishments Across Buildings Within 0.3 Miles of Grand Central Station

Panel D: Law Ignoring Space Constraints Panel D: Law Allowing for Space Constraints

Panel E: Manufacturing Ignoring Space Constraints Panel E: Manufacturing Allowing for Space Constraints

Panel F: All Others Ignoring Space Constraints Panel F: All OthersAllowing for Space Constraints

37

Table 1a: Summary Measures

Panel A: Observation frequency of buildings by citya Observations Mean Chicago 7,460 0.2214 Los Angeles 6,926 0.2056 New York 10,136 0.3008 San Francisco 4,657 0.1382 Washington DC 4,513 0.1339 Total 33,702 1.0000 Panel B: Observation frequency of buildings with industry employment presenta

Industry Not Present Industry Present Obs Mean Obs Mean

Retail (SIC 53-59) 19,214 0.570 14,488 0.430 Finance (SIC 62, 67) 30,985 0.919 2,717 0.081 Advertising (SIC 7311, 7312, 7319) 32,744 0.972 958 0.028 Law (SIC 81) 30,889 0.917 2,813 0.083 Manufacturing (SIC 20-39) 29,129 0.864 4,573 0.136 Other Industries 8,001 0.237 25,701 0.763 Total 150,962 0.747 51,250 0.253 Panel C: Building quality attributesa Observations Mean Closest transit stop (miles) 5,431 0.387 Building Class (A = highest; C = lowest) A 545 0.0959 B 2,400 0.4223 C 2,738 0.4818 Total 5,683 1.0000 Star Rating (5 = highest; 1 = lowest) 5 37 0.0065 4 517 0.0903 3 2,553 0.4495 2 2,467 0.4345 1 109 0.0192 Total 5,683 1.0000 a The sample in Panel A and B are used for the regressions in Table 4b. The sample in Panel C is used for the regressions in Table 4c. Both samples include only buildings with 10 or more workers in the core counties for each of the cities listed in Panel A.

38

Table 1b: Employment Shares by Industrya

Panel A: By Geographic Unit

Employment Share Within 0.1 miles of the Target Building

(All Buildings)

Employment Share in Adjacent Building (For Buildings with

100 or More Workers)

Employment Share in Target Building (for Buildings with

100 or More Workers)

Retail (SIC 53-59) 0.213 0.123 0.130 Finance (SIC 62, 67) 0.021 0.031 0.027 Advertising (SIC 7311, 7312, 7319) 0.008 0.011 0.011 Law (SIC 81) 0.021 0.039 0.035 Manufacturing (SIC 20-39) 0.057 0.060 0.064 Other Industries 0.679 0.737 0.733 Observations 33,702 2,760 4,730 Employment Share in Buildings with 100 or More WorkersPanel B: By Building Class Class A Class B Class C Retail (SIC 53-59) 0.051 0.090 0.093 Finance (SIC 62, 67) 0.118 0.023 0.014 Advertising (SIC 7311, 7312, 7319) 0.023 0.017 0.024 Law (SIC 81) 0.078 0.040 0.019 Manufacturing (SIC 20-39) 0.071 0.075 0.099 Other Industries 0.659 0.756 0.752 Observations 431 882 472 Employment Share in Buildings with 100 or More WorkersPanel C: By Star Rating 5 Star 4 Star 3 Star 2 Star 1 Star Retail (SIC 53-59) 0.021 0.050 0.086 0.119 0.041 Finance (SIC 62, 67) 0.175 0.106 0.020 0.022 0.000 Advertising (SIC 7311, 7312, 7319) 0.016 0.025 0.020 0.015 0.000 Law (SIC 81) 0.143 0.073 0.034 0.022 0.000 Manufacturing (SIC 20-39) 0.137 0.071 0.090 0.048 0.031 Other Industries 0.509 0.675 0.750 0.773 0.773 Observations 37 419 1,060 264 5 a Building level employment shares are reported for buildings with 50 or more workers. Employment shares for employment within 0.1 miles are based on all employment outside of the target building including employment in buildings with less than 50 workers.

39

Table 1c: Anchor Share of Establishmentsa

Target Buildings Adjacent Buildings Buildings

With 10 or More

Workers

Buildings With

100 or More Workers

Buildings With

10 or More Workers

Buildings With

100 or More Workers

Retail (SIC 53-59) 0.026 0.080 0.014 0.079 Finance (SIC 62, 67) 0.003 0.018 0.002 0.022 Advertising (SIC 7311, 7312, 7319) 0.018 0.008 0.001 0.007 Law (SIC 81) 0.039 0.021 0.003 0.026 Manufacturing (SIC 20-39) 0.0116 0.049 0.007 0.049 Other Industries 0.128 0.516 0.071 0.517 Small Building (Employment < 50) 0.740 - 0.857 - Observations 33,702 4,730 33,702 2,760 a To be designated as an anchor, an establishment must (i) be the largest establishment in the building, (ii) account for 20 percent or more of employment in the building, and (iii) must be housed in a building that has 50 or more workers present. Given this definition, small buildings (with employment < 50) have no anchors. Among large buildings some do not have an anchor and none have more than one anchor.

40

Table 2: Correlation Between Industry-Specific Employment Shares in Target Building Relative to Nearby Employment Sharesa

Panel A: Sample includes target buildings with 10 or more workers

Employment Outside of the

Target Building Within 0.1 Mile

Employment in the Adjacent Building

Employment in the Target Building

Retail (SIC 53-59) 0.064 0.078 1.00 Finance (SIC 62, 67) 0.105 0.157 1.00 Advertising (SIC 7311, 7312, 7319) 0.066 0.060 1.00 Law (SIC 81) 0.220 0.114 1.00 Manufacturing (SIC 20-39) 0.147 0.060 1.00 Other Industries 0.090 0.178 1.00 Observations 33,702 33,702 33,702

Panel B: Sample includes target buildings with 100 or more workers

Employment Outside of the

Target Building Within 0.1 Mile

Employment in the Adjacent Building

Employment in the Target Building

Retail (SIC 53-59) 0.267 0.083 1.00 Finance (SIC 62, 67) 0.156 0.174 1.00 Advertising (SIC 7311, 7312, 7319) 0.132 0.095 1.00 Law (SIC 81) 0.348 0.145 1.00 Manufacturing (SIC 20-39) 0.239 0.062 1.00 Other Industries 0.249 0.062 1.00 Observations 4,730 4,730 4,730 a Building level employment shares are based on buildings with 50 or more workers. Employment shares for employment within 0.1 miles are based on all employment outside of the target building including employment in buildings with less than 50 workers. The sample of target buildings is restricted to buildings with 10 or more workers.

41

Table 3a: Industry Spatial Concentration within 0.3 Miles of the New York Stock Exchange and 0.3 Miles of Grand Central Stationa

Panel A: New York Stock Exchange Number of

Establishments Share of

Establishments Employment Share of

Employment

Share of Buildings with

Industry Present

Spatial G of Industry Emp

Across Buildingsb

Retail (SIC 53-59) 271 0.0653 5,187 0.0503 0.1971 0.1027 Finance (SIC 62, 67) 439 0.1059 11,699 0.1134 0.2000 0.2057 Advertising (SIC 7311, 7312, 7319) 29 0.0070 843 0.0082 0.0551 0.0915 Law (SIC 81) 760 0.1833 7,975 0.0773 0.1758 0.0666 Manufacturing (SIC 20-39) 96 0.0231 2,087 0.0202 0.1130 0.0741 Other Industries 2,552 0.6154 75,366 0.7306 0.2986 0.0312 All Industries 4,147 1.0000 103,157 1.0000 - -

Panel B: Grand Central Station Number of

Establishments Share of

Establishments Employment Share of

Employment

Share of Buildings with

Industry Present

Spatial G of Industry Emp

Across Buildingsb

Retail (SIC 53-59) 928 0.0759 19,895 0.0963 0.2367 0.263 Finance (SIC 62, 67) 1,532 0.1253 24,788 0.1200 0.1772 0.068 Advertising (SIC 7311, 7312, 7319) 109 0.0089 11,828 0.0573 0.0673 0.215 Law (SIC 81) 1,233 0.1008 16,846 0.0816 0.1643 0.045 Manufacturing (SIC 20-39) 534 0.0437 12,319 0.0596 0.1876 0.147 Other Industries 7,891 0.6454 120,856 0.5852 0.3208 0.026 All Industries 12,227 1.0000 206,532 1.0000 - - a The samples for both panels include only buildings with 10 or more workers. b The spatial G is calculated as Gj ≡ ∑i (Sij – Xi)2, where Xi and Sij are respectively building i’s share of total employment and building i’s share of industry j employment for buildings located within 0.3 miles of the target location (NYSE and Grand Central Station for panels A and B, respectively). Specified in this manner Gj ranges equals zero when industry j employment is distributed across buildings as for total employment and converges to one as industry j employment becomes concentrated in a single building.

42

New Table 3b: Spatial G of Industry Employment Across Buildings Close to New York Stock Exchangea,b

Panel A: Within 0.3 Miles (Based on 419 Buildings)

Actual Spatial Gc

Simulated Distribution for Spatial G Based on 100 Repetitions

Mean St Dev 5th Pctl 10th Pctl 90th Pctl 95th Pctl Retail (SIC 53-59) 0.1027*** 0.0847 0.0119 0.0776 0.0782 0.0899 0.1032 Finance (SIC 62, 67) 0.2057*** 0.0776 0.0126 0.0681 0.0691 0.0888 0.1085 Advertising (SIC 7311, 7312, 7319) 0.0915*** 0.0729 0.0080 0.0632 0.0640 0.0813 0.0865 Law (SIC 81) 0.0666*** 0.0592 0.0078 0.0532 0.0538 0.0683 0.0781 Manufacturing (SIC 20-39) 0.0741 0.0728 0.0078 0.0653 0.0658 0.0832 0.0887 Other Industries 0.0312*** 0.0413 0.0030 0.0360 0.0374 0.0448 0.0455

Panel B: Within 1.0 Miles (Based on 2,049 Buildings)

Actual Spatial Gc

Simulated Distribution for Spatial G Based on 100 Repetitions

Mean St Dev 5th Pctl 10th Pctl 90th Pctl 95th Pctl Retail (SIC 53-59) 0.0204*** 0.0183 0.0025 0.0165 0.0167 0.0212 0.0242 Finance (SIC 62, 67) 0.0891*** 0.0517 0.0110 0.0430 0.0441 0.0621 0.0716 Advertising (SIC 7311, 7312, 7319) 0.0552*** 0.0407 0.0030 0.0379 0.0381 0.0442 0.0466 Law (SIC 81) 0.0314*** 0.0256 0.0025 0.0232 0.02351 0.02831 0.0312 Manufacturing (SIC 20-39) 0.0453*** 0.0391 0.0045 0.0354 0.0357 0.0459 0.0494 Other Industries 0.0139*** 0.0169 0.0014 0.0144 0.0149 0.0185 0.0186 a Empirical distributions were calculated based on 100 simulations in which establishments in Panels A and B were randomly reassigned to buildings in their respective areas making allowance for space constraints in individual buildings. b The spatial G is calculated as Gj ≡ ∑i (Sij – Xi)2, where Xi and Sij are respectively building i’s share of total employment and building i’s share of industry j employment for buildings located in the specified area (e.g. within 0.3 or 1.0 miles of the target location). Specified in this manner Gj ranges equals zero when industry j employment is distributed across buildings as for total employment and converges to one as industry j employment becomes concentrated in a single building. c Significance tests are based on a comparison of the actual spatial G to the mean value over the 100 repetitions. Significance at the 99% level is denoted by three star; significance at the 95% level is denoted by two stars.

43

New Table 3c: Spatial G of Industry Employment Across Buildings Close to Grand Central Stationa,b

Panel A: Within 0.3 Miles (Based on 622 Buildings)

Actual Spatial Gc

Simulated Distribution for Spatial G Based on 100 Repetitions

Mean St Dev 5th Pctl 10th Pctl 90th Pctl 95th Pctl Retail (SIC 53-59) 0.263*** 0.243 0.037 0.163 0.186 0.271 0.276 Finance (SIC 62, 67) 0.068** 0.063 0.008 0.055 0.056 0.073 0.080 Advertising (SIC 7311, 7312, 7319) 0.215*** 0.185 0.032 0.167 0.167 0.221 0.274 Law (SIC 81) 0.045*** 0.035 0.005 0.030 0.031 0.040 0.043 Manufacturing (SIC 20-39) 0.147*** 0.122 0.012 0.114 0.115 0.135 0.141 Other Industries 0.026*** 0.023 0.003 0.019 0.019 0.026 0.026

Panel B: Within 1.0 Miles (Based on 6,637 Buildings)

Actual Spatial Gc

Simulated Distribution for Spatial G Based on 100 Repetitions

Mean St Dev 5th Pctl 10th Pctl 90th Pctl 95th Pctl Retail (SIC 53-59) 0.019 0.019 0.0039 0.016 0.017 0.020 0.033 Finance (SIC 62, 67) 0.049*** 0.040 0.0096 0.024 0.030 0.046 0.063 Advertising (SIC 7311, 7312, 7319) 0.065*** 0.055 0.0046 0.053 0.053 0.057 0.061 Law (SIC 81) 0.012*** 0.009 0.0005 0.008 0.008 0.009 0.009 Manufacturing (SIC 20-39) 0.021** 0.020 0.0031 0.016 0.018 0.023 0.028 Other Industries 0.006 0.006 0.0005 0.004 0.005 0.006 0.006 a Empirical distributions were calculated based on 100 simulations in which establishments in Panels A and B were randomly reassigned to buildings in their respective areas making allowance for space constraints in individual buildings. b The spatial G is calculated as Gj ≡ ∑i (Sij – Xi)2, where Xi and Sij are respectively building i’s share of total employment and building i’s share of industry j employment for buildings located in the specified area (e.g. within 0.3 or 1.0 miles of the target location). Specified in this manner Gj ranges equals zero when industry j employment is distributed across buildings as for total employment and converges to one as industry j employment becomes concentrated in a single building. c Significance tests are based on a comparison of the actual spatial G to the mean value over the 100 repetitions. Significance at the 99% level is denoted by three star; significance at the 95% level is denoted by two stars.

44

Table 4a: Tobit Model of Building Employment Share Outside of Own-Industry Anchora

Retail Finance Advert Law Manf Other Log Employment < 0.1 miles (1,000s) 0.1054 0.0278 0.0529 0.0498 0.0124 -0.0380 (34.14) (10.57) (10.91) (17.69) (3.27) (-24.98) Log Employment in bldg. (1,000s) 0.0225 0.1155 0.1445 0.1071 0.1817 0.0233 (4.21) (28.70) (18.46) (25.97) (28.64) (7.75) Emp share < 0.1 miles – Financeb -0.9478 0.5110 -0.0648 0.1043 0.0260 0.3566 (-15.00) (10.76) (-0.71) (2.12) (0.29) (9.26) Emp share < 0.1 miles – Advertisingb -1.0774 0.0293 0.8855 0.0654 0.5635 0.5488 (-8.02) (0.30) (7.27) (0.68) (3.73) (7.92) Emp share < 0.1 miles – Lawb -0.8635 0.5822 0.0362 1.0600 0.3836 0.2284 (-10.10) (9.87) (0.32) (17.15) (3.41) (4.69) Emp share < 0.1 miles – Manufacturingb -1.3094 -0.0202 0.0961 -0.2735 1.4845 0.1935 (-32.23) (-0.49) (1.60) (-5.02) (33.42) (8.09) Emp share < 0.1 miles – Otherb -1.0625 0.1123 0.0682 0.0980 0.1875 0.5234 (-49.55) (4.69) (1.64) (4.02) (5.43) (37.31) Anchor In Target Building Retailc 0.4563 -0.0723 -0.1349 -0.1108 -0.1890 -0.1971 (18.81) (-3.43) (-3.75) (-5.13) (-5.56) (-11.21) Financec -0.1288 0.3329 -0.1936 0.0666 -0.0810 -0.0382 (-2.74) (10.94) (-3.17) (2.19) (-1.52) (-1.44) Advertisingc -0.0635 0.0348 0.4147 0.0292 -0.0594 0.0353 (-0.98) (0.91) (6.49) (0.52) (-0.67) (0.99) Lawc -0.0877 0.1412 -0.1024 0.3412 -0.0325 -0.1065 (-2.30) (5.21) (-1.87) (11.16) (-0.61) (-4.50) Manufacturingc -0.0271 0.0018 -0.0177 -0.0337 0.4423 -0.1296 (-0.83) (0.08) (-0.44) (-1.50) (13.33) (-6.17) Otherc -0.1096 0.0069 -0.0621 -0.0178 -0.1417 0.1286 (-7.12) (0.70) (-3.70) (-1.79) (-8.02) (16.50) Small Building (Employment < 50) 0.1999 0.0460 0.0143 0.0079 0.0664 -0.0329 (10.94) (3.70) (0.69) (0.64) (3.11) (-3.26) Sigma 0.7686 0.3838 0.4863 0.4037 0.7669 0.4970 (241.58) (42.45) (24.92) (45.89) (105.19) (250.66) City Fixed Effects 5 5 5 5 5 5 Observations 48,662 48,662 48,662 48,662 48,662 48,662 Censored 29,760 44,712 47,296 44,692 41,913 10,679 Uncensored 18,902 3,950 1,366 3,970 6,749 37,983 Pseudo R-squared 0.062 0.230 0.201 0.247 0.095 0.054 a Sample includes buildings with 10 or more workers in the following counties: New York (Manhattan), Cook (Chicago) (Chicago), Los Angeles, San Francisco, Washington DC. T-ratios based on robust standard errors in parentheses. b Omitted category is retail share of employment. c An establishment is coded as an anchor (1 if yes, 0 if no) if all three of the following conditions hold: (i) it is the largest establishment in the building; (ii) it includes at least 20% of building employment; (iii) it has 10 or more workers.

45

Table 4b: Tobit Model of Building Employment Share Including Adjacent Building Anchora

Retail Finance Advert Law Manf Other Log Employment < 0.1 miles (1,000s) 0.1113 0.0268 0.0550 0.0475 0.0133 -0.0463 (26.84) (7.63) (8.18) (12.28) (2.52) (-19.53) Log Employment in bldg. (1,000s) 0.0024 0.1112 0.1476 0.0987 0.1735 0.0309 (0.37) (21.83) (14.66) (19.08) (21.71) (7.45) Log Employment in adj bldg. (1,000s) 0.0028 -0.0008 0.0058 0.0058 0.0193 -0.0028 (0.61) (-0.23) (0.79) (1.42) (3.07) (-0.99) Emp share < 0.1 miles - Financeb -0.8525 0.4724 -0.1094 0.1291 0.0809 0.3607 (-11.26) (8.83) (-0.90) (2.03) (0.72) (6.85) Emp share < 0.1 miles – Advertisingb -1.2261 -0.0586 0.8365 0.0428 0.5819 0.7216 (-8.19) (-0.51) (5.56) (0.36) (3.28) (8.72) Emp share < 0.1 miles – Lawb -0.9625 0.5334 0.1447 1.1280 0.3951 0.3028 (-9.37) (7.47) (0.99) (14.53) (2.82) (4.79) Emp share < 0.1 miles – Manufacturingb -1.4166 0.0072 0.1779 -0.2432 1.6491 0.2680 (-27.38) (0.14) (2.18) (-3.72) (28.65) (8.19) Emp share < 0.1 miles – Otherb -1.0699 0.1283 0.1035 0.1673 0.2963 0.5748 (-41.08) (4.48) (1.92) (5.43) (6.98) (31.10) Anchor In Target Building Retailc 0.3290 -0.0705 -0.1491 -0.1152 -0.1880 -0.1664 (11.78) (-3.03) (-3.52) (-4.54) (-4.89) (-7.73) Financec -0.2029 0.2473 -0.1309 0.0646 -0.0888 0.0349 (-3.56) (7.13) (-1.83) (1.66) (-1.35) (1.07) Advertisingc -0.1118 0.0521 0.4088 0.0030 -0.1435 0.0519 (-1.48) (1.12) (5.33) (0.04) (-1.43) (1.25) Lawc -0.1890 0.0873 -0.0666 0.3913 -0.0915 -0.0934 (-3.89) (2.96) (-1.04) (9.96) (-1.39) (-2.85) Manufacturingc -0.1029 -0.0443 -0.0126 -0.0583 0.4021 -0.0752 (-2.62) (-1.64) (-0.26) (-2.13) (10.30) (-2.94) Otherc -0.2064 0.0059 -0.0709 -0.0209 -0.1295 0.1597 (-11.06) (0.52) (-3.35) (-1.71) (-6.14) (14.76) Small Building (Employment < 50) 0.0969 0.0447 0.0020 -0.0238 0.0507 -0.0094 (4.40) (3.14) (0.08) (-1.57) (2.00) (-0.68) Anchor In Adjacent Building Retailc 0.0785 -0.0374 0.0330 0.0046 -0.0359 -0.0104 (2.10) (-1.28) (0.64) (0.15) (-0.75) (-0.37) Financec -0.1670 0.0191 0.0161 0.0499 -0.1270 0.0776 (-2.04) (0.42) (0.15) (1.05) (-1.29) (1.72) Advertisingc -0.0574 -0.0976 -0.1020 -0.0476 0.1235 0.0933 (-0.51) (-1.04) (-0.76) (-0.48) (0.95) (1.39) Lawc -0.0591 0.0289 -0.1519 0.0148 0.0815 -0.0068 (-0.77) (0.61) (-1.58) (0.27) (0.95) (-0.15) Manufacturingc -0.0955 0.0427 0.0780 0.0504 0.0461 0.1082 (-1.72) (1.15) (1.06) (1.30) (0.72) (3.42) Otherc -0.0656 -0.0022 -0.0124 0.0196 0.0031 0.0507 (-2.59) (-0.12) (-0.36) (0.99) (0.10) (3.29) Small Building (Employment < 50) 0.0243 -0.0275 0.0335 0.0338 0.0716 -0.0106 (0.97) (-1.38) (0.91) (1.55) (2.17) (-0.67)

Continued on next page

46

Table 4b: Tobit Model of Building Employment Share Including Adjacent Building Anchor continueda

Retail Finance Advert Law Manf Other Sigma 0.7457 0.3606 0.4963 0.4037 0.7276 0.5140 (212.06) (35.33) (21.70) (39.07) (82.89) (217.54) City Fixed Effects 5 5 5 5 5 5 Observations 33,702 33,702 33,702 33,702 33,702 33,702 Censored 19,214 30,985 32,744 30,889 29,129 8,001 Uncensored 14,488 2,717 958 2,813 4,573 25,701 Pseudo R-squared 0.057 0.222 0.191 0.231 0.100 0.053 a Sample includes buildings with 10 or more workers in Manhattan, Cook County (Chicago), Los Angeles, San Francisco, Washington DC. T-ratios based on robust standard errors in parentheses. b Omitted category is retail share of employment. c An establishment is coded as an anchor (1 if yes, 0 if no) if all three of the following conditions hold: (i) it is the largest establishment in the building; (ii) it includes at least 20% of building employment; (iii) it has 10 or more workers.

47

Table 4c: Tobit Model of Building Employment Share Outside of Own-Industry Anchora

Retail Finance Advert Law Manf Other Log Employment < 0.1 miles (1,000s) 0.0877 0.0166 0.0277 0.0350 0.0362 -0.0473 (10.98) (2.93) (2.90) (5.17) (5.13) (-10.55) Log Employment in bldg. (1,000s) 0.0133 0.0568 0.0885 0.0605 0.0766 0.0126 (1.48) (8.00) (8.40) (7.67) (9.14) (2.04) Log Employment in adj bldg. (1,000s) 0.0019 -0.0058 0.0074 -0.0066 0.0066 -0.0069 (0.26) (-1.13) (0.90) (-1.16) (1.00) (-1.56) Emp share < 0.1 miles – Financeb -0.6705 0.4461 -0.1456 0.0497 -0.3361 0.2427 (-5.93) (4.95) (-0.98) (0.54) (-3.06) (3.29) Emp share < 0.1 miles – Advertisingb -0.6443 -0.0025 0.5462 -0.1244 0.0500 0.2549 (-4.00) (-0.02) (3.32) (-0.93) (0.34) (2.56) Emp share < 0.1 miles – Lawb -0.2778 0.3147 -0.2123 0.8715 -0.4440 -0.1429 (-1.70) (2.84) (-1.04) (7.24) (-2.58) (-1.32) Emp share < 0.1 miles – Manufacturingb -0.3464 -0.0439 0.2428 -0.2044 0.2807 0.1158 (-3.67) (-0.56) (2.12) (-2.10) (3.12) (1.98) Emp share < 0.1 miles - Otherb -0.5188 0.0663 0.0618 0.0813 -0.1023 0.2424 (-8.11) (1.34) (0.69) (1.44) (-1.69) (6.49) Anchor In Target Building Retailc 0.1423 -0.0518 -0.1698 -0.0972 -0.0560 -0.0836 (3.48) (-1.89) (-3.15) (-2.96) (-1.38) (-2.83) Financec -0.1619 0.1789 -0.1959 -0.0300 -0.0859 -0.0579 (-3.41) (4.03) (-3.12) (-0.70) (-1.86) (-1.55) Advertisingc -0.0328 -0.0187 0.2272 -0.0724 -0.1947 -0.0385 (-0.62) (-0.44) (3.41) (-1.09) (-3.17) (-0.98) Lawc -0.1465 -0.0107 -0.0468 0.2657 -0.1695 -0.0983 (-3.03) (-0.27) (-0.76) (5.35) (-3.39) (-2.57) Manufacturingc -0.0236 -0.0795 -0.0811 -0.1193 0.2095 -0.1053 (-0.83) (-2.82) (-1.86) (-4.11) (6.57) (-4.14) Otherc -0.1001 -0.0321 -0.0816 -0.0674 -0.1069 0.0510 (-5.12) (-2.53) (-3.64) (-4.61) (-6.32) (3.91) Small Building (Employment < 50) 0.0534 -0.0388 -0.0353 -0.0817 -0.0557 -0.0391 (1.76) (-1.83) (-1.04) (-3.51) (-2.04) (-1.95) Anchor In Adjacent Building Retailc -0.0314 -0.0490 0.0092 0.0405 0.0035 0.0697 (-0.65) (-1.42) (0.16) (1.08) (0.09) (2.20) Financec -0.0533 -0.0407 -0.0298 -0.0076 -0.0214 0.0347 (-0.59) (-0.80) (-0.34) (-0.13) (-0.26) (0.61) Advertisingc 0.0236 0.0116 -0.0937 -0.0414 0.0315 0.1022 (0.32) (0.14) (-0.75) (-0.39) (0.53) (2.02) Lawc 0.0653 -0.0110 -0.0207 0.0123 -0.0231 -0.0594 (0.64) (-0.17) (-0.21) (0.18) (-0.34) (-0.89) Manufacturingc -0.0696 0.0239 0.0125 0.0366 0.0763 0.0443 (-1.37) (0.70) (0.22) (0.91) (1.59) (1.25) Otherc -0.0233 -0.0196 -0.0483 -0.0037 0.0177 0.0087 (-0.75) (-0.85) (-1.35) (-0.14) (0.60) (0.43) Small Building (Employment < 50) 0.0017 -0.0356 0.0128 -0.0041 0.0249 -0.0166 (0.05) (-1.26) (0.31) (-0.13) (0.72) (-0.73)

Continued next page

48

Table 4c: Tobit Model of Building Employment Share Outside of Own-Industry Anchor continued

Retail Finance Advert Law Manf Other Building Class (A = highest; C = lowest) Class A 0.0117 0.1479 -0.0221 0.0299 -0.0660 -0.0350 (0.33) (6.13) (-0.58) (1.19) (-1.96) (-1.46) Class B -0.0340 0.0570 -0.0245 0.0159 -0.0328 0.0105 (-1.94) (3.96) (-1.15) (1.04) (-1.95) (1.01) Star Rating (5 = highest; 1 = lowest) Rating = 5 0.0280 0.0991 0.0898 0.1663 0.0926 -0.1506 (0.28) (1.09) (0.53) (1.53) (0.86) (-2.35) Rating = 4 0.0672 0.0870 0.1434 0.1468 0.0893 -0.0665 (0.75) (1.06) (0.97) (1.47) (0.94) (-1.41) Rating = 3 0.0746 0.0441 0.1726 0.1188 0.0995 -0.0230 (0.90) (0.56) (1.21) (1.22) (1.12) (-0.56) Rating = 2 0.0391 0.0250 0.0934 0.1099 0.0525 -0.0354 (0.48) (0.32) (0.65) (1.13) (0.59) (-0.88) Miles to Closest Transit Stop -0.0118 0.0236 -0.0069 0.0237 -0.0281 0.0157 (-0.61) (1.60) (-0.28) (1.56) (-1.63) (1.56) Sigma 0.4816 0.2671 0.3472 0.3170 0.3847 0.3347 (59.47) (21.86) (14.63) (23.48) (31.98) (74.55) City Fixed Effects 5 5 5 5 5 5 Observations 5,431 5,431 5,431 5,431 5,431 5,431 Censored 3,028 4,380 4,936 4,287 3,969 438 Uncensored 2,403 1,051 495 1,144 1,461 4,993 Pseudo R-squared 0.062 0.346 0.187 0.230 0.165 0.086 a Sample includes buildings with 10 or more workers in Manhattan, Cook County (Chicago), Los Angeles, San Francisco, Washington DC. T-ratios based on robust standard errors in parentheses. b Omitted category is retail share of employment. c An establishment is coded as an anchor (1 if yes, 0 if no) if all three of the following conditions hold: (i) it is the largest establishment in the building; (ii) it includes at least 20% of building employment; (iii) it has 10 or more workers.

49

Table 4d: Building Fixed Effect Models of Within Building Industry Employment Share Outside of Own-Industry Anchora

Including Retail Excluding Retail Buildings

With 10 or More

Workers

Buildings With

10 to 100 Workers

Buildings With

100 or More Workers

Buildings With

10 or More Workers

Buildings With

10 to 100 Workers

Buildings With

100 or More Workers

Own Ind Emp share < 0.1 milesb 0.4515 0.4545 0.3276 0.3786 0.3871 0.3281

(47.90) (43.69) (15.77) (37.38) (34.40) (15.00)

Own Ind Anchor in Target Bldgc 0.1927 0.2023 0.1805 0.1657 0.1760 0.1472

(35.24) (24.70) (18.81) (32.03) (22.81) (16.20)

Own Ind Anchor in Adjacent Bldgc 0.0239 0.0312 0.0134 0.0151 0.0165 0.0090

(3.17) (3.12) (1.29) (2.11) (1.75) (0.86)

Finance (1 if yes) -0.1728 -0.1926 -0.0612 - - -

(-65.49) (-63.45) (-14.01) - - -

Advertising (1 if yes) -0.1739 -0.1928 -0.0709 -0.0021 -0.0009 -0.0101

(-65.15) (-62.78) (-16.33) (-4.11) (-1.80) (-5.72)

Law (1 if yes) -0.1696 -0.1901 -0.0535 0.0032 0.0025 0.0078

(-64.09) (-62.47) (-12.09) (5.34) (4.08) (3.70)

Manufacturing (1 if yes) -0.1521 -0.1701 -0.0490 0.0237 0.0253 0.0133

(-54.67) (-53.51) (-10.22) (22.33) (21.91) (4.98)

All Other Industries (1 if yes) 0.1322 0.1103 0.3267 0.3568 0.3498 0.4049

(19.68) (15.18) (20.72) (48.38) (42.77) (25.00)

Constantd 0.1748 0.1934 0.0753 0.0036 0.0020 0.0148

(65.20) (62.63) (17.28) (6.15) (3.25) (8.35)

Building Fixed Effects 33,702 28,972 4,730 33,702 28,972 4,730 Observations 202,212 173,832 28,380 168,510 144,860 23,650 Within R-squared 0.483 0.463 0.654 0.632 0.612 0.749 a Sample includes buildings with 10 or more workers in Manhattan, Cook County (Chicago), Los Angeles, San Francisco, Washington DC. T-ratios based on robust standard errors in parentheses. b Omitted category is retail share of employment. c An establishment is coded as an anchor (1 if yes, 0 if no) if all three of the following conditions hold: (i) it is the largest establishment in the building; (ii) it includes at least 20% of building employment; (iii) it has 10 or more workers. d The constant captures the average building employment share for the omitted industry, retail for the first three models and finance for the last three columns. The coefficients on the industry dummy variables reflect the average difference from the constant term.

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Table 5: Tobit Models of Building Level Average Sales/Worker Not Including Own-Industry Anchors in the Target Buildinga

Retail Law Employment < 0.1 miles (1,000s) 1,630 2,254 (11.00) (9.83) Employment in bldg. (1,000s) 13,103 22,531 (4.03) (4.89) Employment in adj bldg. (1,000s) 89.00 1,177 (0.04) (0.38) Emp share < 0.1 miles – Financeb -58,813 85,959 (-4.22) (3.52) Emp share < 0.1 miles – Advertisingb -28,997 179,474 (-1.25) (4.00) Emp share < 0.1 miles – Lawb 4,637 488,753 (0.28) (18.02) Emp share < 0.1 miles – Manufacturingb -92,214 -87,009 (-11.74) (-4.12) Emp share < 0.1 miles - Otherb -83,601 58,732 (-19.87) (5.44)

Anchor Establishment In Target Building

In Adjacent Building

In Target Building

In Adjacent Building

Retailc 26,990 6,228 -62,310 727.8 (5.00) (0.95) (-6.38) (0.06) Financec 8,670 -36,385 26,231 -5,664 (0.46) (-1.79) (1.67) (-0.23) Advertisingc 21,607 3,107 -3,079 -38,912 (1.26) (0.12) (-0.13) (-0.97) Lawc -290.4 -333.9 105,345 -16,643 (-0.02) (-0.02) (10.35) (-0.96) Manufacturingc 16,155 10,734 -22,996 12,052 (1.94) (1.00) (-2.02) (0.73) Otherc -7,951 -2,129 -12,557 4,257 (-2.26) (-0.47) (-2.50) (0.54) Small Building (Employment < 50) -6,003 -2,207 -105,246 -15,824 (-2.02) (-0.59) (-21.43) (-2.30) Sigma 112,761 151,935 (47.36) (38.67) City Fixed Effects 5 5 Observations 33,635 33,692 Censored 21,501 30,942 Uncensored 12,134 2,750 a Sample includes buildings with 10 or more workers in New York County (Manhattan), Cook County (Chicago), Los Angeles MSA, San Francisco County, Washington DC County. Building level sales per worker is calculated omitting own-industry anchor establishments within the target building. T-ratios based on robust standard errors. b Omitted category is retail share of employment. c An establishment is coded as an anchor (1 if yes, 0 if no) if all three of the following conditions hold: (i) it is the largest establishment in the building; (ii) it includes at least 20% of building employment; (iii) it has 10 or more workers.

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Appendix: Supplemental Tables

Table A-1: Frequency Counts of Establishments by Industry Groupings and SIC 2 Subcategoriesa

Panel A: Retail Panel E: Manufacturing SIC 2 Freq. Prcnt. Cum. SIC 2 Freq. Prcnt. Cum. 52 3,982 2.45 2.45 20 2,939 5.93 5.93 53 3,339 2.06 4.51 21 43 0.09 6.02 54 18,323 11.29 15.8 22 1,185 2.39 8.41 55 12,370 7.62 23.42 23 3,539 7.15 15.56 56 14,108 8.69 32.11 24 1,456 2.94 18.50 57 10,270 6.33 38.44 25 1,371 2.77 21.27 58 63,560 39.16 77.6 26 835 1.69 22.95 59 36,357 22.40 100 27 8,932 18.03 40.99 Total 162,309 100 28 2,929 5.91 46.90

29 236 0.48 47.38 30 1,489 3.01 50.38 Panel B: Finance 31 521 1.05 51.44 SIC 2 Freq. Prcnt. Cum. 32 1,135 2.29 53.73 62 8,021 35.62 35.62 33 893 1.80 55.53 67 14,497 64.38 100 34 3,855 7.78 63.31 Total 22,518 100 35 5,382 10.87 74.18 36 4,168 8.42 82.60 37 1,575 3.18 85.78 Panel C: Advertising 38 2,792 5.64 91.41 SIC 2 Freq. Prcnt. Cum. 39 4,253 8.59 100 73 3,941 100 100 Total 49,528 100 Total 3,941 100 Panel D: Law SIC 2 Freq. Prcnt. Cum. 81 34,324 100 100 Total 34,324 100

Continued on next page

52

Table A-1 continued: Frequency Counts of Establishments by Industry Groupings and SIC 2 Subcategories

Panel F: Other Industries Panel F: Other Industries SIC 2 Freq. Prcnt. Cum. SIC 2 Freq. Prcnt. Cum. 1 842 0.12 0.12 60 8,617 1.22 21.73 2 254 0.04 0.15 61 7,885 1.11 22.84 7 7,089 1.00 1.16 63 2,582 0.36 23.21 8 61 0.01 1.17 64 9,752 1.38 24.59 9 50 0.01 1.17 65 40,326 5.70 30.29 10 50 0.01 1.18 70 5,896 0.83 31.12 12 18 0.00 1.18 72 27,954 3.95 35.07 13 381 0.05 1.24 73 111,931 15.81 50.88 14 155 0.02 1.26 75 15,795 2.23 53.11 15 17,706 2.50 3.76 76 7,895 1.12 54.23 16 1,985 0.28 4.04 78 9,622 1.36 55.59 17 25,224 3.56 7.60 79 17,449 2.47 58.05 40 113 0.02 7.62 80 102,367 14.46 72.52 41 3,614 0.51 8.13 82 21,081 2.98 75.50 42 8,860 1.25 9.38 83 24,460 3.46 78.95 43 385 0.05 9.44 84 1,734 0.24 79.20 44 488 0.07 9.51 86 37,747 5.33 84.53 45 1,051 0.15 9.65 87 82,578 11.67 96.20 46 32 0.00 9.66 89 8,023 1.13 97.33 47 12,070 1.71 11.36 91 2,253 0.32 97.65 48 10,066 1.42 12.79 92 2,252 0.32 97.97 49 2,551 0.36 13.15 93 217 0.03 98.00 50 30,984 4.38 17.52 94 1,187 0.17 98.17 51 21,160 2.99 20.51 95 1,131 0.16 98.33 96 1,141 0.16 98.49 97 1,665 0.24 98.72 99 9,047 1.28 100 Total 707,776 100 aFrequency counts include all establishments in the Dun and Bradstreet data with 1 or more workers present.

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Table A-2: Building Employment Share Outside of Own-Industry Anchor By Building Sizea

Panel A: Bldgs with 10 or More Workers Retail Finance Advert Law Manf Other

Own Ind Emp share < 0.1 milesb - 0.0801 0.0482 0.2228 0.4273 0.4328

- (7.39) (3.14) (11.38) (22.53) (32.53)

Own Ind Anchor in Target Bldgc 0.1785 0.1157 0.1097 0.2192 0.1496 0.1161

(11.25) (5.70) (3.62) (8.74) (8.49) (13.70)

Own Ind Anchor in Adjacent Bldgc 0.0376 0.0183 -0.0155 0.0222 -0.0149 0.0383

(1.85) (1.14) (-2.22) (1.12) (-0.81) (3.18)

City Fixed Effects 5 5 5 5 5 5 Observations 33,702 33,702 33,702 33,702 33,702 33,702 R-squared 0.114 0.040 0.015 0.077 0.087 0.085 Panel B: Bldgs With 10 to 100 Workers Retail Finance Advert Law Manf Other

Own Ind Emp share < 0.1 milesb - 0.0539 0.0340 0.1397 0.4441 0.4440

- (4.37) (2.54) (7.84) (21.63) (31.08)

Own Ind Anchor in Target Bldgc 0.1272 0.0419 0.0599 0.2499 0.1375 0.1710

(5.68) (2.30) (1.56) (4.28) (4.66) (12.60)

Own Ind Anchor in Adjacent Bldgc 0.0527 -0.0118 -0.0082 0.0285 -0.0210 0.0516

(2.01) (-1.66) (-3.09) (1.06) (-0.87) (3.22)

City Fixed Effects 5 5 5 5 5 5 Observations 28,972 28,972 28,972 28,972 28,972 28,972 R-squared 0.100 0.010 0.004 0.029 0.083 0.079 Panel C: Bldgs With 100 or More Workers Retail Finance Advert Law Manf Other

Own Ind Emp share < 0.1 milesb - 0.1483 0.0894 0.4381 0.2811 0.2892

- (6.39) (1.82) (8.07) (5.82) (8.03)

Own Ind Anchor in Target Bldgc 0.2443 0.1301 0.1327 0.1868 0.1622 0.0715

(10.77) (4.97) (3.28) (6.73) (7.49) (6.89)

Own Ind Anchor in Adjacent Bldgc 0.0070 0.0344 -0.0290 -0.0040 0.0021 0.0156

(0.25) (1.16) (-1.87) (-0.13) (0.09) (0.97)

City Fixed Effects 5 5 5 5 5 5 Observations 4,730 4,730 4,730 4,730 4,730 4,730 R-squared 0.146 0.119 0.061 0.201 0.129 0.126 a Each of the models below includes the full set of controls from Table 4b, most of which are not shown to focus on the own-industry patterns tabled out above. Samples include buildings of the size range specified for each panel and are drawn from the same five cities as before, New York (New York County), Chicago (Cook County), Los Angeles (Los Angeles County), San Francisco (San Francisco County) and Washington DC. T-ratios based on robust standard errors in parentheses. b Omitted category is retail share of employment which is why the own-industry employment share coefficient is not shown for retail. c An establishment is coded as an anchor (1 if yes, 0 if no) if all three of the following conditions hold: (i) it is the largest establishment in the building; (ii) it includes at least 20% of building employment; (iii) it has 10 or more workers.