What does Wall Street tell usabout Main Street?*

Sean J. Flynn Jr.Tulane University

Andra GhentUniversity of North Carolina, Chapel Hill

May 4, 2021

First draft: September 28, 2020

Abstract

We use detailed establishment-level data to understand whether and how thecomposition of the US stock market differs from the composition of US firms as awhole. Although the locational composition of employment in public firms is simi-lar to that of all US firms, we find certain industries significantly overrepresented.Further, the gap between the industrial composition of publicly-traded and allfirms has grown over the last thirty years, and public firms display markedlydifferent growth dynamics than private firms. Despite this, we show that stockreturns within industries and geographies predict employment changes in thoseindustries and geographies.

*Flynn: sflynn1@tulane.edu ; Ghent: andra ghent@kenan-flagler.unc.edu. The authors began this work while Ghent was afaculty member at the University of Wisconsin-Madison; we are grateful to the staff at YTS for their assistance with the data.We thank seminar participants at the Bank of Canada, UNC-Chapel Hill, Tulane University, and CUHK for feedback on anearlier draft as well as Greg Brown, Eric Ghysels, Paige Ouimet, Harry Turtle, and Ross Valkanov for helpful conversations.

1 Introduction

Observers often look to the stock market as a real-time barometer of the health of the

economy. The efficiency with which the stock market incorporates information has

also led to economists frequently using the reaction of publicly traded firms to gauge

the effect of various events on the economy.1 Every major news outlet reports on the

stock market, often as part of headline news, despite stock wealth being a negligible

fraction of wealth for 90% of US households, and many households having no stock

market wealth at all.2 Furthermore, retail investors have highly correlated GDP

growth and stock return expectations (Giglio, Maggiori, Stroebel, and Utkus, 2021),

suggesting that they will significantly change their consumption and production de-

cisions based on publicly-traded stock returns, even if they do not own a significant

amount of stocks.

Rather than directly affecting households’ wealth, perhaps the media focuses heav-

ily on the performance of the stock market because it provides information about fu-

ture employment. Changes in stock prices reflect news about future cash flows that

may provide information about future employment, conditional on 1) firms facing sig-

nificant labor adjustment costs, and 2) publicly-traded firms being representative of

the broader set of firms in the US economy. In the presence of labor adjustment costs,

changes to firm cash flows may not immediately result in changes to employment but

rather could be a leading indicator of future employment prospects for firms, indus-1See, for example, Gormsen and Koijen (2020).2Kuhn, Schularick, and Steins (2020) provide a detailed breakdown of the composition of US house-

hold wealth. See also Poterba (2000) and Smith, Zidar, and Zwick (2020). Households have some indi-rect exposure to the stock market through pension funds; publicly traded equities account for slightlyless than half of the holdings of pension funds (Andonov and Rauh, 2020). Lustig, Van Nieuwerburgh,and Verdelhan (2013) and Palacios (2015) show that human capital accounts for more than 90% ofaggregate wealth.

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tries, and cities.

However, it is unclear how well the stock market represents the firms in the US

economy as a whole. As the number of publicly-traded firms has decreased in the last

twenty years (Doidge, Karolyi, and Stulz, 2017), the extent to which the stock market

represents the U.S. economy may have changed. Inferences about the effect of various

events on the macroeconomy may be misleading if the composition of publicly traded

firms differs dramatically from the composition of firms in the economy as a whole.3

In this paper we assess how the composition of the US stock market differs from

the US economy as a whole using detailed establishment-level data. We first docu-

ment the correlation between employment in publicly-traded firms and total employ-

ment by industry and geographic region. In terms of geographic location, the share of

employment in publicly traded firms in a given city is highly correlated with the share

of total US employment in that city. This is particularly true when we disaggregate

the employment of public firms to the establishment level, but even if we attribute

all employment in a firm to the headquarters location, the correlation between public

and total employment remains high. Additionally, there is no clear time trend in the

correlation between location-based measures of employment.

In contrast, the industry representativeness of public firms for the entire economy

displays more variation. On average the correlation between public and total em-

ployment is significantly lower at the industry than at the geography level, and that

correlation has declined from 1990 to 2017. Manufacturing and retail industries are3Alfaro, Chari, Greenland, and Schott (2020) offer one example of using more granular information

from publicly traded firms to make inferences about the macroeconomy. They look at the impact ofCOVID-19 infections on the economy by using the change in the market value of publicly-traded firmswithin an industry and then weighting those changes according to the weight that industry has intotal employment in an area.

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consistently overrepresented in employment in publicly-traded firms, whereas health-

care and other services are typically underrepresented. We show this is not due to

certain industries having an overrepresentation of larger, older firms. This suggests

that the stock market has become less representative of the industry composition of

total employment over time. Our results along this dimension of representativeness

are consistent with the findings of Schlingemann and Stulz (2020). Schlingemann

and Stulz (2020) study how the shift from manufacturing to services impacted the

representativeness of public firms relative to their contribution to employment and

GDP, and their results suggest that the contribution has declined over time.

We further investigate whether public firms are representative of all firms within

industries. Focusing on employment, our results suggest that public firms grow faster

than private firms within the same industry, controlling for size and age. This find-

ing is consistent with public and private firms having significantly different growth

dynamics, and it supports the importance of private equity in a well-diversified port-

folio.

We then exploit the granularity of the establishment-level data to study whether

the returns of public firms predict changes in total employment. We construct a mea-

sure of the exposure of individual geographic and industry units to a given firm and

then weight the stock returns for public firms by their importance to those geographic

and industry units. Finally, we aggregate the weighted stock returns across all firms

with a presence in a given geographic/industry unit. We call this the exposure-

weighted stock return (EWSR) for that geography/industry. This results in a granular

measure of the impact of changes in the stock market on geographic/industry units

that is not based on the location/industry of a firm’s headquarters.

3

We estimate the association between EWSR and total employment growth and

find a positive association at both the geographic and industry level. A one standard

deviation increase in the quarterly EWSR for the average core-based statistical area

(CBSA) is associated with an increase in employment growth the following quarter of

30% relative to the mean. Similarly, the impact of an analogous change in quarterly

EWSR at the 4-digit NAICS level results in an employment growth increase in that

industry of nearly 50% relative to the mean.

The next section describes our data and presents our findings regarding the extent

to which publicly traded firms mirror the composition of US firms as a whole. We

present our regressions of local and industry employment on our indices in Section 3.

Section 4 concludes.

2 How Representative are Publicly Traded Firms?

2.1 Data

Our main dataset is establishment-level employment data from Your-economy Time

Series (YTS). The YTS data begins in 1997 and covers all US public and private es-

tablishments. YTS aggregates data from the Infogroup Business Data historical files,

and these files are provided by the Business Dynamics Research Consortium (BDRC)

at the University of Wisconsin. Kunkle (2018) details Infogroup’s methodology to

gather the data underlying YTS:

To develop its datasets, Infogroup operates a 225-seat call center that makes

contact with over 55,000 businesses each and every day in order to record

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and qualify company information. During a typical month, 15% of the en-

tire Infogroup business dataset is re-verified. On average, 150,000 new

businesses are added while 100,000 businesses are removed each month,

capturing the dynamic business churn happening in the economy. In-

fogroup’s team also identifies new companies through U.S. Yellow Pages,

county-level public sources on new business registrations, industry direc-

tories, and press releases.

Additional information on the YTS data is available at https://wisconsinbdrc.

org/data/.4

We use Compustat to identify publicly-traded firms.We merge the set of all firms

in Compustat with firms in YTS in a series of steps. We begin with the 15,425 Com-

pustat firms active over the 1997-2017 period that were not missing data on assets,

employment, and capital expenditures. Our first step in the merge is to look for a

match in the YTS data using stock market ticker. In the second step we try to match

the remaining Compustat firms with firms in YTS based on headquarter names and

zip codes. In the third and final step we match based on the headquarter two-digit

NAICS code, the headquarter zip code, and a stub of the headquarter firm name.

In total, we are able to match 9,296 firms in the sample. The Compustat firms we

are unable to match to YTS firms are smaller (median assets of $100 million) than the

full sample of 15,425 Compustat firms (median assets of $163 million). The median

and average assets of the Compustat firms we are able to merge are $240 million and

$4.5 billion, respectively. Thus, while we match about two thirds of firms by number,

we match about 80% of firms by asset value.4Kunkle (2018) also compares the YTS data with employment data from the US Bureau of Labor

Statistics (BLS).

5

https://wisconsinbdrc.org/data/https://wisconsinbdrc.org/data/

In addition to Compustat and YTS, we use several datasets from the Bureau of

Labor Statistics. For our comparison between employment in publicly-traded firms

and all firms we use the Quarterly Census of Employment and Wages (QCEW). The

QCEW data is a comprehensive set of employment and wage data that, according to

the BLS, covers “more than 95 percent of U.S. jobs, available at the county, MSA, state

and national levels by industry.” To construct annual employment at various NAICS

and geographic levels of aggregation, we use the QCEW Aggregation Level Codes,

which provides aggregate employment numbers at 2-, 4-, or 6-digit NAICS code, and

at the state and county level using FIPS code.

For our employment growth regressions we rely on two BLS data sources that pro-

vide monthly employment at the geography and industry level. For geography-level

employment we use the Local Area Unemployment Statistics (LAUS). The LAUS data

is the “official source of civilian labor force and unemployment data for over 7,500

unique subnational areas” and are used by federal programs to allocate unemploy-

ment benefit funds. For industry-level employment we rely on Current Employment

Statistics (CES) data. The CES data is based on a comprehensive monthly survey

of over 145,000 establishments and nearly 700,000 workers. We restrict our sam-

ple to private sector employment (as opposed to government-related employment) by

excluding NAICS 2-digit codes 92 and 99.

Finally, we gather firm-level stock return data from CRSP and factor returns from

Ken French’s website.

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Headquarter vs establishment-level information

A key benefit of matching the YTS data to Compustat is that it allows us to identify

employment of Compustat firms within a geographic area or industry that is not the

same as the firm’s headquarters. For example, if a firm has headquarters in New

York state but operations and employees in Texas and California as well, we are able

to use the YTS match to identify the number of employees at the California and Texas

locations.

This is important because, as the top panel of Figure 1 shows, most employment

in publicly-traded firms is not at the firm’s headquarters location. This panel uses

the YTS-Compustat merged data to display, for the average firm in each year, the

percentage of all employees that are located in the headquarters state (top series) or

headquarters CBSA (bottom series). At the CBSA level, the average firm has roughly

22% of employees in its headquarters location in 1997, but by 2017 that number drops

to about 15%. While this panel uses firms in all industries, the finding is not driven

by firms that have most of their employment in nontradable industries; the figure

looks broadly similar when we exclude firms in establishments in NAICS codes that

Mian and Sufi (2014) define as nontradable or construction industries. Figure A.1 de-

composes the time trends based on whether firms are in nontradable/construction or

tradable industries. The top panel illustrates a similar downward trend in nontrad-

able and construction industries, and the bottom panel also displays a decline over

time even in tradable industries.

As an alternative way to demonstrate the importance of establishment-level ag-

gregation, we show in the bottom panel of Figure 1 the number of distinct CBSAs,

states, and 2-digit NAICS industries in which publicly-traded firms have at least one

7

employee. This panel aggregates the Compustat-YTS merged data across the entire

1997-2017 sample period and buckets firm-years into five quintiles based on total as-

sets. The first cluster of bars is for all firms, whereas the Q1 (Q5) cluster summarizes

data for the smallest (largest) 20% of firm-years. Within each bucket, the number of

CBSAs, states, and 2-digit NAICS industries in which the median firm has at least

one employee is reported. As an example, the median firm in the Q3 bucket (which

comprises from the 40th to the 60th percentile of total assets) has at least one em-

ployee in five CBSAs, three states, and two industries.

The bottom panel illustrates that, with the exception of the smallest firms, the

median publicly-traded firm has operations in multiple cities and states, with larger

firms having more geographically dispersed operations. Similarly, it shows that most

firms have operations in multiple industries and that larger firms are more likely

to have operations in multiple industries.5 This is consistent with the fact that the

industry code of the firm’s headquarters that appears in regulatory filings is usually

not the industry code of all the firm’s employment, particularly for large firms.6

2.2 Comparing total employment to public firm employment

Our analysis of the representativeness of the public market for the broader econ-

omy begins by measuring the association between publicly-traded firm employment5Garcı́a and Norli (2012) and Bernile, Kumar, and Sulaeman (2015) previously studied firm geo-

graphic diversification using 10-K statements.6Cohen and Lou (2012) use the Compustat segment data to document that less than half of the

value-weighted CRSP universe consists of firms that operate in only one industry. A large literaturestudies whether industrially diversified stocks have higher or lower returns than firms concentrated inone industry (e.g., Whited (2001) and Custódio (2014)). Villalonga (2004) and Tate and Yang (2015) usemore detailed data on establishments than is available in Compustat and find a greater degree of di-versification compared to studies that measure industrial diversification based only on the Compustatsegment data.

8

and total employment. Specifically, we establish the correlation between the share

of public firm employment and the share of total employment that are accounted for

by each industry and geographic unit. We compute two measures of employment for

this analysis: compustat share and bls share. Compustat share measures the percent

of total Compustat (public firm) employees in a given industry or geographic unit in

a given year, whereas bls share measures the percent of total employees (both public

and private firm) in a given industry or geographic unit in a given year.

As an example of how we construct the shares, assume that in 2005 there are

1,000 total employees reported in the entire cross-section of Compustat. Assume also

that 100 of these employees are at firms with two-digit NAICS code 52 (finance and

insurance), and the other 900 are at firms with different NAICS codes. Then the

variable compustat share for NAICS code 52 in year 2005 is equal to 100/1000 = 0.10.

We treat the geographic units analogously.

For both the industry and geographic units, the BLS data on total employment

does not allow us to disentangle establishment from headquarter employment. How-

ever, using the Compustat-YTS merged dataset, we are able to construct public em-

ployment at both the establishment-level and headquarter-level. We do so for both the

industry and geographic analysis. This is important because a single firm may have

establishments in distinct states or industries. For example, assume that in 2005

there are 1,000 total employees reported in the entire cross-section of Compustat.

Assume also that 100 of these employees are at firms headquartered in North Car-

olina, but that the firms headquartered in North Carolina also have establishments

in Louisiana. If the establishments in Louisiana comprise 50 of the 100 employees of

these firms, and the establishments in North Carolina comprise the other 50, then the

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state-level measure of compustat share based on headquarters is 0.10 for NC and 0 for

LA. In contrast, the state-level measure of compustat share based on establishments

is 0.05 for NC and 0.05 for LA.

We estimate the association between the public and total employment shares us-

ing the following equation

bls sharei,t = β0 + β1compustat sharei,t + �i,t(1)

where bls sharei,t is the share of total employment in industry or geographic region i

in year t and compustat sharei,t is the share of employment in publicly-traded firms in

industry or geographic region i in year t, using either headquarter or establishment

level aggregation. When compustat sharei,t is at the HQ level we use the full Compu-

stat database, and when compustat sharei,t is at the establishment level we use the

YTS-Compustat merged database.

Table 1 defines our variables and Table 2 summarizes the data used in the repre-

sentativeness analysis. Because the YTS data begin in 1997, we compute statistics at

the establishment level over the 1997-2017 period. However, we compute statistics at

the HQ level over the period from 1990-2017 given we have Compustat data back to

1990, which is also when the disaggregated BLS data start. The statistics in Table 2

illustrate that there is no meaningful difference between public and total employment

shares when the data are summarized over the entire sample period.

To investigate time and cross-sectional variation, Figures 2-5 plot the differences

between public and total employment summarized in Table 2 over time.7 In each

graph we plot the total share of employment in a given industry/geography on the7Tables A.1 and A.2 provide the data underlying Figures 2 and 3.

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horizontal axis and the public share of employment on the vertical axis. We also plot

a line at 45 degrees. If the total employment share is equal to the public employment

share, then the dot for a given industry/geography will lie on the 45 degree line. How-

ever, if the public employment share is larger (smaller) than the total employment

share, the dot lies above (below) the 45 degree line. A greater deviation from the

45 degree line indicates a larger difference between employment in publicly-traded

companies and employment as a whole. We plot the deviation in ten year periods

that span our sample period for 2-digit NAICS industry and state-level geographic

groupings.

To more formally measure the strength of the correlation, we also estimate regres-

sions of total employment on public employment using equation 1. The regressions

are weighted by the BLS share of employment in a given state or 2-digit NAICS. Fig-

ure 6 plots the R2s for each regression at both the state and 2-digit NAICS levels over

time.

At the industry level, two aspects of the results are of note. First, Figures 3 and 5

illustrate that, during our sample period, certain industries are consistently over and

underrepresented in the public market. Specifically, manufacturing (2-digit NAICS

31-33) is consistently overrepresented, with its employment share in publicly traded

firms being on average 2.1 times as high than in total U.S. employment. Retail trade

(2-digit NAICS 44-45) is also overrepresented in publicly-traded firms, with its public

employment share being 1.3 times as high as total firms. Conversely, the healthcare

industry (2-digit NAICS 62) is underrepresented in public firms relative to the overall

economy, with the share of employment in public firms relative to all firms being less

than 25%.

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The second key industry-level result is that, cross-sectionally, as Figure 6 illus-

trates, public employment explains less than 70% of the variation in total employ-

ment in all years in the sample. Moreover, the explanatory power of the publicly-

traded market have declined markedly over time. The R2s for the 2-digit NAICS

regressions decline consistently from 1990 to 2017 and are particularly low following

the 2008 financial crisis. By the end of the sample period, the R2s at the HQ level (es-

tablishment level) indicate that publicly-traded firms explain only about 40% (16%)

of the variation in total employment. These findings with respect to time variation

are consistent with the results of Schlingemann and Stulz (2020) who show, using HQ

aggregation, that the industrial representativeness of the public market for the total

economy has declined over time.

In contrast to our industry-level results, Figures 2 and 4 reveal that the geographic

employment distribution of the U.S. is generally well represented by the geographic

distribution of publicly traded firms. There is relatively little difference between pub-

lic and total employment as illustrated by the fact that most states lie close to the

diagonal, regardless of whether we use HQ state (Figure 4) or establishment state

(Figure 2). This is further borne out in Figure 6, which shows a high correlation be-

tween public and total employment, particularly when using establishment location.

Although the association becomes weaker when using headquarters location, the av-

erage explanatory power of public employment for total employment is still nearly

75%. As such, there is unlikely to be a significant bias against certain geographies

when trying to infer future total employment in U.S. regions from data on publicly

traded firms.

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2.3 Why does the industrial composition of public firms differ

from that of all firms?

Given the findings in Figures 3 and 5, a natural question is why certain industries are

overrepresented or underrepresented in public firms relative to their share of employ-

ment in the US economy. One possibility is that certain industries are characterized

by larger or older firms that are more likely to be publicly traded. If this is the case,

then firm size and age should explain the differences in industrial composition. On

the other hand, there may be certain industries that are underrepresented for other

reasons.

We investigate this by aggregating the YTS data to the firm-year level and then

estimating regressions in which the dependent variable takes a value of 1 if the firm-

year is publicly traded and 0 otherwise. We estimate these regressions using various

combinations of firm characteristics, including firm size fixed effects, firm age fixed

effects, and year and two-digit NAICS fixed effects, where industry is based on the

firm’s headquarters NAICS code.

Table 3A presents the results of probit regressions. Firm size categories are 1-9

employees, 10-49 employees, 50-99 employees, 100-499 employees, and 500+ employ-

ees, and age categories are 1 year, 2-5 years, 6-10 years, 11-25 years, and 26+ years.

Column 1 includes size indicator variables only (with the excluded category being 1-

9 employees), column 2 adds age indicator variables (with 1 year being the excluded

category), column 3 adds year indicators (with 1997 being the excluded category), and

column 4 adds industry indicators (with agriculture being the excluded category). The

employment and age categories are positive and significant across specifications, con-

sistent with larger, more mature firms being more likely to be publicly traded.

13

Despite this, size and age do not entirely explain the difference in the industry

composition of public firms relative to all firms. Table 3B displays the coefficients,

labeled according to 2-digit NAICS code, for the industry indicators in column 4 of

Table 3A. For example, the coefficient on the Education indicator variable, which is

among the most underrepresented industry in Figures 3 and 5, is negatively corre-

lated with the probability of being public. The coefficients are nearly all significant,

indicating that industry effects, controlling for size and age, are also correlated with

the likelihood of being public.

2.4 Are public firms representative of all firms within an in-

dustry?

A related question is whether publicly-traded firms within an industry are represen-

tative of all firms within that industry. This is important because if public firms are

representative of all firms, then inferences from stock market data about broader

industry trends may be better. Indeed, Yan (2020) finds that private firms make in-

vestment decisions based on the stock market returns of public firms in the same

industry. Furthermore, if public firms within an industry are representative of the

private firms in that industry, it may be less important to include private equity in a

well-diversified portfolio.

We assess the similarity between public and private firms within industries by

focusing on employment dynamics. Specifically, we examine whether employment

growth in publicly-traded firms differs significantly from employment growth in pri-

vate firms. To do so, we regress annual firm-level employment growth8 on industry8We use annual frequency data because private firm employment from YTS is only available annu-

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fixed effects (where industry is based on the firm’s headquarters NAICS code), year

fixed effects, size and age fixed effects (defined in the same way as in Table 3A), and

an indicator variable for whether the firm is public (public). Table 4A contains the

results.9 The variable public and the size and age categories are all lagged one year

(denoted by the prefix “L.”). As the positive and statistically significant coefficients on

public in columns 1 and 2 show, public firms indeed have faster employment growth

than their private counterparts even after accounting for firm size, firm age, and in-

dustry and year effects.

In columns 3-4, we also include as controls industry-by-public interactions terms,

which are constructed by interacting the variable public with the indicators for each

of the 2-digit NAICS codes. In these two specifications we drop the standalone public

variable. The coefficients for the industry-by-public terms from column 4 of Table

4A (the most stringent specification) are reported individually in Table 4B. Each in-

teraction term captures the impact on employment growth of being public within

that industry. For example, the “Healthcare” coefficient represents the coefficient on

the public × NAICS62 term. The positive sign indicates that public firms within the

healthcare industry experience greater employment growth than private firms in that

industry, controlling for size, age, year, and industry-wide fixed effects.

As Table 4B illustrates, public firms grow significantly faster within most indus-

tries (as evidenced by the mostly positive signs). This is consistent with the findings

of Feldman, Kawano, Patel, Rao, Stevens, and Edgerton (2021) that observationally

similar public firms invest more than private firms. While we do not have exten-

ally. As in Section 2.3, for multiestablishment firms we sum employment across all establishments.9We only include firm-years with five or more employees in the regressions, as firms with fewer

than five employees have a very low probability of being public.

15

sive information on the private firms in our dataset, the results are consistent with

private firms having different growth dynamics than public firms such that a port-

folio that excludes private equity is unlikely to span the market. Brown, Hu, and

Kuhn (2019) show more formally that including investment in private equity funds

improves portfolio Sharpe ratios.

3 Predicting Employment with Stock Returns

Having established that the representativeness of the stock market for the overall

economy has weakened over time at the industry level but remained relatively con-

stant at the geographic level, we now focus on the predictive power of public firm

stock returns for total employment. For this we move to a more granular definition

of industry and geography. Whereas the previous analysis focused on the state- and

2-digit NAICS-levels, we now move to the CSBA- and 4-digit NAICS-levels.

We exploit cross-sectional variation in the geographic location of employment in

public firms to identify the relation between stock returns and employment at the

local level. Similarly, we use heterogeneity in the industrial composition of public

firm employment to identify the relation at the industry level. All of our regressions

include time period fixed effects such that we do not identify the impact of changes

in discount rates on employment, but instead focus on the impact of changes in firm-

specific cash flow news.

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3.1 How might stock returns predict employment?

There are multiple channels through which the returns of publicly-traded firms may

be associated with subsequent changes in total employment. First, news about future

firm-specific cash flows that stock prices capitalize will be correlated with changes in

public firm employment. To the extent that firms face labor adjustment costs, there

will be a lag between stock returns and employment changes.10 Increases in public

firm employment will not only directly affect total employment, but they should have

a spillover effect as changes in the number of public firm employees will change de-

mand for goods and services produced by private firms. If this is the main mechanism

through which stock returns predict employment, the marginal effect of stock returns

may be small given the small share of employment in public firms in many cities and

industries.

Second, the cash flow news that drives public firm returns may be correlated with

cash flow news that also impacts private firms. The importance of this channel de-

pends, of course, on the correlation between returns.

Shocks to private firms in an industry may be highly correlated with shocks to pub-

lic firms in the same industry. While it is perhaps less obvious that shocks impacting

firms within the same city are highly correlated, Dougal, Parsons, and Titman (2015)

find that, at least among publicly-traded firms, firm investment is highly sensitive to

firms headquartered in the same city but in different industries.

Third, home bias in portfolios may generate spillovers with respect to consumption10Belo, Lin, and Bazdresch (2014) show that significant labor adjustment costs are necessary to

reconcile asset pricing facts. Labor adjustment costs are especially important for firms that rely onmore skilled labor (Belo, Li, Lin, and Zhao, 2017; Ghaly, Dang, and Stathopoulos, 2017).

17

at the geographic level.11 If investors hold a significant number of local stocks in their

portfolios then shocks to geographically proximate public firms may have an outsized

impact on their wealth. This may lead to changes in consumption, which affects

demand for products and services of private firms.

Finally, shocks to the discount rate, such as from monetary policy shocks, may

affect both firm returns and future employment. Similarly, fiscal policy shocks that

affect both the returns to physical and human capital will generate a correlation be-

tween stock market returns and future employment. A large recent literature stud-

ies the joint dynamics of the return processes of financial wealth and human capi-

tal.12 Given the growth in income inequality and changes in the labor share, this is

an important literature. However, because our predictive regressions exploit cross-

sectional differences in stock market returns, time fixed effects capture the impact of

any aggregate shocks or changes in discount rates. Similarly, we use abnormal stock

market returns in most of our analysis such that the coefficients on local and industry

returns do not capture heterogeneity in the regional effects of aggregate stock market

prices that is due to regional heterogeneity in stock market wealth (Chodorow-Reich,

Nenov, and Simsek, 2021).

3.2 Measuring returns and employment

Our primary independent variable captures returns to firms with a presence in a par-

ticular geography or industry. We begin by measuring returns over the time period11See, for example, Coval and Moskowitz (1999), Ivković and Weisbenner (2005), Pirinsky and Wang

(2006), Seasholes and Zhu (2010), and Branikas, Hong, and Xu (2020) for evidence on domestic homebias in stocks.

12See, for example, Lustig and Van Nieuwerburgh (2008), Berk and Walden (2013), Eiling (2013),Athreya, Ionescu, and Neelakantan (2018), and Greenwald, Lettau, and Ludvigson (2020).

18

leading up to when the employment data is measured. For analysis at the monthly

frequency, we use the monthly returns reported in CRSP. For quarterly and six-month

frequency analysis, we cumulate the monthly returns to the quarterly or six-month

level. We use both raw and abnormal returns in the analysis, and we compute abnor-

mal returns using the five factor model of Fama and French (2015).

To generate a geography-level or industry-level return, we then weight firms’ cu-

mulative returns according to the importance of that firm to the relevant geography

or industry. To illustrate this process more concretely, consider measuring the geo-

graphic impact on employment of a 29% positive return to the firm Biogen’s stock that

occurred during July 1999. In 1998, Biogen operates plants in two CBSAs: Durham-

Chapel Hill, NC, and Boston-Cambridge-Newton, MA-NH.

To measure the impact of this 29% return on these distinct geographic units,

we first compute the proportion of total employment in each geography that is ac-

counted for by Biogen in the year prior to when the shock occurred. In 1998, Bio-

gen accounts for 0.034% of Durham-Chapel Hill’s employment and 0.014% of Boston-

Cambridge-Newton’s employment. We then weight the return based on these pro-

portions to arrive at our localized measure of stock return exposure. For Durham-

Chapel Hill, the employment exposure-weighted return is 0.034% ∗ 29% = 0.01%, and

for Boston-Cambridge-Newton the employment exposure-weighted return is 29% ∗

0.014% = 0.0042%. Even though Biogen has most of its employment in Boston-Cambridge-

Newton, the shock is more important for Durham-Chapel Hill because Biogen is more

important for Durham-Chapel Hill than for Boston.

We follow this process for each public firm in our sample. As in the example,

we always lag the employment exposure weights one year such that the price shock

19

is allocated based on the previous year’s share of total employment accounted for

by a particular firm. After computing weighted returns for each firm, we sum these

returns over geographic and industry units. The result is a measure that captures the

net impact of publicly-traded stock price changes on an industry and/or geographic

unit.

We call this measure the Exposure-Weighted Stock Return (EWSR) of a given

geography/industry over a given horizon. Mathematically we express this measure at

the year-geography/industry level as:

EWSRm,t =S∑

i=1

ωi,m,y−1Reti,t

where Reti,t is the cumulative log return of firm i during period t (either abnormal re-

turn or raw return), ωi,m,y−1 is the weight of firm i in unit m during the previous year,

and S is the number of publicly-traded firms in year y−1. If a firm has no employment

in unit m in year y− 1, ωi,m,y−1 = 0. Note that although the exposure weights are con-

structed based on y − 1 employment, we subscript EWSR with t because cumulative

returns are measured during a period in the current year.

Our primary dependent variable is total employment growth (i.e., the percent

change in employment) in industry/geographic unit m from period t to t + 1. For the

CBSA-level analysis, we use monthly employment levels from the BLS LAUS dataset,

and for the industry-level analysis, we use monthly employment levels from the BLS

CES dataset. We average the monthly employment over quarters or half-years for the

analysis at those frequencies.

20

We estimate the following regression:

Empm,t+1 = β0 + β1EWSRm,t + βxContm + �m,t(2)

where Empm,t+1 is employment growth from t to t + 1, EWSRm,t is the contempora-

neous exposure-weighted stock return measure for m (computed from t − 1 to t), and

Contm,t is a set of controls that include lags of employment growth and EWSR, as well

as fixed effects. In addition to time period fixed effects, we include unit by calendar

month (or quarter or half-year) fixed effects. We do so because the BLS employment

data we use is not deseasonalized and city or industry employment may have differ-

ent degrees of seasonality. For example, one would expect Miami, Florida, to exhibit

greater seasonality in its employment given its dependence on tourism in the winter

months than a city like Syracuse, NY.

3.3 Main Results

Table 5 summarizes the data used in the employment prediction analysis. Monthly

data is summarized at the CBSA- or industry-month level, and quarterly and half-

year data are summarized at the unit-quarter or unit-half-year level, respectively.

Tables 6A and 6B report the results of estimating equation 2. We only include CBSA-

periods in excess of 10,000 total employees in our regressions, and the data are win-

sorized at the 1% level in both tails.

Columns 1-4 are at the monthly frequency, columns 5-7 are at the quarterly fre-

quency, and columns 8 and 9 are at a six-month frequency. The dependent variable

is one period ahead employment growth whereas EWSR is measured contemporane-

21

ously. Variables prefixed with “L” are lagged relative to EWSR. Time and calendar

period by unit fixed effects are included and standard errors are robust.13

Focusing first on Table 6A, which shows the results at the CBSA level, we find

that EWSR is not significantly related to employment at a monthly frequency. This

is unsurprising given that labor adjustment costs likely prevent significant changes

in employment over a single month. However, EWSR has a positive and significant

association with employment growth at the quarterly frequency and is weakly signif-

icant in one six-month specification (column 8). This is consistent with employment

adjusting over a period of several months. Focusing on the quarterly results, columns

5-7 indicate that an increase in exposure-weighted cumulative returns during quarter

q are positively associated with employment growth during quarter q + 1.

As an example of how to interpret the coefficients, take the specification in column

7. The coefficient on EWSR (Q) is such that a one standard deviation increase in the

quarterly EWSR is associated with an increase in employment growth the following

quarter of 0.03%. Because the average quarterly employment growth within a CBSA

during the sample period is 0.1% (see CBSA emp gr (Q) in Table 5), this increase is

roughly 30% relative to the mean.

Moving to the industry level, Table 6B report the results for employment growth

and EWSR at the 4-digit NAICS level. The results mirror those at the CBSA level.

EWSR is insignificant at the monthly level, but positive and significant at both the

quarterly and six-month horizons. Again focusing on the quarterly results, the co-

efficient on EWSR (Q) in the column 7 specification indicates that a one standard

deviation increase in 4-digit NAICS EWSR is associated with a 0.05% increase in13Although we use robust standard errors in our analysis, the significance of our coefficients is un-

changed if we instead two-way cluster by time-unit (CBSA-by-time or 4-digit NAICS-by-time).

22

quarterly employment growth. This is nearly 50% of the mean industry employment

growth of 0.1% (Ind emp gr (Q) in Table 5).

3.4 Sensitivity Analysis

To understand whether our results are sensitive to changes in how we define both

the dependent and main independent variables, we estimate a number of alternative

specifications using the quarterly frequency data and report the results in Table 7.

First, we weight our regressions by either CBSA or industry size (measured as the

total number of employees in that CBSA or industry relative to total employment

overall). The results, reported in columns 1 and 4, suggest that while the city-level

results are sensitive to weighting, the industry results are not. Second, we use raw

returns instead of Fama-French 5 factor abnormal returns. These results are reported

in columns 2 and 5 and indicate the main results hold when using raw returns. Fi-

nally, we estimate equation 2 without winsorizing the dependent variable (employ-

ment growth). The results, reported in columns 3 and 6, indicate our main results are

not sensitive to winsorization.

4 Conclusions

We show that the subset of firms in the US that are publicly traded have similar

geographic employment patterns to all US firms. However, publicly-traded firms in-

creasingly overrepresent certain industries such as retail and manufacturing relative

to these sectors’ share of total US employment. Additionally, public firms have growth

dynamics that differ significantly from their private counterparts. Despite this, stock

23

market indices of geographically- and industrially-weighted firms are excellent pre-

dictors of employment growth in a city or industry.

Our results show that good news in the stock market translates into at least a

short-term increase in labor demand. As such, the findings indicate that the stock

market remains relevant for the majority of US households that do not own a signif-

icant amount of stock. The results also suggest that there is a significant correlation

between shocks to public firms and those affecting private firms both among firms

within the same industry and among firms operating within the same city.

24

References

ALFARO, L., A. CHARI, A. GREENLAND, AND P. K. SCHOTT (2020): “Aggregate and

Firm-Level Stock Returns, in Real Time,” Tech. rep., Harvard University.

ANDONOV, A. AND J. D. RAUH (2020): “The Return Expectations of Institutional

Pension Investors,” Tech. rep., University of Amsterdam.

ATHREYA, K., F. IONESCU, AND U. NEELAKANTAN (2018): “Stock Market Participa-

tion: The Role of Human Capital,” Tech. rep., Federal Reserve Bank of Richmond

WP 15-07R.

BELO, F., J. LI, X. LIN, AND X. ZHAO (2017): “Labor-Force Heterogeneity and Asset

Prices: The Importance of Skilled Labor,” The Review of Financial Studies, 30,

3669–3709.

BELO, F., X. LIN, AND S. BAZDRESCH (2014): “Labor Hiring, Investment, and Stock

Return Predictability in the Cross Section,” Journal of Political Economy, 122, 129–

177.

BERK, J. B. AND J. WALDEN (2013): “Limited Capital Market Participation and Hu-

man Capital Risk,” The Review of Asset Pricing Studies, 3, 1–37.

BERNILE, G., A. KUMAR, AND J. SULAEMAN (2015): “Home away from Home: Ge-

ography of Information and Local Investors,” The Review of Financial Studies, 28,

2009–2049.

BRANIKAS, I., H. HONG, AND J. XU (2020): “Location choice, portfolio choice,”

Journal of Financial Economics, 138, 74 – 94.

25

BROWN, G., W. HU, AND B.-K. KUHN (2019): “Why Defined Contribution Plans Need

Private Investments,” Tech. rep., University of North Carolina, Chapel Hill.

CHODOROW-REICH, G., P. T. NENOV, AND A. SIMSEK (2021): “Stock Market Wealth

and the Real Economy: A Local Labor Market Approach,” American Economic

Review, 111, 1613–57.

COHEN, L. AND D. LOU (2012): “Complicated firms,” Journal of Financial Economics,

104, 383 – 400, special Issue on Investor Sentiment.

COVAL, J. AND T. MOSKOWITZ (1999): “Home bias at home: Local equity preferences

in domestic portfolios,” Journal of Finance, 54, 2045–2073.

CUSTÓDIO, C. (2014): “Mergers and Acquisitions Accounting and the Diversification

Discount,” The Journal of Finance, 69, 219–240.

DOIDGE, C., G. A. KAROLYI, AND R. M. STULZ (2017): “The U.S. listing gap,” Journal

of Financial Economics, 123, 464 – 487.

DOUGAL, C., C. A. PARSONS, AND S. TITMAN (2015): “Urban Vibrancy and Corpo-

rate Growth,” The Journal of Finance, 70, 163–210.

EILING, E. (2013): “Industry-Specific Human Capital, Idiosyncratic Risk, and the

Cross-Section of Expected Stock Returns,” The Journal of Finance, 68, 43–84.

FAMA, E. AND K. FRENCH (2015): “A five-factor asset pricing model,” Journal of

Financial Economics, 116, 67–88.

FELDMAN, N., L. KAWANO, E. PATEL, N. RAO, M. STEVENS, AND J. EDGERTON

26

(2021): “Investment differences between public and private firms: Evidence from

U.S. tax returns,” Journal of Public Economics, 196, 104370.

GARCÍA, D. AND O. NORLI (2012): “Geographic dispersion and stock returns,”

Journal of Financial Economics, 106, 547 – 565.

GHALY, M., V. DANG, AND K. STATHOPOULOS (2017): “Cash Holdings and Labor

Heterogeneity: The Role of Skilled Labor,” The Review of Financial Studies, 30,

3636–3668.

GIGLIO, S., M. MAGGIORI, J. STROEBEL, AND S. UTKUS (2021): “Five Facts about

Beliefs and Portfolios,” American Economic Review, 111, 1481–1522.

GORMSEN, N. J. AND R. S. J. KOIJEN (2020): “Coronavirus: Impact on Stock Prices

and Growth Expectations,” The Review of Asset Pricing Studies, 10, 574–597.

GREENWALD, D. L., M. LETTAU, AND S. C. LUDVIGSON (2020): “How the Wealth

Was Won: Factor Shares as Market Fundamentals,” Tech. rep., MIT.

IVKOVIĆ, Z. AND S. WEISBENNER (2005): “Local Does as Local Is: Information Con-

tent of the Geography of Individual Investors’ Common Stock Investments,” The

Journal of Finance, 60, 267–306.

KUHN, M., M. SCHULARICK, AND U. I. STEINS (2020): “Income and Wealth Inequal-

ity in America, 1949–2016,” Journal of Political Economy, 128, 3469–3519.

KUNKLE, G. (2018): “Business Employment Data: YTS versus CES and CPS,”

Tech. rep., Business Dynamics Research Consortium, available at https :

27

//wisconsinbdrc.org/wp − content/uploads/sites/6/2019/10/Y TSreview.pdf . Last ac-

cessed July 27, 2020.

LUSTIG, H. AND S. VAN NIEUWERBURGH (2008): “The Returns on Human Capital:

Good News on Wall Street is Bad News on Main Street,” The Review of Financial

Studies, 21, 2097–2137.

LUSTIG, H., S. VAN NIEUWERBURGH, AND A. VERDELHAN (2013): “The Wealth-

Consumption Ratio,” The Review of Asset Pricing Studies, 3, 38–94.

MIAN, A. AND A. SUFI (2014): “What Explains the 2007–2009 Drop in Employment?”

Econometrica, 82, 2197–2223.

PALACIOS, M. (2015): “Human Capital as an Asset Class Implications from a General

Equilibrium Model,” The Review of Financial Studies, 28, 978–1023.

PIRINSKY, C. AND Q. WANG (2006): “Does Corporate Headquarters Location Matter

for Stock Returns?” The Journal of Finance, 61, 1991–2015.

POTERBA, J. M. (2000): “Stock Market Wealth and Consumption,” Journal of

Economic Perspectives, 14, 99–118.

SCHLINGEMANN, F. AND R. STULZ (2020): “Has the stock market become less repre-

sentative of the economy?” Tech. rep., Ohio State University.

SEASHOLES, M. S. AND N. ZHU (2010): “Individual Investors and Local Bias,” The

Journal of Finance, 65, 1987–2010.

SMITH, M., O. ZIDAR, AND E. ZWICK (2020): “Top Wealth in America: New Esti-

mates and Implications for Taxing the Rich,” Tech. rep., US Treasury Department.

28

TATE, G. AND L. YANG (2015): “The Bright Side of Corporate Diversification: Evi-

dence from Internal Labor Markets,” The Review of Financial Studies, 28, 2203–

2249.

VILLALONGA, B. (2004): “Diversification Discount or Premium? New Evidence from

the Business Information Tracking Series,” The Journal of Finance, 59, 479–506.

WHITED, T. M. (2001): “Is It Inefficient Investment that Causes the Diversification

Discount?” The Journal of Finance, 56, 1667–1691.

YAN, D. (2020): “Do Private Firms (Mis)-Learn from the Stock Market,” Tech. rep.,

Stockholm School of Economics.

29

Figures and Tables

30

(a) Share of employment of publicly traded firms in headquarters loca-tion

(b) Number of locations and industries in which firms have employment

Figure 1: Geographic and industry dispersion of employment in publicly-traded firms.

Notes: (1) Both panels use the YTS-Compustat merged data. (2) The top panel plotsthe proportion of employees of publicly-traded firms in the HQ state or CBSA for theaverage firm in each year. (3) The bottom panel plots, for the median firm withineach size bucket, the number of states, CBSAs, or 2-digit NAICS industries in whichthere is at least one employee. The size quintiles are based on total assets inCompustat.

31

(a) 1997

(b) 2007

(c) 2017

Figure 2: Employment shares by state based on establishment location

Notes: (1) Compustat employment shares are based on location (state) of firmestablishments using the YTS-Compustat merged database. bls share is plotted onthe x-axis and compustat share is plotted on the y-axis. (2) All variables defined inTable 1.

32

(a) 1997

(b) 2007

(c) 2017

Figure 3: Employment shares by 2-digit NAICS based on establishment industry.

Notes: (1) Compustat employment shares are based on the 2-digit NAICS industry offirm establishments using the YTS-Compustat merged database. bls share is plottedon the x-axis and compustat share is plotted on the y-axis. (2) All variables defined inTable 1.

33

(a) 1990 (b) 1997

(c) 2007 (d) 2017

Figure 4: Employment shares by state based on firm HQ location

Notes: (1) Compustat employment shares are based on location (state) of firmheadquarters using the full Compustat database. bls share is plotted on the x-axisand compustat share is plotted on the y-axis. (2) All variables defined in Table 1.

34

(a) 1990 (b) 1997

(c) 2007 (d) 2017

Figure 5: Employment shares by 2-Digit NAICS based on firm HQ industry.

Notes: (1) Compustat employment shares are based on the 2-digit NAICS industry ofheadquarters using the full Compustat database. bls share is plotted on the x-axisand compustat share is plotted on the y-axis. (2) All variables defined in Table 1.

35

Figure 6: Explanatory power of public employment for total employment over time

Notes: (1) The figure plots the R2 from a weighted cross-sectional regression of thetotal employment share in a particular NAICS code or geography (bls share) on thepublic firm employment share (compustat share). The weights are based on the totalemployment share for the given industry or geographic unit. (2) Larger valuesindicate that employment in publicly-traded firms is more representative of allemployment. (3) “Estab” indicates that employment is allocated based on the actualestablishment location or industry while “HQ” indicates that all employment in thefirm is allocated to the location or industry of the headquarters.

36

Table 1: Variable definitions

Variable Descriptioncompustat share Employment of Compustat firms within an industry or geographic region as a percent of total Compustat employmentbls share Employment of all firms within an industry or geographic region as a percent of total BLS employmentCBSA emp gr (M) Monthly employment growth at CBSA levelCBSA emp gr (Q) Quarterly employment growth at CBSA levelCBSA emp gr (H) Six-month employment growth at CBSA levelCBSA EWSR (M) Monthly EWSR at CBSA levelCBSA EWSR (Q) Quarterly EWSR at CBSA levelCBSA EWSR (H) Six-month EWSR at CBSA levelInd emp gr (M) Monthly employment growth at NAICS4 levelInd emp gr (Q) Quarterly employment growth at NAICS4 levelInd emp gr (H) Six-month employment growth at NAICS4 levelInd EWSR (M) Monthly EWSR at NAICS4 levelInd EWSR (Q) Quarterly EWSR at NAICS4 levelInd EWSR (H) Six-month EWSR at NAICS4 level

37

Table 2: Employment shares: full sample

Variable N Mean Median SD Min MaxNAICS 2-digit - establishmentcompustat share 378 0.056 0.021 0.078 0 0.361bls share 378 0.056 0.047 0.041 0.004 0.159NAICS 2-digit - headquartercompustat share 504 0.056 0.025 0.078 0.001 0.407bls share 504 0.056 0.049 0.045 0.004 0.205State level - establishmentcompustat share 1071 0.020 0.014 0.020 0.001 0.104bls share 1071 0.020 0.013 0.021 0.002 0.119State level - headquartercompustat share 1428 0.020 0.008 0.025 0.000 0.114bls share 1428 0.020 0.013 0.021 0.001 0.125

Notes: (1) Employment shares for the YTS-Compustat merge, BLS, and full Compustat datasets. For

the HQ-level results, the time period is 1990-2017 and the compustat share is based on the full

Compustat dataset. For the establishment-level results, the time period is 1997-2017 and the

compustat share is based on the YTS-Compustat merged dataset. (2) All variables defined in Table 1.

38

Table 3A: Likelihood of being public

(1) (2) (3) (4)emp2 0.00036*** 0.00017*** 0.00017*** 0.00012***

(6.1e-06) (3.7e-06) (3.7e-06) (2.5e-06)emp3 0.0021*** 0.00095*** 0.00094*** 0.00076***

(0.000025) (0.000014) (0.000014) (0.000011)emp4 0.011*** 0.0048*** 0.0048*** 0.0037***

(0.000074) (0.000038) (0.000038) (0.000033)emp5 0.12*** 0.061*** 0.061*** 0.058***

(0.00058) (0.00033) (0.00033) (0.00033)age2 0.000038*** 0.000042*** 0.000047***

(8.3e-06) (8.2e-06) (6.0e-06)age3 0.000077*** 0.000081*** 0.000081***

(9.7e-06) (9.7e-06) (7.3e-06)age4 0.000094*** 0.000097*** 0.000087***

(7.6e-06) (7.6e-06) (5.3e-06)age5 0.00076*** 0.00080*** 0.00061***

(0.000030) (0.000031) (0.000024)Observations 227,175,079 227,175,079 227,175,079 227,175,079Pseudo-R2 0.35 0.38 0.38 0.43Time FE N N Y YInd FE N N N YSE Clust by Firm HQ Y Y Y Y

Notes: 1) Results of estimating probit regressions of an indicator for whether a firm-year is public on size, age, year, andindustry controls. Marginal effects are reported. Data is from Compustat and YTS from 1997-2017. 2) emp2 is equal to 1 for10-49 employees, emp3 is equal to 1 for 50-99 employees, emp4 is equal to 1 for 100-499 employees, and emp5 is equal to 1 for500+ employees. age2 is equal to 1 for 2-5 years, age3 is equal to 1 for 6-10 years, age4 is equal to 1 for 11-25 years, and age5is equal to 1 for 26+ years. All other variables defined in Table 1. Variables are winsorized at the 1% level in each tail. 3).∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, and ∗p < 0.1. Robust standard errors reported.

39

Table 3B: Industries and the likelihood of being public

2D NAICS Coeff 2D NAICS CoeffMining 0.0063*** Real Est 0.00027***

(0.00045) (0.000045)Utilities 0.00034*** Tech 0.00031***

(0.000062) (0.000045)Construction 5.1e-06 Mgmt 0.0088***

(0.000012) (0.00057)Manufacturing 0.00021*** Admin 0.00021***

(0.000039) (0.000039)Whole Trade 0.00020*** Education -0.000020***

(0.000037) (6.2e-06)Retail Trade 0.000075*** Healthcare 1.1e-06

(0.000021) (0.000012)Transp 0.000097*** Entertainment 0.000063***

(0.000027) (0.000023)Info 0.00043*** Hospitality 0.000034*

(0.000062) (0.000017)Finance 0.00052*** Other 0.000082***

(0.000069) (0.000022)

Notes: 1) 2-digit NAICS industry indicator variable coefficients from column 4 of Table 3A. Marginal effects are reported.Data is from Compustat and YTS from 1997-2017. 2) All variables defined in Table 1. Variables are winsorized at the 1%level in each tail. 3). ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, and ∗p < 0.1. Robust standard errors reported.

40

Table 4A: Employment growth in public firms by industry

(1) (2) (3) (4)L.Public 0.0045*** 0.023*** X X

(0.00090) (0.00093)L.emp2 -0.026*** -0.026***

(0.000037) (0.000037)L.emp3 -0.025*** -0.025***

(0.000079) (0.000079)L.emp4 -0.034*** -0.034***

(0.00012) (0.00012)L.emp5 -0.044*** -0.045***

(0.00040) (0.00040)L.age2 -0.014 -0.014

(0.014) (0.014)L.age3 -0.014 -0.014

(0.014) (0.014)L.age4 -0.0054 -0.0054

(0.014) (0.014)L.age5 -0.0044 -0.0045

(0.014) (0.014)Observations 61,574,795 61,574,795 61,574,795 61,574,795R2 0.005 0.015 0.005 0.015Year FEs Yes Yes Yes YesIndustry FEs Yes Yes Yes YesIndustry×public indicators No No Yes YesSE Clust by Firm Yes Yes Yes Yes

Notes: 1) Results of estimating linear regressions of annual employment growth on controls and fixed effects. Data is fromCompustat and YTS from 1997-2017. Only firm-years with five or more employees are included. 2) Variables prefixed by “L.”are lagged one year. public is equal to 1 if the firm-year is public and 0 otherwise, emp2 is equal to 1 for 10-49 employees,emp3 is equal to 1 for 50-99 employees, emp4 is equal to 1 for 100-499 employees, and emp5 is equal to 1 for 500+ employees.age2 is equal to 1 for 2-5 years, age3 is equal to 1 for 6-10 years, age4 is equal to 1 for 11-25 years, and age5 is equal to 1 for26+ years. All other variables defined in Table 1. Variables are winsorized at the 1% level in each tail. 3). ∗ ∗ ∗p < 0.01,∗ ∗ p < 0.05, and ∗p < 0.1. Standard errors clustered by firm.

41

Table 4B: Employment growth in public firms by industry

2D NAICS Coeff 2D NAICS CoeffAg 0.042* Real Est 0.035***

(0.026) (0.0047)Mining 0.0099 Tech 0.022***

(0.0066) (0.0022)Utilities 0.0068 Mgmt 0.041***

(0.012) (0.0019)Construction -0.011 Admin 0.035***

(0.011) (0.0037)Manufacturing -0.0071*** Education 0.028***

(0.0026) (0.0092)Whole Trade 0.0086*** Healthcare 0.043***

(0.0033) (0.0047)Retail Trade 0.023*** Entertainment 0.020***

(0.0036) (0.0071)Transp 0.024*** Hospitality 0.056***

(0.0062) (0.0055)Info 0.012** Other 0.044***

(0.0049) (0.0028)Finance 0.0089**

(0.0036)

Notes: 1) Coefficients on the 2-digit NAICS industry × public variables from column 4 of Table 4A. Data is from Compustatand YTS from 1997-2017. Only firm-years with five or more employees are included. 2) All variables defined in Table 1.Variables are winsorized at the 1% level in each tail. 3). ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, and ∗p < 0.1. Standard errors clustered byfirm.

42

Table 5: Summary statistics for employment prediction

Variable N mean p50 sd min maxCBSA emp gr (M) 202826 0.0005 0.0009 0.0143 -0.0483 0.0495CBSA emp gr (Q) 67042 0.001 0.000 0.026 -0.076 0.098CBSA emp gr (H) 33096 0.002 0.001 0.037 -0.114 0.137CBSA EWSR (M) 210192 -0.180 -0.096 0.777 -2.887 1.958CBSA EWSR (Q) 70064 -0.546 -0.308 1.505 -5.820 3.276CBSA EWSR (H) 35032 -1.079 -0.729 2.117 -8.426 3.828raw CBSA EWSR (M) 210192 0.122 0.155 1.769 -6.417 5.619raw CBSA EWSR (Q) 70064 0.372 0.359 3.295 -11.636 10.400raw CBSA EWSR (H) 35032 0.721 0.956 4.489 -16.840 13.461Ind emp gr (M) 54729 0.000 0.001 0.021 -0.087 0.091Ind emp gr (Q) 18099 0.001 0.001 0.042 -0.163 0.179Ind emp gr (H) 8941 0.002 0.003 0.063 -0.210 0.286Ind EWSR (M) 73301 -0.235 -0.004 2.800 -15.103 11.349Ind EWSR (Q) 24435 -0.727 -0.035 5.253 -29.808 19.245Ind EWSR (H) 12219 -1.443 -0.104 7.470 -43.536 23.945raw Ind EWSR (M) 73301 0.106 0.003 3.880 -17.953 17.201raw Ind EWSR (Q) 24435 0.345 0.031 7.256 -32.720 32.890raw Ind EWSR (H) 12219 0.648 0.067 9.905 -44.668 46.097

Notes: (1) Summary statistics for variables used in employment prediction model. Data come from

Compustat, YTS, and BLS. Raw indicates EWSR is based on raw returns, as opposed to Fama-French

5 factor model abnormal returns. All EWSRs are computed using log returns. (2) All variables defined

in Table 1.

43

Table 6A: City (CBSA) employment growth and stock returns

One-period ahead employment growthMonthly Quarterly Six-month

(1) (2) (3) (4) (5) (6) (7) (8) (9)EWSR (M) -3.9e-06 5.0e-06 5.7e-06 0.000010

(0.000035) (0.000035) (0.000035) (0.000035)EWSR (Q) 0.00020*** 0.00020*** 0.00020***

(0.000064) (0.000064) (0.000064)EWSR (H) 0.00018* 0.00016

(0.00010) (0.00010)emp gr (M) -0.084*** -0.086*** -0.085***

(0.0032) (0.0032) (0.0032)L1.emp gr (M) -0.029*** -0.029***

(0.0029) (0.0029)L1.EWSR (M) -0.000040

(0.000034)L2.EWSR (M) 0.00014***

(0.000036)L3.EWSR (M) -0.000071**

(0.000034)emp gr (Q) -0.100*** -0.100***

(0.0059) (0.0058)L1.EWSR (Q) 0.000047

(0.000060)emp gr (H) -0.11***

(0.0080)Observations 202,826 201,910 200,994 200,078 67,042 66,126 66,126 33,096 32,180R-squared 0.631 0.634 0.634 0.635 0.649 0.651 0.651 0.652 0.658Returns Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal AbnormalTime FE Y Y Y Y Y Y Y Y YCBSA x Cal Mo FE Y Y Y Y N N N N NCBSA x Cal Q FE N N N N Y Y Y N NCBSA x Cal Half FE N N N N N N N Y Y

Notes: 1) Results of estimating linear regressions of employment growth on EWSR and controls. The dependent variable ismeasured over the period following when EWSR is measured. Variables with a “L.” prefix are lagged relative to when EWSRis measured. All EWSRs are computed using log returns. An observation is a CBSA-period, and we limit the data toCBSA-periods with greater than 10,000 total employees. Data is from Compustat and YTS from 1997-2017. 2) All variablesdefined in Table 1. Variables are winsorized at the 1% level in each tail. 3). ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, and ∗p < 0.1. Robuststandard errors reported.

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Table 6B: Industry (NAICS4) employment growth and stock returns

One-period ahead employment growthMonthly Quarterly Six-month

(1) (2) (3) (4) (5) (6) (7) (8) (9)EWSR (M) 0.000022 0.000022 0.000022 0.000020

(0.000019) (0.000019) (0.000019) (0.000019)EWSR (Q) 0.000091*** 0.000088*** 0.000087***

(0.000031) (0.000031) (0.000031)EWSR (H) 0.00016*** 0.00016***

(0.000045) (0.000044)emp gr (M) 0.020** 0.019** 0.018**

(0.0085) (0.0085) (0.0085)L1.emp gr (M) 0.020** 0.019**

(0.0078) (0.0078)L1.EWSR (M) 0.000057***

(0.000018)L2.EWSR (M) 0.000019

(0.000018)L3.EWSR (M) 0.000032*

(0.000018)emp gr (Q) 0.053*** 0.052***

(0.013) (0.013)L1.EWSR (Q) 0.000047

(0.000031)emp gr (H) -0.028

(0.020)Observations 54,727 54,496 54,265 54,034 18,099 17,868 17,868 8,941 8,710R-squared 0.782 0.783 0.783 0.783 0.834 0.834 0.834 0.827 0.828Returns Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal Abnormal AbnormalTime FE Y Y Y Y Y Y Y Y YNAICS4 x Cal Mo FE Y Y Y Y N N N N NNAICS4 x Cal Q FE N N N N Y Y Y N NNAICS4 x Cal Half FE N N N N N N N Y Y

Notes: 1) Results of estimating linear regressions of employment growth on EWSR and controls. The dependent variable ismeasured over the period following when EWSR is measured. Variables with a “L.” prefix are lagged relative to when EWSRis measured. All EWSRs are computed using log returns. An observation is a 4-digit NAICS industry-period. Data is fromCompustat and YTS from 1997-2017. 2) All variables defined in Table 1. Variables are winsorized at the 1% level in each tail.3). ∗ ∗ ∗p < 0.01, ∗ ∗ p < 0.05, and ∗p < 0.1. Robust standard errors reported.

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Table 7: Sensitivity analysis of employment growth regressions

One-quarter ahead CBSA employment growth One-quarter ahead NAICS4 employment growth(1) (2) (3) (4) (5) (6)

EWSR (Q) 7.0e-06 0.00023*** 0.000077*** 0.000096***(0.000058) (0.000075) (0.000027) (0.000032)

EWSR (Q) 0.000079*** 0.000070***(0.000030) (0.000025)

empgr (Q) -0.019*** -0.100*** -0.12*** 0.16*** 0.053*** -0.013(0.0072) (0.0059) (0.0080) (0.021) (0.013) (0.025)

Observations 66,126 66,126 66,126 17,868 17,868 17,868R-squared 0.711 0.651 0.697 0.896 0.834 0.900Time FE Y Y Y Y Y YReturns Abnormal Raw Abnormal Abnormal Raw AbnormalCBSA x Cal Q FE Y Y Y N N NWeighted by City Size Y N Y N N NNAICS4 x Cal Q FE N N N Y Y YWeighted by NAICS Size N N N Y N YLHS Winsorized Y Y N Y Y N

Notes: 1) Results of estimating linear regressions of employment growth on EWSR and controls. The dependent variable ismeasured over the period following when EWSR is measured. Variables with a “L.” prefix are lagged relative to when EWSRis measured. All EWSRs are computed using log returns. An observation in columns 1-3 is a CBSA-period, and anobservation in columns 4-6 is a 4-digit NAICS industry-period. Data is from Compustat and YTS from 1997-2017. 2) Allvariables defined in Table 1. Right-hand-side variables are winsorized at the 1% level in each tail. 3). ∗ ∗ ∗p < 0.01,∗ ∗ p < 0.05, and ∗p < 0.1. Robust standard errors reported.

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A Appendix

47

(a) Nontradable and construction

(b) Tradable and other

Figure A.1: Share of employment of publicly traded firms in headquarters location

Notes: Both panels use the YTS-Compustat merged data and plot the proportion ofemployees of publicly-traded firms in the HQ state or CBSA for the average firm ineach year. The top panel only includes nontradable and construction industries, andthe bottom panel only includes tradable and other industries. Industry definitionsare based on Mian and Sufi (2014).

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Table A.1: State-level differences between public (establishment-level) and total mar-ket share

State 1997 2007 2017Compustat BLS Compustat BLS Compustat BLS

AK 0.001 0.002 0.002 0.002 0.002 0.002AL 0.015 0.015 0.015 0.014 0.015 0.013AR 0.013 0.008 0.012 0.009 0.011 0.008AZ 0.017 0.016 0.022 0.020 0.020 0.019CA 0.103 0.114 0.101 0.116 0.104 0.119CO 0.019 0.016 0.020 0.017 0.019 0.018CT 0.019 0.013 0.014 0.012 0.013 0.011DC 0.002 0.003 0.003 0.004 0.002 0.004DE 0.004 0.003 0.003 0.003 0.003 0.003FL 0.051 0.053 0.063 0.059 0.065 0.060GA 0.034 0.029 0.037 0.030 0.036 0.030HI 0.003 0.004 0.003 0.004 0.004 0.004IA 0.010 0.011 0.011 0.011 0.011 0.011ID 0.003 0.004 0.004 0.005 0.004 0.005IL 0.047 0.048 0.048 0.044 0.044 0.042IN 0.029 0.024 0.025 0.022 0.024 0.021KS 0.012 0.010 0.011 0.010 0.011 0.009KY 0.015 0.014 0.015 0.013 0.015 0.013LA 0.015 0.014 0.014 0.013 0.014 0.013MA 0.022 0.026 0.020 0.024 0.021 0.025MD 0.017 0.018 0.016 0.018 0.016 0.018ME 0.004 0.005 0.004 0.004 0.003 0.004MI 0.034 0.036 0.031 0.031 0.027 0.030

MN 0.022 0.020 0.021 0.020 0.020 0.020MO 0.022 0.022 0.021 0.020 0.022 0.019MS 0.011 0.008 0.010 0.008 0.009 0.008MT 0.002 0.003 0.002 0.003 0.003 0.003NC 0.028 0.030 0.029 0.030 0.031 0.030ND 0.002 0.002 0.002 0.002 0.002 0.003NE 0.006 0.007 0.008 0.007 0.007 0.007NH 0.004 0.004 0.004 0.004 0.005 0.004NJ 0.024 0.030 0.026 0.029 0.026 0.028

NM 0.005 0.005 0.005 0.006 0.006 0.005NV 0.009 0.007 0.017 0.010 0.013 0.009NY 0.053 0.065 0.044 0.062 0.046 0.064OH 0.058 0.045 0.048 0.040 0.042 0.038OK 0.011 0.011 0.011 0.011 0.012 0.011OR 0.009 0.013 0.011 0.013 0.011 0.013PA 0.038 0.045 0.038 0.043 0.038 0.041RI 0.003 0.003 0.003 0.003 0.003 0.003SC 0.014 0.013 0.014 0.013 0.015 0.013SD 0.002 0.003 0.002 0.003 0.002 0.003TN 0.026 0.020 0.024 0.020 0.027 0.021TX 0.085 0.072 0.084 0.078 0.088 0.085UT 0.007 0.008 0.008 0.009 0.009 0.010VA 0.027 0.026 0.028 0.027 0.030 0.026VT 0.002 0.002 0.001 0.002 0.002 0.002WA 0.017 0.021 0.018 0.022 0.022 0.023WI 0.019 0.022 0.019 0.021 0.018 0.020

WV 0.005 0.005 0.005 0.005 0.005 0.005WY 0.001 0.002 0.002 0.002 0.002 0.002

Notes: 1) Data underlying Figure 2. 2) All variables are defined in Table 1.

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Table A.2: 2-digit NAICS-level differences between public (establishment-level) andtotal market share

NAICS 2 1997 2007 2017Compustat BLS Compustat BLS Compustat BLS

11 0.000 0.011 0.001 0.009 0.001 0.00921 0.014 0.005 0.011 0.005 0.007 0.00522 0.021 0.008 0.014 0.006 0.013 0.00623 0.006 0.052 0.008 0.062 0.006 0.052

31-33 0.361 0.156 0.237 0.111 0.184 0.09342 0.026 0.048 0.037 0.048 0.036 0.044

44-45 0.204 0.129 0.280 0.124 0.274 0.11948-49 0.027 0.044 0.021 0.042 0.021 0.044

51 0.060 0.029 0.045 0.025 0.049 0.02252 0.092 0.047 0.101 0.048 0.103 0.04453 0.036 0.017 0.023 0.017 0.044 0.01654 0.017 0.052 0.027 0.061 0.016 0.06856 0.014 0.061 0.016 0.067 0.015 0.06961 0.002 0.085 0.003 0.094 0.001 0.09462 0.022 0.120 0.024 0.135 0.043 0.15971 0.005 0.016 0.008 0.018 0.007 0.01972 0.090 0.084 0.141 0.091 0.175 0.10281 0.003 0.035 0.005 0.036 0.004 0.033

Notes: 1) Data underlying Figure 3. 2) All variables are defined in Table 1.

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IntroductionHow Representative are Publicly Traded Firms?DataComparing total employment to public firm employmentWhy does the industrial composition of public firms differ from that of all firms?Are public firms representative of all firms within an industry?

Predicting Employment with Stock ReturnsHow might stock returns predict employment?Measuring returns and employmentMain ResultsSensitivity Analysis

ConclusionsAppendix