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WORKING PAPER · NO. 2019-64

Tax-Related Human Capital: Evidence from Employee MovementsJohn Barrios and John GallemoreAPRIL 2019

Tax-Related Human Capital: Evidence from Employee Movements

John Barrios

University of Chicago john.barrios@chicagobooth.edu

John Gallemore University of Chicago

john.gallemore@chicagobooth.edu

April 2019

The authors thank Kathleen Andries, Mary Billings, Jennifer Blouin, Jenny Brown, Ted Christensen,

Scott Dyreng, Merle Erickson, Michelle Hanlon, Martin Jacob, April Klein, Eva Labro, Ed Maydew,

Stephanie Sikes, Terry Shevlin, Jaron Wilde, Xiang Zheng, and seminar participants at the Frankfurt School of Finance and Management, University of Georgia, MIT, New York University, and the

University of Pennsylvania for helpful comments. The authors gratefully acknowledge the support of the

Accounting Research Center, the Charles E. Merrill Faculty Research Fund, and the Centel Foundation/Robert P. Reuss Fund at the University of Chicago Booth School of Business.

Tax-Related Human Capital: Evidence from Employee Movements

Abstract

Despite the human capital in corporate tax departments representing the average firm’s most direct and

substantial investment in tax compliance and planning, our understanding of it is limited. We shed light on the determinants and consequences of tax-related human capital by examining employee movement

between the tax departments of large U.S. corporations. We first show that deteriorations in firm tax

performance, measured either by increases in cash effective tax rates (ETRs) or tax-related internal control weaknesses or restatements, are associated with an increased likelihood of tax department hiring.

Second, we find that tax departments tend to hire from firms with similar characteristics (such as industry

membership, size, and the extent of foreign operations), suggesting that tax-related human capital is highly specific in nature. Finally, we document that firms exhibit meaningful increases in tax avoidance

when they hire from low ETR firms, and that this association varies predictably with the employee’s prior

role and experience, consistent with employee movement being a mechanism through which tax planning

knowledge spreads across firms. Overall, we provide some of the first evidence on tax-related human capital and its relation to tax planning outcomes. Our findings have implications for recent trends in

firms’ investments in internal tax departments.

Keywords: human capital, employee movements, tax departments, tax avoidance

JEL Classification Codes: H25, H26, J23, J24, J44

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1. Introduction

Tax planning is an important issue for most firms, because tax payments to the government

generally represent one of the most substantial corporate expenses. Consequently, the last decade has seen

widespread public and academic interest in corporate tax planning (Hanlon and Heitzman 2010; Wilde

and Wilson 2018). This has led to numerous studies on the determinants and consequences of corporate

tax planning, but little systematic evidence on the group of individuals directly responsible for developing

and implementing the firm’s tax strategies – the corporate tax department.

Understanding the role of the corporate tax department in tax planning is important for two

reasons. First, these employees traditionally represent the most substantial direct investment in corporate

tax planning (Slemrod and Venkatesh 2002). Second, prior research suggests that a significant amount of

the variation in firms' practices and performance are explained by individuals (Bertrand and Schoar

2003), specifically their human capital (Malmendier and Tate 2005; Custódio and Metzger 2013, 2014).

The complexity and uncertainty in tax planning make it a natural setting to examine how human capital

shapes firm performance. Along these lines, several studies have begun to explore the role of individuals

in corporate tax planning, such as executives (Dyreng, Hanlon, and Maydew 2010) and specific tax

department employees (Armstrong, Blouin, and Larcker 2012; Jiang, Robinson, and Wang 2017; Klassen,

Lisowsky, and Mescall 2016). However, these studies do not directly explore the determinants and

consequences of human capital in tax departments, which is the focus of our study.

We define tax-related human capital as the sum of an individual’s training and work experience

in corporate tax departments. We explore three fundamental questions regarding firms’ investments in

tax-related human capital: what triggers firms to invest in it, where firms source it, and what firms receive

for investing in it. Studying these questions provides a deeper understanding of the role of human capital

in corporate tax planning. For example, Becker (1962) proposes a distinction between two types of human

capital: general human capital, which helps productivity not only at the current firm but also at other

firms, and firm-specific human capital, which raises productivity at the current firm but not elsewhere.

The concept of firm-specific human capital has been generalized to skills that are specific to firms in a

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given industry or sector of the economy (Kletzer 1993; Neal 1995; Kim 1998). The extent to which tax-

related human capital is primarily general or specific will determine whether tax departments can be fully

outsourced, as some have suggested after General Electric moved its tax department to

PriceWaterhouseCoopers in 2017, or continue to be housed within the firm.

To explore these questions, we construct a unique dataset of tax department employees at large,

publicly traded U.S. corporations that have posted their resumes on a major professional networking

website.1 In contrast to past surveys with limited sample sizes and little individual-specific information,

these data allow us to explore the role of tax department personnel for an extensive sample of firms and

individuals. Additionally, the resumes allow us to examine employment histories, providing insight into

the tax-related human capital responsible for creating and implementing a firm’s tax strategies.

Furthermore, we focus on the movement of employees between tax departments for several reasons. First,

hiring is a visible manifestation of the firm’s investment in tax-related human capital, allowing us to

explore the determinants and consequences of this investment. Moreover, by exploring the hired

employees’ backgrounds, we can speak to the specific sources of tax-related human capital demanded by

firms. Second, examining employee movement allows us to explore whether employees are a plausible

mechanism through which tax planning knowledge spreads across firms. Third, focusing on movements

allow us to mitigate various measurement concerns present in aggregate department measures.2

We begin by exploring when firms invest in tax-related human capital. We find that firms are

more likely to hire a tax employee from another S&P 1500 firm, but not from a public accounting firm,

1 Alternatively, one could explore other sources of human capital, such as experience in public accounting firms.

However, we have chosen to focus on prior experience at S&P 1500 firms, as we hypothesize that work

experiences can vary predictably by firm characteristics (e.g., experience at a low ETR firm leads to greater

knowledge of successful tax avoidance strategies). That said, it is not clear whether or how tax-related human

capital varies across public accounting firms. However, in section 4.3 we explore hiring from public accounting

firms around negative compliance events (e.g., internal control weaknesses and restatements) and the

implementation of FIN 48. 2 Because we are unable to observe employees who do not voluntarily list their resume, aggregate measures of

tax department human capital (e.g., total department size or experience) are likely measured with error and/or

bias. That said, it is likely that we do not observe all employee movement between our sample firms. However,

we are less concerned about this source of measurement error for two reasons. First, employees who move

between firms are more likely to use the website, suggesting that our ability to capture employee movement is

greater than our ability to capture aggregate tax department characteristics. Second, to the extent that we fail to

capture employee movement, doing so likely weakens our ability to find associations in our tests.

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when they experience an increase in their cash ETR. This is consistent with deteriorations in tax planning

performance triggering investments in tax-related human capital, and specifically human capital built in

corporate tax departments. Furthermore, this result is stronger when the new employee is coming from a

firm with a low cash ETR (i.e., a firm with better tax planning performance). We also find that the recent

occurrence of a tax-related internal control weakness or restatement is associated with increased hiring

from other corporate tax departments. Overall, these findings suggest that a deterioration in tax

performance (e.g., planning or risk management) triggers subsequent investments in tax-related human

capital. We also explore hiring around the implementation of FIN 48, which changed the way firms

accounted for uncertain tax positions. We find that low ETR firms are more likely to hire employees from

public accounting firms, but not from other S&P 1500 tax departments, relative to high ETR firms after

FIN 48. This finding is consistent with low ETR firms being more affected by FIN 48, and thus

increasing hiring to deal with the additional compliance burden.

Next, we explore where tax departments source their tax-related human capital. We find S&P

1500 firms tend to hire from other S&P 1500 firms of a similar size, within close geographical proximity,

in the same industry, and having similar multinational operations. This finding is consistent with tax-

related human capital having a strong aspect of industry or firm specificity. Interestingly, we do not find

evidence that firms hire from other firms that exhibit similar effective tax rates, consistent with at least

some firms seeking human capital built at a prior firm with different tax planning outcomes.3

Given that prior research suggests that geographic distance is one of the principal constraints to

labor mobility (Schwartz 1973; Clark and Cosgrove 1991; Rogers 1997), we examine situations in which

firms go outside their local labor markets for tax-related human capital. We find that firms’ hiring occurs

at a greater distance when they do not have low cash ETRs (i.e., bottom quartile). This association is

driven by hires made from firms with low cash ETRs, and when the new employee was in a senior role at

3 We follow prior tax research (e.g., Hanlon and Heitzman (2010); Rego and Wilson (2012)) in using “tax

planning” and “tax avoidance” interchangeably to refer to any actions that reduce firms’ taxes.

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their prior firm and has above-median tax experience. These findings are consistent with firms going

outside the local labor market when they have a strong need for tax-related human capital.

Finally, we explore whether firms’ tax avoidance is associated with investments in a certain type

of tax-related human capital, prior experience at a low ETR firm. We find that when a firm hires a tax

department employee from a low ETR firm, it experiences an increase in tax avoidance (i.e., a decline in

the cash ETR) in subsequent years. This finding is consistent with employee movement being a

mechanism through which tax planning knowledge spreads across firms. In additional tests, we find that

this association is larger when hiring individuals who previously held senior roles (e.g., manager or

director) in low ETR firms, and thus more likely played a significant role in the prior firm’s tax planning.

Additionally, this association is also larger when the individual has more overall tax experience. Because

the human capital of senior and/or more experienced employees is more likely to be specific in nature

(Becker 1964; Hashimoto 1981), these findings are consistent with tax-related human capital playing an

important role in tax avoidance through its specificity. Finally, we find that the effect is larger when the

hiring and former firms are in the same industry, consistent with tax-related human capital having a

strong aspect of industry specificity and tax planning having a substantial industry-specific component.

Our study is subject to several important caveats. First, inferences based on our data are

potentially limited by the voluntary nature of the data source and the reporting errors or biases that may

be present in individuals' resumes. That said, the extensive use of these resumes for credible networking

and job search purposes provides some assurance as to their integrity. Moreover, we take several steps to

validate the accuracy of our data. Second, given the matching nature of the employment relationship

between individuals and firms, we do not claim to measure causal effects. By focusing on the firm-level

determinants and consequences of tax department hiring, we take a demand-side perspective on the topic

of tax-related human capital. Furthermore, firms that invest in tax-related human capital built at low ETR

firms may make concurrent investments in other corporate tax functions and/or make changes to the tax

planning strategy. To mitigate these concerns, we attempt to account for certain alternative explanations

in our research design. Additionally, our cross-sectional tests show that the relation between tax-related

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human capital and tax avoidance is larger where one would have expected ex ante. That said, it is unlikely

that we fully account for all possible omitted factors. For that reason, we emphasize that our results are

descriptively consistent with the idea that tax-related human capital is an important component of a firm’s

overall tax planning strategy. We see our study as a starting point for examining the determinants and

consequences of tax-related human capital and look forward to more research on this topic.

Our study contributes to the literature on corporate tax planning in several ways. First, we add to

the collective understanding of a firm's internal investments in corporate tax planning. Prior research on

the role of internal tax investments finds that tax-related expenditures, internal information quality, firm

executives, specific tax department employees (such as former IRS employees), or aggregate tax

department size are associated with observed tax liabilities (Mills, Erickson, and Maydew 1998; Dyreng

et al. 2010; Gallemore and Labro 2015; Jiang et al. 2017; Chen, Cheng, Chow, and Liu 2017; Klassen et

al. 2016). We contribute by providing large-sample descriptive evidence on the determinants and

consequences of tax-related human capital. We believe these facts are relevant given the widespread

interest in corporate tax planning and the fact that tax departments have traditionally constituted the

firm’s most significant investment in tax planning activities. A key difference between our study and prior

work is that we focus on the human capital of tax department employees, rather than specific employees

or aggregate characteristics such as total tax expenditures or tax department size. Furthermore, prior

research generally focuses on consequences, whereas we also model the potential determinants of tax-

related human capital. Finally, our study is the first to explore whether tax-related human capital is

general in nature or whether it exhibits specificity.

Second, we contribute by examining how tax planning knowledge spreads across firms. Prior

research suggests that tax planning spreads through board interlocks and other network ties (Brown 2011;

Brown and Drake 2014). We find that firms experience increases in their tax avoidance when hiring

employees who built their tax-related human capital at low ETR firms. This finding is consistent with the

movement of tax department employees—who are directly responsible for creating and implementing tax

strategies—being a mechanism through which tax planning knowledge spreads across firms.

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The examination of tax-related human capital is especially timely given recent developments in

the outsourcing of internal corporate income tax planning functions to public accounting firms. The extent

to which tax-related human capital exhibits specificity will naturally limit the extent of outsourcing.

Moreover, even if some outsourcing occurs, specificity in tax-related human capital would suggest that

public accounting firms will specialize along certain characteristics (e.g., industry and foreign operations)

as opposed to having a general corporate tax practice. Furthermore, if tax employees are a mechanism

through which knowledge and strategies spread across firms, then this trend could accelerate the pace at

which that information is shared.4

Finally, our study contributes to a growing body of literature in personnel economics that

examines the relation between investments in human capital and organizational performance (Stiles and

Kulvisaechana 2003). A number of studies have attempted to show the link between firm-level human

capital and performance, generally focusing on overall firm outputs such as employee turnover,

productivity, and financial performance (Delaney and Huselid 1996).5 One issue with studying overall

firm performance is that different business units within the organization likely have distinct objectives;

for example, some units may be focused on market share and others on profit. Therefore, research that

takes place at the organizational level, rather than business unit, may suffer from a weak linkage between

human capital and unit performance (Becker and Gerhart 1996). By focusing on tax employees and tax

outcomes, we provide insight into the role of business unit human capital and its closely linked

outcomes.6 Finally, by exploiting employee movement between firms, we establish a novel

4 Unfortunately, we are unable to study the effect of outsourcing on tax planning, for several reasons. First, we

are unaware of databases that track the outsourcing of corporate tax functions. Second, this phenomenon is

relatively new (it was sparked by GE moving its tax group to PriceWaterhouseCoopers in 2017), and our data

collection was primarily conducted before it began. Instead, we view our study as having implications for this

trend, and we look forward to future research exploring tax department outsourcing more directly. 5 Relatedly, Call, Campbell, Dhaliwal, and Moon Jr. (2017) document that the average workforce education in

the MSAs in which a firm operates is associated with greater financial reporting quality. 6 In addition to the closer linkage between employees and the outcomes associated with their efforts, the tax

setting also provides us with the ability to identify employees in this particular business unit, as tax department

employees almost always have the word “tax” in their title. In contrast, it is more difficult to identify employees

in other business units (e.g., marketing, accounting) due to heterogeneity in titles across firms.

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methodological approach that future researchers can use to further explore the relation between human

capital and firm performance.

2. Conceptual Framework

2.1. Corporate tax departments

Most corporate tax departments have several objectives: tax compliance (e.g., preparing and

filing corporate tax returns in the appropriate jurisdictions), income tax planning, supporting other

business units and advising on transactions, accounting for income taxes (e.g., compliance with FIN 48

reporting), and transaction taxes (e.g., sales and property taxes). A survey of corporate tax departments in

large firms conducted by the Tax Executives Institute found that the top three tax department

responsibilities (in terms of time spent) were tax compliance (average of 36 percent of total time spent),

accounting for income taxes (14 percent), and income tax planning (10.5 percent) (Tax Executives

International Inc. 2012). Most of the responsibility for income tax planning is kept in-house, with only

approximately 40 (3) percent of firms partially (entirely) outsourcing these responsibilities (Tax

Executives International Inc. 2012). Tax departments are evaluated based on avoiding compliance

surprises; common measures include audit results (e.g., avoiding penalties), meeting deadlines, and actual

expenditures relative to budget (Tax Executives International Inc. 2012). Moreover, most tax

departments’ tax planning performance is evaluated using effective tax rates and cash taxes paid.

Prior literature on the role of internal tax departments and corporate tax planning has generally

focused on tax departments’ broad, non-employee characteristics or on specific tax department

employees. Mills et al. (1998) study the determinants of corporate tax-related expenditures and document

that greater expenditures are associated with lower tax liabilities, consistent with investments in tax

planning resources paying off in terms of increased tax avoidance. Dunbar and Phillips (2001) find that

smaller and faster growing firms are more likely to outsource tax compliance functions. Gallemore and

Labro (2015) find that the quality of the firm’s international information is associated with the firm’s tax

planning outcomes, consistent with better quality information enabling firms to achieve both lower

effective and less volatile effective tax rates. However, greater tax department resources or better

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information quality will likely only affect tax planning outcomes through tax department employees. For

example, for higher quality information to lead to better tax outcomes, tax department employees must

use the information in tax-related decisions.

There are few studies on the individuals in corporate tax department. Armstrong et al. (2012)

study the compensation of the tax director and document that greater incentive compensation leads to

lower GAAP effective tax rates, but does not appear to be associated with other tax planning outcomes.

Jiang et al. (2017) find that hiring a former IRS employee is associated with a reduction in the firm’s ETR

volatility. Klassen et al. (2016) explore the relation between the signer of the corporate tax return and tax

aggressiveness and find that internal signers are associated with greater unrecognized tax benefit

balances, suggesting that the firm is more likely to take aggressive tax positions when the internal tax

department is in charge of tax compliance. Similarly, few studies examine individuals who are outside,

but related to, the tax department. Dyreng et al. (2010) find that top-level corporate executives (e.g.,

CEOs and CFOs) are associated with tax planning outcomes, suggesting that the “tone at the top” has a

significant impact on the firm’s tax planning liabilities. Koester, Shevlin, and Wangerin (2016) find that

the managerial ability of CEOs is associated with greater tax avoidance. Overall, these studies focus on

either individuals outside the tax department or on particular tax employees (e.g., tax form signees, tax

directors, and former IRS employees).7 In contrast, our study aims to study a broader set of tax

department employees and their prior tax experience.

One exception is Chen et al. (2017). Using a sample of tax department employees from LinkedIn,

they find that larger tax departments are associated with lower effective tax rates. Our study differs from

their study in that we focus on tax employee movements, rather than the tax department’s aggregate

characteristics (e.g., number of employees). Movements allow us to explore whether employees are a

mechanism through which tax planning knowledge spreads across firms. Furthermore, we focus on these

7 Relatedly, Chyz, Ching Leung, Zhen Li, and Meng Rui (2013) find that labor unions are associated with less tax

aggressiveness.

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employees’ tax-related human capital, specifically their prior work experience, which shapes their tax

planning strategy and its implementation.

Finally, prior research documents that parties outside of the firm are involved in corporate tax

compliance and tax planning. For example, corporations often pay for tax services from their auditor, and

auditors that are industry experts have been shown to be able to assist clients with tax planning (McGuire,

Omer, and Wang 2012). Other research suggests that banks and supply chain partners play an important

role in corporate tax planning (Gallemore, Gipper, and Maydew 2018; Cen, Maydew, Zhang, and Zuo

2017). Despite this evidence, it seems likely that the corporate tax department has a first order effect on

the firm’s tax compliance and tax planning outcomes, which is the focus of our study.

2.2. Prior research on human capital

The neoclassical view of the firm implicitly assumes employees are homogeneous inputs and

perfect substitutes for one another. Yet, empirical studies document large variation in firm performance,

even among those that operate in the same narrowly defined market (Haltiwanger, Lane, and Spletzer

1999). Moreover, simple firm characteristics are unable to explain most of this variation, suggesting

employee human capital could play a role. Human capital theory proposes that firms have an economic

incentive to invest in human capital, with the expectation that such an investment will lead to future

profits in excess of wages paid (Becker 1964).

Furthermore, Becker (1962) proposes a distinction between two types of human capital, general

and specific. General human capital helps productivity not only at the current firm but also at other firms,

whereas firm-specific human capital raises the productivity of the worker at the current firm but not

others. The concept of firm-specific human capital has been extended to industries or sectors, where

certain skills may be useful in one industry but not others (Kletzer 1993; Neal 1995; Kim 1998). Most

models on human capital and job search are built on the assumption that skills are specific in nature,

implying that the value of these skills is fully or partially lost when an individual leaves a particular firm

or sector. Under this assumption, human capital accumulated while working in a job is specific to that

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firm and/or industry to the extent that the firm or industry places different values on the combinations of

skills required for the job (Lazear 2009).

Prior empirical research on human capital largely focuses on the impact of general education on

individual productivity and firm outcomes. For example, Black and Lynch (1996) find that a 1-year

increase in the average education of employees is associated with an 8.5 percent increase in

manufacturing industry productivity and a 12.7 percent increase in non-manufacturing industries.

Research on non-C-suite executives (e.g., middle managers) shows that particular personality traits and

positions facilitate innovation (Kanter 1982), communication (Allen 1971), and the selection of projects

to pursue (Burgelman 1991).

While previous studies examine the effect of individuals on overall firm-level outcomes, we focus

on tax employees and firm tax outcomes. We believe that this setting is advantageous for two reasons.

First, the complex and uncertain nature of tax planning suggests that developing corporate tax strategies

requires critical thinking and skill. Therefore, individuals and their human capital should play a critical

role in explaining variation in firm tax outcomes. Second, the tax setting provides methodological

advantages. Specifically, we are able to study a particular type of employee and the direct outcome

measure of their performance (e.g., corporate tax avoidance). In contrast to prior work that examines

general management and overall firm performance, our setting allows for greater power to study the

relationship between individuals’ human capital and its associated outcomes. Furthermore, due to the

differences in how tax planning is accomplished across jurisdictions, industries, and other firm

characteristics, tax departments provide a useful setting to explore whether there is variation in the

specificity of human capital within a given occupation.

3. Data

To conduct our empirical analyses, we create a sample of tax department employees and their

career histories. We obtain these data from resumes posted to a prominent professional networking

website. In this section, we provide an overview of the method used to construct the sample. We also

attempt to validate our sample using several different approaches.

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3.1. Data collection process

We begin our data collection process by creating a directory of firms that have been included in

the S&P 1500 at any point between 1993 and 2015. We focus on S&P 1500 firms for several reasons.

First, these firms generally represent the largest, most important corporations in the U.S. economy. The

size of these firms also increases the likelihood of having both (1) a tax department and (2) tax

department employees posting profiles to the website. Second, significant academic, media, and political

attention has been paid to the tax planning done by these firms, suggesting that examining tax employees

in these firms is likely of broad interest. Third, these firms’ operating and tax environments involve

substantial complexity and uncertainty, suggesting that human capital is more likely to play an important

role in understanding their tax planning outcomes. Finally, the S&P 1500 index represents a diverse set of

firms from different regions and industries, which allows us to explore whether tax-related human capital

is primarily general or specific in nature. We begin our sample period in 1993 with the enactment of

SFAS No. 109. This allows us to have consistent accounting for income taxes throughout the sample, as

some of our analyses employ effective tax rates as tax planning proxies.

For each firm, we searched the networking site for individuals who either currently or previously

worked in a tax position.8 This search took place over 2016 and 2017, and therefore we end our sample

period in 2015. To identify employees who worked in the firm’s tax department, we perform a keyword

search for “tax” within each company. This search identifies profiles that have either: (1) a current or

prior title with the word “tax” for that given firm, or (2) a current or prior role at the given firm for which

the job description includes the word “tax.”9 An inspection of the data indicated that many profiles

containing the word “tax” in the job description are not in the corporate tax department, whereas nearly

8 Our search employs simple variations of the firm’s name. If the employee worked in the tax department of a

subsidiary firm, our search will capture these employees to the extent that one of the following conditions is

satisfied: (1) the employee listed the parent company’s name in their resume or (2) the employee listed the

subsidiary’s name, which contains a variation of the firm’s name. We are relatively confident that our search process will captures most of the tax employees who work at the firm and have listed their resume on the

website, especially because the tax department is located at the corporate headquarters for most firms. 9 Examples of these titles include “Tax Analyst,” “Tax Specialist,” “Tax Associate,” “Tax Manager,” “Senior

Tax Lawyer,” “Tax Attorney,” “Global Tax Manager” and “Chief Tax Counsel.” It is important to note that if

the tax department employee does not have the word tax in the job title, we may omit them in our subsequent

analyses. A manual inspection of our data suggests this is very unlikely.

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all of the titles that included the word “tax” are likely in the corporate tax department. Therefore, we only

classify an individual as being in the tax department when their title includes the word “tax.”

For each tax department employee, we collect information on their career and educational history.

For career histories, we identify separate job positions, and for each position, the company name, start

date, and end date. We determine the chronological order of the positions using their arrangement on the

profile page as well as the beginning and end dates. We exclude individuals for which we cannot identify

start and end years for their different positions. Table 1 describes our sample selection process.

Ultimately, we have a sample of 64,614 unique individuals who have worked at any point in time during

their careers in the tax department of an S&P 1500 firms.

3.2. Data validation

To validate the accuracy of our hand-collected data, we begin by providing some general

descriptive evidence on our sample. First, in Table OA.1 of the Online Appendix, we present the largest

25 corporations (Panel A) and locations (Panel B) in our sample by the average number of tax department

employees over 2011 to 2015. We focus on the last five years of the sample for this table as these are the

years in which we are more likely to accurately capture the full corporate tax department. We find that

our sample generally contains the largest firms in the U.S. economy.10 In particular, we are reassured to

find that General Electric is one of the top three firms in our list, as it is well known as having one of the

largest tax departments. Also on the list are other well-known examples of large tax departments, such as

Exxon Mobil, Amazon, and several large financial institutions. Our list of largest locations roughly

conforms to the list of the largest U.S. cities, with a few non-U.S. cities (e.g., London and Toronto) also

present.11 This list is reassuring as it suggests that our data collection does not suffer from regional biases.

Next, we compare aspects of our sample to those in prior surveys of corporate tax departments.

First, we compare our sample to the 2011-12 Tax Executives International (TEI)’s Corporate Tax

10 In untabulated analyses, we document a large positive correlation (0.41) between the average count of tax

department employees with the average count of total employees (from Compustat) over the last five years of

our sample (2011-2015). This further increases our confidence in the data collection process. 11 It is important to note that our data collection captures the individual’s most recent location without necessarily

indicating the location of their employment when working at the S&P 1500 firm.

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Department Survey. This survey was sent to the institute’s roughly 2,500 corporate members plus several

select larger corporations in 2011 and responses were received from 518 corporations. Roughly 60

percent of the respondents had total revenues of $1 billion or greater and roughly 70 percent had assets of

$1 billion or greater, compared to 78 and 83 percent for our sample firms (averaged over the 2011-2015

period), respectively. This suggests that on average, our sample firms are larger in terms of assets and

revenues than the sample firms in the TEI survey. The TEI survey reports an average of 11 employees per

company, with 18 percent of respondents having between 10 and 25 employees and 9 percent having 26

or more employees. When we average sample firms’ tax department size over the 2011-2015 period, we

find an average of 14 department employees. Furthermore, we find that 14 percent of our sample firms

averaged between 11 and 25 employees and 13 percent averaged greater than 26 employees a year. These

amounts are relatively similar, but point to our sample firms having more tax employees than the firms in

the TEI sample on average, consistent with our sample firms being larger than TEI firms (as suggested by

our comparison of assets and revenues discussed above). Furthermore, the TEI survey finds that 85

percent of tax departments have at least one individual who previously worked in public accounting,

whereas we find that 78 percent of firm-years between 2011 and 2015 have one such employee. Overall,

our sample appears to be relatively similar to the sample in the 2011-12 TEI survey.

We also compare our sample to a 2016 survey of tax departments conducted by KPMG (KPMG

2016). This survey received responses from 294 people in charge of tax operations in corporations across

the globe. Two-thirds of the respondents were publicly traded companies, and approximately two-thirds

had annual revenues of $5 billion or greater. Our sample is composed entirely of publicly traded

companies, and 36 percent of our firms average $5 billion or more in revenue between 2011 and 2015.

This finding suggests that our sample firms are smaller on average. The KPMG survey reported an

average of 43 full-time tax employees per company, compared to an average of 14 employees in our

sample across 2011-2015. The reason(s) for the sizeable difference could be twofold: (1) the KPMG

survey appears to have targeted larger companies, which could have led to its higher average tax

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department size, and (2) our collection may have missed some tax department employees, leading to a

lower average in our sample.

3.3. Caveats regarding data quality

Overall, we believe that the extensive amount of data collection, descriptive evidence, and

comparisons to other data sources on tax departments suggest that our sample represents a reasonably

accurate collection of individuals who work in the tax departments of publicly traded U.S. corporations.

However, despite our efforts to collect, clean, and validate the data, it is unlikely that we have accurately

identified all individuals and their backgrounds for all of our sample tax departments. This is the result of

three different issues: (1) tax department individuals not registering their resumes on the website, (2) tax

department individuals potentially either omitting or incorrectly listing information on their profiles

(preventing us from accurately pairing them to firm-years or identifying their prior work experience), and

(3) errors in our collection and parsing of these profiles. As a result, inferences based on our data should

be interpreted with these caveats in mind. Furthermore, because of these issues, our primary focus is on

tax department individuals rather than the characteristics of the tax department itself (which are more

likely to be measured with error and/or bias).

3.4. Movement between S&P 1500 firms

We exploit the movement of tax employees between S&P 1500 firms in many of our empirical

analyses. We focus on employee movement for several reasons. First, our underlying assumption is that

firms’ hiring of tax department employees is a visible manifestation of their demand for tax-related

human capital. Exploring the timing of the hiring and where the hiring firm sources the new employee

provides insight into factors associated with the demand for tax-related human capital. Second, the

movements allow us to explore whether tax-related human capital is primarily general in nature, or

whether there exists some specificity that leads firms to hire certain individuals with the requisite

experience to match their needs. Finally, this setting allows us to examine whether the movement of tax

department employees between firms is a probable mechanism through which tax planning knowledge

can spread across firms.

15

To construct our sample of S&P 1500 tax department employee movements, we require

individuals to meet the following criteria: (1) they need to have been employed at a sample firm for at

least one year, (2) they need to then move to another sample firm and remain there for at least one year,

and (3) the time between jobs must be no greater than twelve months. Furthermore, in order to classify

prior firms based on their tax avoidance, we require the prior firm to have a non-missing 5-year cash ETR

(measured in the employee’s year of departure). Given these data requirements, we focus on employee

movements during the period from 1999 to 2015. Overall, we find that 3,948 of our sample individuals

move between firms during our sample period. The exact number of movements varies across analyses

because we require that firms have non-missing values for all covariates employed in each analysis.

3.5. Other data sources

We gather financial accounting data from Compustat and equity data from CRSP. Following

Dyreng and Lindsey (2009), we set the following variables to zero if they are missing in Compustat:

advertising expense, research and development expense, tax loss carryforwards, intangible assets, special

items, and long-term debt. Additionally, we employ their method to correct for errors in foreign tax

expense, foreign and domestic pre-tax income, total pre-tax income, federal current tax expense, and

worldwide current tax expense.

4. When do firms invest in tax-related human capital?

We begin our analysis by exploring when firms invest in tax-related human capital. This question

can be approached from both a supply-side perspective (by focusing on the reasons why individuals seek

employment in tax departments) and a demand-side perspective (by focusing on the need for tax-related

human capital). We take the demand-side perspective by examining factors that are associated with firms’

investments in tax-related human capital via hiring. Firms likely make hiring decisions based on an

evaluation of their current investment in labor resources and current performance. This view is supported

by prior research that suggests that firms, especially larger firms, became more likely to run their tax

departments as profit centers during our sample period (Robinson, Sikes, and Weaver 2010). Therefore,

we explore whether unfavorable changes in a firm’s tax performance lead to tax department hiring.

16

4.1. Research design

To empirically examine whether changes in the firm’s tax performance affects subsequent hiring

decisions, we estimate the following regression on our sample of S&P 1500 employee movements using a

linear probability model:

Hirei,t = β1Tax Performance Proxyi,t + Firm-level Controlsi,t + Fixed Effects + εi,t. (1)

Our dependent variable is Hire from S&P 1500, an indicator variable equal to one if the firm

hired from another S&P 1500 firm in a given year and zero otherwise. We use several variations of this

variable: Hire from Low ETR Firm, an indicator variable equal to one if the firm hires at least one

employee from an S&P 1500 firm with a 5-year cash effective tax rate that is in the bottom quartile of the

sample, and Hire from Non-Low ETR Firm, an indicator variable equal to one if Hire from S&P 1500 is

equal to one and Hire from Low ETR Firm is equal to zero. We also use Hire from Public Accounting, an

indicator equal to one if the firm hired from a public accounting firm in that year and zero otherwise.12

Our first proxy of the firm’s tax performance is based on its 5-year cash ETR. The long-run cash

effective tax rate is a summary measure of the firm’s tax environment, capturing whether the firm has

been successful in avoiding taxes over a long period (Dyreng, Hanlon, and Maydew 2008; Dyreng,

Hanlon, Maydew, and Thornock 2017). Prior survey evidence suggests that the corporate tax

department’s performance is in part evaluated on cash taxes paid (Tax Executives International Inc.

2012), and prior research suggests that during our sample period corporate tax departments are often run

as profit centers, with strong incentives to lower the cash taxes paid (Robinson et al. 2010). Our key

independent variable is ETR Increase Indicator, which is an indicator variable equal to one when the firm

experiences an increase in its 5-year cash ETR in at least two of the three last years (year t-3 through year

t-1) and zero otherwise. We measure ETR over a 5-year window to capture changes in ETR that are likely

economically important, rather than transitory shocks. Furthermore, we focus on ETR changes rather than

levels in order to capture shifts in the firm’s tax performance. Decisions regarding additional tax labor

12 We define the following firms as public accounting firms: Deloitte, EY, PWC, KPMG, Grant Thornton, as well

as various regional accounting firms.

17

investments should be driven by shifts in the firm’s effective tax rate (i.e., whether it is worsening or

improving). We assume that experiencing ETR increases in at least two of the prior three years signals

deteriorating tax planning performance. If this triggers the firm to invest in tax-related human capital, we

expect a positive coefficient on the ETR Increase Indicator.

Our second measure of the firm’s tax performance is based on the tax department’s risk

management responsibilities. Prior research suggests that the focus of the corporate tax department has

grown to include managing risk around financial reporting, especially after the implementation of FIN 48

(Donohoe, McGill, and Outslay 2014). Therefore, we measure deterioration in the tax department’s

management of financial reporting risk using Tax-Related ICW or Restatement, an indicator variable

equal to one if the firm experienced a tax-related weakness in the internal controls over financial reporting

or a tax-related restatement in year t or t-1, zero otherwise.13 We define an internal control weakness or a

restatement as being tax-related if at least one of the identified issues in Audit Analytics involves tax.

To help isolate the effect of tax performance on hiring, we control for a number of additional

factors. First, we include industry-year fixed effects to account for industry-level shocks that can affect

tax performance and hiring decisions, where industry membership is defined at the two-digit SIC code

level. We include firm fixed effects to account for time-invariant firm-level factors that may impact tax

performance and hiring. We also include a number of firm-level control variables that prior research has

shown to be associated with firm tax planning outcomes and could affect the demand for tax department

employees: Size, Leverage, MTB Ratio, Domestic Income, Foreign Income, and Foreign Income

Indicator.14 We define all variables in the appendix. Descriptive statistics for the variables used in these

tests are presented in Table OA.2 of the online appendix.

13 We measure this variable over a two-year period because it is unclear when the firm will become aware of the

issue. For example, an internal control weakness is not publicly disclosed until the following year, but

conversations with auditors may make the firm aware of the issue before disclosure. 14 We measure these controls contemporaneously with the hiring decision. Inferences are not affected if we

measure them with a one-year lag (i.e., in year t-1) (results untabulated).

18

4.2. Main results

Table 2, Panel A reports the results of estimating equation 1 using ETR Increase Indicator as the

measure of tax performance. In column 1, we use Hire as the dependent variable. As expected, we find

that firms that experience increases in their long-run cash effective tax rate in two of the prior three years

are more likely to hire tax department employees from other S&P 1500 firms. Relative to the sample

average of 4.65 percent, the coefficient in column 1 translates to roughly a 13 percent increase in the

underlying probability of hiring from another S&P 1500 firm. Columns 2 and 3 replace Hire with Hire

from Low ETR Firm and Hire from Non-Low ETR Firm, respectively.15 Consistent with firms that

experience poor tax planning performance seeking tax-related human capital built at low ETR firms, we

find a positive and significant coefficient in column 2. This translates into a 26 percent increase in the

likelihood of hiring from a low ETR firm. On the other hand, we fail to find an association between poor

tax performance and subsequent hiring from a non-low ETR firm. These findings suggest that ETR

increases signal poor tax department performance and thus a need to invest in tax-related human capital,

and human capital built in low ETR firms in particular.

We conduct four robustness tests of this result. First, in column 4, we find similar results when re-

estimating the column 2 results using only the firms that hired at least one employee during the sample

period (e.g., dropping firms that never hire anyone). Second, we use Senior Hire from Low ETR Firm,

which allows us to focus in on the employees who likely had a fundamental role in the former firm’s

successful tax avoidance. We find that firms with poor tax performance are more likely to hire such

individuals. Furthermore, the result is economically important: it translates to a 30 percent increase in the

likelihood of hiring a senior tax employee. In untabulated analyses, we fail to find an association between

ETR increases and non-senior hiring from a low ETR firm, suggesting that the column 2 results are driven

primarily by employees who held senior roles at, and thus likely played a critical role in, their prior firm’s

tax performance. Third, we explore whether we find a similar effect when examining firms that have been

15 In an untabulated test, we replace Hire with Hire from Public Accounting, and find that the coefficient on ETR

Increase Indicator is roughly one-third of the coefficient in column 1 and not statistically significant.

19

successful in increasing their tax avoidance. In column 6, we find a negative association between Hire

from a Low ETR Firm and ETR Decrease Indicator, an indicator variable that equals one when the firm

experiences an ETR decline in two of the prior three years. This finding is consistent with ETR decreases

signaling that a firm has a positive tax performance and sufficient tax resources and is therefore less likely

to hire individuals who previously worked at low ETR firms.16 Finally, in Panel B of Table 2 we replace

ETR Increase Indicator with a continuous measure of tax performance: ETR Change Over Three Years,

the firm’s 5-year cash ETR in year t minus the 5-year cash ETR in year t-3, and find similar results.17,18

In Table 3, Panel A, we estimate equation 1 using Tax-Related ICW or Restatement as our

measure of tax performance. We find that experiencing a tax-related ICW or restatement leads to an

increase in hiring (column 1). In terms of economic significance, the coefficient in column 1 translates to

roughly an 18 percent increase in the underlying hiring probability of 15.42 percent. The increase comes

from S&P 1500 hires (column 3) rather than from public accounting (column 2). Furthermore, the

increase in hiring comes from both Low ETR (column 4) and Non-Low ETR firms (column 5). These

findings suggest that firms are more likely to invest in tax-related human capital when their corporate tax

departments experience a decrease in their ability to manage risk around financial reporting.19

4.3. Hiring around FIN 48

FASB Interpretation Number 48 (commonly referred to as FIN 48), enacted in 2007, changed the

way firms accounted for uncertain tax positions in their SEC filings. Prior research suggests that the

16 We conduct two additional, untabulated tests. First, we include the firm’s five-year cash effective tax rate as an

additional control. Second, we explore deteriorations in tax planning performance relative to peer firms by

calculating ETR Increase Indicator after first demeaning the five-year cash effective tax rate within each

industry-year. We find similar inferences to those reported in panel A of table 2. 17 These analyses have fewer observations than those in Panel A because we require non-missing values for the

five-year Cash ETR in year t and year t-3 to calculate the change variable. In contrast, the indicator approach in

Panel A only requires calculating the change in the five-year Cash ETR in at least two of the three years. 18 Given that tax departments’ mandates also include managing tax risk, one could also explore whether volatility

or uncertainty in annual effective tax rates triggers investments in tax-related human capital. In untabulated tests, we find that the absolute value of ETR Change Over Three Years (which should be positively associated

with ETR volatility) is not significantly associated with subsequent tax department hiring. This finding is not

consistent with ETR volatility triggering investments in tax human capital. 19 In untabulated analyses, we find a positive, but slightly weaker, relation between non-tax related internal control

weaknesses and restatements and tax department hiring. This suggests that general decreases in the firm’s

ability to manage financial reporting risk can trigger further investments in tax-related human capital.

20

implementation of FIN 48 increased the tax department’s responsibilities in financial reporting and likely

shifted the tax department’s focus towards one of risk management (Donohoe et al. 2014). We explore

whether tax departments were more likely to hire post-FIN 48, due to the increased responsibilities.

Moreover, FIN 48 may have had more of an impact on firms that exhibited greater tax avoidance in the

pre-FIN 48 period, as these firms were more likely to be implementing aggressive tax strategies.

Therefore, we estimate the following regression:

Hirei,t = β1Low Pre-FIN 5-year Cash ETR x Post-FIN 48i,t + Firm Controlsi,t + Fixed Effects + εi,t. (2)

Our “treatment” variable is Low Pre-FIN 48 5-year Cash ETR, which is an indicator equal to one

if the firm’s 5-year cash effective tax rate in the pre-FIN 48 period (i.e., ending in 2006) is below the

sample median and zero otherwise. Post-FIN 48 is an indicator variable equal to one if the year is in the

post-FIN period (i.e., 2007 or later) and zero otherwise. Thus, the coefficient on the interaction of these

two variables captures low ETR firms’ incremental propensity to hire in the post-FIN 48 period, relative

to high ETR firms. We estimate this regression on firm-year observations between 2004 and 2009 (to

focus on a short window around FIN 48) and include the full set of control variables and fixed effects.

The results of this analysis are presented in Table 3, Panel B. In contrast with the findings from

Panel A, we find no evidence that FIN 48 is associated with increased hiring from other S&P 1500 firms

among firms with low ETRs relative to those with high ETRs: the coefficients on the interaction term are

insignificant in columns 1, 4, and 5. On the other hand, we do find that FIN 48 is associated with

increased hiring from public accounting firms for firms with greater tax avoidance, relative to those with

less tax avoidance (columns 2 and 3). In terms of economic significance, the coefficient of 3.14 represents

an increase of 24 percent over the base public accounting hiring rate of 13.04 percent. One possible

explanation for this finding is that because the regulation affected all firms (even if its impact across firms

was different), the demand shock made it difficult for low ETR firms to hire from other firms in the

immediate aftermath of FIN 48. This in turn led firms to focus on hiring from public accounting firms.

21

5. Where do firms source tax-related human capital?

Next, we explore where firms source their tax department employees, which allows us to examine

whether tax-related human capital is general or specific in nature.

5.1. Prominent sources of tax department employees

We begin by examining when individuals start their careers in the corporate tax departments of

our sample firms. Figure 1 describes the timing of the employee’s first S&P 1500 corporate tax

department job. The majority (54 percent) of our sample individuals begin their careers at an S&P 1500

corporate tax department, while 39 percent are hired into the tax department in either their second or third

job. In Figure 2, we explore where tax employees came from before they joined an S&P tax department.

After university or other educational programs, the most significant source of tax professionals is public

accounting (24 percent) and non-S&P 1500 firms (21 percent). Despite concerns about a revolving door

with the IRS or state revenue departments, former revenue department employees only account for 0.2

percent of employee movements into S&P 1500 tax departments.

Next, we explore a specific source of tax-related human capital: prior experience in another S&P

1500 corporate tax department. Figure 3 presents the percentage of individuals in our full raw sample who

move between S&P 1500 firms.20 Approximately 47 percent of unique individuals and 40 percent of

unique movements in our sample occur between two S&P firms. This finding suggests that prior

experience at an S&P firm is a unique source of tax-related human capital that these firms value. In Table

4, we explore the top suppliers and demanders of tax professionals at both the firm (Panel A) and industry

(Panel B) levels. We find that certain firms, such as General Electric, IBM, and JP Morgan are among the

largest suppliers of tax ‘alumni,’ employees who are subsequently hired by other S&P 1500 firms, as well

as being the largest hirers of tax department employees from other S&P 1500 firms.21 We find that

Amazon has been the second largest hirer of S&P tax alumni during our sample period. In Panel B, we

20 For the figures, we use the raw sample of tax employees linked to S&P 1500 firms. The sample does not require

us to estimate the cash ETR nor have any of the control variables for our multivariable tests. 21 Before its move to PriceWaterhouseCoopers, General Electric’s tax department was often referred to as the

“Harvard of corporate tax departments.”

22

aggregate employee movements at the two-digit SIC-industry level to examine the top supplying and

demanding industries for tax professionals. The most active industries (in terms of both the supply and

demand of S&P 1500 tax department employees) are the business and financial services industries, and

various industrials. In untabulated analyses, we find that movements tend to occur between either

different financial industries or different non-financial industries. This is consistent with tax-related

human capital containing some sector-level specificity and tax planning involving an industry-specific

component (McGuire et al. 2012); we explore this idea further in subsequent analyses.

5.2. Do employees move between similar firms?

In this section, we explore the matching process between hiring firms and the firms from which

they hire (“supplying firms”). This allows us to explore whether tax-related human capital is general or

specific in nature. In particular, we examine whether firms tend to hire from other firms that are similar or

dissimilar along certain characteristics. If firms’ operating and tax environments lead to unique tax

scenarios that require specific sources of tax-related human capital, we expect that firms will be more

likely to hire from similar firms, where the employees developed the required human capital. On the other

hand, if tax human capital is primarily general in nature, movements may occur without regards to the

similarity between the hiring and supplying firm.

We focus on several firm characteristics that could proxy for unique operating and tax

environments: firm size, geographic proximity, industry membership, foreign operations, and the level of

tax avoidance. Larger firms likely have more complex tax environments than smaller firms due to the

scale of their operations. Similarly, multinational corporations are likely to have unique tax environments:

operating in multiple tax jurisdictions leads to both additional complexity (e.g., complying with different

regulations) as well as tax avoidance opportunities (e.g., shifting income from high-tax to low-tax

jurisdictions). Similarly, firms within close geographic proximity likely face similar tax rules and

regulations (e.g., state and local taxation). Furthermore, the pecuniary (e.g., search costs, moving costs)

and non-pecuniary costs of hiring an employee make firms more likely to hire from other firms within

close geographical proximity. Prior research shows that tax planning often involves industry-specific

23

components (McGuire et al. 2012), suggesting that employee movements are more likely to occur within

an industry. Finally, firms that have low ETRs (i.e., an ETR in the bottom quintile) may be using

aggressive tax strategies; the maintenance and/or development of those strategies may require specific

experience at an aggressive tax avoiding firm.

We first explore this idea in a univariate analysis employing the sample of employee movements

described in section 3.4. Panel A of Table 5 describes the percentage of employee movements that occur

within certain groupings, based on the firm characteristics described above. We document that 17 percent

of employee movements in our sample occur within the same 2-digit SIC. This is much higher than would

be expected if the movements were random across industries (approximately 1 percent). Turning to firm

size and foreign income, we find that 35 (29) percent of movement occurs within the same total asset

(foreign income) quintile, which is higher than the 25 percent that would be observed if the movement

was random with respect to firm size. The evidence on industry membership, firm size, and foreign

income is consistent with tax-related human capital being specific in nature. Furthermore, these findings

suggest that certain firm characteristics lead to a unique operating and tax environment, in response to

which firms seek tax professionals who have specific types of human capital.22 Interestingly, we find that

24 percent of employee movement occurs within the same quartile of the five-year cash effective tax rate

(Cash ETR), which is not different from what would be expected under random movement. This suggests

that tax aggressive (non-tax aggressive) firms are not more likely to demand tax-related human capital

built up at other tax aggressive (non-tax aggressive) firms.

We further explore this idea using multivariable regression. We create a sample of possible hiring

firm-employee combinations, using all firms that hired in a given year and all employees who moved in

that year. We then estimate the following model on this dataset:

22 Ideally, we would compare these percentages to employee movement in other business units or departments

(e.g., marketing). Unfortunately, we are unaware of studies that conduct similar analyses for other business

units or departments and doing so ourselves would require an extensive data collection effort on our part.

Furthermore, it is unclear whether we would be able to accurately identify employees in other departments,

given the heterogeneity in titles that can exist across firms. This is in contrast to the tax department setting, as

tax department employees likely have the word “tax” in their title.

24

Matchi,j,t = β1Same Industryi,j,t +β2Distancei,j,t + β3Size Differentiali,j,t + β4Foreign Income

Differentiali,j,t+ β5ETR Differentiali,j,t + Fixed Effects + εi,t. (3)

The dependent variable is Match, an indicator variable equal to one if the firm hired that

employee in that year and zero otherwise. We include various measures that capture the difference (or

similarity) between the hiring and supplying firms as independent variables: whether the hiring and

supplying firms are in the same industry (Same Industry), the geographic distance between their

headquarters in miles (Distance), the absolute difference in total assets (Size Differential), the absolute

difference in foreign income (Foreign Income Differential), and the absolute difference between their 5-

year cash effective tax rates (ETR Differential). We also include several types of fixed effects.

The results of this analysis are presented in Panel B of Table 5. Consistent with the univariate

results in Panel A, we find that firms are more likely to hire from firms that share industry membership,

are closer in geographic proximity, and are similar in terms of total assets and foreign income. These

effects are economically meaningful. For example, a standard deviation increase in differences between

firms’ foreign income is associated with a 24 percent decrease in the likelihood of hiring between firms.

These findings again support the idea that tax-related human capital is specific rather than general in

nature: Firms appear to on average demand specific types of tax-related human capital that fit their

particular operating and tax environments. On the other hand, we fail to find evidence that the ETR

similarity between the hiring and supplying firms impacts the likelihood of matching. Overall, these

results are consistent with the idea that certain firm characteristics shape the corporate tax environment,

including its responsibilities and opportunities for tax planning, and thus the firm’s need for specific

sources of tax-related human capital.

5.3. Going outside the local labor market for tax-related human capital

The evidence above suggests that S&P 1500 firms seek tax-related human capital from other

nearby S&P 1500 firms. This is not surprising, as prior research suggests that one of the principal

constraints to labor mobility is geographic distance (Schwartz 1973; Clark and Cosgrove 1991; Rogers

1997). In the presence of search frictions (such as geographic distance), individuals will tend to match

25

with firms in the local labor market. However, there may be instances where firms will go outside the

local labor market for human capital. Specifically, when a firm is constrained and in need of a scarce

resource, it will be willing to go beyond the local labor market to find it.23 Likewise, because moving is

costly, the employee’s decision to relocate outside the local labor market must be accompanied by the

expectation of higher benefits at the new firm. In this framework, employees migrate longer distances in

an attempt to increase the return on their human capital and firms that are constrained will go further out

in order to obtain scarce resources.

We hypothesize that firms that are more likely to benefit from adding tax-related human capital

will be willing to go outside the local labor market. Specifically, a non-tax aggressive firm may be more

likely to go outside the local labor market, because the benefits of adding tax-related human capital to

these firms outweigh the additional costs. To explore this idea empirically, we employ the sample of

employee movements between S&P 1500 firms, and estimate the following regression:24

Log Distancei,j,t = β1Non-Low ETR Firmi,t + Firm-level Controlsi,t + Fixed Effects + εi,t. (4)

We capture the distance between the hiring and supplying firms with Log Distance, the natural

logarithm of the total miles between the headquarters of the hiring and supplying firms. Our primary

independent variable is Non-Low ETR Firm, which is an indicator variable equal to one if the hiring

firm’s 5-year cash effective tax rate is in the top three quartiles of the sample and zero otherwise. If firms

that are not aggressive tax avoiders are more likely to go outside the local labor market in their search for

tax-related human capital, we expect the coefficient on Non-Low ETR Firm to be positive. We include

industry and year fixed effects as well as several control variables for the hiring firm: Size, Leverage,

MTB Ratio, Domestic Income, Foreign Income, and Foreign Income Indicator.

We present the results of this analysis in Table 6, Panel A. In column 1, we include industry and

year fixed effects. We add control variables in column 2. In both models, we find that non-low ETR firms

23 In expectation, the higher benefits from obtaining that resource will outweigh the additional costs incurred

going outside the local market (e.g., search and moving costs). 24 For these tests, we remove all employees who have specific non-income tax responsibilities, such as sales,

payroll, or property, as these employees will likely have little to do with income tax planning.

26

go further in distance on average when hiring a tax department employee. In terms of economic

significance, the results in column 2 suggest that non-low ETR Firms are on average willing to go

approximately ten percent further, relative to the sample average for Log Distance. In columns 3 and 4,

we replace Log Distance with Large Distance Indicator, an indicator variable equal to one if the hiring

and supplying firms are greater than 200 miles apart in distance and zero otherwise. We continue to find

that non-low ETR Firms are more likely to go lengthy distances for tax-related human capital; in column

4, we find that these firms are approximately 11 percentage points more likely to go further than 200

miles to hire the employee. These findings are consistent with firms in greater need of tax-related human

capital being willing to incur greater costs to obtain it.

In Table 6, Panel B, we explore heterogeneity in the extent to which non-low ETR firms are

willing to go outside the local labor market for tax-related human capital. First, if non-low ETR firms are

seeking tax-related human capital in order to improve future tax planning outcomes, then we expect that

these firms are more likely to go outside the local labor market when hiring employees with specific tax-

related human capital: for example, prior experience at a low ETR firm. We test this by estimating

equation 4 over two subsamples based on the 5-year cash effective tax rate of the supplying firm: low

ETR firms (column 1) and non-low ETR firms (column 2). We find that non-low ETR firms go further

for tax-related human capital when hiring from a low ETR firm, but not when hiring from other firms.

Second, we explore whether these firms are willing to go further in distance for tax employees

with greater tax-related human capital. We measure tax-related human capital using both the cumulative

tax experience (in years) and whether the new employee held a senior role at the prior firm, as senior

employees are more likely to have played a fundamental role in the prior firm’s tax strategies. We then

create two movement subsamples: high tax-related human capital (above-median tax experience or a

senior role in the prior firm) and low tax-related human capital (below median tax experience or junior

role in the prior firm). In columns 3 and 5 of Panel B, we find that non-low ETR firms go further to hire

employees when the employee has greater-than-median experience and when the employee was in a

senior role at the prior firm. On the other hand, we find in columns 4 and 6 that non-low ETR firms do

27

not go further in distance when hiring employees with below-median tax experience or who had a junior

role in their prior firms. These findings suggest that firms are more willing to go outside the local labor

market when they have a strong need for tax-related human capital, and when the employee has unique

tax-related human capital.25

6. What do firms receive for investing in tax-related human capital?

In this section, we investigate whether firm’s tax avoidance changes after investing in tax-related

human capital. In examining this question, we focus on a particular source of tax-related human capital:

prior experience at a low ETR firm. A tax department employee who is involved in the creation, design,

and implementation of tax planning strategies at a firm that is ultimately successful at avoiding taxes has

likely developed a valuable type of human capital and may seek to apply that human capital at a new firm.

Therefore, we predict that a firm will experience an increase in its tax avoidance when it hires an

employee who previously worked for a low ETR firm. In doing so, we explore whether employee

movements serve as a mechanism through which tax planning knowledge can spread across firms.

However, there are reasons why hiring an employee from a low ETR firm may not lead to an

increase in tax avoidance for the hiring firm. First, the employee may not have been intimately involved

in the prior firm’s income tax planning. For example, the individual may have only been involved with

simple implementation aspects of the tax strategy, or might have non-income tax responsibilities (e.g.,

sales taxation). Second, the tax-related human capital the employee built in his/her prior firm may not be

as applicable to the hiring firm. For example, some tax planning strategies have an industry-specific

component and hiring an individual from a different industry may therefore provide little benefit to the

firm if tax-related human capital is not general in nature. Third, the firm may not have sufficient

unexploited tax planning opportunities to benefit from the increase in tax-related human capital. Finally,

tax departments’ primary objectives may have changed over time, from focusing on avoidance to

25 Our expectation is that tax professionals, due to their greater training and education, may be more mobile than

other types of employees. Therefore, these findings may not generalize to employees in roles that do not require

specific types of human capital.

28

managing risk (Donohoe et al. 2014; Robinson et al. 2010). We explore these predictions in subsequent

analyses.

6.1. Research design

To test this hypothesis, we again focus on the sample of employees who moved from one S&P

1500 firm to another firm during our sample period (described in section 3.4). Using this sample of

employee movements, we estimate the following regression on firm-year observations within a seven-

year window centered on the employee movement:26

Cash ETRi,t = β1Hire from Low ETR Firmi x Postt + Firm-level Controlsi,t + Fixed Effects + εi,t. (5)

Cash ETR is the hiring firm’s cash ETR rate in year t. We measure the cash effective tax rate as

cash taxes paid scaled by adjusted pre-tax income (pre-tax income minus special items). Hire from Low

ETR Firm is an indicator variable equal to one if the employee is moving from a firm with a five-year

cash effective tax rate that is in the bottom quartile of the sample, zero otherwise. Post is an indicator

equal to one for the hiring year and every year afterward, zero otherwise. The coefficient of interest is β1,

which is the incremental effect on the average Cash ETR for firms after hiring an employee from a low

ETR firm, relative to employees hired from other firms. We predict that this coefficient will be negative,

consistent with these firms engaging in more tax avoidance. We measure the prior firm’s tax avoidance

over a five-year window in order to capture a low ETR that is likely the result of deliberate and

sustainable tax planning efforts, rather than unsustainable tax avoidance or transitory shocks. On the other

hand, we measure the hiring firm’s tax planning using a one-year effective tax rate in order to detect any

shift in tax avoidance that occurs around the hiring of the new employee.

We control for a number of additional factors. First, we include industry-year fixed effects to

account for industry-level shocks that can affect cash effective tax rates, where industry membership is

defined at the two-digit SIC code level. We include event-time fixed effects to account for general effects

26 We focus on a seven-year window centered on employee movement in order to balance two competing factors:

(1) having a window sufficiently long enough to observe the changes in tax avoidance that occur around the

employee’s hiring and (2) having a window sufficiently short enough to minimize the chance that other non-

hiring factors impact the firm’s tax avoidance.

29

that occur around the hiring of an additional tax department employee, regardless of the former firm’s

observed tax planning. These fixed effects take on a different value for each year in the seven-year

window (e.g., -3, -2, etc.). We include firm fixed effects to account for time-invariant (within our

window) firm-level factors that may impact tax planning outcomes. Finally, we include former firm times

hiring-year fixed effects. This fixed effect effectively accounts for any time-invariant factors that led to

employees being hired from a given firm in a given year. We also include firm-level control variables that

prior research has shown to be associated with firm tax planning outcomes: Size, Domestic Income,

Foreign Income, Sales Growth, Special Items, Extraordinary Items, Advertising Expense, R&D Expense,

PPE, Intangible Assets, Leverage, MTB Ratio, NOL Indicator, and FI Indicator (all defined in the

appendix). Table OA.3 of the online appendix presents descriptive statistics for the variables used in these

analyses, partitioned by the former firm’s tax planning classification. Untabulated results show that the

average five-year cash effective tax rate for the prior firms designated as low ETR firms is approximately

9 percent, consistent with these firms maintaining very low cash ETRs over a long period.

6.2. Main results

Columns 1 through 3 of Table 7 present the results from estimating equation 5 on our sample of

employee movements. The main effects of Hire from Low ETR Firm and Post are subsumed by the fixed

effects in columns 2 and 3. The primary variable of interest, Hire from Low ETR Firm x Post, is

statistically significant in each model. We find that when a firm hires an employee from a low ETR firm,

they experience a decline in Cash ETR of approximately 2 percent. This result is relatively stable

regardless of the inclusion of control variables or fixed effects.27

It is important to note that this result is not an unbiased estimate of the individual’s “treatment”

effect on the firm’s observed tax avoidance, for several reasons. First, the assignment of employees to

firms is non-random and involves double-sided matching. It is likely that a firm hires an employee from a

low ETR firm when it desires to increase its tax avoidance. Randomly placing this same employee at a

different firm likely would not lead to the same decline in the Cash ETR unless the new firm had the same

27 The exact number of observations varies across models due to the differing fixed effect structures.

30

incentives and opportunities to increase its tax avoidance. Second, a firm hiring an employee from a low

ETR firm may be taking other actions that impact its tax avoidance, such as sourcing new tax planning

advice from third parties (e.g., lawyers, audit firms, or banks) or improving the firm’s internal information

environment. That is, the new hire is likely to be part of a broader strategy to increase tax avoidance.

Although we attempt to account for these factors with our extensive set of control variables and fixed

effects, some of these actions are clearly unobservable to the researcher. Therefore, we cannot attribute

the entire reduction in Cash ETR to the new employee. Instead, we view our results as being descriptively

consistent with the idea that hiring employees with this specific source of tax-related human capital

(experience at a low ETR firm) is valuable to firms seeking to increase their tax avoidance.28

Furthermore, our findings are consistent with individuals and their movement between firms being a

mechanism through which tax knowledge and strategies spread across firms.

As a placebo test, we examine whether the association between tax avoidance and hiring from a

low ETR firm exists for employees who mention in their job title or description that they are involved in

non-income tax responsibilities (e.g., property, sales, and payroll taxation). We assume that if the

individual’s job title or job description mentions any of these three taxes, then these taxes made up a non-

trivial portion of the employee’s responsibilities. These employees are unlikely to play a substantial role

in the firm’s income tax planning. On the other hand, we assume that if an employee’s title or job

description does not mention any of these three taxes, then they likely play a role in the firm’s income tax

compliance and/or planning. Our theory suggests that the movement of an employee from a low ETR firm

should have implications for the new firm’s tax avoidance when the individual has income tax

responsibilities, rather than non-income tax responsibilities. We test this idea by splitting out movements

28 One might wonder whether this association depends on whether the newly hired employee is a “replacement”

for a recently departed employee, or whether the hire represents an “incremental” investment in tax human

capital (i.e., is not replacing anyone). However, this is challenging to examine empirically for several reasons.

First, while we believe that employees moving to new jobs are more likely to use the professional networking website (improving our odds of capturing these movements), those leaving a job may be less likely to actively

use the website. For example, someone who passes away or retires may fail to update their resume, meaning we

would not capture these departures. Furthermore, even if we could, we would be unable to classify the nature of

the departure (e.g., whether it was voluntary or involuntary). Finally, our hypothesis does not depend on

whether the hire is a replacement or incremental, just that the newly hired employee is able to apply human

capital built at the prior firm in his or her new role.

31

by whether the employee is involved in an income or non-income tax role at their new firm. In column 4,

we find that our results are concentrated in the subsample of employees who have income tax

responsibilities. Furthermore, in column 5 we fail to find that the movement of employees from low ETR

firms has an effect on the hiring firm’s Cash ETR when the new employee has non-income tax

responsibilities. We discuss additional analyses and robustness tests supporting these findings in sections

OA.1 and OA.2 of the Online Appendix, respectively.

6.3. Individual-level cross-sectional tests

Next, we explore whether the association between employee hiring from low ETR firms and the

hiring firm’s Cash ETR varies by employee characteristics. Specifically, we examine whether the effect is

stronger for employees (1) with greater overall tax experience and (2) who held a senior-level position in

their prior firm. Employees with greater overall tax experience are more likely to have contributed to the

successful tax planning at their prior firm. Furthermore, if the employee held a senior level position at the

prior firm, they were likely intimately involved in devising and implementing the tax strategies that led to

its low long-run cash ETR. Senior personnel are also more likely to drive the corporate tax department’s

culture and overall attitude towards tax avoidance (e.g., whether it is aggressive or conservative).

We test these predictions in Table 8.29 First, we explore how the employee’s total tax experience,

measured as the number of years spent in tax positions prior to moving to the new firm, affects the results

from Table 7. We find that the negative association between hiring an employee from a low ETR firm and

the hiring firm’s cash effective tax rate is greater when the employee has above-median total tax

experience (column 1) versus when the employee has below-median total tax experience (column 2).

Next, we examine how the seniority of the employee’s role at the prior firm affects our primary findings.

We classify an employee as having a senior role at the prior firm if their title contains one of the

following words: manager, supervisor, director, or vice president (or associated abbreviations). We find

that the Cash ETR decrease associated with hiring an employee from a low ETR firm is stronger when the

29 The analyses in Tables 8 and 9 are estimated on the subsample of employee movements that do not have non-

income tax responsibilities (e.g., payroll, property, or sales in job title).

32

employee held a senior role at the former firm (column 3) versus when the employee held a junior role

(column 4). Finally, we find that the association is stronger when the individual has both above-median

tax experience and was a senior employee in the former firm (column 5) relative to when one of these

conditions does not hold (column 6). The results in Table 8 are broadly consistent with tax-related human

capital built in the low ETR firm playing a role in the hiring firm’s increased tax avoidance. Furthermore,

because the human capital of senior and/or more experienced employees is more likely to be specific in

nature (Becker 1964; Hashimoto 1981), our findings are consistent with tax-related human capital playing

an important role in tax avoidance through its specificity.

6.4. Firm-level cross-sectional tests

We also explore whether the association between hiring employees from low ETR firms and the

hiring firm’s Cash ETR varies by firm-level characteristics. First, we explore whether the association

varies by firm size. We hypothesize that the hiring of an employee with this specific type of tax-related

human capital will have a greater effect on smaller firms. Smaller firms likely have smaller tax

departments pre-hiring, and therefore the addition of an employee likely has a bigger impact on the

cumulative amount of tax-related human capital relative to a similar hire in a larger firm. To examine this

prediction, we split the sample into two subsamples based on whether the firm’s total assets are above or

below the sample median in the year prior to the employee’s hiring. In Table 9, we find that the

association is stronger within the small firm subsample (column 1) relative to the large firm sample

(column 2), consistent with our prediction.

Second, we explore whether the association is stronger for firms with greater foreign income.

Firms with greater foreign income may have a larger tax planning opportunity set, and therefore may

benefit more from hiring an employee with experience at a low ETR firm. We test this notion by splitting

the sample into two subsamples based on the sample median for foreign income and then re-estimating

our primary model on these two subsamples. We present the results from these analyses in columns 3 and

4 of Table 9. As predicted, we find that the association between hiring from the low ETR firm and the

33

change in the hiring firm’s Cash ETR is greater in the subsample of firms with high foreign income

(column 3) relative to the subsample with low foreign income (column 4).

Third, we examine whether the association varies by the level of auditor-provided tax services

(APTS). It could be that firms with lower APTS keep most of their tax planning in-house, and therefore

they may benefit more from hiring an employee from a low ETR firm. On the other hand, it could be that

greater APTS indicates that the firm is interested in increasing its tax avoidance and therefore would

benefit more from bringing in this type of tax-related human capital. We test this prediction using an

approach similar to the previous tests, now splitting the sample by the amount of tax fees to the firm’s

auditor (scaled by total assets). We present these findings in columns 5 and 6 of Table 9. Consistent with

the latter prediction, we find that the Cash ETR reduction associated with hiring from a low ETR firm is

greater in the above-median APTS subsample, relative to the subsample with below-median APTS.30

Finally, we explore whether the association varies depending on whether the new and prior firms

share industry membership. Prior research suggests that tax strategies are often industry-specific

(McGuire et al. 2012). Therefore, we predict that the Cash ETR reduction associated with hiring an

employee from a low ETR firm will be greater when the hiring and former firms are in the same industry.

In column 7 (8) of Table 9, we re-estimate equation 5 within the subsample of observations where the

industries of the hiring and prior firms are the same (different), where industry membership is measured

using two-digit SIC codes. We find evidence consistent with our hypothesis; hiring employees from low

ETR firms is associated with a greater reduction in Cash ETR when the firms share industry membership.

This finding is consistent with tax-related human capital affecting tax avoidance through its industry

specificity. Furthermore, this result suggests that the spread of tax planning knowledge across firms can

occur via hiring employees from aggressive tax avoiders in the same industry.

30 This finding is also consistent with firms being reluctant to engage their auditor for the purposes of aggressive

tax planning, preferring to keep their substantial tax planning in-house. Regardless of explanation, our findings

are consistent with APTS and in-house tax-related human capital being complements, rather than substitutes.

34

7. Conclusion

We study tax-related human capital in corporate tax departments of publicly traded U.S.

corporations. The training and prior experiences in corporate tax departments (which we term “tax-related

human capital”) likely shapes employees’ ability to interpret tax law, deal with operating and tax

uncertainties, and craft and implement tax planning strategies, and thus are likely important for

understanding the firm’s tax outcomes. With few exceptions, prior research has generally been unable to

explore the determinants and consequences of tax-related human capital. Using data collected from

resumes of corporate tax department employees and implementing a novel methodological framework

based on employee movement between firms, we consider several questions. First, we find that firms are

more likely to hire from another S&P 1500 firm when experiencing deteriorations in tax performance

(e.g., recent ETR increases or tax-related internal control weaknesses and restatements). Second, we find

that employee movement between sample firms are more common when size, geographic location,

industry membership, and foreign operations are similar between the hiring and prior firms, suggesting

that tax-related human capital is specific in nature. Furthermore, we find that firms that do not have low

ETRs on average hire from further away and that this result is concentrated in matches where the new

employee is coming from a low ETR firm, suggesting that firms with a strong need for tax-related human

capital are more willing to go outside the local labor market. Finally, we find that firms experience a

decrease in their cash ETR when hiring an employee from a low ETR firm, consistent with employee

movements being a mechanism through which tax planning knowledge spreads across firms.

Our study contributes to the literatures on the effects of individual employees and their human

capital on firm outcomes and the determinants of corporate tax avoidance. We hope that our study serves

as a starting point for future research that connects labor economics with corporate taxation. For example,

future research could explore the role of other sources of tax-related human capital in understanding firm

tax outcomes (i.e., prior experience in public accounting).

35

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38

Appendix: Variable Definitions

Main variables: Hiring analyses (Tables 2 and 3) VARIABLE DEFINITION

Any Hire An indicator equal to one if the firm hired an employee from another S&P 1500

firm or from a public accounting firm in that year and zero otherwise; multiplied

by 100

Hire from S&P 1500 An indicator equal to one if the firm hired an employee from another S&P 1500

firm in that year and zero otherwise; multiplied by 100

Hire from Low ETR Firm An indicator equal to one if the firm hired an employee from a prior firm with a 5-

year cash ETR that was in the bottom quartile of the sample and zero otherwise;

multiplied by 100

Hire from Non-Low ETR

Firm

An indicator equal to one if Hire from S&P 1500 is equal to one and Hire from

Low ETR Firm is equal to zero and zero otherwise; multiplied by 100

Senior Hire from Low ETR

Firm

An indicator equal to one if the firm hired an employee who had a senior role at a

prior firm with a 5-year cash ETR that was in the bottom quartile of the sample and

zero otherwise; multiplied by 100

Hire from Public

Accounting

An indicator equal to one if the firm hired an employee from a public accounting

firm in that year and zero otherwise; multiplied by 100

ETR Increase Indicator An indicator equal to one if the firm's 5-year cash ETR has increased in at least two of the three prior years and zero otherwise; multiplied by 100

ETR Decrease Indicator An indicator equal to one if the firm's 5-year cash ETR has decreased in at least

two of the three prior years and zero otherwise; multiplied by 100

ETR Change Over 3 Years The firm's 5-year cash ETR at year t minus the firm's 5-year cash ETR at year t-3

Tax-Related ICW or

Restatement

An indicator equal to one if the firm experienced a tax-related internal control

weakness or restatement in either year t or t-1, zero otherwise; multiplied by 100

Low Pre-FIN 48 Cash ETR An indicator equal to one if the firm's 5-year cash ETR as of year-end 2006 is below the sample median and zero otherwise

Post-FIN 48 An indicator equal to one if the fiscal year is in the post-FIN 48 period (i.e., 2007

or later), zero otherwise

Main variables: Similarity and distance analyses (Tables 5 and 6) VARIABLE DEFINITION

Distance Miles between the hiring firm's headquarters and the employee's prior firm's

headquarters

Log Distance Natural logarithm of Distance

Large Distance Indicator An indicator equal to one if the distance between the hiring firm's headquarters and

the employee's prior firm’s headquarters is greater than 200 miles, zero otherwise

Same Industry An indicator equal to one if the hiring firm and the prior firm are in the same two-digit SIC industry and zero otherwise

Size Differential The absolute value of the difference between the hiring firm's total assets and the

prior firm's total assets

Foreign Income Differential The absolute value of the difference between the hiring firm's foreign income and

the prior firm's foreign income (each scaled by assets)

ETR Differential The absolute value of the difference between the hiring firm's 5-year cash ETR and

the prior firm's 5-year cash ETR

High ETR Firm Indicator variable equal to one if the firm's five-year cash effective tax rate is in the top three quartiles, zero otherwise

39

Appendix: Variable Definitions (continued)

Main variables: ETR analyses (Tables 7 through 9) VARIABLE DEFINITION

Cash ETR The cash effective tax rate, defined as cash taxes paid scaled by adjusted pre-tax

income (pre-tax income minus special items); this variable is measured either over

a one- or 5-year horizon

Hire from Low ETR Firm An indicator equal to one if the prior firm's 5-year cash ETR that was in the bottom

quartile of the sample and zero otherwise

Post An indicator equal to one in the year when the employee is hired and all later

years, zero otherwise

Senior Role An indicator equal to one if the employee's job title indicates a senior role and zero

otherwise. We consider a role as senior if it includes one of the following phrases

(or similar abbreviations): "Vice President", "Director", "Manager", or

"Supervisor".

Tax Experience The total number of years (through the end of the prior year) spent in tax positions,

regardless of firm.

Control variables: All analyses VARIABLE DEFINITION

Size Natural logarithm of assets

Leverage Long-term debt scaled by total assets

MTB Ratio Market-to-book ratio

Domestic Income Domestic pre-tax income scaled by total assets

Foreign Income Foreign pre-tax income scaled by total assets

FI Indicator Indicator equal to one if foreign income is non-zero and non-missing, zero

otherwise

Control variables: ETR analyses VARIABLE DEFINITION

Sales Growth Annual change in sales scaled by lagged sales

Special Items Special items scaled by total assets

Extraordinary Items Extraordinary items scaled by total assets

Advertising Expense Advertising expense scaled by total assets

R&D Expense R&D expense scaled by total assets

PPE Property, plant and equipment scaled by total assets

Intangible Assets Intangible assets scaled by total assets

NOL Indicator Indicator equal to one if there was a tax loss carryforward at beginning of the year

and zero otherwise

40

Figure 1: Timing of the first S&P 1500 job This figure plots the percent of individuals and the point in their careers when they were first employed at an S&P

tax department. We use the full sample of individuals who have worked at least once in an S&P 1500 tax department.

Figure 2: Where employees came from prior to their first S&P 1500 job This figure plots the categories of where S&P tax employees came from prior to their first employment in an S&P

tax department. We use the full sample of individuals who have worked at least once in an S&P 1500 tax

department.

41

Figure 3: Percent of the sample with S&P to S&P movement

This figure plots the percent of individuals and their movement between jobs at S&P firms in our sample. The sample consists of tax department employees who worked at least once in a tax department at an S&P 1500 firm

irrespective of whether we have information on the firm’s characteristics or the timing of the turnover. Column 1

graphs the percentage of the individuals in our sample who transitioned between two tax departments in an S&P

1500 firm at least once during our sample period. In column 2, we graph the percentage of total job movements that

occur between S&P firms.

42

Table 1: Sample selection

This table describes the sample creation process and the final sample employed in subsequent analyses. Specifically, it documents the construction of the individual sample given our data requirements as well as

the firms that make up our sample.

Selection Criteria - Individuals Number of

Individuals

Raw Tax Professionals 276,365

Less: No tax titles in at least one firm or start and end dates for positions 211,751

Individuals in the Tax Department Sample 64,614

Less: Not in previous S&P 1500 firm, no firm tax

information, or turnover falls outside 1999 and 2015 60,666

Individuals in the Movements Sample 3,948

Selection Criteria - Firms Number of

Firms

Total Firms Listed in S&P 1500 since 1993 3,085

less: Firms with no Individuals data on networking site 1,450

Firms in Sample 1,635

43

Table 2: Changes in tax avoidance and tax department hiring

This table presents the results of estimating equation 1 on the sample of S&P 1500 firms from 1999 to 2015. The dependent variable is one of the following variables: Hire from S&P 1500, an indicator variable equal to one if the firm hired a tax department employee from another S&P 1500 firm in that year and zero

otherwise; Hire from Low ETR Firm, an indicator variable equal to one if the firm hired a tax department employee from an S&P 1500 firm with a 5-year cash

effective tax rate that is in the bottom quartile in that year and zero otherwise; Hire from Non-Low ETR Firm, an indicator variable equal to one if Hire from S&P

1500 is equal to one and Hire from Low ETR Firm is equal to zero and zero otherwise; or Senior Hire from Low ETR Firm, an indicator variable equal to one if

the firm hired a senior (e.g., title containing manager, vice president, or executive) tax department employee from an S&P 1500 firm with a 5-year cash effective

tax rate that is in the bottom quartile in that year and zero otherwise. The primary independent variable is one of the following variables: ETR Increase Indicator

(ETR Decrease Indicator), an indicator variable equal to one if the firm’s 5-year effective tax rate increased (decreased) in two of the prior three years and zero

otherwise; or ETR Change Over 3 Years, the firm’s 5-year effective tax rate in year t-1 minus the firm’s 5-year effective tax rate in year t-4. Column 4 only

contains firms that hired at least once during the sample period; all other columns contain the full sample of eligible observations. All regressions include control

variables, firm fixed effects, and industry-year fixed effects. Standard errors clustered by firm are presented in parentheses below the coefficient estimates. *, **,

and *** represent coefficients that are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively.

Panel A: Indicator ETR change measure

(1) (2) (3) (4) (5) (6)

SAMPLE: All Firms All Firms All Firms

Hiring Firms

Only All Firms All Firms

DEP VAR: Hire from S&P

1500 Hire from Low

ETR Firm Hire from Non-Low ETR Firm

Hire from Low ETR Firm

Senior Hire from Low ETR Firm

Hire from Low ETR Firm

ETR Increase Indicator 0.602* 0.693*** 0.132 1.971*** 0.442**

(0.338) (0.261) (0.195) (0.729) (0.207)

ETR Decrease Indicator

-0.623**

(0.272)

Controls Yes Yes Yes Yes Yes Yes

Firm FE Yes Yes Yes Yes Yes Yes

Industry x Year FE Yes Yes Yes Yes Yes Yes

Observations 30,688 30,688 30,688 9,532 30,688 30,688

Adjusted R-squared 0.158 0.095 0.062 0.059 0.070 0.095

44

Table 2: Changes in tax avoidance and tax department hiring (continued)

Panel B: Continuous ETR change measure

(1) (2) (3) (4) (5)

SAMPLE: All Firms All Firms All Firms

Hiring Firms

Only All Firms

DEP VAR: Hire from S&P

1500

Hire from

Substantial Tax

Avoider

Hire from Non-

Substantial Tax

Avoider

Hire from

Substantial Tax

Avoider

Senior Hire

from Substantial Tax

Avoider

ETR Change Over 3 Years 1.286 1.703** 0.510 5.682** 1.223*

(0.972) (0.858) (0.676) (2.752) (0.644)

Controls YES YES YES YES YES

Industry x Year FE YES YES YES YES YES

Firm FE YES YES YES YES YES

Observations 18,094 18,094 18,094 6,773 18,094

Adjusted R-squared 0.164 0.096 0.060 0.055 0.080

45

Table 3: Changes in tax risk management and tax department hiring

This table presents the results of estimating equations 1 and 2. Panel A contains the estimation of equation 1 on the sample of S&P 1500 firms from 2005 to 2015. Panel B contains the estimation of equation 2 on the sample of S&P 1500 firms from 2004 to 2009. The dependent variable is one of the following

variables: Any Hire, an indicator variable equal to one if the firm hired a tax department employee from another S&P 1500 firm or from a public accounting firm

in that year and zero otherwise; Hire from Public Accounting (Hire from Only Public Accounting), an indicator equal to one if the firm hired from a public

accounting firm in that year (and did not hire from an S&P 1500 firm), zero otherwise; Hire from Low ETR Firm, an indicator variable equal to one if the firm

hired a tax department employee from an S&P 1500 firm with a 5-year cash effective tax rate that is in the bottom quartile in that year and zero otherwise; Hire

from Non-Low ETR Firm, an indicator variable equal to one if Hire from S&P 1500 is equal to one and Hire from Low ETR Firm is equal to zero and zero

otherwise. The primary independent variable in Panel A is Tax-Related ICW or Restatement, an indicator equal to one if the firm experienced an internal control

weakness or financial restatement that involved tax issues in either year t or t-1, zero otherwise. The primary independent variable in Panel B is the interaction of

Low Pre-FIN 48 Cash ETR, an indicator equal to one if the firm’s five-year cash effective tax rate ending in year 2006 was below the sample median and zero

otherwise, and Post-FIN 48, an indicator equal to one if the fiscal year was 2007 through 2009 and zero otherwise. All regressions include control variables, firm

fixed effects, and industry-year fixed effects. Standard errors clustered by firm are presented in parentheses below the coefficient estimates. *, **, and *** represent coefficients that are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively.

Panel A: Tax-related internal control weaknesses and restatements

(1) (2) (3) (4) (5)

SAMPLE: All Firms All Firms All Firms All Firms All Firms

DEP VAR: Any Hire Hire from

Public Accounting

Hire from S&P

1500

Hire from

Low ETR Firm

Hire from

Non-Low ETR Firm

Tax-Related ICW or Restatement 2.706** 1.436 1.775** 1.307** 1.272*

(1.273) (1.206) (0.836) (0.638) (0.738)

Controls YES YES YES YES YES

Industry x Year FE YES YES YES YES YES

Firm FE YES YES YES YES YES

Observations 20,416 20,416 20,416 20,416 20,416

Adjusted R-squared 0.414 0.396 0.182 0.113 0.128

46

Table 3: Changes in tax risk & compliance and tax department hiring (continued)

Panel B: FASB Implementation No. 48

(1) (2) (3) (4) (5)

SAMPLE: All Firms All Firms All Firms All Firms All Firms

DEP VAR: Hire from S&P

1500

Hire from

Public Accounting

Hire from Only

Public Accounting

Hire from

Low ETR Firm

Hire from Non-Low ETR

Firm

Low Pre-FIN 48 Cash ETR

x Post-FIN 48

0.488 2.839** 3.140** -0.439 0.373

(0.998) (1.426) (1.456) (0.712) (0.872)

Controls YES YES YES YES YES

Industry x Year FE YES YES YES YES YES

Firm FE YES YES YES YES YES

Observations 10,934 10,934 10,934 10,934 10,934

Adjusted R-squared 0.158 0.385 0.251 0.078 0.120

47

Table 4: Descriptive evidence on employee movements between sample firms

This table presents the largest suppliers (i.e., the employee’s former firm) and hirers (i.e., the employee’s new firm) of tax department employees in our sample over the period from 2011 to 2015. Panel A presents the top suppliers

and hirers of tax department employees by firm, whereas Panel B presents the top suppliers and hirers of tax

department employees by industry (2-digit SIC code).

Panel A: Largest firm suppliers and hirers of tax department employees

Top Suppliers of Tax Professionals Top Hirers of Tax Professionals

Rank Firms Rank Firms

1 General Electric Co. 1 General Electric Co.

2 Intl Business Machines Corp. 2 Amazon.Com Inc.

3 Exxon Mobil Corp. 3 Verizon Communications Inc.

4 Citigroup Inc. 4 JPMorgan Chase & Co.

5 JPMorgan Chase & Co. 5 Citigroup Inc.

6 Johnson Controls Intl Plc. 6 Intl Business Machines Corp.

7 Schlumberger Ltd. 7 Schlumberger Ltd.

8 AT&T Inc. 8 Oracle Corp.

9 HP Inc. 9 Johnson Controls Intl Plc.

10 Verizon Communications Inc. 10 Bank of New York Mellon Corp.

Panel B: Largest industry suppliers and hirers of tax department employees

Top Suppliers of Tax Professionals Top Hirers of Tax Professionals

Rank Industries Rank Industries

1 Business Services 1 Business Services

2 Chemicals and Allied Products 2 Chemicals and Allied Products

3 Industrial & Commercial Machinery,

Computer Equipment 3

Industrial & Commercial Machinery,

Computer Equipment

4 Communications 4 Electric, Gas and Sanitary Services

5 Electric, Gas and Sanitary Services 5 Communications

6 Depository Institutions 6 Electronic & Other Electrical Equipment & Components

7 Electronic & Other Electrical

Equipment & Components 7 Depository Institutions

8 Transportation Equipment 8 Measuring, Photographic, Medical, &

Optical Goods, & Clocks

9 Measuring, Photographic, Medical, & Optical Goods, & Clocks

9 Transportation Equipment

10 Oil and Gas Extraction 10 Oil and Gas Extraction

48

Table 5: Similarity between hiring and prior firms

This table presents evidence on the similarity between the employee’s prior and new firms along several dimensions. Panel A provides the percentage of employee movement that occurs within the same industry, size (assets) quartile,

5-year cash effective tax rate quartile, or foreign income quartile. Panel B presents the results of estimating equation

3 on a sample of realized and possible movements between the corporate tax departments of S&P 1500 firms from

1999 to 2015. The dependent variable is Match, an indicator variable equal to one if the firm hired the moving

employee and zero otherwise. The primary independent variables are Same Industry, Distance, Size Differential,

Foreign Income Differential, and ETR Differential. All variables are defined in the appendix. The sample is

constructed by crossing all firms that hire a tax department employee from another firm in a given year with all tax

department employees who were hired by any sample firm’s corporate tax department in that year. Standard errors

clustered by firm are presented in parentheses below the coefficient estimates. *, **, and *** represent coefficients

that are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively.

Panel A: Univariate analysis

Group Same Different Total

Industry 409 2,060 2,469

17% 83%

Size Quartile 827 1,552 2,379

35% 65%

Foreign Inc. Quartile 424 1,039 1463

29% 71%

ETR Quartile 401 1,250 1,651

24% 76%

Panel B: Multivariate analysis

(1) (2) (3)

SAMPLE: All Possible Matches All Possible Matches All Possible Matches

DEP VAR: Match Match Match

Same Industry 1.513*** 1.503*** 1.503***

(0.215) (0.216) (0.219)

Distance -0.001*** -0.001*** -0.001***

(0.000) (0.000) (0.000)

Size Differential -0.000** -0.000** -0.000***

(0.000) (0.000) (0.000)

Foreign Income Differential -0.240*** -0.239*** -0.253***

(0.047) (0.049) (0.051)

ETR Differential -0.009 -0.014 -0.048

(0.133) (0.141) (0.143)

Year FE YES NO NO

Industry FE YES NO NO

Industry x Year FE NO YES YES

Former Industry FE NO NO YES

Observations 138,043 138,043 138,043

Adjusted R-squared 0.006 0.006 0.006

49

Table 6: Hiring outside the local labor market This table presents the results of estimating equation 4 on a sample of realized movement between the corporate tax departments of S&P 1500 firms from 1999 to

2015. The dependent variable is either Log Distance, the natural logarithm of miles between the headquarters of the hiring firm and the employee’s former firm, or Large Distance Indicator, an indicator variable equal to one if the distance between the headquarters of the hiring firm and the former firm is greater than X

and zero otherwise. The primary independent variable is High ETR Firm, an indicator variable equal to one if the hiring firm’s five-year cash effective tax rate is

in the top three quartiles of the sample and zero otherwise. In Panel B, equation 4 is estimated over different subsamples. The sample employed in column 1 (2)

is all former firms that have five-year cash effective tax rates in the bottom quartile (top three quartiles) of the sample. The sample employed in column 3 (4) is

all hired employees with above (below) median years in tax positions at prior firms. The sample employed in column 5 (6) is all hired employees who held a

senior (non-senior) tax position at the former firm. Standard errors clustered by firm are presented in parentheses below the coefficient estimates. *, **, and ***

represent coefficients that are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively.

Panel A: Hiring distance

(1) (2) (1) (2)

SAMPLE: All Movements All Movements All Movements All Movements

DEP VAR: Log Distance Log Distance Large Distance Indicator Large Distance Indicator

High ETR Firm 0.520** 0.469** 0.128*** 0.116***

(0.209) (0.199) (0.042) (0.041)

Controls YES YES YES YES

Year FE YES YES YES YES

Industry FE NO YES NO YES

Observations 955 955 955 955

Adjusted R-squared 0.052 0.056 0.064 0.071

Panel B: Hiring distance, heterogeneity

(1) (2) (1) (2) (1) (2)

SAMPLE: Low ETR Prior Firm

High ETR Prior Firm

High Tax Exp Low Tax Exp Senior Role in Prior Firm

Non-Senior in Prior Firm

DEP VAR: Log Distance Log Distance Log Distance Log Distance Log Distance Log Distance

High ETR Firm 1.426*** 0.260 0.697** 0.261 0.748** 0.080

(0.435) (0.265) (0.294) (0.278) (0.318) (0.303)

Controls YES YES YES YES YES YES

Year FE YES YES YES YES YES YES

Industry FE YES YES YES YES YES YES

Observations 186 596 416 526 436 955

Adjusted R-squared 0.124 0.069 0.067 0.053 0.062 0.070

50

Table 7: Employee movement from low ETR firms and subsequent hiring firm tax avoidance

This table presents the results of estimating equation 5. The dependent variable is Cash ETR, defined as the cash taxes paid divided by pre-tax income minus special items. The independent variable of interest is the interaction of Hire from Low ETR Firm, an indicator variable equal to one if the employee’s prior firm

had a five-year cash effective tax rate that is in the bottom quartile of the sample and zero otherwise, and Post, an indicator variable equal to one in the year when

the employee joins the hiring firm and each year afterward and zero otherwise. The sample includes a seven-year window centered on the year the employee

starts at the new firm. Panel A presents the main analyses. Columns 1 through 3 include the full sample; column 4 (5) contains only employees with income

(non-income tax) responsibilities at the new firm. Columns 3 through 5 include control variables, industry times year fixed effects, firm fixed effects, and prior

firm-hiring year fixed effects. Standard errors clustered by firm are presented underneath the coefficient estimates. *, **, and *** represent coefficients that are

statistically significant at the 0.10, 0.05, and 0.01 levels, respectively.

Panel A: Main tests

(1) (2) (3) (4) (5)

SAMPLE: All movements All movements All movements Income tax

movements only Non-income tax movements only

DEP VAR: Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR

Hire from Low ETR Firm x Post -0.012* -0.018*** -0.018*** -0.022*** 0.011

(0.006) (0.007) (0.007) (0.007) (0.026)

Hire from Low ETR Firm 0.007

(0.007)

Controls NO NO YES YES YES

Industry x Year FE YES YES YES YES YES

Event-time FE YES YES YES YES YES

Firm FE NO YES YES YES YES

Prior Firm-Hiring Year FE NO YES YES YES YES

Observations 12,019 12,002 12,002 10,774 968

Adjusted R-squared 0.204 0.410 0.443 0.434 0.324

51

Table 8: Employee movement from low ETR firms and subsequent tax avoidance, employee-level heterogeneity

This table presents the results of estimating equation 5 using the income tax employee subsample. The dependent variable is Cash ETR, defined as the cash taxes paid divided by pre-tax income minus special items. The independent variable of interest is the interaction of Hire from Low ETR Firm, an indicator variable

equal to one if the employee’s prior firm had a five-year cash effective tax rate that is in the bottom quartile of the sample and zero otherwise, and Post, an

indicator variable equal to one in the year when the employee joins the hiring firm and each year afterward and zero otherwise. The sample includes a seven-year

window centered on the year the employee starts at the new firm. Column 1 (2) estimates the model on the subsample of employees with an above (below)

median number of years in a tax role prior to joining the new firm. Column 3 (4) estimates the model on the subsample of employees who held (did not hold) a

senior role in their prior firm, where a senior role is defined as a supervisor, manager, director, or vice president. Column 5 estimates the model on the subsample

of employees who had an above-median number of years in a tax role prior to joining the new firm and held a senior role in their prior firm, where column 6

estimates the model on the subsample of all employees not in column 5. All regressions include control variables, industry times year fixed effects, firm fixed

effects, and prior firm-hiring year fixed effects. Standard errors clustered by firm are presented underneath the coefficient estimates. *, **, and *** represent

coefficients that are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively.

(1) (2) (3) (4) (5) (6)

SAMPLE: High Tax Exp Low Tax Exp Senior Role Non-Senior

Role High Tax Exp & Senior Role

All Others

DEP VAR: Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR

Hire from Low ETR Firm x Post -0.035*** -0.013 -0.025** -0.017 -0.030* -0.014

(0.013) (0.010) (0.012) (0.011) (0.016) (0.009)

Controls YES YES YES YES YES YES

Industry x Year FE YES YES YES YES YES YES

Event-time FE YES YES YES YES YES YES

Firm FE YES YES YES YES YES YES

Prior Firm-Hiring Year FE YES YES YES YES YES YES

Observations 4,914 5,308 5,349 4,909 3,411 6,815

Adjusted R-squared 0.359 0.433 0.380 0.430 0.337 0.440

52

Table 9: Employee movement from low ETR firms and subsequent tax avoidance, firm-level heterogeneity

This table presents the results of estimating equation 5 using the income tax employee subsample. The dependent variable is Cash ETR, defined as the cash taxes paid divided by pre-tax income minus special items. The independent variable of interest is the interaction of Hire from Low ETR Firm, an indicator variable

equal to one if the employee’s prior firm had a five-year cash effective tax rate that is in the bottom quartile of the sample and zero otherwise, and Post, an

indicator variable equal to one in the year when the employee joins the hiring firm and each year afterward and zero otherwise. The sample includes a seven-year

window centered on the year the employee starts at the new firm. Column 1 (2) estimates the model on the subsample of firms with below (above) median assets.

Column 3 (4) estimates the model on the subsample of firms with above (below) median foreign income scaled by total assets. Column 5 (6) estimates the model

on the subsample of firms with below (above) median tax fees paid to the firm’s auditor scaled by total assets. Column 7 (8) estimates the model on the

subsample of firms where the firm’s industry membership is the same (not the same) as the industry membership of the former firm, where industry membership

is measured at the two-digit SIC code level. All regressions include control variables, industry times year fixed effects, firm fixed effects, and prior firm-hiring

year fixed effects. Standard errors clustered by firm are presented underneath the coefficient estimates. *, **, and *** represent coefficients that are statistically

significant at the 0.10, 0.05, and 0.01 levels, respectively.

(1) (2) (3) (4) (5) (6) (7) (8)

SAMPLE: Small Firm Large Firm

High

Foreign

Income

Low

Foreign

Income

Low APTS High APTS

Same Industry

Different Industry

DEP VAR: Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR

Hire from Low ETR Firm x Post -0.032** -0.014* -0.030*** -0.015 -0.013 -0.024* -0.053** -0.017** (0.015) (0.007) (0.011) (0.012) (0.009) (0.013) (0.025) (0.008)

Controls YES YES YES YES YES YES YES YES

Industry x Year FE YES YES YES YES YES YES YES YES

Event-time FE YES YES YES YES YES YES YES YES

Firm FE YES YES YES YES YES YES YES YES

Prior Firm-Hiring Year FE YES YES YES YES YES YES YES YES

Observations 5,115 5,271 5,177 5,194 4,603 4,655 1,490 8,929

Adjusted R-squared 0.331 0.576 0.450 0.457 0.579 0.329 0.443 0.428

53

ONLINE APPENDIX

OA.1. Employee movement and firm tax avoidance, additional analyses

In Panel A of Table OA.4, we conduct a number of additional tests to support the findings in

Table 7. First, in column 1, we find that the effect of hiring a tax employee from a low ETR firm on tax

avoidance is robust to the inclusion of additional control variables that capture the firm’s overall

profitability, asset growth, inventory, number of employees, the change in PP&E over the year, the

change in net operating loss over the year, and the tax loss carryforward at the beginning of the year.

Next, we explore whether our movement results are driven by recent CEO changes and concurrent shifts

in corporate strategy. Prior research suggests that the CEO is an important determinants of a firm’s

effective tax rates (Dyreng et al. 2010), and that culture, as it is driven from the top level, affects tax

compliance (DeBacker, Heim, and Tran 2015). A new CEO may decide to overhaul the corporate tax

department as part of a broader shift in strategy (e.g., to become more aggressive or risk-seeking), and

therefore our findings may simply capture this change in strategy rather than the new human capital. In

column 2, we re-estimate our primary specification after removing any observations where there was a

CEO change in year t or t-1, and find similar results to those in our primary analyses. This suggests that

our primary findings are not driven entirely by CEO turnover. Similarly, in column 3, we explore whether

our Panel A findings are driven by concurrent mergers and acquisition activity, which could lead to

employee movement and affect tax planning outcomes. We continue to find an association between

employee movement from low ETR firms and an increase in tax avoidance after eliminating firm-year

observations where acquisition cash outflows are greater than 10 percent of lagged total assets. This

suggests that the findings in Table 7 are not the result of significant merger and acquisition activity.

In columns 4 through 6, we explore whether the impact of hiring an employee from a low ETR

firm has changed over time. Prior research suggests that corporations ran corporate tax departments as

profit centers starting in the mid-to-late 1990s, but that the focus of the tax department has gradually

shifted over time to one of risk management (Donohoe et al. 2014). Furthermore, cash effective tax rates

have gradually declined over time, consistent with tax knowledge and strategies spreading across firms

54

(Dyreng et al. 2017). Therefore, the impact of hiring an employee from a low ETR firm on subsequent tax

avoidance may have been stronger earlier in our sample period and declined over time as tax strategies

became widely known and the focus of the department shifted away from tax minimization. To explore

this idea, we split our sample into three subsamples of approximately equal size, based on the year of the

employee movement. In column 4, we find a robust and significant effect of employee movement from

low ETR firms on the ETR of the firm that subsequently hired the employee in the early part of our

sample period (1999 to 2006). This association declines in the second part of our sample period (2007 to

2011) and is approximately zero at the end of it (2012 to 2015). Overall, this finding is consistent with the

impact of employee movement on ETRs declining due to the increased dissemination of tax planning

knowledge and/or the shift in tax department focus.

Finally, we explore whether the usage and effectiveness of tax haven subsidiaries is a potential

mechanism driving the Panel A findings. In Panel C, we find that hiring an employee from a low ETR

firm is associated with a reduction in the hiring firm’s Cash ETR when the prior firm had tax subsidiaries

(column 1) but not when it did not (column 2). Similarly, we find that the effect is concentrated in hiring

firms that increased their usage of tax haven subsidiaries in the post-hiring period (column 3), relative to

when it did not (column 4). These results suggest that income shifting via tax haven subsidiaries could be

a specific tax strategy through which the new employee affects the hiring firm’s tax avoidance.31

OA.2. Employee movement and firm tax avoidance, untabulated robustness tests

We conduct several untabulated robustness tests of the table 7 findings. First, we cluster standard

errors by the former firm. Second, we employ different cut-offs (the five-year cash effective tax rate for

the bottom 20 or 30 percent of the sample) when defining Hire from Low ETR Firm. In each of these

tests, we find results consistent with those reported in Table 7.

31 We also explore whether the propensity to have tax haven subsidiaries increases after hiring an employee from

a low ETR firm. In untabulated analyses using an indicator for tax haven subsidiary usage as the dependent

variable, we find that the coefficient on Hire from Low ETR Firm x Post is statistically significant when we

exclude firm fixed effects, but that it becomes marginally insignificant when including these fixed effects.

Therefore, it appears that the Panel C findings may be driven, in part, by the increased effectiveness of existing

tax haven subsidiaries; unfortunately, we cannot explore this empirically.

55

We also examine the robustness of our findings to an alternative definition of Hire from Low ETR

Firm. In our main analyses, we define a prior firm as being a Low ETR Firm (Hire from Low ETR Firm =

1) when it is has a 5-year cash ETR in the bottom quartile of the entire sample. This allows for the amount

of low ETR firms to vary across industries and years. Our results are robust to using an alternative

measure that defines a low ETR firm as one that is in the bottom quartile of the 5-year cash ETR within

each industry-year (where industry membership is defined at the two-digit SIC code level). The economic

significance of this result (1.2 percentage points) is weaker than our primary findings, perhaps because

this alternative definition is less effective in identifying low ETR firms (the average 5-year cash ETR of

these firms is 18 percent).

Next, we examine whether we find symmetric effects when a firm hires an employee from a high

ETR firm. We find that hiring firms exhibit no discernable change in their Cash ETR after hiring an

employee from a high ETR firm (a former firm whose 5-year cash ETR is in the top quartile of the

sample). This test supports our primary hypothesis that it is the tax knowledge gained from low ETR

firms, rather than mean reversion in the hiring firm’s effective tax rate, that is driving our findings.

Finally, we explore the robustness of our findings to using the book effective tax rate (defined as

the current tax expense scaled by adjusted pre-tax income. While the coefficient on Hire from Low ETR

Firm x Post remains economically significant (-0.011), it is not statistically significant at conventional

levels (one-tailed p-value of 0.081). However, when we use this proxy in the cross-sectional tests

discussed above (e.g., total tax experience), we generally find results consistent with those reported in

Tables 8 and 9. Specifically, we find economically and statistically significant reductions in the book

current effective tax rate when the employee has above-median tax experience and was a senior employee

in their prior firm. We find economically significant reductions in the book effective tax rate for smaller

firms, firms with greater foreign income, and when the hiring and prior firms are in the same industry,

although these findings are not statistically significant at conventional levels. We also explore a book

effective tax rate that includes the total tax expense, but do not find significant effects of employee

56

movement when using this proxy. This suggests that the tax planning strategies the new firm implements

likely have a substantial deferral component.

57

Table OA.1: Largest firms and areas by average total department size

This table presents the top 25 firms and areas with the largest corporate tax departments by the average number of tax department employees from 2011 to 2015. Panel A presents the largest corporations and Panel B presents the

largest locations.

Panel A: Largest Firms Panel B: Largest Areas

Rank Firm Rank Location

1 JP Morgan Chase & Co 1 Greater New York City Area

2 Intl Business Machines Corp 2 Houston, Texas Area

3 General Electric Co 3 San Francisco Bay Area

4 Bank Of New York Mellon Corp 4 Dallas & Fort Worth Area

5 Schlumberger Ltd 5 Greater Chicago Area

6 State Street Corp 6 Greater Boston Area

7 Bank Of America Corp 7 Greater Atlanta Area

8 Citigroup Inc. 8 Greater Philadelphia Area

9 Procter & Gamble Co 9 London, United Kingdom

10 Exxon Mobil Corp 10 Washington D.C. Metro Area

11 Accenture Plc 11 Greater Seattle Area

12 Chevron Corp 12 Greater Minneapolis & St. Paul Area

13 Wells Fargo & Co 13 Greater Los Angeles Area

14 Morgan Stanley 14 Greater Detroit Area

15 Wal-Mart Stores Inc. 15 Charlotte, North Carolina Area

16 HP Inc. 16 Greater Denver Area

17 Verizon Communications Inc. 17 Toronto, Canada Area

18 Johnson Controls Intl Plc 18 Greater San Diego Area

19 Dell Technologies Inc. 19 Greater Pittsburgh Area

20 Amazon.Com Inc. 20 Columbus, Ohio Area

21 Goldman Sachs Group Inc. 21 Ireland

22 AT&T Inc. 22 Greater St. Louis Area

23 American International Group 23 Tampa St. Petersburg, Florida Area

24 Oracle Corp 24 Phoenix, Arizona Area

25 PepsiCo Inc. 25 Cleveland Akron, Ohio Area

58

Table OA.2: Descriptive statistics for hiring tests

This table presents descriptive statistics on the variables used in the hiring analyses in Tables 2 and 3. The descriptive statistics are calculated using the full sample of hiring observations from 1999 to 2015. All variables are

defined in the appendix.

VARIABLE MEAN STD DEV MEDIAN

Any Hire 15.421 36.115 0.000

Hire from S&P 1500 4.659 21.076 0.000

Hire from Low ETR Firm 2.651 16.064 0.000

Hire from Non-Low ETR Firm 3.144 17.450 0.000

Hire from Public Accounting 13.046 33.681 0.000

ETR Increase Indicator 0.332 0.471 0.000

ETR Change over 3-years -0.007 0.157 -0.003

Tax-Related ICW or Restatement 4.924 21.638 0.000

Size 7.555 1.773 7.475

Domestic Income 0.042 0.114 0.041

Foreign Income 0.014 0.031 0.000

Leverage 0.193 0.182 0.158

MTB Ratio 2.906 3.206 2.081

FI Indicator 0.486 0.500 0.000

59

Table OA.3: Descriptive statistics for employee movement tests

This table presents descriptive statistics on the variables used in the employee movement analyses in Tables 7 through 9 and Table OA.4, separately for the movements where Hire from Low ETR Firm = 1 and Hire from Low

ETR Firm = 0, where Hire from Low ETR Firm is an indicator variable equal to one if the employee’s prior firm had

a five-year cash effective tax rate in the bottom quartile of the sample and zero otherwise. All variables are defined

in the appendix.

SAMPLE: Hire from Low ETR Firm = 1 Hire from Low ETR Firm = 0

VARIABLE MEAN STD DEV MEDIAN MEAN STD DEV MEDIAN

Cash ETR 0.232 0.175 0.211 0.241 0.172 0.220

Size 9.739 1.891 9.775 9.697 1.885 9.634

Domestic Income 0.056 0.062 0.043 0.057 0.064 0.045

Foreign Income 0.030 0.040 0.013 0.032 0.042 0.014

Sales Growth 0.080 0.181 0.057 0.090 0.190 0.061

Special Items -0.009 0.027 -0.002 -0.010 0.027 -0.002

Extraordinary Items 0.000 0.006 0.000 0.000 0.006 0.000

Advertising Expense 0.013 0.026 0.000 0.013 0.027 0.000

R&D Expense 0.028 0.043 0.003 0.024 0.041 0.003

PPE 0.247 0.215 0.170 0.237 0.207 0.163

Intangible Assets 0.208 0.192 0.146 0.213 0.194 0.152

Leverage 0.206 0.143 0.195 0.204 0.142 0.191

MTB Ratio 3.615 4.220 2.496 3.735 4.322 2.616

NOL Indicator 0.435 0.496 0.000 0.438 0.496 0.000

FI Indicator 0.725 0.446 1.000 0.758 0.428 1.000

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Table OA.4: Employee movement from low ETR firms and subsequent hiring firm tax avoidance This table presents the results of estimating equation 5. The dependent variable is Cash ETR, defined as the cash taxes paid divided by pre-tax income minus

special items. The independent variable of interest is the interaction of Hire from Low ETR Firm, an indicator variable equal to one if the employee’s prior firm had a five-year cash effective tax rate that is in the bottom quartile of the sample and zero otherwise, and Post, an indicator variable equal to one in the year when

the employee joins the hiring firm and each year afterward and zero otherwise. The sample includes a seven-year window centered on the year the employee

starts at the new firm. Panel A presents additional tests using the income tax employee subsample. In column 1, the regression includes the following additional

control variables: Log Total Employment (natural logarithm of total employees), ROA (operating income before depreciation scaled by total assets), Asset Growth

(change in total assets over the year, scaled by lagged total assets), Inventory (inventory scaled by total assets), TLCF (an indicator variable equal to one if the

firm had a non-zero tax loss carryforward at the beginning of the year, zero otherwise), ΔNOL (the change in the net operating loss over the year, scaled by

lagged total assets), and ΔPPE (the change in property, plant, and equipment over the year, scaled by lagged total assets). Column 2 (3) excludes observations

with a CEO change in year t or t-1 (observations where the cash funds related to acquisitions is greater than 10 percent of lagged assets). Columns 4, 5, and 6

contain observations related to employee movements that occurred between 1999 and 2006, 2007 and 2011, and 2012 and 2015, respectively. Panel B presents

tests related to the usage of tax haven subsidiaries, using the income tax employee subsample. Column 1 (2) contains movements where the prior firm did (did

not) have subsidiaries in tax havens using the Dyreng and Lindsey (2009) definition. Column 3 (4) contains movements where the hiring firm increased (did not increase) the number of tax haven subsidiaries in the post-hiring period. All regressions include control variables, industry times year fixed effects, firm fixed

effects, and prior firm-hiring year fixed effects. Standard errors clustered by firm are presented underneath the coefficient estimates. *, **, and *** represent

coefficients that are statistically significant at the 0.10, 0.05, and 0.01 levels, respectively.

Panel A: Additional tests

(1) (2) (3) (4) (5) (6)

SPECIFICATION: Additional

controls

Excluding

CEO changes

Excluding large

M&A activity

Moves from

1999 to 2006

Moves from

2007 to 2011

Moves from

2012 to 2015

DEP VAR: Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR

Hire from Low ETR Firm x Post -0.021*** -0.025*** -0.018** -0.041** -0.025* 0.000

(0.007) (0.008) (0.007) (0.017) (0.015) (0.011)

Controls YES YES YES YES YES YES

Industry x Year FE YES YES YES YES YES YES

Event-time FE YES YES YES YES YES YES

Firm FE YES YES YES YES YES YES

Prior Firm-Hiring Year FE YES YES YES YES YES YES

Observations 10,645 8,337 9,060 3,205 3,778 3,663

Adjusted R-squared 0.448 0.426 0.457 0.327 0.435 0.457

61

Panel B: Role of tax haven subsidiaries

(1) (2) (3) (4)

SAMPLE: Prior firm had

haven subs Prior firm had no haven subs

Hiring firm adds haven subs

Hiring firm did not add haven subs

DEP VAR: Cash ETR Cash ETR Cash ETR Cash ETR

Hire from Low ETR Firm x Post -0.038*** 0.013 -0.031** -0.020**

(0.009) (0.013) (0.015) (0.009)

Controls YES YES YES YES

Industry x Year FE YES YES YES YES

Event-time FE YES YES YES YES

Firm FE YES YES YES YES

Prior Firm-Hiring Year FE YES YES YES YES

Observations 6,956 3,529 3,012 7,015

Adjusted R-squared 0.403 0.429 0.421 0.440